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Speaker 1: Comic Con dot com slash NYCC hyphen presents Welcome to

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Speaker 1: Stuff to Blow Your Mind from How Stuffworks dot com. Hey,

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Speaker 1: are you welcome to Stuff to Blow Your Mind? My

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Speaker 1: name is Robert Lamb and I'm Joe McCormick and Robert

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Speaker 1: I got a question. How often do you have this

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Speaker 1: experience of finding out one subtle way that somebody is

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Speaker 1: like you, and it's suddenly changing your whole attitude toward them.

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Speaker 1: So you're talking to somebody new you haven't met before.

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Speaker 1: You know, you're getting to know somebody who's been an

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Speaker 1: acquaintance and you find out that they really like one

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Speaker 1: movie that you like, or they really or they've traveled

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Speaker 1: to one place you've traveled to, or something like this,

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Speaker 1: and suddenly your brain just unlocks and you say, oh, okay,

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Speaker 1: we're cool now. Yeah, I encountered this a lot. You know,

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Speaker 1: they'll be I tend to be a little you know,

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Speaker 1: distrusting of new people and uh. And then sometimes you know,

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Speaker 1: you get to talk to them a little bit, or

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Speaker 1: you check out their social media activity or something in

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Speaker 1: your lies. Hey, they they really like this film, or

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Speaker 1: they they really like this artist that not many of

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Speaker 1: us are into, or they're into this h this other

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Speaker 1: a kind of niche thing that I love and there

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Speaker 1: and then you you you sort of give them the

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Speaker 1: benefit of a doubt based on that information. You're like, well,

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Speaker 1: they're they're probably pretty good, they're probably pretty cool, Like, babe,

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Speaker 1: I should talk to them a little bit more. What

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Speaker 1: else are they into that I like? But why why

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Speaker 1: is that? I mean, why is it that just using

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Speaker 1: like a shared favorite movie for example, Like what basis

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Speaker 1: do you have for thinking that somebody who likes the

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Speaker 1: same movie you like is a good person. Well, it

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Speaker 1: falls apart under the scrutiny of other things, because, for instance, um,

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Speaker 1: I'll use Tool as an example. Tool is one of

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Speaker 1: my my my favorite bands and Uh and their band

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Speaker 1: I loved in in high school and I've never I've

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Speaker 1: never stopped loving. There's been a lot of bands that

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Speaker 1: a lot of musical styles that have sort of come

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Speaker 1: and gone that I'll rediscover and and or that I'll

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Speaker 1: hate for a little home. But but but I've always liked Tool.

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Speaker 1: So what you're saying is the best rubric for discovering

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Speaker 1: whether a person is good or not is do they

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Speaker 1: like Tool? Yes and no. So, for instance, here at work,

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Speaker 1: Nathan our our our social media guru, uh, shortly after

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Speaker 1: he started, he saw that I had some some Tool

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Speaker 1: memorabilia at my desk, and we instantly struck up conversation

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Speaker 1: about it, and that was like our initial uh uh,

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Speaker 1: you know, bonding moment, or the fact that we both

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Speaker 1: like this group. On the other hand, I've gone to

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Speaker 1: enough Tool concerts to witness people that are also fans

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Speaker 1: of Tool and sometimes interact with them where I end

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Speaker 1: up getting a bit judge, and I think to myself, well,

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Speaker 1: there's no there's no way that they are appreciating this

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Speaker 1: music on the same level that I am. So it

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Speaker 1: can kind of cut both ways. You can be very

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Speaker 1: choosy about about how you feel regarding other people's appreciation

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Speaker 1: of your stuff. But ideally it would mean that if

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Speaker 1: if someone else is into something that that the that

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Speaker 1: you dig, then they're they're going to be into some

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Speaker 1: of the same ideas, some of the same values, some

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Speaker 1: of the same you know, aesthetic quality days what have you,

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Speaker 1: that that there's going to be more than just this

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Speaker 1: one thing that lines up between the two of you, right,

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Speaker 1: But you're using that one little detail, that one tiny

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Speaker 1: thing is some kind of like totem. It's this one

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Speaker 1: it's the signifier, this tiny flag flying above their head

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Speaker 1: that you have decided, for whatever reason, is good enough

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Speaker 1: evidence to open yourself up to this person and essentially

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Speaker 1: give them a fair shake that you might not give

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Speaker 1: to any other stranger. I've caught myself doing the same

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Speaker 1: thing too, even though I realized it makes no sense.

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Speaker 1: I mean, I think generally that you might be able

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Speaker 1: to make some kind of case that in some cases

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Speaker 1: shared aesthetics or shared values. Uh, you know that if

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Speaker 1: you like the same movie as somebody, it might embody

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Speaker 1: some kinds of values or intellectual predispositions that you would

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Speaker 1: find in common with other people who tend to like it.

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Speaker 1: But a lot of times that's not the case. I

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Speaker 1: don't know. I mean, I love Blade Runner, but there's

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Speaker 1: probably a lot of people who love Blade Runner who

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Speaker 1: are just absolute vampire. You saying they don't appreciate it

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Speaker 1: on the same level as you. Maybe that's it. I

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Speaker 1: wonder if you don't appreciate it on the same level

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Speaker 1: I do. I don't know. I've I've actually spoken about

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Speaker 1: this a little bit, but my thoughts on Blade Run

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Speaker 1: are mixed. But I do I do. I do love

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Speaker 1: Blade Runner. So today we're gonna be talking about a

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Speaker 1: hypothesis in genetics and genomics that is going to be

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Speaker 1: a kind of weird analogy to the personality tests that

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Speaker 1: we've just been discussing, and it's the concept of green

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Speaker 1: beard genes or the green beard effect. Now that the

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Speaker 1: sad news is that this episode does not really deal

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Speaker 1: with actual green beards. If you're looking for if you go,

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Speaker 1: if you're coming into this expecting an episode and a

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Speaker 1: weird genetic anomaly that makes people grow green beards, or

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Speaker 1: say a famous pirate with a green beard. Um, you're

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Speaker 1: gonna be disappointed on those counts. But the but the

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Speaker 1: underlying science we're going to discuss here in the genetics

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Speaker 1: is pretty amazing and pretty fascinating. Now I wanted to

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Speaker 1: come up with the backstory of the pirate green beard.

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Speaker 1: I couldn't think of what it is. What it's like

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Speaker 1: he's got algae in there or something. He just never

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Speaker 1: cleans his beard. Yeah, you're just um, I'm guessing you

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Speaker 1: probably have like really green teeth as well, you know,

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Speaker 1: and just maybe he eats a lot of plant matter

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Speaker 1: and it just kind of drolls down in his unwashed beard.

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Speaker 1: But you know, this does bring if there was if

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Speaker 1: there was a pirate with a green beard and he

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Speaker 1: met another pirate with a green beard, perhaps he would

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Speaker 1: have this effect. He would say, look, there is another

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Speaker 1: pirate who shares my esthetic qualities, perhaps my diet. Maybe

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Speaker 1: we have more thoughts on hygiene. Yeah. So the name

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Speaker 1: of the green beard hypothesis, which we will explain, comes

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Speaker 1: to us from Richard Dawkins, based on an example he

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Speaker 1: used to illustrate the concept in his nineteen seventy six

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Speaker 1: book The Selfish Gene. But the idea goes back to

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Speaker 1: the influential English biologists of the twentieth century W. D.

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Speaker 1: Hamilton's um And so the idea arises in the context

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Speaker 1: of a question about explaining how biology creates social behavior. Now,

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Speaker 1: this is one of the biggest, most difficult questions in biology, right,

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Speaker 1: So humans and other animals display these complex things like culture,

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Speaker 1: like aesthetic preferences, taste, uh, social behavior, all these things

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Speaker 1: that are complicated and have all these weird semi coded rules,

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Speaker 1: and we know at some level must be at least

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Speaker 1: semi dictated by genetic predispositions. But it's hard to know

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Speaker 1: what parts of culture and behavior are genetic and what

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Speaker 1: parts are just happenstances that you know that that arise

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Speaker 1: naturally and somewhat randomly. Well, we have all these levels

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Speaker 1: of cognition and culture that are kind of an illusion

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Speaker 1: and and and they're they're overriding the biology. But since

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Speaker 1: we are the illusion, it's hard to it's it's often

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Speaker 1: hard for us to really think about and in terms

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Speaker 1: of underlying biological properties. Yeah, so let's let's explore this

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Speaker 1: question that this problem with explaining social behavior through biology.

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Speaker 1: So we all know that species arise from the propagation

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Speaker 1: of self replicating molecules like DNA, the fundamental units of

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Speaker 1: heredity and DNA or genes. These are the parts of

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Speaker 1: your DNA that do something that are inherited from your parents.

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Speaker 1: And because genes make copies of themselves, we find some

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Speaker 1: of the same genes spread out across many different organisms

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Speaker 1: in the same gene pool. Now, obviously, genes that cause

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Speaker 1: an organism to die immediately at birth become less numerous

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Speaker 1: and eventually disappear. Genes that help an organism survive and

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Speaker 1: produce more offspring become more numerous in the gene pool.

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Speaker 1: These are the fundamentals of evolution by natural selection. This

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Speaker 1: is basic stuff. We we know it up to this point.

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Speaker 1: But here's a question. Why would an organism produced by

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Speaker 1: this process, that arises out of evolution by natural selection

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Speaker 1: ever displayed behaviors that we would call selfless or generous

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Speaker 1: or altruistic, for example, things like sharing food with another

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Speaker 1: organism or defending another organism in a fight. Now, I

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Speaker 1: do want to heavily qualify that by saying I think

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Speaker 1: It's probably not hard to explain why humans in particular altruistic, because,

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Speaker 1: after all, there appear to be a lot of ways

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Speaker 1: in which our complex brains can generate motivation for behaviors

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Speaker 1: that run contrary to our genetic fitness, and the simplest

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Speaker 1: example one of thousands would be celibacy. Whether for religious

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Speaker 1: reasons or philosophical reasons, or simply out of personal preference,

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Speaker 1: some people voluntarily choose never to have sexual relationships. This

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Speaker 1: runs absolutely counter to our genetic programming. Another more common

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Speaker 1: example would be the use of contraception. Many people who

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Speaker 1: are sexually active still choose to have no children, or

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Speaker 1: choose to have far fewer children than they technically could,

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Speaker 1: for a variety of reasons. So obviously there are some

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Speaker 1: ways in which the complex human brain has acquired the

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Speaker 1: capacity to override the genes that programmed it well, I think.

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Speaker 1: One one example that comes to mind is say, for instance,

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Speaker 1: my own son UH has adopted and so he is

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Speaker 1: not my biological offspring, but he he fulfills all the

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Speaker 1: needs and desires that that I had that I have

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Speaker 1: as a human for for offspring, for a son, right,

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Speaker 1: your genes don't care about him, but you do. Uh,

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Speaker 1: it's it's there in your brain, it is there in

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Speaker 1: your personality. So if you were only wondering about humans,

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Speaker 1: it might not be so hard to explain why we

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Speaker 1: would share scarce resources with a stranger, or risk our

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Speaker 1: own safety to intervene in somebody else's defense, or any

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Speaker 1: number of other selfless acts. We're smart enough to recognize,

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Speaker 1: maybe philosophical or religious reasons why we think we should

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Speaker 1: to help other people and override those selfish instincts to

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Speaker 1: act on those reasons. Or if you look at it

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Speaker 1: from another angle, maybe the more cynical person would say, well,

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Speaker 1: our complex, language obsessed brains are highly vulnerable to memes

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Speaker 1: that have the power to override the optimization of our

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Speaker 1: genetic fitness. Either way, human mentality appears to be fairly

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Speaker 1: unique on Earth, and it can account for behavior that

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Speaker 1: you wouldn't expect to find in other animals. But altruism

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Speaker 1: isn't only present in human beings with their religions and

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Speaker 1: moral philosophy and complex cognition. Altruism seems to be robustly

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Speaker 1: observed in other animals, from like complex mammals all the

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Speaker 1: way down to insects. Right, yeah, yeah, we have. We

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Speaker 1: have a few I think very illustrative examples here to

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Speaker 1: run through. Here's a crazy one. Vampire bats will regurgitate

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Speaker 1: blood for other bats who were unable to find a meal,

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Speaker 1: sacrificing their own immediate nutrition to keep another bat in

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Speaker 1: their community from starving. And this has been documented since

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Speaker 1: it was discovered in the nineteen eighties by a biologist

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Speaker 1: named Gerald Wilkinson. And that that's no small feat because

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Speaker 1: these bats are sort of operating constantly near the edge

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Speaker 1: of starvation. As we've talked about before. You know, if

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Speaker 1: a vampire bat goes a few days without food, it's

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Speaker 1: in a bad place. It can it can starve um.

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Speaker 1: And so sharing food like this is a significant sacrifice

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Speaker 1: for the good of another uh not necessarily related bat.

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Speaker 1: And you have to ask yourself, would your friends and

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Speaker 1: coworkers be selfless enough to vomit up blood in your

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Speaker 1: mouth if you've missed a meal. None of them have

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Speaker 1: ever offered, And it makes me feel rather sour. But

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Speaker 1: it's not even just the mammals, right, We see classic

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Speaker 1: altruistic behaviors in in uh animals that are thought of

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Speaker 1: as less complex. Right, Oh yeah, I mean bees are

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Speaker 1: a prime example of altruism, and I mean you social

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Speaker 1: insects in general have pretty much perfected altruism in many respects.

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Speaker 1: So worker bee works for the benefit of the queen's

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Speaker 1: offspring while for going reproduction herself. Genetically speaking, she is

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Speaker 1: not the genetic future the hive. The queen is, the

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Speaker 1: drones are, but the workers do all the work. In

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Speaker 1: a weird way, you can almost think about the insects

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Speaker 1: like this, being that the queen is the organism and

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Speaker 1: the worker bees are like fingers or appendages of the

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Speaker 1: major organism. Yeah, exactly, and it's just completely completely selfless

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Speaker 1: if you look at it from the right angle. Yeah,

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Speaker 1: but how about birds too, well, ravens who we've talked

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Speaker 1: about ravens a bit when we talked about bird intelligence. Uh,

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Speaker 1: Ravens have been observed to call in additional ravens when

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Speaker 1: they happen upon food, so like a mouse or something,

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Speaker 1: which doesn't make any sense that they have it, but

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Speaker 1: they have apparently have a special call that they use,

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Speaker 1: so they raise the alarm and say, hey, other ravens

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Speaker 1: that are not me or my children, come in here

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Speaker 1: and get part of this mouse. Why would you do that?

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Speaker 1: It doesn't seem to make sense. But some ravens even

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Speaker 1: return to their roost and recruit more eaters to come

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Speaker 1: and share in the feast. Yet again, so your friends

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Speaker 1: and co workers, they've never offered to vomit blood in

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Speaker 1: your mouth. They've also probably never offered to share a

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Speaker 1: dead mouse with you. Well, no, but on the rare,

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Speaker 1: on the on the instances where we've had catered dead mouse, Uh,

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Speaker 1: then it certainly people will come around and say, hey,

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Speaker 1: there's food on the question mark table, come and eat

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Speaker 1: with us. So, well, that's just their moral philosophy or

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Speaker 1: their religion or whatever. Alright, Well we have some some

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Speaker 1: other just quick examples throughout. Dolphins and elephants are fairly

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Speaker 1: complex social animals and display uh various forms of altruism.

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Speaker 1: They form social bonds, and then then they seem to

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Speaker 1: make conscious decisions to help members of their group. Yeah,

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Speaker 1: and so we have all these examples of these are

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Speaker 1: by far not the only examples. There are just tons

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Speaker 1: of documented examples of animals in the wild or in

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Speaker 1: a conditions seeming to help other animals who are not

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Speaker 1: their direct offspring at their own at their own expense.

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Speaker 1: And obviously we can see why evolution would select for

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Speaker 1: genes that cause organisms to be altruistic towards their own children. Right,

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Speaker 1: that's kind of the whole point. Your genes optimize your

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Speaker 1: body to have lots of children that can carry as

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Speaker 1: many copies of those genes into the future as possible.

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Speaker 1: But why would evolution produce creatures that sacrifice some of

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Speaker 1: their own safety or resources to help save another creature

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Speaker 1: that wasn't their direct offspring? I mean, wouldn't wouldn't genes

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Speaker 1: that promoted selfishness and cowardice produce the most grandchildren on average? Well?

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Speaker 1: I mean one obvious answer here is that there's an

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Speaker 1: individual survival advantage in groups, right, Right, The group kind

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Speaker 1: of becomes the meta organism, and natural selection encourages the

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Speaker 1: pro group members of the metal organism and not and

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Speaker 1: not the lone wolves. Now, this is attempting direction to

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Speaker 1: go in, but there are critic of this type of thinking,

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Speaker 1: So lots of scientists have tried to go in this direction,

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Speaker 1: including EO. Wilson in later years. Uh. And this is

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Speaker 1: sort of related to the concept known as group selection. Right.

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Speaker 1: The idea that natural selection can select for not just

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Speaker 1: the survival of individual genes or the survival of individual organisms,

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Speaker 1: but groups of organisms together. This is also known as

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Speaker 1: multi level selection. But, like I mentioned, this idea has

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Speaker 1: a lot of harsh critics, including Dawkins. I mean, he's

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Speaker 1: criticized this idea. And here's one potential snag. It's true

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Speaker 1: that teamwork pays off, right, But you know what pays

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Speaker 1: off even more than teamwork? Letting other people do the

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Speaker 1: team Ah. This is the This is the classic um

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Speaker 1: A classroom situation where you've been assigned to groups and

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Speaker 1: you're gonna have somebody in the group who's a real

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Speaker 1: hard work, are very dedicated. You have some other individuals,

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Speaker 1: and you can have at least one person who's just

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Speaker 1: gonna coast right right, Yeah, So you let the rest

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Speaker 1: of the team do the work while you sand bag

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Speaker 1: and reap the rewards. So if you imagine if you've

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Speaker 1: got wild dogs that have a gene that causes cooperative

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Speaker 1: pack hunting behavior where every dog chips in and does

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Speaker 1: their share of work in the hunt, and in turn,

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Speaker 1: this increases the group's chances of catching prey and then

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Speaker 1: they all share the benefits. That would be great. Everything's

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Speaker 1: going great if you've got dogs with those genes, But

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Speaker 1: then one of the dogs in the pack acquires a

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Speaker 1: mutation that says, actually, don't help with the hunt, just

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Speaker 1: hang around until your pack mates catch something, and then

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Speaker 1: show up and plunge your face into that delicious viscera.

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Speaker 1: A dog with this trait would spend less energy hunting,

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Speaker 1: and it would risk less injury, and it would still

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Speaker 1: get the rewards of the hunt, so it would ultimately

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Speaker 1: benefit more than any of the team players, and on

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Speaker 1: average that that freeloader dog would have more offspring, meaning

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Speaker 1: that over time the freeloader gene would become more common

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Speaker 1: than the team player gene, and the pack hunting system

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Speaker 1: would sort of fall apart. So systems that rely on

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Speaker 1: benefits of group cooperation for the sake of the group

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Speaker 1: as a whole are sort of pervasively undermined by the

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Speaker 1: interests of individual organisms and individual genes within the group,

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Speaker 1: which will sort of inevitably find ways to run counter

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Speaker 1: to the success of the group as a whole. So

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Speaker 1: to sum up, we can show that in general, natural

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Speaker 1: selection will tend to push organisms to favor their own

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Speaker 1: success at the expense of others and at the expense

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Speaker 1: of their groups. And yet at the same time, group

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Speaker 1: cooperation and selflessness is present in nature, so there must

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Speaker 1: be some way to reconcile these observations, right, yes, and

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Speaker 1: I imagine this is where the green beard comes in. Well,

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Speaker 1: the green beard is going to be one weird way

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Speaker 1: of branching off of this question. Actually, the main answers

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Speaker 1: to this question are not going to be the focus

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Speaker 1: of today's episode, but we should mention them. So two

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Speaker 1: of the main answers that emerged in the twentieth century

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Speaker 1: to answer this question, or what's known as kin selection

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Speaker 1: and what's known as reciprocal altruism. So reciprocal altruism is

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Speaker 1: basically another way of summing it up, is just the

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Speaker 1: phrase tip for tat. And while in many cases it

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Speaker 1: can be hard to verify outside of lab conditions, you

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Speaker 1: can sort of build models of organisms that behave on

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Speaker 1: this principle and it does appear to be stable if

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Speaker 1: and the way this works is like this, So if

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Speaker 1: a member of the pack is mean to you, or

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Speaker 1: if they try to cheat and be selfish, you repay

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Speaker 1: them back the same way you're mean to them back

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Speaker 1: you you don't share with them. But if another member

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Speaker 1: of the pack is nice to you, you're nice back.

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Speaker 1: So essentially you just mirror. You start with the basis

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Speaker 1: of being nice, and then you mirror the you mirror

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Speaker 1: the behavior that people display towards you. Okay, well that

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Speaker 1: that makes sense. I think everybody can fall fall behind that. Yeah.

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Speaker 1: And if you run models like this, it is mathematically stable.

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Speaker 1: Genes that encourage this type of behavior don't tend to

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Speaker 1: get eliminated from the gene pool, but they can reach

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Speaker 1: a kind of stable equilibrium. But the other main idea

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Speaker 1: is what's known as kin selection, and this was explored

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Speaker 1: in the nineteen sixties by the English biologist we mentioned earlier, W. D. Hamilton's.

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Speaker 1: Now Hamilton's used these quantitative genetics modeling methods, which is

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Speaker 1: like math about how genes are inherited and distributed to

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Speaker 1: show something interesting, It makes mathematical sense for genes to

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Speaker 1: optimize for altruism toward blood relatives other than our own offspring.

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Speaker 1: So it turns out that for a sexually reproducing species

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Speaker 1: like us, you share the same percent of your genes

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Speaker 1: with each of your children that you do with your

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Speaker 1: full brothers and sisters and with your parents. In each case,

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Speaker 1: you share fifty percent of your genes with them, and

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Speaker 1: to a lesser extent, the same goes for other members

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Speaker 1: of your family. Further removed, you'll share more genes with

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Speaker 1: your aunts and uncles and nieces and nephews than you

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Speaker 1: do with the general population and so forth. And given

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Speaker 1: this calculus, you can work out how genes that direct

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Speaker 1: organisms to be generous and altruistic toward family members become

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Speaker 1: more numerous in the gene pool than genes that direct

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Speaker 1: organisms to be exclusively selfish and unhelpful to anybody. So

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Speaker 1: if I have a cousin that appears on America's Got Talent,

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Speaker 1: I'm going to root for them in part because they

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Speaker 1: share more of my genes than anybody else that's contestant

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Speaker 1: on that show. I mean, on average, they probably do.

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Speaker 1: You might have like a secret unknown brother or sister,

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Speaker 1: but yeah, yeah, on average, that's going to be the case. Now.

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Speaker 1: And of course, there are lots of weird ways that

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Speaker 1: maybe things like kin selection or reciprocal altruism uh could

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Speaker 1: be could be sort of modified in various circumstances to

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Speaker 1: generate the mechanisms that create altruism that get applied in

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Speaker 1: other ways. But it is in the context of discussing

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Speaker 1: this this whole question about altruism and biology in the

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Speaker 1: Selfish Gene that Dawkins also came up with this weird

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Speaker 1: hypothetical idea for one weird type of selective altruism, the

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Speaker 1: green Beard. All Right, we're gonna take a quick break

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Speaker 1: and when we come back we will join back up

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Speaker 1: with Captain green Beard and discuss the details of of

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Speaker 1: of this idea. Alright, we're back. So we were mentioning

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Speaker 1: how the name of the green Beard gene comes from

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Speaker 1: an example given by Richard Dawkins in The Selfish Gene

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Speaker 1: in n So, Dawkins is pointing out how any arbitrary

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Speaker 1: gene will become more numerous if it makes carriers of

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Speaker 1: that gene friendly and helpful toward other carriers, right, and

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Speaker 1: that sort of makes sense. It says like, if you've

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Speaker 1: got gene A in your body, gene A does especially

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Speaker 1: well if it makes your body give food and night,

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Speaker 1: you know, nice behavior toward other carriers of gene A. Right,

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Speaker 1: it's just a boost for gene A across the board.

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Speaker 1: So he starts talking about the example of the gene

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Speaker 1: for albinism, which is a genetic condition that causes the

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Speaker 1: carrier to have unusually low levels of the pigment melanin,

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Speaker 1: leading to paleness of skin, hair, and eyes. And hypothetically,

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Speaker 1: a really successful version of this gene for albino traits

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Speaker 1: would not only produce the visible traits, but it would

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Speaker 1: motivate a carrier to be helpful toward other people who

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Speaker 1: have these visible traits. Now keep in mind, this is

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Speaker 1: not how the albino traits work in in reality. This

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Speaker 1: is just a hypothetical alternate version of them um And

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Speaker 1: if this were to exist that way, everyone who has

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Speaker 1: this particular gene would have more offspring and the gene

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Speaker 1: would become even more abundant. But would we find something

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Speaker 1: like that in nature? That's the question, and Dawkins writes

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Speaker 1: the following quote. It is theoretically possible that a gene

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Speaker 1: could arise which conferred an externally visible label, say a

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Speaker 1: pale skin or a green beard, or anything conspicuous, and

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Speaker 1: also attendance to be specially nice to bearers of that

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Speaker 1: conspicuous label. It is possible, but not particularly likely. Green

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Speaker 1: beardedness is just as likely to be linked to a

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Speaker 1: tendency to develop ingrown toenails or any other trait, and

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Speaker 1: a fondness for green beards is just as likely to

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Speaker 1: go together with an inability to smell frieze is it

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Speaker 1: is not very probable that one and the same gene

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Speaker 1: would produce both the right label and the right sort

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Speaker 1: of altruism. Nevertheless, what may be called the green beard

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Speaker 1: altruism effect is a theoretical possibility. So this is where

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Speaker 1: the green beard idea comes from. And to be clear,

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Speaker 1: Dawkins is not saying that he expected to find anything

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Speaker 1: like this in nature, right, I mean, it would be

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Speaker 1: it would be a tough sell to say that there's

431
00:24:46,000 --> 00:24:49,320
Speaker 1: just one gene or one tightly linked group of genes

432
00:24:50,359 --> 00:24:52,760
Speaker 1: and that does all that work. It's got to produce

433
00:24:52,840 --> 00:24:56,760
Speaker 1: the externally detectable trait. It's got to detect that trait

434
00:24:56,960 --> 00:24:59,919
Speaker 1: in other people, and it's got to make you a special,

435
00:25:00,000 --> 00:25:03,280
Speaker 1: really nice or in some way manage a positive interaction

436
00:25:03,880 --> 00:25:08,080
Speaker 1: with other people who have that externally detectable trade. And

437
00:25:08,240 --> 00:25:10,879
Speaker 1: uh so, so he wasn't saying you'd expect to find it,

438
00:25:10,960 --> 00:25:14,680
Speaker 1: just that it was an interesting hypothetical possibility, Like he's saying,

439
00:25:15,160 --> 00:25:18,320
Speaker 1: it's possible that the murder was committed in the library

440
00:25:18,600 --> 00:25:22,200
Speaker 1: with the candlestick by Colonel Mustard with a green beard

441
00:25:22,200 --> 00:25:26,399
Speaker 1: by Colonel ver dear Beard. But but it's just a

442
00:25:26,520 --> 00:25:30,080
Speaker 1: hypothetical situation, Yeah, and one thing one that would be

443
00:25:30,080 --> 00:25:32,399
Speaker 1: difficult to come about. But if it did exist, it

444
00:25:32,400 --> 00:25:35,240
Speaker 1: would be encouraged by natural selection. Right, he's sort of

445
00:25:35,240 --> 00:25:38,040
Speaker 1: pointing out how natural selection would favor this. You just

446
00:25:38,080 --> 00:25:41,760
Speaker 1: wouldn't expect it to arise in the first place. But

447
00:25:41,960 --> 00:25:46,119
Speaker 1: nature is stranger than our imagination. Ah, and this is

448
00:25:46,160 --> 00:25:51,280
Speaker 1: where we get into actual examples in nature, the green beard.

449
00:25:51,400 --> 00:25:54,320
Speaker 1: So this was not what we originally expected, right, I mean,

450
00:25:54,400 --> 00:25:57,679
Speaker 1: geneticist didn't think that you you discover things like this

451
00:25:57,760 --> 00:26:02,960
Speaker 1: in nature. But man, the actual world is always surprising us.

452
00:26:03,000 --> 00:26:08,840
Speaker 1: So let's meet a species. Solenopsis invicta. It sounds pretty serious,

453
00:26:08,880 --> 00:26:12,280
Speaker 1: it sounds pretty pretty indomitable. It's like the it's like

454
00:26:12,320 --> 00:26:15,960
Speaker 1: the you know, the Roman Emperor species, And it kind

455
00:26:16,000 --> 00:26:18,720
Speaker 1: of is because that is the name of the red

456
00:26:18,840 --> 00:26:22,720
Speaker 1: imported fire ant, which is native South America. It has

457
00:26:22,720 --> 00:26:26,199
Speaker 1: spread everywhere from Australia to Asia, the United States. It's

458
00:26:26,240 --> 00:26:29,560
Speaker 1: all over the place now, and woe to us for

459
00:26:29,600 --> 00:26:32,600
Speaker 1: that fact. They were first introduced to the United States

460
00:26:32,600 --> 00:26:35,439
Speaker 1: sometime in the nineteen thirties or forties, and now they

461
00:26:35,440 --> 00:26:39,040
Speaker 1: are known as an invasive pest with hot needle venom

462
00:26:39,080 --> 00:26:42,280
Speaker 1: and a ferocious fighting response when their nests are disturbed.

463
00:26:42,320 --> 00:26:45,840
Speaker 1: In fact, in our episode about the biophelia hypothesis, we

464
00:26:45,880 --> 00:26:48,800
Speaker 1: talked about that great video of EO. Wilson trudging through

465
00:26:48,840 --> 00:26:51,879
Speaker 1: the woods until he finds a red fire ant nest

466
00:26:51,920 --> 00:26:54,680
Speaker 1: and shoving his hand into it so he can show

467
00:26:54,720 --> 00:26:57,040
Speaker 1: you how much it hurts. Oh, yes, this is this

468
00:26:57,080 --> 00:26:59,199
Speaker 1: wonderful footage. I love that he just he stirs them

469
00:26:59,240 --> 00:27:01,199
Speaker 1: up less and krawling hand lets them sting him in

470
00:27:01,440 --> 00:27:04,760
Speaker 1: almost like a religious moment. For Yeah, he's so happy

471
00:27:04,800 --> 00:27:07,720
Speaker 1: about it. If only people in general could find something

472
00:27:07,800 --> 00:27:10,120
Speaker 1: that they love as much as this man loves these

473
00:27:10,160 --> 00:27:14,959
Speaker 1: ants that are furiously pumping their venom into his body. Anyway,

474
00:27:15,000 --> 00:27:18,000
Speaker 1: another perhaps interesting thing about these fire ants they exist

475
00:27:18,119 --> 00:27:21,760
Speaker 1: in two sub populations. So they've got so they've got

476
00:27:21,760 --> 00:27:25,720
Speaker 1: this species, and then two subpopulations have very distinct ways

477
00:27:25,760 --> 00:27:29,880
Speaker 1: of making a living. One tolerates only one queen per

478
00:27:29,920 --> 00:27:34,920
Speaker 1: colony and the other has multiple queens per colony. Uh,

479
00:27:34,960 --> 00:27:37,160
Speaker 1: and those are known as the mono guy in versus

480
00:27:37,160 --> 00:27:41,440
Speaker 1: the polygone types. You know, one one queen multiple queens.

481
00:27:41,480 --> 00:27:46,240
Speaker 1: So in Lauren Keller and Kenneth g Ross published an

482
00:27:46,280 --> 00:27:50,560
Speaker 1: interesting report in Nature about Solenopsis invicta, and Keller and

483
00:27:50,680 --> 00:27:54,120
Speaker 1: Ross were studying red fire ants of the polygone variety,

484
00:27:54,359 --> 00:27:57,520
Speaker 1: that's the kind that has multiple queens. So within the

485
00:27:57,560 --> 00:28:01,800
Speaker 1: genome of these polyguine sol Anopsis invicta, there is a

486
00:28:01,920 --> 00:28:04,960
Speaker 1: gene locust called g P nine and a couple of

487
00:28:05,080 --> 00:28:08,359
Speaker 1: terms to explain real quick. In genetics, a locus is

488
00:28:08,440 --> 00:28:11,040
Speaker 1: just the location on a chromosome where you know you

489
00:28:11,040 --> 00:28:13,200
Speaker 1: can look to find something, where you know you can

490
00:28:13,200 --> 00:28:16,000
Speaker 1: look to find a gene or one of its alleles.

491
00:28:16,680 --> 00:28:21,000
Speaker 1: And allele's are variable forms of a gene appearing at

492
00:28:21,000 --> 00:28:23,800
Speaker 1: the same locus. So, for a simple example, at the

493
00:28:23,880 --> 00:28:27,919
Speaker 1: locusts q x one, you might contain a gene that

494
00:28:28,040 --> 00:28:31,880
Speaker 1: regulates the speed at which your toneails grow. And if

495
00:28:31,920 --> 00:28:34,360
Speaker 1: you have one allele at q x one, your toneails

496
00:28:34,400 --> 00:28:36,720
Speaker 1: grow fast, and if you've got a different allele at

497
00:28:36,800 --> 00:28:40,200
Speaker 1: q x one, your tone nails grow slow. Now back

498
00:28:40,240 --> 00:28:42,680
Speaker 1: to the fire ants. They've got this gene locust called

499
00:28:42,720 --> 00:28:45,720
Speaker 1: g P nine with two allele's big B and little B.

500
00:28:47,080 --> 00:28:50,280
Speaker 1: And because they have two sets of chromosomes. The ants

501
00:28:50,320 --> 00:28:53,320
Speaker 1: can have three different types of arrangements at g P nine.

502
00:28:53,680 --> 00:28:56,600
Speaker 1: They can have big B, big B, Big B little

503
00:28:56,600 --> 00:29:00,640
Speaker 1: B or little by little by. All right, now, if

504
00:29:00,640 --> 00:29:03,440
Speaker 1: you were to watch these colonies up close, where a

505
00:29:03,520 --> 00:29:07,280
Speaker 1: new queen reaches sexual maturity and starts trying to mate

506
00:29:07,280 --> 00:29:11,080
Speaker 1: and lay eggs, you notice something weird. Only the queens

507
00:29:11,440 --> 00:29:14,040
Speaker 1: with the two different alleles, the big B little be

508
00:29:14,200 --> 00:29:17,520
Speaker 1: jeans at g P nine live long enough to reproduce.

509
00:29:18,280 --> 00:29:21,200
Speaker 1: Why is that what happens to the others. So first,

510
00:29:21,240 --> 00:29:24,440
Speaker 1: it appears that queens with little be little bee die

511
00:29:24,680 --> 00:29:27,840
Speaker 1: very young of natural complications. This just appears to be

512
00:29:27,880 --> 00:29:30,440
Speaker 1: a lethal recessive combination. And we see this a lot

513
00:29:30,480 --> 00:29:33,040
Speaker 1: of times in nature where if you've got two copies

514
00:29:33,080 --> 00:29:36,320
Speaker 1: of this recessive gene, it's it's bad news for you

515
00:29:36,360 --> 00:29:38,680
Speaker 1: and it can lead to a genetic condition that kills you.

516
00:29:40,160 --> 00:29:43,520
Speaker 1: But what happens to the big B big B queens. Well,

517
00:29:43,720 --> 00:29:47,680
Speaker 1: Keller and Ross found out they get violently executed by

518
00:29:47,720 --> 00:29:51,320
Speaker 1: their own workers. The ants of the colony tear their

519
00:29:51,360 --> 00:29:56,520
Speaker 1: soul apart. So furthermore, they discovered that it was primarily

520
00:29:56,600 --> 00:30:00,240
Speaker 1: workers bearing the little bee allele that did the any

521
00:30:00,240 --> 00:30:03,760
Speaker 1: work of killing the queens without it. So if you've

522
00:30:03,760 --> 00:30:06,960
Speaker 1: got a queen that's big B big B, the workers

523
00:30:07,000 --> 00:30:10,800
Speaker 1: that have a little bee allele will surround it, bite

524
00:30:10,800 --> 00:30:13,959
Speaker 1: it and kill it because it is it's essentially in

525
00:30:14,040 --> 00:30:16,600
Speaker 1: some respects, it's like a foreign queen. It does not

526
00:30:17,440 --> 00:30:22,560
Speaker 1: share the same allegals as it does. Yeah, that's what

527
00:30:22,600 --> 00:30:25,120
Speaker 1: appears to be going on. But how how does that happen?

528
00:30:25,720 --> 00:30:28,120
Speaker 1: I mean, normally you wouldn't be able to look at

529
00:30:28,160 --> 00:30:32,520
Speaker 1: somebody and say you're missing one particular gene that I

530
00:30:32,680 --> 00:30:36,280
Speaker 1: have and I'm going to kill you. So what's going on?

531
00:30:37,080 --> 00:30:39,320
Speaker 1: So the worker ants with that little bijeene at g

532
00:30:39,480 --> 00:30:43,160
Speaker 1: P nine tend to violently massacre any potential queens that

533
00:30:43,320 --> 00:30:46,200
Speaker 1: don't carry the little bee gene at g P nine.

534
00:30:46,760 --> 00:30:49,360
Speaker 1: Uh So it sounds like we might have a slightly

535
00:30:49,400 --> 00:30:52,280
Speaker 1: modified version of the green beer gene on our hands.

536
00:30:52,320 --> 00:30:55,280
Speaker 1: But here's a question. How how do those workers know

537
00:30:55,520 --> 00:30:57,719
Speaker 1: when the new queen has the right a leal or not?

538
00:30:58,800 --> 00:31:02,520
Speaker 1: Here's a clue. The author's noticed that sometimes after a

539
00:31:02,560 --> 00:31:05,840
Speaker 1: worker ant took part in a mob executing a big

540
00:31:05,880 --> 00:31:09,040
Speaker 1: B big B queen, the worker aunt would then be

541
00:31:09,080 --> 00:31:12,760
Speaker 1: attacked by other ants in the colony. So you take

542
00:31:12,840 --> 00:31:15,680
Speaker 1: part in the mob, you kill that big Bi big

543
00:31:15,680 --> 00:31:18,800
Speaker 1: b queen, then you walk away. Then other worker ants

544
00:31:18,880 --> 00:31:21,680
Speaker 1: kill you and you know it. And there's no politics

545
00:31:21,680 --> 00:31:23,479
Speaker 1: in the insect world, so you know, it's not just

546
00:31:23,520 --> 00:31:26,160
Speaker 1: some sort of a situation where the assassin has to

547
00:31:27,040 --> 00:31:31,160
Speaker 1: absorb the blame for the regicide, right right, right, It's

548
00:31:31,160 --> 00:31:33,560
Speaker 1: not like the king slayer is now being you know,

549
00:31:33,640 --> 00:31:36,800
Speaker 1: talking to me, like, so what was going on? You

550
00:31:36,840 --> 00:31:39,960
Speaker 1: have to wonder was something rubbing off on the killer

551
00:31:40,000 --> 00:31:43,280
Speaker 1: worker And this led the authors to suspect the culprit

552
00:31:43,400 --> 00:31:47,640
Speaker 1: is pheromone based. So they tested this by rubbing worker

553
00:31:47,720 --> 00:31:50,760
Speaker 1: ants against two different types of queens, the big b

554
00:31:50,920 --> 00:31:53,400
Speaker 1: big be queens and the big Bee little bee queens,

555
00:31:53,880 --> 00:31:57,600
Speaker 1: and then releasing those workers to their colleagues, and sure enough,

556
00:31:57,920 --> 00:32:01,000
Speaker 1: the the other workers tended to a hack the ants

557
00:32:01,080 --> 00:32:03,560
Speaker 1: that have been rubbed against the doom big Bi big

558
00:32:03,600 --> 00:32:06,440
Speaker 1: Bee queens, but not against the queens with a copy

559
00:32:06,480 --> 00:32:09,680
Speaker 1: of the little be allele. So to know that we're

560
00:32:09,680 --> 00:32:12,120
Speaker 1: looking at an actual green beer gene, we've got to

561
00:32:12,160 --> 00:32:14,680
Speaker 1: remember the gene should be doing three things. It's got

562
00:32:14,680 --> 00:32:18,480
Speaker 1: to produce an externally detectable trade. It's got to detect

563
00:32:18,600 --> 00:32:21,520
Speaker 1: that trade and other individuals, and it's got to provide

564
00:32:21,560 --> 00:32:26,520
Speaker 1: preferential treatment to individuals bearing that trade. So it looks

565
00:32:26,560 --> 00:32:29,560
Speaker 1: like maybe maybe what's going on here is this ants

566
00:32:29,600 --> 00:32:32,600
Speaker 1: with the little bee gene at g P nine will

567
00:32:32,640 --> 00:32:36,360
Speaker 1: attack and kill anything that smells like a sexually mature

568
00:32:36,520 --> 00:32:41,400
Speaker 1: queen unless it also carries the chemical signature generated by

569
00:32:41,400 --> 00:32:44,680
Speaker 1: the little bee gene, which switches off that kill drive

570
00:32:44,720 --> 00:32:49,200
Speaker 1: in the workers. Interesting. Okay, so this looks like, if

571
00:32:49,240 --> 00:32:52,760
Speaker 1: these findings are correct, a genuine green beer gene found

572
00:32:52,800 --> 00:32:56,440
Speaker 1: in nature, there's this one gene or some incredibly tightly

573
00:32:56,520 --> 00:33:02,080
Speaker 1: linked group of genes going off the g P nine locusts.

574
00:33:02,120 --> 00:33:05,840
Speaker 1: That says, if the queen doesn't have this one gene

575
00:33:06,160 --> 00:33:10,040
Speaker 1: killer al Right, well, we're gonna take a quick break

576
00:33:10,040 --> 00:33:11,960
Speaker 1: and when we come back, we have some more possible

577
00:33:11,960 --> 00:33:15,440
Speaker 1: examples of the green beard gene going on, and we

578
00:33:15,480 --> 00:33:18,520
Speaker 1: will talk about the wood mouse and the sperm train.

579
00:33:23,000 --> 00:33:26,320
Speaker 1: All right, we're back. So, Robert, you promised before we

580
00:33:26,400 --> 00:33:28,960
Speaker 1: left that you were going to tell me about sperm trains. Yes,

581
00:33:29,360 --> 00:33:32,760
Speaker 1: Uh so I'm gonna gonna go ahead and back up

582
00:33:32,800 --> 00:33:34,920
Speaker 1: here a little bit for everybody before we get into

583
00:33:34,960 --> 00:33:37,640
Speaker 1: just you know, full on with the sperm train. If

584
00:33:37,640 --> 00:33:40,600
Speaker 1: you were like us, then you've probably read and viewed

585
00:33:40,640 --> 00:33:44,880
Speaker 1: a great deal of science content about sperm over the years. Uh.

586
00:33:45,000 --> 00:33:47,640
Speaker 1: This is a topic that pops up in nature documentaries,

587
00:33:47,640 --> 00:33:51,200
Speaker 1: science news articles, and seemingly endless academic papers. Well, the

588
00:33:51,240 --> 00:33:54,200
Speaker 1: sperm it kind of creates a microcosm of the whole

589
00:33:54,280 --> 00:33:57,040
Speaker 1: natural world, right, Yeah, it is because you know, it's

590
00:33:57,040 --> 00:34:00,880
Speaker 1: a situation where when when the sperm are competing against

591
00:34:00,880 --> 00:34:03,880
Speaker 1: each other to go after the goal that's happening on

592
00:34:03,880 --> 00:34:06,479
Speaker 1: the inside. On the outside, of course, we have all

593
00:34:06,560 --> 00:34:10,680
Speaker 1: these various uh mating competition scenarios that factor into our

594
00:34:10,800 --> 00:34:14,560
Speaker 1: nature documentaries, you know, where different males are competing uh

595
00:34:14,560 --> 00:34:18,200
Speaker 1: to mate with the female, or multiple males mating with

596
00:34:18,239 --> 00:34:21,120
Speaker 1: the female, what have you. Uh. So it's interestingly we

597
00:34:21,160 --> 00:34:24,480
Speaker 1: have kind of this outer war of mating war going on,

598
00:34:24,560 --> 00:34:28,320
Speaker 1: and then there is an intermating war as well. Because

599
00:34:28,320 --> 00:34:31,600
Speaker 1: we have all these examples of of sperm uh playing

600
00:34:31,600 --> 00:34:34,320
Speaker 1: a role in this outer war. We we learned, um,

601
00:34:34,360 --> 00:34:37,560
Speaker 1: you know, the violent details of copulation and how sometimes

602
00:34:37,600 --> 00:34:41,800
Speaker 1: they can deter females from acquiring additional males um. There's

603
00:34:42,000 --> 00:34:48,080
Speaker 1: also the interesting hypothesis regarding Kama kaze uh spermatozoa in

604
00:34:48,200 --> 00:34:52,680
Speaker 1: rats and humans, with the hypothetical primary function in these

605
00:34:52,680 --> 00:34:57,880
Speaker 1: particular sperm of incapacitating sperm of arrival male. It's kind

606
00:34:57,880 --> 00:35:01,600
Speaker 1: of like dog fighting sperm or It also makes me

607
00:35:01,640 --> 00:35:04,920
Speaker 1: think of say a miniature Star Wars space battle taking

608
00:35:04,960 --> 00:35:09,080
Speaker 1: place inside and that the egg is quite ironically the

609
00:35:09,120 --> 00:35:11,319
Speaker 1: death star maybe the life star. Well yeah, if you're

610
00:35:11,320 --> 00:35:12,959
Speaker 1: a sperm, you don't want to blow up the egg.

611
00:35:13,000 --> 00:35:15,399
Speaker 1: You want to join in harmony with the egg. Well yeah,

612
00:35:15,480 --> 00:35:18,680
Speaker 1: but but but you have that one individual sperm wants

613
00:35:18,680 --> 00:35:20,400
Speaker 1: to do it, and it's it's it's a race. It's

614
00:35:20,440 --> 00:35:23,960
Speaker 1: a competition life star like that. Yeah, now you kind

615
00:35:23,960 --> 00:35:27,040
Speaker 1: of grow used to this trend, right, there's competition, ruthless aggression.

616
00:35:27,280 --> 00:35:31,080
Speaker 1: Sperm is the life seeking missiles at war with their rivals.

617
00:35:31,640 --> 00:35:36,120
Speaker 1: Multiple matings resultant sperm competition and even mixed paternity. But

618
00:35:36,239 --> 00:35:39,839
Speaker 1: sperm are also competing against all of their their brethren. Uh.

619
00:35:39,840 --> 00:35:42,040
Speaker 1: And therefore it's a bit shocking when you realize that

620
00:35:42,080 --> 00:35:47,520
Speaker 1: there are cases of sperm cooperation. So most examples of

621
00:35:47,560 --> 00:35:50,880
Speaker 1: this occurs in the let's face it, freaky world of mosques,

622
00:35:52,120 --> 00:35:56,239
Speaker 1: as well as the the aforementioned politics free realm of insects.

623
00:35:56,719 --> 00:35:59,920
Speaker 1: But there's actually an example in the mammal realm as well,

624
00:36:00,000 --> 00:36:03,040
Speaker 1: as demonstrated in the two thousand two paper. And I

625
00:36:03,080 --> 00:36:07,080
Speaker 1: love this title, exceptional sperm cooperation in the wood mouse

626
00:36:07,160 --> 00:36:12,799
Speaker 1: exceptional A plus sperm cooperation. So the wood mouse this

627
00:36:12,880 --> 00:36:15,640
Speaker 1: is a you know, a Western European rodent. It's small,

628
00:36:16,040 --> 00:36:19,640
Speaker 1: but it has tremendous sperm output. The mice in general,

629
00:36:20,000 --> 00:36:23,239
Speaker 1: uh that they have rather large test these tests or

630
00:36:23,320 --> 00:36:26,160
Speaker 1: five percent of their body. And if we were if

631
00:36:26,160 --> 00:36:28,240
Speaker 1: this were the case with humans, to put that in perspective,

632
00:36:28,400 --> 00:36:32,280
Speaker 1: a male human would have seven points points seven pounds

633
00:36:32,440 --> 00:36:38,040
Speaker 1: or three point five ms of tests. So these uh,

634
00:36:38,280 --> 00:36:41,120
Speaker 1: the fact that they're over sexed, it would seem this

635
00:36:41,160 --> 00:36:45,719
Speaker 1: is helpful because they engage in quote scramble competition, uh

636
00:36:45,840 --> 00:36:52,040
Speaker 1: to mate polygamously with promiscuous females. As such, you would

637
00:36:52,040 --> 00:36:55,439
Speaker 1: expect a lot of sperm competition going on there. Race

638
00:36:55,520 --> 00:36:58,399
Speaker 1: to the life star. And this is where the train

639
00:36:58,520 --> 00:37:03,200
Speaker 1: comes in because the wood mouth sperm feature quote extremely

640
00:37:03,400 --> 00:37:07,640
Speaker 1: long apical hooks and they use these hooks to form

641
00:37:07,719 --> 00:37:09,960
Speaker 1: together into a store to sort of think of it

642
00:37:10,000 --> 00:37:14,120
Speaker 1: as a sperm vultron, or, as the paper puts it,

643
00:37:14,600 --> 00:37:21,920
Speaker 1: motile trains of spermatozoa comprising hundreds to several thousands of cells.

644
00:37:21,960 --> 00:37:25,480
Speaker 1: These trains these sort of you know hooked together, you know,

645
00:37:25,560 --> 00:37:29,600
Speaker 1: wheels of sperm. Uh. These trains allow the bound sperm

646
00:37:29,680 --> 00:37:34,440
Speaker 1: to travel at greater speeds up to fifty faster than

647
00:37:34,600 --> 00:37:37,960
Speaker 1: loan sperm out there going on their own. But as

648
00:37:37,960 --> 00:37:40,520
Speaker 1: they do that, they've got to be given a leg

649
00:37:40,640 --> 00:37:43,359
Speaker 1: up to these other sperm that they're in competition with.

650
00:37:43,719 --> 00:37:46,399
Speaker 1: That's the thing, because ultimately only one sperm can get

651
00:37:46,400 --> 00:37:49,880
Speaker 1: in there. But they're they're all working together. It's it

652
00:37:50,000 --> 00:37:52,319
Speaker 1: makes me think of these scenarios where a bunch of

653
00:37:52,400 --> 00:37:55,160
Speaker 1: villains team up, uh, and then they're all going to

654
00:37:55,239 --> 00:37:57,040
Speaker 1: turn in turn on each other at the end, but

655
00:37:57,080 --> 00:37:58,960
Speaker 1: for a little while they're working together, like a sinister

656
00:37:59,040 --> 00:38:01,399
Speaker 1: six situation. Right. I thought you were going to say

657
00:38:01,440 --> 00:38:04,200
Speaker 1: suicide squad. Well, I guess that's a similar example. They're

658
00:38:04,200 --> 00:38:08,520
Speaker 1: all villains, right, are they so untold? I I didn't

659
00:38:08,520 --> 00:38:10,880
Speaker 1: see it. I I tried to check it out on HBO,

660
00:38:11,280 --> 00:38:13,520
Speaker 1: uh to see what everyone was talking about and made

661
00:38:13,520 --> 00:38:16,160
Speaker 1: it made it just about like five minutes in. But

662
00:38:16,239 --> 00:38:19,520
Speaker 1: maybe it's a great film. I could be wrong. So

663
00:38:19,640 --> 00:38:22,440
Speaker 1: in these cases, the hook up occurs one to two

664
00:38:22,480 --> 00:38:26,400
Speaker 1: minutes after ejaculation, and that's when head to head or

665
00:38:26,480 --> 00:38:29,840
Speaker 1: tail tail to head hookups occur. So they have hooks

666
00:38:29,880 --> 00:38:32,720
Speaker 1: on their heads as well in this scenario. But again,

667
00:38:32,719 --> 00:38:35,279
Speaker 1: only one sperm may enter the life star. It's it's

668
00:38:35,400 --> 00:38:36,960
Speaker 1: it's like the scene with the you know, trying to

669
00:38:36,960 --> 00:38:39,640
Speaker 1: shoot the bolts into the death Star. Uh. So the

670
00:38:39,680 --> 00:38:44,040
Speaker 1: train starts to decouple about twenty to thirty minutes after forming,

671
00:38:44,480 --> 00:38:47,040
Speaker 1: and in doing this, many of the individual sperms seemed

672
00:38:47,080 --> 00:38:51,680
Speaker 1: to sacrifice themselves and uh and some might be even

673
00:38:51,760 --> 00:38:53,640
Speaker 1: marked for death ahead of time, or there might be

674
00:38:53,719 --> 00:38:56,080
Speaker 1: competition there at the end. So again, think of a

675
00:38:56,120 --> 00:38:59,640
Speaker 1: group of supervillains. They've they've worked together so far, but

676
00:38:59,680 --> 00:39:02,680
Speaker 1: after they kill the Batman or the Superman, they're just

677
00:39:02,719 --> 00:39:05,000
Speaker 1: gonna turn on each other. So how do the researchers

678
00:39:05,040 --> 00:39:08,520
Speaker 1: connect this to the idea of the green beer gene. Well,

679
00:39:08,640 --> 00:39:11,240
Speaker 1: this is what they say. Quote a green beer gene

680
00:39:11,520 --> 00:39:17,600
Speaker 1: might evoke such cellular cooperation, although other genetic mechanisms cannot

681
00:39:17,600 --> 00:39:20,520
Speaker 1: be excluded at this stage. A green beard is a

682
00:39:20,600 --> 00:39:23,200
Speaker 1: gene that recognizes copies of itself and other individuals and

683
00:39:23,239 --> 00:39:25,920
Speaker 1: directs benefits to those individuals. So the idea, here's the

684
00:39:26,280 --> 00:39:29,160
Speaker 1: green beer gene would be the deciding and factor with

685
00:39:29,640 --> 00:39:33,760
Speaker 1: which sperm you're going to team up with in this battle,

686
00:39:33,800 --> 00:39:36,120
Speaker 1: because you certainly are not gonna gonna want to hook

687
00:39:36,200 --> 00:39:41,400
Speaker 1: up with sperm from other uh individual mice that have

688
00:39:41,600 --> 00:39:45,040
Speaker 1: made it with this particular female. Right. Interesting, So so

689
00:39:45,080 --> 00:39:48,680
Speaker 1: they're saying this could be one explanation of the behavior

690
00:39:48,719 --> 00:39:51,520
Speaker 1: they're seeing here. Yeah, this is Life Race two thousand.

691
00:39:52,000 --> 00:39:54,160
Speaker 1: All the cars are heading out, but some of these

692
00:39:54,160 --> 00:39:56,920
Speaker 1: cars are realizing that they can work together and form

693
00:39:57,000 --> 00:40:00,759
Speaker 1: a train uh to to to out maneuver and and

694
00:40:01,000 --> 00:40:04,279
Speaker 1: uh and and outperform their competitors because some of the

695
00:40:04,280 --> 00:40:07,920
Speaker 1: cars have exact copies of the driver in each one. Yeah,

696
00:40:08,040 --> 00:40:13,440
Speaker 1: like multiple Frankenstein's that was a deep cut. I scared

697
00:40:13,480 --> 00:40:16,120
Speaker 1: somebody off there. Roger Corman's Death Rays two thousand, if

698
00:40:16,120 --> 00:40:17,800
Speaker 1: you want to want to get all of that, Okay,

699
00:40:17,840 --> 00:40:20,759
Speaker 1: So that's another So there really does appear to be

700
00:40:20,800 --> 00:40:23,359
Speaker 1: a green beer, you know, if the observations are correct,

701
00:40:23,400 --> 00:40:25,359
Speaker 1: there really does appear to be a green beer gene

702
00:40:25,400 --> 00:40:28,279
Speaker 1: working in the fire ants. Uh. It looks like green

703
00:40:28,320 --> 00:40:31,399
Speaker 1: beer jeans are a good candidate to explain what's going

704
00:40:31,440 --> 00:40:34,319
Speaker 1: on here in the sperm trains, but it's not a

705
00:40:34,360 --> 00:40:38,440
Speaker 1: sure thing. Other mechanisms might explain it. In two thousand

706
00:40:38,480 --> 00:40:41,319
Speaker 1: and eight, I should just mention quickly there was one

707
00:40:41,360 --> 00:40:44,319
Speaker 1: paper that seemed to identify a green beer gene that

708
00:40:44,520 --> 00:40:49,160
Speaker 1: was driving cooperation in yeast cells, and that that gene

709
00:40:49,200 --> 00:40:53,439
Speaker 1: was called f l O one. But just recently I

710
00:40:53,480 --> 00:40:57,440
Speaker 1: was reading about how there is apparently green beer gene

711
00:40:57,440 --> 00:41:01,279
Speaker 1: activity in slime molds. Our old friends. Oh yes, yeah, yeah,

712
00:41:01,480 --> 00:41:06,319
Speaker 1: this was pretty interesting. So to refresh slime molds, which

713
00:41:06,560 --> 00:41:08,960
Speaker 1: which I like to compare to science itself at times.

714
00:41:09,000 --> 00:41:11,960
Speaker 1: I think it's a good metaphor for it. Slime molds

715
00:41:12,000 --> 00:41:15,960
Speaker 1: are microbes that live as single celled organisms, but they

716
00:41:16,040 --> 00:41:19,320
Speaker 1: kind of vultron together into a great communal metal organism

717
00:41:19,320 --> 00:41:22,839
Speaker 1: that quests after food. It's a two vulturo on episode. Yeah. Uh,

718
00:41:22,880 --> 00:41:25,839
Speaker 1: and you know, there's some fabulous experiments that show the

719
00:41:25,880 --> 00:41:29,200
Speaker 1: sort of communal intelligence that it has, like a very

720
00:41:29,239 --> 00:41:34,080
Speaker 1: non non non human form of intelligence. So it's problem

721
00:41:34,120 --> 00:41:39,120
Speaker 1: solving ability. Yeah. Well, in March of this year, geneticists

722
00:41:39,160 --> 00:41:43,320
Speaker 1: from the Universities of Manchester and Bath discovered that these

723
00:41:43,360 --> 00:41:48,040
Speaker 1: individual cells are capable of choosing who they joined forces with.

724
00:41:49,080 --> 00:41:52,960
Speaker 1: So it's not just you know, every um slime mold

725
00:41:53,320 --> 00:41:55,440
Speaker 1: individual is gonna come together like they're gonna be a

726
00:41:55,480 --> 00:41:58,439
Speaker 1: little bit choosy and uh. And it seems that green

727
00:41:58,480 --> 00:42:01,840
Speaker 1: beer jeans factor into it. Oh okay, So you're a

728
00:42:01,840 --> 00:42:05,920
Speaker 1: slime mold cell and you've got this one gene that says,

729
00:42:06,120 --> 00:42:08,960
Speaker 1: if there's another slime slime mold cell that has this

730
00:42:09,080 --> 00:42:15,000
Speaker 1: same gene, join with them and conquer the maze. Okay, yeah,

731
00:42:15,120 --> 00:42:17,279
Speaker 1: it's so it's pretty straightforward in that regard that the

732
00:42:17,360 --> 00:42:20,799
Speaker 1: gene in question encodes a molecule on the surface of

733
00:42:20,840 --> 00:42:23,799
Speaker 1: a slime mold cell and it can bind to the

734
00:42:23,880 --> 00:42:27,840
Speaker 1: same molecule of another slime mold cell. Now, most my

735
00:42:28,080 --> 00:42:32,960
Speaker 1: slime molds strains have a unique version of the gene. Okay. Now,

736
00:42:32,960 --> 00:42:35,719
Speaker 1: there's a great deal of diversity in slime mold green

737
00:42:35,760 --> 00:42:38,319
Speaker 1: beer genes, according to the study, but individuals show a

738
00:42:38,400 --> 00:42:43,040
Speaker 1: preference for cells with like or similar green beer genes,

739
00:42:43,480 --> 00:42:47,680
Speaker 1: and the assembled whole the resulting you know, larger mental

740
00:42:47,760 --> 00:42:51,959
Speaker 1: organism slime mold. Um, it actually performs better. It does

741
00:42:52,000 --> 00:42:56,480
Speaker 1: better if it's formed of preferred partners as opposed to

742
00:42:56,560 --> 00:43:00,680
Speaker 1: you know, non preferred partners. As such, these beer genes

743
00:43:00,719 --> 00:43:04,120
Speaker 1: listening to govern in a particular specific cooperative behavior in

744
00:43:04,200 --> 00:43:07,600
Speaker 1: the slime molds. So the way it works is it

745
00:43:07,680 --> 00:43:11,160
Speaker 1: just makes you sticky for other slime mold cells that

746
00:43:11,239 --> 00:43:14,520
Speaker 1: have this same gene. Yeah, basically like on a very

747
00:43:14,560 --> 00:43:17,439
Speaker 1: it's it's kind of just like the real pro gloves. Yeah,

748
00:43:17,480 --> 00:43:21,080
Speaker 1: it's like the simplified version of why liking the same

749
00:43:21,200 --> 00:43:26,560
Speaker 1: band um binds you to somebody else. So to summarize

750
00:43:27,600 --> 00:43:31,160
Speaker 1: going off the paper here, Uh, the green beer genes

751
00:43:31,200 --> 00:43:33,960
Speaker 1: achieved the following in the slime mold. So they predict

752
00:43:34,480 --> 00:43:39,760
Speaker 1: partners specific patterns of cooperation by underlying variation in partner

753
00:43:39,840 --> 00:43:45,839
Speaker 1: specific protein protein binding strength and recognition specificity. Uh. They

754
00:43:45,840 --> 00:43:48,799
Speaker 1: also increase fitness because they help avoid potential costs of

755
00:43:48,840 --> 00:43:56,040
Speaker 1: cooperating with incompatible partners. And also they generate a homophilic

756
00:43:56,080 --> 00:44:00,680
Speaker 1: binding spectrum that allows individuals to identify appropriate partners with

757
00:44:00,719 --> 00:44:04,520
Speaker 1: whom to engage in cooperation. And its states that it

758
00:44:04,560 --> 00:44:08,040
Speaker 1: also serves to stabilize cooperation in the face of selective

759
00:44:08,080 --> 00:44:12,480
Speaker 1: pressure for the emergence of quote false bearded cheaters who

760
00:44:13,000 --> 00:44:17,600
Speaker 1: by providing information that can be used to differentiate compatible

761
00:44:17,680 --> 00:44:20,600
Speaker 1: from incompatible partners. So this is a new ripple. This

762
00:44:20,680 --> 00:44:23,399
Speaker 1: is a new ripple. So we started with this hypothetical

763
00:44:23,480 --> 00:44:27,680
Speaker 1: idea that Hamilton and Dawkins talked about, saying that it's

764
00:44:28,120 --> 00:44:30,319
Speaker 1: not necessarily going to be found in nature. It's just

765
00:44:30,440 --> 00:44:33,040
Speaker 1: one hypothetical thing that could work if it ever were

766
00:44:33,080 --> 00:44:36,440
Speaker 1: to arise. Now we're finding examples of it in nature.

767
00:44:36,480 --> 00:44:39,040
Speaker 1: The green beard gene, the gene that says, hey, I

768
00:44:39,080 --> 00:44:41,440
Speaker 1: see you have this gene too. I like you. I'm

769
00:44:41,440 --> 00:44:44,319
Speaker 1: going to treat you right. But now we see that

770
00:44:44,400 --> 00:44:48,120
Speaker 1: there are false beard pretenders. You could have a gene

771
00:44:48,200 --> 00:44:52,120
Speaker 1: that in fact signals the same thing as having the gene,

772
00:44:52,280 --> 00:44:54,960
Speaker 1: or something that could easily trick you into thinking they

773
00:44:54,960 --> 00:44:57,560
Speaker 1: have the gene but they don't have it. Well, you know,

774
00:44:57,640 --> 00:45:00,000
Speaker 1: I'm reminded of the slime mold moving through the maize

775
00:45:00,040 --> 00:45:05,040
Speaker 1: that you know, our scientific uh quest to understand the universe.

776
00:45:05,080 --> 00:45:07,960
Speaker 1: So anytime we find something new, there's always some new ripple,

777
00:45:08,040 --> 00:45:11,719
Speaker 1: some new complication, uh. And and and some new level

778
00:45:11,760 --> 00:45:15,160
Speaker 1: of the complexity of life. So what is the what

779
00:45:15,320 --> 00:45:18,719
Speaker 1: is the liking the same band equivalent of the false

780
00:45:18,760 --> 00:45:24,320
Speaker 1: spirited cheaters? Somebody who you think signals liking the same band,

781
00:45:24,440 --> 00:45:26,880
Speaker 1: but in fact they do not really like them. Is

782
00:45:26,960 --> 00:45:29,520
Speaker 1: that the the fake tool fans you were talking about

783
00:45:29,560 --> 00:45:32,719
Speaker 1: at the beginning, maybe, or maybe a better example would be,

784
00:45:34,000 --> 00:45:36,920
Speaker 1: like there might be a band that was really popular

785
00:45:37,360 --> 00:45:41,560
Speaker 1: and you dig them, somebody else doesn't. Really they're not

786
00:45:41,760 --> 00:45:43,759
Speaker 1: They're not a true fan. They just they're maybe a

787
00:45:43,800 --> 00:45:47,080
Speaker 1: little nostalgic for it. Or they remember listening to this

788
00:45:47,160 --> 00:45:50,200
Speaker 1: band in high school. But where have they been, you know,

789
00:45:50,840 --> 00:45:53,920
Speaker 1: the subsequent decades. So they've got the bumper sticker on

790
00:45:53,960 --> 00:45:56,640
Speaker 1: the back of their car. But if you started talking

791
00:45:56,640 --> 00:45:58,839
Speaker 1: to them, you would find that the compatibility you were

792
00:45:58,880 --> 00:46:02,240
Speaker 1: seeking is not really. It's all superficial, right, or maybe

793
00:46:02,280 --> 00:46:04,880
Speaker 1: it's Uh. It also reminds me I think it was

794
00:46:05,400 --> 00:46:08,080
Speaker 1: Kids in the Hall skit where somebody's trying to buy

795
00:46:08,120 --> 00:46:10,279
Speaker 1: a copy of The Door's Greatest Hits at the CD

796
00:46:10,440 --> 00:46:13,080
Speaker 1: store and this guy comes in Elections and was like, no,

797
00:46:13,239 --> 00:46:15,680
Speaker 1: you have to start with the first album, and then

798
00:46:15,719 --> 00:46:18,120
Speaker 1: you have to listen to it, like there's very specific

799
00:46:18,120 --> 00:46:20,440
Speaker 1: instructions about where you listen to it, how long you

800
00:46:20,520 --> 00:46:23,080
Speaker 1: listen to it, and then only then can you move

801
00:46:23,080 --> 00:46:24,640
Speaker 1: on to the second album, and you have to go

802
00:46:24,680 --> 00:46:26,799
Speaker 1: in order. So he's instructing him how to be a

803
00:46:26,840 --> 00:46:30,160
Speaker 1: true long term fan and not like a cheap skip

804
00:46:30,200 --> 00:46:32,160
Speaker 1: to the greatest hits fan. How to be a true

805
00:46:32,200 --> 00:46:36,160
Speaker 1: pretentious jerk. Do pretentious jerks glom onto each other in

806
00:46:36,200 --> 00:46:39,160
Speaker 1: the same way that Greenbeard slime mold cells do? I

807
00:46:39,160 --> 00:46:41,040
Speaker 1: don't know, or maybe I mean I guess they do.

808
00:46:41,160 --> 00:46:43,360
Speaker 1: But you could also argue that they don't have the

809
00:46:43,400 --> 00:46:46,160
Speaker 1: binding factor. They're just too pretentious about their their stuff.

810
00:46:46,200 --> 00:46:48,839
Speaker 1: They can't actually enjoy it with other people because they

811
00:46:48,840 --> 00:46:50,400
Speaker 1: have to be the they have to be the biggest

812
00:46:50,400 --> 00:46:52,080
Speaker 1: fan in the room. They have to be the smartest

813
00:46:52,080 --> 00:46:55,920
Speaker 1: fan in the room. But we're getting into the human

814
00:46:55,960 --> 00:46:59,239
Speaker 1: complexities here of of of sharing and things. I think

815
00:46:59,280 --> 00:47:02,080
Speaker 1: we've run more fild with our metaphors and we've gone

816
00:47:02,080 --> 00:47:06,320
Speaker 1: off the deep end. Anyway, picking up from our intro

817
00:47:06,480 --> 00:47:08,600
Speaker 1: to this episode, where we had to lay the context

818
00:47:08,640 --> 00:47:12,840
Speaker 1: about the discussion about altruism and biology and the idea

819
00:47:12,840 --> 00:47:15,640
Speaker 1: about the different levels of selection I've been thinking that

820
00:47:15,840 --> 00:47:20,640
Speaker 1: this would be daunting because it makes biologists uh ruffle

821
00:47:20,680 --> 00:47:23,480
Speaker 1: their feathers and get very upset. I think maybe in

822
00:47:23,520 --> 00:47:25,440
Speaker 1: the future we should try to do a big episode

823
00:47:25,520 --> 00:47:28,360
Speaker 1: or maybe a two partter on the levels of selection

824
00:47:28,480 --> 00:47:33,160
Speaker 1: debate in biology, like this big controversy over whether group

825
00:47:33,239 --> 00:47:37,680
Speaker 1: selection is a real thing or not, or whether whether

826
00:47:38,400 --> 00:47:42,360
Speaker 1: there's also arguments about the arguments, like whether the argument

827
00:47:42,520 --> 00:47:46,600
Speaker 1: is real or whether it's just semantic um and so

828
00:47:47,200 --> 00:47:49,640
Speaker 1: I think I think there's a lot of interesting stuff there,

829
00:47:49,640 --> 00:47:52,319
Speaker 1: but I know it's a big, thorny, controversial issue that

830
00:47:52,400 --> 00:47:58,080
Speaker 1: has a lot of uh petty sub issues. Well, no,

831
00:47:58,200 --> 00:48:00,160
Speaker 1: that could be fun. I mean, who knows. Maybe it's

832
00:48:00,280 --> 00:48:02,480
Speaker 1: a topic that it would be Uh it would be

833
00:48:02,480 --> 00:48:05,000
Speaker 1: sensible to bring in a guest to help us and

834
00:48:05,080 --> 00:48:07,960
Speaker 1: navigate all the controversies. Well, maybe we need to bring

835
00:48:08,000 --> 00:48:11,360
Speaker 1: to two guests, one that take each side. Ah, and

836
00:48:11,400 --> 00:48:14,719
Speaker 1: then we turned into a debate show, but we're not

837
00:48:14,800 --> 00:48:17,919
Speaker 1: we're not going to do that. Well anyway, if that's

838
00:48:17,960 --> 00:48:19,799
Speaker 1: something you would like to hear, maybe you should let

839
00:48:19,840 --> 00:48:23,440
Speaker 1: us know. Yeah, yeah, for sure. Hey, before we close

840
00:48:23,520 --> 00:48:26,400
Speaker 1: out here, Uh, this is one of the first episodes

841
00:48:26,480 --> 00:48:31,080
Speaker 1: that we're recording after the total solar eclipse here in

842
00:48:31,160 --> 00:48:35,160
Speaker 1: North America. Uh And, as previously stated, you went up

843
00:48:35,200 --> 00:48:38,640
Speaker 1: to Tennessee to to check it out, to be within

844
00:48:38,680 --> 00:48:41,200
Speaker 1: the line of totality, and and I went up to

845
00:48:41,560 --> 00:48:46,000
Speaker 1: Blue Ridge, Georgia in the North Georgia Mountains and observed

846
00:48:46,040 --> 00:48:49,040
Speaker 1: it as well. So what are your thoughts? It was?

847
00:48:49,160 --> 00:48:54,320
Speaker 1: It was amazing. I was trying to uh convince everyone

848
00:48:54,880 --> 00:48:57,839
Speaker 1: that we should go up to the path of totality,

849
00:48:57,880 --> 00:49:01,520
Speaker 1: and they were like, but hey, isn't it like totality

850
00:49:01,600 --> 00:49:03,440
Speaker 1: here in Atlanta. And I was trying to explain the

851
00:49:03,480 --> 00:49:07,000
Speaker 1: difference between like ninety nine point nine and is is

852
00:49:07,040 --> 00:49:10,200
Speaker 1: apparently huge. And I'm glad we went. I'm glad we

853
00:49:10,239 --> 00:49:13,440
Speaker 1: did it. It's I don't want to sound flip when

854
00:49:13,440 --> 00:49:16,879
Speaker 1: I say this, but it was a religious experience. I mean,

855
00:49:16,920 --> 00:49:20,640
Speaker 1: it was a really truly astonishing thing. I've never seen

856
00:49:20,680 --> 00:49:23,640
Speaker 1: anything like it before. It was amazing watching all the

857
00:49:23,640 --> 00:49:28,000
Speaker 1: different physical changes going on around us. As the totality approached.

858
00:49:28,360 --> 00:49:30,919
Speaker 1: There was this moment where it was probably about as

859
00:49:30,960 --> 00:49:34,000
Speaker 1: the sun was about seventy covered, maybe a little bit more,

860
00:49:34,560 --> 00:49:37,040
Speaker 1: that I started to notice that there was still still

861
00:49:37,080 --> 00:49:39,239
Speaker 1: the appearance of sunlight all around me. So it was

862
00:49:39,280 --> 00:49:42,200
Speaker 1: like a bright summer day, but suddenly there was I

863
00:49:42,239 --> 00:49:45,160
Speaker 1: didn't feel the heat anymore. The sunlight felt cool, and

864
00:49:45,200 --> 00:49:47,600
Speaker 1: I could stand in the direct sun and my skin

865
00:49:47,719 --> 00:49:51,879
Speaker 1: didn't have that that radiation reaction it normally does when

866
00:49:51,880 --> 00:49:55,480
Speaker 1: you're standing out in the direct sunlight. There. I don't

867
00:49:55,480 --> 00:49:58,680
Speaker 1: know that. The colors became so strange as it came on,

868
00:49:58,840 --> 00:50:01,560
Speaker 1: and then once the totality hit it was true what

869
00:50:01,640 --> 00:50:04,200
Speaker 1: I read in advance that said, you know, it can

870
00:50:04,239 --> 00:50:06,480
Speaker 1: feel like it's over in seconds. It totally did. I

871
00:50:06,520 --> 00:50:09,560
Speaker 1: was just standing there gaping at it, and then I

872
00:50:10,800 --> 00:50:13,520
Speaker 1: after almost no time at all, it was over. Yeah.

873
00:50:13,600 --> 00:50:16,279
Speaker 1: I was really impressed by it as well. I mean,

874
00:50:16,320 --> 00:50:20,200
Speaker 1: aside from just the the the actual spectacle of the

875
00:50:20,239 --> 00:50:24,520
Speaker 1: full eclipse, just seeing the ring there in the sky. Uh,

876
00:50:24,600 --> 00:50:27,920
Speaker 1: you know, all the subtle stuff was very in a

877
00:50:27,960 --> 00:50:31,000
Speaker 1: way unnerving, and and and and he felt like you

878
00:50:31,040 --> 00:50:33,840
Speaker 1: were kind of crossing over into, uh, you know, an

879
00:50:33,880 --> 00:50:38,200
Speaker 1: abnormal realm because that you had this sense that it

880
00:50:38,280 --> 00:50:41,840
Speaker 1: was it was dusk, but there was no sunset on

881
00:50:41,880 --> 00:50:45,720
Speaker 1: the horizon, you know, there was this uh this also,

882
00:50:46,160 --> 00:50:49,319
Speaker 1: I certainly observed the changes in the animals because I

883
00:50:49,360 --> 00:50:52,239
Speaker 1: was in the forest in the mountains, So the cicadas

884
00:50:52,560 --> 00:50:55,839
Speaker 1: all died down, and it suddenly the cicadas stopped making

885
00:50:55,840 --> 00:50:59,240
Speaker 1: their noises and the crickets started up. Yeah, I observed

886
00:50:59,239 --> 00:51:02,920
Speaker 1: that too. Up in Tennessee. We heard the nighttime insects

887
00:51:02,960 --> 00:51:06,120
Speaker 1: begin to whir to life. Another thing when we saw

888
00:51:06,239 --> 00:51:09,080
Speaker 1: moths as well, Like suddenly there were moths flying around.

889
00:51:10,520 --> 00:51:13,320
Speaker 1: So I got to see because I knew in advance

890
00:51:13,360 --> 00:51:15,279
Speaker 1: to look to the west, like we talked about in

891
00:51:15,320 --> 00:51:18,960
Speaker 1: our eclipse episode, there were some clouds in the sky

892
00:51:19,080 --> 00:51:21,600
Speaker 1: to the west of where we were, and I kept

893
00:51:21,640 --> 00:51:24,560
Speaker 1: my eyes focused to the west right as the last

894
00:51:24,600 --> 00:51:26,680
Speaker 1: of the Bailey's beads were fading, and as it was

895
00:51:26,719 --> 00:51:29,360
Speaker 1: coming on, and I saw the shadow pass over the

896
00:51:29,400 --> 00:51:31,479
Speaker 1: clouds to the west of us, and it was so

897
00:51:31,560 --> 00:51:34,040
Speaker 1: cool to see like that, we had white clouds and

898
00:51:34,080 --> 00:51:37,120
Speaker 1: then suddenly black straight over them. And of course everyone

899
00:51:37,160 --> 00:51:41,000
Speaker 1: experienced the drop in temperature, but we also witnessed what

900
00:51:41,040 --> 00:51:43,560
Speaker 1: I believe was, you know, weather changes because of that,

901
00:51:43,640 --> 00:51:46,400
Speaker 1: because the it was a little bit cloudy, and the

902
00:51:46,480 --> 00:51:49,920
Speaker 1: clouds were on the just teasing with us with the

903
00:51:49,920 --> 00:51:54,839
Speaker 1: possibility of obscuring the total eclipse, but it held off.

904
00:51:54,880 --> 00:51:58,080
Speaker 1: But then right after the eclipse occurred and was and

905
00:51:58,320 --> 00:52:00,759
Speaker 1: and and the moon was big getting to move out

906
00:52:00,760 --> 00:52:03,239
Speaker 1: of the way of getting away again. Uh, there was

907
00:52:03,320 --> 00:52:07,600
Speaker 1: this um suddenly a lot of cloud cover moved in

908
00:52:07,840 --> 00:52:10,640
Speaker 1: and then it almost immediately started raining. Yeah. I think

909
00:52:10,719 --> 00:52:12,839
Speaker 1: we had some strange weather the rest of the day.

910
00:52:13,160 --> 00:52:15,920
Speaker 1: How about your dog, Charlie? Was he there? No, we

911
00:52:16,080 --> 00:52:19,239
Speaker 1: left him at home. I we thought about bringing him,

912
00:52:20,360 --> 00:52:23,040
Speaker 1: but the place we were going was it was a

913
00:52:23,080 --> 00:52:25,919
Speaker 1: college campus up in up In, Tennessee, and we didn't

914
00:52:25,920 --> 00:52:27,759
Speaker 1: know how many people would be there, and we didn't

915
00:52:27,760 --> 00:52:30,879
Speaker 1: want him to get upset if there was a big crowd. Um,

916
00:52:31,239 --> 00:52:33,040
Speaker 1: so we we just left him at home and he

917
00:52:33,080 --> 00:52:37,960
Speaker 1: was fine. Well, Uh, my son came. He really enjoyed it.

918
00:52:37,960 --> 00:52:40,759
Speaker 1: It was just a great experience. So uh, And I

919
00:52:40,800 --> 00:52:43,080
Speaker 1: know I reached out on the Facebook on social media.

920
00:52:43,200 --> 00:52:46,560
Speaker 1: A number of our followers there, our listeners there, they

921
00:52:46,680 --> 00:52:50,880
Speaker 1: chimed in those that either experienced the partial or the

922
00:52:51,120 --> 00:52:53,960
Speaker 1: total eclipse. And certainly we'd love to hear from anybody

923
00:52:54,000 --> 00:52:58,360
Speaker 1: else who has particular observations about how the environment reacted

924
00:52:58,480 --> 00:53:01,560
Speaker 1: or or how you reacted to the total eclipse. Uh,

925
00:53:01,719 --> 00:53:04,480
Speaker 1: you know, we would love to hear your experience as well.

926
00:53:04,680 --> 00:53:09,799
Speaker 1: One last thought, photos don't do it justice. Yeah, absolutely not.

927
00:53:09,920 --> 00:53:13,600
Speaker 1: I mean I've seen tons of photos of past solar eclipses,

928
00:53:13,640 --> 00:53:17,480
Speaker 1: but it's not like seeing it in person for yourself.

929
00:53:17,560 --> 00:53:19,120
Speaker 1: So if you ever get a chance in the future,

930
00:53:19,200 --> 00:53:22,120
Speaker 1: if you want to, you know, if you're in South America,

931
00:53:22,320 --> 00:53:24,000
Speaker 1: or you want to fly to where one of those

932
00:53:24,000 --> 00:53:26,480
Speaker 1: total eclipses in upcoming years is coming, or if you're

933
00:53:26,480 --> 00:53:28,040
Speaker 1: in the United States and you just want to go

934
00:53:28,040 --> 00:53:31,719
Speaker 1: ahead and start planning for it's really worth it. It's

935
00:53:31,920 --> 00:53:37,720
Speaker 1: something unlike anything else you'll see in your life. All right, Hey,

936
00:53:37,760 --> 00:53:39,719
Speaker 1: In the meantime, waiting for the as you wait for

937
00:53:39,719 --> 00:53:41,360
Speaker 1: the next episode of Stuff to Blow your Mind to

938
00:53:41,400 --> 00:53:43,080
Speaker 1: come out, head on over to Stuff to Blow your

939
00:53:43,080 --> 00:53:46,440
Speaker 1: Mind dot com. That's where you'll find all the podcast episodes,

940
00:53:46,600 --> 00:53:49,840
Speaker 1: including our episode on the on the the the solar

941
00:53:49,920 --> 00:53:52,439
Speaker 1: eclipse then that came out in the last week or two.

942
00:53:52,760 --> 00:53:55,279
Speaker 1: Be sure to check that out. Also, you'll find links

943
00:53:55,280 --> 00:53:59,840
Speaker 1: out to all our various social media accounts such as Instagram, Twitter, Um, Tumbler,

944
00:54:00,120 --> 00:54:02,760
Speaker 1: and Facebook. Hey, and on Facebook, we have a group

945
00:54:02,840 --> 00:54:06,279
Speaker 1: now the discussion module. Be sure to check that out

946
00:54:06,320 --> 00:54:08,319
Speaker 1: because that's a great place to have more long form

947
00:54:08,480 --> 00:54:12,279
Speaker 1: form discussions with other listeners and UH and sometimes the

948
00:54:12,560 --> 00:54:15,200
Speaker 1: hosts as well. And you can also just bring up

949
00:54:15,239 --> 00:54:18,160
Speaker 1: like cool ideas, cool articles, et cetera, and there'll be

950
00:54:18,239 --> 00:54:22,000
Speaker 1: some some other like minded individuals to bounce that off of.

951
00:54:22,360 --> 00:54:24,080
Speaker 1: And hey, if you want to get in touch with

952
00:54:24,160 --> 00:54:27,799
Speaker 1: us directly, as always the old fashioned way, you can

953
00:54:27,840 --> 00:54:30,840
Speaker 1: email us at blow the Mind at how stuff works

954
00:54:31,200 --> 00:54:44,160
Speaker 1: dot com for more on this and thousands of other topics.

955
00:54:44,280 --> 00:55:01,600
Speaker 1: Does it how stuff works dot com. He graduated in

956
00:55:01,719 --> 00:55:03,080
Speaker 1: the part of proper