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So, Mark Twain wrote,

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Any government could have told her that the best way to increase wolves in America,

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rabbits in Australia, and snakes in India is to pay a bounty on their scalps.

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Then every patriot goes to raising them.

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Welcome to Wild Turkey Science,

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a podcast made possible by Turkeys for Tomorrow.

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I'm Dr. Marcus Lashley, Professor of Wildlife Ecology at the University of Florida.

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And I'm Dr. Will Goolsby, Professor of Wildlife Ecology and Management at Auburn University.

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We're both lifelong hunters and devoted scientists who are passionate about hunting,

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managing, and researching wild turkeys.

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In this podcast, we'll explore turkey research, speak to the experts in the field,

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and address the difficult questions related to wild turkey ecology and management.

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Our goal is to serve as your connection to wild turkey science.

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It does kind of look like you're up to something nefarious.

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Well, I'm on the edge of my seat about this one.

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I hope you brought a ladle.

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A ladle?

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Yeah, because we're gonna stir the pot.

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Well, I was thinking we're gonna lap it up.

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I'm about to lap it up.

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Let me ask you a question, Will.

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I think you are going to lap up some of this info.

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I mean, it's cool stuff.

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Oh, I'm going to like it, I know.

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But why do I feel like it's going to be a hot take?

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To be clear to everybody out there, we are doing a deep dive on a topic that comes up a lot.

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How many times, Will, have you heard or been told?

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How many times have you heard or been told or gotten comments?

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I know we do on our episodes sometimes.

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We hear about it a lot that to solve our turkey woes, we need to have a predator bounty.

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So many times.

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I mean, it's like common knowledge.

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It's like almost every single one of our YouTube episodes has that comment on it somewhere.

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But you have now done a deep dive on that topic.

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I have.

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From what I understand.

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And to be clear to everybody out there, I did not do a deep dive on the topic.

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And I don't know what you found or what you're going to talk about other than that topic.

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Well, it's going to be fun because I'm the one armed with the information today.

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And I can quiz you on this stuff.

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Like you have been with me on a couple of recent episodes.

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Hey, you've done that to me before.

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I know I have.

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So see the jokes on you though, because I don't care if it makes me sound dumb.

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I mean, did you think I do based on our history on this show?

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No, obviously we just let it ride.

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Yeah.

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So you're going to get my genuine response and my genuine not lack of knowledge whenever it emerges.

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Okay.

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Well, you ready to go?

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Yeah, but I do feel like it's going to be kind of a hot take just because of the way

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you had presented yourself when we came to the episode.

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How was that?

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You're just like sitting all over there with your arms crossed.

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Like I'm about to tell y'all something.

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Well, I think.

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I think that, uh, well, maybe I do feel that way.

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Maybe I shouldn't be that.

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Maybe I shouldn't be so eager to ruin, ruin people's thoughts or perceptions of the way

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things work, but I don't know.

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Maybe that's one of the aspects that I enjoy about science and why I enjoy being a scientist

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is because that's part of our job is to figure out if things really work like we think they

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would logically.

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And sometimes it's fun when they don't, to be honest, it's kind of more interesting.

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I mean, it's frustrating, but interesting.

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Well, you know, it's a fundamental aspect of our work and, you know, it's, it's disappointing

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sometimes when scientists don't react to things the way that I'm about to lay out.

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But I commonly go into things with a hypothesis and I have a prediction that I think, okay,

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this is the way this is going to happen.

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And I collect data and that doesn't work like that.

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And it's like, dang.

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Yeah, well, that's interesting.

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I mean, just like probably happens more often than not.

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Right.

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Yeah.

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All the time.

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But it hooks me in and now I'm like, all right, why didn't it work that way?

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We got to figure out and not, we got to do something different to figure out how to make

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sure that we know it works this way.

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Like, that's not, you know, we don't want that kind of, of approach and, uh, you know,

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this sounds like might be one of those topics where we think it works away and then it doesn't.

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And then I'm going to be really intrigued by that if that's the case.

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Yeah.

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Okay.

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Okay.

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You ready?

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Well, yeah.

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Do you have a place that you want to start?

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I do.

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Well, let's start there.

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With a glossary term.

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A glossary term.

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Yeah.

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Have you ever heard of the term of a perverse incentive?

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Perverse.

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I don't think I've heard that terminology, but I might be able to work out the definition

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based on the terms.

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A perverse incentive is an incentive structure with undesirable results.

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Particularly one.

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It's interesting that you would start there.

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A particularly one where those effects are unexpected and contrary to the intentions

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of its designers.

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I think that falls perfectly within what we've just been talking about.

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Yeah.

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And it is a pretty apt description of what happens a lot of times with these predator

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bounty programs.

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And so I'm going to kind of the way that I did this and have it planned out, I'm going

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to start out with a couple of kind of classic examples of how this plays out that may not

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be really directly related to turkeys.

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And then we'll get into some literature that is more so directly related to the topic.

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I think that's good.

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But to start with, since you started this out with a Webster's Dictionary entry, what

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is a predator bounty program just in general?

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How would you describe that?

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In some way of incentivizing, typically monetarily, the harvesting of a particular species of

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animal in a particular area.

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With the intention of...

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Reducing the population of that animal.

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Yeah.

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But for some purpose, right?

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For some purpose.

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And it's not always...

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Like if it was for turkeys, we might remove coyotes to increase hen survival.

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Yes.

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Or whatever.

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Yes.

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Something like that.

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And as it turns out, I have some information on coyote bounties too.

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Oh.

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So that'll be directly relevant to that.

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Cool.

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Okay.

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Okay.

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So have you ever heard of the cobra effect?

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That's where I'm going to start out today.

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The cobra?

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The cobra effect.

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The only cobra I know is Cobra Kai, baby.

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Okay.

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Strike first, strike hard.

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Isn't that what the saying is?

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I was, man, when I was about eight years old, I was swinging nunchucks, you know, like the

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plastic ones trying to be Karate Kid.

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Well, you know, they remade a show centered around the Cobra Kai gym a few years ago.

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Oh yeah.

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Yeah.

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I watched it.

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Okay, good.

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It's weird saying Karate Kid as an adult.

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Oh, I know.

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He's not as intimidating as I remember him being when I was a kid.

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So I am aware of the cobra.

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When I watched Karate Kid as a kid, I thought he was a badass.

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And it's like, now that I've grown up and I watch him, I'm like, I don't know if he

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sells the part that well.

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Yeah.

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All right.

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The cobra effect.

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So backstory here.

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This was in the-

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I'm sorry, I haven't recovered yet.

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I have heard of the cobra.

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We'll keep working on your recovery over there.

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So the British government, it was occupying India, right.

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And they were concerned about the number of venomous cobras.

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Shit, we're going way back.

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I am.

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So they were concerned about the number of venomous cobras in Delhi.

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So what'd they do?

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Put a bounty on them.

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They put a bounty on them.

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So initially, the bounty program seemed to be pretty successful.

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People were bringing in huge numbers of snakes all the time, trying to get reward.

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But then what happens?

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Somebody starts breeding snakes and they start trying to game the system.

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Yeah.

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So the program, I don't know if we want to talk about this or not.

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Just tell me if you do or don't.

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But this makes me think of like giving up density.

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You know what I'm saying?

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Like with, with regards to wildlife that, you know, if they're trying to acquire a food

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resource or something like that, there's a certain point past which there's too few of

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that food item within an area for it to be worthwhile for them to search for it.

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And I think, and that, and that is kind of an overarching principle that I think we'll

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touch on several times today is if you're implementing a program that people are

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getting financially rewarded for, that is intended to control or eradicate individuals

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of a certain species, you eventually get to this point of diminishing returns where you're

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putting in a whole lot of effort for very few animals that are captured.

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And if the money is being paid off of how many animals you capture, you're probably

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either going to one, quit when you get to that density and give up like we were just

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talking about, or two, you're going to move to another area that has more of that animal.

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And then you take the pressure off of the area where the population has been reduced

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and it starts to respond positively again.

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MIKE Yeah.

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And then we could go a little further than that.

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Another thing that could happen is it becomes part of the, the culture or livelihood.

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And then you start to cultivate that for, to, to ensure that the sustainability of that

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opportunity.

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And that is, that is exactly what happened in India.

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People began to breed the cobras for income.

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And the program eventually got scrapped once, uh, the British government caught on to it.

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And you know what, and you know what, wait, wait, wait, wait, wait.

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You know what happened to, to all these breeding populations that people had curated?

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They just released them.

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They released them.

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And the cobra population ended up higher than it was before the bounty program was implemented.

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So first important point that I want to make here today is that human psychology

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plays a huge role in the effectiveness of these programs.

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And that's just one example.

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MIKE That's not what would happen with turkeys.

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MIKE Okay.

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All right.

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I got another one for you.

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I got another one for you on our little historical adventure.

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MIKE Is it another cobra example?

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MIKE No, this one is the Great Hanoi Rat Massacre of 1902 in Vietnam.

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You ever heard about that one?

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MIKE The Great Rat Massacre?

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MIKE The Great Hanoi Rat Massacre.

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MIKE I don't know what that word is.

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MIKE No one knew that they were going to be hearing about the Great Hanoi Rat Massacre today.

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MIKE What is Hanoi?

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Can you say that, like spell that word?

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MIKE It's in Vietnam.

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MIKE It's just a place?

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MIKE Yes.

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MIKE Okay.

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MIKE It's a place in Vietnam.

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MIKE I don't know.

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I'm not, not familiar with that geography.

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MIKE So this was in the early 1900s.

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Vietnam was under French colonial rule and the government created a bounty that paid

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one cent for each rat killed.

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And so to prove that they had killed a rat, they brought the tail back to somebody.

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They killed the rat, they severed the tail, brought it back and they got paid for it.

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So it wasn't too long that officials started noticing rats that were running around Hanoi

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that were missing what?

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MIKE A foot.

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MIKE No, their tail.

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God, you're being difficult today for the sake of difficulty.

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Yes, they were, they were catching them in live traps, cutting off their tails and releasing

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them so that they would keep breeding to make more young rats with tails that they could

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cut off.

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MIKE Well, where does the massacre come in?

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MIKE Well, they were trying to make it into a rat massacre, but it ended up just being

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a massive tail docking event, I guess.

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MIKE So the, all right, my last little, wait, wait, wait, wait.

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MIKE Oh, okay.

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MIKE I'm not past it yet.

250
00:12:51,240 --> 00:12:51,640
MIKE All right.

251
00:12:51,640 --> 00:12:53,960
MIKE So the bounty on rats.

252
00:12:53,960 --> 00:12:57,080
MIKE Yeah, a penny a rat.

253
00:12:57,080 --> 00:13:05,000
MIKE A penny a rat resulted in all the rats getting their tail cut off.

254
00:13:05,000 --> 00:13:09,960
So now there's a bunch of rats running around with no tails that are still breeding.

255
00:13:09,960 --> 00:13:10,920
MIKE They're still breeding.

256
00:13:10,920 --> 00:13:13,880
MIKE And now that is amazing.

257
00:13:13,880 --> 00:13:19,720
MIKE I mean, never, what is the, what is the term about?

258
00:13:19,720 --> 00:13:21,080
I don't know.

259
00:13:21,080 --> 00:13:27,640
It's just like never underestimate people's ability to work smarter, not harder, I guess.

260
00:13:27,640 --> 00:13:32,280
They'll find a work, people will find a workaround for anything.

261
00:13:32,280 --> 00:13:38,440
MIKE I'm just, I was trying to think through the, is there, I guess that could happen with

262
00:13:38,440 --> 00:13:48,040
raccoons, you know, with ratcheting tails, but it really matters how you measure, you know,

263
00:13:48,040 --> 00:13:50,200
the, uh, the bounty.

264
00:13:50,200 --> 00:13:57,080
MIKE Well, so I can tell you giving more modern examples, the same thing, I don't know about

265
00:13:57,080 --> 00:14:03,400
leaving them alive, but there's a lot of issues with bounty programs typically require you to

266
00:14:03,400 --> 00:14:06,360
bring some part of the animal back to prove, to prove that you killed it.

267
00:14:07,240 --> 00:14:13,320
And I know that people have taken advantage of those programs that were designed for feral pig

268
00:14:13,320 --> 00:14:15,640
control, where they had to bring back pig tails.

269
00:14:15,640 --> 00:14:17,160
I know they've done that with beaver tails.

270
00:14:17,160 --> 00:14:19,320
I have a personal experience with that.

271
00:14:19,320 --> 00:14:21,400
Um, and then I know.

272
00:14:21,400 --> 00:14:24,040
MIKE Where you just cut the tail off a beaver and then let it go.

273
00:14:24,040 --> 00:14:25,000
MIKE Uh-huh.

274
00:14:25,000 --> 00:14:26,200
MIKE You just cut the tail off.

275
00:14:26,200 --> 00:14:26,440
MIKE No, no, no.

276
00:14:26,440 --> 00:14:29,880
They were still, in this, in that instance, people were still killing them, but they were

277
00:14:29,880 --> 00:14:33,240
killing them outside of the area that they were supposed to be getting paid for the bounty and

278
00:14:33,240 --> 00:14:36,680
bringing them, bringing the tail back to the other area.

279
00:14:37,560 --> 00:14:43,640
Um, and then I know of an example where that was done on preparing for this episode

280
00:14:43,640 --> 00:14:45,000
with actually seals.

281
00:14:45,000 --> 00:14:51,240
So there's a bounty on seals, um, that I found some information about we might get into today.

282
00:14:51,240 --> 00:14:52,120
MIKE Interesting.

283
00:14:52,120 --> 00:14:53,000
All right.

284
00:14:53,000 --> 00:14:54,040
Well, I'll let you move on.

285
00:14:54,040 --> 00:14:54,360
MIKE Okay.

286
00:14:54,360 --> 00:14:54,840
MIKE I just.

287
00:14:54,840 --> 00:15:00,840
MIKE Well, my last little bit of, my last little historical reference, um, I actually

288
00:15:00,840 --> 00:15:06,840
pulled from Mark Twain's autobiography, um, and his wife decided to pay their son to control

289
00:15:06,840 --> 00:15:07,800
flies at their house.

290
00:15:07,800 --> 00:15:12,200
And of course, you know, what does the son see?

291
00:15:12,200 --> 00:15:14,360
He's like, man, I'm going to make some money.

292
00:15:14,360 --> 00:15:15,240
Absolutely.

293
00:15:15,240 --> 00:15:21,800
And, uh, his, so Mark Twain wrote, any government could have told her that the best way to

294
00:15:21,800 --> 00:15:27,240
increase wolves in America, rabbits in Australia and snakes in India is to pay a bounty on

295
00:15:27,240 --> 00:15:27,880
their scalps.

296
00:15:28,520 --> 00:15:30,680
Then every Patriot goes to raising them.

297
00:15:30,680 --> 00:15:36,840
And I, I mean, I don't think we would probably, you know, I don't think people would be trying

298
00:15:36,840 --> 00:15:41,960
to raise animals like that to take advantage of a bounty program today, the way they would

299
00:15:41,960 --> 00:15:45,160
have historically when people were more in a subsistence state of living.

300
00:15:45,160 --> 00:15:47,320
But it's, I think the point still stands.

301
00:15:47,320 --> 00:15:52,360
And anyways, it was interesting to me that Mark Twain had a quote on this.

302
00:15:52,360 --> 00:15:53,320
So I had to include it.

303
00:15:53,320 --> 00:15:57,320
That's a really interesting quote.

304
00:15:57,320 --> 00:16:04,040
And I'm just thinking like, you're going to have some hate messages on YouTube.

305
00:16:04,040 --> 00:16:05,480
Oh, I know.

306
00:16:05,480 --> 00:16:08,120
We've got so many people telling us about that.

307
00:16:08,120 --> 00:16:13,240
But before y'all, before y'all send me that hate message, before y'all send me that hate

308
00:16:13,240 --> 00:16:14,360
message, think about this.

309
00:16:14,360 --> 00:16:15,320
Okay.

310
00:16:15,320 --> 00:16:17,640
They've already said it, man.

311
00:16:17,640 --> 00:16:18,520
No, no, no, no.

312
00:16:18,520 --> 00:16:20,040
They said it three minutes ago.

313
00:16:20,040 --> 00:16:22,680
I might get them on that backspace button right here.

314
00:16:22,680 --> 00:16:23,160
We'll see.

315
00:16:23,160 --> 00:16:29,160
We make this same argument about game species.

316
00:16:29,160 --> 00:16:36,520
When you place a value on an animal, people are incentivized to conserve it.

317
00:16:36,520 --> 00:16:41,160
And so, but it's the opposite situation.

318
00:16:41,160 --> 00:16:45,880
So in the bounty system, they're incentivized to conserve it to make money.

319
00:16:45,880 --> 00:16:51,560
In the hunting situation, like when we recovered wild turkeys, for example, the only reason

320
00:16:51,560 --> 00:16:57,000
we recovered wild turkeys is because they had value as a game species, right?

321
00:16:57,000 --> 00:17:03,640
So animals that have value, people find a way to keep those animals around in general.

322
00:17:03,640 --> 00:17:04,600
Yeah.

323
00:17:04,600 --> 00:17:09,640
So we make the same argument when it comes to the positive aspects of wildlife conservation.

324
00:17:09,640 --> 00:17:11,960
I heard this argument the other day.

325
00:17:15,400 --> 00:17:20,040
I think this is 100% accurate, but I have not vetted it myself.

326
00:17:20,040 --> 00:17:28,840
But this is what was said, that there have been no game species under protections as

327
00:17:28,840 --> 00:17:30,680
a game species that have gone extinct.

328
00:17:30,680 --> 00:17:31,720
Yep.

329
00:17:31,720 --> 00:17:35,640
It's pretty incredible.

330
00:17:35,640 --> 00:17:36,840
Yeah.

331
00:17:36,840 --> 00:17:44,440
Especially when there are many, many, many, many, many, many species that have gone extinct.

332
00:17:44,440 --> 00:17:50,520
But I think that the point here is that some of those same forces that are in effect on

333
00:17:50,520 --> 00:17:56,600
this bounty system that perhaps make it less effective than we think it'd be, I do actually

334
00:17:56,600 --> 00:18:00,120
have an example that we can pull from the literature of one that was somewhat successful

335
00:18:00,120 --> 00:18:04,440
that we'll get to at the end, because it really is kind of the exception, not the norm.

336
00:18:04,440 --> 00:18:11,480
I think it, I'm glad we are going to cover that, but I think your point

337
00:18:13,240 --> 00:18:15,480
is a very interesting one and well-taken.

338
00:18:15,480 --> 00:18:25,480
When we think about it from the game species, it's a positive viewpoint, and everybody,

339
00:18:25,480 --> 00:18:28,520
I mean, that's pretty ubiquitously accepted.

340
00:18:28,520 --> 00:18:31,080
Right?

341
00:18:31,080 --> 00:18:34,280
As if we put a value on a species, it will get conserved.

342
00:18:34,280 --> 00:18:39,080
It's one of our main talking points about game management and conservation.

343
00:18:39,640 --> 00:18:43,400
But when it's a species that we view as negative, like a predator

344
00:18:43,400 --> 00:18:50,280
or an invasive species or whatever, that logic breaks down.

345
00:18:50,280 --> 00:18:55,080
Yep.

346
00:18:55,080 --> 00:18:55,880
It's interesting.

347
00:18:55,880 --> 00:19:01,560
But the fundamental way that it operates does not.

348
00:19:01,560 --> 00:19:02,440
Yeah, that's right.

349
00:19:04,440 --> 00:19:09,080
The next thing that I want to get into is about the pig bounty example.

350
00:19:09,080 --> 00:19:12,120
I'm pretty familiar with this one.

351
00:19:12,120 --> 00:19:16,920
This was from a paper that was written by one of my colleagues, Steve Ditchcoff,

352
00:19:16,920 --> 00:19:18,760
who's also here at Auburn.

353
00:19:18,760 --> 00:19:25,880
They looked at a pig bounty program that was implemented on Fort Benning in Georgia, which

354
00:19:25,880 --> 00:19:31,240
for those of you that don't know, that's directly east of me here in Auburn, so right across the

355
00:19:31,240 --> 00:19:37,000
line in kind of west central Georgia, and I think it's the home for all of the US Army's

356
00:19:37,000 --> 00:19:37,800
infantry training.

357
00:19:37,800 --> 00:19:42,680
So they have tons of wildlands across the property.

358
00:19:42,680 --> 00:19:45,640
They've got a lot of pines and prescribed fire and everything like that.

359
00:19:45,640 --> 00:19:49,880
And it's known for having a relatively high pig population.

360
00:19:49,880 --> 00:19:53,080
And so they tried to implement a bounty there.

361
00:19:53,080 --> 00:19:54,760
And we'll talk about the results of that program.

362
00:19:54,760 --> 00:19:58,760
But before I go into that, there were a couple of additional little points that I gleaned

363
00:19:58,760 --> 00:20:01,160
from the introduction of that article that I wanted to share.

364
00:20:01,160 --> 00:20:08,600
And so we've already talked about, they phrased them in this paper as the bad intentions of

365
00:20:08,600 --> 00:20:09,560
participants.

366
00:20:09,560 --> 00:20:14,680
I would phrase it more so as the misaligned objectives of the bounty program versus the

367
00:20:14,680 --> 00:20:17,080
people participating in the bounty program.

368
00:20:17,080 --> 00:20:19,640
Their objectives are not congruent with each other, right?

369
00:20:19,640 --> 00:20:22,040
So we talked about that.

370
00:20:22,040 --> 00:20:28,040
But then also in the Ditchcoff et al paper, they point out that bounty failure is also

371
00:20:28,040 --> 00:20:33,560
often attributed to the biology of the species being poorly understood or incorporated into

372
00:20:33,560 --> 00:20:34,200
the program.

373
00:20:34,200 --> 00:20:40,360
So that it's not considering the biological attributes of that species and how a control

374
00:20:40,360 --> 00:20:46,120
effort should be shaped or prescribed in reference to that.

375
00:20:46,120 --> 00:20:46,920
Does that make sense?

376
00:20:46,920 --> 00:20:52,120
So they pointed that out.

377
00:20:54,360 --> 00:20:58,520
And then we'll just get right into the story of the Pig Bounty Program.

378
00:20:58,520 --> 00:21:05,880
So the Army decided when they implemented this, this was back in 2007 and 2008, and

379
00:21:05,880 --> 00:21:11,560
they instituted a bounty program that paid out $40 for every feral pigtail that was brought

380
00:21:11,560 --> 00:21:11,800
back.

381
00:21:11,800 --> 00:21:18,280
So during the study, which again, it was only, it wasn't even two full years.

382
00:21:18,280 --> 00:21:21,800
It was most of 2007 and a part of 2008, I believe.

383
00:21:23,400 --> 00:21:27,800
And over the course of that time period, they removed about 1,100 pigs from the site.

384
00:21:27,800 --> 00:21:28,760
Okay.

385
00:21:28,760 --> 00:21:30,920
So 1,138 pigs.

386
00:21:30,920 --> 00:21:38,760
And the total cost of removal of those was $57,000, almost $300.

387
00:21:38,760 --> 00:21:41,800
Okay.

388
00:21:41,800 --> 00:21:49,160
So you know what I meant to calculate the cost per pig?

389
00:21:49,160 --> 00:21:50,920
How many sacks of corn is that?

390
00:21:50,920 --> 00:21:52,120
Oh, we're getting there too.

391
00:21:52,120 --> 00:21:52,600
Trust me.

392
00:21:53,160 --> 00:21:56,760
That's, I'm so glad you brought that up because that's a part of this conversation.

393
00:21:56,760 --> 00:21:57,960
Oh man.

394
00:21:57,960 --> 00:22:02,600
Well, that's a cost of about $50 per pig, not horrible.

395
00:22:02,600 --> 00:22:03,480
Yeah.

396
00:22:03,480 --> 00:22:06,200
Okay.

397
00:22:06,200 --> 00:22:07,320
Well, if it worked, it isn't.

398
00:22:07,320 --> 00:22:10,520
Well, here's the problem.

399
00:22:10,520 --> 00:22:14,040
You want to guess what the removal rate was?

400
00:22:14,040 --> 00:22:17,160
The density of removal?

401
00:22:17,160 --> 00:22:22,040
So 1,100 pigs on Fort Benning?

402
00:22:22,040 --> 00:22:22,280
Yeah.

403
00:22:22,280 --> 00:22:25,800
I don't know how big is Fort Benning.

404
00:22:25,800 --> 00:22:30,360
Well, I mean, then I'd just be giving you the numbers to make the calculation.

405
00:22:30,360 --> 00:22:32,520
One per 150 acres.

406
00:22:32,520 --> 00:22:37,720
No, they would have probably had very different results if that was the case.

407
00:22:37,720 --> 00:22:46,840
The removal rate was less than one pig per square mile and a third, 1.3 square miles.

408
00:22:46,840 --> 00:22:50,600
And the reason they use that weird number is because that was the average

409
00:22:50,600 --> 00:22:55,400
home range size of a sounder, was 1.3 square miles.

410
00:22:55,400 --> 00:22:56,840
And they removed-

411
00:22:56,840 --> 00:22:58,840
Like 800 acres or something?

412
00:22:58,840 --> 00:23:02,280
1.3 times 640.

413
00:23:02,280 --> 00:23:03,800
Or 900, something like that.

414
00:23:03,800 --> 00:23:05,720
832.

415
00:23:05,720 --> 00:23:09,800
So less than one pig for every 832 acres.

416
00:23:09,800 --> 00:23:13,720
I mean, anybody that has ever dealt with pigs knows that that's not going to get it.

417
00:23:14,520 --> 00:23:22,520
So that removal program, that was the agency removal,

418
00:23:22,520 --> 00:23:25,160
or that's how many got incentivized to be removed?

419
00:23:25,160 --> 00:23:26,360
That's how many got incentivized.

420
00:23:26,360 --> 00:23:32,840
Well, interestingly, they were not able, because they had been documenting,

421
00:23:32,840 --> 00:23:35,800
you know, everything that gets killed off that base gets recorded.

422
00:23:35,800 --> 00:23:42,920
And the removal rate pre-bounty program was similar to the removal rate post-bounty program.

423
00:23:43,640 --> 00:23:45,080
It did not change the removal rate.

424
00:23:45,080 --> 00:23:47,640
Okay.

425
00:23:47,640 --> 00:23:52,600
So Fort Benning was removing pigs and then they implemented a bounty program to help,

426
00:23:52,600 --> 00:23:56,760
and they still kept the same rate of removing pigs.

427
00:23:56,760 --> 00:23:57,800
Exactly.

428
00:23:57,800 --> 00:23:58,760
So it doesn't work.

429
00:23:58,760 --> 00:24:00,040
And here's part of the problem.

430
00:24:00,040 --> 00:24:07,160
Some reports were that people were buying tails from game processors or killing them elsewhere,

431
00:24:07,160 --> 00:24:09,320
and then reselling them to the army at a higher price.

432
00:24:09,320 --> 00:24:13,160
So, you know, you could maybe go to a meat processor-

433
00:24:13,160 --> 00:24:13,660
Capitalism.

434
00:24:13,660 --> 00:24:16,920
Capitalism always wins, man.

435
00:24:16,920 --> 00:24:21,080
I mean, they were going down to, you know, Bob's Deer Processing

436
00:24:21,080 --> 00:24:24,200
and buying a pig tail for $20 and selling it to the army for $40.

437
00:24:24,200 --> 00:24:31,880
And then the processor makes a bunch of money off a pig tail.

438
00:24:31,880 --> 00:24:32,600
Absolutely.

439
00:24:32,600 --> 00:24:34,600
Absolutely.

440
00:24:34,600 --> 00:24:34,920
Okay.

441
00:24:34,920 --> 00:24:37,160
Are you ready for this?

442
00:24:37,160 --> 00:24:37,960
Yeah.

443
00:24:37,960 --> 00:24:39,320
Are you still on pigs?

444
00:24:39,320 --> 00:24:39,880
Yes.

445
00:24:39,880 --> 00:24:40,200
Okay.

446
00:24:40,280 --> 00:24:44,120
Well, let's don't leave there because I want to talk about a different aspect of this,

447
00:24:44,120 --> 00:24:44,680
but go ahead.

448
00:24:44,680 --> 00:24:49,320
What do you think happened to the pig density?

449
00:24:49,320 --> 00:24:51,720
It increased.

450
00:24:51,720 --> 00:24:52,760
I'm just guessing.

451
00:24:52,760 --> 00:24:53,880
It did actually increase.

452
00:24:53,880 --> 00:24:55,720
I'm guessing.

453
00:24:55,720 --> 00:24:57,560
Even worse than not changing, it increased.

454
00:24:57,560 --> 00:24:59,160
Yeah.

455
00:24:59,160 --> 00:25:00,840
Do you want to guess why?

456
00:25:00,840 --> 00:25:04,440
People brought pigs in.

457
00:25:04,440 --> 00:25:04,680
No.

458
00:25:04,680 --> 00:25:08,440
It's back to something you said a second ago.

459
00:25:08,760 --> 00:25:13,000
They cut all the tails off and released them.

460
00:25:13,000 --> 00:25:13,480
I don't know.

461
00:25:13,480 --> 00:25:16,120
Quit linking me hanging.

462
00:25:16,120 --> 00:25:19,160
See, this is why I like being the one armed with the information.

463
00:25:19,160 --> 00:25:26,120
They dispersed 40 tons of corn and 30 tons of slop throughout the installation over a

464
00:25:26,120 --> 00:25:27,000
six month period.

465
00:25:27,000 --> 00:25:36,280
And they think that, I mean, this is a quote from the article, litter size and offspring

466
00:25:36,280 --> 00:25:41,880
survival increased under the bounty scheme due to the bait used to attract animals to

467
00:25:41,880 --> 00:25:43,720
hunting and trapping sites.

468
00:25:43,720 --> 00:25:48,840
The productivity rate we observed was similar to that of adult female pigs in a population

469
00:25:48,840 --> 00:25:50,200
provided with supplemental feed.

470
00:25:50,200 --> 00:26:00,040
Sounder size increased 144 to 233 percent and the number of piglets per female increased

471
00:26:00,040 --> 00:26:07,320
by 191 to 219 percent.

472
00:26:07,320 --> 00:26:08,040
That didn't work.

473
00:26:08,040 --> 00:26:12,840
That's something right there.

474
00:26:12,840 --> 00:26:18,040
And that was from giving them a bunch of corn and slop.

475
00:26:18,040 --> 00:26:19,400
Yeah.

476
00:26:19,400 --> 00:26:24,840
Total 70 tons of feed over a six month period.

477
00:26:28,120 --> 00:26:30,920
That 70 tons.

478
00:26:30,920 --> 00:26:36,440
How much is that per square mile in that area?

479
00:26:36,440 --> 00:26:40,120
I can't, I realized I was trying to back construct that.

480
00:26:40,120 --> 00:26:44,520
Well, that's 140,000 pounds.

481
00:26:44,520 --> 00:26:48,680
Of corn and slop?

482
00:26:48,680 --> 00:26:49,880
Yeah, I think it's combined.

483
00:26:49,880 --> 00:26:50,760
I combined it all.

484
00:26:53,560 --> 00:26:58,680
Let's see, Fort Benning is 197,000 acres.

485
00:26:58,680 --> 00:27:00,040
So it was pretty spread out.

486
00:27:00,040 --> 00:27:06,840
Yeah, it was only like a, what did you say, 120,000?

487
00:27:06,840 --> 00:27:08,600
140,000 pounds.

488
00:27:08,600 --> 00:27:09,080
Yeah.

489
00:27:09,080 --> 00:27:11,320
So less than a pound per acre.

490
00:27:11,320 --> 00:27:15,720
But you would presume that it was probably concentrated in certain areas of the property.

491
00:27:15,720 --> 00:27:17,800
It's not going to be evenly distributed across the-

492
00:27:17,800 --> 00:27:21,320
They didn't walk through there with a spinner.

493
00:27:21,320 --> 00:27:22,040
Yeah, right.

494
00:27:22,040 --> 00:27:23,240
Just distributing it equally.

495
00:27:23,240 --> 00:27:29,400
One thing I will mention too is they noted that a problem was that the hunters generally

496
00:27:29,400 --> 00:27:34,840
were targeting the large boars and they weren't really killing females and juveniles.

497
00:27:34,840 --> 00:27:43,080
So all that to say, especially when you consider the before versus after harvest rate, it doesn't

498
00:27:43,080 --> 00:27:47,160
really seem like the bounty program actually incentivized people to change their behavior

499
00:27:47,160 --> 00:27:50,680
that much in such a way that it would actually lead to a reduction in the population.

500
00:27:51,640 --> 00:27:52,520
Mm-hmm.

501
00:27:52,520 --> 00:27:54,840
And that's not the only example of that that I have.

502
00:27:54,840 --> 00:27:57,160
I've got another one from another paper.

503
00:27:57,160 --> 00:28:03,400
Well, another thing that's really interesting, and I did not prepare for this, so I can't

504
00:28:03,400 --> 00:28:10,840
remember where this is cited, and maybe you will, or maybe they cited it even in this

505
00:28:10,840 --> 00:28:18,600
paper, but it's interesting to me that from the 1500s all the way up until the early 1980s,

506
00:28:19,400 --> 00:28:21,240
pigs were in what, five states?

507
00:28:21,240 --> 00:28:23,080
Oh yeah, mm-hmm.

508
00:28:23,080 --> 00:28:31,800
And then from 1980 to the early 2000s, they went from five, I think, to 45, I'm pretty

509
00:28:31,800 --> 00:28:32,280
sure.

510
00:28:32,280 --> 00:28:32,780
Yeah.

511
00:28:32,780 --> 00:28:38,680
Like the range expansion over that, I mean, it's just a fraction of the time that there

512
00:28:38,680 --> 00:28:40,280
have been pigs in the United States.

513
00:28:40,280 --> 00:28:45,000
Yeah, I mean, I can remember being a little kid, like late 80s, early 90s, and my dad

514
00:28:45,000 --> 00:28:50,120
had a good buddy that was big into pig hunting down there in South Alabama, and he would

515
00:28:50,120 --> 00:28:55,720
have to go way back in the Delta, he'd be crawling back through there trying to find

516
00:28:55,720 --> 00:28:59,000
pigs when he'd go hunt and have to access it by boat and all that kind of stuff.

517
00:28:59,000 --> 00:28:59,800
Yeah.

518
00:28:59,800 --> 00:29:01,880
It's like now people are just tired of killing them.

519
00:29:01,880 --> 00:29:03,260
Yeah.

520
00:29:03,260 --> 00:29:12,120
Well, my point is that it became valuable as a thing to hunt.

521
00:29:12,120 --> 00:29:16,760
We also started incentivizing people through these kinds of programs and stuff.

522
00:29:16,760 --> 00:29:24,920
And what it did was facilitate moving pigs to new areas and releasing pigs in places

523
00:29:24,920 --> 00:29:30,840
that they start to cause a problem to incentivize it and all this kind of, I mean, it just,

524
00:29:30,840 --> 00:29:33,720
like the pigs are, I think are a really good example.

525
00:29:33,720 --> 00:29:39,560
I'm glad you brought that up because some of the states have banned pig hunting.

526
00:29:39,560 --> 00:29:40,920
I was going to come to that.

527
00:29:40,920 --> 00:29:46,120
For that very reason, because just like we talked about earlier, if you incentivize having

528
00:29:46,120 --> 00:29:52,200
them around, you make, whether that's financially or recreationally, people try to propagate

529
00:29:52,200 --> 00:29:52,700
them.

530
00:29:52,700 --> 00:29:53,500
Yeah.

531
00:29:53,500 --> 00:30:00,440
But exactly what you just said was where I was going to go as a couple of states took

532
00:30:00,440 --> 00:30:03,560
a radical approach.

533
00:30:03,560 --> 00:30:05,640
And it seems very counterintuitive.

534
00:30:05,640 --> 00:30:07,000
Yeah, it seems counterintuitive.

535
00:30:07,000 --> 00:30:09,000
And that's my point here.

536
00:30:09,000 --> 00:30:15,480
And the reason that people keep saying this, it is intuitive that this actually would address

537
00:30:15,480 --> 00:30:15,980
a problem.

538
00:30:15,980 --> 00:30:19,800
That makes sense, just intuitively.

539
00:30:19,800 --> 00:30:20,360
Right.

540
00:30:20,360 --> 00:30:28,440
But it just doesn't work out that way because of the way that it plays out.

541
00:30:28,440 --> 00:30:32,760
I mean, we've got examples all the way back to whenever that was in India.

542
00:30:32,760 --> 00:30:33,900
Yeah.

543
00:30:34,520 --> 00:30:37,640
And did you say you found an example where it worked?

544
00:30:37,640 --> 00:30:38,360
Yes.

545
00:30:38,360 --> 00:30:39,880
So at least we've got one.

546
00:30:39,880 --> 00:30:40,380
Yeah.

547
00:30:40,380 --> 00:30:45,000
And I'm curious to hear about that because I don't know what it is right off.

548
00:30:45,000 --> 00:30:54,360
But the best examples with pigs are the couple of instances where the value of it was taken

549
00:30:54,360 --> 00:30:55,080
away.

550
00:30:55,080 --> 00:30:55,580
Right.

551
00:30:55,580 --> 00:31:02,280
So this is an interesting intersection between wildlife biology and human psychology and

552
00:31:02,280 --> 00:31:02,920
economics.

553
00:31:03,160 --> 00:31:08,280
And human behavior plays a much larger role in this than wildlife biology does.

554
00:31:08,280 --> 00:31:09,900
Yeah.

555
00:31:09,900 --> 00:31:12,440
Which kind of makes it hard to talk about.

556
00:31:12,440 --> 00:31:13,080
Very interesting.

557
00:31:13,080 --> 00:31:13,640
Yeah.

558
00:31:13,640 --> 00:31:17,880
I'm really, really curious to see what the comments on this episode are going to be.

559
00:31:17,880 --> 00:31:20,040
That's the only reason you had me lead it.

560
00:31:20,040 --> 00:31:21,720
You wanted it all directed at me.

561
00:31:21,720 --> 00:31:22,760
No.

562
00:31:22,760 --> 00:31:24,680
I mean, it's going to be on my YouTube channel.

563
00:31:24,680 --> 00:31:27,320
All right, you ready to move to coyotes?

564
00:31:27,320 --> 00:31:30,600
I think so.

565
00:31:30,600 --> 00:31:31,100
Okay.

566
00:31:31,580 --> 00:31:32,140
All right.

567
00:31:32,140 --> 00:31:37,580
So I got a paper from Bartell and Brunson, 2003.

568
00:31:37,580 --> 00:31:39,340
It was in Utah.

569
00:31:39,340 --> 00:31:46,060
So Utah reinstated, so I guess they had one previously that they had canceled and they

570
00:31:46,060 --> 00:31:49,660
reinstated it, a coyote bounty program in 2000.

571
00:31:49,660 --> 00:31:55,500
And so the goal of this paper is mostly survey work and they wanted to assess the effects

572
00:31:55,500 --> 00:31:58,700
of the bounty program on coyote take and hunter participation.

573
00:31:59,580 --> 00:32:06,380
So the first thing that they looked at that I included in my notes here is the number

574
00:32:06,380 --> 00:32:14,300
of people that had not been killing coyotes previously that started to because of the

575
00:32:14,300 --> 00:32:15,020
bounty program.

576
00:32:15,020 --> 00:32:21,660
So of the people that they sent surveys to, what percentage of them would you guess started

577
00:32:21,660 --> 00:32:25,180
harvesting coyotes because the bounty program was introduced?

578
00:32:26,620 --> 00:32:31,100
So if you've got a hundred guys that are participating in this bounty program, what

579
00:32:31,100 --> 00:32:34,700
number do you think just started killing coyotes because of the program?

580
00:32:34,700 --> 00:32:37,660
Half of them.

581
00:32:37,660 --> 00:32:38,960
8%.

582
00:32:38,960 --> 00:32:40,940
8%?

583
00:32:40,940 --> 00:32:41,420
Yeah.

584
00:32:41,420 --> 00:32:42,460
Eight out of a hundred.

585
00:32:42,460 --> 00:32:44,620
So the rest of them were already killing coyotes.

586
00:32:44,620 --> 00:32:49,660
So over 90% of participants had reported harvesting coyotes within the previous few

587
00:32:49,660 --> 00:32:50,160
years.

588
00:32:50,160 --> 00:32:55,180
They also reported that they had been taking coyotes out of the wild.

589
00:32:55,180 --> 00:33:00,220
They also reported that they had been taking red fox, skunks, bobcats, and badgers in the

590
00:33:00,220 --> 00:33:00,940
previous year.

591
00:33:00,940 --> 00:33:06,300
So generally speaking, these were probably people that were already trapping and probably

592
00:33:06,300 --> 00:33:07,020
for harvesting.

593
00:33:07,020 --> 00:33:11,100
So in addition to that.

594
00:33:11,100 --> 00:33:13,260
They're just diversifying their portfolio.

595
00:33:13,260 --> 00:33:14,620
Yeah, exactly.

596
00:33:14,620 --> 00:33:17,420
It's a new income stream, right?

597
00:33:17,420 --> 00:33:18,060
Yeah.

598
00:33:18,060 --> 00:33:19,900
Which I'm not knocking them for that.

599
00:33:19,900 --> 00:33:19,900
No.

600
00:33:19,900 --> 00:33:21,580
I'm just saying that's why it doesn't work.

601
00:33:21,580 --> 00:33:22,220
Absolutely.

602
00:33:23,180 --> 00:33:26,780
Also noted participants did not change how they harvested coyotes.

603
00:33:26,780 --> 00:33:31,500
So 82% of those involved in the program were still just calling and shooting.

604
00:33:31,500 --> 00:33:37,740
So they didn't change into trapping or running like a long line of a long line trap line

605
00:33:37,740 --> 00:33:39,660
when the bounty program was implemented.

606
00:33:39,660 --> 00:33:47,260
And this part is the most telling to me and was also the most interesting to me.

607
00:33:48,380 --> 00:33:53,500
They reported that because they wanted to look at the motivations of why they participated

608
00:33:53,500 --> 00:33:54,540
and understand that.

609
00:33:54,540 --> 00:34:00,460
And one would think, you know, we keep talking about how logical some of this is.

610
00:34:00,460 --> 00:34:03,900
One would think that they're mostly doing it to be incentivized because of the bounty

611
00:34:03,900 --> 00:34:04,780
reward, the money.

612
00:34:04,780 --> 00:34:14,380
But in fact, what they reported is that enjoying the outdoors, big game protection, and ensuring

613
00:34:14,380 --> 00:34:18,140
hunting rights were the most important motives for their participation.

614
00:34:18,140 --> 00:34:25,100
The next four motives, which were recreation, testing outdoor skills, livestock protection,

615
00:34:25,100 --> 00:34:29,100
and participation in conservation were all equal in importance.

616
00:34:29,100 --> 00:34:35,100
But gaining additional income was identified as the least important factor in participating

617
00:34:35,100 --> 00:34:35,740
in that program.

618
00:34:41,980 --> 00:34:45,820
I don't even know how to digest that.

619
00:34:45,820 --> 00:34:50,380
What I'm taking away from it is that.

620
00:34:50,380 --> 00:35:01,980
So basically, let me see if I understand that only 8% of the people started into the bounty

621
00:35:01,980 --> 00:35:04,300
program because there was a bounty program.

622
00:35:04,300 --> 00:35:07,660
The rest of them just were trapping because they do that.

623
00:35:07,660 --> 00:35:07,900
Yeah.

624
00:35:07,900 --> 00:35:08,540
For whatever reason.

625
00:35:08,540 --> 00:35:11,340
They were trapping or calling and shooting because they already do that.

626
00:35:11,340 --> 00:35:18,380
So then when you ask them all why they're participating, 92 out of 100 of them are responding

627
00:35:18,380 --> 00:35:23,980
for why they are participating in trapping because they aren't actually, they weren't

628
00:35:23,980 --> 00:35:25,980
incentivized to start by the program.

629
00:35:25,980 --> 00:35:27,420
No.

630
00:35:27,420 --> 00:35:33,420
So let me back up and we may have speculated too much and gotten off on the wrong track.

631
00:35:33,420 --> 00:35:37,900
82% of people were harvesting coyotes by calling and shooting.

632
00:35:39,820 --> 00:35:41,820
So it wasn't like there were many trappers in this.

633
00:35:41,820 --> 00:35:47,740
Even though people reported taking fox, skunks, bobcats, and badgers.

634
00:35:47,740 --> 00:35:50,780
I guess they were taking them while they were out there calling and shooting.

635
00:35:50,780 --> 00:35:51,180
I'm not sure.

636
00:35:51,180 --> 00:35:52,460
It didn't specify in the article.

637
00:35:52,460 --> 00:35:58,940
Oh, well, yeah, I see.

638
00:35:58,940 --> 00:36:01,740
I said trapping, but I meant taking predators.

639
00:36:01,740 --> 00:36:02,060
Okay.

640
00:36:02,060 --> 00:36:07,420
I've just kind of condensed that down to the one word, but I meant they're participating

641
00:36:07,420 --> 00:36:09,900
in predator removal somehow or another.

642
00:36:09,900 --> 00:36:10,620
Yeah.

643
00:36:10,620 --> 00:36:15,580
And I think basically the take-home story is these people are doing what, it's the same

644
00:36:15,580 --> 00:36:17,820
people doing it as we're doing it previously.

645
00:36:17,820 --> 00:36:20,300
They're doing it the same way that they were doing it previously.

646
00:36:20,300 --> 00:36:23,820
They're doing it for the same reasons that they always were.

647
00:36:23,820 --> 00:36:27,180
And probably the only reason they're participating in the bounty program is they were going to

648
00:36:27,180 --> 00:36:27,740
do it anyway.

649
00:36:27,740 --> 00:36:28,780
Why not get paid for it?

650
00:36:28,780 --> 00:36:29,980
Yeah.

651
00:36:29,980 --> 00:36:35,980
Well, clearly it wasn't incentivized, not even in the top many reasons that they do it.

652
00:36:35,980 --> 00:36:36,780
Right.

653
00:36:36,780 --> 00:36:37,500
But you could say.

654
00:36:37,500 --> 00:36:41,900
They're not ranking the incentive, even in the top many reasons that they're doing it

655
00:36:41,900 --> 00:36:42,460
to begin with.

656
00:36:42,460 --> 00:36:45,500
Well, and to play devil's advocate here, maybe the reward wasn't high enough.

657
00:36:45,500 --> 00:36:47,660
Maybe so.

658
00:36:47,660 --> 00:36:50,700
You know, that would be a factor, but then the problem.

659
00:36:50,700 --> 00:36:51,980
But if you go through all the other examples.

660
00:36:51,980 --> 00:36:56,620
Well, that's the problem is if you incentivize it too much, then you create dishonesty in

661
00:36:56,620 --> 00:36:57,500
the system.

662
00:36:57,500 --> 00:36:57,740
Yeah.

663
00:36:57,740 --> 00:36:58,700
All right.

664
00:36:58,700 --> 00:37:00,380
Let me get into the biology of this too.

665
00:37:00,380 --> 00:37:01,420
There was a little bit of that.

666
00:37:01,420 --> 00:37:02,220
That's a good point.

667
00:37:02,220 --> 00:37:07,820
If you had gotten it into a, if you had moved it up enough to where it actually was one

668
00:37:07,820 --> 00:37:11,500
of the top ranking, then it's a financial decision.

669
00:37:11,500 --> 00:37:17,020
Not that they're not doing it for the sake of the other objectives associated with the

670
00:37:17,020 --> 00:37:17,500
program.

671
00:37:17,500 --> 00:37:19,500
And that's when you're going to start having people cheat the system.

672
00:37:19,500 --> 00:37:20,960
Yeah.

673
00:37:20,960 --> 00:37:22,220
All right.

674
00:37:22,220 --> 00:37:28,380
So a little bit, one last little fact before we leave this, the highest rate of county

675
00:37:28,380 --> 00:37:31,580
level removal, they put a table in there and they had all these counties that the program

676
00:37:31,580 --> 00:37:33,260
was implemented in and the total removal.

677
00:37:33,260 --> 00:37:40,060
So they removed 332 coyotes over a nine month period.

678
00:37:40,060 --> 00:37:45,340
And that was done by 126 individuals.

679
00:37:45,340 --> 00:37:52,380
So 126 people removed 332 coyotes over a nine month period in Beaver County, Utah, which

680
00:37:52,380 --> 00:37:56,380
is, I calculated 2,592 square miles.

681
00:37:56,940 --> 00:38:01,900
So that comes out to a coyote harvest of 0.13 coyotes per square mile.

682
00:38:01,900 --> 00:38:10,060
Not enough to move the needle.

683
00:38:10,060 --> 00:38:11,680
No.

684
00:38:11,680 --> 00:38:18,460
Even if it, let's say a quarter of that's actually somewhere that could be occupied

685
00:38:18,460 --> 00:38:19,580
by a coyote, it's still not.

686
00:38:19,580 --> 00:38:20,080
Yeah.

687
00:38:21,100 --> 00:38:31,260
And my study was not designed to do this, but we did estimate coyote density as part

688
00:38:31,260 --> 00:38:32,620
of my PhD research.

689
00:38:32,620 --> 00:38:38,540
And I know that John Kilgo and I shared some numbers back and forth several years ago,

690
00:38:38,540 --> 00:38:41,980
cause we were kind of trying to come up with a general idea of, you know, like what is

691
00:38:41,980 --> 00:38:45,580
probably a region wide average that coyotes occur at.

692
00:38:45,580 --> 00:38:50,380
And I think where we were ending up was somewhere around like four to six coyotes per square

693
00:38:50,380 --> 00:38:51,500
mile on average.

694
00:38:51,500 --> 00:38:56,540
And they were removing in this particular study, you know, basically a 10th of a coyote

695
00:38:56,540 --> 00:38:57,260
per square mile.

696
00:38:57,260 --> 00:39:05,420
I think we probably have higher densities here than they do, just to be clear.

697
00:39:05,420 --> 00:39:05,920
Yeah.

698
00:39:05,920 --> 00:39:12,060
Well, the other thing is the compensatory immigration, compensatory reproduction.

699
00:39:12,060 --> 00:39:13,260
We haven't even gotten into all that.

700
00:39:13,260 --> 00:39:14,320
Yeah.

701
00:39:14,320 --> 00:39:14,960
Yeah.

702
00:39:16,780 --> 00:39:23,340
Well, I don't feel very good about predator Mounties right now.

703
00:39:23,340 --> 00:39:27,660
So do you have, are we ready to move on to where, when it worked?

704
00:39:27,660 --> 00:39:30,300
Yeah, almost, uh, we were almost there.

705
00:39:30,300 --> 00:39:32,300
I've got one more stop before we get there.

706
00:39:32,300 --> 00:39:34,460
Cause this one was kind of just an interesting one.

707
00:39:34,460 --> 00:39:35,980
And I really thought.

708
00:39:35,980 --> 00:39:36,860
Lay it on me.

709
00:39:36,860 --> 00:39:42,460
I really thought that it spoke to the human psychology of this whole issue.

710
00:39:43,740 --> 00:39:46,700
I mean, it like it hit it right on the nose.

711
00:39:46,700 --> 00:39:46,940
Okay.

712
00:39:46,940 --> 00:39:48,460
So I'll give you a little bit of background.

713
00:39:48,460 --> 00:39:49,900
There's not a lot of results in this.

714
00:39:49,900 --> 00:39:52,140
I kind of want to just get to how people felt about it.

715
00:39:52,140 --> 00:39:58,220
But, um, this paper was by, uh, Lely et al 2009.

716
00:39:58,220 --> 00:39:59,820
I'm probably not saying that right.

717
00:39:59,820 --> 00:40:00,460
Apologies.

718
00:40:00,460 --> 00:40:08,940
Um, but there, they recorded, or they, um, they did some work on a historical seal bounty

719
00:40:08,940 --> 00:40:15,100
program that had been implemented in the late 1800s, early 1900s in the states of Maine and

720
00:40:15,100 --> 00:40:15,980
Massachusetts.

721
00:40:15,980 --> 00:40:22,940
And during the part of, uh, during the course of that, they estimated that 72 to 135,000

722
00:40:22,940 --> 00:40:24,780
seals were killed as part of the program.

723
00:40:24,780 --> 00:40:30,780
And they said that it probably had a substantial impact, particularly around areas that had high

724
00:40:30,780 --> 00:40:33,820
population, high human population densities.

725
00:40:34,620 --> 00:40:39,580
Um, and what was really interesting though, like, like I was talking about earlier, getting

726
00:40:39,580 --> 00:40:44,620
to the human psychology side of this equation, it said they were likely successful at reducing

727
00:40:44,620 --> 00:40:46,620
conflict between seals and fishermen.

728
00:40:46,620 --> 00:40:51,100
However, cause that was the main, you know, reason that they wanted to control them.

729
00:40:51,100 --> 00:40:56,620
However, it is also true that fishermen never ceased complaining about the impact of seals

730
00:40:56,620 --> 00:40:59,740
on fish, no matter how low the seal population fell.

731
00:41:00,380 --> 00:41:05,900
Eventually the bounties were repealed because the costs were felt, uh, to outweigh the benefits

732
00:41:05,900 --> 00:41:07,580
and people still weren't satisfied.

733
00:41:07,580 --> 00:41:14,060
So even causing the species to basically go extinct.

734
00:41:14,060 --> 00:41:14,560
Yeah.

735
00:41:14,560 --> 00:41:23,580
Well, and seals are not even close to being a seal biologist, but the little bit, relative

736
00:41:23,580 --> 00:41:25,900
to the average person, you're pretty damn close.

737
00:41:25,900 --> 00:41:31,740
The little bit that I know about them is, it tells me that they have a much lower reproductive

738
00:41:31,740 --> 00:41:36,540
rate than, you know, coyotes or coons, or a lot of these turkey predators that we're

739
00:41:36,540 --> 00:41:37,100
dealing with.

740
00:41:37,100 --> 00:41:39,340
So they're probably a lot easier to control.

741
00:41:39,340 --> 00:41:43,580
And then, you know, they have this convenient habit of swimming up around fishing boats

742
00:41:43,580 --> 00:41:46,140
and hanging out around docks and marinas and makes them easy to kill.

743
00:41:46,140 --> 00:41:47,840
Yeah.

744
00:41:47,840 --> 00:41:50,940
Those habits.

745
00:41:50,940 --> 00:41:51,440
Yeah.

746
00:41:51,440 --> 00:41:52,620
All right.

747
00:41:52,620 --> 00:41:54,620
So you're ready to get to where it worked or you want to.

748
00:41:55,740 --> 00:41:56,460
Hold on.

749
00:41:56,460 --> 00:41:56,940
Okay.

750
00:41:56,940 --> 00:42:01,900
I've been thinking through this while you're laying all this, this on me.

751
00:42:01,900 --> 00:42:03,260
I'm dropping the note.

752
00:42:03,260 --> 00:42:07,260
And I'm, I'm also thinking about how people are going to react to it.

753
00:42:07,260 --> 00:42:13,580
Cause this one's got the potential to, to circulate we'll say.

754
00:42:13,580 --> 00:42:13,820
Yeah.

755
00:42:13,820 --> 00:42:16,700
And if you're listening out there, share it with everybody.

756
00:42:16,700 --> 00:42:17,260
Let's get it.

757
00:42:17,260 --> 00:42:20,140
Let's get Will in front of as many people as we can.

758
00:42:21,420 --> 00:42:28,140
But I did think about the incentivize, like the, like what the incentive is.

759
00:42:28,140 --> 00:42:35,740
So when you have a general incentive like this, where the whole population doesn't have

760
00:42:35,740 --> 00:42:40,380
a vested interest in outcome, they just have a vested incentive.

761
00:42:40,380 --> 00:42:40,880
Yeah.

762
00:42:40,880 --> 00:42:46,780
I don't know if that's the right way to say that, but everyone gets equal incentive,

763
00:42:47,340 --> 00:42:53,420
but they don't all get equal, uh, investment in the outcome.

764
00:42:53,420 --> 00:43:04,220
That's very different than having a landowner that wants more turkeys who has a vested interest

765
00:43:04,220 --> 00:43:09,900
in making sure that, you know, the predator population has been suppressed so that they

766
00:43:09,900 --> 00:43:12,780
get more turkeys, the different kind of incentive.

767
00:43:12,780 --> 00:43:16,380
And that one, I would say is much more likely to work.

768
00:43:16,380 --> 00:43:19,660
Well, it's an entirely different scale too.

769
00:43:19,660 --> 00:43:21,420
Yeah, I know.

770
00:43:21,420 --> 00:43:27,580
But I'm just saying when everyone has a vested interest in the outcome,

771
00:43:27,580 --> 00:43:36,780
then the incentive is now the vested interest in the outcome rather than a financial incentive.

772
00:43:36,780 --> 00:43:37,280
Right.

773
00:43:37,280 --> 00:43:38,400
Right.

774
00:43:38,400 --> 00:43:42,380
So anyway, I don't know where you're going with that other one,

775
00:43:42,380 --> 00:43:47,820
but I was thinking through that, like, it's not that, I mean, we're, we're actively

776
00:43:47,820 --> 00:43:54,300
both involved in research to try to understand how predator removal can impact turkeys.

777
00:43:54,300 --> 00:43:54,800
Right.

778
00:43:54,800 --> 00:44:02,460
That's very different than just having a blanket incentive for everybody to kill predators.

779
00:44:02,460 --> 00:44:06,300
Yeah, we're not speaking at all to the efficacy of predator control right now.

780
00:44:06,300 --> 00:44:12,060
We are speaking to the efficacy of bounty programs to achieve predator control.

781
00:44:12,780 --> 00:44:13,280
That's right.

782
00:44:13,280 --> 00:44:14,940
Is this a good way to go about it or not?

783
00:44:14,940 --> 00:44:20,460
Right. And we're not talking about whether or not predator removal is a feasible strategy.

784
00:44:20,460 --> 00:44:23,100
We've done that elsewhere and we can link those episodes.

785
00:44:23,100 --> 00:44:23,600
Oh yeah.

786
00:44:23,600 --> 00:44:34,380
Also, in terms of an incentive structure, one that tries to have a local buy-in to an outcome

787
00:44:34,380 --> 00:44:40,780
is much more likely to work than one that has a incentive to try to achieve that outcome without

788
00:44:40,780 --> 00:44:43,340
a vested interest in the outcome being achieved.

789
00:44:43,340 --> 00:44:48,300
Yeah. Aren't we already there with turkeys? I mean, don't we, don't you think that

790
00:44:48,300 --> 00:44:54,220
turkey hunters have a shared incentive to reduce predator populations to benefit turkeys?

791
00:44:54,220 --> 00:44:57,740
I do on their property, but I don't think so.

792
00:44:57,740 --> 00:44:59,740
You don't think they're concerned about it at a larger scale?

793
00:44:59,740 --> 00:45:01,280
No.

794
00:45:01,280 --> 00:45:03,120
Yeah.

795
00:45:03,120 --> 00:45:08,380
Yeah. So people aren't going out and trapping, obviously, property that's not theirs,

796
00:45:08,380 --> 00:45:11,340
even if they could legally to control populations there.

797
00:45:11,340 --> 00:45:13,820
And they're not worried about it. In fact,

798
00:45:13,820 --> 00:45:17,340
there's probably people that would be de-incentivized for that.

799
00:45:17,340 --> 00:45:17,840
Yeah.

800
00:45:17,840 --> 00:45:20,540
They don't want their neighbors to have their turkeys.

801
00:45:20,540 --> 00:45:23,660
Nobody thinks like that, do they, Marcus?

802
00:45:23,660 --> 00:45:25,180
I hope not, but I'm just saying.

803
00:45:25,180 --> 00:45:26,620
All right.

804
00:45:26,620 --> 00:45:30,860
We have seen some lines of thought that are very similar to that.

805
00:45:30,860 --> 00:45:33,660
So let's get to where it worked.

806
00:45:33,660 --> 00:45:36,140
All right. Yeah. Let me hear it.

807
00:45:36,140 --> 00:45:40,460
In the context of the situation where it worked is very different

808
00:45:40,460 --> 00:45:42,540
from those other programs where it didn't work.

809
00:45:42,540 --> 00:45:48,060
And it's probably not very applicable to the situation with turkeys,

810
00:45:48,060 --> 00:45:52,860
but I thought it would be dishonest of me not to present a scenario where it did work, right?

811
00:45:52,860 --> 00:45:53,360
Yeah.

812
00:45:53,360 --> 00:45:55,260
Is this the only one you could find?

813
00:45:55,260 --> 00:45:56,700
This is the only one I could find. Yes.

814
00:45:56,700 --> 00:46:01,180
Well, I think that's important. We've done that throughout the whole

815
00:46:03,580 --> 00:46:08,780
theme of the show, and I think that has hopefully built some trust with people that

816
00:46:08,780 --> 00:46:14,140
we're trying to be balanced and cover what is available to cover.

817
00:46:14,140 --> 00:46:20,540
And I don't care whether they work or not, but you just lined up a whole bunch of compelling

818
00:46:20,540 --> 00:46:24,700
instances where they very clearly did not work.

819
00:46:24,700 --> 00:46:25,200
Yeah.

820
00:46:25,200 --> 00:46:28,780
And you even started it with Webster's Dictionary to

821
00:46:28,780 --> 00:46:33,020
give it a name of why it doesn't work before we even started.

822
00:46:33,100 --> 00:46:40,220
You know what? I'm going to buy a Webster's Dictionary right now.

823
00:46:40,220 --> 00:46:42,540
I don't know whether to take that as a compliment or an insult.

824
00:46:42,540 --> 00:46:49,260
I thought it was a, of, I mean, now hindsight's 2020, right?

825
00:46:49,260 --> 00:46:55,500
Now looking back on it, I see what you did there and I think it was perfect.

826
00:46:55,500 --> 00:46:57,820
Well, I appreciate it. I needed your validation today.

827
00:46:58,380 --> 00:47:02,940
All right, let's get into Gosling and Baker. And this is really informative,

828
00:47:02,940 --> 00:47:05,900
not only because it's an example of a successful program,

829
00:47:05,900 --> 00:47:12,940
but it also gives us a lot of clues as to what we, the way we might want to go about this if we did

830
00:47:12,940 --> 00:47:17,740
want to implement a program that would potentially be effective at the desired objective.

831
00:47:17,740 --> 00:47:23,820
All right, so this paper is Gosling and Baker. It was published in 1989 and it focused on

832
00:47:24,380 --> 00:47:29,900
two species that were introduced into Britain during the 1920s and they were muskrats and

833
00:47:29,900 --> 00:47:36,220
nutria rats, which I'd never heard them called. Apparently they call it over there, they call

834
00:47:36,220 --> 00:47:41,580
them coipus, nutria, they call them coipus. Good name.

835
00:47:41,580 --> 00:47:43,340
Did you know that? No.

836
00:47:43,340 --> 00:47:47,900
I had to look it up. I had to look up the scientific name and that's why we use

837
00:47:47,900 --> 00:47:52,700
scientific names. So all of you students out there that are listening and that complain,

838
00:47:52,700 --> 00:47:54,780
particularly if- I don't speak Latin.

839
00:47:54,780 --> 00:48:01,100
Particularly those in my Habitat class that are mad about learning Latin for the lab exam next

840
00:48:01,100 --> 00:48:05,340
week, this is why we do it. None of them are listening right now, Will.

841
00:48:05,340 --> 00:48:09,820
I have at least one student in my current Habitat class that listens, I know. So shout out.

842
00:48:09,820 --> 00:48:13,660
You better be studying. Yeah, you better be studying right now.

843
00:48:13,660 --> 00:48:19,500
All right, so muskrats and nutria rats in Britain. So two species that were introduced,

844
00:48:19,500 --> 00:48:27,100
happened in the 1920s. When it happened, apparently the folks in that area were familiar

845
00:48:27,100 --> 00:48:34,540
with muskrats potentially being a damaging invasive species. So they started an eradication

846
00:48:34,540 --> 00:48:43,900
campaign really soon after that species was first introduced in 1932 and they made use of overseas

847
00:48:43,900 --> 00:48:50,380
expertise as well as a control strategy that was designed by pest control specialists that

848
00:48:50,380 --> 00:48:57,100
had experience working with the species. So they ended up being successful in that regard and they

849
00:48:57,100 --> 00:49:04,940
were paying trappers as part of this program, but they were also specifying where they trapped

850
00:49:04,940 --> 00:49:10,940
and their level of trapping effort. And they were continuing to maintain this relatively high

851
00:49:10,940 --> 00:49:17,980
consistent level of trapping effort by paying those trappers in this scenario. And that's what

852
00:49:17,980 --> 00:49:25,980
led, they believe, to the success of the program. You combine that approach with the fact that they

853
00:49:25,980 --> 00:49:31,740
included the experts. And then on top of that, it was shortly after the species was introduced while

854
00:49:31,740 --> 00:49:35,980
its population was still becoming established. So they weren't completely naturalized within

855
00:49:35,980 --> 00:49:43,420
that area yet. Yeah. And you definitely have, you're more effective when you're in that part

856
00:49:43,420 --> 00:49:49,420
of their establishment. Exactly. Because perfect case in point, the Nutria program wasn't as

857
00:49:49,420 --> 00:49:53,980
successful because they weren't as familiar with the species and they didn't start eradication

858
00:49:53,980 --> 00:50:03,420
efforts as quickly after introduction. Well, what I was thinking about, it sounds like to me,

859
00:50:03,420 --> 00:50:12,460
instead of the incentive being on a per animal basis, they were incentivizing by paying a person

860
00:50:12,460 --> 00:50:19,100
to focus on an area. Yes. And it didn't depend on how many they caught. Here's the details. So I'll

861
00:50:19,100 --> 00:50:23,580
go ahead and lay that out for you. I'm sorry, I should have went ahead and done that. So the way

862
00:50:23,580 --> 00:50:29,820
they went about it is they used detailed population simulations to plan the number of trappers that

863
00:50:29,820 --> 00:50:34,380
they needed. They planned out the time that they estimated they'd need for eradication and the

864
00:50:34,380 --> 00:50:41,020
likely cost of the campaign. And then they used an incentive bonus scheme to overcome the problem

865
00:50:41,020 --> 00:50:48,460
that trappers would work themselves out of a job. Trapper deployment was planned using capture to

866
00:50:48,460 --> 00:50:57,100
trapping effort ratios and progress was monitored by biologists. So it was a very hands-on program.

867
00:50:57,820 --> 00:51:06,540
Mm-hmm. Yeah. I was just trying to think of how could you possibly implement that at scale

868
00:51:06,540 --> 00:51:11,660
for turkeys? That's the problem. How do you do it at scale? How does he scale that up?

869
00:51:11,660 --> 00:51:15,980
Well, even- It's conceivable maybe to do it in a small area.

870
00:51:15,980 --> 00:51:19,900
Well, even a group, let's say a group of landowners hires a trapper.

871
00:51:22,220 --> 00:51:27,660
Like now you're focusing on an area, they're getting paid

872
00:51:27,660 --> 00:51:34,060
to trap that area, not necessarily based on the number of individuals they remove.

873
00:51:34,060 --> 00:51:40,380
I'm just trying to think of what is, you know, that might be a more realistic thing to accomplish.

874
00:51:40,380 --> 00:51:45,660
A group of landowners get together and they want to focus a bunch of effort on their area.

875
00:51:47,660 --> 00:51:54,380
I'm just thinking how could we take what we learned in that study and design that?

876
00:51:54,380 --> 00:51:54,880
Right.

877
00:51:54,880 --> 00:52:02,460
And maybe it already is designed that way where you pay a trapper to catch as many as possible

878
00:52:02,460 --> 00:52:07,260
during a window of time in an area. Mm-hmm. I mean, is that what you do

879
00:52:07,260 --> 00:52:12,780
when you pay somebody to come trap your land? Yeah. Maybe it's already structured like that.

880
00:52:12,780 --> 00:52:16,140
So in that type of incentive program already works fine.

881
00:52:16,140 --> 00:52:16,640
Yeah.

882
00:52:16,640 --> 00:52:26,860
That's interesting. I don't see how we could do it at a larger scale.

883
00:52:26,860 --> 00:52:27,360
Yeah.

884
00:52:27,360 --> 00:52:33,100
But I'm sure somebody has ideas and they're going to leave them in our comments.

885
00:52:33,100 --> 00:52:33,600
Okay.

886
00:52:33,600 --> 00:52:38,940
So once we get through all the hate, maybe we'll have some good ideas down there.

887
00:52:38,940 --> 00:52:44,700
Hey, and you added a new word to your, a new term to your vocabulary today, perverse incentive.

888
00:52:44,700 --> 00:52:49,900
I'm going to highlight it in my Webster's dictionary.

889
00:52:49,900 --> 00:52:51,020
That's highlighted in my notes.

890
00:52:51,020 --> 00:53:05,020
Well, I feel like, how did I start out the episode now? I already forgot where you're...

891
00:53:05,020 --> 00:53:07,180
I asked you if you had your ladle.

892
00:53:07,180 --> 00:53:10,620
Oh, it was a hot take. I do feel like it was a hot take.

893
00:53:10,620 --> 00:53:11,980
Was it a hot take?

894
00:53:11,980 --> 00:53:18,300
It was a hot take. Essentially what I heard is the way that they have been implemented

895
00:53:18,300 --> 00:53:25,260
for a long time, that a predator bounties largely just don't work.

896
00:53:25,260 --> 00:53:31,260
Yeah. And if this does end up being a widely circulated episode,

897
00:53:31,260 --> 00:53:39,420
and you were listening to us for the first time, know that our goal in covering information like

898
00:53:39,420 --> 00:53:46,780
this is not to prevent someone from doing something, but to help them allocate their

899
00:53:46,780 --> 00:53:52,940
effort towards the most productive activities possible. And from what we've gone through,

900
00:53:52,940 --> 00:53:59,020
it doesn't seem like predator bounties are a good way for us to spend our time, money, and efforts

901
00:54:00,300 --> 00:54:07,580
to try to restore or increase turkey populations. And so the benefit that that gives us is now we

902
00:54:07,580 --> 00:54:09,820
know we can focus on something else that will be.

903
00:54:09,820 --> 00:54:20,780
Right. Well, let's just walk through that real quick. And I agree with you 100%. That's what

904
00:54:20,780 --> 00:54:27,500
we're trying to accomplish here. But I know that, for example, the state agency catches a lot of

905
00:54:27,500 --> 00:54:33,820
pressure and you can just name one, it doesn't matter. They get pressure to have a bounty program,

906
00:54:33,820 --> 00:54:43,500
but that would require them to use part of their funding to pay for it.

907
00:54:43,500 --> 00:54:50,860
And it's very unlikely from what we just talked about to be an effective strategy since we've

908
00:54:50,860 --> 00:54:56,220
repeatedly tried it in all kinds of systems and situations and it basically just didn't work,

909
00:54:56,220 --> 00:55:02,940
except for in the one that would cost a lot more time and effort and money where you'd have to have

910
00:55:02,940 --> 00:55:06,780
really close tracking of it. And I don't know how you could do that even at the state level.

911
00:55:06,780 --> 00:55:12,460
Well, that's the problem is like in the muskrat nutria example, I mean, those were newly

912
00:55:12,460 --> 00:55:17,100
established populations that were very, very geographically limited in their distribution.

913
00:55:17,100 --> 00:55:23,420
Yeah. Maybe in a county you could do that, but it'd be very hard to do that at a larger scale.

914
00:55:23,980 --> 00:55:30,140
But my point is, that's not to say that there isn't a place for predator removal.

915
00:55:30,140 --> 00:55:30,700
Right.

916
00:55:30,700 --> 00:55:36,620
Like I said earlier, you and I are both actively in research to try to understand how effective

917
00:55:36,620 --> 00:55:45,420
that can be. But it would, based on what we've just talked about, it would be a waste of resources

918
00:55:45,420 --> 00:55:53,260
to do a bounty program to achieve that. But those same resources could be diverted to something more

919
00:55:53,260 --> 00:56:03,420
effective like training people how to trap, for example. Or what we're more fond of is teaching

920
00:56:03,420 --> 00:56:09,420
them to manage their land, the habitat. Or from the state's perspective, let's hire more

921
00:56:09,420 --> 00:56:13,500
technical assistance biologists that can go out and consult with private landowners to help them

922
00:56:13,500 --> 00:56:15,820
improve the wildlife habitat quality on their land.

923
00:56:16,380 --> 00:56:24,220
Right. That's what I'm, my whole point is, you know, there's plenty of scrutiny about how we

924
00:56:24,220 --> 00:56:33,660
allocate funding. And, you know, there's a lot of pressure in some situations, I see it and I hear

925
00:56:33,660 --> 00:56:39,100
about it all the time, I assume there's a lot of pressure to allocate resources to something like

926
00:56:39,100 --> 00:56:44,940
a bounty program. And it would probably be better served to allocate those resources and other

927
00:56:44,940 --> 00:56:49,180
things that are more likely to move the needle. We only have so much time and money, let's make

928
00:56:49,180 --> 00:56:52,940
sure that we're putting it where it's going to most effectively help us accomplish our objective

929
00:56:52,940 --> 00:56:55,980
of increasing turkey populations. Yes.

930
00:56:55,980 --> 00:57:03,980
Was that a mic? I think so.

931
00:57:03,980 --> 00:57:06,780
It's a really small one. I like to use the...

932
00:57:06,780 --> 00:57:09,420
It's like a little violin. I use the finesse mics.

933
00:57:09,420 --> 00:57:13,020
I don't know. A finesse mic?

934
00:57:13,020 --> 00:57:15,660
I don't know. I'm just making it up.

935
00:57:15,660 --> 00:57:21,180
A finesse mic. I'm going to include that in my vernacular.

936
00:57:21,180 --> 00:57:22,380
Yeah, that's a real thing, right?

937
00:57:22,380 --> 00:57:26,220
I'm just learning that.

938
00:57:26,220 --> 00:57:27,260
No, I'm just making it up.

939
00:57:27,260 --> 00:57:32,860
A finesse mic. Well, that was a really good one.

940
00:57:32,860 --> 00:57:33,580
I enjoyed it.

941
00:57:33,580 --> 00:57:39,820
Hope you all enjoyed it out there. We're trying to, you know, keep up with timely topics. And

942
00:57:40,780 --> 00:57:45,980
we thought this would be one that folks want to hear about because...

943
00:57:45,980 --> 00:57:49,660
It was based on listener feedback because we get so much feedback about bounties.

944
00:57:49,660 --> 00:57:56,620
Yeah. It's brought up all the time. And I've, you know, we kind of de facto end up in this

945
00:57:56,620 --> 00:58:03,340
in lots of ways when we're working together, you kind of end up being the predator guy,

946
00:58:03,340 --> 00:58:09,900
which I guess stems from the fact that you've done work, like you've researched a lot of stuff

947
00:58:09,900 --> 00:58:17,660
related to it. But yeah, I was really interested in the topic and to see where you went with it.

948
00:58:17,660 --> 00:58:23,020
Yeah. Was it as interesting as you had hoped? Did it live up to your expectations?

949
00:58:23,020 --> 00:58:30,220
You know, I feel like it was a little bit of a finesse mic drop instead of a,

950
00:58:30,220 --> 00:58:34,300
you know, like a big Yeti mic drop.

951
00:58:34,300 --> 00:58:37,020
You wanted a boom mic drop.

952
00:58:38,540 --> 00:58:44,380
All right. No, it was. I think that was really good. And it did not turn out exactly how I

953
00:58:44,380 --> 00:58:50,540
thought, but that's probably a good thing. Yes. All right. That was fun. Thanks everybody for

954
00:58:50,540 --> 00:58:55,660
listening. Keep the feedback coming and we'll keep exploring these kinds of topics.

955
00:58:55,660 --> 00:59:03,180
Wild Turkey Science is part of the Natural Resources University Podcast Network and is

956
00:59:03,180 --> 00:59:07,180
made possible by Turkeys for Tomorrow, a grassroots organization dedicated to the

957
00:59:07,180 --> 00:59:19,500
wild turkey. To learn more about TFT, check out turkeysfortomorrow.org.