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Speaker 1: This is me eater podcast coming at you shirtless, severely

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Speaker 1: bug bitten in my case, underwear listening podcast. You can't

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Speaker 1: predict anything presented by on X. Hunt creators are the

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Speaker 1: most comprehensive digital mapping system for hunters. Download the Hunt

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Speaker 1: app from the iTunes or Google play store. Nor where

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Speaker 1: you stand with on X. When I was a kid,

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Speaker 1: I took out a hit on a beaver. Um does

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Speaker 1: the show started this? The show might as well be started.

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Speaker 1: You guys are cool if it started already. Yeah. Uh,

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Speaker 1: there was a draint in my home township. Do they

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Speaker 1: have townships where you live, Clay Now, I was I

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Speaker 1: was gonna make a comment about that that I'm not.

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Speaker 1: I don't understand townships, Spencer. Do you come from township country?

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Speaker 1: I own many a plot books where uh play of

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Speaker 1: the time I was like seventeen years old. I feel

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Speaker 1: like I knew every township I was in without looking

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Speaker 1: at one. Yeah. So a township is six by six miles.

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Speaker 1: Very common. Yeah, very common in the Midwest to have townships.

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Speaker 1: And then there's a small amount of government is run

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Speaker 1: out of the township. So most townships you look at

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Speaker 1: be like thirty six So it's thirty six square miles.

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Speaker 1: One of those square miles is State School trust land,

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Speaker 1: you know, and that's is how it works. So uh,

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Speaker 1: are we had our township had a drain commissioner. I

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Speaker 1: understand now the drain commissioner there is very The drain

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Speaker 1: commissioner in my home township, which is Dalton Township, is

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Speaker 1: very uh a controversial sort of or maybe the county commissioner.

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Speaker 1: Either way, you have a drain commissioner. The drain commissioner

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Speaker 1: would hire me two get problem beavers out of the

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Speaker 1: drain system. M hm. One time I was working this

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Speaker 1: beaver and it was in the summertime and UH shot

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Speaker 1: at it sank and I went and told him, I said, man,

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Speaker 1: you know, I can't really present it to you because

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Speaker 1: it sank. And he paid me my bucks. Anyways, you

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Speaker 1: think about that, that's that's honorable. I'd say that's like

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Speaker 1: a hat tip to the trapper kind of honorable. I

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Speaker 1: ain't that guy's name, uh Merle, His name was Merle,

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Speaker 1: not Haggard. Do you think he'd give the money to

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Speaker 1: you an hour? I'd be pretty shocked. Here is alive

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Speaker 1: right now, so no, um no, he just know way

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Speaker 1: that do is a live still nice guy. So uh,

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Speaker 1: I don't off you guys are reading the New York

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Speaker 1: Times recently. If you see a wolf article in the

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Speaker 1: New York Times, you can virtually guarantee that it will

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Speaker 1: be uh something about just how nice wolves are? You know?

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Speaker 1: In the New York Times. There's an article in the

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Speaker 1: New York Times. It's kind of a collision of two things.

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Speaker 1: I'm super interested in because the article that the name

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Speaker 1: of the articles is using wolves as first responders against

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Speaker 1: the deadly brain disease, and it's saying it's talking about how, um,

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Speaker 1: these researchers are in Yellowstone are taking a look at

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Speaker 1: whether wolves because they have a magical ability. You know,

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Speaker 1: everyone who's watched um Never cry Wolf knows that they

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Speaker 1: have an uncanny magical ability to sniff out disease and

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Speaker 1: kill diseased animals. Uh that wolves and all that will

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Speaker 1: take to stop c w D is wolves, because they'll

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Speaker 1: go and eat all the c w D positive deer.

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Speaker 1: And so they're they're studying this in Yellowstone. Our article

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Speaker 1: goes on and on. You know, I'll give him this,

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Speaker 1: like I'll give him this. I think it's interesting to watch.

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Speaker 1: Um if you could ever I have to ask my

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Speaker 1: brother if you could do this, because this is kind

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Speaker 1: of like that he sort of specializes in like designing

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Speaker 1: studies and statistics and stuff like that. But if you

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Speaker 1: could ever see, like does the spread of chronic wasting

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Speaker 1: disease which is a deer and elk it's like a

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Speaker 1: dear family version for you folks at home. It's a deer,

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Speaker 1: it's a servet. So dear elk moves care, you know, right,

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Speaker 1: taeli ammule deer. Right, it's the scrapy or mad cow

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Speaker 1: disease version. It's a deer nelt version of that of

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Speaker 1: scrape or mad cow disease. Um, it's a transmissible sponge

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Speaker 1: ofform encephalopathy and it's spreading very rapidly all around the country. Um,

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Speaker 1: it's main spread. This is a I'm gonna tell you

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Speaker 1: a controversial statement right now. CAP is not listening, no,

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Speaker 1: because no, it's the big spreads that have happened around. Honest,

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Speaker 1: gonna come out and say this. Uh, the captive servit industry,

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Speaker 1: the captive deer industry has done a mighty lot, done

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Speaker 1: a mighty lot to help c w D get spread around. Uh,

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Speaker 1: even a bunch of money. They just see now, I'm

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Speaker 1: way off on the off on the wolf thing. Let

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Speaker 1: me lay out this c w D deal a little bit. First,

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Speaker 1: c w D there's no evidence that it passes to humans, Like,

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Speaker 1: no humans gotten it. They even had this group of people.

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Speaker 1: They have a group of like a hundred and some

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Speaker 1: people that all eight c w D unknowingly eight c

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Speaker 1: w D infected meet at some kind of fundraiser and

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Speaker 1: then I don't remember the bulk of them all voluntarily

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Speaker 1: submitted themselves for annual testing and none of them have

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Speaker 1: gotten it. Tens of thousands of of American hunters have

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Speaker 1: eaten c w D positive meat, never jumped. Seth, don't

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Speaker 1: even care, do you, set Tell him, Seth, tell them

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Speaker 1: how little you care about eating it. I don't care

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Speaker 1: at all. Let me ask you this, Seth. If I

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Speaker 1: made Let's say I I went to Doug's house and

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Speaker 1: I got a bunch of the I went to the

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Speaker 1: wherever they do the testing, and I said, I want

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Speaker 1: a bunch of those positive deer brains, okay, And I

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Speaker 1: made a burger in which I took ten c w

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Speaker 1: D positive deer yep and ground up their meat and

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Speaker 1: put some of the brain in there and put some

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Speaker 1: of the spinal column tissue in there and made a burger?

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Speaker 1: Would you eat that burger? Well, don't. I don't eat

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Speaker 1: the brains and spinal column anyway. Okay, let's say I'll

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Speaker 1: eat the burger. Really. Yeah. See that's how I test

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Speaker 1: the c w D deny or are U s c

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Speaker 1: w D deny or you just don't think it's gonna

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Speaker 1: jump to humans? No, I I believe in c NEED

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Speaker 1: one thousand. Accept that it's a disease and it's spreading around,

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Speaker 1: but you don't take precautions to not spread it around.

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Speaker 1: And you know, I don't deny that at all, But

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Speaker 1: when it comes to human health, like you're not worried

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Speaker 1: that you're gonna be the dude that gets it. No,

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Speaker 1: there seems to be like a direct correlation between how

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Speaker 1: many people you're feeding in your family and like how

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Speaker 1: serious he takes c w D. Absolutely, I just how

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Speaker 1: big is your family? I mean me and my girlfriend

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Speaker 1: that So, who's probably who's emerging as the best wildlife

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Speaker 1: artist in America? Tell tell everybody her name Johnson, Tell

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Speaker 1: him her ka Ray Johns is a better check to

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Speaker 1: make sure that's right emerging? Did you hear what I'm saying.

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Speaker 1: Clay emerging as probably America's greatest wildlife artist. I've seen

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Speaker 1: some of your stuff. It's cool, it's cool stuff. I

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Speaker 1: have a prong horn picture of hers hanging up in

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Speaker 1: my house. K Ray Artworks dot com. Spell it for everybody,

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Speaker 1: kay r a e Artworks dot Com. Yep, check it out.

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Speaker 1: That's girlfriend. Uh anyway, Seth Seth like the burger. That's

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Speaker 1: how I test whether someone that's like people that say

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Speaker 1: c w DS nobody deal. I need to get a

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Speaker 1: badge these burgers. So when I when someone says I

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Speaker 1: don't know, I'm not worried about c w D, I

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Speaker 1: can fry him up one of my c w D burgers.

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Speaker 1: If they eat it, then I'm like, dude, I believe

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Speaker 1: everything you say, or I believe that you believe everything

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Speaker 1: you say. If they pause, then I'm like, we got

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Speaker 1: a problem. Yeah, I want to get back. We're in

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Speaker 1: a CWD zone here in Arkansas. Yeah. And I was

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Speaker 1: just gonna say, I'm I'm in some ways with Seth

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Speaker 1: like I'm not that worried about it. And that is

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Speaker 1: displayed to me because I have not had my dear tested,

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Speaker 1: which I'm I do, and I'm not necessarily proud of that.

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Speaker 1: But we've been eating deer from this region for twenty years.

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Speaker 1: I mean, my kids have grown up with it. So

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Speaker 1: my point is is that if we if we start,

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Speaker 1: I mean, we've already had it pretesting. I mean, that's

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Speaker 1: the thing. So I'm gonna start making a habit of

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Speaker 1: getting deer tested. But we never have, you know, uh,

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Speaker 1: Bubbly Doug during he gets ear tat and when people

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Speaker 1: say that, like if if you were to say to two, Doug,

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Speaker 1: I don't care about c w D because I'm not

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Speaker 1: worried about catching c w D. Doug feels you're missing

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Speaker 1: the point, right, because as prevalence goes up, um, it's

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Speaker 1: always fatal. Like you don't know, dear survive c w D.

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Speaker 1: As prevalence goes up, you're gonna have population wide impacts.

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Speaker 1: And he feels some people can test this. Doug feels

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Speaker 1: it's beginning to happen, and you're gonna see that in

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Speaker 1: these areas that have seventy five you know, and climbing

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Speaker 1: um infection rates that you're gonna get to where you're

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Speaker 1: gonna see population crashes dying from c w D and Also,

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Speaker 1: you're not going to have old mature bucks because deer

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Speaker 1: don't live long enough. Because I killed the CDBD positive

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Speaker 1: dear this year, do you it? No? Man? Well, at

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Speaker 1: the time, I offered it up to anybody on the

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Speaker 1: editorial team, um, and nobody took it. I was even

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Speaker 1: offering it to people for like them to feed their

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Speaker 1: dogs with, and nobody took it. Um to feed a dog. Yeah,

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Speaker 1: I I offered it to Yanni and it was a

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Speaker 1: hard no. He wouldn't feed it to his dog. Yanni

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Speaker 1: also brought up a good point that if he would

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Speaker 1: feed it to his dog and ignored the potential health thing,

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Speaker 1: his dog would then go shut it out in his

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Speaker 1: yard and be spreading CWD all over his property. Um,

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Speaker 1: which I can respect that stance. Yeah, that's a good point.

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Speaker 1: You know what's funny about this article? Back back to

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Speaker 1: the article here, So it's like this article where it's

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Speaker 1: you know, you know, tomorrow there'll be an article like

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Speaker 1: wolves actually helped bring more rainbows. You know, if we

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Speaker 1: had more wolves, we have more rainbows. Um. They're never

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Speaker 1: just like a large while canine in their eyes a

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Speaker 1: magical creature. And uh so they're doing this thing, and

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Speaker 1: I am interested to see, like, like I'd be interested

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Speaker 1: to see if c w D spread slows in places

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Speaker 1: that have high wolf densities. Um. This thing goes on

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Speaker 1: and on like saying like this could be a reason

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Speaker 1: why we should reintroduce more wolves around the country because

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Speaker 1: they'll sniff out the c w D. And they finally

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Speaker 1: give a fishing game guy. They give um chief of

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Speaker 1: the Wildlife Division of Montana Fish, Wildlife and Parks. They

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Speaker 1: give him like at the end of the article, they

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Speaker 1: throw him a bone and give him a quote. He

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Speaker 1: expresses doubts that wolves would prevent chronic waste and disease.

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Speaker 1: He says, wolves help remove sick animals, but animals don't

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Speaker 1: get visibly ill for about two years, so they are

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Speaker 1: carriers and spreaders, but they don't get the symptoms. And

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Speaker 1: to counter this, they go on and say, um that uh.

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Speaker 1: The the miss Brandle who's involved with this research goes

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Speaker 1: on to say that, um that these magical wolves, she

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Speaker 1: doesn't used magical. Said the wolves made detective disease long

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Speaker 1: before it becomes apparent to people. Mm hmm. Through smell

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Speaker 1: like a cancer dog dogs that dear smells like it's

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Speaker 1: got c w D. I'm automatically thinking about the welfare

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Speaker 1: of the wolves. Man. I mean, we won't feel it.

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Speaker 1: We won't feed this stuff to our kids, but we're

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Speaker 1: will feed it to these uh majestic wolves. You see

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Speaker 1: what I'm saying, Like, no concern for the wolves here. Yeah,

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Speaker 1: but wolves always get like I listen to I go,

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Speaker 1: I like wolves. I like seeing wolves. I like seeing

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Speaker 1: wolves tracks. I like wolves. I like to see wolves

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Speaker 1: get restored. But it's like to also be like realistic

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Speaker 1: about it and not do all the hyperbolic bs that

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Speaker 1: people do to like to to proselytize wolves. And that's

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Speaker 1: hard for people like they have a phenomenal marketing engine

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Speaker 1: behind them. Uh, how's that? Is that good? That article

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Speaker 1: feels an awful out like the one that the New

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Speaker 1: York Times ran in about letting mountain lions eat the

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Speaker 1: feral horses and that'll be the solution to the problem.

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Speaker 1: Do you remember that? Yeah? And did you ever hear

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Speaker 1: Carl Malcolm uh tear that we try to get that

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Speaker 1: right around the podcast. He wouldn't come on the podcast.

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Speaker 1: Carl wrote a Carl wrote a scathing critique of that article. Um,

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Speaker 1: who's he's a professional biologist. You know, he wrote a

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Speaker 1: scathing critique of that article, but the Times didn't publish

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Speaker 1: it because their rebuttals are generally very short. And Carl

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Speaker 1: had all these statistics to point out areas where some

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Speaker 1: of the highest mountain lion densities overlap with some of

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Speaker 1: the highest wild horse densities, and also laid out a

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Speaker 1: lot about how mountain lions used the landscape um in

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Speaker 1: a way that wouldn't it all be benef official to

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Speaker 1: killing off horses, Like where they hunt, where they occupy,

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Speaker 1: isn't where horses go. It was just an assinine, an

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Speaker 1: assinine article, um. And the writer wouldn't even stand by

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Speaker 1: it to come on the show and talk about it.

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Speaker 1: It was just it was a joke. Guy died of

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Speaker 1: it was an article floating around right now. This guy

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Speaker 1: they they've had this body for a long time. So

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Speaker 1: there's there's this dude that was alive one thousand to

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Speaker 1: one thousand four years ago, UM in the desert southwest.

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Speaker 1: He died a constipation. Mhm, jeez. That's terrible. And it

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Speaker 1: seems as though as they're looking at his his diet

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Speaker 1: that uh, he had for months been someone had been

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Speaker 1: for months feeding him grasshoppers with the legs torn off.

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Speaker 1: He ate mainly grasshoppers and the painful months prior to

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Speaker 1: his death. Now, why why do you say someone was

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Speaker 1: feeding him? Well, it goes on to say that he

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Speaker 1: was in such bad shape. He had what's called a

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Speaker 1: mega colon. There's a he had a parasite trip pana

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Speaker 1: soma cruzy, which I've never heard of, had blocked up

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Speaker 1: the man's gastro intestinal system so as cold and swelled

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Speaker 1: to six times normal size. It's a condition called mega colon.

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Speaker 1: So the guy can't digest food properly, and he was

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Speaker 1: becoming malnourished. They think that he would have been so

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Speaker 1: bad by looking at his body that he probably couldn't

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Speaker 1: even walk or eat on his own. He was in

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Speaker 1: that bad of shape, and then for the last two

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Speaker 1: or three months of his life, someone was feeding him

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Speaker 1: or he was feeding on legless grasshoppers, like the squishy part.

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Speaker 1: I wonder what, I wonder why? I don't know. They

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Speaker 1: found this guy back in seven and a rock shelter

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Speaker 1: along the Rio Grande and Pecos rivers in South Texas.

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Speaker 1: You can't say that. You can't say Rio Grande River.

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Speaker 1: Do you know that because you're saying River Grande River.

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Speaker 1: Oh yeah, yeah, I got you. I just saw I

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Speaker 1: just let me bathe phrase that the Rio Grande. I'm

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Speaker 1: just gonna say the Rio grand and Pacos River. So

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Speaker 1: this dude was was living. Was he like living in

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Speaker 1: a town somewhere just out in the bush. We mean

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Speaker 1: a town somewhere. Oh, we said, like like a pueblo

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Speaker 1: or something. They they found him like in a rock shelter. Yeah,

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Speaker 1: So he was like a primitive fellow. Two point six

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Speaker 1: pounds of feces in a vast the amount of food

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Speaker 1: remains that were never processed. That's not that much feces. No,

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Speaker 1: I never go on a scale, but I'm not like

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Speaker 1: blown away by that. Do you ever a weigh at spencer? No,

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Speaker 1: He's shaking his head. No, no, No, I feel like

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Speaker 1: I feel like wrestlers do that. I had had a

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Speaker 1: guy one time tell me that he said, you'd be

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Speaker 1: surprised what you'd eat when you're hungry. And he told

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Speaker 1: he said that statement after he told the story of

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Speaker 1: riding a train from somewhere in New Mexico to San Antonio,

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Speaker 1: Texas in a box car that he jumped in. He

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Speaker 1: he rode the entire way for three days. The train

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Speaker 1: didn't stop when he got to San Antonio, Texas. He

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Speaker 1: was sixteen years old. He'd run away from home and

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Speaker 1: he jumped out of the box car and went and

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Speaker 1: found in a dumpster some molded biscuits and raw bacon,

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Speaker 1: and he ate him. I worked with this guy. He

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Speaker 1: was like in his sixties. This was twenty years ago

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Speaker 1: and at the time he's in his sixties. And he

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Speaker 1: looked at me and he said, Clay, you'd be surprised

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Speaker 1: what you'd eat if you're hungry. And I believed him that.

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Speaker 1: I don't think so I take that back about um

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Speaker 1: Seth said, wrestlers probably know how much it weighs. And

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Speaker 1: I guess when I was in high school wrestling, I

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Speaker 1: wasn't eating a bunch. But before ways, if you'd go

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Speaker 1: go to the bathroom, you could usually if you had

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Speaker 1: a a nice, good crap, you could usually lose about

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Speaker 1: a pound. So that's what wrestlers do. Yeah, that's interesting.

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Speaker 1: Just try and get get all everything out when you

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Speaker 1: can before you hop on this skated. Uh. Well, what's

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Speaker 1: interesting about how much it goes on to say, like

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Speaker 1: how much pressure this guy had going on there's a

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Speaker 1: thing um a final list, which is a plant part,

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Speaker 1: and usually a final list can pass through a humans

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Speaker 1: digestive track on scathed. But the final lists and this

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Speaker 1: guy's digestive track or split open and crushed, which points

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Speaker 1: to an incredible pressure that was exerted on a microscopic

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Speaker 1: level in this guy's intestinal system. This is gonna be

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Speaker 1: detailed in the forthcoming book called the Handbook of Mummy Studies.

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Speaker 1: And if you think I'll not be buying the Handbook

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Speaker 1: of Mummy Studies, you are wrong. That sounds very painful. Uh.

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Speaker 1: Speaking of painful, did you guys hear about Seth almost

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Speaker 1: getting uh attacked by a wild weasel this morning? He's

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Speaker 1: lucky to be alive. Tell what happens, Seth Um, we

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Speaker 1: identified a culvert pipe that would be good for trapping weasels.

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Speaker 1: And I dumped down over the bank and in the dark,

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Speaker 1: in the dark, I reached down in the culvert pipe

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Speaker 1: and to put a trap box, a weasel box with

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Speaker 1: with a trap in it, And Uh, I heard something

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Speaker 1: growled at me and Seth Seth won't make okay, tell

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Speaker 1: the story. I don't. I do not think I can

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Speaker 1: accurately mimic the sound because it sounded bigger than a weasel. Um,

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Speaker 1: he's he cried, he cried like a little baby. That

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Speaker 1: that's false. What what types of weasels? That's like long

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Speaker 1: tail weasel? Yeah, long tail is that that was in

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Speaker 1: a culvert. So it's like it was like it was

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Speaker 1: like growing in probably a fourteen inch sixteen inch colvert.

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Speaker 1: I mean that would make it sound really loud. Yeah,

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Speaker 1: here's the thing. He just keeps talking about this, this

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Speaker 1: horrific growl. I knew. I ran down there with a flash,

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Speaker 1: like I thought we might have a bobcat. That's what

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Speaker 1: I thought. I thought, by the way, And I went

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Speaker 1: down there and shined light in there, and we went

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Speaker 1: to the other end and it was just a little

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Speaker 1: teeny weasel come out the other end. And then we

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Speaker 1: spent an hour try Seth won't say what the sound

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Speaker 1: sounded like because I I can't even begin to miss

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Speaker 1: and we've tried every like her, No, it's like we're

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Speaker 1: a weasel growl Like well, I don't know how to

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Speaker 1: mimic that. He won't he will not do the thing

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Speaker 1: like it wasn't like it wasn't like it was like no, no,

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Speaker 1: it's it was like, to try to explain it, it

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Speaker 1: was like a very like soft, quiet growl. But it

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Speaker 1: startled you. But yeah, because I could tell how startled

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Speaker 1: he was because when I went down there, the weaselbox

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Speaker 1: is just kind of thrown down into pipe. He didn't

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Speaker 1: even properly place it. There's a YouTube video that says

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Speaker 1: sound of weasel, and it's a weasel in a cage

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Speaker 1: making a growl. Oh, keep playing that for us real quick.

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Speaker 1: I'm trying here. Is that what you heard? Said? That's

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Speaker 1: not what I heard. It's not very loud, guys. Oh,

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Speaker 1: there it is there it is. It's like a chuckle.

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Speaker 1: Is that what you heard? That the coock? That part

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Speaker 1: sounds a little familiar, that's what's scared. But it didn't.

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Speaker 1: It didn't sound like it was like ok it was

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Speaker 1: like it was like two of those okay, But it

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Speaker 1: was in a damn culvert. I would like to bring

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Speaker 1: up that that's some good weasel trapping. That's some d

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Speaker 1: good weasel trapping. Set of weasel trap. Stick your hand

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Speaker 1: in a hole and there's already a weasel there. In fact, Yeah,

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Speaker 1: the sign reading that went into this that we identified

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Speaker 1: such a hot low coal that he was actually in

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Speaker 1: the culvert, and then we shine too. You couldn't see

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Speaker 1: all the way through, so I ran up in over

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Speaker 1: the road and down the other embankment and couldn't find

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Speaker 1: the exit track. Then I realized that weasel was so

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Speaker 1: scared of Seth that when he come out the other

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Speaker 1: end of the culver, he must clear snow before his

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Speaker 1: feet hit the ground. Here's a great question, though, Steve.

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Speaker 1: Here's a trapping question. And this is gonna show the

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Speaker 1: heart of the trapper, and and I don't know which

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Speaker 1: way it is gonna go. Would you have killed the

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Speaker 1: weasel in the culvert if he wasn't in a trap? Oh?

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Speaker 1: I thought we had a bobcat, and that bobcat would

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Speaker 1: have been in trouble. Okay, because it's like when you're

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Speaker 1: coon hunting with dogs, like we'll we'll see a coon

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Speaker 1: in a tree and not kill it because our dogs

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Speaker 1: didn't tree it. We'll walk past the training thing. And

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Speaker 1: I'm not worried about training up Seth. I'm not gonna

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Speaker 1: like spoil Seth by by getting a weasel that he

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Speaker 1: didn't tree up. Uh. So she hasn't been she has

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Speaker 1: been approved yet, but by made his selection of Interior Secretary. Um,

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Speaker 1: if you if you're hunting fish or if you like

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Speaker 1: to if you're just generally like to be outdoors, I

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Speaker 1: don't care. If you're a skier, biker, hiker, definitely, if

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Speaker 1: you're hunter and angler, Probably the most influential person in

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Speaker 1: the country for you, particularly if you live in the

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Speaker 1: Western US, probably the most influential person in your life,

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Speaker 1: whether you know it or not, is the Interior Secretary.

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Speaker 1: So the secretary of the Interior um outside of like

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Speaker 1: the egg Secretary, who under Trump was Sonny Perdue, who

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Speaker 1: overseas like U. S. D A, you know, Forest Service lands,

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Speaker 1: the Interior Secretary is like BLM, refugees, National Parks, all

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Speaker 1: these land management you know, all though these land management

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Speaker 1: agencies sit under this Interior Secretary, and they kind of

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Speaker 1: set the tone for what goes on in this country

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Speaker 1: around and management on public lands, federal public lands highly influential.

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Speaker 1: When Trump came in, he initially appointed Zinky Ryan Zinky

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Speaker 1: from Montana, who had been a Navy seal. He ran

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Speaker 1: into a lot of ethics troubles and left and was

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Speaker 1: replaced by a guy named Bernhardt and Bernhardt Trump so

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Speaker 1: he Bernhard almost did four years from Trump. Bernhard did

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Speaker 1: a lot of great stuff. He did a lot of

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Speaker 1: annoying stuff, but he had a lot of good stuff.

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Speaker 1: And it was very good on access, so very good

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Speaker 1: on opening up lands the hunting and fishing that that

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Speaker 1: previously weren't. It was good on migration corridors. Was good

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Speaker 1: on some management issues like overturning some Obama air rules

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Speaker 1: that restricted Alaska's ability to use certain management practices on

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Speaker 1: refuge lands. There was a real slap in the face

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Speaker 1: two the state of Alaska. Um, they did a lot

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Speaker 1: of goods. He did a lot of good stuff. He

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Speaker 1: did a lot of bad stuff. They had this energy dominance.

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Speaker 1: The Trump administration had this you know energy dominance plan, right, Um,

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Speaker 1: and it and their their m O for the history

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Speaker 1: of Trump's term was to really try to up energy

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Speaker 1: extraction on public lands. UM. I'm not a I'm not

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Speaker 1: a big fan of just coming out and saying that

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Speaker 1: you just want to maximize output and not talk about

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Speaker 1: how you want to do it in a responsible, gradual way. Uh.

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Speaker 1: But Biden just did his pick, and I was really

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Speaker 1: hoping that he was gonna pick. Martin Heinrich from New Mexico,

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Speaker 1: and I know there's all kinds of reasons, all kinds

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Speaker 1: of political stuff, but he was my He was my

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Speaker 1: Like I would have been very very happy because here

424
00:27:48,760 --> 00:27:54,880
Speaker 1: you would have had an avid hunter, avid angler, Um,

425
00:27:55,200 --> 00:28:03,000
Speaker 1: great conservation ethic, extremely knowledgeable about the landscape ape out there. Um,

426
00:28:03,119 --> 00:28:06,160
Speaker 1: his head's on straight, he like knows what stuff matters

427
00:28:06,560 --> 00:28:12,040
Speaker 1: for hunters and anglers. You couldn't have done better than Heinrich,

428
00:28:12,240 --> 00:28:14,679
Speaker 1: who's a senator from New Mexico. And it was rumored

429
00:28:14,680 --> 00:28:16,600
Speaker 1: that he was going to get the NOD or you know,

430
00:28:17,320 --> 00:28:19,879
Speaker 1: there's a lot of people pushing for him to get

431
00:28:19,880 --> 00:28:25,040
Speaker 1: the NOD, but it went to subject to approval, went

432
00:28:25,080 --> 00:28:30,840
Speaker 1: to Deb Hayland from New Mexico, the first Native American

433
00:28:32,000 --> 00:28:36,720
Speaker 1: two get appointed as Interior Secretary. So that that that

434
00:28:36,720 --> 00:28:42,400
Speaker 1: that's that's a big win for Native Americans here. Um,

435
00:28:42,880 --> 00:28:48,920
Speaker 1: I am like trying to be optimistic here. I'm I'm

436
00:28:48,920 --> 00:28:53,320
Speaker 1: worried about a couple of things that would happen. Uh.

437
00:28:53,560 --> 00:28:59,120
Speaker 1: I'm worried that under Biden and under Halan, that we

438
00:28:59,240 --> 00:29:06,000
Speaker 1: might do too much renewable energy stuff on public lands. Um,

439
00:29:06,080 --> 00:29:14,800
Speaker 1: these solar arrays and wind farms are we can't just

440
00:29:14,840 --> 00:29:19,040
Speaker 1: turn the landscape into a solar farm. It's very destructive

441
00:29:19,080 --> 00:29:22,959
Speaker 1: the wildlife habitat. Uh. And so this kind of like

442
00:29:23,200 --> 00:29:25,600
Speaker 1: I feel that there's gonna be this like knee jerk

443
00:29:25,800 --> 00:29:30,640
Speaker 1: push into doing put creating like industrial landscapes out on

444
00:29:30,680 --> 00:29:35,920
Speaker 1: our public lands of solar arrays. It's you know, that

445
00:29:36,000 --> 00:29:39,160
Speaker 1: stuff can be catastrophic to wildlife. So I'm a little

446
00:29:39,160 --> 00:29:41,960
Speaker 1: afraid of that. There's a couple of other things I'm

447
00:29:41,960 --> 00:29:43,880
Speaker 1: afraid of, but maybe I'll be pleased in the end.

448
00:29:45,760 --> 00:29:49,920
Speaker 1: You guys got any thoughts on this? Mhmm, you ought too.

449
00:29:50,960 --> 00:29:53,320
Speaker 1: I think so it should just take that solar energy

450
00:29:53,400 --> 00:29:57,800
Speaker 1: and make everyone get the elon Musk's U solar energy

451
00:29:57,840 --> 00:30:00,680
Speaker 1: on the roofs of everyone's houses instead of out on

452
00:30:00,760 --> 00:30:03,440
Speaker 1: the environment. Yeah, that comes to complications too, but I'd

453
00:30:03,520 --> 00:30:09,360
Speaker 1: rather that. Yeah. Um, watching massive mounts of grassland and

454
00:30:09,440 --> 00:30:13,520
Speaker 1: massive mounts of you know, open country converged into wind farms.

455
00:30:16,040 --> 00:30:18,920
Speaker 1: They're so noisy too. Where I grew up, we had

456
00:30:18,960 --> 00:30:22,400
Speaker 1: a bunch of them, and um, the only thing they

457
00:30:22,440 --> 00:30:26,320
Speaker 1: were good for it was obviously the energy they're creating.

458
00:30:26,400 --> 00:30:28,440
Speaker 1: But you could check the wind real easy when you're

459
00:30:28,440 --> 00:30:31,200
Speaker 1: gonna go hunt and see which way they were pointed, No,

460
00:30:31,360 --> 00:30:34,080
Speaker 1: that's a good point. There's enough. See, that's that's always

461
00:30:34,120 --> 00:30:37,920
Speaker 1: look at the bright side. There's a there's a three

462
00:30:38,080 --> 00:30:42,840
Speaker 1: thousand acre solar solar farm that's proposed to go in

463
00:30:43,480 --> 00:30:47,720
Speaker 1: right next to my family's Honing property and Pennsylvania is

464
00:30:47,720 --> 00:30:51,760
Speaker 1: that right, But it's not public, but it is like

465
00:30:51,880 --> 00:30:55,760
Speaker 1: some of the best elk cabitat you'll find in the state.

466
00:30:56,760 --> 00:30:59,960
Speaker 1: And it's there's a shipload of elk on it. Because

467
00:31:00,040 --> 00:31:03,600
Speaker 1: that I've seen the big wind the big wind turbans

468
00:31:03,600 --> 00:31:08,840
Speaker 1: out west, the solar farms I'm less I know less

469
00:31:08,880 --> 00:31:11,000
Speaker 1: about just because I haven't seen them. I mean, are

470
00:31:11,040 --> 00:31:15,000
Speaker 1: these some massive like like him saying that a three

471
00:31:15,000 --> 00:31:17,640
Speaker 1: thousand acre solar farm, that's news to me. I didn't

472
00:31:17,680 --> 00:31:20,280
Speaker 1: know we had them that big. So that would effectively,

473
00:31:21,320 --> 00:31:23,840
Speaker 1: I mean, they would fence these things so that it

474
00:31:23,920 --> 00:31:28,040
Speaker 1: would impede movement of wildlife. Yeah, it's like it's an

475
00:31:28,080 --> 00:31:31,680
Speaker 1: it's an industrial it winds up being an industrial landscape.

476
00:31:32,120 --> 00:31:36,880
Speaker 1: They basically they basically um blanket the landscape. Yeah, and

477
00:31:36,920 --> 00:31:40,240
Speaker 1: then you have all you know roads. I mean, it's

478
00:31:40,240 --> 00:31:42,000
Speaker 1: just like it's like it's like it's it's an industrial

479
00:31:42,000 --> 00:31:45,520
Speaker 1: development project. And so and I and the little bit

480
00:31:45,520 --> 00:31:48,000
Speaker 1: I've not a little bit. The fair bit of reevening

481
00:31:48,080 --> 00:31:51,600
Speaker 1: I've done on Hayland is that that's like a higher priority.

482
00:31:51,640 --> 00:31:57,920
Speaker 1: Other people speculated a high priority could be UM trying

483
00:31:57,960 --> 00:32:02,080
Speaker 1: to seed lands bad two tribes, which is you know,

484
00:32:02,440 --> 00:32:09,680
Speaker 1: as an enormous minefield, UM and I and and would

485
00:32:09,680 --> 00:32:16,760
Speaker 1: be highly controversial and may distract Like that conversation may

486
00:32:16,840 --> 00:32:22,120
Speaker 1: distract from some of the things that I would view

487
00:32:22,680 --> 00:32:26,320
Speaker 1: as high priority that we're likely to get taken care of.

488
00:32:26,960 --> 00:32:41,120
Speaker 1: But we'll see, hopefully we'll all be real pleased. We

489
00:32:41,160 --> 00:32:43,640
Speaker 1: had a conversation recently about castration bands. I found a

490
00:32:43,680 --> 00:32:45,920
Speaker 1: coyote ship with a castration band in it. Was I

491
00:32:45,960 --> 00:32:51,080
Speaker 1: telling you about this spencer you talked about on the podcast. Yeah,

492
00:32:51,280 --> 00:32:53,160
Speaker 1: I found a coyote ship. I was hunting anilop my

493
00:32:53,200 --> 00:32:55,600
Speaker 1: kids and found a coyote ship with a with a

494
00:32:55,640 --> 00:32:58,200
Speaker 1: castration band. Is you can take a lamb and I

495
00:32:58,200 --> 00:33:00,320
Speaker 1: remember this kid named Paul Anderson do it to his

496
00:33:00,400 --> 00:33:05,600
Speaker 1: dog when I was growing up. Um, you take this

497
00:33:05,640 --> 00:33:08,160
Speaker 1: little rubber band and wrap it around his scroll, and

498
00:33:08,560 --> 00:33:11,200
Speaker 1: the causes the scroll to fall off. Do you ever

499
00:33:11,240 --> 00:33:14,360
Speaker 1: have that done to you? Seth no, I tried to

500
00:33:14,400 --> 00:33:16,120
Speaker 1: avoid that kind of stuff. A guy wrote in a

501
00:33:16,200 --> 00:33:22,840
Speaker 1: rancher from um southeastern Montana, and he wrote in he said,

502
00:33:22,880 --> 00:33:29,719
Speaker 1: people use those castration bands to dock sheep tails. So

503
00:33:29,880 --> 00:33:33,000
Speaker 1: you notice like now and then you'll see, you know,

504
00:33:33,080 --> 00:33:35,520
Speaker 1: lambs aren't supposed to have like lambs are born with

505
00:33:35,560 --> 00:33:38,360
Speaker 1: a big, long, damn tail, but everybody cuts the tail

506
00:33:38,400 --> 00:33:40,800
Speaker 1: off him. And he was saying that you really cut

507
00:33:40,880 --> 00:33:43,520
Speaker 1: lambs off tails. You prevent dung build up in maggot

508
00:33:43,520 --> 00:33:47,400
Speaker 1: infestation on the lamb's backside, so that tail builds up

509
00:33:47,400 --> 00:33:52,440
Speaker 1: and lamb feces flies land on there. It's badford the lamb.

510
00:33:52,480 --> 00:33:56,040
Speaker 1: So what they do is they put that castration band

511
00:33:56,040 --> 00:33:58,320
Speaker 1: around the tail and it cuts off circulation. The tails

512
00:33:58,360 --> 00:34:03,400
Speaker 1: falls off. He's saying that heat thinks this kyote ship

513
00:34:03,440 --> 00:34:06,760
Speaker 1: they had a castration band in it. He said. Quote

514
00:34:06,760 --> 00:34:11,520
Speaker 1: odds are the lamb had lost his tail in a

515
00:34:11,640 --> 00:34:15,760
Speaker 1: pasture somewhere and the kyote found himself a little lamb

516
00:34:15,800 --> 00:34:19,840
Speaker 1: tail jerky laying on the ground and a everything including

517
00:34:19,880 --> 00:34:22,880
Speaker 1: the band. We see it in our ranch dogs scat

518
00:34:22,960 --> 00:34:29,240
Speaker 1: all the time. Seth Price, still running a hotmail address,

519
00:34:30,440 --> 00:34:33,800
Speaker 1: really think of that I don't know that's the thing anymore.

520
00:34:34,920 --> 00:34:37,080
Speaker 1: Clay tell us about this Canadian Links that just did

521
00:34:37,120 --> 00:34:40,160
Speaker 1: this this kind of cool thing. Yeah, you know, Steve,

522
00:34:40,239 --> 00:34:44,560
Speaker 1: this actually took place a while back. It just resurfaced

523
00:34:45,360 --> 00:34:51,319
Speaker 1: in the media. But oh, there's all time many still.

524
00:34:51,360 --> 00:34:55,880
Speaker 1: A story center producer a story about a guy who outfitter,

525
00:34:55,920 --> 00:34:59,280
Speaker 1: who had gotten so much wild had so many wildly

526
00:34:59,480 --> 00:35:02,879
Speaker 1: violation that he had what's called the global He had

527
00:35:02,960 --> 00:35:07,839
Speaker 1: like a global forfeiture of hunting privileges. And she wrote back,

528
00:35:07,920 --> 00:35:09,799
Speaker 1: She's like, you know, this was nineteen years ago. Are

529
00:35:09,840 --> 00:35:11,800
Speaker 1: you sure you want me to put this in the show.

530
00:35:12,239 --> 00:35:15,880
Speaker 1: I'm like, oh, sorry, well, hey, this is worthy, Steve.

531
00:35:15,960 --> 00:35:20,200
Speaker 1: This uh, this is the longest documented travel of the

532
00:35:20,320 --> 00:35:25,520
Speaker 1: links in in biological history. And obviously you know how

533
00:35:25,520 --> 00:35:28,560
Speaker 1: many links in the history of the planet have been

534
00:35:28,680 --> 00:35:32,680
Speaker 1: have been monitored. But basically, so the big story is

535
00:35:32,680 --> 00:35:36,879
Speaker 1: is that in the early two thousands, there were two

536
00:35:37,000 --> 00:35:40,959
Speaker 1: hundred and eighteen links, one source says. Another source said

537
00:35:41,360 --> 00:35:47,719
Speaker 1: ninety links transplanted from Canada into the high country of Colorado.

538
00:35:48,200 --> 00:35:51,879
Speaker 1: So basically they were trying to restore the links population

539
00:35:52,280 --> 00:35:54,680
Speaker 1: in Colorado, so they were catching them up in Canada,

540
00:35:54,719 --> 00:35:57,160
Speaker 1: where there's a bunch of links bringing them down in

541
00:35:57,160 --> 00:36:00,759
Speaker 1: the Colorado where there were no links. And you haven't

542
00:36:00,760 --> 00:36:05,800
Speaker 1: know what year links got Endangered Species Act protections. Yes, uh,

543
00:36:06,000 --> 00:36:08,440
Speaker 1: Canadian links were listed about the US Fish and Wildlife

544
00:36:08,480 --> 00:36:12,520
Speaker 1: Service has threatened in the contiguous US and two thousands,

545
00:36:12,560 --> 00:36:14,880
Speaker 1: so they're not on the Endangered Species list. I was

546
00:36:14,960 --> 00:36:18,200
Speaker 1: understand it. They're on the threatened list, but still they're

547
00:36:18,320 --> 00:36:20,880
Speaker 1: on the s A list has threatened, that's right. So

548
00:36:20,920 --> 00:36:24,680
Speaker 1: the only the only US state that you can harvest

549
00:36:24,800 --> 00:36:29,000
Speaker 1: and hunt links is Alaska. But in Canada they're thick,

550
00:36:29,360 --> 00:36:33,840
Speaker 1: you know, they're they're doing very well. And um I

551
00:36:34,400 --> 00:36:37,799
Speaker 1: read an interesting statute Steve that you'll appreciate. So at

552
00:36:37,840 --> 00:36:41,000
Speaker 1: the peak of the fur trade, the modern fur trade,

553
00:36:41,600 --> 00:36:45,480
Speaker 1: in the early nineteen eighties, they were exporting about thirty

554
00:36:45,520 --> 00:36:50,400
Speaker 1: five thousand links pelts out of Alaska and Canada, and

555
00:36:50,480 --> 00:36:54,520
Speaker 1: obviously today that's much less just because of supply demand,

556
00:36:54,840 --> 00:36:58,200
Speaker 1: you know. But that's off top, that's off the topic here.

557
00:36:58,280 --> 00:37:00,879
Speaker 1: This so this links and he's got a science name,

558
00:37:00,920 --> 00:37:04,200
Speaker 1: you know, b C O three m O two. They

559
00:37:04,480 --> 00:37:07,960
Speaker 1: dropped him off in Colorado when he was two years old,

560
00:37:08,920 --> 00:37:15,399
Speaker 1: and he lived in Colorado for four years and then

561
00:37:15,400 --> 00:37:19,640
Speaker 1: he just just he settled right in. Man, the amount

562
00:37:19,680 --> 00:37:22,680
Speaker 1: of surveillance they do on these cats is kind of creepy. Man.

563
00:37:22,760 --> 00:37:28,080
Speaker 1: They knew how many sires of kittens this cat had, uh,

564
00:37:28,160 --> 00:37:31,680
Speaker 1: how many sets of kittens this cat had sired and

565
00:37:31,760 --> 00:37:34,279
Speaker 1: said it's sired two letters of kittens, one in two

566
00:37:34,280 --> 00:37:36,320
Speaker 1: thousand five, one in two thousand. So he was getting

567
00:37:36,360 --> 00:37:41,160
Speaker 1: some play in Colorado. He was and lived there four years.

568
00:37:41,600 --> 00:37:44,959
Speaker 1: It must have been like a marital dispute that pushed

569
00:37:45,040 --> 00:37:47,120
Speaker 1: him out. I don't know, but he left in two

570
00:37:47,160 --> 00:37:50,359
Speaker 1: thousand seven. He went missing, and so the biologists just

571
00:37:50,600 --> 00:37:52,400
Speaker 1: felt like it was just a loss, you know, like

572
00:37:52,480 --> 00:37:55,000
Speaker 1: all these radio colored animals that they're tracking all over

573
00:37:55,000 --> 00:37:58,719
Speaker 1: the place, bears lines like collars just go dead. You

574
00:37:58,719 --> 00:38:01,600
Speaker 1: know that it's just the it happens. It happens here

575
00:38:01,640 --> 00:38:05,000
Speaker 1: in Arkansas with our bears a lot, just randomly, something

576
00:38:05,040 --> 00:38:08,600
Speaker 1: goes wrong, collar goes dead, animal disappears off the data

577
00:38:08,840 --> 00:38:14,200
Speaker 1: points of these biologists and um. But in two thousand

578
00:38:14,239 --> 00:38:21,440
Speaker 1: and ten, Steve, a trapper in nordag Alberta catches a

579
00:38:21,520 --> 00:38:26,120
Speaker 1: big lynx with a with a collar on it, and uh,

580
00:38:26,320 --> 00:38:28,520
Speaker 1: the cat was already dead in the trap, or he

581
00:38:28,560 --> 00:38:31,839
Speaker 1: would have he would have he would have released it,

582
00:38:32,120 --> 00:38:36,040
Speaker 1: he said. But anyway, he gets the number off the collar,

583
00:38:36,640 --> 00:38:41,320
Speaker 1: calls the number, and the in the people are just amazed.

584
00:38:41,360 --> 00:38:45,080
Speaker 1: And the cat had traveled about twelve hundred miles and

585
00:38:45,400 --> 00:38:47,759
Speaker 1: the cat was nine years old at the time and

586
00:38:47,800 --> 00:38:51,840
Speaker 1: went trick to Alberta. Okay, it was caught in British Columbia.

587
00:38:53,000 --> 00:38:56,120
Speaker 1: It went to Alberta, so it didn't it didn't quite

588
00:38:56,160 --> 00:39:00,440
Speaker 1: make it home. Um, but it it just went north

589
00:39:00,960 --> 00:39:03,359
Speaker 1: for and there's no you know, these things are so

590
00:39:03,440 --> 00:39:05,920
Speaker 1: mysterious because we just don't know why it did it.

591
00:39:06,080 --> 00:39:09,400
Speaker 1: We we don't know why. But the the travel was

592
00:39:09,640 --> 00:39:13,640
Speaker 1: you know, obviously the cats crossing major interstate highways. I mean,

593
00:39:13,680 --> 00:39:16,480
Speaker 1: there's all kind of hazards that this cat would have

594
00:39:16,480 --> 00:39:19,000
Speaker 1: had to have gone through. So traveled north through the

595
00:39:19,000 --> 00:39:23,279
Speaker 1: bulk of because he started out in southern Colorado, right

596
00:39:23,400 --> 00:39:31,360
Speaker 1: San Juan Mountains. Yeahs through Colorado presumably like I don't know,

597
00:39:31,560 --> 00:39:39,200
Speaker 1: swung through Utah, western Wyoming, traveled through Idaho or Montana,

598
00:39:40,680 --> 00:39:47,280
Speaker 1: and then made his way were ass into Canada. Yeah. Yeah.

599
00:39:47,320 --> 00:39:51,120
Speaker 1: These so these cats, Steve typically have a home range

600
00:39:51,160 --> 00:39:56,640
Speaker 1: of twelve to eighty three miles. That's what the the

601
00:39:56,640 --> 00:40:02,360
Speaker 1: the website, the wells, it was the FED website. Um,

602
00:40:03,280 --> 00:40:06,160
Speaker 1: it was really specific. But so you know, the males

603
00:40:06,200 --> 00:40:08,840
Speaker 1: have bigger home ranges, the females are gonna have smaller

604
00:40:08,840 --> 00:40:12,319
Speaker 1: home ranges. So you know they're known to travel. You know,

605
00:40:12,800 --> 00:40:16,920
Speaker 1: links most of his diet, his snowshoe hairs. They're highly specific.

606
00:40:16,920 --> 00:40:19,239
Speaker 1: I did a little research on why. How are they

607
00:40:19,280 --> 00:40:21,200
Speaker 1: different than bobcats. You know, they look a lot like

608
00:40:21,200 --> 00:40:24,759
Speaker 1: a bobcat and it it mainly has. They can be

609
00:40:24,880 --> 00:40:29,080
Speaker 1: slightly larger than bobcat, but they're super specialized. That's pretty

610
00:40:29,160 --> 00:40:32,319
Speaker 1: much what makes them different. You know, bobcats live all

611
00:40:32,400 --> 00:40:37,000
Speaker 1: across the US. I mean they're very widely distributed. Links

612
00:40:37,040 --> 00:40:41,239
Speaker 1: are highly specialized for hunting snowshoe hairs and snow and

613
00:40:41,280 --> 00:40:43,799
Speaker 1: then the boarder typically the well. Now they're in the

614
00:40:43,840 --> 00:40:46,040
Speaker 1: boreal forest, but at one time they were in all

615
00:40:46,120 --> 00:40:49,480
Speaker 1: the highlands of North America. As I understand it, So

616
00:40:49,600 --> 00:40:52,759
Speaker 1: I have a big, a big gass foot and bigger tough.

617
00:40:52,840 --> 00:40:54,160
Speaker 1: I was talking about this on the show with a

618
00:40:54,160 --> 00:40:57,239
Speaker 1: handful of times, but um, I've only laid eyes on

619
00:40:57,239 --> 00:40:59,920
Speaker 1: one links man, and it was just and they have

620
00:41:00,040 --> 00:41:02,280
Speaker 1: a face that kind of looks like a human baby

621
00:41:02,320 --> 00:41:06,520
Speaker 1: in a weird way. Man, you're wild looking. I guess

622
00:41:06,760 --> 00:41:09,120
Speaker 1: if you want to preorder the Handbook of Mummy Studies

623
00:41:09,160 --> 00:41:13,080
Speaker 1: Guests with that Bad Boys coming in at it's free

624
00:41:13,080 --> 00:41:22,120
Speaker 1: delivery on prime hard cover, there's four hundred forty nine cents. Oddly,

625
00:41:22,680 --> 00:41:27,520
Speaker 1: the paperback is six hundred nine dollars and cents to

626
00:41:27,600 --> 00:41:31,920
Speaker 1: pre order it. Why would it be that expensive? I

627
00:41:31,960 --> 00:41:34,080
Speaker 1: don't know, but I hope these good people are listening

628
00:41:34,080 --> 00:41:37,719
Speaker 1: and send me one of these damn books. That's crazy.

629
00:41:38,040 --> 00:41:40,960
Speaker 1: And then mum is the mountain lion that recently made

630
00:41:41,000 --> 00:41:45,319
Speaker 1: a long trip. Uh that was more recent, Yeah, that

631
00:41:45,440 --> 00:41:48,719
Speaker 1: was so there was a young lion collared in New

632
00:41:48,760 --> 00:41:52,440
Speaker 1: Mexico February the twelve, in kind of like northwest New

633
00:41:52,480 --> 00:41:58,000
Speaker 1: Mexico and uh he he was documented to have traveled

634
00:41:58,040 --> 00:42:00,319
Speaker 1: five hundred and fifty eight miles and stead in the

635
00:42:00,360 --> 00:42:04,279
Speaker 1: Mason Verda National Park. So that's that's like, that's not

636
00:42:04,480 --> 00:42:10,239
Speaker 1: standard mountain lion protocol, but that's like not terribly uncommon.

637
00:42:12,040 --> 00:42:14,120
Speaker 1: I mean, pretty cool, no doubt, oh yeah, but just

638
00:42:14,160 --> 00:42:17,960
Speaker 1: like amazing that they can move around and avoid trouble.

639
00:42:19,120 --> 00:42:28,120
Speaker 1: M hm. You know all those highways. Yeah, passing through towns,

640
00:42:28,200 --> 00:42:33,359
Speaker 1: crossing highways and not starving to death. Steve, we have

641
00:42:33,880 --> 00:42:37,839
Speaker 1: so too. Three years ago, the Arkansas Game and Fish

642
00:42:37,840 --> 00:42:41,840
Speaker 1: Commission changed the title of one of our biologists from

643
00:42:41,880 --> 00:42:46,359
Speaker 1: the bar biologists to the large carnivore biologists because we

644
00:42:46,440 --> 00:42:50,799
Speaker 1: had enough legitimate mountain lion sightings in the state that

645
00:42:50,920 --> 00:42:56,320
Speaker 1: it deemed a biologist to be over mountain lions in Arkansas.

646
00:42:57,120 --> 00:43:00,640
Speaker 1: We're super fascinating. And I talked of the guy the

647
00:43:00,640 --> 00:43:05,919
Speaker 1: other day for a podcast, and basically he thinks we're

648
00:43:05,920 --> 00:43:10,760
Speaker 1: getting mountain lions on these big treks, these big journeys,

649
00:43:10,800 --> 00:43:14,799
Speaker 1: like they're they're coming down the Missouri River coming into Arkansas,

650
00:43:15,400 --> 00:43:18,520
Speaker 1: and they'll will have just like a spasm of lyon

651
00:43:19,040 --> 00:43:23,200
Speaker 1: sightings that span like all of you know, I mean

652
00:43:23,239 --> 00:43:25,879
Speaker 1: like counties. You know, like there's a lie there, there's

653
00:43:25,920 --> 00:43:28,400
Speaker 1: a line there, there's a line there, and it's the

654
00:43:28,440 --> 00:43:32,440
Speaker 1: same lion and he's on this big walk about and

655
00:43:32,520 --> 00:43:36,080
Speaker 1: they leave though there there's they have yet to document

656
00:43:36,160 --> 00:43:39,680
Speaker 1: a lion that is a breeding populations of lions here.

657
00:43:39,719 --> 00:43:43,160
Speaker 1: But every year we have lions and so He speculates

658
00:43:43,200 --> 00:43:47,239
Speaker 1: that it's this it's this edge of lyon habitat where

659
00:43:47,239 --> 00:43:50,879
Speaker 1: the males are dispersing deep. And he said, as soon

660
00:43:50,920 --> 00:43:54,440
Speaker 1: as a female decides to live here, then we'll start getting,

661
00:43:54,840 --> 00:43:57,879
Speaker 1: you know, males that come and stay like because they're

662
00:43:58,040 --> 00:44:01,000
Speaker 1: basically looking for mates and looking for terry tory. They're

663
00:44:01,040 --> 00:44:04,359
Speaker 1: not male is not gonna come in and inhabited place

664
00:44:04,400 --> 00:44:07,680
Speaker 1: that doesn't have a female. Was this point historically it

665
00:44:07,800 --> 00:44:16,960
Speaker 1: was the most widely distributed large mammal in the Western hemisphere. Yeah,

666
00:44:17,080 --> 00:44:22,839
Speaker 1: down to the southern tip of Patagonia, right all through

667
00:44:22,840 --> 00:44:28,000
Speaker 1: South America, Central America, up into Canada coast to coast.

668
00:44:28,000 --> 00:44:33,759
Speaker 1: Here in the US, it was everywhere. It's really had

669
00:44:33,760 --> 00:44:38,440
Speaker 1: this little population hang on in Florida and then just

670
00:44:38,520 --> 00:44:40,640
Speaker 1: get wiped out in the Eastern US and then just

671
00:44:40,880 --> 00:44:45,759
Speaker 1: very slowly but gradually coming back. You think, you know,

672
00:44:45,800 --> 00:44:49,920
Speaker 1: how they like they seem to be doing um fairly

673
00:44:49,960 --> 00:44:55,279
Speaker 1: well in like California where it's heavily populated. They're eating

674
00:44:55,280 --> 00:44:58,279
Speaker 1: house cats and dogs and ship like that. Do you

675
00:44:58,320 --> 00:45:05,840
Speaker 1: think eventually the they'll slowly start populating like the Eastern

676
00:45:05,880 --> 00:45:10,439
Speaker 1: States where it's where it's like like they've they're gonna

677
00:45:10,560 --> 00:45:14,239
Speaker 1: like kind of learned to live amongst humans. Oh yeah,

678
00:45:14,320 --> 00:45:17,799
Speaker 1: I mean you're definitely. I don't know how many generations

679
00:45:17,840 --> 00:45:19,719
Speaker 1: it will take, but I think that you'll have I mean,

680
00:45:20,200 --> 00:45:24,960
Speaker 1: at this moment, like we're having like expanding you know,

681
00:45:25,000 --> 00:45:28,640
Speaker 1: mountain lions are expanding range. Black bears are actively right

682
00:45:28,719 --> 00:45:31,920
Speaker 1: now expanding range. So I think definitely, I don't know

683
00:45:31,960 --> 00:45:33,480
Speaker 1: what it would be. I don't know if like my

684
00:45:33,640 --> 00:45:37,200
Speaker 1: kids kids will be surprised to hear that once upon

685
00:45:37,239 --> 00:45:39,239
Speaker 1: a time it was unusual to have a mountain lion

686
00:45:39,360 --> 00:45:44,040
Speaker 1: like in Ohio, um because it's just like it's one

687
00:45:44,040 --> 00:45:46,359
Speaker 1: thing to pass through. But like they gotta have enough

688
00:45:46,480 --> 00:45:49,360
Speaker 1: room to not get in that much trouble. But the

689
00:45:49,400 --> 00:45:54,440
Speaker 1: fact that they're able to um do well, not do well,

690
00:45:54,480 --> 00:45:58,840
Speaker 1: but to to live in huge population centers in California.

691
00:45:59,040 --> 00:46:03,080
Speaker 1: But then you have like a lot of topography, steep

692
00:46:03,120 --> 00:46:08,960
Speaker 1: brushy country they can still hide. They've got to have

693
00:46:09,000 --> 00:46:11,759
Speaker 1: a ton of deer, they've got you know, elk, they've

694
00:46:11,800 --> 00:46:14,600
Speaker 1: got to have some type of ungula. You know. Cultural

695
00:46:14,640 --> 00:46:16,840
Speaker 1: tolerance will be the main thing. I think in the East,

696
00:46:17,520 --> 00:46:21,319
Speaker 1: just like how many people, how how many lions will

697
00:46:21,400 --> 00:46:25,000
Speaker 1: people put up with which I think the tolerance would

698
00:46:25,000 --> 00:46:27,600
Speaker 1: be left in the eastern part of the US would

699
00:46:27,600 --> 00:46:30,920
Speaker 1: be less than the cultural tolerance in the West for

700
00:46:31,200 --> 00:46:34,319
Speaker 1: large predators. Yeah. I mean there's celebrities around California, man,

701
00:46:34,400 --> 00:46:37,080
Speaker 1: I mean, and like around the population hubs in California

702
00:46:37,120 --> 00:46:40,319
Speaker 1: population centers there. Uh. One of the one of the

703
00:46:40,440 --> 00:46:42,360
Speaker 1: I think probably an now standing question on it is

704
00:46:42,360 --> 00:46:45,240
Speaker 1: how well they can do in an agricultural landscape too,

705
00:46:46,440 --> 00:46:49,759
Speaker 1: where so much of the ground is tilled and open um.

706
00:46:49,880 --> 00:46:52,000
Speaker 1: I don't know, man, but they're they're definitely expanding out.

707
00:46:52,360 --> 00:46:56,760
Speaker 1: You know. Years ago now, California banned mountain lion hunting.

708
00:46:56,800 --> 00:46:59,200
Speaker 1: First they went after houndsman, then they just banned it

709
00:46:59,200 --> 00:47:02,320
Speaker 1: all together. It's kind of like the playbook there is,

710
00:47:02,360 --> 00:47:06,160
Speaker 1: you like, like it's the Death Back thousand cuts playbook. Uh.

711
00:47:06,800 --> 00:47:09,520
Speaker 1: They were looking at this dispersal study. They thought that

712
00:47:09,600 --> 00:47:14,560
Speaker 1: because California had no lion hunting, they were expecting to

713
00:47:14,600 --> 00:47:19,960
Speaker 1: see lions like sort of flowing out of California. And

714
00:47:19,960 --> 00:47:21,680
Speaker 1: they did this. They were looking at this study of

715
00:47:21,719 --> 00:47:25,839
Speaker 1: like dispersal and it was funny because uh, Nevada, which

716
00:47:25,880 --> 00:47:29,680
Speaker 1: still has like a thriving lion hunting culture, Nevada actually

717
00:47:29,719 --> 00:47:35,080
Speaker 1: has lions that they're producing that are dispersing westward into California.

718
00:47:36,120 --> 00:47:42,160
Speaker 1: Like they're still creating them and kicking them out. That's wild. Yeah,

719
00:47:42,239 --> 00:47:46,000
Speaker 1: it's good stuff man. Uh. And in the early two thousands,

720
00:47:46,040 --> 00:47:47,920
Speaker 1: I was trying to do a magazine story for Outside.

721
00:47:47,920 --> 00:47:49,840
Speaker 1: I could never get him to assign me the story,

722
00:47:49,880 --> 00:47:53,600
Speaker 1: but I kept wanting to do a story about, um,

723
00:47:53,719 --> 00:47:57,000
Speaker 1: these guys and like, you know anywhere like North Carolina, Tennessee,

724
00:47:57,440 --> 00:48:02,920
Speaker 1: like the local crazy guy that would see a mountain lion. Yeah,

725
00:48:03,040 --> 00:48:04,799
Speaker 1: and everybody's like, you're crazy, you don't know what you're

726
00:48:04,800 --> 00:48:07,360
Speaker 1: talking about. Um, And I was kind of and I

727
00:48:07,360 --> 00:48:08,560
Speaker 1: was a little bit ahead of my time because this

728
00:48:08,719 --> 00:48:10,960
Speaker 1: guy even started this thing called like the Eastern Puma

729
00:48:11,000 --> 00:48:13,200
Speaker 1: Research Center or something like that because he had run

730
00:48:13,239 --> 00:48:16,000
Speaker 1: into one on a road hunting turkeys, I think, and

731
00:48:16,040 --> 00:48:18,719
Speaker 1: it haunted him. Every thought he's a nut job. And

732
00:48:18,760 --> 00:48:21,040
Speaker 1: then ten years later they're getting hit that one gets

733
00:48:21,120 --> 00:48:24,840
Speaker 1: hit on the road in Connecticut. Yeah, a wild born

734
00:48:24,960 --> 00:48:30,640
Speaker 1: one in Pennsylvania. I mean, like I always heard like

735
00:48:30,840 --> 00:48:34,960
Speaker 1: everyone knew someone that either seen one or like had

736
00:48:35,120 --> 00:48:39,600
Speaker 1: a track that they found in like cast or. It

737
00:48:39,680 --> 00:48:43,440
Speaker 1: was just like you know it was. It was. There

738
00:48:43,520 --> 00:48:46,480
Speaker 1: was always stories of people seeing mountain lions and people

739
00:48:46,520 --> 00:48:49,520
Speaker 1: having like I haven't seen him, but like heard of

740
00:48:49,560 --> 00:48:52,480
Speaker 1: people who had like has pictures of mount lines that

741
00:48:52,520 --> 00:48:54,840
Speaker 1: they had killed because they were like killing the chickens

742
00:48:54,920 --> 00:48:57,359
Speaker 1: or something that kind of ship. You know, This dude

743
00:48:57,400 --> 00:48:59,480
Speaker 1: in my home state wrote this book, Beast of Never

744
00:48:59,600 --> 00:49:01,640
Speaker 1: Category odd. I think it was what the book was called,

745
00:49:02,040 --> 00:49:04,040
Speaker 1: but it was about him getting all obsessed with mountain

746
00:49:04,040 --> 00:49:09,799
Speaker 1: lions in Michigan, which wanted him not being bullshit. Yeah,

747
00:49:10,000 --> 00:49:12,640
Speaker 1: they just moved. Yeah. Well, I mean, like this dude

748
00:49:12,719 --> 00:49:14,640
Speaker 1: right here northing. It's not even like that big of

749
00:49:14,640 --> 00:49:19,399
Speaker 1: a deal. Dude, five six miles man, Yeah, a freaking links.

750
00:49:19,400 --> 00:49:23,880
Speaker 1: How far that links? There was a documented lion that

751
00:49:23,920 --> 00:49:28,839
Speaker 1: went from the Dakotas to Connecticut in the last ten years. Yeah,

752
00:49:28,880 --> 00:49:31,520
Speaker 1: I think they think he swung around up through Uh.

753
00:49:31,560 --> 00:49:34,160
Speaker 1: I think they think that that dude swung around up

754
00:49:34,200 --> 00:49:37,520
Speaker 1: through Michigan's Upper Peninsula, dropped down from Canada into the East.

755
00:49:38,320 --> 00:49:40,280
Speaker 1: That's like one guess of how he might have gone.

756
00:49:40,640 --> 00:49:44,520
Speaker 1: I heard he got on a FedEx cargo plane chasing

757
00:49:44,680 --> 00:49:51,000
Speaker 1: chickens and got out on the other side. Okay, so Spencer,

758
00:49:51,040 --> 00:49:53,000
Speaker 1: you were right. Spencer sent me a thing where he's like,

759
00:49:53,040 --> 00:49:56,719
Speaker 1: I bet you A hundred people sent you this, and

760
00:49:56,760 --> 00:49:59,759
Speaker 1: they had tell, tell talk about what it was that

761
00:49:59,840 --> 00:50:03,959
Speaker 1: the hundred people have sent me. Spencer recently this week

762
00:50:04,000 --> 00:50:08,640
Speaker 1: out of Kentucky and elk antler turned off that looks

763
00:50:08,920 --> 00:50:15,280
Speaker 1: very old and carved into it, says d boone Um.

764
00:50:15,360 --> 00:50:20,800
Speaker 1: And then it has the year seventeen and the story

765
00:50:20,880 --> 00:50:25,240
Speaker 1: goes this, this antler was found in the late eighteen

766
00:50:25,280 --> 00:50:28,160
Speaker 1: hundreds and was passed through some generations and has now

767
00:50:28,680 --> 00:50:32,319
Speaker 1: wound up with the r M E f um. And

768
00:50:32,400 --> 00:50:35,000
Speaker 1: there's all this excitement around it on Facebook, for example,

769
00:50:35,120 --> 00:50:38,359
Speaker 1: Rocky Mountain Foundation. On Facebook, for example, it's been liked

770
00:50:38,360 --> 00:50:41,560
Speaker 1: four thousand times, commented on seven hundred times, and shared

771
00:50:41,600 --> 00:50:45,919
Speaker 1: five thousand times because people are excited that this antler um,

772
00:50:45,960 --> 00:50:50,440
Speaker 1: which came from the species of elk that is now extinct,

773
00:50:51,520 --> 00:50:55,719
Speaker 1: turned up in Kentucky. It's not a species of elk,

774
00:50:55,800 --> 00:51:03,960
Speaker 1: Spencer subspecies if not not prop No, no, okay, I

775
00:51:04,000 --> 00:51:07,680
Speaker 1: don't buy Okay, go on, here's the thing, I I said,

776
00:51:07,680 --> 00:51:09,320
Speaker 1: do you see them like, I'm sure this has crossed

777
00:51:09,360 --> 00:51:12,200
Speaker 1: your inbox? So I don't want to get into the

778
00:51:12,200 --> 00:51:15,160
Speaker 1: subspecies thing. But I'll point this out, at the time

779
00:51:15,320 --> 00:51:17,040
Speaker 1: you had elk just from one end of the country

780
00:51:17,080 --> 00:51:20,920
Speaker 1: to the other, there wasn't like unique population groups. They

781
00:51:21,040 --> 00:51:27,560
Speaker 1: probably all intermingled. But but sure, that's fine. A geneticist

782
00:51:27,560 --> 00:51:30,480
Speaker 1: would would not agree with that. Well, I'm gonna tell

783
00:51:30,520 --> 00:51:32,799
Speaker 1: you what the Facebook post said and why I said that.

784
00:51:33,080 --> 00:51:37,719
Speaker 1: It says resource private land biologists Joe lace Field the

785
00:51:37,840 --> 00:51:41,480
Speaker 1: Rocky Mountain Elk Foundation funded the Carbon fourteen dating of

786
00:51:41,520 --> 00:51:44,040
Speaker 1: the antler and it was traced back to the extinct

787
00:51:44,160 --> 00:51:48,520
Speaker 1: subspecies of eastern elk. The carbon fourteen test dated the

788
00:51:48,560 --> 00:51:50,399
Speaker 1: elk to have died in the range of the years

789
00:51:50,440 --> 00:51:55,600
Speaker 1: between seventeen thirty and eighteen o six. I would like, here,

790
00:51:55,680 --> 00:51:58,520
Speaker 1: here's there a thing I would like to see that report,

791
00:52:00,680 --> 00:52:05,960
Speaker 1: because having done some C fourteen submissions, they usually give

792
00:52:06,000 --> 00:52:09,200
Speaker 1: you these things in like sigmas. Okay, they they give

793
00:52:09,239 --> 00:52:14,200
Speaker 1: you these things where it's like percentage, like these like

794
00:52:14,280 --> 00:52:22,040
Speaker 1: percentage likelihoods, and it'll go like thirty three p sixt likelihood.

795
00:52:22,800 --> 00:52:27,120
Speaker 1: I would like to see is the report included there no, no,

796
00:52:27,440 --> 00:52:30,680
Speaker 1: the that's that's the best evidence they give. And then

797
00:52:30,680 --> 00:52:32,880
Speaker 1: they end the thing by saying, there's no way to

798
00:52:32,920 --> 00:52:36,239
Speaker 1: prove that Boone inscribed the antler, but the evidence says

799
00:52:36,400 --> 00:52:40,520
Speaker 1: it is likely. Okay, is not a legitimate I mean

800
00:52:40,560 --> 00:52:45,960
Speaker 1: like we're we're saying that this is a legitimate news report. Yes, well,

801
00:52:46,080 --> 00:52:50,200
Speaker 1: we're saying that they there is an elk Antler that's undisputed.

802
00:52:50,640 --> 00:52:53,879
Speaker 1: It has D. Boon written on it, that's undisputed. They

803
00:52:53,920 --> 00:52:58,759
Speaker 1: submitted it for C fourteen dating radio carbon dating. That's undisputed.

804
00:53:00,160 --> 00:53:02,479
Speaker 1: It came back with that date range. But I would

805
00:53:02,480 --> 00:53:05,920
Speaker 1: just be curious to see what probability they abscribed to

806
00:53:05,960 --> 00:53:09,600
Speaker 1: the accuracy of that date range. Can I Can I

807
00:53:09,680 --> 00:53:13,879
Speaker 1: jump in here a minute? Top police radio carbon? Yes, okay,

808
00:53:16,239 --> 00:53:18,799
Speaker 1: that's pretty fascinating, man boy. Yeah, oh yeah, No, No, no,

809
00:53:18,880 --> 00:53:20,560
Speaker 1: it is. It is, And I'm not even saying that

810
00:53:20,560 --> 00:53:24,319
Speaker 1: it's not. D. Boon's Daniel Boone. Boone would have been

811
00:53:24,400 --> 00:53:27,319
Speaker 1: during that time from eighteen o six eighteen thirty. He

812
00:53:27,320 --> 00:53:32,040
Speaker 1: didn't he die in the eighteen twenties. Steve in Missouri. Uh,

813
00:53:32,440 --> 00:53:35,080
Speaker 1: someone could pull that up, teens, pull it up, seth,

814
00:53:35,120 --> 00:53:37,160
Speaker 1: See how fast you know what I would type in? Seth?

815
00:53:37,200 --> 00:53:41,960
Speaker 1: I would type in um Daniel Boone Whearing. When did

816
00:53:42,000 --> 00:53:46,040
Speaker 1: Daniel Boone die Well, He died in Missouri. Boon first

817
00:53:46,120 --> 00:53:49,040
Speaker 1: arrived in in Tucky in seventeen sixty nine and settled

818
00:53:49,200 --> 00:53:54,200
Speaker 1: with his family at Boonsboro in seventeen seventy and again

819
00:53:54,320 --> 00:53:58,040
Speaker 1: this through the Revolution. This antler was dated between seventeen

820
00:53:58,080 --> 00:54:01,600
Speaker 1: thirty and eighteen o six. The Earth, Boys and Girls

821
00:54:01,719 --> 00:54:08,279
Speaker 1: is bombarded by cosmic rays from the sun. Okay, mm

822
00:54:08,360 --> 00:54:13,759
Speaker 1: hmmm uh. The thing that is produced by the bombardment

823
00:54:13,800 --> 00:54:17,840
Speaker 1: of these cosmic rays is a radioactive substance called C fourteen.

824
00:54:19,040 --> 00:54:24,640
Speaker 1: You tracking spencer, you're fact checking. It's taken up by plants.

825
00:54:25,760 --> 00:54:31,760
Speaker 1: Anything that is alive that eats plants or eats things

826
00:54:31,800 --> 00:54:43,160
Speaker 1: that have eaten plants accumulates C fourteen. C fourteen stops

827
00:54:43,160 --> 00:54:46,840
Speaker 1: accumulating in these organisms when they die, So you stop

828
00:54:46,920 --> 00:54:51,960
Speaker 1: consuming plants, you stop consuming things that consume plants, and

829
00:54:52,080 --> 00:54:57,320
Speaker 1: you stop your intake of C fourteen that is building

830
00:54:57,400 --> 00:55:01,040
Speaker 1: up in your bones. C fourteen has an known half life,

831
00:55:02,520 --> 00:55:04,719
Speaker 1: So if you take a bone and you can look

832
00:55:04,760 --> 00:55:08,040
Speaker 1: at the rate of decay of the C fourteen in it,

833
00:55:08,239 --> 00:55:13,160
Speaker 1: you can tell how long ago the things stopped being alive. Now,

834
00:55:14,080 --> 00:55:18,839
Speaker 1: once they started testing atomic weapons, so from anything that

835
00:55:18,960 --> 00:55:21,080
Speaker 1: was alive like C four team won't work any like,

836
00:55:21,080 --> 00:55:23,080
Speaker 1: you can't radio carbon anything that was alive from the

837
00:55:23,160 --> 00:55:29,319
Speaker 1: nineteen fifties on, because then like atmospheric radioactive substances are

838
00:55:29,360 --> 00:55:32,560
Speaker 1: so prevalent that our our systems are all out of whack.

839
00:55:32,719 --> 00:55:34,279
Speaker 1: Like if you checked us, you wouldn't be able to

840
00:55:34,280 --> 00:55:36,080
Speaker 1: do you wouldn't be able to radio carbon. Date I

841
00:55:36,160 --> 00:55:39,719
Speaker 1: remains in the future because we were alive for all

842
00:55:39,800 --> 00:55:46,360
Speaker 1: that radioactive material that's in the atmosphere now from a

843
00:55:46,440 --> 00:55:50,200
Speaker 1: testing atomic bombs, detonating atomic bombs and whatnot, So it

844
00:55:50,200 --> 00:55:54,040
Speaker 1: doesn't work anymore. Then there's it comes into this thing

845
00:55:54,080 --> 00:55:59,360
Speaker 1: called dendro chronology where the rate at which these cosmic

846
00:55:59,480 --> 00:56:04,040
Speaker 1: rays but barred the atmosphere fluctuates through time. It's not constant.

847
00:56:05,400 --> 00:56:08,120
Speaker 1: So what they're able to do is take, for instance,

848
00:56:08,840 --> 00:56:13,839
Speaker 1: tree rings from old trees, and they're able to count back.

849
00:56:13,920 --> 00:56:15,440
Speaker 1: Like if you cut a tree down right now, and

850
00:56:15,480 --> 00:56:16,960
Speaker 1: I can count back. Let's say I find some three

851
00:56:17,360 --> 00:56:20,120
Speaker 1: year old tree. I can be like, okay, I just

852
00:56:20,160 --> 00:56:22,160
Speaker 1: cut the tree down, and I can look and I'd

853
00:56:22,160 --> 00:56:25,839
Speaker 1: be like, this ring was laid down. I know three

854
00:56:26,239 --> 00:56:31,239
Speaker 1: years ago, because I've counted from the core right to

855
00:56:31,560 --> 00:56:33,839
Speaker 1: get to where this ring was laid down, and then

856
00:56:33,880 --> 00:56:37,000
Speaker 1: you can look in that ring at the Sea fourteen

857
00:56:37,040 --> 00:56:39,520
Speaker 1: that was laid down in that ring that year, and

858
00:56:39,560 --> 00:56:43,879
Speaker 1: you can get an idea of the relative bombardment of

859
00:56:43,880 --> 00:56:48,799
Speaker 1: of these rays, and that's what helps you calibrated out.

860
00:56:49,560 --> 00:56:55,719
Speaker 1: But it's like imprecise. When I had my buffalo skull done,

861
00:56:55,760 --> 00:56:58,680
Speaker 1: I came in with a sixty six percent probability that

862
00:56:58,760 --> 00:57:01,600
Speaker 1: it died within a couple of dead cads of seventeen seventy.

863
00:57:03,560 --> 00:57:04,840
Speaker 1: So I would just like to look at the report.

864
00:57:05,120 --> 00:57:10,280
Speaker 1: We'll go on Spencer. Well, maybe like the best reason

865
00:57:10,280 --> 00:57:14,560
Speaker 1: to be skeptical is because there have been trees in

866
00:57:14,640 --> 00:57:18,560
Speaker 1: like Kentucky and Tennessee that were these landmarks at one

867
00:57:18,600 --> 00:57:22,320
Speaker 1: point where Daniel Boone supposedly carved his name in there

868
00:57:22,560 --> 00:57:24,280
Speaker 1: and like left a cute little note like one of

869
00:57:24,320 --> 00:57:28,640
Speaker 1: them was like d Boone killed a bar on tree

870
00:57:29,160 --> 00:57:32,800
Speaker 1: year seven year, seventeen sixty And a number of these

871
00:57:32,840 --> 00:57:36,080
Speaker 1: like trees exists in that area. But there's there's like

872
00:57:36,120 --> 00:57:39,840
Speaker 1: a lot disputing like, well, actually he spelled his name correctly,

873
00:57:39,840 --> 00:57:43,160
Speaker 1: but in this tree it was spelled incorrectly. There are

874
00:57:43,160 --> 00:57:46,360
Speaker 1: other receipts of him writing Bear correctly, but in this

875
00:57:46,440 --> 00:57:49,680
Speaker 1: tree again, like this is extra folksy. How he spelled

876
00:57:49,680 --> 00:57:53,479
Speaker 1: his name wrong, spelled Bear wrong, etcetera. Um. So there's

877
00:57:53,520 --> 00:57:56,880
Speaker 1: just like it seems like some pride in that area

878
00:57:57,240 --> 00:58:01,440
Speaker 1: of having Daniel Boone are to facts and you know,

879
00:58:01,480 --> 00:58:03,960
Speaker 1: going all the way back to the eighteen hundreds when

880
00:58:04,000 --> 00:58:08,400
Speaker 1: these trees were you know, cut down and like put

881
00:58:08,400 --> 00:58:10,520
Speaker 1: on display and stuff like that, and it feels a

882
00:58:10,600 --> 00:58:13,400
Speaker 1: lot like, Uh, I'm from South Dakota, but I consider

883
00:58:13,440 --> 00:58:16,520
Speaker 1: myself like an honorary Minnesotan um. And they have the

884
00:58:16,560 --> 00:58:19,080
Speaker 1: Kensington run still in there. Are you guys familiar with that?

885
00:58:19,400 --> 00:58:23,560
Speaker 1: Like the the giant piece of rock late in the

886
00:58:23,600 --> 00:58:25,320
Speaker 1: earth that came up in the roots of a tree

887
00:58:25,840 --> 00:58:30,400
Speaker 1: where some vikings had inscribed in this rock that like

888
00:58:30,520 --> 00:58:36,040
Speaker 1: through the Great Lakes and stuff. Um. But of historians say, no,

889
00:58:36,240 --> 00:58:39,000
Speaker 1: that's that's a lie. But this popped up, you know,

890
00:58:39,720 --> 00:58:45,480
Speaker 1: hundreds of years ago. Because they have like this great ah,

891
00:58:45,640 --> 00:58:48,000
Speaker 1: like they take a lot of pride in in Daniel Boone,

892
00:58:48,160 --> 00:58:50,520
Speaker 1: or people in Minnesota take a lot of pride in

893
00:58:50,760 --> 00:58:55,640
Speaker 1: vikings beating Christopher Columbus to America. That's what it feels like.

894
00:58:55,920 --> 00:58:58,680
Speaker 1: I can't remember if it was in Robert Morgan's Boone

895
00:58:58,720 --> 00:59:03,200
Speaker 1: biography or the fair Riger biography, one of these Boom biographies.

896
00:59:04,280 --> 00:59:10,200
Speaker 1: In the end, he talks about um all of the

897
00:59:10,280 --> 00:59:15,000
Speaker 1: alleged artifacts, that there was a cottage industry of writing

898
00:59:15,040 --> 00:59:20,000
Speaker 1: boon on stuff, and that you could fill ten houses

899
00:59:21,240 --> 00:59:23,880
Speaker 1: with all the stuff that supposedly came out of Boone's

900
00:59:23,920 --> 00:59:29,160
Speaker 1: house and guns that have boon on him and hatches

901
00:59:29,240 --> 00:59:31,600
Speaker 1: that have It was like because he was a celebrity

902
00:59:31,720 --> 00:59:34,480
Speaker 1: his own era, you know, he was like he was

903
00:59:34,520 --> 00:59:40,560
Speaker 1: like famous while he was alive, and so I don't know, man,

904
00:59:40,840 --> 00:59:44,000
Speaker 1: it's like modern social media, like people writing stuff in

905
00:59:44,080 --> 00:59:47,959
Speaker 1: trees like back in the eight hundreds, it's like making

906
00:59:47,960 --> 00:59:54,000
Speaker 1: a Facebook post. Or Albert Morgan, who you were referring

907
00:59:54,000 --> 00:59:56,400
Speaker 1: to you is going to be a guest on Kentucky

908
00:59:56,520 --> 01:00:01,320
Speaker 1: Outdoors Media UM to talk about the pose it Boone Antler.

909
01:00:01,600 --> 01:00:03,840
Speaker 1: What's his take on the Antler? We don't know. He

910
01:00:03,880 --> 01:00:05,520
Speaker 1: hasn't been on yet, but he's done some other He

911
01:00:05,520 --> 01:00:08,400
Speaker 1: hasn't done any other interviews about this Antler. The Antler

912
01:00:08,480 --> 01:00:13,480
Speaker 1: came up on December three days ago. So this is

913
01:00:13,520 --> 01:00:16,600
Speaker 1: all fresh. I'll put it to you this way. If

914
01:00:16,640 --> 01:00:22,000
Speaker 1: God came down, Let's say Chester. Let's say Chester came over,

915
01:00:22,800 --> 01:00:26,960
Speaker 1: and Chester said, I know, because somehow I've had contact

916
01:00:26,960 --> 01:00:31,520
Speaker 1: with an omniscient being, I know the answer, this is

917
01:00:31,600 --> 01:00:36,360
Speaker 1: Boone's antler or not? Like I know through magical abilities,

918
01:00:36,400 --> 01:00:39,000
Speaker 1: I know the true answer. And he said to me,

919
01:00:39,000 --> 01:00:42,120
Speaker 1: I'm gonna give you. You have to take a guess

920
01:00:42,720 --> 01:00:45,920
Speaker 1: Boone's antler not Boone's antler. If you get it wrong,

921
01:00:46,000 --> 01:00:49,439
Speaker 1: I'm gonna shoot you in the head. Okay, So here

922
01:00:49,440 --> 01:00:51,680
Speaker 1: I am. I have to get it right. Is it

923
01:00:51,680 --> 01:00:57,160
Speaker 1: Boone's antler or not? I'll be shot dead if I

924
01:00:57,240 --> 01:00:59,960
Speaker 1: get the answer wrong. You know what I would say,

925
01:01:01,800 --> 01:01:07,720
Speaker 1: I'd say not Boone's aniler. Put in that situation the

926
01:01:07,760 --> 01:01:10,360
Speaker 1: Boone artifact thing is is funny that you bring up.

927
01:01:11,000 --> 01:01:13,320
Speaker 1: We're publishing an article on the meat eat or dot

928
01:01:13,360 --> 01:01:17,920
Speaker 1: com today called the Guns of wyatt Earp, looking at like,

929
01:01:18,600 --> 01:01:22,360
Speaker 1: what guns did the cinematic wyatt Earp carry? What guns

930
01:01:22,440 --> 01:01:27,080
Speaker 1: did the dime novel wha earb carry? And what guns

931
01:01:27,120 --> 01:01:31,400
Speaker 1: did the real life while wyatt Earp carry? The reality

932
01:01:31,520 --> 01:01:35,040
Speaker 1: is that nobody really knows um and There's been guns

933
01:01:35,040 --> 01:01:38,640
Speaker 1: sold many times at auction that claimed to be wide ARBs,

934
01:01:39,240 --> 01:01:44,320
Speaker 1: but everyone would dispute it um and and nobody really knows.

935
01:01:44,840 --> 01:01:47,560
Speaker 1: So that that feels very much like this Daniel Boone thing.

936
01:01:48,120 --> 01:01:50,320
Speaker 1: So you don't actually know if he carried a cold peacemaker.

937
01:01:51,520 --> 01:01:53,800
Speaker 1: You're gonna have to read the read the article to

938
01:01:54,440 --> 01:01:57,640
Speaker 1: get an idea of what what people think he actually carried.

939
01:01:57,720 --> 01:02:00,240
Speaker 1: In the history of the cinematic and the now bold

940
01:02:00,320 --> 01:02:04,160
Speaker 1: version of wider stuff, Spencer, what would you do if

941
01:02:04,200 --> 01:02:08,240
Speaker 1: Chester held the gun to your head? You say, not

942
01:02:08,320 --> 01:02:11,200
Speaker 1: Boone's antler. I'd say that bone. You're gonna be living

943
01:02:11,200 --> 01:02:13,480
Speaker 1: to tell a notre tale, To tell a tale, what

944
01:02:13,600 --> 01:02:15,560
Speaker 1: are the odds that would be Boone's and not just

945
01:02:15,640 --> 01:02:20,000
Speaker 1: like a newcomon there or Ranella or some or Morris

946
01:02:20,120 --> 01:02:24,320
Speaker 1: anything like that. How did this one antler from an

947
01:02:24,320 --> 01:02:30,760
Speaker 1: extinct not subspecies of ELCV Daniel Boone's subscripture signature on it?

948
01:02:31,200 --> 01:02:33,240
Speaker 1: You think it's old. I think it was a phony

949
01:02:33,320 --> 01:02:35,040
Speaker 1: from a long time ago. If I had to guess,

950
01:02:35,080 --> 01:02:36,280
Speaker 1: I don't really know. I mean, who the hell am

951
01:02:36,280 --> 01:02:38,720
Speaker 1: I don't know? Does it look like it's been drammled

952
01:02:38,760 --> 01:02:42,800
Speaker 1: in there that Yeah, that was what I thought, And

953
01:02:42,840 --> 01:02:45,520
Speaker 1: surely they tested this, But I mean, like you could

954
01:02:45,560 --> 01:02:47,600
Speaker 1: find it. It wouldn't be that big a deal to

955
01:02:47,640 --> 01:02:50,640
Speaker 1: find an elk candler from the early eighteen hundreds and

956
01:02:50,720 --> 01:02:54,720
Speaker 1: then drimble into it and maybe leave it in the

957
01:02:54,800 --> 01:02:57,440
Speaker 1: mud for five years and then pull it out. I'll

958
01:02:57,480 --> 01:03:00,560
Speaker 1: be clear. I think a dude scrap said in their

959
01:03:00,640 --> 01:03:05,680
Speaker 1: long as time ago. That's what I think. Uh, Spencer,

960
01:03:05,720 --> 01:03:10,760
Speaker 1: you you had provided a you had provided a recommendation

961
01:03:10,920 --> 01:03:16,280
Speaker 1: of a transition, a segue to another topic of conversation.

962
01:03:16,320 --> 01:03:17,880
Speaker 1: But I don't want to steal your transition. So do

963
01:03:17,880 --> 01:03:19,880
Speaker 1: you want to do your own transition? No, you do it.

964
01:03:19,880 --> 01:03:24,120
Speaker 1: You'll you'll probably do it better. You're such a fan. Okay, Um,

965
01:03:24,240 --> 01:03:26,640
Speaker 1: you know how Spencer was just talking about Daniel Boone.

966
01:03:27,800 --> 01:03:30,560
Speaker 1: Well we're gonna talk about Boone and Crockett Club for

967
01:03:30,600 --> 01:03:35,280
Speaker 1: a minute. How is that? That was a great Yeah,

968
01:03:35,560 --> 01:03:40,600
Speaker 1: our podcast guest Jim Heffelfinger sent us in this article, Um,

969
01:03:40,640 --> 01:03:42,040
Speaker 1: and I think that he might have been kind of

970
01:03:42,040 --> 01:03:44,760
Speaker 1: responding to when we had him on and we were

971
01:03:44,760 --> 01:03:48,000
Speaker 1: talking about, uh, when we had the Boone and Crockett

972
01:03:48,000 --> 01:03:51,919
Speaker 1: Club people from the Booing Crockett Club on and we're

973
01:03:51,920 --> 01:03:54,400
Speaker 1: talking about like how things like Boone and Crockett Club.

974
01:03:54,400 --> 01:03:56,240
Speaker 1: And see when people talk about like a deer having

975
01:03:56,480 --> 01:03:59,120
Speaker 1: a shot at one sixty white tail, right, you're talking

976
01:03:59,120 --> 01:04:00,960
Speaker 1: about like a measurement system. There's a way to do

977
01:04:01,040 --> 01:04:05,920
Speaker 1: you measure bear skulls is the way you measure dear antlers. Um.

978
01:04:06,040 --> 01:04:08,320
Speaker 1: And there's like record books, so if you shoot a

979
01:04:08,320 --> 01:04:10,280
Speaker 1: big buck you measure You're go like wow, this box

980
01:04:10,280 --> 01:04:14,440
Speaker 1: a white tail and you send it off and they

981
01:04:14,600 --> 01:04:19,440
Speaker 1: put in their record books and um. There's this article

982
01:04:19,480 --> 01:04:22,120
Speaker 1: the heffle Finger sent us that that spencer will break

983
01:04:22,120 --> 01:04:28,280
Speaker 1: down a little bit about criticisms of that system, like

984
01:04:28,440 --> 01:04:32,280
Speaker 1: is it really helpful because it's kind of become like

985
01:04:32,280 --> 01:04:36,120
Speaker 1: like Boone and Crockett Club. The score has become like

986
01:04:36,160 --> 01:04:38,680
Speaker 1: a so it's a social thing. It's like a way

987
01:04:38,720 --> 01:04:41,480
Speaker 1: to brag up whatever. But they like they but they

988
01:04:41,520 --> 01:04:45,200
Speaker 1: point out that it had like a scientific foundation and

989
01:04:45,280 --> 01:04:47,680
Speaker 1: this heffel Finger sent us this article that kind of

990
01:04:47,760 --> 01:04:51,120
Speaker 1: lays into this a little bit. And the criticism would

991
01:04:51,120 --> 01:04:54,720
Speaker 1: be that if if you are trying to track a

992
01:04:54,800 --> 01:04:58,480
Speaker 1: population of critters, how is it helpful to only look

993
01:04:58,520 --> 01:05:01,000
Speaker 1: at the biggest ones. If there's a minute mom size

994
01:05:01,080 --> 01:05:03,640
Speaker 1: to get in, what are you really learning that minimum

995
01:05:03,680 --> 01:05:05,560
Speaker 1: size being? For like, I think a typical white tail

996
01:05:05,640 --> 01:05:09,320
Speaker 1: is one sixty and non typical is I don't know,

997
01:05:09,480 --> 01:05:12,720
Speaker 1: one seventy or one eight. Um are are you really

998
01:05:12,760 --> 01:05:17,480
Speaker 1: actually like learning trends about the health of populations or not?

999
01:05:17,800 --> 01:05:21,600
Speaker 1: So if you're only interested in the big ones, yes, well,

1000
01:05:21,640 --> 01:05:25,800
Speaker 1: because the big ones are the indicators of a healthy

1001
01:05:25,840 --> 01:05:28,720
Speaker 1: population of animals. Did y'all know I'm a boon an

1002
01:05:28,720 --> 01:05:33,000
Speaker 1: official boone Crocket score? No? I didn't know that. So

1003
01:05:34,200 --> 01:05:38,000
Speaker 1: this is this. I love this topic because on the

1004
01:05:38,040 --> 01:05:43,280
Speaker 1: surface it does seem it's easy to buy what this

1005
01:05:43,360 --> 01:05:46,200
Speaker 1: guy's saying. But it's a little bit deeper than that.

1006
01:05:46,280 --> 01:05:50,080
Speaker 1: To understand it. In that you gotta go back to

1007
01:05:50,120 --> 01:05:53,480
Speaker 1: originally when this system was built. It was built before

1008
01:05:53,600 --> 01:05:57,080
Speaker 1: much of our modern science, and the way to understand

1009
01:05:57,160 --> 01:05:59,920
Speaker 1: the health of systems. An indicator was the number of

1010
01:06:00,000 --> 01:06:05,080
Speaker 1: older mature males. So basically they quantified the a number

1011
01:06:05,480 --> 01:06:10,040
Speaker 1: that's that said this is a mature, healthy species and

1012
01:06:10,080 --> 01:06:13,040
Speaker 1: so UH typical Boon and crockert act. The reason BOODH

1013
01:06:13,080 --> 01:06:17,520
Speaker 1: Crocket UH awards and gives preference to symmetry. Is because

1014
01:06:17,560 --> 01:06:24,320
Speaker 1: symmetry typically indicates health. That's I I know that argument,

1015
01:06:24,360 --> 01:06:28,160
Speaker 1: but that is bs well, but it's it's actually not

1016
01:06:28,560 --> 01:06:31,600
Speaker 1: uh the back in the day, back in the day,

1017
01:06:31,800 --> 01:06:35,200
Speaker 1: if a big but that throws a little sticker out

1018
01:06:35,280 --> 01:06:37,600
Speaker 1: his right antler, you know, you don't look at and

1019
01:06:37,640 --> 01:06:39,800
Speaker 1: be like, oh, something must be wrong with them. Well

1020
01:06:39,920 --> 01:06:42,720
Speaker 1: now now it's not talking about that though. And again

1021
01:06:42,760 --> 01:06:45,800
Speaker 1: you gotta think this, this was this system was designed

1022
01:06:45,800 --> 01:06:49,120
Speaker 1: in the you know, a hundred years ago. What it

1023
01:06:49,200 --> 01:06:52,440
Speaker 1: is talking about is when you have an animal that's stressed,

1024
01:06:52,640 --> 01:06:56,040
Speaker 1: you get massive amounts of dissymmetry. I mean, like, you know,

1025
01:06:56,080 --> 01:06:59,120
Speaker 1: the back right leg is messed up, the right antler

1026
01:06:59,160 --> 01:07:03,800
Speaker 1: is going to be messed up up left opposite, Yeah, yeah, yeah,

1027
01:07:03,800 --> 01:07:06,120
Speaker 1: with the front leg. If the front right leg is

1028
01:07:06,160 --> 01:07:08,560
Speaker 1: messed up, then the front right antler is messed up

1029
01:07:08,720 --> 01:07:12,919
Speaker 1: with the leg it's the opposite, yes, And and hey

1030
01:07:13,400 --> 01:07:15,800
Speaker 1: the other thing. And I love it that this came

1031
01:07:15,880 --> 01:07:17,920
Speaker 1: up and just stop me because I could take the

1032
01:07:17,960 --> 01:07:20,000
Speaker 1: next hour and talk about this. And I'm not a

1033
01:07:20,000 --> 01:07:22,600
Speaker 1: big score guy, Like I'm not gonna go like I

1034
01:07:22,640 --> 01:07:27,520
Speaker 1: have no goals to like kill Boone Crockett animals. But

1035
01:07:28,160 --> 01:07:31,640
Speaker 1: I mean, like I would just assume kill a hundred

1036
01:07:31,760 --> 01:07:33,959
Speaker 1: forty deer on the mountain over here. That I love

1037
01:07:34,120 --> 01:07:37,160
Speaker 1: is go to Canada and kill one. You know what

1038
01:07:37,200 --> 01:07:39,240
Speaker 1: I'm saying. I mean, like, I don't. I'm not a

1039
01:07:39,240 --> 01:07:43,360
Speaker 1: score guy, but I think there's relevance to it because

1040
01:07:43,360 --> 01:07:49,880
Speaker 1: of its historical precedence and at the time, actually trophy

1041
01:07:49,960 --> 01:07:53,360
Speaker 1: hunting quote unquote like and that meaning targeting an animal

1042
01:07:53,400 --> 01:07:55,840
Speaker 1: because the size of its head gear is actually what

1043
01:07:55,920 --> 01:07:58,560
Speaker 1: helps save North American hunting. Because the brown is Down

1044
01:07:58,640 --> 01:08:01,040
Speaker 1: philosophy was the way it went. We we're coming out

1045
01:08:01,040 --> 01:08:04,240
Speaker 1: of an era of market hunting, going into an era

1046
01:08:04,280 --> 01:08:06,480
Speaker 1: when all these guys were saying, Nah, don't you know,

1047
01:08:06,480 --> 01:08:08,760
Speaker 1: we gotta leave the females and young, we gotta leave

1048
01:08:08,800 --> 01:08:11,480
Speaker 1: the juvenile males. I'll tell you what I'll do. Let's

1049
01:08:11,520 --> 01:08:14,720
Speaker 1: make let's let's make a number. Let's make a scoring

1050
01:08:14,760 --> 01:08:18,880
Speaker 1: system that rewards killing an older mature mail. And let's

1051
01:08:18,960 --> 01:08:23,080
Speaker 1: make that culturally cool to kill an older mature mail.

1052
01:08:23,439 --> 01:08:26,960
Speaker 1: And it totally took the pressure off of females and

1053
01:08:27,040 --> 01:08:29,839
Speaker 1: young and juvenile males and put it on older mature species,

1054
01:08:29,840 --> 01:08:32,639
Speaker 1: which you've already contributed to the gene pool, and which

1055
01:08:32,680 --> 01:08:36,639
Speaker 1: are the best animals from a conservation standpoint to take out,

1056
01:08:36,720 --> 01:08:39,960
Speaker 1: especially in a vulnerable herd. So like, you gotta think

1057
01:08:39,960 --> 01:08:43,120
Speaker 1: about it that way. And now you know, a hundred

1058
01:08:43,160 --> 01:08:47,560
Speaker 1: and twenty five years later, the Boonoo Crockett Club is

1059
01:08:47,640 --> 01:08:50,559
Speaker 1: doing the main focus of the organization is not their

1060
01:08:50,600 --> 01:08:54,519
Speaker 1: record keeping. They still keep records, but the main focus

1061
01:08:54,520 --> 01:09:01,440
Speaker 1: of their their thrust is conservation efforts and dissemination of information.

1062
01:09:01,560 --> 01:09:04,040
Speaker 1: And they're funding a whole bunch of stuff. You know.

1063
01:09:04,040 --> 01:09:07,360
Speaker 1: They just have this niche that they work in. And so, man,

1064
01:09:07,720 --> 01:09:11,519
Speaker 1: when people give BC a bad rap, I got the man,

1065
01:09:11,640 --> 01:09:14,800
Speaker 1: I'm not I'll kick your I'm not giving BC a

1066
01:09:14,800 --> 01:09:18,160
Speaker 1: bad rap. No, no, no, not you, Steve. I know

1067
01:09:18,240 --> 01:09:21,080
Speaker 1: you would. I know you're not. Really, I'm not saying you.

1068
01:09:21,120 --> 01:09:23,880
Speaker 1: I'm just saying people because people do. And it's because

1069
01:09:23,880 --> 01:09:25,920
Speaker 1: they don't know. They couldn't tell you what I just

1070
01:09:25,960 --> 01:09:29,240
Speaker 1: told you. You know, it's super embarrassing to me, Clay.

1071
01:09:29,840 --> 01:09:31,800
Speaker 1: I spent my whole life saying my old man was

1072
01:09:31,880 --> 01:09:38,160
Speaker 1: a score because he was like, dude, I know he

1073
01:09:38,240 --> 01:09:39,960
Speaker 1: scored for Pope and young, But I always said he

1074
01:09:39,960 --> 01:09:42,840
Speaker 1: was a scorer for commemorative Bucks of Michigan, Boone and

1075
01:09:42,880 --> 01:09:44,439
Speaker 1: Crockett and Pope and young. But then the dudes that

1076
01:09:44,439 --> 01:09:46,800
Speaker 1: Boone and crock had told me, your old man wasn't

1077
01:09:46,840 --> 01:09:49,719
Speaker 1: a score because they went and looked in the database

1078
01:09:49,760 --> 01:09:52,840
Speaker 1: and his old his name wasn't in there, so he

1079
01:09:52,920 --> 01:09:55,640
Speaker 1: was lying or I didn't remember right, But he was

1080
01:09:55,680 --> 01:09:58,120
Speaker 1: a score because everybody, I've all few grown up people

1081
01:09:58,120 --> 01:10:02,799
Speaker 1: would bring their gear over to have a score them. Yeah.

1082
01:10:02,880 --> 01:10:06,720
Speaker 1: And originally the scoring of deer was was like very rudimentary.

1083
01:10:06,880 --> 01:10:10,400
Speaker 1: It was like spread and length of time and that

1084
01:10:10,479 --> 01:10:13,439
Speaker 1: was it. But then in nineteen fifties we got the

1085
01:10:13,479 --> 01:10:15,560
Speaker 1: model that we now know today. We're on like a

1086
01:10:15,720 --> 01:10:19,560
Speaker 1: five by five buck, you would have twenty measurements or

1087
01:10:19,600 --> 01:10:23,040
Speaker 1: something like that. Um, So, you know, I think the

1088
01:10:23,080 --> 01:10:26,160
Speaker 1: main thing to think about too, like to just say

1089
01:10:26,200 --> 01:10:30,439
Speaker 1: this is like solid rational thinking that takes into account

1090
01:10:30,479 --> 01:10:34,120
Speaker 1: the last hundred years of what's happened in American North

1091
01:10:34,120 --> 01:10:39,080
Speaker 1: American conservation is that a scoring system turned the hunting

1092
01:10:39,120 --> 01:10:42,840
Speaker 1: culture from a market hunting culture into a conservation hunting

1093
01:10:42,840 --> 01:10:45,920
Speaker 1: culture by putting emphasis on older mature males. I think

1094
01:10:45,920 --> 01:10:48,640
Speaker 1: it's that simple and That's why we had tipped to

1095
01:10:48,640 --> 01:10:50,760
Speaker 1: it today. That's why we say, oh man, he killed

1096
01:10:50,760 --> 01:10:52,519
Speaker 1: a buono crocket buck. Guys say that and they have

1097
01:10:52,560 --> 01:10:54,720
Speaker 1: no idea what they're saying. I mean, people say it

1098
01:10:54,760 --> 01:10:58,880
Speaker 1: all the time and and and it's it's like, heck, yeah,

1099
01:10:58,960 --> 01:11:03,280
Speaker 1: I'm glad somebody stepped up to the plate and changed

1100
01:11:03,600 --> 01:11:17,559
Speaker 1: us from a bunch of market hunting fools. So the

1101
01:11:17,600 --> 01:11:21,120
Speaker 1: criticism of that decay would be like in, and he's

1102
01:11:21,160 --> 01:11:25,440
Speaker 1: not hacking on BC, That's right, I understand the criticism

1103
01:11:25,560 --> 01:11:30,840
Speaker 1: would be there like in it's not useful, like there

1104
01:11:30,880 --> 01:11:34,360
Speaker 1: there are other ways that we could uh measure the

1105
01:11:34,360 --> 01:11:37,439
Speaker 1: health of a population of critters, And so they we

1106
01:11:37,560 --> 01:11:41,080
Speaker 1: and we do. That's right, And so hefl Finger and

1107
01:11:41,800 --> 01:11:44,160
Speaker 1: Taylor Lashar, who has written for the Meat Eater dot

1108
01:11:44,200 --> 01:11:50,080
Speaker 1: com very recently, they looked at Pope and Young Dallas,

1109
01:11:50,080 --> 01:11:53,360
Speaker 1: Safari Club and Boone and Crockett tracked the trends over time,

1110
01:11:53,760 --> 01:11:56,439
Speaker 1: and they wanted to see if they all basically agreed

1111
01:11:56,479 --> 01:12:00,559
Speaker 1: with each other on like the trend of the or

1112
01:12:00,880 --> 01:12:03,840
Speaker 1: of deer um. And if they did agree with each other,

1113
01:12:04,120 --> 01:12:06,519
Speaker 1: then you could see why this is relevant and why

1114
01:12:06,520 --> 01:12:08,559
Speaker 1: this information would be helpful if they didn't agree with

1115
01:12:08,800 --> 01:12:12,360
Speaker 1: with each other, Say you had the you know, Boone

1116
01:12:12,439 --> 01:12:15,200
Speaker 1: Crockett club was really high in the fifties and now

1117
01:12:15,240 --> 01:12:18,840
Speaker 1: it's really low. For like, the score of critters and

1118
01:12:18,840 --> 01:12:20,640
Speaker 1: Pope and Young was really low in the fifties and

1119
01:12:20,640 --> 01:12:23,559
Speaker 1: now it's really high. Then then you could argue that

1120
01:12:23,600 --> 01:12:28,400
Speaker 1: these criticisms, um like are legit. What they noticed, though,

1121
01:12:28,680 --> 01:12:31,760
Speaker 1: is that all three clubs agree with each other on

1122
01:12:32,000 --> 01:12:35,360
Speaker 1: the trend of the score of antlers from like the

1123
01:12:35,400 --> 01:12:39,720
Speaker 1: fifties up until now, meaning that this is relevant. You

1124
01:12:39,800 --> 01:12:43,400
Speaker 1: can like assess something based off of only looking at

1125
01:12:43,439 --> 01:12:47,599
Speaker 1: the top one per cent of critters only because they

1126
01:12:47,600 --> 01:12:53,480
Speaker 1: all agree that that was my understanding from a criticism,

1127
01:12:53,680 --> 01:12:57,120
Speaker 1: that's a criticism. No, No, he is saying that because

1128
01:12:57,120 --> 01:13:00,360
Speaker 1: Pope and Young has the same trend as d C I,

1129
01:13:00,760 --> 01:13:04,120
Speaker 1: and because d C I has the same trend as

1130
01:13:04,120 --> 01:13:07,599
Speaker 1: Boone and Crockett, that you could actually look at these

1131
01:13:07,680 --> 01:13:12,120
Speaker 1: numbers and like make some informed decisions by only looking

1132
01:13:12,160 --> 01:13:14,720
Speaker 1: at the biggest things that have the biggest antlers and

1133
01:13:14,760 --> 01:13:17,000
Speaker 1: the biggest horns. I wish I would have read this

1134
01:13:17,040 --> 01:13:20,439
Speaker 1: thing myself, because I'm telling you that's what it says

1135
01:13:21,400 --> 01:13:25,200
Speaker 1: oh my god. But okay, think about places where where

1136
01:13:25,240 --> 01:13:27,800
Speaker 1: they are not big deer. Let's just take white tails

1137
01:13:27,880 --> 01:13:30,200
Speaker 1: that we're all familiar with, Like, there's not a lot

1138
01:13:30,200 --> 01:13:33,920
Speaker 1: of Boone and Crockett deer coming out of Mississippi, Georgia, Arkansas,

1139
01:13:34,520 --> 01:13:39,640
Speaker 1: and it's it's because are populations of deer for the

1140
01:13:39,760 --> 01:13:47,160
Speaker 1: habitat are are sometimes overpopulated. Um, you know, like big

1141
01:13:47,200 --> 01:13:49,200
Speaker 1: deer coming out of the Midwest, or we probably have

1142
01:13:49,360 --> 01:13:51,479
Speaker 1: some of the healthiest deer herds in the country minus

1143
01:13:51,520 --> 01:13:55,000
Speaker 1: c W D. But man, here's why Spencer didn't read

1144
01:13:55,000 --> 01:13:59,120
Speaker 1: the article right. Think about it like this. Let's say

1145
01:13:59,560 --> 01:14:01,720
Speaker 1: I said, I'm gonna find a way to measure the

1146
01:14:01,720 --> 01:14:05,080
Speaker 1: health of humans by measuring their thumbs. And I'm like,

1147
01:14:05,160 --> 01:14:07,599
Speaker 1: what I do is I measure the length of their thumb.

1148
01:14:08,720 --> 01:14:12,519
Speaker 1: Then I measure around the big knuckle, and that thumb

1149
01:14:12,600 --> 01:14:14,639
Speaker 1: gets a score. And this is how I'm gonna track

1150
01:14:14,800 --> 01:14:18,040
Speaker 1: how well humans are doing. Is are they making big

1151
01:14:18,080 --> 01:14:22,160
Speaker 1: people with big thumbs. At the same time, Chester here says,

1152
01:14:22,240 --> 01:14:25,080
Speaker 1: you know, I'm gonna attract how well humans are doing

1153
01:14:25,120 --> 01:14:27,639
Speaker 1: by measuring thumbs. But what I'm gonna do is I'm

1154
01:14:27,640 --> 01:14:31,440
Speaker 1: gonna measure the length of the thumb, and then the

1155
01:14:31,479 --> 01:14:35,240
Speaker 1: circumference of the thickest part of that thumb, regardless of

1156
01:14:35,280 --> 01:14:38,960
Speaker 1: where it sits. And Seth then says, yeah, I'm gonna

1157
01:14:39,040 --> 01:14:41,680
Speaker 1: check the well being of humans by measuring thumbs. I'm

1158
01:14:41,680 --> 01:14:46,280
Speaker 1: gonna measure the length of the thumb. Plus I'm gonna

1159
01:14:46,280 --> 01:14:50,600
Speaker 1: take four circumferences off the thumb and the thickness of

1160
01:14:50,640 --> 01:14:52,439
Speaker 1: the nail, and the thickness of the nail. He's gonna

1161
01:14:52,439 --> 01:14:55,800
Speaker 1: measure the thickest of the nail too. So here we

1162
01:14:55,920 --> 01:14:59,960
Speaker 1: have Buona Crock, I'm boone Crock, Club Chester so far

1163
01:15:00,200 --> 01:15:04,479
Speaker 1: club Seth is poping. Okay, we're all measuring thumbs a

1164
01:15:04,479 --> 01:15:09,720
Speaker 1: little different, but we're all measuring thumbs. But they do.

1165
01:15:10,160 --> 01:15:14,760
Speaker 1: They do have difference, very minute different. That's why I'm

1166
01:15:14,800 --> 01:15:19,840
Speaker 1: trying to make these thumb measurements minutely different. If later

1167
01:15:20,280 --> 01:15:23,519
Speaker 1: someone said, oh, what his Ronella's thing about measuring these

1168
01:15:23,520 --> 01:15:28,000
Speaker 1: stupid thumbs to see how well people are doing? Um,

1169
01:15:28,120 --> 01:15:32,000
Speaker 1: let's test whether it's appropriate to see what Chester and

1170
01:15:32,040 --> 01:15:34,920
Speaker 1: Seth thumb measurements are like. And Chester and Seth are like.

1171
01:15:34,920 --> 01:15:38,160
Speaker 1: Oh yeah, bro, um, I've noticed the same thing with

1172
01:15:38,200 --> 01:15:41,960
Speaker 1: my thumb measurements. That doesn't tell you anything. Well, I mean,

1173
01:15:42,400 --> 01:15:45,200
Speaker 1: there's a lot this this analogy. I'm not sure it

1174
01:15:45,240 --> 01:15:50,040
Speaker 1: flies because it's actually it's actually a perfect analogy. We

1175
01:15:50,240 --> 01:15:54,760
Speaker 1: don't know that human thumb length has any correlation to

1176
01:15:54,880 --> 01:15:58,800
Speaker 1: human health. We do know antler development. We did know

1177
01:15:58,880 --> 01:16:03,160
Speaker 1: that antler development, which is directly related to animal health,

1178
01:16:03,800 --> 01:16:08,000
Speaker 1: is is massively correlated. Hey here, I think this could

1179
01:16:08,080 --> 01:16:10,400
Speaker 1: help solve some of this debate. I want to make

1180
01:16:10,439 --> 01:16:13,559
Speaker 1: up quick bet with you, Clay. Okay, I'll bet you

1181
01:16:13,680 --> 01:16:16,639
Speaker 1: five dollars that Spencer is not doing a good job

1182
01:16:16,680 --> 01:16:18,640
Speaker 1: of telling us what the article is about. Can I

1183
01:16:18,680 --> 01:16:21,519
Speaker 1: read you a few sentences from the article? Let me

1184
01:16:21,560 --> 01:16:26,400
Speaker 1: make my bet first. I mean, who's deciding if he's

1185
01:16:26,439 --> 01:16:34,160
Speaker 1: done this? I'll bet you a dollar then, okay, alright, Spencer,

1186
01:16:34,240 --> 01:16:37,639
Speaker 1: prove that you're reading the article right. This is from

1187
01:16:37,680 --> 01:16:40,559
Speaker 1: the article. If trends in horn and antler size were

1188
01:16:40,560 --> 01:16:43,600
Speaker 1: being influenced by a minimum entry score biased, then we

1189
01:16:43,600 --> 01:16:48,120
Speaker 1: would expect that different record different records programs with different

1190
01:16:48,160 --> 01:16:53,479
Speaker 1: minimum size requests that hard story about the direction and

1191
01:16:53,479 --> 01:16:56,679
Speaker 1: the strength of trends. For example, the strength of trends

1192
01:16:56,840 --> 01:16:58,960
Speaker 1: in the horns and antlers recorded by the Boone and

1193
01:16:58,960 --> 01:17:01,800
Speaker 1: Crockett Club with a higher minimum should be less than

1194
01:17:01,840 --> 01:17:04,599
Speaker 1: the trends in the poping young records with a lower

1195
01:17:04,720 --> 01:17:07,400
Speaker 1: requirement from well, you didn't tell me that part? What

1196
01:17:07,400 --> 01:17:09,240
Speaker 1: what part did I leave out about the minimums and

1197
01:17:09,240 --> 01:17:12,360
Speaker 1: all that? Yeah, I said that the minimums that there's

1198
01:17:12,400 --> 01:17:16,320
Speaker 1: like check or cash. No, because here's the thing, here's

1199
01:17:16,360 --> 01:17:21,439
Speaker 1: the thing. Just because he understood it doesn't mean that

1200
01:17:21,479 --> 01:17:26,760
Speaker 1: he delivered it properly. I've been working with this guy

1201
01:17:26,840 --> 01:17:29,599
Speaker 1: for a little while, and I'll tell you if there's

1202
01:17:29,640 --> 01:17:32,559
Speaker 1: one thing that Spencer new heart is, it is it

1203
01:17:32,760 --> 01:17:37,080
Speaker 1: is like correct in you know, Okay, he he's on

1204
01:17:37,160 --> 01:17:40,479
Speaker 1: his game. So he was, Yes, I'm changing my complaint.

1205
01:17:41,240 --> 01:17:45,080
Speaker 1: He understood it well, but in explaining it to me

1206
01:17:45,240 --> 01:17:47,720
Speaker 1: now he left out the part that would have made

1207
01:17:47,760 --> 01:17:54,000
Speaker 1: me understand. Now here's here's my Now it's making total sense.

1208
01:17:55,000 --> 01:17:58,559
Speaker 1: And they saw the trends were the exact same. See,

1209
01:17:58,560 --> 01:18:00,800
Speaker 1: I knew if Hefflefinger sent it over, it had to

1210
01:18:00,800 --> 01:18:03,800
Speaker 1: have been reasonable. That's why that's why I thought you

1211
01:18:03,840 --> 01:18:07,680
Speaker 1: were messing it up. My gripe with the Boone and

1212
01:18:07,680 --> 01:18:10,200
Speaker 1: Crocket Club is not the Boone and crock Club's fault.

1213
01:18:11,080 --> 01:18:13,920
Speaker 1: It is when people hold up states like Wisconsin and

1214
01:18:13,960 --> 01:18:17,000
Speaker 1: say that they are the big buck king because they

1215
01:18:17,040 --> 01:18:19,880
Speaker 1: have more Boone and Crockett and Pulping Young records than anybody.

1216
01:18:20,479 --> 01:18:23,320
Speaker 1: But I am sure that they do not kill two

1217
01:18:23,439 --> 01:18:26,000
Speaker 1: or three times as many giant deer as a state

1218
01:18:26,080 --> 01:18:29,920
Speaker 1: like Texas. It's just that the culture prioritizes entering them

1219
01:18:30,120 --> 01:18:33,120
Speaker 1: in places like Wisconsin. Yeah, and then in Texas. You

1220
01:18:33,120 --> 01:18:35,280
Speaker 1: gotta remember if it's high fence that they won't know,

1221
01:18:35,439 --> 01:18:39,880
Speaker 1: they'll still accept it in some other not Boon and Crockett. Well, no,

1222
01:18:40,120 --> 01:18:42,040
Speaker 1: they do, but they also don't they keep track of

1223
01:18:42,120 --> 01:18:45,679
Speaker 1: road kills and stuff and some other sort of thing. Well,

1224
01:18:45,920 --> 01:18:50,000
Speaker 1: so Boone Crockett Club keeps track of all animals despite

1225
01:18:50,120 --> 01:18:53,920
Speaker 1: method of kill, and they even keep records of pickups

1226
01:18:53,920 --> 01:18:56,439
Speaker 1: because again it's a biological record. So it doesn't matter

1227
01:18:56,479 --> 01:19:00,000
Speaker 1: if a hunter kills it or not. Spencer, you can't

1228
01:19:00,120 --> 01:19:02,639
Speaker 1: get in the like, you can't sort of be honored

1229
01:19:02,680 --> 01:19:06,160
Speaker 1: if it's a high fenced deer. Texas doesn't have to

1230
01:19:06,160 --> 01:19:08,719
Speaker 1: be Texas in this argument either, it can be any state.

1231
01:19:08,840 --> 01:19:13,120
Speaker 1: Wisconsin does not kill twice as many big giant bucks

1232
01:19:13,160 --> 01:19:16,720
Speaker 1: as Kansas, despite them having twice as many entries in

1233
01:19:16,800 --> 01:19:19,880
Speaker 1: the books. Yeah, you think so dudes in Wisconsin have

1234
01:19:19,920 --> 01:19:23,880
Speaker 1: a higher proclivity or have there's a greater chance that

1235
01:19:23,960 --> 01:19:28,280
Speaker 1: some bugg in Wisconsin is gonna register his buck through BNC. Think.

1236
01:19:28,680 --> 01:19:31,680
Speaker 1: I think that's because all the buck pools at the

1237
01:19:31,680 --> 01:19:35,639
Speaker 1: bars could be because the score their deer to win

1238
01:19:35,720 --> 01:19:39,960
Speaker 1: the stuff, to win the money. You know, I buy that.

1239
01:19:40,400 --> 01:19:43,880
Speaker 1: I think you're I think you're sort of onto something there.

1240
01:19:43,880 --> 01:19:46,080
Speaker 1: But you're also talking about hunter numbers when you're talking

1241
01:19:46,080 --> 01:19:50,880
Speaker 1: about Wisconsin and Kansas. I mean, like incredibly more hunters

1242
01:19:50,880 --> 01:19:54,439
Speaker 1: in Wisconsin than Kansas. But I think your points well taken,

1243
01:19:54,600 --> 01:19:58,719
Speaker 1: And I don't think the Boone Crockett Boone Crockett Club

1244
01:19:58,840 --> 01:20:02,559
Speaker 1: is trying to say that this is an infallible biological record.

1245
01:20:03,000 --> 01:20:05,600
Speaker 1: You know, at the time it was created, it was

1246
01:20:05,960 --> 01:20:09,240
Speaker 1: the best we had. It was innovative in it. It

1247
01:20:09,320 --> 01:20:14,840
Speaker 1: worked to turn a market hunting culture into a conservation

1248
01:20:14,920 --> 01:20:18,240
Speaker 1: culture where we said we're going to we're gonna give

1249
01:20:18,640 --> 01:20:21,160
Speaker 1: value to older mature males and a species. So that's

1250
01:20:21,160 --> 01:20:23,439
Speaker 1: a good thing. But what you're saying, Spencer is is

1251
01:20:23,600 --> 01:20:26,200
Speaker 1: right there. There could be holes inside of it, But

1252
01:20:26,320 --> 01:20:28,880
Speaker 1: that doesn't mean that it's totally invalid. You know, in

1253
01:20:28,880 --> 01:20:33,719
Speaker 1: Boone and crock it's not saying that. You know, certainly

1254
01:20:33,800 --> 01:20:36,519
Speaker 1: every deer that net Boone and Croc nets Boone and

1255
01:20:36,520 --> 01:20:41,840
Speaker 1: Crockett is not being scored, that's for sure. But but ah,

1256
01:20:43,640 --> 01:20:46,200
Speaker 1: I think it. I think it has I think it

1257
01:20:46,240 --> 01:20:49,800
Speaker 1: has good historical precedents. One thing I will say is

1258
01:20:49,840 --> 01:20:53,120
Speaker 1: that the net score is what people get tripped up

1259
01:20:53,160 --> 01:20:55,400
Speaker 1: on as they say, that's not relevant. And that's where

1260
01:20:55,439 --> 01:20:59,559
Speaker 1: you take the symmetrical difference from one side and deducted

1261
01:20:59,600 --> 01:21:01,800
Speaker 1: from the other. They're in the animal is penalized in

1262
01:21:01,840 --> 01:21:04,960
Speaker 1: the scoring system, and people are like, that's not relevant.

1263
01:21:05,360 --> 01:21:09,200
Speaker 1: And you know, I think I think Boone and Crockett

1264
01:21:10,120 --> 01:21:12,880
Speaker 1: they can't change the way they score animals because they've

1265
01:21:12,880 --> 01:21:15,600
Speaker 1: been scoring animals for a hundred and twenty years. Do

1266
01:21:15,640 --> 01:21:20,200
Speaker 1: you understand that. It's like right here, yeah, So, I

1267
01:21:20,240 --> 01:21:22,400
Speaker 1: mean I think all those guys would say, yeah, there

1268
01:21:22,400 --> 01:21:25,320
Speaker 1: there may be a better way to actually evaluate the

1269
01:21:25,400 --> 01:21:27,519
Speaker 1: size of a white tailed dear rac but we're not

1270
01:21:27,560 --> 01:21:30,280
Speaker 1: gonna do it because we can't because they would invalidate

1271
01:21:30,320 --> 01:21:32,840
Speaker 1: all the scores behind it. You know. So I think

1272
01:21:32,920 --> 01:21:35,080
Speaker 1: when you look at it, like the idea that the

1273
01:21:35,120 --> 01:21:37,840
Speaker 1: Boone and Crockett Club is just a bunch of ego

1274
01:21:37,960 --> 01:21:40,439
Speaker 1: driven guys wanting to see you as the biggest buck.

1275
01:21:40,640 --> 01:21:42,559
Speaker 1: It's just not true. I mean, it's just really not

1276
01:21:42,680 --> 01:21:45,320
Speaker 1: I mean, I and we know these guys, Steve, you

1277
01:21:45,360 --> 01:21:46,920
Speaker 1: know a lot of these guys, and I mean they're

1278
01:21:47,760 --> 01:21:51,840
Speaker 1: their conservation minded hunters just like us that you know,

1279
01:21:52,120 --> 01:21:57,799
Speaker 1: very few, very few guys are chasing scores. That's my thoughts.

1280
01:21:58,880 --> 01:22:03,000
Speaker 1: That's fine, that's good. Well are you gonna debate me

1281
01:22:03,080 --> 01:22:05,400
Speaker 1: on Oh? I don't know, we can. I don't know.

1282
01:22:05,479 --> 01:22:07,799
Speaker 1: I just kind of feel like I feel like it's

1283
01:22:07,840 --> 01:22:09,880
Speaker 1: it's like leading to something, man like, it's gonna lead

1284
01:22:09,880 --> 01:22:13,439
Speaker 1: to us going toe to toe on something. Oh I

1285
01:22:13,479 --> 01:22:15,680
Speaker 1: don't know. Okay, you didn't have it. I thought you

1286
01:22:15,720 --> 01:22:20,720
Speaker 1: had a specific Oh no, no no, oh now I'm disappointed.

1287
01:22:21,360 --> 01:22:24,680
Speaker 1: I just want to swear off on something, man so

1288
01:22:25,640 --> 01:22:32,200
Speaker 1: real quick, speaking of watch this segue, Spencer speaking of

1289
01:22:34,120 --> 01:22:41,880
Speaker 1: deer having um big old antlers. Uh. I magine if

1290
01:22:41,880 --> 01:22:46,960
Speaker 1: they had big old tusks take it away, seth, And

1291
01:22:47,000 --> 01:22:52,439
Speaker 1: they used to that was good over good. They don't anymore.

1292
01:22:53,600 --> 01:23:00,799
Speaker 1: But some dear it's rare. Some dear still have fangs

1293
01:23:00,920 --> 01:23:06,840
Speaker 1: or K nine's and the month Jack. Yeah, there's there's

1294
01:23:06,840 --> 01:23:09,280
Speaker 1: still species of deer out there that have them. Yeah,

1295
01:23:09,400 --> 01:23:12,080
Speaker 1: his name. One of them is the month Remy shot

1296
01:23:12,120 --> 01:23:16,799
Speaker 1: one month. Where are they from? Keep talking anyway, there's

1297
01:23:16,840 --> 01:23:20,559
Speaker 1: a South Asia, there's Asia. He's got little tusks, yeah,

1298
01:23:20,720 --> 01:23:27,599
Speaker 1: great big tuscans. Um. There's a podcast listener that that

1299
01:23:27,640 --> 01:23:30,120
Speaker 1: wrote in and sent a picture of a deer he

1300
01:23:30,240 --> 01:23:40,120
Speaker 1: shot in South Texas, um that had canines. Um, And

1301
01:23:40,360 --> 01:23:43,880
Speaker 1: so I started looking in a deer with K nine's

1302
01:23:43,920 --> 01:23:48,000
Speaker 1: and UM found an article written by Kip Adams back

1303
01:23:48,040 --> 01:23:54,560
Speaker 1: in two thousand sixteen where he cited a lot of

1304
01:23:54,560 --> 01:24:00,760
Speaker 1: people in here. Um. But basically, uh K nine is

1305
01:24:00,800 --> 01:24:04,920
Speaker 1: like an evolutionary throwback to ancestral deer that had big

1306
01:24:05,000 --> 01:24:11,080
Speaker 1: K nine's big fangs. Um, and that's what like elk ivory. Yeah,

1307
01:24:11,280 --> 01:24:13,800
Speaker 1: because the vestigial tusk like it used to have a

1308
01:24:13,840 --> 01:24:17,160
Speaker 1: tusk out doesn't. Yeah, which do you think you think

1309
01:24:17,240 --> 01:24:22,719
Speaker 1: like thousand years from now they'll eventually just be gone?

1310
01:24:23,360 --> 01:24:26,120
Speaker 1: You think they'll evolve away from that? I don't think

1311
01:24:26,120 --> 01:24:29,680
Speaker 1: a thousand years is ten thousand million. I don't know

1312
01:24:29,760 --> 01:24:32,280
Speaker 1: I think it's headed towards the going away. Yeah, they

1313
01:24:32,320 --> 01:24:35,240
Speaker 1: just don't need them. It's headed towards going away. Um.

1314
01:24:35,240 --> 01:24:39,360
Speaker 1: But there was interesting studies done, um, you know, across

1315
01:24:39,479 --> 01:24:43,720
Speaker 1: the US, and it kind of seems as if like

1316
01:24:43,840 --> 01:24:47,240
Speaker 1: really like depending on the region, it's more prevalent than

1317
01:24:47,280 --> 01:24:53,040
Speaker 1: other places. For instance, UM, in Michigan's Upper Peninsula there

1318
01:24:53,120 --> 01:24:56,120
Speaker 1: was they looked at a hundred and sixty six deer

1319
01:24:56,320 --> 01:24:59,920
Speaker 1: and found that four of those deer, which is two points,

1320
01:25:00,000 --> 01:25:07,000
Speaker 1: we had k nines. Um. They looked deer in the

1321
01:25:07,040 --> 01:25:12,320
Speaker 1: Lower Peninsula, they looked at a hundred thirty four which

1322
01:25:12,360 --> 01:25:19,679
Speaker 1: one of them had K nine which is point zero seven. UM.

1323
01:25:19,720 --> 01:25:24,880
Speaker 1: In Florida they looked at deer. Four of those had

1324
01:25:24,960 --> 01:25:31,720
Speaker 1: K nine's four. So I don't know, could be that

1325
01:25:32,080 --> 01:25:35,760
Speaker 1: deer in in certain areas they're still holding onto that

1326
01:25:36,560 --> 01:25:39,960
Speaker 1: ancient ancestral trade. Yeah. We had a guy right in

1327
01:25:40,080 --> 01:25:42,240
Speaker 1: I remember this dude right and in. But another dude

1328
01:25:42,280 --> 01:25:46,760
Speaker 1: wrote in recently and he had shot a fanged a

1329
01:25:46,920 --> 01:25:50,599
Speaker 1: tusked buck. I think it was in Florida that had

1330
01:25:50,640 --> 01:25:55,960
Speaker 1: this crazy facial matt marking, like it had like this

1331
01:25:56,160 --> 01:25:59,200
Speaker 1: mask on its face black and they were saying that

1332
01:25:59,200 --> 01:26:02,640
Speaker 1: that was sort of like this this you know, this

1333
01:26:02,840 --> 01:26:06,800
Speaker 1: like this recessive gene that had been triggered in that

1334
01:26:06,880 --> 01:26:12,040
Speaker 1: deer for whatever reason. Louisiana was it Louisiana a crazy

1335
01:26:12,200 --> 01:26:19,880
Speaker 1: looking like face mask similar to other deer forms. It

1336
01:26:20,000 --> 01:26:22,519
Speaker 1: had a black y on its face. At the top

1337
01:26:22,560 --> 01:26:26,040
Speaker 1: of the y started it like its pedicles, and then

1338
01:26:26,240 --> 01:26:29,360
Speaker 1: it met like like where its eyes are, and then

1339
01:26:29,400 --> 01:26:31,760
Speaker 1: that why the bottom of it continued all the way

1340
01:26:31,800 --> 01:26:34,040
Speaker 1: down to its nose. And that was the marketing. Jim

1341
01:26:34,080 --> 01:26:37,760
Speaker 1: Heffinger was quoted an article about that deer saying, yeah,

1342
01:26:37,760 --> 01:26:41,240
Speaker 1: that's just like a throwback um piece of genetics that

1343
01:26:41,400 --> 01:26:45,400
Speaker 1: made it into this deer. In he also talked about

1344
01:26:45,439 --> 01:26:47,240
Speaker 1: the black markings on their face. If you look at

1345
01:26:47,280 --> 01:26:50,800
Speaker 1: a white tails lower jaw, can you picture white tails

1346
01:26:50,880 --> 01:26:54,880
Speaker 1: lower jaw where it has that black ring like right

1347
01:26:54,920 --> 01:26:58,600
Speaker 1: behind its nose. That is where the fangs would have

1348
01:26:58,640 --> 01:27:01,519
Speaker 1: stuck out in a white tail, and it'd be black

1349
01:27:01,600 --> 01:27:05,800
Speaker 1: there to make them show better other deer and show

1350
01:27:05,840 --> 01:27:09,320
Speaker 1: them how big and impressive their fangs are. So you

1351
01:27:09,360 --> 01:27:13,479
Speaker 1: see that black ring along the bottom jaw. I like that, man.

1352
01:27:14,439 --> 01:27:16,639
Speaker 1: One last thing, when I touch on real quick, another

1353
01:27:16,720 --> 01:27:22,040
Speaker 1: dude wrote in tell this story south Yep. So there's

1354
01:27:22,080 --> 01:27:25,760
Speaker 1: another dude that that rode in. He's from Um, Southeast

1355
01:27:25,760 --> 01:27:32,400
Speaker 1: New Hampshire, and he wrote in about a buck. He

1356
01:27:32,439 --> 01:27:34,120
Speaker 1: thought it was a buck, a buck that he killed

1357
01:27:34,800 --> 01:27:37,920
Speaker 1: um in southeast New Hampshire. He said it was in

1358
01:27:37,960 --> 01:27:40,800
Speaker 1: an area that has very high hunter density, a lot

1359
01:27:40,800 --> 01:27:47,080
Speaker 1: of pressure. Um. He shot at November nine and it

1360
01:27:47,680 --> 01:27:51,360
Speaker 1: was with three other doughs. It was four deer altogether,

1361
01:27:51,479 --> 01:27:53,519
Speaker 1: and he thought it was weird when the deer came

1362
01:27:53,560 --> 01:27:57,920
Speaker 1: through that the buck was the first of the four.

1363
01:27:58,120 --> 01:28:00,760
Speaker 1: So the buck was leading the dos, which but it

1364
01:28:00,800 --> 01:28:04,720
Speaker 1: wasn't a buck. It wasn't a buck. You would think

1365
01:28:04,760 --> 01:28:06,720
Speaker 1: November nine that the buck would be in the back,

1366
01:28:06,800 --> 01:28:10,120
Speaker 1: but it wasn't a buck. He shoots this deer. Oh,

1367
01:28:10,160 --> 01:28:12,280
Speaker 1: I got what he's saying. It was unusual because yeah,

1368
01:28:12,280 --> 01:28:14,880
Speaker 1: the buck would always be following the dos. Yeah, I

1369
01:28:14,920 --> 01:28:17,000
Speaker 1: got you. Yeah. Let me let me plug an article

1370
01:28:17,040 --> 01:28:19,240
Speaker 1: real quick. Pat Jrking is publishing an article in the

1371
01:28:19,240 --> 01:28:21,960
Speaker 1: media dot com in January that talks about why the

1372
01:28:22,000 --> 01:28:24,920
Speaker 1: bucks and bulls are always last when they're walking out

1373
01:28:25,000 --> 01:28:27,559
Speaker 1: at night and you're waiting to kill him. Why are

1374
01:28:27,560 --> 01:28:31,360
Speaker 1: they always last ones I already know. I have have to

1375
01:28:31,400 --> 01:28:37,720
Speaker 1: read that one. I don't really know. Um. Anyway, So

1376
01:28:37,880 --> 01:28:41,240
Speaker 1: this this guy, he shoots his deer and it it

1377
01:28:41,320 --> 01:28:45,000
Speaker 1: goes a short distance and falls, and he calls a

1378
01:28:45,040 --> 01:28:48,200
Speaker 1: buddy to have him help him, and why he's waiting

1379
01:28:48,240 --> 01:28:52,920
Speaker 1: for his buddy goes down and looks like for first blood. Um.

1380
01:28:53,000 --> 01:28:56,280
Speaker 1: And while he's doing that, he noticed a a spike

1381
01:28:56,360 --> 01:28:58,639
Speaker 1: comes around and by a grunting and all hot and bother,

1382
01:28:58,680 --> 01:29:00,559
Speaker 1: because can you do that grunt even though you can't

1383
01:29:00,560 --> 01:29:03,280
Speaker 1: do the squeal of or the roar of a weasel?

1384
01:29:03,800 --> 01:29:12,559
Speaker 1: Yeah right, all right, all right, um. Anyway, he comes

1385
01:29:12,560 --> 01:29:14,479
Speaker 1: all he comes by, all hot and bothered. The close

1386
01:29:14,560 --> 01:29:20,360
Speaker 1: distance finally sees him, spooks off, and the guy here's

1387
01:29:20,400 --> 01:29:24,120
Speaker 1: body shows up. They started tracking the deer and uh,

1388
01:29:24,760 --> 01:29:30,400
Speaker 1: same buck comes through again grunting and sees him again

1389
01:29:30,520 --> 01:29:33,679
Speaker 1: spooks off. So on you know one of these deer,

1390
01:29:34,880 --> 01:29:36,280
Speaker 1: I think they assumed that it was the one that

1391
01:29:36,360 --> 01:29:45,040
Speaker 1: d shot um, even though it was carrying round antlers. Um.

1392
01:29:45,080 --> 01:29:50,519
Speaker 1: So they they took they took this deer to get aged,

1393
01:29:51,400 --> 01:29:54,680
Speaker 1: and the biologists just recently got back to him and

1394
01:29:54,680 --> 01:29:59,960
Speaker 1: said it was seventeen years old that is unbelievable. Transgendered

1395
01:30:00,080 --> 01:30:03,880
Speaker 1: dear go in too estras though I don't know. I

1396
01:30:03,880 --> 01:30:07,600
Speaker 1: mean it's it had all the female organs, That's what

1397
01:30:07,720 --> 01:30:10,719
Speaker 1: That's what I was gonna say. I'm I'm not sold

1398
01:30:10,760 --> 01:30:14,479
Speaker 1: on the idea that antler dough would be fertile. I

1399
01:30:14,479 --> 01:30:16,840
Speaker 1: don't know. I mean maybe only maybe it didn't throw

1400
01:30:16,880 --> 01:30:21,040
Speaker 1: antlers every year. And the guy that wrote it wrote transgender,

1401
01:30:21,080 --> 01:30:23,040
Speaker 1: but spelled it d e e er like a little

1402
01:30:23,120 --> 01:30:29,759
Speaker 1: joke transgender transgender. Um, I wonder, and I honestly wondered.

1403
01:30:29,840 --> 01:30:35,760
Speaker 1: This does the but if she comes into heat, she

1404
01:30:35,840 --> 01:30:38,000
Speaker 1: comes into estras and the and the buck goes the breeder.

1405
01:30:38,560 --> 01:30:43,920
Speaker 1: Does the bucket thrown off? Is there? Does the buck

1406
01:30:44,080 --> 01:30:49,919
Speaker 1: register like something's not right when it sees the antlers?

1407
01:30:50,720 --> 01:30:53,000
Speaker 1: Or does it just like he he smells what he

1408
01:30:53,040 --> 01:30:57,840
Speaker 1: needs to smell and that's all he cares about. I

1409
01:30:58,200 --> 01:31:02,679
Speaker 1: think it from like a year behavior standpoint, a buck

1410
01:31:02,720 --> 01:31:06,520
Speaker 1: would see the visual queue of antlers from a long distance,

1411
01:31:06,960 --> 01:31:09,880
Speaker 1: and he might come in to investigate, thinking he's about

1412
01:31:09,880 --> 01:31:12,200
Speaker 1: to get in a fight. Once he got closer and

1413
01:31:12,240 --> 01:31:16,440
Speaker 1: actually engaged the attention of the dough that had antlers,

1414
01:31:16,560 --> 01:31:19,439
Speaker 1: that dough would begin to send body signals that she

1415
01:31:19,640 --> 01:31:23,280
Speaker 1: wasn't a buck, and he would then that would then

1416
01:31:23,439 --> 01:31:26,320
Speaker 1: override the visual que that this is a buck because

1417
01:31:26,360 --> 01:31:29,800
Speaker 1: of antlers. I don't care what you got on your head.

1418
01:31:30,400 --> 01:31:33,360
Speaker 1: Yeah here for I think I think that smell just

1419
01:31:34,400 --> 01:31:38,800
Speaker 1: just trumps everything. Yeah, that time of year, that's all

1420
01:31:38,840 --> 01:31:46,080
Speaker 1: they're thinking about. No judgment. Just curious, man, Yeah, just curious.

1421
01:31:46,800 --> 01:31:50,200
Speaker 1: Someone's gonna fill up our inbox with like a trail

1422
01:31:50,240 --> 01:31:53,280
Speaker 1: camp photo of a buck mounting another buck. It's happened,

1423
01:31:53,400 --> 01:31:57,519
Speaker 1: for sure. Uh. Now we're gonna start getting pictures of that. Yeah.

1424
01:31:57,680 --> 01:32:00,639
Speaker 1: And then uh when when you're separating Kyle's in their calves,

1425
01:32:00,680 --> 01:32:04,439
Speaker 1: and and and when they're coming into you know, uh,

1426
01:32:06,680 --> 01:32:10,360
Speaker 1: never mind, there's situations where out of it, there's situations

1427
01:32:10,360 --> 01:32:13,720
Speaker 1: where cows mount cows, steers jumping on each other all

1428
01:32:13,760 --> 01:32:18,519
Speaker 1: the time. So the stuff goes on. Now, Clay, I'm

1429
01:32:18,520 --> 01:32:24,519
Speaker 1: gonna see you very soon. We're gonna go deer hunting.

1430
01:32:25,560 --> 01:32:27,400
Speaker 1: You can't decide if you're gonna bring your bow or

1431
01:32:27,400 --> 01:32:31,519
Speaker 1: your gun. You don't need to decide right now. I

1432
01:32:31,560 --> 01:32:37,400
Speaker 1: want to tell you something I do not believe and

1433
01:32:37,479 --> 01:32:45,720
Speaker 1: bringing both. I think it's wrong. I don't believe in

1434
01:32:45,800 --> 01:32:51,040
Speaker 1: bringing both. If you're a bow hunter, then be a

1435
01:32:51,120 --> 01:32:54,759
Speaker 1: damn bow hunter. Don't come and be like I'm gonna

1436
01:32:54,840 --> 01:32:57,800
Speaker 1: try with my bow and then switch to my gun

1437
01:32:57,840 --> 01:33:00,880
Speaker 1: because I'm a bowl hunter only up to the point

1438
01:33:00,880 --> 01:33:06,520
Speaker 1: where I might get afraid that I won't be successful. Okay,

1439
01:33:06,600 --> 01:33:10,960
Speaker 1: can I it's immoral. I would like to refer to

1440
01:33:10,960 --> 01:33:13,400
Speaker 1: my lawyer, Spencer me heart, and I would also like

1441
01:33:13,479 --> 01:33:16,440
Speaker 1: to say that I am not like fighting for this position,

1442
01:33:16,560 --> 01:33:19,840
Speaker 1: but you've been hitting that. You hinted to me, Oh,

1443
01:33:19,920 --> 01:33:25,759
Speaker 1: I've been bringing both and I will not hunt with you. Well,

1444
01:33:25,800 --> 01:33:30,040
Speaker 1: what Spencer, I'm gonna let Spencer talk first, he's itching

1445
01:33:30,080 --> 01:33:33,639
Speaker 1: to go. I I was fighting for Clay to bring both.

1446
01:33:33,760 --> 01:33:35,720
Speaker 1: Me like take your both for a few days. Third

1447
01:33:35,800 --> 01:33:40,120
Speaker 1: day rival, I think one of the worst sayings in hunting.

1448
01:33:40,240 --> 01:33:42,559
Speaker 1: And it's something that I heard on the outdoor channel

1449
01:33:42,720 --> 01:33:45,439
Speaker 1: when I was probably like twelve or thirteen years old,

1450
01:33:45,600 --> 01:33:48,719
Speaker 1: and uh it it like ruined me for a period

1451
01:33:48,720 --> 01:33:50,840
Speaker 1: of time. And it was this outdoor show that I

1452
01:33:50,880 --> 01:33:55,080
Speaker 1: was watching where the woman was at some big buck

1453
01:33:55,080 --> 01:34:00,000
Speaker 1: outfitter and I don't recall. I can take a guess,

1454
01:34:00,040 --> 01:34:01,599
Speaker 1: doesn't matter. I could take a guess, but it wouldn't

1455
01:34:01,600 --> 01:34:03,920
Speaker 1: be fair to them if I was wrong. They were

1456
01:34:03,920 --> 01:34:07,080
Speaker 1: at like some outfitter in Iowa or Kansas, and it

1457
01:34:07,200 --> 01:34:09,040
Speaker 1: was the last day of the hunt, and like a

1458
01:34:09,160 --> 01:34:11,519
Speaker 1: hunter and twenty inch probably three and a half year

1459
01:34:11,520 --> 01:34:14,559
Speaker 1: old white tail buck walks by and they choose to pass,

1460
01:34:15,120 --> 01:34:17,439
Speaker 1: and then uh they cut to the interview and the

1461
01:34:17,479 --> 01:34:20,400
Speaker 1: woman says, now, you never shoot something on the last

1462
01:34:20,479 --> 01:34:22,760
Speaker 1: day that you wouldn't shoot on the first, and that

1463
01:34:22,880 --> 01:34:25,599
Speaker 1: I was like, oh, like, that's that's good advice, right,

1464
01:34:25,600 --> 01:34:29,799
Speaker 1: Like that's good advice. That she stole that from Yanni

1465
01:34:29,840 --> 01:34:34,320
Speaker 1: and messed it all up. So so I I take

1466
01:34:34,400 --> 01:34:37,200
Speaker 1: a lot of issue with that saying. And you're saying

1467
01:34:37,360 --> 01:34:40,439
Speaker 1: a version of that that, Like I you're saying that,

1468
01:34:40,479 --> 01:34:43,040
Speaker 1: like you cannot go into a haunt and move the goalpost.

1469
01:34:43,160 --> 01:34:44,639
Speaker 1: You can't go into hunt and be like, I'm gonna

1470
01:34:44,720 --> 01:34:47,040
Speaker 1: kill a hunter and fifty inch buck and then on

1471
01:34:47,120 --> 01:34:50,840
Speaker 1: the second to last day you're like, well, of course,

1472
01:34:51,200 --> 01:34:53,760
Speaker 1: of course, and then then a hunter and buck walks,

1473
01:34:53,800 --> 01:34:58,320
Speaker 1: but kill that instead. How is that okay? How is

1474
01:34:58,360 --> 01:35:01,280
Speaker 1: that different than what you're telling I just think showing

1475
01:35:01,360 --> 01:35:03,680
Speaker 1: up with all kinds of weapons that you have like

1476
01:35:04,160 --> 01:35:07,280
Speaker 1: in your head sort of teared out as like best

1477
01:35:07,360 --> 01:35:10,360
Speaker 1: case you know, I'd be like, well, I'm really trying

1478
01:35:10,360 --> 01:35:12,800
Speaker 1: to get one with my bow now. Failing that, I'm

1479
01:35:12,800 --> 01:35:15,519
Speaker 1: gonna try to get with my rifle. Failing that, I

1480
01:35:15,560 --> 01:35:17,400
Speaker 1: will try to get on with my truck. It's just

1481
01:35:17,400 --> 01:35:20,040
Speaker 1: like I just like, just make up your mind. Man,

1482
01:35:20,160 --> 01:35:22,720
Speaker 1: isn't here, Here's here's the nick of your mind. What

1483
01:35:22,880 --> 01:35:26,800
Speaker 1: when you when you say that what you are prioritizing

1484
01:35:26,880 --> 01:35:32,639
Speaker 1: in your hedgemon of of ideas is that the method

1485
01:35:32,640 --> 01:35:36,160
Speaker 1: of kill is most important. And I used to know

1486
01:35:36,320 --> 01:35:39,720
Speaker 1: you are, but can I used to be I I

1487
01:35:40,240 --> 01:35:43,160
Speaker 1: steve you're This is touching on some deep rooted issues

1488
01:35:43,439 --> 01:35:46,439
Speaker 1: inside of me because I grew up with such a

1489
01:35:46,560 --> 01:35:50,360
Speaker 1: strict bow hunting, in such a strict bow hunting world

1490
01:35:50,840 --> 01:35:54,280
Speaker 1: that like, I mean, we we didn't have guns. We

1491
01:35:54,280 --> 01:35:58,400
Speaker 1: we we bow hunted. And as I became an adult,

1492
01:35:58,560 --> 01:36:00,920
Speaker 1: I feel like, and I still all loved bow hunt,

1493
01:36:00,960 --> 01:36:04,160
Speaker 1: That's like my default thing to do, but I kind

1494
01:36:04,160 --> 01:36:08,160
Speaker 1: of just became okay with using a gun, and so

1495
01:36:08,240 --> 01:36:11,439
Speaker 1: I kind of and and obviously I still love to

1496
01:36:11,479 --> 01:36:13,920
Speaker 1: bow hunt, but so I look at it more from

1497
01:36:13,920 --> 01:36:17,920
Speaker 1: a the macro hunt picture like I would like to

1498
01:36:18,000 --> 01:36:21,400
Speaker 1: go where we're going and take a good white tail deer.

1499
01:36:21,840 --> 01:36:23,800
Speaker 1: I would prefer to do it with a bow. I

1500
01:36:23,800 --> 01:36:28,120
Speaker 1: don't know what I'm against, don't I don't know what

1501
01:36:28,160 --> 01:36:31,599
Speaker 1: I'm up against. But Steve, I want to say too,

1502
01:36:31,640 --> 01:36:34,240
Speaker 1: I'm on your team on this. I like what you

1503
01:36:34,320 --> 01:36:36,559
Speaker 1: said that that I was like, yeah, I like that.

1504
01:36:36,920 --> 01:36:39,080
Speaker 1: I mean, I like the idea of not switching. I

1505
01:36:39,120 --> 01:36:43,320
Speaker 1: don't like to switch. I've switched before, and I've brought

1506
01:36:43,360 --> 01:36:46,040
Speaker 1: a bow and I've brought a rifle or a bow

1507
01:36:46,120 --> 01:36:48,679
Speaker 1: and a shotgun. It just never seems to work out

1508
01:36:48,720 --> 01:36:50,880
Speaker 1: for me that well because I'm not I like the

1509
01:36:50,960 --> 01:36:54,240
Speaker 1: commitment at least when for me it's just it hasn't

1510
01:36:54,280 --> 01:36:58,360
Speaker 1: worked out that well. It's like bring the bowl, stick

1511
01:36:58,400 --> 01:37:01,360
Speaker 1: with it, or bring the situation too. Is we have

1512
01:37:01,400 --> 01:37:04,720
Speaker 1: a very short time period, you know, we we don't

1513
01:37:04,720 --> 01:37:07,400
Speaker 1: have a lot of time to get this done. But

1514
01:37:07,520 --> 01:37:10,840
Speaker 1: so I'm on your team, Steve. But it's like a

1515
01:37:10,880 --> 01:37:15,720
Speaker 1: hard never do that philosophy, you know, I got I

1516
01:37:15,760 --> 01:37:17,280
Speaker 1: think we give I think we've got to give some

1517
01:37:18,000 --> 01:37:23,680
Speaker 1: grace to me. That's like some version of what that

1518
01:37:23,720 --> 01:37:25,439
Speaker 1: woman said on the outdoor channel, and that was a

1519
01:37:25,479 --> 01:37:27,360
Speaker 1: mantra that it went buy for a while that I

1520
01:37:27,400 --> 01:37:30,559
Speaker 1: would like to go back and retroactively kick my own

1521
01:37:30,640 --> 01:37:32,920
Speaker 1: ass for like passing on deer, because I was like,

1522
01:37:33,080 --> 01:37:34,640
Speaker 1: you know what, you never shoot a deer in the

1523
01:37:34,680 --> 01:37:38,960
Speaker 1: last day that you wouldn't shoot on the first. It's

1524
01:37:39,000 --> 01:37:44,080
Speaker 1: saying there, there, it goes both ways. Are day ever

1525
01:37:44,200 --> 01:37:48,759
Speaker 1: pass up on the first day what you'd be happy

1526
01:37:48,840 --> 01:37:52,080
Speaker 1: to have on the last I'm telling you what this

1527
01:37:52,120 --> 01:37:56,080
Speaker 1: woman said on the outdoor channel that caused me to

1528
01:37:56,120 --> 01:37:58,679
Speaker 1: pass on many deer in the future, that I would

1529
01:37:58,680 --> 01:38:01,240
Speaker 1: I wish I could go back and does deer because

1530
01:38:01,240 --> 01:38:04,200
Speaker 1: I would have been happier in the moment shooting that

1531
01:38:04,360 --> 01:38:07,240
Speaker 1: younger buck than not shooting it. And then just like

1532
01:38:07,320 --> 01:38:09,080
Speaker 1: having that matra rolling around in my head, like you

1533
01:38:09,120 --> 01:38:11,320
Speaker 1: don't shoot a buck on the last day, you wouldn't

1534
01:38:11,320 --> 01:38:15,800
Speaker 1: shot them the first. That's not good, dear management. I'll

1535
01:38:15,800 --> 01:38:24,719
Speaker 1: have the last word. Uh No, that's different. That's all Clay.

1536
01:38:24,800 --> 01:38:26,479
Speaker 1: I don't care what in the world you bring down there.

1537
01:38:26,520 --> 01:38:30,479
Speaker 1: You just bring one of them. No, I actually have

1538
01:38:30,560 --> 01:38:33,320
Speaker 1: your rifle on bringing it, so you either decide either way.

1539
01:38:33,360 --> 01:38:36,599
Speaker 1: I'm bringing to the rifle. Yeah. I like the archery

1540
01:38:36,680 --> 01:38:39,080
Speaker 1: idea though. Oh yeah, I think that's what you should do. Man,

1541
01:38:39,120 --> 01:38:40,920
Speaker 1: we'll have a hell of a lot of fun. Let's

1542
01:38:40,920 --> 01:38:42,720
Speaker 1: do it. The cool part about that this one is

1543
01:38:42,720 --> 01:38:48,559
Speaker 1: gonna be peak rot um rattling is gonna be very effective.

1544
01:38:49,520 --> 01:38:52,160
Speaker 1: And the interaction you're gonna have at close distances by

1545
01:38:52,240 --> 01:38:55,760
Speaker 1: rattling bucks in is gonna be pretty sweet. And we're

1546
01:38:55,800 --> 01:38:58,479
Speaker 1: on like private land in Texas that barely no one

1547
01:38:58,520 --> 01:39:03,519
Speaker 1: gets the hunt on. It could be really good. It

1548
01:39:03,560 --> 01:39:05,880
Speaker 1: could be really good. I think it's gonna be. If

1549
01:39:05,880 --> 01:39:07,320
Speaker 1: it was anything like last year when we were there,

1550
01:39:07,600 --> 01:39:09,559
Speaker 1: when Steve was hunting Neil Guy last year, yeah, I'd

1551
01:39:09,560 --> 01:39:12,640
Speaker 1: be like, oh, look at Zebra. It was. It was

1552
01:39:12,680 --> 01:39:16,240
Speaker 1: insanely good and we weren't hunting white Tail's. Well, I've

1553
01:39:16,240 --> 01:39:19,479
Speaker 1: got a decoy. I picked up a decoy today, a

1554
01:39:19,520 --> 01:39:22,559
Speaker 1: real mobile decoy that we can just pop out real quick.

1555
01:39:22,680 --> 01:39:25,640
Speaker 1: It's a pop up decoy, double sided, pop up decoy,

1556
01:39:25,840 --> 01:39:29,040
Speaker 1: Montana decoy. So man, we'll pop that thing out. We'll

1557
01:39:29,080 --> 01:39:32,040
Speaker 1: just have to be pretty strategic with our setup because

1558
01:39:32,080 --> 01:39:35,519
Speaker 1: you're gonna be rattling for me, Steve, right, yeahs are

1559
01:39:35,640 --> 01:39:39,599
Speaker 1: unifairy bit about too much rattling and not enough randoling.

1560
01:39:39,840 --> 01:39:44,160
Speaker 1: We'll get it all sorted Outcoy. I don't know if

1561
01:39:44,160 --> 01:39:46,120
Speaker 1: our friendship is gonna hold up, but we'll get it

1562
01:39:46,200 --> 01:39:52,800
Speaker 1: sorted out. Oh man, Alright, guys see you, guys see

1563
01:40:00,640 --> 01:40:01,240
Speaker 1: sa