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Speaker 1: So, Mary, what is the most embarrassing situation you've ever

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Speaker 1: been in as part of your book research.

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Speaker 2: That's an easy one. That one, not surprisingly was for Bonk.

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Speaker 2: The subtitle of that book is The Curious Coupling of

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Speaker 2: Science and Sex. So that is a book about the

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Speaker 2: delightful awkwardness of taking sex, arousal, orgasm, all of that,

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Speaker 2: all the physiological components of it, and putting it in

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Speaker 2: a laboratory setting with a couple doing things and then

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Speaker 2: somebody in a white coat taking notes. So I wanted

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Speaker 2: to get at that central awkwardness of what I just described.

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Speaker 2: And I found this researcher in London who's doing something

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Speaker 2: with four dimensional ultrasounds, so three D movies basically in ultrasound.

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Speaker 2: It was a diagnostic tool, and he wanted to do

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Speaker 2: one It was actually a penis and a vagina interacting,

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Speaker 2: and I said, well, I would like to be there

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Speaker 2: when you do that to observe, and he wrote back, well,

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Speaker 2: if your organization can provide some volunteers, then I'd be

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Speaker 2: happy to do that. So my organization asked its husband.

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Speaker 1: If you want to hear the rest of that story,

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Speaker 1: you'll have to listen to the rest of the podcast.

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Speaker 1: So on today's episode of Daniel and Kelly's Extraordinary Universe,

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Speaker 1: we have the amazing Mary Roach on the show.

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Speaker 2: Hi.

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Speaker 3: I'm Daniel, I'm a particle physicist, and I'm rarely embarrassed

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Speaker 3: by my own science.

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Speaker 1: I am Kelly Wiener Smith, and my science has put

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Speaker 1: me in loads of embarrassing situations or maybe gross and embarrassing.

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Speaker 3: All right, tell us quickly, what is the grossest thing

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Speaker 3: you've done, ever done for science?

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Speaker 1: Oh? My gosh, all right, hands down. So I hate

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Speaker 1: the smell of fish. And I worked on a project

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Speaker 1: where we were trying to figure out what kind of

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Speaker 1: impact the coal powered power plants on Lake Erie we're

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Speaker 1: having on the local fish populations. These surveys happen every

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Speaker 1: twenty years or so, and the way you do it

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Speaker 1: is you essentially put a net into the water that

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Speaker 1: gets sucked into these power plants, and the water is

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Speaker 1: used to like cool things down, generate steam, blah blah blah.

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Speaker 1: But the fish gets smushed against the net and as

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Speaker 1: the temperature of the water heats up, they all die.

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Speaker 1: And so not all of them, but many of them.

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Speaker 1: Some of the power plants pull the nets out and

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Speaker 1: dump them into dump trucks which literally fill up halfway

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Speaker 1: or more with dead fish, and somebody has to go

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Speaker 1: in there, including in the middle of the summer, with

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Speaker 1: waiters on, and like subsample and identify what species of

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Speaker 1: fish are in here? How many are in here? Do

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Speaker 1: any of them look like they have fungle diseases and

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Speaker 1: would have died anyway? So I had to like become

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Speaker 1: very intimate with dump trucks full of dead fish under

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Speaker 1: a hot summer sun. And that is the most disgusting

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Speaker 1: thing I've done in my whole life. And I hate

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Speaker 1: the smell of fish. And I still don't eat fish.

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Speaker 1: I just can't. I just can't.

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Speaker 3: Sounds like a good job for your future robot research assistant.

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Speaker 1: Bringing the robot overlords on. That sounds great. I mean

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Speaker 1: I learned a lot about fish identification that summer, but

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Speaker 1: I would maybe give up that knowledge if I could

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Speaker 1: have avoided that job.

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Speaker 3: Well, sometimes doing science means getting all mucky and dirty,

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Speaker 3: and sometimes explaining science requires you to get in weird situations,

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Speaker 3: sometimes scary, sometimes embarrassing, sometimes just downright on, but.

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Speaker 1: Always when they're explained by Mary Roach, absolutely hilarious. So

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Speaker 1: let's just jump right into the interview. Let's not make

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Speaker 1: people wait. On today's show, We're incredibly lucky to have

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Speaker 1: Mary Roach. Mary has been my absolute favorite popsey author

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Speaker 1: since I picked up Stiff as an undergrad in two

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Speaker 1: thousand and three. Stiff, Spook, Bonk, Packing from Mars, Gulp, Grunt,

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Speaker 1: and Falls have all been New York Times bestsellers, and

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Speaker 1: I have been at the bookstore the day each of

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Speaker 1: them have been released so that I could get my

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Speaker 1: copy as soon as possible. So if you've ever wondered,

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Speaker 1: how many lasers does it take to keep birds away

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Speaker 1: from the Vatican's flowers? Can you lower the national debt

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Speaker 1: by doing a more thorough job of chewing your food?

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Speaker 1: What's it like to go to the bathroom in space?

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Speaker 1: What happens at a body farm? Have scientists been able

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Speaker 1: to find the soul? Or any other equally weird questions.

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Speaker 1: We've got the Gael for you. Her latest book is

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Speaker 1: Fuzz When Nature Breaks the Law. Welcome to the.

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Speaker 2: Show, Mary, Hey, thanks, thank you so much.

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Speaker 1: Guys, I'm so excited to have you on the show.

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Speaker 1: I got to talk to you once about cosmonauts and

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Speaker 1: that was so much fun.

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Speaker 2: Oh yeah, the cosmonauts love those guys.

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Speaker 1: Yeah, they're special to start off. So one of the

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Speaker 1: things that I love the most about your books is

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Speaker 1: that their topics that I didn't know I was dying

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Speaker 1: to learn about, and then as soon as I start reading,

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Speaker 1: it turns out I was dying to learn about these topics.

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Speaker 1: So let's get started with Fuzz. How did you pick

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Speaker 1: the topic for the book Fuzz? What was the thought

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Speaker 1: process there? And then in general, how do you pick

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Speaker 1: your book topics?

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Speaker 2: I always wish that I had the really succinct and

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Speaker 2: fantastic origin story for a book, you know, like that

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Speaker 2: there was something in my past that triggered this passion

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Speaker 2: that I had to spend two years down this rabbit

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Speaker 2: hole that had meant everything to me. Is that like that?

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Speaker 2: It's like I turned in a book and I would

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Speaker 2: like to do another one. That means I need an

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Speaker 2: idea for another book, and I don't have one. So

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Speaker 2: it's this process of just sometimes looking back at things

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Speaker 2: that I've covered before or something I heard about. In

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Speaker 2: this case, I had heard about lab up in Ashland, Oregon,

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Speaker 2: the National Wildlife Forensics Laboratory, and there's a woman there

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Speaker 2: who was an expert and published a paper on how

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Speaker 2: to tell real versus artificial or counterfeit tiger penis, which

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Speaker 2: is that this is used medicinally, sometimes for virility. And

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Speaker 2: she wrote this paper on how to tell real versus

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Speaker 2: counterfeit tiger penis. And that's the thing I get kind

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Speaker 2: of like that there's a scientist out there whose area

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Speaker 2: of expertise is detection of counterfeit tiger penis because of course,

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Speaker 2: you know, if somebody's smuggling this stuff in, you need

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Speaker 2: to prove that it is an endangered species in order

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Speaker 2: to arrest the person. So she needs to be able

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Speaker 2: to tell is it real or not? And happily, it

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Speaker 2: is almost always fake. It's usually cowerd deer penis, which

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Speaker 2: is a lot bigger than a tiger penis.

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Speaker 1: Anyway, does she do it by morphology or genetics?

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Speaker 2: Oh, by morphology it is so easy to tell. Like

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Speaker 2: a tiger piece you can't see because this is audio,

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Speaker 2: but tiger pens is like it's a little tiny thing,

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Speaker 2: you know, what a dear penis is.

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Speaker 3: But does either one actually have an effect on you know,

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Speaker 3: male performance? Does it really matter if it's counterfeit?

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Speaker 2: Like most of these things, I think, you know, maybe

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Speaker 2: kind of psychologically inspiring, but not the tigre penis that'd

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Speaker 2: be a little depressing. Yeah. I don't think there's any

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Speaker 2: peer reviewed controlled studies suggesting that any of these penises

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Speaker 2: have any solid effect on male performance, but regardless, people

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Speaker 2: smuggle them in and out of the country. I thought, oh, well,

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Speaker 2: that's an interesting line of forensics. Forensics is interesting. Well,

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Speaker 2: maybe I'll go up there and I'll talk to her,

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Speaker 2: which I did, and it was a wonderful morning. We

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Speaker 2: had dried animal penises laid out on the table and

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Speaker 2: I took some pictures, which is still on my phone,

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Speaker 2: and every now and then the grandkids are like, what's

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Speaker 2: on your phone. I'm like, no, no, no, no, there's a

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Speaker 2: lot of stuff on my phone we can't look at

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Speaker 2: because I'll have to explain. So I thought this is

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Speaker 2: going well. And then I went in to talk to

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Speaker 2: the director of the lab and I said, well, here's

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Speaker 2: what I love to do, or that I need to

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Speaker 2: do with the book. I have to be on the scene.

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Speaker 2: And I imagined, you know, going in when they rated

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Speaker 2: a fake targer penis sweatshop in China, Like you know,

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Speaker 2: we'd go in and there'd be these four people and

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Speaker 2: they have to do this. They carve little barbs, because

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Speaker 2: felines have barbs on there. The penis little sticky eppie things,

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Speaker 2: sort of like get the rental car, you know, don't

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Speaker 2: back up on this spikes, so somebody has to carve those.

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Speaker 2: And I thought we'd be hunched over carving these penises,

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Speaker 2: and we'll go and it'll be this incredible scene. I

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Speaker 2: had this whole scenario in my head, and then I'd

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Speaker 2: find other things to write about. And then the guy goes,

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Speaker 2: you know, legally, if it's an open case investigation, you

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Speaker 2: can't go along. You just can't. It's the law. No.

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Speaker 2: So that just shut me down. And so I went

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Speaker 2: home and I was like, ah, damn.

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Speaker 4: But then I came across this book, The Criminal Prosecution

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Speaker 4: and Capital Punishment of Animals, which made me think, this

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Speaker 4: is a historical book, you know, about people used to

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Speaker 4: put animals on trial that had misbehaved.

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Speaker 2: And I thought, what if the animals were the perpetrators?

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Speaker 2: And I say perpetrators in quotes. Obviously animals follow their instinct,

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Speaker 2: not laws. So what if we turn it around and

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Speaker 2: what if the animals are not the victims but the

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Speaker 2: perpetrators And then it turns out there's this whole area

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Speaker 2: of science called human wildlife conflict, with conferences and experts

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Speaker 2: and textbooks, And I had never heard of human wildlife conflict,

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Speaker 2: had no idea people devote their lives to figuring out

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Speaker 2: how to coexist with wild animals. So that's a very

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Speaker 2: long winded way of saying that's what happened. This is

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Speaker 2: how I got my idea for the book.

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Speaker 3: I mean, it's a great story in general. I'd love

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Speaker 3: to hear more about how you got into science writing.

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Speaker 3: I mean, how did you identify this sort of opening

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Speaker 3: in people's appetite that wasn't being completely served, or you know,

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Speaker 3: this itch that people wanted scratching in a certain way.

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Speaker 2: Well, again, not through a longstanding passion of mine. I

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Speaker 2: was one of those people who thought, in high school

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Speaker 2: and in college, oh, science is boring. I want to

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Speaker 2: be creative, which is so stupid. There's so much creativity

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Speaker 2: in science. It wasn't something that I had thought about

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Speaker 2: in college or even when I first graduated. I was

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Speaker 2: writing for the magazine of those Sunday paper here in

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Speaker 2: the Bay Area San Francisco Chronicle, just general stories, kind

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Speaker 2: of a little bit weird Mary Rochie but not science related.

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Speaker 2: And my editor went to another publication called Hippocrates, which

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Speaker 2: was more focused on health and medicine, but they still

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Speaker 2: let me be Mary Roach and do kind of odd

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Speaker 2: stories and which was fun. And then from there an

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Speaker 2: editor from Discover magazine said, would you like to do

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Speaker 2: these weird Mary Roach stories for us? We are a

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Speaker 2: science magazine. I know you're not a science writer, but

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Speaker 2: why don't you do this? And I was like, okay,

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Speaker 2: So I did that and those were the most interesting

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Speaker 2: stories I'd done. And I loved writing about science. Since

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Speaker 2: I don't have a science background, they tend to be

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Speaker 2: not heavy science, you know, they're definitely science for people

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Speaker 2: who have mistakenly thought that they don't like science or

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Speaker 2: that they don't think science is interesting. I'm kind of

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Speaker 2: a gateway drug to science writing. I'm heavy on narrative

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Speaker 2: and fun and quirk. For me, the science and the

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Speaker 2: discovery of learning about these different fields is tremendous fun

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Speaker 2: and really gratifying. But am I really a science writer?

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Speaker 2: I guess so I don't know.

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Speaker 1: Of course you are.

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Speaker 2: You look at real science writers and you know, they

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Speaker 2: just I can't understand their books.

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Speaker 1: I think you are the kind of science writer where

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Speaker 1: people actually learn something because they make it to the

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Speaker 1: end of the book, which I think is huge and

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Speaker 1: like that's a real skill getting people to stay to

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Speaker 1: the end of the book. Thank you, thank you, Yes,

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Speaker 1: you're welcome.

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Speaker 2: That is my goal, even just to the end of

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Speaker 2: the chapter. Man. I am always fighting for that, I

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Speaker 2: really am. I'm always sort of policing my words and

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Speaker 2: my sentences going in has been it's been a couple

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Speaker 2: of pages, but since there's been anything really fun here

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Speaker 2: or surprising, like let's punch it up, they're going to

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Speaker 2: put the book down. I know they're going to put

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Speaker 2: the book down any second.

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Speaker 1: I feel like it's an interesting activity when you're writing

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Speaker 1: to figure out how long has it been since the

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Speaker 1: funny thing that I said, and then trying to not

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Speaker 1: like overdo it. Yes, Zach and I had a problem

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Speaker 1: where a couple chapters we felt like we had gotten

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Speaker 1: corny and we were just overdoing it. But you know,

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Speaker 1: you want to make sure they come at a regular pace.

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Speaker 2: It's an art, and you guys do it so well,

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Speaker 2: some really tough stuff that you're asking people to come

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Speaker 2: along with you for and you managed to keep it fun,

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Speaker 2: and that is extraordinary. Thank you.

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Speaker 3: I think we identified something really important, though, which is

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Speaker 3: that a lot of science writing is done in a

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Speaker 3: way that isn't actually that understandable, and yet a lot

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Speaker 3: of people still love to consume it, which has always

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Speaker 3: been something of a mystery to me. Like I read

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Speaker 3: Stephen Hawking and I don't understand a lot of his books,

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Speaker 3: and then people tell me like I loved A Brief

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Speaker 3: History of Time, and I'm like, really, did you actually

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Speaker 3: understand it? Did you just appreciate being in the presence

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Speaker 3: of what felt like a great mind. So I wonder

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Speaker 3: what people are going for sometimes with those books, if

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Speaker 3: they actually want to understand, if they just want to

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Speaker 3: hang out with Stephen Hawking.

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Speaker 2: I do too. I don't have an advanced degree, but

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Speaker 2: I don't feel that I'm a moron, you know, I'll

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Speaker 2: pick up I don't know, Brian Green, maybe these are

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Speaker 2: really gifted, super intelligent, successful writers, but I'm like, I

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Speaker 2: don't know, or even like astronomy for people in a

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Speaker 2: hurry Neil deGrasse.

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Speaker 3: Tyson astrophysics for people in hurry, yeah.

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Speaker 2: And I'm like, I don't know. I'm not following along

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Speaker 2: I'm falling behind. Help me, I'm falling behind. And I

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Speaker 2: know these books are huge sellers, and I'm always curious

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Speaker 2: about that. How many people read it all the way

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Speaker 2: through and really understand it in the way the author

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Speaker 2: means it to be understood. I'd love to know that.

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Speaker 2: Can we get Ai to give us an answer on that.

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Speaker 1: I actually threw out my copy of Hawkings's book because

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Speaker 1: I realized I only had it on my shelf so

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Speaker 1: that I could be the kind of person who looked

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Speaker 1: like they could read that book. But I gotten through

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Speaker 1: ten pages and I was like, this book being on

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Speaker 1: my shelf is a lie, and so I kindly got

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Speaker 1: rid of it.

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Speaker 2: But I'm glad to hear that. I mean no disrespect

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Speaker 2: to mister Hawking.

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Speaker 1: Then, oh yeah, he's clearly way smarter than I will

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Speaker 1: ever be.

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Speaker 3: Well, when you're writing your books, you dive deep into

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Speaker 3: a topic, You talk to the experts. You must at

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Speaker 3: some point pass over the threshold where you're something of

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Speaker 3: an expert in the topic yourself. How do you keep

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Speaker 3: hold of what the reader knows and what they don't understand,

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Speaker 3: and what this must feel like to them the first

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Speaker 3: time they're hearing about it. That's a real challenge something

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Speaker 3: you pull off amazingly in your book.

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Speaker 2: That is always a concern that, over the course of

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Speaker 2: the two or three years that I work on a book,

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Speaker 2: have I actually learned enough that I'm assuming a level

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Speaker 2: of knowledge on the part of my reader that doesn't

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Speaker 2: exist because we are starting out at the same place,

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Speaker 2: the place of just curiosity and near total ignorance. And

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Speaker 2: then I'm moving along through speaking to researchers and experts,

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Speaker 2: I'm actually getting to the point where I'm understanding things

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Speaker 2: in a way that I now think, is it completely

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Speaker 2: unclear what I'm saying here? And that's a little stuff

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Speaker 2: I do have. My editor at Norton is she's a

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Speaker 2: poet and a novelist. She has no background in science.

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Speaker 2: She's a good editor for me for that reason because

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Speaker 2: she will sometimes just put a note in the margin,

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Speaker 2: I Mary, you've lost me here, or I don't know

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Speaker 2: what you're talking about. That is helpful. I'm so excited

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Speaker 2: to talk to somebody who is an expert in their field,

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Speaker 2: and when their field is something you know, it's a

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Speaker 2: little complicated, like you know, stem cells say. I mean,

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Speaker 2: I'm so grateful to find somebody who can explain it

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Speaker 2: to me, who has a sense of what I don't know,

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Speaker 2: which is almost everything, and can then unpack it in

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Speaker 2: a way that I understand. So part of what my

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Speaker 2: job is is looking for those people who I'm going

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Speaker 2: to then force to be an unpaid tutor for days

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Speaker 2: on end and for endless emails. So I'm not entirely

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Speaker 2: clear on can you just explain that's so necessary to

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Speaker 2: keep in your head where people are at and whether

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Speaker 2: you've left them behind, because I get, let's be honest,

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Speaker 2: left behind by a lot of science books.

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Speaker 3: I have this theory that Agatha Christie would be a

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Speaker 3: great science writer because she seems to have this ability

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Speaker 3: of like obviously understanding what's actually happening in her story

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Speaker 3: and introducing the facts in a way that the reader

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Speaker 3: to discovers them and unravels the mystery in an organic way.

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Speaker 3: It seems to be sort of the same challenge to

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Speaker 3: write a mystery novel and keep in mind what the

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Speaker 3: reader knows and doesn't know. I find that impossible. I

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Speaker 3: can't imagine writing a murder mystery.

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Speaker 2: Yeah, this is why I never read murder mysteries, or

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Speaker 2: even when I see knives out I'm like, wait, wait,

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Speaker 2: did she wait a minute? You know? And I'm always

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Speaker 2: making my husband stop, we stop, stop, wait a minute?

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Speaker 2: Is that? And he hates it. It's like, can't we

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Speaker 2: just keep going with it? We just keep this going? No,

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Speaker 2: but I don't know who's the guy? Is that the brother? Who?

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Speaker 2: Or is that the wait? What? This is the same

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Speaker 2: kind of thing you're asking people to keep in their head,

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Speaker 2: the facts that you explained three chapters ago. And it's

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Speaker 2: easy for you because you learned it as the author

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Speaker 2: I have in the book that I just turned in.

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Speaker 2: There's a moment where I'm like, as we learned and

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Speaker 2: forgot in chapter three, it's nobody remembers.

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Speaker 1: How did the conversations with the experts often go and

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Speaker 1: has it changed over the like twenty so years that

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Speaker 1: you've been writing, because you know, sometimes I'll try to

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Speaker 1: interview someone and they'll go right over my and I'm like, no, no, no,

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Speaker 1: bring it down. And some of them can and some

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Speaker 1: of them can't. And I also feel like the relationship

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Speaker 1: with some of the experts has changed over time, as

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Speaker 1: NSF keeps telling all the scientists that we are also communicators,

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Speaker 1: and they feel like but they're not what is that like?

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Speaker 2: You get a sense of this in the first five minutes.

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Speaker 2: I think of a conversation by phone or on zoom

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Speaker 2: of whether a person's going to really be able to

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Speaker 2: do that, And sometimes I just in my head, stop

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Speaker 2: the ner. You know, I just asked one or two

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Speaker 2: more token questions, and I thank the person and says, no,

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Speaker 2: it never happened. You know. I will say, can we

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Speaker 2: back up and can you explain whatever it is? And

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Speaker 2: then the explanation is different but also incomprehensible for me.

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Speaker 2: So there isn't much to be done there. And that's

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Speaker 2: why I just when I find someone who actually enjoys

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Speaker 2: explaining things in an accessible way, I just want to

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Speaker 2: You know, I can't kiss them because we are on

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Speaker 2: the phone, but I'm just like, Okay, you're now going

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Speaker 2: to be my guy for induced pluripotent stem cells. I'm

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Speaker 2: going to be pestering you on and off for the

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Speaker 2: next six months and not paying you for that. I'll

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Speaker 2: send you a book. But what hasn't changed. No, I

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Speaker 2: think that's always been there. I mean I once was

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Speaker 2: a sign to do a story I wanted to go.

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Speaker 2: And you know those planes that fly into hurricanes. I

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Speaker 2: had this idea that'd be a fun thing to do.

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Speaker 2: And there was a hurricane hunter plane that was going

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Speaker 2: into a certain kind of storm, not infect a hurricane.

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Speaker 2: I'm like, okay, I'm going to do that. And there

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Speaker 2: was this meteorologist on board, and it was almost like

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Speaker 2: a form of hostility. He would not explain things in

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Speaker 2: a way that anybody who's not a meteorologist could understand.

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Speaker 2: And I kept trying to well, I what if I'm

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Speaker 2: let's pretend I'm a sixth grader at a somebody's barbecue

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Speaker 2: party and just talk to me like that, and he wouldn't,

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Speaker 2: And so we got down. It was this in Monterey.

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Speaker 2: We landed, and I called my editor and just said,

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Speaker 2: forget it. You know, maybe you can pay my expenses,

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Speaker 2: which aren't a lot from here to Monterey and we're

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Speaker 2: done because nothing can't do it.

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Speaker 1: What a bummer. Yeah, I know, I love you are

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Speaker 1: expecting someone to be a tutor approach. I was in

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Speaker 1: trouble figuring out how much research I should do before

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Speaker 1: I reach out to someone. And I think that's because

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Speaker 1: early on I asked for an interview and they said, well,

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Speaker 1: tell me what you want to ask me about and

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Speaker 1: I sent them my questions and they said, Nope, these

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Speaker 1: are not the most interesting things that I do. I

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Speaker 1: will not be interviewed by you, and they like shut

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Speaker 1: me down. And I was like, oh, I mean I

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Speaker 1: was hoping you'd help me understand this stuff, but okay.

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Speaker 1: And so I also feel like some people I talk

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Speaker 1: to it's almost like they don't respect the job of

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Speaker 1: a popsie author, like NSF keeps telling every when you

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Speaker 1: do these broader impacts, scientists can communicate, And I feel

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Speaker 1: like a lot of scientists I've talked to now feel like, oh,

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Speaker 1: if you're a scientist, you also can do science communication

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Speaker 1: just as easily, Like you learn that along the way.

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Speaker 1: But it's a very different kind of writing. And so

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Speaker 1: I feel like I encounter people who are very hostile.

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Speaker 1: They don't feel like I've done enough. And then also

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Speaker 1: people who are like, well I could do what you do.

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Speaker 2: I think to go back to your previous question, but

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Speaker 2: not that there's anything wrong with the current question. To

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Speaker 2: go back to your previous question. One thing that has

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Speaker 2: changed for me over twenty years is that this is

407
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Speaker 2: a wonderful thing when it happens. If I write to

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Speaker 2: someone that I've never met, and I say, hey, you know,

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Speaker 2: I saw that you're working on such and such. I'm

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Speaker 2: working on a book, and I also at a little

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Speaker 2: bio at the bottom of my email. And when someone

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Speaker 2: writes back and says, oh, I've read one of your books,

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Speaker 2: or I'm familiar with what you do. Sure, happy to talk.

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Speaker 2: I'd love to talk. I know that that's going to

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Speaker 2: go well because they know the kind of weirdo that

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Speaker 2: I am and the level at which I'm writing, and

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Speaker 2: their enthusiasm suggests this is going to be just what

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Speaker 2: I need. Another thing that's changed is a lot of

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Speaker 2: times when you reach out by email, you just don't

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Speaker 2: get a reply. And I don't know if that's one

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Speaker 2: of those people who hates science communicators, or it's somebody

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Speaker 2: who doesn't check their email, or it's not a useful

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Speaker 2: I don't know what it is. But there's a lot

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Speaker 2: more of just never hearing back through, whether it's LinkedIn

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Speaker 2: or a university email address. I think it's easier now

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Speaker 2: for people to feel okay ignoring you because no one

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Speaker 2: who checks their email. Nobody checks their email anymore. You know,

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Speaker 2: you got to text me, well, I don't have your number,

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Speaker 2: so that's different.

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Speaker 3: There's a gentleman in my department who I often go

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Speaker 3: to with really hardcore questions about like general relativity, and

432
00:21:14,320 --> 00:21:17,440
Speaker 3: his attitude is, you're wasting your time. There's no way

433
00:21:17,440 --> 00:21:19,919
Speaker 3: you could ever explain these topics to the general public.

434
00:21:20,080 --> 00:21:22,760
Speaker 3: And then he gives me a really excellent explanation, which

435
00:21:22,800 --> 00:21:26,120
Speaker 3: I rely on for the podcast, and he keeps doing it.

436
00:21:26,359 --> 00:21:28,960
Speaker 3: So he's quite grumpy about it sometimes, but he's also

437
00:21:29,000 --> 00:21:31,680
Speaker 3: excellent at it. And it seems to me that there's

438
00:21:32,040 --> 00:21:34,720
Speaker 3: this sort of conflict in a lot of scientists. They

439
00:21:34,760 --> 00:21:36,560
Speaker 3: feel like, I just want to be left alone to

440
00:21:36,600 --> 00:21:38,800
Speaker 3: do my science, but they also feel like, hey, the

441
00:21:38,800 --> 00:21:42,119
Speaker 3: public should support us, and this is important, and I

442
00:21:42,119 --> 00:21:44,160
Speaker 3: think it's vital that people do the kind of work

443
00:21:44,160 --> 00:21:46,280
Speaker 3: you do, because not every scientist can be out there

444
00:21:46,320 --> 00:21:49,320
Speaker 3: explaining it to sixth graders. And I'm wondering, like, if

445
00:21:49,359 --> 00:21:52,720
Speaker 3: you feel sort of any hostility from scientists or any

446
00:21:52,720 --> 00:21:55,680
Speaker 3: sort of like, you know, weirdness that you're telling their

447
00:21:55,720 --> 00:21:57,960
Speaker 3: story instead of them, What do you feel like is

448
00:21:57,960 --> 00:22:00,120
Speaker 3: your relationship with the science community.

449
00:21:59,640 --> 00:22:03,800
Speaker 2: There, I do feel hostility from the people who respond

450
00:22:04,280 --> 00:22:07,240
Speaker 2: to me and agree to talk. I think that usually

451
00:22:07,280 --> 00:22:10,879
Speaker 2: goes well. There was a woman who on Amazon was

452
00:22:11,040 --> 00:22:14,760
Speaker 2: very put off by Gulp, which is Adventures on the

453
00:22:14,760 --> 00:22:17,560
Speaker 2: Alimentary Canal. It's you know, the tube between nose and

454
00:22:17,640 --> 00:22:20,920
Speaker 2: apple and all the weird things that go on in there.

455
00:22:20,960 --> 00:22:24,880
Speaker 2: And she was very, very angry that I hadn't mentioned

456
00:22:24,960 --> 00:22:31,239
Speaker 2: the mucus layer in the stomach. Basically, her feeling was

457
00:22:31,320 --> 00:22:38,320
Speaker 2: that people like me are stealing science, making money off

458
00:22:38,359 --> 00:22:42,159
Speaker 2: of it, cheapening it, dumbing it down. I looked her

459
00:22:42,280 --> 00:22:44,960
Speaker 2: up onpe my professor, and she had It was very mixed.

460
00:22:45,000 --> 00:22:46,800
Speaker 2: There were people who are like, she's a really great

461
00:22:46,800 --> 00:22:48,919
Speaker 2: professor and I learned a lot, and then there are

462
00:22:48,960 --> 00:22:52,639
Speaker 2: people like, she's such a hater. I do not deserve that.

463
00:22:52,760 --> 00:22:55,520
Speaker 2: D These are people who aren't reaching out to me.

464
00:22:55,600 --> 00:22:58,640
Speaker 2: They're obviously talking about for the most part, behind my back.

465
00:22:58,720 --> 00:23:00,520
Speaker 2: I'm sure that that's out there. I don't get a

466
00:23:00,560 --> 00:23:04,560
Speaker 2: lot of it directed at me through email or I

467
00:23:04,560 --> 00:23:08,479
Speaker 2: don't read my Amazon review reviews anymore, and as I

468
00:23:08,520 --> 00:23:11,440
Speaker 2: hope that you don't either. No, there was a reviewer

469
00:23:11,520 --> 00:23:16,000
Speaker 2: who said the same kind of thing that making science successible,

470
00:23:16,040 --> 00:23:19,119
Speaker 2: I was disrespecting it, or you know, that kind of

471
00:23:19,119 --> 00:23:23,000
Speaker 2: reaction exists out there for sure. For sure, probably far

472
00:23:23,080 --> 00:23:25,160
Speaker 2: more people than I want to believe feel that way

473
00:23:25,200 --> 00:23:28,400
Speaker 2: about me, but they're nice enough to keep it to themselves.

474
00:23:28,800 --> 00:23:31,600
Speaker 1: I bet that's the minority opinion. But let's take a

475
00:23:31,640 --> 00:23:34,200
Speaker 1: break and then when we come back, we will talk

476
00:23:34,280 --> 00:23:54,399
Speaker 1: about animals getting in troule. So Daniel and I have

477
00:23:54,520 --> 00:23:57,240
Speaker 1: this long running debate about whether or not Virginia or

478
00:23:57,240 --> 00:24:00,600
Speaker 1: California is better, and oh, irobably going to fall on

479
00:24:00,600 --> 00:24:03,640
Speaker 1: the California side, but I think Virginya is clearly better.

480
00:24:03,680 --> 00:24:05,520
Speaker 1: But we were going to each ask a question about

481
00:24:05,560 --> 00:24:09,199
Speaker 1: animal problems that are in our area. And I have

482
00:24:09,280 --> 00:24:11,960
Speaker 1: tons of trouble with deer. We have like forty deer

483
00:24:12,480 --> 00:24:15,280
Speaker 1: in our hay fields last night I counted. And they

484
00:24:15,280 --> 00:24:19,840
Speaker 1: are also in the roads, yes, and why don't they

485
00:24:19,840 --> 00:24:22,480
Speaker 1: get out of the way what I'm driving towards them? So, yeah,

486
00:24:22,480 --> 00:24:23,800
Speaker 1: what's going on with the deer and how do we

487
00:24:23,800 --> 00:24:24,680
Speaker 1: get them out of the roads.

488
00:24:25,040 --> 00:24:27,679
Speaker 2: Yeah, the deer in the headlights phenomenon. When you think

489
00:24:27,720 --> 00:24:30,159
Speaker 2: about animals not getting out of the way, an animal

490
00:24:30,160 --> 00:24:33,120
Speaker 2: in the road sees a car coming at it and

491
00:24:33,160 --> 00:24:36,119
Speaker 2: it perceives it as a predator, which makes sense, Like

492
00:24:36,160 --> 00:24:39,080
Speaker 2: this thing is coming at me, I don't know. And

493
00:24:39,160 --> 00:24:42,800
Speaker 2: so there's this thing flight initiation distance, which is how

494
00:24:42,840 --> 00:24:44,640
Speaker 2: long can I stay where I am? Maybe if I'm

495
00:24:44,640 --> 00:24:47,240
Speaker 2: eating something delicious, how long can I stay here before

496
00:24:47,400 --> 00:24:49,199
Speaker 2: I actually really need to get out of the way

497
00:24:49,200 --> 00:24:51,720
Speaker 2: because I'm going to be hit or grabbed or eaten

498
00:24:51,840 --> 00:24:54,800
Speaker 2: or whatever it is. But in general, the car hasn't

499
00:24:54,840 --> 00:24:58,680
Speaker 2: been around that long the automobile, so the evolution hasn't

500
00:24:58,720 --> 00:25:01,439
Speaker 2: kind of incorporated you know what, There's things going sixty

501
00:25:01,480 --> 00:25:05,160
Speaker 2: miles per hour, so you need to update your sense

502
00:25:05,200 --> 00:25:08,040
Speaker 2: of how quickly do I need to run? How quickly

503
00:25:08,080 --> 00:25:09,480
Speaker 2: do I need to get out of the way. So

504
00:25:09,480 --> 00:25:11,840
Speaker 2: there's some confusion with that. I mean, pigeons are obviously

505
00:25:11,920 --> 00:25:13,800
Speaker 2: really good at it. You can never hit a pigeon

506
00:25:13,840 --> 00:25:15,520
Speaker 2: with your car, and I'm not saying that I try

507
00:25:15,520 --> 00:25:18,119
Speaker 2: to do that. But the other thing going on is

508
00:25:18,240 --> 00:25:21,119
Speaker 2: one of the ways that an animal can detect or

509
00:25:21,160 --> 00:25:23,040
Speaker 2: a person for them, and the way we can tell

510
00:25:23,080 --> 00:25:26,200
Speaker 2: how quickly something's coming at us is looming like it's

511
00:25:26,720 --> 00:25:29,080
Speaker 2: getting bigger and bigger, Like how quickly is it getting

512
00:25:29,080 --> 00:25:31,200
Speaker 2: bigger and bigger? That kind of tells us how quickly

513
00:25:31,240 --> 00:25:34,000
Speaker 2: it's coming at us, and animals are not with cars

514
00:25:34,080 --> 00:25:37,040
Speaker 2: again because they are very fast pedestrians too. You know,

515
00:25:37,080 --> 00:25:39,280
Speaker 2: the faster the car, the harder it is to judge.

516
00:25:39,600 --> 00:25:42,399
Speaker 2: You know, how quickly is it coming at us? Deer active,

517
00:25:42,560 --> 00:25:46,080
Speaker 2: you know, it's dusk and night, and when what's coming

518
00:25:46,160 --> 00:25:50,720
Speaker 2: at you is just two pinpoints of light, all you

519
00:25:50,760 --> 00:25:53,440
Speaker 2: can see is these two pinpoints of light. It's very

520
00:25:53,480 --> 00:25:56,600
Speaker 2: hard to see is that looming? Is it getting bigger?

521
00:25:56,800 --> 00:25:59,880
Speaker 2: It just may look like two points of light, not chain.

522
00:26:00,480 --> 00:26:03,640
Speaker 2: So you can imagine the deer looking at that, these

523
00:26:03,680 --> 00:26:06,240
Speaker 2: two points of light going huh A couple points of

524
00:26:06,320 --> 00:26:10,480
Speaker 2: light huh boo. By the time they figure out that

525
00:26:10,520 --> 00:26:14,119
Speaker 2: there's a car attached, it's too late, and that is

526
00:26:14,760 --> 00:26:18,159
Speaker 2: really bad for deer but also really bad for people

527
00:26:18,560 --> 00:26:21,000
Speaker 2: because you know, if people often say, oh, what's the

528
00:26:21,040 --> 00:26:23,560
Speaker 2: most danger people ask me, what are these the most

529
00:26:23,640 --> 00:26:26,919
Speaker 2: dangerous animal of all the animals that you wrote about. Well,

530
00:26:26,960 --> 00:26:28,960
Speaker 2: first of all, I would say, you know, bacteria or

531
00:26:29,080 --> 00:26:31,840
Speaker 2: virus is, but they're not really animals. So deer in

532
00:26:31,920 --> 00:26:34,720
Speaker 2: terms of the number of people not because you hit

533
00:26:34,760 --> 00:26:37,359
Speaker 2: the deer, but you swerved to not hit the deer

534
00:26:37,720 --> 00:26:40,119
Speaker 2: and you hit a tree where you went off on cliff.

535
00:26:40,280 --> 00:26:43,600
Speaker 2: So deer are very very dangerous in that way. The

536
00:26:43,640 --> 00:26:45,800
Speaker 2: other thing that happens is a deer's running across the

537
00:26:45,840 --> 00:26:47,359
Speaker 2: road in front of you, and your eye is on

538
00:26:47,440 --> 00:26:50,439
Speaker 2: that deer, and deer there's usually more than one, so

539
00:26:51,240 --> 00:26:53,800
Speaker 2: you are looking at that deer, you hit the brakes,

540
00:26:53,800 --> 00:26:55,840
Speaker 2: and then what you end up hitting is the deer

541
00:26:56,080 --> 00:26:59,680
Speaker 2: behind that one that's also crossing at the same time.

542
00:27:00,080 --> 00:27:02,359
Speaker 3: That exact thing happened to me as a teenager.

543
00:27:02,520 --> 00:27:03,680
Speaker 1: Did you hit the second one?

544
00:27:03,840 --> 00:27:06,560
Speaker 3: I hit the second one, Yeah, exactly, went through the windshield.

545
00:27:07,000 --> 00:27:09,440
Speaker 3: I like looked at it deep in the eyes forward

546
00:27:09,520 --> 00:27:10,040
Speaker 3: bounced off.

547
00:27:10,160 --> 00:27:14,040
Speaker 2: Yeah, my god. Yeah. And it's terrifying, really dangerous when

548
00:27:14,359 --> 00:27:16,560
Speaker 2: it's a tall animal like a moose or an elk,

549
00:27:16,760 --> 00:27:20,040
Speaker 2: because you're just hitting the legs and you're knocking the

550
00:27:20,119 --> 00:27:22,240
Speaker 2: legs out from under and the whole body and the

551
00:27:22,280 --> 00:27:26,240
Speaker 2: antlers come crashing down on the roof and the windshield.

552
00:27:26,359 --> 00:27:28,840
Speaker 2: So camels are a problem in the Middle East. A

553
00:27:28,880 --> 00:27:31,400
Speaker 2: lot of broken necks. It's bad. It's bad stuff.

554
00:27:32,800 --> 00:27:35,359
Speaker 1: I was interested in the various ways that people have

555
00:27:35,440 --> 00:27:37,280
Speaker 1: come up with to try to keep the deer off

556
00:27:37,280 --> 00:27:39,520
Speaker 1: the road. Some of them were fairly hilarious, and I

557
00:27:39,560 --> 00:27:41,840
Speaker 1: love that you introduced this topic with the woman who

558
00:27:41,880 --> 00:27:45,840
Speaker 1: called into the radio show. Would you share that story.

559
00:27:45,600 --> 00:27:48,960
Speaker 2: With us, Yeah, Donna. Donna called it was I forget

560
00:27:49,040 --> 00:27:52,840
Speaker 2: where this was a call in radio program and this

561
00:27:52,880 --> 00:27:55,159
Speaker 2: woman called in and said, I have a pet pee.

562
00:27:55,440 --> 00:28:00,000
Speaker 2: Why do they put deer crossing signs in these areas

563
00:28:00,080 --> 00:28:02,560
Speaker 2: of high traffic? Why would you put it there that

564
00:28:02,640 --> 00:28:07,280
Speaker 2: The guy's like, there's a silence, he goes. It sounds

565
00:28:07,640 --> 00:28:13,920
Speaker 2: like you think that they're placing the signs. So it's

566
00:28:14,000 --> 00:28:16,640
Speaker 2: like you shouldn't understand that you put the signs where

567
00:28:16,680 --> 00:28:19,119
Speaker 2: the deer are crossing. It nothing. You don't just choose

568
00:28:19,160 --> 00:28:29,479
Speaker 2: where you're going to tell the deers. Exactly the tone

569
00:28:29,520 --> 00:28:33,639
Speaker 2: of the DJs, there's a mix of incredulity and kindness. Donna,

570
00:28:34,080 --> 00:28:35,359
Speaker 2: it sounds as though.

571
00:28:36,760 --> 00:28:38,920
Speaker 1: I appreciate the kindness though, I feel like that's part

572
00:28:38,960 --> 00:28:41,480
Speaker 1: of what made it such a charming, funny story.

573
00:28:41,720 --> 00:28:43,960
Speaker 2: They put the sign right here, Let's like right before

574
00:28:44,000 --> 00:28:46,000
Speaker 2: there's a curve, and it's a terrible place to tell

575
00:28:46,040 --> 00:28:46,920
Speaker 2: the deer to cross.

576
00:28:49,560 --> 00:28:52,160
Speaker 3: If only we could tell the deer what to do. Well,

577
00:28:52,200 --> 00:28:55,960
Speaker 3: these stories of like human animal interactions human wildlife interactions

578
00:28:55,960 --> 00:28:58,320
Speaker 3: I think are really fascinating from like a public health

579
00:28:58,320 --> 00:29:00,800
Speaker 3: point of view and in forest management point view and

580
00:29:00,880 --> 00:29:03,000
Speaker 3: out here in the wonderful state of California. As of course,

581
00:29:03,080 --> 00:29:05,640
Speaker 3: you know, we deal a lot with bears. My family

582
00:29:05,680 --> 00:29:08,440
Speaker 3: goes camping a lot. Recently we had this experience where

583
00:29:08,440 --> 00:29:10,480
Speaker 3: we went camping, and of course we had a bear bag.

584
00:29:10,760 --> 00:29:14,000
Speaker 3: We made a mistake of leaving one can of Seltzer

585
00:29:14,080 --> 00:29:17,000
Speaker 3: in the backseat of the car, and there was a

586
00:29:17,000 --> 00:29:20,080
Speaker 3: bear and it came in. It opened the car door

587
00:29:20,800 --> 00:29:24,240
Speaker 3: and got the seltzer, crunched the seltzer, drank all of it,

588
00:29:24,520 --> 00:29:27,120
Speaker 3: and didn't harm the car at all. It was incredible,

589
00:29:27,520 --> 00:29:31,360
Speaker 3: like it used its snout to like open the door somehow. Anyway,

590
00:29:31,440 --> 00:29:34,440
Speaker 3: bears are very very smart, and I often wonder like

591
00:29:34,520 --> 00:29:37,680
Speaker 3: it must be very challenging to design bear proof trash

592
00:29:37,720 --> 00:29:40,760
Speaker 3: cans that are complex enough for bear to not be

593
00:29:40,840 --> 00:29:43,400
Speaker 3: able to figure them out, but simple enough for humans

594
00:29:43,640 --> 00:29:44,280
Speaker 3: to be able to do.

595
00:29:44,400 --> 00:29:46,840
Speaker 2: This is the problem. This is the problem if you

596
00:29:46,880 --> 00:29:49,520
Speaker 2: go as I did. I was in Colorado, actually not

597
00:29:49,680 --> 00:29:53,840
Speaker 2: California for this chapter, but at one point the researcher

598
00:29:53,840 --> 00:29:57,440
Speaker 2: and I were walking down this alley where behind all

599
00:29:57,520 --> 00:30:02,320
Speaker 2: the restaurants in downtown Aspen, these bear proof containers were

600
00:30:02,440 --> 00:30:06,480
Speaker 2: either broken or not being used hardly because it's difficult.

601
00:30:06,720 --> 00:30:12,360
Speaker 2: I mean, it's like reading a Stephen Hawking book. It's like,

602
00:30:12,600 --> 00:30:14,440
Speaker 2: you know, harder than one of those you know press

603
00:30:14,480 --> 00:30:18,880
Speaker 2: while turning aspirin bottles. It was it required two hands.

604
00:30:18,920 --> 00:30:22,640
Speaker 2: And you could imagine if you're some restaurant worker who's

605
00:30:22,680 --> 00:30:25,440
Speaker 2: poorly paid, was middle of a shift running out with

606
00:30:25,440 --> 00:30:28,360
Speaker 2: a bag of garbage, you know, not bothering to lock

607
00:30:28,440 --> 00:30:31,360
Speaker 2: it again. And also one of the things that was

608
00:30:31,400 --> 00:30:35,200
Speaker 2: being done well in an area near Aspen, not Aspen itself,

609
00:30:35,600 --> 00:30:37,760
Speaker 2: was that they were holding educational talks for the people

610
00:30:37,800 --> 00:30:40,120
Speaker 2: who worked in the kitchen. The patrons and the restaurant

611
00:30:40,120 --> 00:30:43,400
Speaker 2: owners have been educated. You know, okay, fed bear is

612
00:30:43,440 --> 00:30:45,920
Speaker 2: a dead bear. Like this is what happens. The bears

613
00:30:45,960 --> 00:30:48,120
Speaker 2: get used to being fed, they keep coming here. There's

614
00:30:48,160 --> 00:30:49,880
Speaker 2: going to be a conflict and then the bear will

615
00:30:49,920 --> 00:30:52,160
Speaker 2: be killed. But the people who worked in the kitchen,

616
00:30:52,200 --> 00:30:55,959
Speaker 2: some of them didn't speak English. They hadn't been told

617
00:30:56,320 --> 00:30:59,800
Speaker 2: why it's important to lock this container. So they were

618
00:30:59,800 --> 00:31:03,040
Speaker 2: doing outreach in Spanish in this community, just so that

619
00:31:03,120 --> 00:31:05,880
Speaker 2: people got it like, oh, this is why we do it,

620
00:31:05,960 --> 00:31:07,840
Speaker 2: and it's a nice thing to do for the bears.

621
00:31:08,400 --> 00:31:10,840
Speaker 2: But yeah, they're very smart. The guy that I spent

622
00:31:10,920 --> 00:31:14,120
Speaker 2: time with had done a study on bears breaking into

623
00:31:14,160 --> 00:31:17,680
Speaker 2: minivans in Yosemite. They would break into other cars, but

624
00:31:17,720 --> 00:31:19,440
Speaker 2: for some reason it was so they all knew there's

625
00:31:19,440 --> 00:31:22,120
Speaker 2: a certain minivan and he wasn't sure, Like I said,

626
00:31:22,160 --> 00:31:24,760
Speaker 2: is it the fault of the minivan maker? Are they not?

627
00:31:25,200 --> 00:31:26,840
Speaker 2: Is there something about the doors that are easy to

628
00:31:26,880 --> 00:31:28,760
Speaker 2: pop open? He said, it might be that, but I

629
00:31:28,760 --> 00:31:31,840
Speaker 2: think it's more that a mini van is a place

630
00:31:31,960 --> 00:31:35,000
Speaker 2: with lots of children and lots of spilled food and

631
00:31:35,080 --> 00:31:38,080
Speaker 2: crumbs and soda and a lot of nice smells. And

632
00:31:38,160 --> 00:31:41,320
Speaker 2: the bears are like, that's the place for me, mm, the.

633
00:31:41,360 --> 00:31:45,160
Speaker 3: Same reason dogs like babies. Yeah, exactly. Well, you know,

634
00:31:45,200 --> 00:31:47,760
Speaker 3: I've been to Aspen Center for Physics. Of course, is

635
00:31:47,800 --> 00:31:50,920
Speaker 3: there an Aspen, And many times I've seen a genius

636
00:31:50,960 --> 00:31:53,560
Speaker 3: theoretical physicist standing in front of one of those things

637
00:31:53,760 --> 00:31:57,880
Speaker 3: and like unevil, you know, And it's like when you

638
00:31:57,920 --> 00:32:00,000
Speaker 3: stand in a shower in a new hotel and you're like,

639
00:32:00,040 --> 00:32:01,680
Speaker 3: how you turned this thing on? And what's hot and

640
00:32:01,680 --> 00:32:03,560
Speaker 3: what's cold. It's like a new puzzle everything.

641
00:32:03,880 --> 00:32:05,840
Speaker 2: Oh my god, the number of times I called the

642
00:32:05,880 --> 00:32:10,040
Speaker 2: fronts like I'm standing here naked, I can't take a

643
00:32:10,080 --> 00:32:14,720
Speaker 2: shower because I'm too stupid to figure out your shower

644
00:32:15,040 --> 00:32:16,960
Speaker 2: system exactly.

645
00:32:17,200 --> 00:32:20,040
Speaker 1: And the fancy or the hotel, the more obscure the

646
00:32:20,080 --> 00:32:24,560
Speaker 1: system is. Let's look that they are have coffee, which sucks.

647
00:32:25,920 --> 00:32:28,040
Speaker 3: Probably bears could figure it out, though they'd be happily

648
00:32:28,040 --> 00:32:28,640
Speaker 3: taking showers.

649
00:32:28,800 --> 00:32:31,160
Speaker 2: Pars could figure it out. The bears could figure out

650
00:32:31,160 --> 00:32:34,160
Speaker 2: there also. And there was a guy from Colorado Fish

651
00:32:34,160 --> 00:32:36,600
Speaker 2: and Wildlife or fish and Game, I forget what they

652
00:32:36,640 --> 00:32:39,840
Speaker 2: call themselves there, but he was describing how, you know,

653
00:32:39,920 --> 00:32:41,760
Speaker 2: up in the hills where there's a lot of delicious

654
00:32:41,760 --> 00:32:45,080
Speaker 2: food for bears, natural and man made, they got a

655
00:32:45,080 --> 00:32:49,120
Speaker 2: lot of break ins. And he was describing how he

656
00:32:49,200 --> 00:32:51,840
Speaker 2: came to this one place where the bear had literally

657
00:32:52,160 --> 00:32:56,360
Speaker 2: just pulled the door out of the frame, just like

658
00:32:56,360 --> 00:32:59,520
Speaker 2: like you know, incredible hulk style pulled it off, but

659
00:32:59,560 --> 00:33:03,840
Speaker 2: then carefully just set it aside and leaned it against

660
00:33:03,920 --> 00:33:06,800
Speaker 2: the wall, not like threw it over the deck or

661
00:33:06,880 --> 00:33:10,120
Speaker 2: knocked it down, just sort of carefully laid it aside.

662
00:33:10,520 --> 00:33:13,040
Speaker 2: I just I love that. I love bears.

663
00:33:13,440 --> 00:33:15,600
Speaker 3: I love megafauna in general. I wish we had more

664
00:33:15,640 --> 00:33:18,720
Speaker 3: megafauna still in North America, you know, like.

665
00:33:18,680 --> 00:33:19,560
Speaker 1: The Giant's loss.

666
00:33:19,920 --> 00:33:23,880
Speaker 2: Yeah, the Giant's loss. Giant anything would be great. I

667
00:33:23,920 --> 00:33:28,160
Speaker 2: once did a story on the Gippsland giant earthworm, which

668
00:33:28,240 --> 00:33:31,040
Speaker 2: is this worm that is so this is the size

669
00:33:31,080 --> 00:33:34,160
Speaker 2: of a forty five record around big. And I was like,

670
00:33:34,320 --> 00:33:37,840
Speaker 2: I'm going to interview this woman who studies the Gippsland

671
00:33:37,880 --> 00:33:38,760
Speaker 2: giant earthworm.

672
00:33:38,880 --> 00:33:40,840
Speaker 3: You're saying, this is an earthworm like the size of

673
00:33:40,880 --> 00:33:41,680
Speaker 3: a fire hose.

674
00:33:41,960 --> 00:33:44,960
Speaker 2: Yeah, oh my god, it is definitely at least that big.

675
00:33:45,480 --> 00:33:48,480
Speaker 2: And you can hear them moving underground. She goes stomping

676
00:33:48,480 --> 00:33:51,440
Speaker 2: around on the field and you hear this this sort

677
00:33:51,480 --> 00:33:54,880
Speaker 2: of gurgling sound as they moved through their tunnels. And

678
00:33:54,920 --> 00:33:57,719
Speaker 2: she was able to pinpoint where it was dig with

679
00:33:57,720 --> 00:34:00,280
Speaker 2: the shovel, and like we saw this glimpse of the

680
00:34:00,600 --> 00:34:03,880
Speaker 2: giant earthworm like going away down the tunnel, and happily

681
00:34:03,920 --> 00:34:08,160
Speaker 2: she didn't cut it in half. But anyway, any giant thing.

682
00:34:08,200 --> 00:34:09,719
Speaker 2: But I was going to say, getting back to you,

683
00:34:09,719 --> 00:34:12,680
Speaker 2: you're talking about how it had broke in without to

684
00:34:12,719 --> 00:34:15,920
Speaker 2: get the seltzer water without causing damage. And there are

685
00:34:16,120 --> 00:34:19,239
Speaker 2: bears that kind of get a reputation. And again this

686
00:34:19,360 --> 00:34:22,120
Speaker 2: was in Colorado. There's this one bear. It was breaking

687
00:34:22,160 --> 00:34:24,279
Speaker 2: into cabins and going in and people would be like,

688
00:34:24,360 --> 00:34:26,640
Speaker 2: you know what, he didn't touch anything. He just went

689
00:34:26,680 --> 00:34:28,920
Speaker 2: to the fridge. He took out the honey and the

690
00:34:29,000 --> 00:34:31,799
Speaker 2: yogurt and the beer and shut the door on the

691
00:34:31,880 --> 00:34:35,359
Speaker 2: left and everything was fo Yeah, And because of that,

692
00:34:35,760 --> 00:34:38,200
Speaker 2: no one wanted to call him in. And so there

693
00:34:38,239 --> 00:34:40,600
Speaker 2: was this thought that maybe, you know, that these are

694
00:34:40,640 --> 00:34:43,480
Speaker 2: the bears that don't get killed by fish and wildlife,

695
00:34:43,800 --> 00:34:46,840
Speaker 2: and that maybe over time will select for bears that

696
00:34:46,920 --> 00:34:51,439
Speaker 2: are mellower and better mannered, and that maybe one day

697
00:34:51,440 --> 00:34:53,880
Speaker 2: bears will just be like you know, big raccoons, you know,

698
00:34:54,080 --> 00:34:57,440
Speaker 2: just kind of bumbling around getting into the garbage.

699
00:34:57,480 --> 00:34:59,479
Speaker 1: Are they checking to see if someone is home first?

700
00:34:59,520 --> 00:35:01,359
Speaker 1: I feel like I'd feel different if I came home

701
00:35:01,400 --> 00:35:03,240
Speaker 1: and was like, oh, my yogurt is on the counter,

702
00:35:03,280 --> 00:35:05,640
Speaker 1: a bear must have been here, or if I was like, there, well,

703
00:35:05,640 --> 00:35:06,640
Speaker 1: it was eating my yogurt.

704
00:35:08,200 --> 00:35:11,279
Speaker 2: Yeah. Yeah. They prefer to come in when no one's there,

705
00:35:11,280 --> 00:35:13,319
Speaker 2: but sometimes they don't know there are stories of a

706
00:35:13,360 --> 00:35:16,080
Speaker 2: bear coming in grabbing a chicken off the dining table

707
00:35:16,080 --> 00:35:20,120
Speaker 2: while people are eating, or somebody like they're sitting at

708
00:35:20,120 --> 00:35:22,879
Speaker 2: home and a bear comes through the door and they

709
00:35:23,040 --> 00:35:26,000
Speaker 2: run and lock themselves in the bathroom. They are sometimes

710
00:35:26,080 --> 00:35:29,040
Speaker 2: coming in when you're there, and when it's dangerous is

711
00:35:29,480 --> 00:35:31,520
Speaker 2: often if the person has a dog and the dog

712
00:35:31,640 --> 00:35:34,080
Speaker 2: is you know, of course, freaked out, and the dog's

713
00:35:34,120 --> 00:35:36,840
Speaker 2: going after the bear and the bear is in freak

714
00:35:36,880 --> 00:35:40,640
Speaker 2: out defensive mode, and if the person tries to come

715
00:35:40,680 --> 00:35:43,400
Speaker 2: between the dog and the bear, and they can redirect

716
00:35:43,480 --> 00:35:46,440
Speaker 2: the attack onto the person, So that's a bad schoys.

717
00:35:46,800 --> 00:35:48,920
Speaker 3: Well, what do you think more broadly about this interaction

718
00:35:49,000 --> 00:35:51,759
Speaker 3: between humans and wildlife. A lot of people feel like, oh,

719
00:35:51,760 --> 00:35:53,440
Speaker 3: we have to protect the humans. We should get rid

720
00:35:53,440 --> 00:35:55,280
Speaker 3: of the bear. It's like, this is why we hunted

721
00:35:55,280 --> 00:35:58,160
Speaker 3: a lot of megafauna to extinction. But you know, the

722
00:35:58,200 --> 00:36:01,359
Speaker 3: bears were there first. Right in my neighborhood. People worry

723
00:36:01,400 --> 00:36:03,759
Speaker 3: about their cats because there's coyotes in the neighborhood and

724
00:36:03,800 --> 00:36:06,400
Speaker 3: I'm like, well, you know, coyotes live here. We moved

725
00:36:06,400 --> 00:36:07,920
Speaker 3: in and brought our cats.

726
00:36:08,280 --> 00:36:10,120
Speaker 2: People are funny about it. Oh, it's so cruel to

727
00:36:10,200 --> 00:36:13,320
Speaker 2: keep a cat inside. I've had cats as indoor cats.

728
00:36:13,320 --> 00:36:16,919
Speaker 2: They're fine with it. You're just projecting on your cat.

729
00:36:17,480 --> 00:36:19,920
Speaker 2: A cat doesn't give it. It wants food.

730
00:36:19,920 --> 00:36:23,319
Speaker 3: And a lap, and the birds are happy if your

731
00:36:23,320 --> 00:36:24,000
Speaker 3: cat stays.

732
00:36:23,760 --> 00:36:26,640
Speaker 2: Inside exactly, the birds are happier. Yeah.

733
00:36:26,719 --> 00:36:28,919
Speaker 1: So, speaking of the cats, I've talked to a couple

734
00:36:28,920 --> 00:36:32,440
Speaker 1: different people who do animal control work, and nobody wants

735
00:36:32,520 --> 00:36:34,920
Speaker 1: to see someone out there like shooting the feral cats,

736
00:36:34,920 --> 00:36:37,359
Speaker 1: because we all like have warm and fuzzy feelings about

737
00:36:37,360 --> 00:36:39,399
Speaker 1: the cats, even though they're killing the birds, they're killing

738
00:36:39,400 --> 00:36:41,920
Speaker 1: the rodents, and so there are a lot of popular

739
00:36:41,920 --> 00:36:45,160
Speaker 1: programs where the cats are captured, spaine or neutered and

740
00:36:45,200 --> 00:36:49,480
Speaker 1: then released again, where they're still of course being ecological menaces,

741
00:36:49,640 --> 00:36:52,400
Speaker 1: but you hope that the numbers don't grow. And it

742
00:36:52,400 --> 00:36:54,840
Speaker 1: seems like they were doing something similar with the monkeys

743
00:36:55,000 --> 00:36:57,200
Speaker 1: that you studied in one of the chapters. Can you

744
00:36:57,239 --> 00:36:58,920
Speaker 1: tell us about the monkeys and also what it was

745
00:36:58,960 --> 00:37:00,000
Speaker 1: like to get rubbed by a monkey?

746
00:37:00,800 --> 00:37:03,120
Speaker 2: Sure? But I did one on the topic of cats

747
00:37:03,239 --> 00:37:06,280
Speaker 2: first here. The program in New Zealand is really interesting

748
00:37:06,320 --> 00:37:09,760
Speaker 2: Predator Free New Zealand twenty thirty, which is an attempt

749
00:37:09,800 --> 00:37:15,120
Speaker 2: to rid the island of stoats rats. What's it's like

750
00:37:15,160 --> 00:37:20,600
Speaker 2: a weasel, a long, tubular, fierce, vicious thing. So cute

751
00:37:20,719 --> 00:37:22,960
Speaker 2: possums that's the other one. So they're non native species

752
00:37:23,000 --> 00:37:25,600
Speaker 2: and you know New Zealand has flightless birds, so that's

753
00:37:25,719 --> 00:37:29,560
Speaker 2: like easy pickens if you're a stout. And the possums,

754
00:37:29,600 --> 00:37:31,400
Speaker 2: you know, strip the trees and do other things. So

755
00:37:31,440 --> 00:37:35,080
Speaker 2: there's this desire in order to preserve the native species.

756
00:37:35,120 --> 00:37:40,200
Speaker 2: Is there's a program by using poisons to eliminate predators,

757
00:37:40,640 --> 00:37:43,520
Speaker 2: but there were also and again these were brought in.

758
00:37:43,880 --> 00:37:47,120
Speaker 2: The cats were not native, and there's a ton of

759
00:37:47,280 --> 00:37:50,640
Speaker 2: feral cats in New Zealand. They brought them in thinking

760
00:37:50,680 --> 00:37:52,640
Speaker 2: they can get rid of the rats. I think it was. Anyway,

761
00:37:52,760 --> 00:37:56,040
Speaker 2: if you're going to do predator free New Zealand, you

762
00:37:56,960 --> 00:37:59,960
Speaker 2: rationally should also be getting rid of the feral cats.

763
00:38:00,040 --> 00:38:03,160
Speaker 2: But that is a flashpoint and you can't do that.

764
00:38:03,640 --> 00:38:05,600
Speaker 2: So that's interesting.

765
00:38:05,920 --> 00:38:08,880
Speaker 1: Yeah. Culture, it's so interesting how you know, human emotions

766
00:38:08,880 --> 00:38:11,120
Speaker 1: sort of make all of this much more complicated. Like

767
00:38:11,160 --> 00:38:13,680
Speaker 1: I think the macaques, they're in a culture where you're

768
00:38:13,680 --> 00:38:15,239
Speaker 1: not supposed to kill animals, and.

769
00:38:15,160 --> 00:38:18,920
Speaker 2: So well, the macacus. Yeah, I mean in India, not

770
00:38:19,000 --> 00:38:21,840
Speaker 2: just macacs, but elephants as well. Some of the nuisance

771
00:38:22,040 --> 00:38:27,360
Speaker 2: quote nuisance animals are also representations of gods hanuman Is

772
00:38:27,360 --> 00:38:29,440
Speaker 2: and the monkey gods. So if you go to a

773
00:38:29,520 --> 00:38:34,799
Speaker 2: honumon temple in India, there's monkeys everywhere and people are

774
00:38:35,120 --> 00:38:37,799
Speaker 2: on the one hand fed up with macaques because they

775
00:38:37,840 --> 00:38:42,279
Speaker 2: break into houses and they steal things and they're very aggressive.

776
00:38:42,880 --> 00:38:44,600
Speaker 2: But on the other people are feeding them. It's like

777
00:38:44,640 --> 00:38:48,279
Speaker 2: making an offering. So they hang around urban areas and

778
00:38:48,320 --> 00:38:50,839
Speaker 2: temples and are fed. At the same time, they are

779
00:38:50,840 --> 00:38:55,000
Speaker 2: despised and the poor people who are the animal control

780
00:38:55,080 --> 00:38:59,879
Speaker 2: types are completely fed up. It's an intractable problem because

781
00:39:00,000 --> 00:39:03,800
Speaker 2: people are encouraging them. Also, there's not reliable garbage pickup.

782
00:39:04,239 --> 00:39:07,680
Speaker 2: I was in Delhi, but also in other cities. It's

783
00:39:07,760 --> 00:39:10,120
Speaker 2: so there's a lot of food for them and they're

784
00:39:10,239 --> 00:39:12,839
Speaker 2: very smart, you know, like bears. I was staying at

785
00:39:12,880 --> 00:39:16,359
Speaker 2: this hotel in Jaipor and around dusk you look at

786
00:39:16,360 --> 00:39:18,359
Speaker 2: the rooftops and all of a sudden, the monkeys are

787
00:39:18,360 --> 00:39:20,600
Speaker 2: coming out and the monkeys. You know, we were in

788
00:39:20,640 --> 00:39:22,680
Speaker 2: a restaurant and this monkey just all of a sudden

789
00:39:22,760 --> 00:39:25,640
Speaker 2: is on the rafters and is dropping down onto tables,

790
00:39:25,680 --> 00:39:28,279
Speaker 2: and the waiters have you know, a stick handy to

791
00:39:28,400 --> 00:39:31,360
Speaker 2: just threaten the monkey and get to leave. They're everywhere.

792
00:39:31,440 --> 00:39:33,720
Speaker 2: It's hard to get approval for any kind of control

793
00:39:33,800 --> 00:39:37,799
Speaker 2: because of the religious overtones of a monkey. To hire

794
00:39:37,840 --> 00:39:40,920
Speaker 2: monkey catchers. I mean what's done is to capture them

795
00:39:40,920 --> 00:39:43,120
Speaker 2: and take them to this big area south in the

796
00:39:43,280 --> 00:39:46,279
Speaker 2: southern part of Delhi used to be a quarry, this

797
00:39:46,360 --> 00:39:49,239
Speaker 2: big chunk of land, and so they're captured and brought there.

798
00:39:49,280 --> 00:39:51,960
Speaker 2: But it's very hard to get somebody to take the

799
00:39:52,080 --> 00:39:55,880
Speaker 2: job of monkey catcher because you're harassing a monkey, and

800
00:39:55,920 --> 00:39:58,440
Speaker 2: that's people are not comfortable with that. They're not comfortable

801
00:39:58,440 --> 00:40:01,280
Speaker 2: with killing them. They're not even comfortable with birth control

802
00:40:01,320 --> 00:40:04,760
Speaker 2: efforts which have been going on just because it's a god.

803
00:40:05,080 --> 00:40:07,200
Speaker 2: I mean it's not literally a god, but it's too

804
00:40:07,239 --> 00:40:09,680
Speaker 2: close for comfort, and so it's a big problem.

805
00:40:10,120 --> 00:40:12,319
Speaker 3: Yeah, So what's going on in this region where it's

806
00:40:12,320 --> 00:40:14,560
Speaker 3: all just the monkeys, Like the monkeys are in charge.

807
00:40:14,600 --> 00:40:15,720
Speaker 3: It's like some monkey kingdom.

808
00:40:15,920 --> 00:40:18,120
Speaker 2: I went there, I got in there. It is. It's

809
00:40:18,320 --> 00:40:21,480
Speaker 2: monkey central. They feed them. It's concentrated in certain areas

810
00:40:21,480 --> 00:40:23,560
Speaker 2: it's an old quarry, but it is all overgrown. It's

811
00:40:23,560 --> 00:40:25,759
Speaker 2: actually a lovely place to be a monkey. It's just

812
00:40:25,800 --> 00:40:30,520
Speaker 2: a wild space, but there are villages close by, and

813
00:40:30,560 --> 00:40:33,000
Speaker 2: the monkeys are really good at climbing right up over

814
00:40:33,040 --> 00:40:36,160
Speaker 2: the wall and harassing people in the village. There was

815
00:40:36,200 --> 00:40:38,160
Speaker 2: a guy there is like, you cannot even eat a

816
00:40:38,239 --> 00:40:41,400
Speaker 2: ROTI there's a monkey wall that's coming to you. They're

817
00:40:41,440 --> 00:40:43,240
Speaker 2: not being kept in very well.

818
00:40:43,480 --> 00:40:44,920
Speaker 1: I think there's a lot of things that we do

819
00:40:45,480 --> 00:40:48,240
Speaker 1: when it comes to animal control that makes us feel good,

820
00:40:48,719 --> 00:40:51,120
Speaker 1: but isn't actually changing anything. I think when you move

821
00:40:51,160 --> 00:40:53,520
Speaker 1: animals from one place to another, so you know you yes,

822
00:40:53,600 --> 00:40:56,040
Speaker 1: you mentioned that the monkeys get out. I think a

823
00:40:56,040 --> 00:40:57,600
Speaker 1: lot of times you move an animal and they've spent

824
00:40:57,640 --> 00:40:59,680
Speaker 1: their whole life getting used to an area they know

825
00:40:59,719 --> 00:41:01,400
Speaker 1: where the food is, and now you've dropped them as

826
00:41:01,440 --> 00:41:03,840
Speaker 1: an adult into an area they don't know. And it

827
00:41:03,880 --> 00:41:05,880
Speaker 1: seems like in the book you discovered that that's a problem.

828
00:41:06,080 --> 00:41:08,239
Speaker 2: Well, it may be effective for the town from which

829
00:41:08,239 --> 00:41:10,200
Speaker 2: you removed them, but you've now put them in an

830
00:41:10,239 --> 00:41:13,279
Speaker 2: area where they're going to figure out where's say it's

831
00:41:13,280 --> 00:41:15,600
Speaker 2: a bear, where's the closest town that has a restaurant

832
00:41:15,640 --> 00:41:19,879
Speaker 2: or that has houses with cat food or just attractants,

833
00:41:19,920 --> 00:41:22,440
Speaker 2: as they say, and they're going to start doing the

834
00:41:22,480 --> 00:41:25,239
Speaker 2: same thing there. So you've just kind of moved the problem.

835
00:41:25,719 --> 00:41:30,200
Speaker 2: That's an issue for the control agency or the wildlife

836
00:41:30,680 --> 00:41:34,640
Speaker 2: folks who decided to do the relocation. If that bear

837
00:41:35,000 --> 00:41:38,560
Speaker 2: gets into any kind of situation where it's becoming aggressive

838
00:41:38,600 --> 00:41:41,600
Speaker 2: and it hurts or kills somebody in that new town,

839
00:41:41,719 --> 00:41:44,560
Speaker 2: Now you the wildlife agency that moved the bear, that

840
00:41:44,640 --> 00:41:48,479
Speaker 2: translocated the bear, now you are liable, so you're open

841
00:41:48,560 --> 00:41:50,640
Speaker 2: to a lawsuit. And that has happened. There have been

842
00:41:50,640 --> 00:41:55,919
Speaker 2: some big payouts in situations where that bear that they

843
00:41:56,000 --> 00:41:59,200
Speaker 2: know is got habituated to food, they know that's a

844
00:41:59,280 --> 00:42:02,400
Speaker 2: high risk bear, and they've moved it and something happened,

845
00:42:02,440 --> 00:42:06,040
Speaker 2: So that's also a consideration. It works a little better

846
00:42:06,080 --> 00:42:09,680
Speaker 2: in a younger bear that hasn't gotten set in its ways,

847
00:42:09,719 --> 00:42:13,040
Speaker 2: but it's very expensive also to do, and the sense

848
00:42:13,160 --> 00:42:16,239
Speaker 2: is among some of the wildlife control people that it's

849
00:42:16,280 --> 00:42:19,160
Speaker 2: done in cases where there's a lot of publicity. In

850
00:42:19,200 --> 00:42:22,400
Speaker 2: other words, you're not going to kill that bear because

851
00:42:22,480 --> 00:42:25,040
Speaker 2: you're going to get a lot of heat and anger

852
00:42:25,200 --> 00:42:28,239
Speaker 2: from the public. So it looks good to do that,

853
00:42:28,400 --> 00:42:30,920
Speaker 2: but it doesn't always work in terms of what the

854
00:42:30,960 --> 00:42:33,479
Speaker 2: bear's behavior is in the new place. And it's also

855
00:42:33,600 --> 00:42:35,720
Speaker 2: not necessarily a nice thing to do. As you mentioned,

856
00:42:35,719 --> 00:42:38,880
Speaker 2: this is an animal that knows food sources, knows the

857
00:42:38,960 --> 00:42:41,600
Speaker 2: terrain in the place that it is, so you're now

858
00:42:41,640 --> 00:42:44,920
Speaker 2: taking it and putting it somewhere else. There were researchers

859
00:42:44,960 --> 00:42:47,800
Speaker 2: that looked at the survival of it was either raccoons

860
00:42:47,880 --> 00:42:49,560
Speaker 2: or squirrels that had been trends. I think it was

861
00:42:49,600 --> 00:42:53,239
Speaker 2: squirrels that had been translocated, and very few of them survived.

862
00:42:53,400 --> 00:42:56,319
Speaker 2: They put radio collars on them. They'd find like a

863
00:42:56,440 --> 00:43:01,640
Speaker 2: piece of radio color. All that remained of squirrel sixty.

864
00:43:01,400 --> 00:43:04,719
Speaker 3: Seven, probably torn apart by the local squirrel gangs. Right,

865
00:43:04,760 --> 00:43:08,840
Speaker 3: it's not happy exactly the person come eating their exactly.

866
00:43:08,880 --> 00:43:11,000
Speaker 3: I mean, I know we feel that way. Whenever Virginians

867
00:43:11,000 --> 00:43:14,319
Speaker 3: moved to California. We're like, hey, you know, that's why

868
00:43:14,360 --> 00:43:14,760
Speaker 3: I left.

869
00:43:15,200 --> 00:43:15,960
Speaker 1: That's why I left.

870
00:43:16,120 --> 00:43:19,040
Speaker 2: Stop eating my nuts exactly.

871
00:43:20,320 --> 00:43:21,799
Speaker 1: All right, Well, let's take a break, and when we

872
00:43:21,800 --> 00:43:23,840
Speaker 1: get back, we're going to ask Mary about some of

873
00:43:23,880 --> 00:43:43,960
Speaker 1: the most extreme situations that her work has put her in, So, Mary,

874
00:43:44,040 --> 00:43:47,920
Speaker 1: what is the most embarrassing situation you've ever been in

875
00:43:48,040 --> 00:43:50,080
Speaker 1: as part of your book research?

876
00:43:50,400 --> 00:43:54,560
Speaker 2: That's an easy one. That one, not surprisingly was for Bonk.

877
00:43:54,680 --> 00:43:57,200
Speaker 2: The subtitle of that book is The Curious Coupling of

878
00:43:57,239 --> 00:43:59,759
Speaker 2: Science and Sex. So that is a book about the

879
00:44:00,000 --> 00:44:06,239
Speaker 2: delightful awkwardness of taking sex, arousal orgasm, all of that,

880
00:44:06,320 --> 00:44:08,840
Speaker 2: all the physiological components of it, and putting it in

881
00:44:08,880 --> 00:44:12,680
Speaker 2: a laboratory setting with you a couple doing things and

882
00:44:12,680 --> 00:44:15,680
Speaker 2: then somebody in a white coat taking notes. So I

883
00:44:15,880 --> 00:44:19,960
Speaker 2: wanted to get at that central awkwardness of what I

884
00:44:20,120 --> 00:44:23,720
Speaker 2: just described. And I found this researcher in London who's

885
00:44:23,800 --> 00:44:27,640
Speaker 2: doing something with four dimensional ultrasound, so three D movies

886
00:44:27,640 --> 00:44:32,640
Speaker 2: basically in ultrasound, which is something that surgeons could use,

887
00:44:32,680 --> 00:44:35,560
Speaker 2: say if you were working on a cleft lip and

888
00:44:35,640 --> 00:44:38,359
Speaker 2: you wanted to get a sense of how that lip

889
00:44:38,440 --> 00:44:42,080
Speaker 2: is moving before you did the surgery. And he had

890
00:44:42,200 --> 00:44:44,279
Speaker 2: I think there was a Pearne study that he had

891
00:44:44,320 --> 00:44:47,560
Speaker 2: done with an erecting penis and looking at before surgery.

892
00:44:47,600 --> 00:44:50,160
Speaker 2: What am I dealing with? It was a diagnostic tool

893
00:44:50,360 --> 00:44:53,840
Speaker 2: and he wanted to do when it was actually a

894
00:44:53,920 --> 00:44:57,880
Speaker 2: penis and a vagina interacting, and I said, well, I

895
00:44:57,960 --> 00:45:00,800
Speaker 2: would like to be there when you do that to observe,

896
00:45:01,600 --> 00:45:06,000
Speaker 2: and he wrote back, well, if your organization can provide

897
00:45:06,120 --> 00:45:10,239
Speaker 2: some volunteers, then I'd be happy to do that. So

898
00:45:10,320 --> 00:45:16,520
Speaker 2: my organization asked its husband, and my husband is such

899
00:45:16,520 --> 00:45:19,360
Speaker 2: a good sport, you know, he had said, oh, sex research,

900
00:45:19,400 --> 00:45:21,400
Speaker 2: sign me up. You know. He was like, well, I

901
00:45:21,440 --> 00:45:23,919
Speaker 2: don't know whatever. I really put the emphasis on, Let's

902
00:45:23,960 --> 00:45:26,200
Speaker 2: go to London and I'll pay for a nice hotel

903
00:45:26,280 --> 00:45:29,160
Speaker 2: and we'll, you know, we'll go to see some plays.

904
00:45:29,239 --> 00:45:31,400
Speaker 2: I think Jeremy Irons is in something right now, and

905
00:45:31,800 --> 00:45:34,040
Speaker 2: it'll be really fun and we have to, you know,

906
00:45:34,120 --> 00:45:41,480
Speaker 2: have sex in front of a guy. I look back

907
00:45:41,520 --> 00:45:43,720
Speaker 2: now on that and I think, how did that even?

908
00:45:43,920 --> 00:45:47,920
Speaker 2: I guess I'm just really committed to getting good material.

909
00:45:48,000 --> 00:45:50,680
Speaker 2: Material would be fun to write up, fun for the reader.

910
00:45:51,200 --> 00:45:52,919
Speaker 2: I knew that that was going to be really fun

911
00:45:52,920 --> 00:45:55,600
Speaker 2: to write and also really not fun to do.

912
00:45:56,920 --> 00:45:57,840
Speaker 3: So you went through with it?

913
00:45:57,920 --> 00:46:02,080
Speaker 2: Oh yes, oh yes, yes I did.

914
00:46:02,480 --> 00:46:04,520
Speaker 3: Yeah, and your marriage survived.

915
00:46:04,640 --> 00:46:07,280
Speaker 2: It's a very fun scene. It's only a couple of pages.

916
00:46:07,320 --> 00:46:10,040
Speaker 2: It's only a couple of pages. It's funny because my

917
00:46:10,160 --> 00:46:12,360
Speaker 2: editor goes, Mary, I don't you know, she has a

918
00:46:12,440 --> 00:46:14,400
Speaker 2: thing about what people need to take you seriously as

919
00:46:14,440 --> 00:46:16,640
Speaker 2: a science writer. You know, this is science writing. It's

920
00:46:16,719 --> 00:46:19,719
Speaker 2: just for people to respect you. And so she was like,

921
00:46:19,760 --> 00:46:22,320
Speaker 2: I don't know if this scene should really be in here,

922
00:46:22,560 --> 00:46:24,319
Speaker 2: And then my agent read it and goes, Mary, you

923
00:46:24,360 --> 00:46:28,160
Speaker 2: need more detail in this scene. So I think I

924
00:46:28,239 --> 00:46:31,000
Speaker 2: hit it just about right. We're going to leave that

925
00:46:31,080 --> 00:46:31,560
Speaker 2: as is.

926
00:46:32,600 --> 00:46:34,799
Speaker 1: I think that's why we take you seriously, because it's

927
00:46:34,840 --> 00:46:37,319
Speaker 1: like she is actually willing to like get involved in

928
00:46:37,360 --> 00:46:40,520
Speaker 1: this stuff and be a data point in a study.

929
00:46:40,600 --> 00:46:41,040
Speaker 2: Yeah.

930
00:46:41,120 --> 00:46:44,080
Speaker 3: Yeah, physics is so much less embarrassing and relieved.

931
00:46:45,719 --> 00:46:48,360
Speaker 1: How about scary? Have you ever signed up for something

932
00:46:48,480 --> 00:46:50,080
Speaker 1: thought this is a great idea, and then when you

933
00:46:50,120 --> 00:46:52,360
Speaker 1: were in the moment, thought this was a bad idea.

934
00:46:52,520 --> 00:46:54,360
Speaker 2: There were things that I did and that I didn't

935
00:46:54,400 --> 00:46:56,960
Speaker 2: really think about it till later that it was probably

936
00:46:57,000 --> 00:47:00,520
Speaker 2: a stupid thing to do. For example, and the leopard

937
00:47:00,600 --> 00:47:02,520
Speaker 2: chapter in the book, this is a section of the

938
00:47:02,520 --> 00:47:07,680
Speaker 2: Middle Himalayas where leopards actually do prey on people, children,

939
00:47:07,800 --> 00:47:11,560
Speaker 2: old people, easy targets. And as we'd come into this area,

940
00:47:11,760 --> 00:47:15,120
Speaker 2: the researcher had sort of done this death tour of

941
00:47:15,160 --> 00:47:18,480
Speaker 2: see this bus stop. An old gentleman was taken waiting

942
00:47:18,520 --> 00:47:20,879
Speaker 2: sitting there, see this field. So it was like this

943
00:47:21,000 --> 00:47:24,680
Speaker 2: tour of maulings. And so I at one point it

944
00:47:24,719 --> 00:47:26,200
Speaker 2: was a nice evening and I said I'm going to

945
00:47:26,280 --> 00:47:29,000
Speaker 2: go out for a walk, and nobody wanted to come

946
00:47:29,040 --> 00:47:30,319
Speaker 2: with me. So I went out for a walk and

947
00:47:30,360 --> 00:47:32,880
Speaker 2: then the sun went down more quickly than I thought,

948
00:47:32,880 --> 00:47:35,600
Speaker 2: and that's the hour at which all these attacks happened.

949
00:47:35,600 --> 00:47:39,000
Speaker 2: And I was walking back thinking, this is really stupid

950
00:47:39,280 --> 00:47:42,319
Speaker 2: of me, and it was spooky because of what I

951
00:47:42,360 --> 00:47:44,640
Speaker 2: knew as I was doing it, but then sort of

952
00:47:44,680 --> 00:47:47,520
Speaker 2: looking back just like that was really stupid of me,

953
00:47:47,840 --> 00:47:51,560
Speaker 2: but not terrifying. I sometimes put myself in a situation

954
00:47:51,640 --> 00:47:53,680
Speaker 2: that my husband will go like, well you did what

955
00:47:54,480 --> 00:47:57,640
Speaker 2: I did. The story before it's on. This is years ago,

956
00:47:57,719 --> 00:47:59,759
Speaker 2: and I wrote for Salon dot com and I did

957
00:47:59,760 --> 00:48:04,720
Speaker 2: a story about bashful bladder paruresis, which is a condition

958
00:48:05,000 --> 00:48:08,800
Speaker 2: you know men at the urinal they can't start to pee,

959
00:48:09,040 --> 00:48:12,680
Speaker 2: and it can be extreme. There are people who bring

960
00:48:12,719 --> 00:48:15,560
Speaker 2: a self catheterization kit when they travel just because they

961
00:48:15,640 --> 00:48:19,000
Speaker 2: just if anybody's close, they can't I don't think this

962
00:48:19,080 --> 00:48:22,880
Speaker 2: is a woman problem much. But anyway, the way that

963
00:48:23,920 --> 00:48:27,000
Speaker 2: these folks get help is similar to if you're afraid

964
00:48:27,000 --> 00:48:29,960
Speaker 2: of spiders. You know, it's a progressive the spiders closer,

965
00:48:30,000 --> 00:48:32,000
Speaker 2: it's in the room, and eventually it's you know, two

966
00:48:32,040 --> 00:48:35,279
Speaker 2: inches away. So there's a treatment that is where you're

967
00:48:35,360 --> 00:48:38,719
Speaker 2: somebody's pea buddy, where you know, they drink a lot

968
00:48:38,719 --> 00:48:40,680
Speaker 2: of water and then you're like out in the living

969
00:48:40,719 --> 00:48:42,879
Speaker 2: room and they're like, okay, I'm gonna pee. Now I'm

970
00:48:42,880 --> 00:48:44,799
Speaker 2: in the bathroom. You're like, okay, I'm in the living room,

971
00:48:44,840 --> 00:48:46,759
Speaker 2: and then you get sort of closer to them and

972
00:48:46,800 --> 00:48:49,799
Speaker 2: to the point where you or I was outside the

973
00:48:49,840 --> 00:48:52,600
Speaker 2: bathroom door and I'm like outside the bathroom door and

974
00:48:52,680 --> 00:48:55,240
Speaker 2: the guy's like, okay, I'm doing and I'm peeing. So anyway,

975
00:48:55,239 --> 00:48:56,839
Speaker 2: I was like, wow, I really helped this guy and

976
00:48:56,840 --> 00:48:58,960
Speaker 2: this is great and it's an interesting story. And my

977
00:48:59,040 --> 00:49:05,760
Speaker 2: husband's like, where were you this afternoon some rando guy's

978
00:49:05,920 --> 00:49:09,560
Speaker 2: house who's peeing and I'm standing outside the door and

979
00:49:09,560 --> 00:49:12,480
Speaker 2: he's like, did you know this person? No? I don't know.

980
00:49:12,760 --> 00:49:15,120
Speaker 2: You know, there's a Google group whatever it was back

981
00:49:15,160 --> 00:49:19,399
Speaker 2: then where I just posted something and somebody said, yes,

982
00:49:19,480 --> 00:49:23,080
Speaker 2: you can help me and be my pea buddy. So

983
00:49:23,320 --> 00:49:25,400
Speaker 2: I'm just always like, ah, that sounds great, that'll be

984
00:49:25,440 --> 00:49:26,080
Speaker 2: really interesting.

985
00:49:26,760 --> 00:49:28,840
Speaker 1: Is there data to suggest that this sort of like

986
00:49:29,080 --> 00:49:31,120
Speaker 1: protocol works in the long term?

987
00:49:31,200 --> 00:49:34,319
Speaker 2: Yeah, it does it. I forget the term in psychology

988
00:49:34,400 --> 00:49:38,080
Speaker 2: for phobias. You start small. He just kind of incrementally

989
00:49:38,480 --> 00:49:43,120
Speaker 2: increase somebody's exposure and so like I'm the spider. He

990
00:49:43,160 --> 00:49:46,280
Speaker 2: wrote to me later and he said he was very grateful.

991
00:49:46,320 --> 00:49:48,920
Speaker 2: He said, you know, I have a girlfriend now and

992
00:49:48,960 --> 00:49:51,600
Speaker 2: I'm able to go to the bathroom off the bedroom

993
00:49:51,640 --> 00:49:53,040
Speaker 2: and pee in the middle of the night. I could

994
00:49:53,120 --> 00:49:56,520
Speaker 2: never do that before. I'm like good, Oh yeah, I know.

995
00:49:56,840 --> 00:50:01,880
Speaker 1: Imagine having Mary Roach be your like pe that's in

996
00:50:01,920 --> 00:50:02,279
Speaker 1: the bar.

997
00:50:04,640 --> 00:50:07,400
Speaker 2: Yeah, that was something good that came from my writing

998
00:50:08,360 --> 00:50:09,480
Speaker 2: or my research. Anyway.

999
00:50:09,680 --> 00:50:11,879
Speaker 3: Well, you mentioned that you sometimes have people who don't

1000
00:50:11,880 --> 00:50:14,920
Speaker 3: respond to interview requests. I wonder if there's ever an

1001
00:50:14,920 --> 00:50:18,120
Speaker 3: interview you really really wanted, that you pushed for, that

1002
00:50:18,160 --> 00:50:21,120
Speaker 3: you were persistent in going after until you got it.

1003
00:50:21,680 --> 00:50:24,000
Speaker 3: How do you convince somebody to talk to you if

1004
00:50:24,040 --> 00:50:25,160
Speaker 3: they're not sure about it.

1005
00:50:25,320 --> 00:50:28,520
Speaker 2: Oh? Yeah, First of all, I just feel that enthusiasm

1006
00:50:29,000 --> 00:50:32,000
Speaker 2: helps a lot, and sometimes enthusiasm takes the form of

1007
00:50:32,640 --> 00:50:35,320
Speaker 2: sending a second and even a third email. It doesn't

1008
00:50:35,320 --> 00:50:39,320
Speaker 2: always work, but people respond, I think to you know,

1009
00:50:39,400 --> 00:50:43,120
Speaker 2: if you're that curious and interested and enthusiastic about their research,

1010
00:50:43,160 --> 00:50:46,400
Speaker 2: and that is appealing for people. And I think people

1011
00:50:46,560 --> 00:50:49,279
Speaker 2: like to talk about their work. You know, maybe their

1012
00:50:49,280 --> 00:50:51,759
Speaker 2: family doesn't want to hear about it anymore. And to

1013
00:50:51,760 --> 00:50:54,440
Speaker 2: have somebody say I'm super interested in what you do

1014
00:50:54,520 --> 00:50:57,279
Speaker 2: and I want to see and observe and learn about it,

1015
00:50:57,400 --> 00:50:59,480
Speaker 2: I think that that goes a long way. Also, I

1016
00:50:59,520 --> 00:51:03,440
Speaker 2: think people don't like to say no three times, so

1017
00:51:04,640 --> 00:51:08,560
Speaker 2: you know, persistence pays off. Also, it just wear them

1018
00:51:08,560 --> 00:51:11,479
Speaker 2: down to a stub. Just keep at them. I've done

1019
00:51:11,480 --> 00:51:14,080
Speaker 2: all's work. I mean, they're definitely people who and it's

1020
00:51:14,080 --> 00:51:17,640
Speaker 2: often something but the nature of their work, they don't

1021
00:51:17,640 --> 00:51:20,640
Speaker 2: want any attention in the media. They don't want to

1022
00:51:20,880 --> 00:51:23,279
Speaker 2: And I think more so now with you know, with

1023
00:51:23,360 --> 00:51:28,680
Speaker 2: social media, and if just one person twists what they

1024
00:51:28,719 --> 00:51:31,680
Speaker 2: do and gets on social media and everybody piles on,

1025
00:51:31,880 --> 00:51:35,439
Speaker 2: I totally understand why that's a scary thing and why

1026
00:51:35,480 --> 00:51:38,759
Speaker 2: you want to avoid that. So that's become harder for me.

1027
00:51:38,880 --> 00:51:42,640
Speaker 2: Any lab where work is done with animals, that's very

1028
00:51:42,680 --> 00:51:45,279
Speaker 2: hard to get in. I had a situation with my

1029
00:51:45,360 --> 00:51:49,959
Speaker 2: last book where the university said no. They didn't really

1030
00:51:50,000 --> 00:51:53,239
Speaker 2: say why, they just said no. And then the guy

1031
00:51:53,239 --> 00:51:56,200
Speaker 2: who runs the lab said, it's my lab. You're going

1032
00:51:56,239 --> 00:51:58,040
Speaker 2: to come to my office and we're going to talk,

1033
00:51:58,200 --> 00:52:00,680
Speaker 2: and then we're going to walk across the hall and

1034
00:52:00,719 --> 00:52:03,239
Speaker 2: we're not going to go to the animal lab. But yes,

1035
00:52:03,360 --> 00:52:05,839
Speaker 2: you can come, and they can't tell me what to do.

1036
00:52:06,080 --> 00:52:08,759
Speaker 2: But you know, he was familiar with my work, and

1037
00:52:08,760 --> 00:52:11,680
Speaker 2: that was he's been there long enough that he didn't

1038
00:52:11,680 --> 00:52:14,160
Speaker 2: give it what the people in the public affairs office

1039
00:52:14,160 --> 00:52:17,680
Speaker 2: had to say. But often there's reasons why people are

1040
00:52:17,719 --> 00:52:20,800
Speaker 2: wary about an outsider coming in and writing about what

1041
00:52:20,840 --> 00:52:23,799
Speaker 2: they're doing. And I completely understand that.

1042
00:52:24,080 --> 00:52:27,040
Speaker 3: I have friends who are climate science researchers and they're

1043
00:52:27,520 --> 00:52:30,759
Speaker 3: very concerned about how they're portrayed, and you know, the

1044
00:52:30,880 --> 00:52:33,640
Speaker 3: nuances are crucial in that field.

1045
00:52:33,880 --> 00:52:36,520
Speaker 2: Yeah, the other thing that I do, as I will

1046
00:52:36,840 --> 00:52:39,839
Speaker 2: tell them you can fact check this to make sure

1047
00:52:39,840 --> 00:52:42,680
Speaker 2: I have it right, and I will never send the

1048
00:52:42,760 --> 00:52:45,239
Speaker 2: material to the person, but I will get on the

1049
00:52:45,239 --> 00:52:47,920
Speaker 2: phone and read it and I'll say stop me if

1050
00:52:47,960 --> 00:52:52,200
Speaker 2: something's wrong, And that works really well. If you give

1051
00:52:52,239 --> 00:52:55,400
Speaker 2: it to them, they'll go in and rewrite it.

1052
00:52:55,480 --> 00:52:57,439
Speaker 1: I made that the same you.

1053
00:52:57,480 --> 00:53:00,840
Speaker 2: No, so that doesn't happen. But just reading it to people,

1054
00:53:00,880 --> 00:53:03,799
Speaker 2: I'm always very nervous to do that. That's always a

1055
00:53:03,800 --> 00:53:06,360
Speaker 2: scary thing to do. But when it goes well and

1056
00:53:06,400 --> 00:53:09,359
Speaker 2: somebody actually likes the way you've portrayed their work, and

1057
00:53:09,400 --> 00:53:13,120
Speaker 2: then that's very gratifying. That's a good day.

1058
00:53:13,880 --> 00:53:16,399
Speaker 3: It's terrifying to write about somebody else's expertise and then

1059
00:53:16,440 --> 00:53:19,640
Speaker 3: send it to them for review and you're like waiting

1060
00:53:19,680 --> 00:53:21,759
Speaker 3: to hear back. I know that.

1061
00:53:22,680 --> 00:53:25,880
Speaker 2: Yeah, Yeah, that's why I do it on the phone.

1062
00:53:26,160 --> 00:53:29,719
Speaker 1: Yeah that's smarter. Yeah, that's I think what I'll do

1063
00:53:29,760 --> 00:53:32,319
Speaker 1: in the future. So we are just about out of time,

1064
00:53:32,360 --> 00:53:34,160
Speaker 1: and I want to respect your time. Are you allowed

1065
00:53:34,200 --> 00:53:35,640
Speaker 1: to tell us what your next book is going to

1066
00:53:35,680 --> 00:53:37,759
Speaker 1: be about? Or is that top secret until it comes out?

1067
00:53:37,840 --> 00:53:40,200
Speaker 2: Oh? No, I can tell you. It's called Replaceable you.

1068
00:53:40,480 --> 00:53:43,320
Speaker 2: So it's all kinds of bits and pieces of the

1069
00:53:43,400 --> 00:53:47,399
Speaker 2: human body and replacement elements. Some of it's historical, some

1070
00:53:47,440 --> 00:53:50,000
Speaker 2: of it's current. You know, it's my usual Mary Roach

1071
00:53:50,680 --> 00:53:52,520
Speaker 2: wandering ding that approach.

1072
00:53:52,960 --> 00:53:54,640
Speaker 1: I cannot wait? Is it coming out soon?

1073
00:53:54,880 --> 00:53:58,879
Speaker 2: September? So almost a year. The lead time is quite

1074
00:53:59,000 --> 00:54:01,040
Speaker 2: long these days for books.

1075
00:54:01,160 --> 00:54:04,200
Speaker 1: Yeah, so it's painful suppending a manuscript and waiting a

1076
00:54:04,200 --> 00:54:05,040
Speaker 1: whole year, I know.

1077
00:54:05,080 --> 00:54:07,200
Speaker 2: And I have the sense that everything is out of date.

1078
00:54:07,280 --> 00:54:11,239
Speaker 2: You know, I've already done one round of Has anything changed? Surprisingly?

1079
00:54:11,320 --> 00:54:13,880
Speaker 2: Things change slowly, you know. You have this sense that

1080
00:54:14,120 --> 00:54:16,879
Speaker 2: their new discoveries every month, and it's like, well, yeah,

1081
00:54:16,960 --> 00:54:21,200
Speaker 2: there's probably not gonna happen for another five to ten years,

1082
00:54:21,280 --> 00:54:24,120
Speaker 2: you know. So it's okay, but it does feel like

1083
00:54:24,920 --> 00:54:27,799
Speaker 2: it's hard to be up to date in anything when

1084
00:54:27,840 --> 00:54:28,920
Speaker 2: you're turning it in.

1085
00:54:29,160 --> 00:54:31,160
Speaker 1: I wouldn't write about AI right now because I feel

1086
00:54:31,200 --> 00:54:33,600
Speaker 1: like that year would be a huge cheerfook would be

1087
00:54:33,640 --> 00:54:35,240
Speaker 1: out of date. The second hit the shelves.

1088
00:54:35,440 --> 00:54:38,160
Speaker 2: Yeah, exactly, how does anybody write about AI? I don't know.

1089
00:54:38,560 --> 00:54:40,640
Speaker 1: All right, Well, thank you so much, Mary, This was

1090
00:54:40,680 --> 00:54:42,719
Speaker 1: so much fun. I will be a little bit more

1091
00:54:42,719 --> 00:54:44,960
Speaker 1: careful with the deer and Daniel. You need to not

1092
00:54:45,000 --> 00:54:46,480
Speaker 1: talk with Seltzer water anymore.

1093
00:54:48,320 --> 00:54:50,400
Speaker 2: Was it even flavored Seltzer water?

1094
00:54:50,640 --> 00:54:51,960
Speaker 3: It was, yes, watermelon?

1095
00:54:52,080 --> 00:54:54,439
Speaker 1: Yeah, are you sure it wasn't stuff that the kids

1096
00:54:54,440 --> 00:54:57,919
Speaker 1: had like ground into the rug beforehand. Seltzer, Why would

1097
00:54:57,920 --> 00:54:58,520
Speaker 1: they go for that?

1098
00:54:58,640 --> 00:55:00,560
Speaker 2: I think the Seltzer water was a read Harry.

1099
00:55:00,920 --> 00:55:05,120
Speaker 1: Yeah, all right, we'll be more careful, Daniel.

1100
00:55:06,920 --> 00:55:08,600
Speaker 2: Thanks you guys. I was so much fun.

1101
00:55:16,160 --> 00:55:20,000
Speaker 1: Daniel and Kelly's Extraordinary Universe is produced by iHeartRadio. We

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