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Speaker 1: You've heard us say many times that we're kind of

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Speaker 1: amazed that our complicated bodies ever work well. There are

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Speaker 1: countless cells that are born in your body and will

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Speaker 1: die in your body, and from an evolutionary perspective, all

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Speaker 1: of those cells are working their tails off to help

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Speaker 1: a particular group of cells kickstart the next generation. And

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Speaker 1: even then, just a few, if any sperm or eggs

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Speaker 1: will get a chance to come together to make a baby.

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Speaker 1: But you don't hear about eggs creating a roadblock in

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Speaker 1: the Filippian tubes as they all race out in a

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Speaker 1: mad rush to be the egg that gets picked that month.

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Speaker 1: Many of our cells do coordinate nicely to give sperm

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Speaker 1: and eggs a chance to strut their stuff.

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Speaker 2: But all of those cells.

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Speaker 1: Are carrying around the same genetic information, so they all

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Speaker 1: benefit from the sperm and the egg knocking it out

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Speaker 1: of the park when their time comes. But what happens

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Speaker 1: when cells need to come together to complete a selfless

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Speaker 1: task and those cells are not one hundred percent.

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Speaker 2: Related to each other.

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Speaker 1: Today we're going to talk about social amiba, who will

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Speaker 1: sometimes join together by the tens of thousands to help

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Speaker 1: a subset of the amiba get to a new and

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Speaker 1: better environment. In the process, about twenty percent of the

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Speaker 1: amiba need to give their lives so the rest of

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Speaker 1: the amiba can get transported to somewhere.

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Speaker 2: Hopefully much better.

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Speaker 1: So how the heck do you convince someone who isn't

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Speaker 1: related to you to give their life for you? And

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Speaker 1: how do you prevent cheaters from avoiding their fair share

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Speaker 1: of the costs. Welcome to Daniel and Kelly's occasionally cooperative universe.

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Speaker 3: Hi, I'm Daniel. I'm a particle physicist who likes speaking

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Speaker 3: about aliens, and I also like cooperating with humans.

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Speaker 2: Hello, I'm Kelly Wienersmith.

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Speaker 1: I study pair sites and space, and I also like

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Speaker 1: cooperating with humans.

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Speaker 3: And I cooperate with microbes all the time. Like I

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Speaker 3: had dinner last night and right now we're working together

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Speaker 3: on digesting that Oh.

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Speaker 1: You're collaborating on a pooh?

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Speaker 2: Is that what you're getting at?

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Speaker 3: I wasn't gonna go there, but you brought us all

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Speaker 3: the way from zero to poo in record time. Thank

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Speaker 3: you very much, and welcome everybody to a biology episode. Guys,

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Speaker 3: we're talking about pooh.

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Speaker 1: I think we might get to cannibalism too, So pull

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Speaker 1: out your dKu Bingo cars.

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Speaker 2: We'll see where we get today.

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Speaker 3: But yes, I'm happily a commensable organism, commensurate, commensal, commensal,

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Speaker 3: thank you very much, with the microbes in my gut,

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Speaker 3: and we're both working on my dinner from last night.

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Speaker 3: That's the way I like to think about it.

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Speaker 1: Okay, yeah, that's a nicer way to think about it.

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Speaker 1: But Daniel, I have a story for you, so you

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Speaker 1: might be amazed to learn that I was ever not

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Speaker 1: good at interviewing people. So so the social abiba that

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Speaker 1: we're talking about today used to be called slime molds.

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Speaker 1: And I don't know if you remember, but for a

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Speaker 1: while there's Zach and I were working on trying to

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Speaker 1: manufacture mazes for slime molds that could be given to

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Speaker 1: like schools as kids to study slime molds. And so

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Speaker 1: I was like, oh, I'm going to interview a slime

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Speaker 1: mold expert so that I can, you know, learn about

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Speaker 1: this stuff. But she doesn't study that kind of slime

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Speaker 1: mold's totally different. And so right when the interview starts,

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Speaker 1: I'm like, well, tell me about and then I say

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Speaker 1: the name of the slime mold that I thought we

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Speaker 1: were going to tell them, and she goes, oh, no,

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Speaker 1: I don't study that.

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Speaker 2: I study and it was a totally different thing. And

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Speaker 2: I was like, what all of my preparation.

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Speaker 1: Clearly I didn't prepare enough, right, And so now when

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Speaker 1: you're like, Kelly, why does it take you fifteen prepared episode,

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Speaker 1: it's because this is living rent free in my brain

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Speaker 1: and I never want to repeat that again.

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Speaker 3: I see everybody's got that potential moment of terror when

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Speaker 3: they're going to be unmasked as totally ignorant on the

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Speaker 3: thing they're supposed to know something.

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Speaker 2: When it happened, it happened to.

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Speaker 3: Me, at least it didn't happen live on the BBC

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Speaker 3: like it did for Naomi Wolf.

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Speaker 2: Oh my gosh. When I think about that, I cringe.

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Speaker 1: All right, tell the listeners what happened for anyone who

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Speaker 1: happens to not be aware of this.

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Speaker 3: Naomi Wolf wrote a book about Let me just make

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Speaker 3: sure I get this right now. I have a Naomi

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Speaker 3: Wolf moment about Naomi Wolf.

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Speaker 2: That was a bad moment to take a sip of water.

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Speaker 3: So Naomi Wolf wrote a whole book about people who

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Speaker 3: she thought were executed in the nineteenth century for being homosexuals.

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Speaker 3: And she based this on legal records where these cases

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Speaker 3: were indicated with the term death recorded, which she interpreted

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Speaker 3: to mean that these men were executed, but live on

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Speaker 3: the BBC being interviewed by historian who revealed to her

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Speaker 3: that the term death recorded actually means that the judges

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Speaker 3: had abstained from handing out a death sentence. She discovered

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Speaker 3: this like as she's being asked about her book in

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Speaker 3: real time. To me, that's like the most terrifying possible experience.

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Speaker 3: And the book was pulled and it was pulped and

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Speaker 3: like years of research on a foundational mistake. So every

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Speaker 3: time I'm doing research, I'm imagining a potential Niaomi Wolf

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Speaker 3: moment and I'm like, let me just go read that

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Speaker 3: paper one more time. Let me just double check that,

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Speaker 3: because nobody wants that to happen to them.

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Speaker 1: My stomach hurts just thinking about that story. And yeah, uh,

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Speaker 1: and to find out about it live on the BBC.

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Speaker 3: Okay, anyway back to social Omiva, which we were talking

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Speaker 3: about in today's episode. What brought us to this topic today, Kelly.

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Speaker 1: Well, we have an amazing Discord community that you can

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Speaker 1: join by going to our website Danielandkelly dot Org and

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Speaker 1: one of our amazing discord members see Dave, asked about

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Speaker 1: these organisms that, when there's not enough food join together

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Speaker 1: and cooperate, but a subset of them die on the way,

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Speaker 1: and I realized that what he was talking about was

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Speaker 1: dictio stellum discoidum. We're just dictis. We're gonna call it

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Speaker 1: DICTI for the rest of the time. Yeah, And I

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Speaker 1: happen to know the lab that does that work, and

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Speaker 1: I was like, you know what, I would love to

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Speaker 1: actually do the research I should have done fifteen years

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Speaker 1: ago when I had Joan Strassman on the show.

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Speaker 2: Oh, and so we're.

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Speaker 1: Going to be talking about the Strassman and Kueller lab research.

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Speaker 1: It's a husband wife team that has been studying these

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Speaker 1: amazing social amiba that are an incredible model for social evolution.

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Speaker 3: All right, So this is not just a fun deep

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Speaker 3: dive into fascinating puzzles in science. It's also a redemption

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Speaker 3: arc for Kelly to finally get to talk about this topic.

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Speaker 2: Maybe I don't know.

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Speaker 1: We'll say if it's a redemption arc or a failure

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Speaker 1: arc by the end of the show. But so this

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Speaker 1: question was inspired by a prior episode we did where

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Speaker 1: we talked about why animals cooperate, which is a bit

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Speaker 1: of a surprising question because you imagine, you know, nature,

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Speaker 1: red and tooth and claw. Everybody should be about trying

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Speaker 1: to like get the best outcomes for themselves, and that

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Speaker 1: involves probably not a lot of cooperation. But yet when

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Speaker 1: we look out in nature, we see loads of animals

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Speaker 1: cooperating with one another and doing nice things, like in

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Speaker 1: honey bees, if a worker bee stings to.

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Speaker 2: Try to protect the rest.

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Speaker 1: Of the hive, she's disemboweled and she like dies to

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Speaker 1: protect the hive.

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Speaker 2: Why would an animal do that?

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Speaker 1: And in that case, the answer is probably because she's

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Speaker 1: very closely related to everybody, and so evolution favors her

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Speaker 1: being willing to risk her life if it saves essentially

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Speaker 1: her genes that are living in the rest of the

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Speaker 1: colony mates.

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Speaker 3: And so for those of us who are not biologists,

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Speaker 3: I guess the mystery is based on a sort of

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Speaker 3: simple reading of evolution that like, every critter is out

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Speaker 3: there for themselves, trying to pass along their genes, and

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Speaker 3: so how could evolution ever produce a system where somebody

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Speaker 3: intentionally kills themselves for the benefit of other organisms and

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Speaker 3: those organisms genes, And I think you're saying that The

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Speaker 3: answer is that that happens when your genes are very

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Speaker 3: close to the genes of the organism you're sacrificing yourself for,

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Speaker 3: so it's actually still benefiting your genes. Is that right?

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Speaker 1: Yes, exactly right. So almost every single example we find

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Speaker 1: of altruism and nature can end up being explained by

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Speaker 1: a benefit to the individual who is doing what appears

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Speaker 1: to be an altruistic act. And so in the example

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Speaker 1: we just talked about, the benefit is that their genes

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Speaker 1: are also found in their family members, and their family

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Speaker 1: members are going to do much better and carry those

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Speaker 1: genes on because of how that individual helped out.

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Speaker 3: That doesn't give me like a Nature is warm and fuzzy,

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Speaker 3: though it's not like, hey, we all care about each other.

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Speaker 3: It's like you don't even matter. All that matters is

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Speaker 3: your genes, and if your genes exist in your brothers

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Speaker 3: and sisters, you should just kill yourself to benefit them.

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Speaker 1: Nature does not care about your feelings, Daniel, It doesn't

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Speaker 1: care about anyone's feelings.

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Speaker 3: So it's still pretty red in toothing claw. Yeah, and

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Speaker 3: like self sacrifices.

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Speaker 1: Yes, absolutely, Nature is not nice, says the parasitologist. And

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Speaker 1: I think my favorite quote that helps you sort of

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Speaker 1: wrap your head around kin selection is by JBS Haldane, who,

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Speaker 1: when asked if he would save a drowning brother if

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Speaker 1: it put his own life, at Ri said no, but

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Speaker 1: I would save two brothers or eight cousins.

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Speaker 3: Sounds like a charming fellow, yes.

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Speaker 1: Yeah, Well, the point that he was trying to make

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Speaker 1: there is that he and his brothers share about fifty

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Speaker 1: percent of their genes, So in order to like replace

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Speaker 1: his loss, you'd have to have two brothers so that

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Speaker 1: you're like, you know, saving enough genes to make it

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Speaker 1: worth it. And you know, the amount that you should

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Speaker 1: be willing to risk as family members become more distantly

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Speaker 1: related goes down over time.

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Speaker 3: Hmmm, I see, sounds like a really cozy Thanksgiving dinner

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Speaker 3: at the hall Dane house.

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Speaker 2: Well, you know.

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Speaker 3: Don't pass the gravy down that end. Those are just cousins.

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Speaker 3: Let's keep it over here on the brother end.

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Speaker 2: Right, That's right, that's right.

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Speaker 1: I mean there's got to be some families like that,

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Speaker 1: You've got there have been some rough Christmases out there

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Speaker 1: I'm sure.

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Speaker 2: I'm sure.

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Speaker 1: In fact, i'm sure some of their listeners are shaking

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Speaker 1: their heads and being like, oh, yeah, I'm in one

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Speaker 1: of those families. So when you're doing these sorts of calculations,

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Speaker 1: it's important to keep in mind what the benefits are

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Speaker 1: going to be and what the call they're going to be,

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Speaker 1: because if you're going to risk your own life, you

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Speaker 1: want to make sure that there's a pretty big benefit

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Speaker 1: for your family members, for example, or maybe you're willing

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Speaker 1: to do something nice if the cost to you is

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Speaker 1: low and you're like, oh why not, I can, Yeah,

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Speaker 1: I can help out. I can listen to you tell

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Speaker 1: me about your boring dream it makes you feel better.

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Speaker 3: And we're couching this in understandable terms from the point

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Speaker 3: of view of the organism, like it's making a decision

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Speaker 3: should I kill myself? Should I jump in the pool

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Speaker 3: to save my brother? But really that's not what's happening, right,

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Speaker 3: And this is sort of a meta process via evolution

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Speaker 3: that's selecting organisms who have made choices that propagate their genes.

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Speaker 1: Yes, that's a great question, And since we're talking about

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Speaker 1: amiba today, it's going to be very easy for us

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Speaker 1: to like wrap our heads around the fact that they

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Speaker 1: are not making decisions about like, oh, right, that's Francine,

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Speaker 1: who I have an emotional connection to, you know, it's

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Speaker 1: just like that's an amoba. That's like me, oh interesting, yeah,

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Speaker 1: So should we talk about the puzzle?

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Speaker 3: Yes, tell us about the puzzle.

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Speaker 2: Okay, So here's the puzzle.

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Speaker 1: There are these social amiba called we're gonna call them dicti,

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Speaker 1: and so these are like free living. They're like one cell.

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Speaker 1: They divide asexually a lot of the time, so they

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Speaker 1: just kind of like split and then there's two of

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Speaker 1: them what and they eat bacteria.

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Speaker 3: But back up and remind us what exactly is an

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Speaker 3: amoeba because they're not viruses, they're not bacteria, but they're

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Speaker 3: sort of on the same end of the size spectrum

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Speaker 3: as those microcritters.

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Speaker 1: Yeah, they're they're small, but they're eukaryotes, okay. And so

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Speaker 1: they've got like a nucleus and organelles that are what

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Speaker 1: is it's right, organitos, and they're sort of like amorphously shaped, right,

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Speaker 1: and so if they come across something that they want

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Speaker 1: to eat, they can sort of like move their body

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Speaker 1: around it to engulf it. And so do you think

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Speaker 1: it's fair to assume that most people like have a

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Speaker 1: mental image for an amoba, or is that just like

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Speaker 1: a bias of this biologists brain of mind.

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Speaker 3: I don't think I've thought about amibas since ninth grade.

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Speaker 2: Oh okay, so.

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Speaker 3: I'm imagining they're like a mini version of the blob.

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Speaker 2: Yeah.

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Speaker 1: So like imagine if you dropped an egg on the

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Speaker 1: ground and then you heated it and it's kind it

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Speaker 1: has a shape sort of like that, but it's also

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Speaker 1: able to move.

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Speaker 3: Wow, I hope nobody ever describes me that way. It's

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Speaker 3: not a flattering description.

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Speaker 2: Your shape is way more defined than that. So I

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Speaker 2: think you're safe.

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Speaker 3: Well, what would you have to drop on the ground

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Speaker 3: so it makes a Daniel shaped splat? Is really the question?

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Speaker 2: Uh, a Daniel?

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Speaker 1: You have to drop a Daniel and then hopefully you're

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Speaker 1: not putting a chalk line around it.

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Speaker 3: I'm not going to make now this flat. When you

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Speaker 3: drop Daniel off of a building, it's not gonna be

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Speaker 3: Daniels shaped. It's gonna be a Daniel splat.

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Speaker 2: Shaped like Daniel mixed with an amiba.

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Speaker 3: We're way out track here anyway. You're saying amiebas are

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Speaker 3: sort of large and amorphous. Not large, Oh, they're not large.

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Speaker 3: They're tiny and.

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Speaker 2: Amorphous, tiny, anamorphous.

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Speaker 1: The egg thing was meant to make you just sort

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Speaker 1: of like visualize the shape, but.

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Speaker 2: They're nowhere near as big as an egg. These are.

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Speaker 2: They are very teeny tiny.

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Speaker 3: Well that's a great name. Actually, the word amoeba actually

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Speaker 3: gives me that mental image of like a tiny little blob.

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Speaker 3: There's something blobby about it?

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Speaker 1: Is that because like you know that it's a tiny

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Speaker 1: little b Yes, maybe I do?

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Speaker 3: Okay, all right, So amibas are tiny little blobs and

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Speaker 3: they slide around absorbing stuff into them. But they're not bacteria,

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Speaker 3: but they're roughly the same size.

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Speaker 1: They're bigger than bacteria. They are actually predators of bacteria.

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Speaker 1: You could see them under a compound microscope. They are

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Speaker 1: usually less than a millimeter long.

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Speaker 3: Okay, so we have these little critters they're less than

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Speaker 3: a millimeter long, and tell us about the puzzle.

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Speaker 2: Okay, So here's the puzzle.

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Speaker 1: So they're going around, they're eating bacteria, doing their amiba thing,

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Speaker 1: and then the environment changes. Maybe you've been living on

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Speaker 1: a turd and all of the good bacteria have been consumed,

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Speaker 1: and now the environment is becoming less good, and you

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Speaker 1: need to figure out some way to high tail off

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Speaker 1: that turd to get to the next one. And so

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Speaker 1: what they do is they start releasing chemicals to aggregate

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Speaker 1: so that they can find each other, and they start

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Speaker 1: pulling together in groups of ten thousand to one hundred

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Speaker 1: thousand individuals.

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Speaker 3: What, right, how can there be so many of them

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Speaker 3: that meet?

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Speaker 1: They're really tiny, and there used to be a lot

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Speaker 1: of food in that turd buffet, but now now there's not,

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Speaker 1: and so now they need to move to the next buffet.

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Speaker 1: And so they join together in what is called a

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Speaker 1: slug and it looks like a slug like and so

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Speaker 1: there so many of them join together that now you

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Speaker 1: can see the trails when they move together. Wow, And

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Speaker 1: these groups can move something like two and a half

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Speaker 1: centimeters on average together, which is a pretty big distance

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Speaker 1: for like, yeah, a tiny little kit, that's right. And

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Speaker 1: so then when it gets to its final location, some

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Speaker 1: of it lifts up into the air and forms a

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Speaker 1: ball on the top. And so now it starts looking

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Speaker 1: like a lollipop. And the bit of the lollipop that's

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Speaker 1: in the stick, those are all ambo that are in there.

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Speaker 1: Twenty percent of the lollipop is the stick, and they die.

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Speaker 1: They die so that they can form a hard stalk

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Speaker 1: so that the stuff on the top can and then

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Speaker 1: transmit to other places.

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Speaker 3: Remind me how many amiba are in this slug again, roughly.

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Speaker 2: Ten thousand to one hundred thousand, wow.

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Speaker 3: And so they all work together to make this like

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Speaker 3: fake macroscopic organism that can move much further away than

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Speaker 3: an individual one could, yep. But along the way a

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Speaker 3: bunch of them have to die. So this is like

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Speaker 3: if I got a bunch of students here you see

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Speaker 3: Irvine together like ten thousand of them to form some

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Speaker 3: huge blob of students and they managed to like lift

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Speaker 3: some of them to safety in some disaster, but some

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Speaker 3: of them died along the way.

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Speaker 2: That's crazy, exactly. It's nuts.

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Speaker 1: And also nobody should work with Daniel because he's.

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Speaker 2: This is just scary.

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Speaker 3: I'm just trying to make it relatable here.

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Speaker 2: Right, all right, it's very visceral. It's visceral now.

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Speaker 1: And so yeah, twenty percent of them will die and

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Speaker 1: harden to create like a lollipop stick so that up

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Speaker 1: at the top the rest of the amiba can transmit.

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Speaker 1: And part of how they transmit is like a fruitfly

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Speaker 1: or some other kind of flaw. I might you know,

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Speaker 1: land on the turd, walk past it, and then pick

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Speaker 1: some of the They're called sportes at this stage, but

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Speaker 1: let's just call the We'll pick some of the amiba

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Speaker 1: up and then bring them to somewhere else, okay. Or

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Speaker 1: like the lollipop head will eventually like dry out and

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Speaker 1: kind of like disperse.

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Speaker 2: In like the wind.

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Speaker 1: And so the amibas can by getting up high either

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Speaker 1: hit your ride on another insects or like get sort

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Speaker 1: of blown somewhere else. And so in that way, they're

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Speaker 1: hoping that they end up in a better environment.

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Speaker 3: And how do they decide who ends up in the

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Speaker 3: head and who ends up in the stick? Is there

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Speaker 3: like a big battle for that.

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Speaker 1: That is like twenty years of research, and that is

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Speaker 1: what we're going to talk about today, okay. And so

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Speaker 1: when we get back we will start to discuss who

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Speaker 1: ends up in the stock, and who gets to survive

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Speaker 1: in the head.

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Speaker 3: Who wins the Amoeba Hunger Games.

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Speaker 1: And we're back in today we are discussing who lives

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Speaker 1: and who.

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Speaker 2: Dies in the Amiga Hunger Games.

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Speaker 3: I'm also wondering how to describe what it is that

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Speaker 3: amiba's do. You said earlier, amiba's doing the thing amibas

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Speaker 3: are doing. It feels like that needs a word, like

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Speaker 3: ami being amibi? What is the word for that?

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Speaker 1: What do amibas do a meebing sounds good, amiboiding or something?

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Speaker 2: And what would be the human equivalent?

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Speaker 1: Like would that be like when I wrap my face

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Speaker 1: around a popsicle or something?

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Speaker 2: I don't I don't know human ing.

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Speaker 3: Yeah, that's sure. Yeah, all right, So amibas be a

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Speaker 3: me being and while they're a meibing, some of them

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Speaker 3: sacrifice themselves so that the rest of them can amibe

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Speaker 3: longer into the future.

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Speaker 2: That's right.

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Speaker 3: How do they decide who's in the stock and who

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Speaker 3: gets to be the special baby at the very top?

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Speaker 1: Okay, well, so the first part of the answer is

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Speaker 1: that the week do not survive, and so there was

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Speaker 1: an experiment where they took clones.

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Speaker 2: So all of the amibas were very closely related.

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Speaker 1: They were like, you know, completely related to each other

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Speaker 1: because they just split many many times to make more clones.

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Speaker 1: And some of the clones were put on a plate

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Speaker 1: without a lot of bacteria, and so they were weak

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Speaker 1: and kind of starving, and some of the clones had

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Speaker 1: a lot of food. And then when you put them

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Speaker 1: all back together and you look to see which ones

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Speaker 1: end up in the stock and which ones end up

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Speaker 1: in the head, the ones that were starving are the

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Speaker 1: ones that end up in the stock.

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Speaker 3: Oh, I see. And so the reason you use clones

395
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Speaker 3: in this experiment is to make sure that there's no

396
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Speaker 3: genetic differentiation. You're focusing just on the week versus the

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Speaker 3: strong guys.

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Speaker 1: Exactly, Yes, all right, honing in on the clones is important,

399
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Speaker 1: and we're going to get to that. But another way

400
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Speaker 1: that you end up in the head instead of in

401
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Speaker 1: the stalk is by being one of the first individuals

402
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Speaker 1: to say, hey, there's there's not.

403
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Speaker 2: A lot of food here. We need to do the

404
00:19:01,800 --> 00:19:02,400
Speaker 2: slug thing.

405
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Speaker 1: Which is surprising because I'd feel like you should have

406
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Speaker 1: expected the opposite, Like whoever starts saying, oh, I'm hungry,

407
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Speaker 1: we need to start the slug, you'd be like, okay,

408
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Speaker 1: But you've got to be one of the ones who dies,

409
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Speaker 1: because otherwise why would anybody join like, yeah, okay, I'll

410
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Speaker 1: help you out. There's like, you know, a twenty percent

411
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Speaker 1: chance I'm gonna kick the bucket if I help you out,

412
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Speaker 1: but I'm gonna give you a hands So that's kind

413
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Speaker 1: of surprising to.

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Speaker 3: Me, but that also makes me wonder how does this begin.

415
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Speaker 3: Is it one amiba decides like, hey guys, it's stock time,

416
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Speaker 3: or is it a collective decision, or how does that happen.

417
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Speaker 2: For the creation of the slug.

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Speaker 1: We don't know exactly because watching this kind of stuff

419
00:19:36,119 --> 00:19:37,879
Speaker 1: in nature is pretty hard, but I think that it

420
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Speaker 1: is like some individuals who are maybe in the part

421
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Speaker 1: of the plate where most of the bacteria have been eaten,

422
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Speaker 1: or the first to start getting hungry. They start releasing

423
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Speaker 1: some chemicals into the environment, and so I guess the

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Speaker 1: other amibas could be like, oh, this is a clue

425
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Speaker 1: that this environment is about to be going downhill, and

426
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Speaker 1: so I'm gonna go ahead and join you. Or maybe

427
00:19:54,320 --> 00:19:56,920
Speaker 1: they also have a sense, but they're just lagging a

428
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Speaker 1: little bit further behind. Maybe they also have a sense

429
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Speaker 1: that the bacteria food in the environment is starting to

430
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Speaker 1: go down. So then they start releasing chemicals, and everybody

431
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Speaker 1: who has quote unquote decided to join the slug starts

432
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Speaker 1: releasing chemicals, and that creates these really big aggregations.

433
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Speaker 3: Interesting, and so when there's like enough of this chemical

434
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Speaker 3: in the environment, it changes all of their behavior. Yeah, yes,

435
00:20:19,119 --> 00:20:20,399
Speaker 3: you know what, I wish we had that for I

436
00:20:20,440 --> 00:20:23,159
Speaker 3: wish we had that for a zoom meetings. Like, if

437
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Speaker 3: enough people on a zoom meeting want it to be over,

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Speaker 3: it should just like automatically end.

439
00:20:28,320 --> 00:20:28,879
Speaker 2: Amen.

440
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Speaker 1: Yes, the screen should just go dark and you should

441
00:20:31,320 --> 00:20:32,040
Speaker 1: be like yes.

442
00:20:32,400 --> 00:20:34,760
Speaker 2: And that way, no one has to declare their preference.

443
00:20:34,760 --> 00:20:39,840
Speaker 1: Suggests you can start secreting things onto your screen exactly.

444
00:20:40,440 --> 00:20:44,000
Speaker 3: That would be lovely, that would let's slug this meeting anyway,

445
00:20:44,320 --> 00:20:46,080
Speaker 3: all right. But part of the mystery is that the

446
00:20:46,119 --> 00:20:50,080
Speaker 3: folks in the bacteria poor the food poor region aren't

447
00:20:50,080 --> 00:20:52,320
Speaker 3: the ones who end up sacrificing themselves. They're the ones

448
00:20:52,359 --> 00:20:55,240
Speaker 3: who end up being safe. So they're like calling for assistance,

449
00:20:55,480 --> 00:20:57,600
Speaker 3: and the people who come to help them end up

450
00:20:57,640 --> 00:20:58,120
Speaker 3: losing out.

451
00:20:58,440 --> 00:21:00,520
Speaker 1: Often they end up losing out. Yeah, that's sort of

452
00:21:00,520 --> 00:21:03,080
Speaker 1: the counterintuitive thing. And so at this moment, I think

453
00:21:03,080 --> 00:21:06,040
Speaker 1: we want to start talking about the costs and benefits here.

454
00:21:06,600 --> 00:21:09,520
Speaker 1: And so one of the benefits of being in a

455
00:21:09,640 --> 00:21:12,440
Speaker 1: group is that the bigger the slug is.

456
00:21:12,960 --> 00:21:15,439
Speaker 2: The farther it travels, makes sense.

457
00:21:15,560 --> 00:21:19,360
Speaker 1: But also if the slug is made up of all

458
00:21:19,480 --> 00:21:22,280
Speaker 1: the same clone like in the lab, if you just

459
00:21:22,359 --> 00:21:24,120
Speaker 1: let the amba di vibe like crazy and then they

460
00:21:24,160 --> 00:21:27,479
Speaker 1: all join and make their own slug slugs that are

461
00:21:27,520 --> 00:21:31,240
Speaker 1: the same size. If you compare the clone slugs to

462
00:21:31,320 --> 00:21:33,600
Speaker 1: what they call a chimeras slug, a slug that's got

463
00:21:33,600 --> 00:21:37,520
Speaker 1: a couple different clones, the clone slug goes farther. Interesting,

464
00:21:37,760 --> 00:21:41,919
Speaker 1: so they work together more nicely when they've got like

465
00:21:42,080 --> 00:21:46,360
Speaker 1: family around. But in the end, we think the benefit

466
00:21:46,480 --> 00:21:49,360
Speaker 1: of working with other amiba is that if you can

467
00:21:49,400 --> 00:21:52,359
Speaker 1: get your slug like twice as big, then you're going

468
00:21:52,400 --> 00:21:54,800
Speaker 1: to go even farther than you would have gone with

469
00:21:54,960 --> 00:21:57,360
Speaker 1: half as many of your clones.

470
00:21:57,680 --> 00:22:02,120
Speaker 3: Yeah, and in the bigger slug to this same proportion survive.

471
00:22:02,359 --> 00:22:05,159
Speaker 3: Is it like still twenty percent end up in the

472
00:22:05,160 --> 00:22:07,400
Speaker 3: stalk or does that change as they get bigger.

473
00:22:07,480 --> 00:22:08,840
Speaker 2: Yeah, it's always about twenty percent.

474
00:22:09,000 --> 00:22:13,480
Speaker 3: Okay, So increasing the slug doesn't change your odds of surviving,

475
00:22:13,920 --> 00:22:16,120
Speaker 3: but it does mean that the slug might get.

476
00:22:15,960 --> 00:22:16,960
Speaker 2: Further that's right.

477
00:22:17,560 --> 00:22:20,560
Speaker 1: Okay, So now you've got these slugs that can either

478
00:22:20,640 --> 00:22:24,120
Speaker 1: form with all clones or they can be chimeras multiple

479
00:22:24,119 --> 00:22:29,199
Speaker 1: different clones together. Yeah, and so you would expect that

480
00:22:29,240 --> 00:22:32,800
Speaker 1: if you've got a situation where multiple clones are in

481
00:22:32,920 --> 00:22:36,560
Speaker 1: the same slug, Yeah, there should be pressure to cheat, right,

482
00:22:36,800 --> 00:22:38,919
Speaker 1: pressure to like try to run into the head and

483
00:22:39,000 --> 00:22:42,360
Speaker 1: get you know, the loser clone to be in the stalk.

484
00:22:42,320 --> 00:22:43,920
Speaker 3: To get them down at the like the dark meat

485
00:22:43,960 --> 00:22:45,200
Speaker 3: side of the Thanksgiving table.

486
00:22:45,320 --> 00:22:48,640
Speaker 1: That's oh my gosh, I also hate dark meat, ah Daniel,

487
00:22:49,600 --> 00:22:54,399
Speaker 1: dark meat bad, white chocolate bad, dark chocolate good, white

488
00:22:54,400 --> 00:22:54,960
Speaker 1: meat good.

489
00:22:55,280 --> 00:22:56,679
Speaker 2: I'm so glad you and I agree.

490
00:22:56,720 --> 00:22:58,399
Speaker 1: And also the listeners should know that you and I

491
00:22:58,440 --> 00:23:01,159
Speaker 1: are wearing very similar color shirts today. We both have

492
00:23:01,200 --> 00:23:05,520
Speaker 1: our black glasses on. It's a good twinsy day for dkayar.

493
00:23:06,400 --> 00:23:06,800
Speaker 2: All right.

494
00:23:06,880 --> 00:23:09,760
Speaker 1: So moving on, So you would expect there to be cheating,

495
00:23:10,640 --> 00:23:15,800
Speaker 1: and in fact you actually find a linear dominance hierarchy.

496
00:23:15,840 --> 00:23:17,879
Speaker 1: So this lab went out in the wild and they

497
00:23:17,880 --> 00:23:21,560
Speaker 1: collected a bunch of different clones from a city called

498
00:23:21,680 --> 00:23:24,800
Speaker 1: Little Butts Gap, which I love so much that I

499
00:23:24,840 --> 00:23:25,679
Speaker 1: had to include it.

500
00:23:26,000 --> 00:23:27,480
Speaker 3: I Mean, that's the kind of place you expect to

501
00:23:27,480 --> 00:23:29,080
Speaker 3: find amba, right, that's right, that's right.

502
00:23:29,080 --> 00:23:30,639
Speaker 1: Well, they live on poop, and so you go to

503
00:23:30,640 --> 00:23:33,720
Speaker 1: a little Butts Gap and so anyway, so at little

504
00:23:33,720 --> 00:23:34,320
Speaker 1: Butts Gap.

505
00:23:34,520 --> 00:23:35,440
Speaker 2: If you get a.

506
00:23:35,359 --> 00:23:39,400
Speaker 1: Bunch of different clones of amba and then you put

507
00:23:39,440 --> 00:23:43,560
Speaker 1: them together in a bunch of different pairwise situations, what

508
00:23:43,640 --> 00:23:48,160
Speaker 1: you find is that there are some clones that are

509
00:23:48,200 --> 00:23:51,040
Speaker 1: just better than other clones at getting into the top

510
00:23:51,119 --> 00:23:54,119
Speaker 1: and avoiding paying the cost. And when I say that

511
00:23:54,160 --> 00:23:57,040
Speaker 1: it's linear, I mean it's like you can rank them

512
00:23:57,080 --> 00:24:00,320
Speaker 1: in order of how good they are at avoiding being

513
00:24:00,320 --> 00:24:02,919
Speaker 1: in the stalk, and so like you can rank them

514
00:24:02,920 --> 00:24:06,159
Speaker 1: from one to ten, and Clone number one is always

515
00:24:06,240 --> 00:24:08,560
Speaker 1: going to be better than Clone two, three, four, five, six, seven,

516
00:24:08,560 --> 00:24:10,879
Speaker 1: eight nine and ten at getting into the head. And

517
00:24:10,960 --> 00:24:13,159
Speaker 1: Clone two isn't as good as Clone one, but it

518
00:24:13,200 --> 00:24:15,480
Speaker 1: can beat Clone three, four, five, six, seven, eight nine, ten.

519
00:24:15,480 --> 00:24:16,200
Speaker 2: Does that make sense?

520
00:24:16,480 --> 00:24:18,160
Speaker 3: Yeah? All right, So we got a lot going on here.

521
00:24:18,200 --> 00:24:21,800
Speaker 3: We said that slugs that only have one species than

522
00:24:21,840 --> 00:24:25,560
Speaker 3: one genetic imprint do the best. When you mix the

523
00:24:25,640 --> 00:24:29,240
Speaker 3: species together, you have this chimera slug. It can still

524
00:24:29,280 --> 00:24:32,080
Speaker 3: do well, but not as well. And then within the

525
00:24:32,200 --> 00:24:35,480
Speaker 3: chimera there's some genetic variants that end up in the

526
00:24:35,520 --> 00:24:38,760
Speaker 3: good part more often and in the stock part less often.

527
00:24:38,840 --> 00:24:41,520
Speaker 3: That's right, and you can rank those. So basically like

528
00:24:41,880 --> 00:24:44,520
Speaker 3: some relatives are better at getting to the white meat

529
00:24:44,600 --> 00:24:46,840
Speaker 3: end of the table, and those guys from Little Butts

530
00:24:46,840 --> 00:24:49,119
Speaker 3: Gap who showed up late always end up in the

531
00:24:49,200 --> 00:24:50,200
Speaker 3: dark meat side of the table.

532
00:24:50,280 --> 00:24:52,959
Speaker 1: That's right, And they don't play nice. They don't like decide, oh,

533
00:24:52,960 --> 00:24:55,119
Speaker 1: I'm going to share to make it even. But again,

534
00:24:55,160 --> 00:24:57,320
Speaker 1: one thing that you need to remember is that that

535
00:24:57,359 --> 00:25:01,560
Speaker 1: comparison you made between slugs of all one clone or chimeras,

536
00:25:02,200 --> 00:25:05,520
Speaker 1: the clone moves better when those slugs are the same size.

537
00:25:05,800 --> 00:25:06,160
Speaker 3: I see.

538
00:25:06,200 --> 00:25:09,159
Speaker 1: But if you can signal hey, I need help and

539
00:25:09,200 --> 00:25:11,639
Speaker 1: the slug ends up being five times bigger because you

540
00:25:11,720 --> 00:25:13,800
Speaker 1: got a bunch of different clones there, then it's going

541
00:25:13,880 --> 00:25:16,159
Speaker 1: to go even farther than you would have gone alone.

542
00:25:16,520 --> 00:25:19,680
Speaker 3: All right, So it's better to include other species because

543
00:25:19,720 --> 00:25:21,280
Speaker 3: it's going to make your slug bigger and you're going

544
00:25:21,320 --> 00:25:23,639
Speaker 3: to go further if you don't have any more of

545
00:25:23,640 --> 00:25:24,679
Speaker 3: your own friends around.

546
00:25:25,000 --> 00:25:27,159
Speaker 1: Maybe, But now there are these trade offs to contend

547
00:25:27,200 --> 00:25:30,560
Speaker 1: with because depending on who you're interacting with, they are

548
00:25:30,600 --> 00:25:34,480
Speaker 1: going to differ in how much they're cheating. Cheating is

549
00:25:34,520 --> 00:25:35,080
Speaker 1: what we call it.

550
00:25:35,119 --> 00:25:38,080
Speaker 3: So for example, if you end up attracting a bunch

551
00:25:38,080 --> 00:25:40,680
Speaker 3: of folks who are really good at getting to the tip,

552
00:25:40,960 --> 00:25:43,119
Speaker 3: you're going to be worse off yourself. You're gonna end

553
00:25:43,200 --> 00:25:44,320
Speaker 3: up somewhere near the middle of the.

554
00:25:44,240 --> 00:25:45,480
Speaker 2: Table, exactly right.

555
00:25:45,520 --> 00:25:47,280
Speaker 1: So what you want to do is you want to

556
00:25:47,320 --> 00:25:51,399
Speaker 1: try to find other cooperators. And so you might remember

557
00:25:51,440 --> 00:25:53,520
Speaker 1: that in the last episode where we talked about cooperation,

558
00:25:53,640 --> 00:25:57,240
Speaker 1: you were like, well, can sort of quote unquote lower

559
00:25:57,359 --> 00:26:01,479
Speaker 1: organisms actually even recognize family members or can they in

560
00:26:01,520 --> 00:26:04,080
Speaker 1: some way recognize another cooperator?

561
00:26:04,320 --> 00:26:05,720
Speaker 3: Oh? I asked that last time. I was about to

562
00:26:05,760 --> 00:26:07,359
Speaker 3: ask that this time, like how did they even know?

563
00:26:07,840 --> 00:26:09,040
Speaker 2: Yeah, so it's a great question.

564
00:26:09,240 --> 00:26:13,119
Speaker 1: And so there's this idea that Richard Dawkins named the

565
00:26:13,240 --> 00:26:16,399
Speaker 1: green beard effect. Okay, and so the idea here is

566
00:26:16,400 --> 00:26:21,400
Speaker 1: that if you want to selectively work with other cooperators, yeah,

567
00:26:21,440 --> 00:26:24,520
Speaker 1: what you need is at least one gene that has

568
00:26:24,640 --> 00:26:29,520
Speaker 1: three different criteria. The gene needs to create something that

569
00:26:29,600 --> 00:26:33,119
Speaker 1: can be seen by other individuals, like a green beard.

570
00:26:33,600 --> 00:26:33,919
Speaker 3: Yeah.

571
00:26:34,200 --> 00:26:36,840
Speaker 1: Other individuals need to be able to like actually recognize

572
00:26:36,840 --> 00:26:39,440
Speaker 1: that trait, Okay, and then you need to be able

573
00:26:39,480 --> 00:26:43,359
Speaker 1: to preferentially work with the individuals that have that trait.

574
00:26:43,840 --> 00:26:46,800
Speaker 3: So it's like a uniform. Right, you're on the battlefield.

575
00:26:46,840 --> 00:26:49,360
Speaker 3: Everybody's got a uniform to indicate which side they're on.

576
00:26:49,760 --> 00:26:52,840
Speaker 3: You can recognize the uniforms and also you know how

577
00:26:52,880 --> 00:26:53,520
Speaker 3: to work together.

578
00:26:53,680 --> 00:26:56,040
Speaker 2: Yes, okay, makes sense, Yeah, exactly okay.

579
00:26:56,080 --> 00:26:59,280
Speaker 1: And so the Strassman Queller lab thought that they found

580
00:26:59,400 --> 00:27:02,639
Speaker 1: a green gene. And so what this gene did was

581
00:27:02,680 --> 00:27:05,359
Speaker 1: it essentially produces a molecule on the outside of the

582
00:27:05,400 --> 00:27:07,680
Speaker 1: amiba that helps it stick to other amiba. So it

583
00:27:07,680 --> 00:27:10,360
Speaker 1: would be like it's like it's reaching out its hands

584
00:27:10,400 --> 00:27:11,680
Speaker 1: to grab another amiba.

585
00:27:11,840 --> 00:27:12,199
Speaker 3: Okay.

586
00:27:12,240 --> 00:27:16,080
Speaker 1: And this is important because if you are not holding

587
00:27:16,160 --> 00:27:18,720
Speaker 1: on to the individuals that are nearby, you have a

588
00:27:18,760 --> 00:27:21,679
Speaker 1: tendency to slide towards the back of the slug. And

589
00:27:21,720 --> 00:27:24,240
Speaker 1: the back of the slug is where the head ends

590
00:27:24,320 --> 00:27:28,080
Speaker 1: up being made. And so you want to find individuals

591
00:27:28,080 --> 00:27:30,000
Speaker 1: who are going to reach out and hold your hand,

592
00:27:30,080 --> 00:27:32,800
Speaker 1: because that's a way of saying, I am not going

593
00:27:32,840 --> 00:27:35,639
Speaker 1: to try to sneak to the good part of the slug.

594
00:27:35,760 --> 00:27:38,760
Speaker 1: I'm gonna stay with you and whatever my fate holds,

595
00:27:38,800 --> 00:27:40,399
Speaker 1: I will be here to see it.

596
00:27:40,440 --> 00:27:42,520
Speaker 2: You know. It's a way of indicating you're an altruist.

597
00:27:42,800 --> 00:27:43,879
Speaker 3: Yeah, you're a cooperator.

598
00:27:44,000 --> 00:27:45,080
Speaker 2: That's right, you're cooperator.

599
00:27:45,119 --> 00:27:47,480
Speaker 1: And so if you knock this gene out, but you

600
00:27:47,680 --> 00:27:50,560
Speaker 1: still find some way to like make the slugs form

601
00:27:50,560 --> 00:27:53,400
Speaker 1: in the lab, you will find that these individuals tend

602
00:27:53,400 --> 00:27:54,760
Speaker 1: to like slide towards the.

603
00:27:54,720 --> 00:27:57,439
Speaker 3: Back, and the back is the good direction.

604
00:27:57,880 --> 00:27:59,840
Speaker 2: The back is the good direction. That's where the heads

605
00:27:59,840 --> 00:28:00,399
Speaker 2: got to be.

606
00:28:00,840 --> 00:28:03,080
Speaker 3: That makes no sense that all the back is where

607
00:28:03,080 --> 00:28:03,840
Speaker 3: the head's going to be.

608
00:28:04,080 --> 00:28:06,679
Speaker 1: Yeah, well you know, nature doesn't Nature doesn't care if

609
00:28:06,720 --> 00:28:09,440
Speaker 1: it's intuitive or not. And so they're arguing that this

610
00:28:09,520 --> 00:28:12,719
Speaker 1: gene is essentially a way to find other individuals who

611
00:28:12,760 --> 00:28:15,040
Speaker 1: are willing to be altruistic. So if you can reach

612
00:28:15,080 --> 00:28:17,119
Speaker 1: out and grab the hand of the amiba next to

613
00:28:17,119 --> 00:28:19,960
Speaker 1: you and hold on to it, you can say, all right,

614
00:28:20,000 --> 00:28:22,320
Speaker 1: you are here with me no matter what our fate

615
00:28:22,400 --> 00:28:24,760
Speaker 1: ends up being. And so you can try to preferentially

616
00:28:25,359 --> 00:28:28,200
Speaker 1: bind to these individuals that have this protein that can

617
00:28:28,200 --> 00:28:29,240
Speaker 1: bind the amba together.

618
00:28:29,560 --> 00:28:32,040
Speaker 3: So I get that they can have this protein that

619
00:28:32,080 --> 00:28:35,240
Speaker 3: can bind them together and help them cooperate, and that

620
00:28:35,280 --> 00:28:37,280
Speaker 3: if you were an amoeba, you would want to find

621
00:28:37,400 --> 00:28:40,280
Speaker 3: other ones that can cooperate. The bit I missed and

622
00:28:40,320 --> 00:28:42,360
Speaker 3: maybe you said, this is how does that get them

623
00:28:42,400 --> 00:28:43,400
Speaker 3: closer to the back.

624
00:28:43,840 --> 00:28:47,760
Speaker 1: So if you don't have this gene, yeah, then you

625
00:28:48,000 --> 00:28:51,240
Speaker 1: aren't sticking your hands out right. And so if you

626
00:28:51,280 --> 00:28:54,520
Speaker 1: can manage to get yourself into a slug, then you

627
00:28:54,560 --> 00:28:57,280
Speaker 1: can slide towards the back because you're not holding anyone's hands.

628
00:28:57,280 --> 00:28:59,440
Speaker 1: So you can just sort of like tiptoe to the

629
00:28:59,480 --> 00:29:01,960
Speaker 1: back of the slug and manage to get yourself in

630
00:29:01,960 --> 00:29:06,800
Speaker 1: the head position. And so by preferentially working with amiba

631
00:29:06,920 --> 00:29:09,680
Speaker 1: that are putting out their hand, then you can make

632
00:29:09,680 --> 00:29:12,600
Speaker 1: sure that your slug is mostly other individuals who are

633
00:29:12,600 --> 00:29:13,360
Speaker 1: willing to play ice.

634
00:29:13,600 --> 00:29:16,200
Speaker 3: I see. So it's not that by holding hands you

635
00:29:16,280 --> 00:29:19,120
Speaker 3: get to the back more often, but by choosing other

636
00:29:19,120 --> 00:29:22,480
Speaker 3: people who will hold hands, you're choosing against folks that

637
00:29:22,520 --> 00:29:24,440
Speaker 3: are going to cheat, sneak around you and get to

638
00:29:24,440 --> 00:29:25,800
Speaker 3: the front of the line for the white meat.

639
00:29:26,000 --> 00:29:28,520
Speaker 1: Yes, exactly, got it right. Okay, So that's one way

640
00:29:28,600 --> 00:29:31,240
Speaker 1: that you can try to find clones that are willing

641
00:29:31,320 --> 00:29:33,920
Speaker 1: to play ice because they're willing to essentially hold your

642
00:29:33,960 --> 00:29:36,880
Speaker 1: little amba hand. I imagine somebody out there who actually

643
00:29:36,920 --> 00:29:40,360
Speaker 1: studies amba is like cringing explanation.

644
00:29:40,440 --> 00:29:41,920
Speaker 2: But then the other thing that.

645
00:29:41,880 --> 00:29:45,040
Speaker 1: You might want to do is preferentially try to find

646
00:29:45,400 --> 00:29:48,240
Speaker 1: your kin. Right, So maybe it would be better if

647
00:29:48,280 --> 00:29:51,640
Speaker 1: your slug had a million cells in it or something, right,

648
00:29:51,760 --> 00:29:54,520
Speaker 1: But if as many of those cells as possible could

649
00:29:54,560 --> 00:29:58,160
Speaker 1: be the exact same clone as you, then you're gonna

650
00:29:58,200 --> 00:30:01,000
Speaker 1: do much better than if you've got you know you,

651
00:30:01,440 --> 00:30:04,200
Speaker 1: and then the clone that you know cheats, right, making

652
00:30:04,240 --> 00:30:04,960
Speaker 1: up fifty percent.

653
00:30:05,120 --> 00:30:07,240
Speaker 3: And do we know why they do better when they're

654
00:30:07,280 --> 00:30:09,560
Speaker 3: all the same clone? Do they just all swim the

655
00:30:09,600 --> 00:30:10,960
Speaker 3: same way or how does that work?

656
00:30:11,040 --> 00:30:13,280
Speaker 1: Yeah, we don't. That's a great question. We don't know exactly.

657
00:30:13,760 --> 00:30:17,000
Speaker 1: But it could be things like, you know, they're all

658
00:30:17,080 --> 00:30:21,160
Speaker 1: moving at the same speed, or they're not like trying

659
00:30:21,200 --> 00:30:23,800
Speaker 1: to jockey for the best position. They're just kind of

660
00:30:23,920 --> 00:30:27,880
Speaker 1: moving forward. Less chaos, yeah, less chaos and so but

661
00:30:27,920 --> 00:30:29,400
Speaker 1: that's just like a handwavy guess we.

662
00:30:29,400 --> 00:30:30,120
Speaker 2: Don't actually know.

663
00:30:30,520 --> 00:30:35,240
Speaker 1: Okay, interesting, Okay, So they did some experiments to try

664
00:30:35,240 --> 00:30:39,200
Speaker 1: to figure out if amiba could essentially find their kin,

665
00:30:40,080 --> 00:30:42,760
Speaker 1: and what they find is that if you put amoeba

666
00:30:43,040 --> 00:30:47,840
Speaker 1: in trays together, the more distantly related the amiba are,

667
00:30:48,840 --> 00:30:51,400
Speaker 1: the less likely they are to come together and end

668
00:30:51,480 --> 00:30:52,440
Speaker 1: up in the same slug.

669
00:30:52,720 --> 00:30:53,000
Speaker 3: Really.

670
00:30:53,160 --> 00:30:54,600
Speaker 2: Yeah, so it does seem like to see, so they

671
00:30:54,600 --> 00:30:55,080
Speaker 2: can do it.

672
00:30:55,200 --> 00:30:57,880
Speaker 1: Yeah, to some extent, they can say, like, I mean,

673
00:30:57,920 --> 00:31:00,640
Speaker 1: so if the clones are sort of close, they probably

674
00:31:00,640 --> 00:31:03,040
Speaker 1: can't tell the difference. But when they get pretty far apart,

675
00:31:03,440 --> 00:31:05,840
Speaker 1: they can say, all right, you are not exactly like me.

676
00:31:06,440 --> 00:31:10,600
Speaker 1: I'm going to try to preferentially associate with my clones

677
00:31:10,640 --> 00:31:12,120
Speaker 1: and like avoid you a little bit.

678
00:31:12,240 --> 00:31:14,520
Speaker 3: When you say far apart, you mean genetically, yes, I

679
00:31:14,520 --> 00:31:17,120
Speaker 3: mean genetically. And so do we know how they do this?

680
00:31:17,160 --> 00:31:19,440
Speaker 3: I mean, they're not running little PCR tests, right, taking

681
00:31:19,480 --> 00:31:22,600
Speaker 3: little samples from each other. There must be something on

682
00:31:22,640 --> 00:31:25,600
Speaker 3: the outside of the amiba which is very sensitive to

683
00:31:25,640 --> 00:31:28,920
Speaker 3: the genetic record that they're using as a signal.

684
00:31:29,240 --> 00:31:31,200
Speaker 1: Yeah, so we don't know exactly what it is, but

685
00:31:31,280 --> 00:31:33,760
Speaker 1: it could be something like, you know, they have those

686
00:31:33,800 --> 00:31:36,120
Speaker 1: hands that reach out, but some of the hands are

687
00:31:36,160 --> 00:31:38,680
Speaker 1: shaped a little bit different, and it's easier to hold

688
00:31:38,720 --> 00:31:40,960
Speaker 1: the hand of a clone or an amba that's closely

689
00:31:41,000 --> 00:31:43,760
Speaker 1: related than an amoba that's much more distantly related.

690
00:31:43,760 --> 00:31:44,520
Speaker 2: We're over time.

691
00:31:44,600 --> 00:31:47,520
Speaker 1: Maybe the hand now has eight fingers instead of five,

692
00:31:47,960 --> 00:31:49,760
Speaker 1: and it just kind of doesn't hold together as well.

693
00:31:49,920 --> 00:31:51,880
Speaker 3: I like to imagine that the Amiba all have like

694
00:31:52,000 --> 00:31:55,480
Speaker 3: their own specialized little greeting, you know, like a secret handshake,

695
00:31:55,920 --> 00:31:57,760
Speaker 3: and if you have the wrong genetics, you like do

696
00:31:57,800 --> 00:31:59,680
Speaker 3: it wrong. You like, no, you're supposed to high five

697
00:31:59,760 --> 00:32:01,880
Speaker 3: and then you're supposed to do a low five and

698
00:32:01,920 --> 00:32:02,479
Speaker 3: then whatever.

699
00:32:02,720 --> 00:32:03,080
Speaker 2: Exactly.

700
00:32:03,200 --> 00:32:05,440
Speaker 3: So maybe that's how they tell who's who and who's

701
00:32:05,440 --> 00:32:06,240
Speaker 3: from what neighborhood.

702
00:32:06,360 --> 00:32:08,440
Speaker 2: Yeah, they've got little voices and they're trying.

703
00:32:08,200 --> 00:32:09,880
Speaker 3: To do like little codes and.

704
00:32:09,840 --> 00:32:12,880
Speaker 1: Hold hands and like your handshakes, and it's yeah, that's

705
00:32:12,880 --> 00:32:13,880
Speaker 1: probably what's happening.

706
00:32:13,640 --> 00:32:15,480
Speaker 3: All right, But here we're speculating, right because we have

707
00:32:15,560 --> 00:32:17,560
Speaker 3: not identified this. We haven't seen this in action.

708
00:32:17,680 --> 00:32:19,320
Speaker 2: That's that's exactly right. Yeah.

709
00:32:19,360 --> 00:32:21,360
Speaker 3: So we know that they can do, we just don't

710
00:32:21,400 --> 00:32:21,960
Speaker 3: know how.

711
00:32:21,880 --> 00:32:22,240
Speaker 2: That's right.

712
00:32:22,240 --> 00:32:22,960
Speaker 3: That's fascinating.

713
00:32:23,120 --> 00:32:24,120
Speaker 2: Yeah, yeah, yeah.

714
00:32:24,160 --> 00:32:28,640
Speaker 1: And so this was becoming this like huge story because

715
00:32:28,800 --> 00:32:31,680
Speaker 1: like everybody wants to study kin selection and try to

716
00:32:31,680 --> 00:32:33,880
Speaker 1: figure out like, well, how do you know who kN

717
00:32:34,080 --> 00:32:36,560
Speaker 1: is and how does that impact cooperation and cheating? And

718
00:32:36,600 --> 00:32:39,720
Speaker 1: by everybody, I'm seeing your face. Your face is saying

719
00:32:40,120 --> 00:32:43,840
Speaker 1: I don't care, but uh, in the field of behaviorally colors.

720
00:32:43,880 --> 00:32:47,200
Speaker 3: No, my face is saying, Okay, biologists are fascinated by this.

721
00:32:47,320 --> 00:32:50,200
Speaker 1: Tell me why, Well, because we want to know about

722
00:32:50,240 --> 00:32:52,800
Speaker 1: like the evolution of cooperation, Like we see a lot

723
00:32:52,800 --> 00:32:54,040
Speaker 1: of cooperation in nature.

724
00:32:54,560 --> 00:32:57,120
Speaker 2: How does this evolve and how does it show up?

725
00:32:57,160 --> 00:33:00,680
Speaker 1: And how do you resist cheating to keep these systems

726
00:33:00,680 --> 00:33:02,400
Speaker 1: going right, because if you have too many cheaters, then

727
00:33:02,480 --> 00:33:04,280
Speaker 1: like it doesn't make sense to cooperate anymore.

728
00:33:04,320 --> 00:33:06,520
Speaker 3: Okay, because of all the puzzles we've laid out.

729
00:33:06,440 --> 00:33:08,560
Speaker 1: Right, yeah, exactly, because of all the puzzles we've laid

730
00:33:08,560 --> 00:33:11,719
Speaker 1: out so cool. The Strassman and Coeller labs were like, well,

731
00:33:11,760 --> 00:33:16,120
Speaker 1: what we really want to know is what do these lollipops,

732
00:33:16,160 --> 00:33:19,560
Speaker 1: these fruiting bodies look like in the wild, Like if

733
00:33:19,560 --> 00:33:22,480
Speaker 1: you collect one in the wild, is it all the

734
00:33:22,520 --> 00:33:23,760
Speaker 1: same clone?

735
00:33:23,840 --> 00:33:25,320
Speaker 2: Is it a bunch of different clones?

736
00:33:25,440 --> 00:33:29,760
Speaker 1: Like how do we make our lab results relevant to

737
00:33:29,840 --> 00:33:30,720
Speaker 1: what's happening in the field.

738
00:33:30,760 --> 00:33:31,800
Speaker 2: We need that baseline.

739
00:33:32,000 --> 00:33:34,320
Speaker 1: And so they kept talking to AMIBA experts who were like,

740
00:33:34,560 --> 00:33:36,640
Speaker 1: you are never going to find them in the field.

741
00:33:36,720 --> 00:33:39,000
Speaker 1: They are these teeny tiny little stalks and none of

742
00:33:39,120 --> 00:33:40,200
Speaker 1: us have been able to find them.

743
00:33:40,640 --> 00:33:42,640
Speaker 3: But how do we know they exist, if how did

744
00:33:43,000 --> 00:33:44,800
Speaker 3: people even know to make them in the lab if

745
00:33:44,800 --> 00:33:46,000
Speaker 3: they'd never seen them in the wild.

746
00:33:46,120 --> 00:33:48,600
Speaker 1: Well, probably, I'm guessing, because somebody forgot to put more

747
00:33:48,640 --> 00:33:51,080
Speaker 1: bacteria on a plate one day and they were like, oh,

748
00:33:51,120 --> 00:33:52,840
Speaker 1: what's going on. There's a slug and now there's a

749
00:33:52,840 --> 00:33:55,080
Speaker 1: fruiting body. And you can reliably get them to do

750
00:33:55,120 --> 00:33:57,280
Speaker 1: it in the lab over and over and over again,

751
00:33:58,120 --> 00:34:00,000
Speaker 1: and so they were like, this must be a thing,

752
00:34:00,000 --> 00:34:01,000
Speaker 1: and they also do in nature.

753
00:34:01,240 --> 00:34:04,040
Speaker 3: So this was discovered first in the lab. Yeah, wow,

754
00:34:04,120 --> 00:34:06,640
Speaker 3: that's incredible. I thought we knew about this, we'd seen

755
00:34:06,640 --> 00:34:09,200
Speaker 3: it in the wild, and people recreated it in the lab.

756
00:34:09,640 --> 00:34:10,719
Speaker 2: That's what I would have guessed too.

757
00:34:10,800 --> 00:34:12,720
Speaker 1: And maybe some people had seen it, but like most

758
00:34:12,719 --> 00:34:16,120
Speaker 1: of the community of people who study amba were telling

759
00:34:16,360 --> 00:34:20,279
Speaker 1: Joan Straussman and her husband David Queller, you're probably not

760
00:34:20,320 --> 00:34:22,000
Speaker 1: gonna find it in the field. But they were like,

761
00:34:22,360 --> 00:34:26,120
Speaker 1: you know what, we're field biologists, and when someone says

762
00:34:26,160 --> 00:34:28,440
Speaker 1: you'll never find it, there's nothing that makes us want

763
00:34:28,440 --> 00:34:31,080
Speaker 1: to go out in the field more than someone's saying that.

764
00:34:31,160 --> 00:34:33,399
Speaker 1: And so they went out and they thought to themselves, Okay,

765
00:34:33,440 --> 00:34:36,319
Speaker 1: they eat bacteria, but what you're looking for is a

766
00:34:36,320 --> 00:34:39,080
Speaker 1: stage where there's not a lot of bacteria anymore, and

767
00:34:39,080 --> 00:34:40,439
Speaker 1: so now they need to go somewhere else.

768
00:34:40,920 --> 00:34:42,240
Speaker 2: We're looking for old poop.

769
00:34:42,320 --> 00:34:45,000
Speaker 3: Hold on, well, you got to back up because you

770
00:34:45,040 --> 00:34:47,480
Speaker 3: said they and sometimes you're referring to the amba, and

771
00:34:47,520 --> 00:34:48,960
Speaker 3: sometimes you're referring to the biologists.

772
00:34:49,040 --> 00:34:52,360
Speaker 1: Oh well, biologists and amba are interested in finding old poop,

773
00:34:52,440 --> 00:34:56,000
Speaker 1: and so the amiva. You know, when they have fresh poop,

774
00:34:56,000 --> 00:34:58,440
Speaker 1: there's loads of bacteria there, just like you and your

775
00:34:58,480 --> 00:35:02,320
Speaker 1: bacteria are collaborating on but you're gonna leave them behind.

776
00:35:02,400 --> 00:35:07,840
Speaker 1: The next day, deer and bacteria are collaborating on some leaves,

777
00:35:08,280 --> 00:35:10,560
Speaker 1: and then the deer leave them behind, and then the

778
00:35:10,600 --> 00:35:14,399
Speaker 1: bacteria become food for amiba. And then at some point

779
00:35:14,440 --> 00:35:17,160
Speaker 1: the amiba have eaten all of the good bacteria, and

780
00:35:17,200 --> 00:35:20,280
Speaker 1: now the deer poop is just not as delicious anymore,

781
00:35:20,280 --> 00:35:22,280
Speaker 1: and they want to try to find a new, fresher

782
00:35:22,600 --> 00:35:27,160
Speaker 1: deer poop. And so they went out looking for old

783
00:35:27,200 --> 00:35:27,880
Speaker 1: deer turds.

784
00:35:29,280 --> 00:35:34,480
Speaker 3: How glamorous is biology really? Not even fresh deer turds

785
00:35:34,920 --> 00:35:39,400
Speaker 3: aged aged deer turds.

786
00:35:40,280 --> 00:35:42,560
Speaker 2: So but but they found them.

787
00:35:42,960 --> 00:35:46,000
Speaker 1: They found a bunch of these like apparently impossible to

788
00:35:46,040 --> 00:35:50,719
Speaker 1: find fruiting bodies by finding old turds. Okay, but we're

789
00:35:50,719 --> 00:35:52,279
Speaker 1: going to take a break, and when we get back,

790
00:35:52,360 --> 00:35:55,200
Speaker 1: I'm going to tell you how related the amoeba were

791
00:35:55,719 --> 00:36:11,200
Speaker 1: on the fruiting bodies on the turds.

792
00:36:17,840 --> 00:36:21,120
Speaker 3: All Right, we're back, and Kelly has already done the amazing,

793
00:36:21,160 --> 00:36:24,560
Speaker 3: the near impossible, which is to get us all interested

794
00:36:24,880 --> 00:36:27,800
Speaker 3: to know what is on old deer turds.

795
00:36:28,160 --> 00:36:29,399
Speaker 2: Oh man, you know, I gotta be honest.

796
00:36:29,480 --> 00:36:32,080
Speaker 1: Usually I'm excited about what is in old deer turds

797
00:36:32,080 --> 00:36:33,960
Speaker 1: because that's where you find the parasite eggs. But now

798
00:36:34,000 --> 00:36:36,960
Speaker 1: I'm also excited about what is on old deer turds.

799
00:36:37,440 --> 00:36:40,759
Speaker 1: And the answer is the amiba who have gone through

800
00:36:40,800 --> 00:36:43,680
Speaker 1: the slug stage and have now made that lollipop shaped

801
00:36:43,680 --> 00:36:46,759
Speaker 1: fruiting body. When you look at how related they are

802
00:36:46,800 --> 00:36:49,600
Speaker 1: to each other, it can go from zero, which is

803
00:36:49,640 --> 00:36:54,360
Speaker 1: like completely unrelated, Like you run into somebody on another continent,

804
00:36:54,400 --> 00:36:55,920
Speaker 1: You're like, we're really not very related.

805
00:36:56,080 --> 00:36:57,920
Speaker 3: Wait, how can they be zero? We had this whole

806
00:36:57,960 --> 00:37:01,360
Speaker 3: episode where we talked about the common ancestors and percentage

807
00:37:01,400 --> 00:37:04,759
Speaker 3: of DNA that's shared and like chimpanzees and humans are

808
00:37:04,840 --> 00:37:07,000
Speaker 3: like ninety eight percent or whatever, So how can it

809
00:37:07,000 --> 00:37:08,359
Speaker 3: be zero? What do you mean by zero?

810
00:37:08,719 --> 00:37:11,200
Speaker 1: Well, so you right, remember that Nathan Lentz explained that

811
00:37:11,239 --> 00:37:13,680
Speaker 1: we share a lot of genes with chimpanzees. But what

812
00:37:13,719 --> 00:37:18,520
Speaker 1: we're talking about in this situation is which exact genes.

813
00:37:18,120 --> 00:37:18,760
Speaker 2: Do you share?

814
00:37:18,840 --> 00:37:22,640
Speaker 1: So, for example, if you have an identical twin, then

815
00:37:22,880 --> 00:37:24,799
Speaker 1: all of your genes are the same. You got the

816
00:37:24,840 --> 00:37:26,960
Speaker 1: same set of genes from your mom and the same

817
00:37:27,000 --> 00:37:29,680
Speaker 1: set of genes from your dad. But your brother got

818
00:37:29,880 --> 00:37:32,920
Speaker 1: fifty percent of their genes randomly from mom, fifty percent

819
00:37:32,920 --> 00:37:35,560
Speaker 1: of their genes randomly from dad, And you also got

820
00:37:35,800 --> 00:37:38,520
Speaker 1: fifty percent of the genes randomly from mom fifty percent

821
00:37:38,560 --> 00:37:41,160
Speaker 1: of the genes randomly from dad. And when that all

822
00:37:41,160 --> 00:37:45,200
Speaker 1: plays out, you and your brother, for example, probably have

823
00:37:45,719 --> 00:37:49,480
Speaker 1: fifty percent of pretty much the exact same gene copies

824
00:37:49,520 --> 00:37:51,920
Speaker 1: because you got them from the same people, which is

825
00:37:51,960 --> 00:37:52,560
Speaker 1: to say.

826
00:37:52,400 --> 00:37:54,520
Speaker 3: Your parents, I see, right, okay.

827
00:37:54,520 --> 00:37:57,880
Speaker 1: Whereas if we were somehow lucky enough to clone Daniel,

828
00:37:58,800 --> 00:38:01,320
Speaker 1: you and the Daniel clone would have relatedness of one.

829
00:38:01,400 --> 00:38:04,160
Speaker 3: Right, I see okay, And so you and I probably

830
00:38:04,160 --> 00:38:05,120
Speaker 3: have a relatedness of.

831
00:38:05,040 --> 00:38:06,959
Speaker 2: What probably pretty close to zero.

832
00:38:07,360 --> 00:38:10,640
Speaker 3: Okay, yeah, all right, so if Daniel Amiba meets Kelly

833
00:38:10,640 --> 00:38:13,920
Speaker 3: Amiba in the wild, we're like, whoa get away from me?

834
00:38:14,160 --> 00:38:16,319
Speaker 1: Well, except I think I'd be like, I see within

835
00:38:16,400 --> 00:38:19,640
Speaker 1: you a fellow altruist, and so maybe we would cooperate.

836
00:38:20,200 --> 00:38:21,680
Speaker 3: But let's start a podcast.

837
00:38:21,760 --> 00:38:25,120
Speaker 2: That's right, that's right, that sounds like fun.

838
00:38:25,280 --> 00:38:28,200
Speaker 1: Let's wear the same shirt on May thirteenth and dressed

839
00:38:28,239 --> 00:38:28,920
Speaker 1: like twinsies.

840
00:38:29,840 --> 00:38:30,240
Speaker 2: Okay.

841
00:38:30,320 --> 00:38:32,799
Speaker 1: So, so first of all, the answer is that some

842
00:38:32,880 --> 00:38:36,000
Speaker 1: of the fruiting bodies that they picked up had a

843
00:38:36,040 --> 00:38:37,640
Speaker 1: relatedness of one.

844
00:38:37,520 --> 00:38:39,400
Speaker 2: So it was all the same clone.

845
00:38:39,640 --> 00:38:44,200
Speaker 1: Oh, somehow they were associating with just similar clones. But

846
00:38:44,280 --> 00:38:47,680
Speaker 1: on average, across all of the fruiting bodies, the relatedness

847
00:38:47,760 --> 00:38:51,680
Speaker 1: was about zero point eighty six, which means, on average,

848
00:38:51,680 --> 00:38:55,680
Speaker 1: they were associating with the same clone, but there were

849
00:38:55,760 --> 00:38:58,200
Speaker 1: some other clones sprinkled in. But they were doing a

850
00:38:58,200 --> 00:39:01,600
Speaker 1: pretty good job of making sure that they were associating

851
00:39:01,600 --> 00:39:03,960
Speaker 1: with very very very very very close relatives.

852
00:39:04,640 --> 00:39:07,120
Speaker 3: And that makes sense because we know that they can

853
00:39:07,160 --> 00:39:09,520
Speaker 3: do this, that they can select their relatives, even if

854
00:39:09,560 --> 00:39:10,719
Speaker 3: we don't know how they're.

855
00:39:10,480 --> 00:39:12,520
Speaker 2: Doing that, right, But it was when we saw it

856
00:39:12,560 --> 00:39:13,120
Speaker 2: in the lab.

857
00:39:13,280 --> 00:39:16,399
Speaker 1: It was pretty weak, so it wasn't like they were

858
00:39:16,440 --> 00:39:19,239
Speaker 1: one hundred percent excluding they would still make slugs with

859
00:39:19,280 --> 00:39:22,160
Speaker 1: other individuals, especially if they were kind of closely related.

860
00:39:22,560 --> 00:39:25,319
Speaker 1: And so it was actually kind of hard to see

861
00:39:25,320 --> 00:39:29,080
Speaker 1: these results and know the extent to which they seemed

862
00:39:29,120 --> 00:39:31,799
Speaker 1: to be able to choose to hang out with the

863
00:39:31,800 --> 00:39:34,600
Speaker 1: same clone in the lab. And that effect was pretty weak,

864
00:39:34,640 --> 00:39:37,000
Speaker 1: and so it didn't explain how do you get such

865
00:39:37,040 --> 00:39:41,800
Speaker 1: a high relatedness in the fruiting body. And the answer,

866
00:39:42,200 --> 00:39:45,040
Speaker 1: it appears, is one that, to be honest, is like

867
00:39:45,239 --> 00:39:51,279
Speaker 1: not super satisfying as from like a framework perspective, because

868
00:39:51,280 --> 00:39:53,680
Speaker 1: you want it to be like, oh, their kin selection framework,

869
00:39:53,680 --> 00:39:54,480
Speaker 1: we get all the support.

870
00:39:54,600 --> 00:39:57,520
Speaker 2: But the answer seems to be if you put ameba

871
00:39:58,239 --> 00:39:58,680
Speaker 2: in like.

872
00:39:58,600 --> 00:40:01,560
Speaker 1: A circle in a dish and you let them grow

873
00:40:01,640 --> 00:40:05,000
Speaker 1: out from there, just based on the way that they grow,

874
00:40:05,080 --> 00:40:07,279
Speaker 1: the farther out from the circle, the more likely you

875
00:40:07,320 --> 00:40:09,680
Speaker 1: are to be surrounded by the exact same clone because

876
00:40:09,719 --> 00:40:12,279
Speaker 1: it just starts like replicating and replicating and replicating and

877
00:40:12,280 --> 00:40:13,600
Speaker 1: moving out from one location.

878
00:40:14,000 --> 00:40:15,240
Speaker 3: It's a geometric argument.

879
00:40:15,320 --> 00:40:17,320
Speaker 2: Basically, it's a geometric argument.

880
00:40:17,400 --> 00:40:20,279
Speaker 1: Yeah, so like if you imagine a pizza and each

881
00:40:20,320 --> 00:40:22,680
Speaker 1: slice is a clone, and they started like in the center,

882
00:40:22,760 --> 00:40:24,319
Speaker 1: you know, like sometimes when you get a pizza, they've

883
00:40:24,320 --> 00:40:25,719
Speaker 1: got that thing that looks like a little stool in

884
00:40:25,719 --> 00:40:26,080
Speaker 1: the middle.

885
00:40:26,280 --> 00:40:29,359
Speaker 3: Yeah, a little stand. Yeah, don't say stool when you're

886
00:40:29,400 --> 00:40:30,840
Speaker 3: talking about pizza.

887
00:40:30,960 --> 00:40:33,120
Speaker 2: You don't get squeamish on me, now, Daniel.

888
00:40:33,520 --> 00:40:36,359
Speaker 1: And so if you put them in the center where

889
00:40:36,440 --> 00:40:38,880
Speaker 1: the stool is, Wait, what did you somebody call it?

890
00:40:39,040 --> 00:40:41,040
Speaker 3: That's a little stand. It's to hold up the box.

891
00:40:41,440 --> 00:40:43,279
Speaker 2: Yeah, yeah, I know what it does. I know what

892
00:40:43,360 --> 00:40:45,440
Speaker 2: it does. I eat a lot of pizza.

893
00:40:45,520 --> 00:40:47,680
Speaker 1: So if you put it, if you put the bacteria

894
00:40:47,719 --> 00:40:50,160
Speaker 1: in the middle where the stand is, they grow out

895
00:40:50,200 --> 00:40:52,680
Speaker 1: from there. And when they grow out from there, like

896
00:40:52,680 --> 00:40:55,120
Speaker 1: if you're looking at the middle of a slice of pizza,

897
00:40:55,520 --> 00:40:57,560
Speaker 1: the middle of that slice of pizza is probably gonna

898
00:40:57,600 --> 00:41:00,239
Speaker 1: have just about all the same clones based just on

899
00:41:00,440 --> 00:41:03,040
Speaker 1: the way they grew out from the center. And so

900
00:41:03,080 --> 00:41:04,520
Speaker 1: when they start to run out of food and they

901
00:41:04,560 --> 00:41:06,880
Speaker 1: send that signal, hey guys, it's time to form a

902
00:41:06,880 --> 00:41:09,880
Speaker 1: slug because we're running out of food, they're mostly talking

903
00:41:09,920 --> 00:41:10,920
Speaker 1: to their relatives.

904
00:41:11,160 --> 00:41:14,719
Speaker 3: Yeah, So the area of each blob grows faster than

905
00:41:15,080 --> 00:41:19,880
Speaker 3: its circumference and so the ratio of area to circumference grows,

906
00:41:19,920 --> 00:41:23,040
Speaker 3: which means that on average they're further away from a boundary.

907
00:41:23,239 --> 00:41:25,920
Speaker 3: That sounds right, Yeah, it's just a simple geometric argument.

908
00:41:26,000 --> 00:41:30,080
Speaker 1: Cool, simple geometric argument, yes, exactly, so elementary, my dear Watson.

909
00:41:30,360 --> 00:41:32,799
Speaker 3: So well, it's for the same reason that like, elephants

910
00:41:32,840 --> 00:41:36,680
Speaker 3: have a larger volume to surface area ratio than ants do. Right,

911
00:41:36,719 --> 00:41:40,200
Speaker 3: the insides grow faster than the surface does, and here

912
00:41:40,360 --> 00:41:43,200
Speaker 3: the like boundary grows more slowly than the inside, and

913
00:41:43,280 --> 00:41:45,480
Speaker 3: so on average, if you pick an amiba, it's going

914
00:41:45,560 --> 00:41:48,400
Speaker 3: to be further from the boundary as the circle grows. Yeaheah,

915
00:41:48,440 --> 00:41:48,920
Speaker 3: that's cool.

916
00:41:49,000 --> 00:41:50,880
Speaker 1: Yeah, right, And so maybe they do a little bit

917
00:41:50,880 --> 00:41:53,840
Speaker 1: of discriminating, Like if you find yourself closer to the

918
00:41:53,920 --> 00:41:56,200
Speaker 1: edge of a slice and you're surrounded by clones that

919
00:41:56,239 --> 00:41:58,680
Speaker 1: aren't closely related, maybe they are able to do a

920
00:41:58,680 --> 00:42:00,759
Speaker 1: little bit to try to edge their slug to be

921
00:42:00,800 --> 00:42:04,080
Speaker 1: a little bit more made up of their own type

922
00:42:04,080 --> 00:42:04,600
Speaker 1: of clone.

923
00:42:04,800 --> 00:42:06,120
Speaker 3: But you don't have to do that much. If you

924
00:42:06,360 --> 00:42:08,480
Speaker 3: get it kind of right in the beginning, then it

925
00:42:08,520 --> 00:42:09,320
Speaker 3: supports itself.

926
00:42:09,520 --> 00:42:12,400
Speaker 1: Yeah, exactly right, And so this is like pretty exciting

927
00:42:12,440 --> 00:42:16,440
Speaker 1: that cooperation is arising to a large extent because of

928
00:42:16,920 --> 00:42:19,799
Speaker 1: random spatial things that happen. When you end up being

929
00:42:19,840 --> 00:42:22,560
Speaker 1: like blown into a new environment and you spread from

930
00:42:22,600 --> 00:42:25,279
Speaker 1: that central location, it just kind of turns out you

931
00:42:25,320 --> 00:42:28,760
Speaker 1: have your family nearby, and so it's easy to associate

932
00:42:28,800 --> 00:42:31,239
Speaker 1: and work with family, which benefits cooperation.

933
00:42:31,440 --> 00:42:33,880
Speaker 3: Yeah, well that's really cool that all you need is

934
00:42:33,920 --> 00:42:37,600
Speaker 3: like a little bit of selective pressure to get the

935
00:42:37,760 --> 00:42:41,960
Speaker 3: family members nearish each other, and then the geometry encourages it, right, Yeah,

936
00:42:42,040 --> 00:42:43,640
Speaker 3: instead of mixing it all up totally.

937
00:42:43,760 --> 00:42:43,960
Speaker 2: Yeah.

938
00:42:43,960 --> 00:42:47,360
Speaker 1: And then this lab did this really amazing evolution experiment

939
00:42:47,400 --> 00:42:51,239
Speaker 1: where essentially they took the amiba and they forced them

940
00:42:51,520 --> 00:42:55,120
Speaker 1: to make slugs with distantly related individuals, and then over

941
00:42:55,200 --> 00:42:57,759
Speaker 1: many many generations they look to see what happened. And

942
00:42:57,840 --> 00:43:01,080
Speaker 1: what they found was that over many many generations, if

943
00:43:01,120 --> 00:43:04,120
Speaker 1: you find yourself in a slug with clones that are

944
00:43:04,160 --> 00:43:07,839
Speaker 1: not closely related to you, then you will sometimes get

945
00:43:08,480 --> 00:43:12,000
Speaker 1: individuals that are obligate cheaters, which is to say, if

946
00:43:12,000 --> 00:43:15,359
Speaker 1: they get into your slug, they will never end up

947
00:43:15,360 --> 00:43:18,440
Speaker 1: in the stalk. They cannot make stalks, but they can

948
00:43:18,480 --> 00:43:21,359
Speaker 1: put themselves in the fruiting body, and you only get

949
00:43:21,400 --> 00:43:24,640
Speaker 1: these obligate cheaters in the lab under these conditions, and

950
00:43:24,680 --> 00:43:27,960
Speaker 1: so it looks like being able to work with closely

951
00:43:27,960 --> 00:43:32,160
Speaker 1: related individuals is great for cooperation. But when you force

952
00:43:32,239 --> 00:43:35,120
Speaker 1: them to work with individuals that are not closely related,

953
00:43:35,400 --> 00:43:37,520
Speaker 1: cheating pops up more and more and more until you

954
00:43:37,560 --> 00:43:41,080
Speaker 1: get some clones that can't even cooperate anymore.

955
00:43:41,280 --> 00:43:43,920
Speaker 2: Cooperation, like you know, is lost in those.

956
00:43:43,719 --> 00:43:48,000
Speaker 3: Clones because the whole slug only happens because of cooperation.

957
00:43:48,160 --> 00:43:50,799
Speaker 3: So if everybody is cheating, then it just doesn't even

958
00:43:50,840 --> 00:43:53,879
Speaker 3: come together. Right, everybody's sitting at the white meat side

959
00:43:53,880 --> 00:43:55,160
Speaker 3: of the table and there's no chairs.

960
00:43:55,719 --> 00:43:58,319
Speaker 2: I know, isn't that sad? And it's so sad, it's

961
00:43:58,360 --> 00:43:58,840
Speaker 2: so sad.

962
00:43:59,480 --> 00:44:02,319
Speaker 1: But you know, these amiba that have to cheat, if

963
00:44:02,320 --> 00:44:04,480
Speaker 1: they're in an environment where there's not a lot of food,

964
00:44:04,680 --> 00:44:06,439
Speaker 1: they're just in a ton of trouble because they can't

965
00:44:06,440 --> 00:44:08,520
Speaker 1: make slugs on their own, they can't make fruiting bodies

966
00:44:08,560 --> 00:44:10,920
Speaker 1: on their own. But as long as there's individuals that

967
00:44:10,960 --> 00:44:13,720
Speaker 1: they can cheat off of, then they do okay.

968
00:44:13,960 --> 00:44:14,640
Speaker 2: But anyway, so.

969
00:44:14,600 --> 00:44:18,440
Speaker 1: The evolution of cooperation depends on being able to associate

970
00:44:18,440 --> 00:44:21,959
Speaker 1: with family members. And the more that association breaks down

971
00:44:22,320 --> 00:44:23,880
Speaker 1: the worse, the cheating seems to get.

972
00:44:24,200 --> 00:44:27,520
Speaker 3: Wow cool, And so what is the sort of cutting

973
00:44:27,680 --> 00:44:30,800
Speaker 3: edge of research right now? Like what are folks doing

974
00:44:31,160 --> 00:44:33,680
Speaker 3: other than hunting for even older.

975
00:44:33,400 --> 00:44:36,080
Speaker 2: Deer poop, which is an exciting thing to be doing.

976
00:44:36,120 --> 00:44:37,799
Speaker 2: But so so you.

977
00:44:37,760 --> 00:44:39,279
Speaker 1: Know, one of the things that they're doing is trying

978
00:44:39,280 --> 00:44:41,520
Speaker 1: to figure out why don't we see chimeras like this

979
00:44:41,719 --> 00:44:45,720
Speaker 1: more often? So why don't we see more organisms cooperating

980
00:44:46,200 --> 00:44:49,520
Speaker 1: because of these like spatial dynamics or whatever. Also there's

981
00:44:49,600 --> 00:44:52,640
Speaker 1: questions about are similar things happening in biofilms. So I

982
00:44:52,719 --> 00:44:55,800
Speaker 1: asked Katrina, you know when there's biofilms or there individuals

983
00:44:55,840 --> 00:44:57,680
Speaker 1: that are cheating to try to be in like the

984
00:44:57,680 --> 00:45:00,319
Speaker 1: center of the biofilm where the antibiotics are less likely

985
00:45:00,360 --> 00:45:01,839
Speaker 1: to be able to get to. And she was like,

986
00:45:02,000 --> 00:45:04,319
Speaker 1: there's probably someone working on that, And I think that

987
00:45:04,320 --> 00:45:07,200
Speaker 1: that's like they're moving into biofilms to try to understand

988
00:45:07,480 --> 00:45:10,400
Speaker 1: cooperation and cheating in biofilms where you also get a

989
00:45:10,400 --> 00:45:14,200
Speaker 1: lot of different kinds of organisms coming together to share

990
00:45:14,200 --> 00:45:17,279
Speaker 1: a common cause that's supposed to help all of them.

991
00:45:17,360 --> 00:45:19,040
Speaker 1: So trying to figure out what's happening there is a

992
00:45:19,040 --> 00:45:19,640
Speaker 1: big question.

993
00:45:20,000 --> 00:45:21,080
Speaker 2: And then they.

994
00:45:21,000 --> 00:45:24,000
Speaker 1: Also discovered this really cool thing where like, okay, so

995
00:45:24,000 --> 00:45:27,960
Speaker 1: they've got these plates, and after the fruiting body, like

996
00:45:28,040 --> 00:45:31,400
Speaker 1: after the amoeba go off and like start new colonies,

997
00:45:31,800 --> 00:45:33,960
Speaker 1: they'll put them on plates. And sometimes they put them

998
00:45:33,960 --> 00:45:36,200
Speaker 1: on a plate that had no bacteria, so no food,

999
00:45:36,640 --> 00:45:38,440
Speaker 1: because they just kind of, you know, we're leaving them

1000
00:45:38,440 --> 00:45:40,360
Speaker 1: there for a little while. And then on some of

1001
00:45:40,360 --> 00:45:43,799
Speaker 1: the plates bacteria started growing anyway, and they were like,

1002
00:45:43,880 --> 00:45:47,200
Speaker 1: that's weird. And then they noticed that bacteria kept growing

1003
00:45:47,480 --> 00:45:51,600
Speaker 1: on plates with particular clones, but it wasn't growing on

1004
00:45:51,719 --> 00:45:55,560
Speaker 1: plates with other clones, okay. And what they discovered was

1005
00:45:55,560 --> 00:45:59,279
Speaker 1: that some clones actually have the ability to bring some

1006
00:45:59,440 --> 00:46:02,480
Speaker 1: bacteria with them so that when they get into the

1007
00:46:02,520 --> 00:46:06,200
Speaker 1: new environment, they can see the new environment with delicious

1008
00:46:06,239 --> 00:46:08,799
Speaker 1: bacteria that they're good at eating. It's kind of like

1009
00:46:08,840 --> 00:46:11,320
Speaker 1: when we take cows with us from like one place

1010
00:46:11,320 --> 00:46:12,799
Speaker 1: to another, so that we can be sure that we've

1011
00:46:12,800 --> 00:46:15,240
Speaker 1: got some good food when we get there, or carrots

1012
00:46:15,280 --> 00:46:18,120
Speaker 1: or cucumbers or you know whatever seeds or seeds, yeah,

1013
00:46:18,200 --> 00:46:22,120
Speaker 1: or seeds, and so some clones are able to carry

1014
00:46:22,239 --> 00:46:24,000
Speaker 1: their own food with them, and so they called those

1015
00:46:24,040 --> 00:46:29,440
Speaker 1: clones farmer clones, and after digging a little deeper, it

1016
00:46:29,520 --> 00:46:32,960
Speaker 1: turned out that the farmer clones were only able to

1017
00:46:33,080 --> 00:46:37,279
Speaker 1: carry bacteria with them if they were also carrying a

1018
00:46:37,320 --> 00:46:39,799
Speaker 1: different kind of bacteria that they couldn't eat. So this

1019
00:46:39,880 --> 00:46:43,960
Speaker 1: bacteria was living inside of them. Wow, as a symbiont.

1020
00:46:43,520 --> 00:46:45,160
Speaker 3: They have like a little garden inside of them.

1021
00:46:45,440 --> 00:46:48,040
Speaker 1: Yeah, they've got sure, Yeah, garden, they've got like a

1022
00:46:48,040 --> 00:46:49,719
Speaker 1: little I think what's really happening is that they have

1023
00:46:49,760 --> 00:46:53,640
Speaker 1: immune cells and those immune cells have encapsulated these bacteria.

1024
00:46:53,719 --> 00:46:56,320
Speaker 1: But the bacteria have found a way to neutralize the

1025
00:46:56,360 --> 00:46:58,800
Speaker 1: immune cells and now they just live in there long term.

1026
00:46:58,840 --> 00:46:59,240
Speaker 3: Wow.

1027
00:46:59,280 --> 00:47:02,759
Speaker 1: And these bacteria that live inside of the amiba are

1028
00:47:02,920 --> 00:47:06,440
Speaker 1: able to give their amiba hosts the ability to carry

1029
00:47:06,440 --> 00:47:10,239
Speaker 1: food with them from one environment to another environment, and

1030
00:47:10,280 --> 00:47:12,640
Speaker 1: they essentially benefit both of them by making sure that

1031
00:47:12,680 --> 00:47:14,640
Speaker 1: when they go into an environment, if it happens to

1032
00:47:14,640 --> 00:47:16,480
Speaker 1: not have a lot of food, that's all right. They

1033
00:47:16,480 --> 00:47:17,399
Speaker 1: brought some food with them.

1034
00:47:17,600 --> 00:47:20,800
Speaker 3: Fascinating, So it benefits the bacteria to be brought along

1035
00:47:21,040 --> 00:47:24,799
Speaker 3: and then basically bread for food because they get to

1036
00:47:24,840 --> 00:47:27,719
Speaker 3: be alive. It's like it's good for cattle to be

1037
00:47:27,920 --> 00:47:29,880
Speaker 3: food crop because then there's lots of cattle.

1038
00:47:30,360 --> 00:47:33,680
Speaker 1: No, not quite, okay, so my mistake. I'm talking about

1039
00:47:33,680 --> 00:47:37,640
Speaker 1: two different kinds of bacteria. So there is bacteria one,

1040
00:47:38,040 --> 00:47:42,920
Speaker 1: which is inedible and lives inside of the amiba, and

1041
00:47:42,960 --> 00:47:47,120
Speaker 1: then there's bacteria two, which are the cows. And the

1042
00:47:47,160 --> 00:47:51,560
Speaker 1: cows are able to be carried only when the inedible

1043
00:47:51,600 --> 00:47:54,719
Speaker 1: bacteria are inside. And we don't know why they give

1044
00:47:54,840 --> 00:47:57,600
Speaker 1: the amiba the ability to bring cows with them, okay,

1045
00:47:57,680 --> 00:48:01,279
Speaker 1: but somehow they do, and this ends up being really

1046
00:48:01,320 --> 00:48:05,600
Speaker 1: good for both the amiba and the inedible bacteria when

1047
00:48:05,640 --> 00:48:07,399
Speaker 1: they go to an environment where there's not a lot

1048
00:48:07,440 --> 00:48:09,960
Speaker 1: of food. But if they go to an environment where

1049
00:48:09,960 --> 00:48:11,919
Speaker 1: there is a lot of food, then it seems it's

1050
00:48:11,920 --> 00:48:13,640
Speaker 1: not good that they spent the energy to try to

1051
00:48:13,680 --> 00:48:16,160
Speaker 1: carry the bacteria with them. But anyway, right now the

1052
00:48:16,239 --> 00:48:19,920
Speaker 1: lab is working on what is happening with these farmers?

1053
00:48:19,960 --> 00:48:22,239
Speaker 1: When is it beneficial to bring food with you? How

1054
00:48:22,280 --> 00:48:26,200
Speaker 1: the heck does this one inedible bacteria living inside of

1055
00:48:26,280 --> 00:48:30,480
Speaker 1: the amiba allow it to bring cows to foreign locations?

1056
00:48:30,800 --> 00:48:33,040
Speaker 1: And so now they're digging into that question. So now

1057
00:48:33,080 --> 00:48:37,520
Speaker 1: they're looking at interactions between various players and how stable

1058
00:48:37,560 --> 00:48:40,640
Speaker 1: they are over time. All right, So the cutting edge

1059
00:48:40,680 --> 00:48:43,440
Speaker 1: to sort of bottom line and eventually finally answer your

1060
00:48:43,480 --> 00:48:46,000
Speaker 1: question is that they're trying to get a general handle

1061
00:48:46,120 --> 00:48:51,080
Speaker 1: on what the results in DICTI mean for other groups

1062
00:48:51,120 --> 00:48:53,600
Speaker 1: of organisms that come together to collaborate.

1063
00:48:53,719 --> 00:48:56,840
Speaker 3: Do these answers generalize or is it specific to these ambis?

1064
00:48:57,280 --> 00:49:00,760
Speaker 1: Right exactly, And now they're trying to understand symbiosi in general,

1065
00:49:00,760 --> 00:49:04,440
Speaker 1: more so symbioceeses when you have multiple different organisms working together,

1066
00:49:04,600 --> 00:49:08,560
Speaker 1: and so why is this bacteria helping the amiba and

1067
00:49:08,719 --> 00:49:10,920
Speaker 1: just kind of looking at those interactions long term.

1068
00:49:10,960 --> 00:49:13,040
Speaker 3: Wow, it seems like there's such a rich set of

1069
00:49:13,120 --> 00:49:16,040
Speaker 3: mysteries here right of like how evolution works not just

1070
00:49:16,080 --> 00:49:18,960
Speaker 3: at the organism level, but at the population level, how

1071
00:49:19,000 --> 00:49:21,080
Speaker 3: those things interact. Fascinating.

1072
00:49:21,400 --> 00:49:22,879
Speaker 1: Yeah, what I love is that when I got into

1073
00:49:22,880 --> 00:49:26,440
Speaker 1: this field, I wanted to study like cheetahs and lions

1074
00:49:26,520 --> 00:49:29,560
Speaker 1: and like the big charismatic megafauna. But at the end

1075
00:49:29,560 --> 00:49:31,279
Speaker 1: of the day, like, yes, you can do a lot

1076
00:49:31,320 --> 00:49:34,200
Speaker 1: of cool behavior work by like sitting in a beautiful

1077
00:49:34,200 --> 00:49:36,600
Speaker 1: savannah and watching them, But at the end of the day,

1078
00:49:36,680 --> 00:49:39,560
Speaker 1: like the evolution experiments that this lab was able to

1079
00:49:39,600 --> 00:49:43,320
Speaker 1: do by having an organism that reproduces quickly where you

1080
00:49:43,360 --> 00:49:45,560
Speaker 1: can really control like what the clones are doing and

1081
00:49:45,600 --> 00:49:47,440
Speaker 1: what their environments are like, and then you can check

1082
00:49:47,480 --> 00:49:49,960
Speaker 1: those results against what's happening in the field, like in

1083
00:49:50,000 --> 00:49:52,680
Speaker 1: the last few decades. You know, I read through most

1084
00:49:52,680 --> 00:49:54,799
Speaker 1: of the papers that the lab has written on this

1085
00:49:54,880 --> 00:49:58,040
Speaker 1: species in the last couple of days. The amount of

1086
00:49:58,120 --> 00:50:00,880
Speaker 1: ground they've been able to cover, yeah, in those decades

1087
00:50:00,920 --> 00:50:03,640
Speaker 1: is incredible, and so I know, it's it's exciting to

1088
00:50:03,640 --> 00:50:05,080
Speaker 1: think about working on cheetahs.

1089
00:50:05,200 --> 00:50:06,640
Speaker 3: Cheetah babies are pretty cute.

1090
00:50:06,719 --> 00:50:09,640
Speaker 1: They're really cute, and these are me but probably much

1091
00:50:09,719 --> 00:50:11,640
Speaker 1: less cute, especially if you're going to find deer poop.

1092
00:50:11,719 --> 00:50:14,240
Speaker 1: But like they've really been able to answer some interesting

1093
00:50:14,320 --> 00:50:16,640
Speaker 1: questions and understand a lot about this system by having

1094
00:50:16,680 --> 00:50:18,799
Speaker 1: a system that just is kind of easier to work

1095
00:50:18,800 --> 00:50:19,399
Speaker 1: with in the lab.

1096
00:50:19,840 --> 00:50:22,880
Speaker 3: Yeah, and there's just so much biology happening at so

1097
00:50:22,960 --> 00:50:26,680
Speaker 3: many scales. It's mostly invisible to you what's going on microscopically,

1098
00:50:27,120 --> 00:50:28,520
Speaker 3: but like it's a war down there.

1099
00:50:28,920 --> 00:50:29,640
Speaker 2: Yeah, it is.

1100
00:50:29,760 --> 00:50:32,000
Speaker 1: I want and like how cool to have been working

1101
00:50:32,040 --> 00:50:34,160
Speaker 1: in the system for decades and then be like, wait

1102
00:50:34,160 --> 00:50:37,560
Speaker 1: a minute, some of those clones always are associated with

1103
00:50:37,600 --> 00:50:41,600
Speaker 1: contamination on the auger plates afterwards, maybe they're bringing bacteria

1104
00:50:41,680 --> 00:50:43,520
Speaker 1: with them, and so it's just also the benefit of

1105
00:50:43,560 --> 00:50:46,400
Speaker 1: like working with an organism for many, many years and

1106
00:50:46,520 --> 00:50:49,319
Speaker 1: just staring at it and like being open to what

1107
00:50:49,440 --> 00:50:51,360
Speaker 1: it might be showing you that you had missed before.

1108
00:50:51,560 --> 00:50:53,200
Speaker 2: Yeah, very exciting.

1109
00:50:52,960 --> 00:50:56,319
Speaker 3: Very exciting discoveries. Somehow compensation for not getting to see

1110
00:50:56,320 --> 00:50:57,080
Speaker 3: baby cheetahs.

1111
00:50:57,320 --> 00:50:59,319
Speaker 1: I you know, I think that they really enjoy it.

1112
00:50:59,320 --> 00:51:02,120
Speaker 1: But Joe Straw Smith also writes books about birding, and

1113
00:51:02,160 --> 00:51:04,400
Speaker 1: so you can check out her books Slow Birding and

1114
00:51:04,440 --> 00:51:07,359
Speaker 1: The Social Lives of Birds, which I recommend. She does

1115
00:51:07,360 --> 00:51:09,239
Speaker 1: amazing work on birds as well.

1116
00:51:09,480 --> 00:51:11,840
Speaker 3: And you dug it in this topic because it's fascinating,

1117
00:51:12,040 --> 00:51:15,279
Speaker 3: not just as personal penance for your flubbed interview from

1118
00:51:15,280 --> 00:51:16,080
Speaker 3: fifteen years ago.

1119
00:51:16,480 --> 00:51:18,600
Speaker 1: No, to be honest, I kind of wanted to not

1120
00:51:18,840 --> 00:51:21,040
Speaker 1: cover this topic because I didn't want to have to

1121
00:51:21,120 --> 00:51:23,360
Speaker 1: listen to that interview, but I knew there was a

1122
00:51:23,360 --> 00:51:25,640
Speaker 1: lot of good information in there, and so I anyway,

1123
00:51:25,680 --> 00:51:28,760
Speaker 1: I did anyway. But I actually did this one because

1124
00:51:29,160 --> 00:51:32,520
Speaker 1: see Dave in our discord channel asked for it, and

1125
00:51:32,560 --> 00:51:34,400
Speaker 1: I give the listeners what they want. So let's go

1126
00:51:34,440 --> 00:51:37,040
Speaker 1: ahead and see if see Dave feels like he's learned

1127
00:51:37,080 --> 00:51:39,600
Speaker 1: everything he wanted to know about the curious lives of

1128
00:51:39,640 --> 00:51:40,720
Speaker 1: social amiba.

1129
00:51:40,840 --> 00:51:43,200
Speaker 4: Thanks Daniel and Kelly. Sorry it was such a traumatic

1130
00:51:43,239 --> 00:51:46,120
Speaker 4: topic for you, involving self sacrifice for an unrelated day.

1131
00:51:46,120 --> 00:51:48,399
Speaker 4: It seems like such a paradox, but this discussion sort

1132
00:51:48,440 --> 00:51:50,799
Speaker 4: of really makes it clear how that happens. Your point

1133
00:51:50,800 --> 00:51:54,239
Speaker 4: about shrinking evolutionary time scales down to human timescales, it's

1134
00:51:54,800 --> 00:51:58,120
Speaker 4: really brilliant anthropomorphing. Aside, it's a little depressing cloth the

1135
00:51:58,160 --> 00:52:01,040
Speaker 4: outerroism only evolved to help those similar and the obligate

1136
00:52:01,080 --> 00:52:05,040
Speaker 4: cheetahs can prosper to the farmer's symbiotic cooperation with the

1137
00:52:05,040 --> 00:52:08,680
Speaker 4: parasitic bacteria. Bringing a third species along is actually really uplifting.

1138
00:52:09,160 --> 00:52:12,320
Speaker 4: My only question is where's the cannibalism from my bingo card?

1139
00:52:13,040 --> 00:52:15,359
Speaker 1: Holy cow, Dave, I can't believe I forgot to get

1140
00:52:15,360 --> 00:52:18,440
Speaker 1: back to cannibalism in the episode. Okay, So the point

1141
00:52:18,440 --> 00:52:22,360
Speaker 1: there was if you take different clones of the amiba

1142
00:52:22,840 --> 00:52:25,600
Speaker 1: and you compete them against one another, there's usually a

1143
00:52:25,640 --> 00:52:28,680
Speaker 1: winner and a loser, and the winner is more likely

1144
00:52:28,719 --> 00:52:30,880
Speaker 1: to end up in the top of the lollipop, so

1145
00:52:30,880 --> 00:52:32,520
Speaker 1: they're more likely to be able to end up in

1146
00:52:32,520 --> 00:52:34,360
Speaker 1: the part that gets to survive and go on to

1147
00:52:34,360 --> 00:52:37,279
Speaker 1: start new populations, and the losers are more likely to

1148
00:52:37,400 --> 00:52:37,960
Speaker 1: end up in.

1149
00:52:37,960 --> 00:52:40,520
Speaker 2: The stock the stick of the lollipop.

1150
00:52:40,920 --> 00:52:43,800
Speaker 1: But when you count up the amiba that are present

1151
00:52:43,920 --> 00:52:46,719
Speaker 1: at the end of the experiment, it looks like some

1152
00:52:46,880 --> 00:52:49,759
Speaker 1: of the losers not only were more likely to end

1153
00:52:49,840 --> 00:52:52,200
Speaker 1: up in the stalk, but they also are just present

1154
00:52:52,280 --> 00:52:54,400
Speaker 1: in lower numbers than you would expect them to be.

1155
00:52:54,440 --> 00:52:56,920
Speaker 1: It's like some of them died or got lost along

1156
00:52:56,960 --> 00:52:59,200
Speaker 1: the way. So one of the ideas is that as

1157
00:52:59,200 --> 00:53:01,880
Speaker 1: the slug moves along, just like a real slug, it

1158
00:53:02,000 --> 00:53:05,040
Speaker 1: leaves something like a slime trail. So maybe the losers

1159
00:53:05,040 --> 00:53:07,400
Speaker 1: are more likely to sort of fall off along the

1160
00:53:07,400 --> 00:53:11,359
Speaker 1: way and get left behind. Or the authors hypothesize that

1161
00:53:11,640 --> 00:53:15,360
Speaker 1: maybe there's some cannibalism going on with the winner amiba

1162
00:53:15,360 --> 00:53:19,839
Speaker 1: clones eating some of the loser amiba clones along the way.

1163
00:53:20,120 --> 00:53:21,399
Speaker 2: This was just a hypothesis.

1164
00:53:21,440 --> 00:53:23,680
Speaker 1: It didn't get tested, but as soon as I saw

1165
00:53:23,719 --> 00:53:25,880
Speaker 1: anything related to cannibalism, I was like, I have to

1166
00:53:25,920 --> 00:53:29,040
Speaker 1: mention that on the show and then somehow I forgot

1167
00:53:29,400 --> 00:53:31,839
Speaker 1: so thank you for keeping us on track.

1168
00:53:31,800 --> 00:53:33,719
Speaker 3: All right, Kelly, thank you for taking us on this

1169
00:53:33,880 --> 00:53:37,800
Speaker 3: deep dive into the lives of Amibe's really fascinating stuff.

1170
00:53:38,000 --> 00:53:40,799
Speaker 3: Great to know that these little farmers work together to

1171
00:53:40,920 --> 00:53:42,319
Speaker 3: make life better for all of them.

1172
00:53:42,760 --> 00:53:45,040
Speaker 1: And if they do it, why can't you, Although actually

1173
00:53:45,040 --> 00:53:47,319
Speaker 1: I guess the answer would be you should specifically help

1174
00:53:47,360 --> 00:53:49,120
Speaker 1: your family and try to keep everyone else out, So

1175
00:53:49,400 --> 00:53:51,440
Speaker 1: forget what the Amiba are doing, but they cooperate and

1176
00:53:51,480 --> 00:53:51,719
Speaker 1: that's me.

1177
00:53:53,239 --> 00:53:55,960
Speaker 3: So everybody makes sure to be nice and don't just

1178
00:53:56,040 --> 00:53:58,600
Speaker 3: grab the spot at the top of the Thanksgiving table.

1179
00:53:58,960 --> 00:54:02,120
Speaker 1: There's enough for everybody, but do avoid the dark meat

1180
00:54:03,200 --> 00:54:03,960
Speaker 1: until next time.

1181
00:54:10,680 --> 00:54:13,120
Speaker 3: Thanks everybody for listening. Please go and do us a

1182
00:54:13,120 --> 00:54:16,399
Speaker 3: favor and rate the show on whatever podcast app you're using.

1183
00:54:16,480 --> 00:54:18,080
Speaker 3: It really helps people find us.

1184
00:54:18,600 --> 00:54:22,520
Speaker 1: Daniel and Kelly's Extraordinary Universe is edited by the amazing

1185
00:54:22,560 --> 00:54:23,280
Speaker 1: Matt Kesselman.

1186
00:54:23,480 --> 00:54:26,759
Speaker 3: He really is a wizard. You can also find us

1187
00:54:26,840 --> 00:54:31,920
Speaker 3: online on Blue Sky, Instagram, and x D and K Universe.

1188
00:54:32,000 --> 00:54:33,239
Speaker 3: Come engage with us.

1189
00:54:33,480 --> 00:54:36,799
Speaker 1: You can email us at questions at Danielankelly dot org.

1190
00:54:36,880 --> 00:54:39,120
Speaker 1: We really do want to hear from you and you.

1191
00:54:39,080 --> 00:54:43,080
Speaker 3: Can find our website www dot danieland Kelly dot org,

1192
00:54:43,360 --> 00:54:46,480
Speaker 3: where you'll also find an invitation to join our discord

1193
00:54:46,520 --> 00:54:50,240
Speaker 3: where everybody comes and talks about the amazing universe.

1194
00:54:50,040 --> 00:54:54,000
Speaker 1: And we also have the most amazing moderators. This is

1195
00:54:54,040 --> 00:54:56,600
Speaker 1: an iHeart podcast. Thanks for joining us.