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

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Speaker 1: bug bitten, and in my case, underwear listening podcast. You

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Speaker 1: can't predict anything presented by on X hunt creators are

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

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Speaker 1: Hunt app from the iTunes or Google play store. Now

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Speaker 1: where you stand with on x okay, first thing we

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Speaker 1: gotta start out with is uh um two fish and

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Speaker 1: related things, never to get into the real meat of

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Speaker 1: the show. Here with the two fish related things on

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Speaker 1: episode to two two, which is a couple of goal.

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Speaker 1: We just did an episode to to three and someone

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Speaker 1: pointed out that we should have commemorated it more carefully

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Speaker 1: because it was a rifle caliber. So the two two three.

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Speaker 1: But the two two two episode, which is all, isn't

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Speaker 1: there another? I believe there's a two two two? Yeah?

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Speaker 1: Search that up? Um. I I challenged the veracity. I

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Speaker 1: challenged the veracity of a man's email who wrote in

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Speaker 1: to say that he snagged a fishing line or snagged

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Speaker 1: a rod ice fishing in a hundred feet of water,

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Speaker 1: pulled up the rod, reeled up all the line and

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Speaker 1: lo and behold on the end of it is a

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Speaker 1: dead whitefish, and I was and I think I must

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Speaker 1: I somehow questioned the veracity of of his tail and

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Speaker 1: unless he went out that day with a rotten whitefish. Um,

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Speaker 1: he sent in photographs that made me that back them up.

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Speaker 1: So my apologies to his name is Steu Beard. My

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Speaker 1: apologies the uh Mr ste Beard that looks like a

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Speaker 1: legitimately rotten whitefish you found on the bottom of of

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Speaker 1: the lake. It's so rotten and looked that he was

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Speaker 1: kind of poking at it in the picture and it

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Speaker 1: doesn't want to quite seem to want to grab it. Um,

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Speaker 1: guy's not interested. This is not a ficient related one,

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Speaker 1: but I think that someone sent in as they sent in.

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Speaker 1: Did you see this, Yanni? A guy sent in a

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Speaker 1: turkey's gizzard? Hm, open, cut open you know what had

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Speaker 1: in it? A marble like a kid's marble. I believe it.

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Speaker 1: I'll put that picture on Instagram. So if you go

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Speaker 1: to Stephen Ronella on Instagram. The question is those you

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Speaker 1: think he was eating it too to add to like

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Speaker 1: the stones, yes, gizzard? Or did you think it was

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Speaker 1: some sort of dude that he hadn't seen before. It's

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Speaker 1: seven eight times bigger than a normal piece of that's

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Speaker 1: a great question, man. I hadn't thought about it like that. Yeah,

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Speaker 1: because it's definitely way bigger than the average piece of

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Speaker 1: stone that's in that gizzard. You know what he could

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Speaker 1: have thought it was? Do you know how they eat snails?

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Speaker 1: M hmm. I hadn't incurred to me that that was marble,

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Speaker 1: had like a little swar roll to it. It could

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Speaker 1: have been like a little snail. I found a bear

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Speaker 1: ship one time that was all Crayola crayons. Yeah, he

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Speaker 1: found a box of Crayola crayons. Um. Yeah, I think

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Speaker 1: you're probably correct. He wasn't picking it up as grit.

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Speaker 1: He thought he's eating something tasty. The last fishing thing

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Speaker 1: is a conundrum, the father and son conundrum that we

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Speaker 1: need to that they had asked that we weigh in on.

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Speaker 1: So this dude and his dad we're fishing, and they

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Speaker 1: both had lines out, and the old man gets distracted,

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Speaker 1: and the kid looks and his the old man's rods

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Speaker 1: going crazy while the old man's off talking to some guy.

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Speaker 1: So the kid runs over and reels the rod and

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Speaker 1: it's like some tanker four pound rainbow. H So, whose

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Speaker 1: fish is it? I think it's the old man's fish

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Speaker 1: really an unintended rod. Yeah, I'm gonna he rigged and

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Speaker 1: he did. He cast it out. It's like, I don't know.

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Speaker 1: If I saw like a video of what was going on,

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Speaker 1: maybe I'd have a different opinion. But I don't know.

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Speaker 1: Maybe this old man was like barely distracted so that

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Speaker 1: his listeners don't think that it's just you and I

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Speaker 1: are sitting here talking to each other all day. I

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Speaker 1: think we should ask our guests, you guys have anything

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Speaker 1: to say, please keep mind he's a representatives here from

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Speaker 1: I would actually defer to you guys. We have three

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Speaker 1: representatives here from the Boone and Crocket Club. So they

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Speaker 1: spent all all day. Uh, they sit there and look

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Speaker 1: at um, you know, whether or not to accept things

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Speaker 1: in the record books and stuff. So I feel like

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Speaker 1: these guys have like a moral uh, they sort of

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Speaker 1: have the moral high ground. You have the well you

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Speaker 1: have there, you have the credentials to weigh it out.

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Speaker 1: So so that let's say you guys were a fish.

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Speaker 1: Let's say you guys kept fish records and this is

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Speaker 1: a record fish and they come to you and be like,

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Speaker 1: here's this record fish, and You're like, clearly it's a

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Speaker 1: record fish, whose name do we put down? And and uh,

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Speaker 1: they tell you the story. A lot of times when

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Speaker 1: that happens, one'll take the ownership of the fish and

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Speaker 1: the other one will say they called it. Now, as

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Speaker 1: long as the person that called it had the fishing license,

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Speaker 1: they would be good, you know, when they followed all

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Speaker 1: the rules. A lot of times that's what a lot

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Speaker 1: of the father's sons do is though, like the sunnell,

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Speaker 1: shoot it or vice versa. And then we have a

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Speaker 1: thing where the father that could then take ownership, which

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Speaker 1: is basically just a sentence letting us know that, hey,

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Speaker 1: this person hook this fish and I'm the owner of it.

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Speaker 1: So we base everything off of the ownership of the

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Speaker 1: of the trophy. So that would mean, that's what I'm saying,

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Speaker 1: who owns the fish? They would have to decide. Yeah, Oh,

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Speaker 1: they would have to decide legally, isn't it whoever reels

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Speaker 1: the fish in, like, because there are certain states that

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Speaker 1: say if you hook it and landed at your fish

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Speaker 1: and it's on your bag limit, So I can't hook

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Speaker 1: a fish and hand it off. It had the legality

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Speaker 1: of it. So the kid that grabbed the rod, I

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Speaker 1: think legally it is his fish, but if he was

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Speaker 1: nice enough, to Kyle's point, he could allow his dad

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Speaker 1: to be listed as a secondary fisherman in this case,

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Speaker 1: assuming they both had the license is necessary for the fish.

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Speaker 1: I like that perspective on it. That like, let's say

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Speaker 1: he had pulled it in and then there was a problem,

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Speaker 1: like he retained I fish, he wasn't allowed to retain.

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Speaker 1: He wouldn't be able to be like, well, the game warden,

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Speaker 1: you better talk to my old man. It was his rod. Yeah,

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Speaker 1: the game ward is gonna be interested in who who

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Speaker 1: hookedn't who landed it? Yeah, alright, so it's the kids fish.

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Speaker 1: Thank you. Settle that problem for the family before you move.

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Speaker 1: Before you move on to to to Remington's triple deuce,

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Speaker 1: first center fire twenty two galiber in our country. There

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Speaker 1: you go, great John, he earned his key. Okay, now

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Speaker 1: we'll do intros. We're talking everybody from the we're talking

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Speaker 1: to folks on Buona Crocker Club. We're gonna explore this

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Speaker 1: whole thing up down sideways. But let's start out with

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Speaker 1: it like what do you what do you guys do

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Speaker 1: each of you? You know? So I'm Tony Shonen and

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Speaker 1: I'm the chief executive officer. Crockett, how long you held

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Speaker 1: that position? Um? About two weeks they just changed. I

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Speaker 1: was the chief of staff for twelve years and then

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Speaker 1: they just decided to change my death for whatever reason.

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Speaker 1: So anyway, I've been running the organizationization for about twelve

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Speaker 1: years and I've been thirty years the outdoor space. About

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Speaker 1: half of that was the for profit sector, half was

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Speaker 1: the nonprofit sector. UM. I went to my first shot

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Speaker 1: show when I was twenty one. I just went to

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Speaker 1: my thirty fifth. I should be get because some kind

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Speaker 1: of a prize, but they haven't offered me anything. Still

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Speaker 1: alive me too. That's a lot of like coming home

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Speaker 1: with like a weird flu. Yeah, I've managed to avoid

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Speaker 1: the weird flues, but uh, but no, So it's been

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Speaker 1: a good rite. UM had a video production company to

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Speaker 1: begin with, and then I went to work for Wonders

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Speaker 1: Wildlife that ran that operation down there, and uh then

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Speaker 1: to Buona. Crockett. Oh, but the Wonders well Life just

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Speaker 1: opened up. They were opened before and then when I

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Speaker 1: left as an executive director there, they shut it down

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Speaker 1: for remodel and then they just opened it up two

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Speaker 1: years ago, which I don't know if you guys been there,

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Speaker 1: but it's an awesome place. I keep hearing people that,

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Speaker 1: um that aren't easily impressed. He would say, it's like

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Speaker 1: really something. Yeah, no, it's it's an awesome I don't

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Speaker 1: know anything about it. Where what is it? And where

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Speaker 1: is it? Said, well, I'll tell you, I've never been there.

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Speaker 1: And now it's a it's a it's a it's a

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Speaker 1: museum and aquarium that was built. The first round was

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Speaker 1: in two thousand and one, and the original intent of

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Speaker 1: John Johnny Morris was to have a home for hunters

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Speaker 1: and anglers and and and all the stories and the

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Speaker 1: traditions surrounding that community. And UM and then UM, when

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Speaker 1: I left two thousand and seven, UM, Johnny wanted to

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Speaker 1: make it a bigger and benner place, and so that's

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Speaker 1: what he did. And so it's still a tribute to

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Speaker 1: the American sportsman and women of the country. But it's UM,

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Speaker 1: it's incredible. You just got to see it too. Something

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Speaker 1: really described. We had our two awards programs there the

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Speaker 1: last two times, like three miles of trail through there. Yeah,

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Speaker 1: is that the one that's down in Florida. It's in Springfield, Missouri.

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Speaker 1: You're thinking of, right, a hotel, right, something like that. No,

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Speaker 1: but yeah, just people know Johnny Morris is the you know,

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Speaker 1: the founder of Bass Pro Shop. That's a hell of

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Speaker 1: a story. Yeah, like that guy's life. He started out

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Speaker 1: like selling bait, selling bait at a marina or something

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Speaker 1: like that. He started in his dad's liquor stores with

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Speaker 1: an in cap. Yeah, and his dad had I don't

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Speaker 1: know how many liquor stores, doesn't or so in Springfield,

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Speaker 1: and he sold he sold off the inn cap and

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Speaker 1: actually there's a rendition of the very first uh uh

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Speaker 1: in cap that he had at in the museum at

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Speaker 1: one of his dad's lager stores. And so yeah, live

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Speaker 1: like a live bait and cap or a tackle in

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Speaker 1: I know, it's just a tackle in cap, you know.

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Speaker 1: He didn't he didn't like the uh the the tackle

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Speaker 1: that the local store was using. So he went to

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Speaker 1: Arkansas about a chuck little back put him in his

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Speaker 1: dad's stores, and that's where the whole story started. As

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Speaker 1: he called the Bass Pro Shop back then. Yeah, it's

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Speaker 1: always bass Bro as far as I know. I mean,

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Speaker 1: he's a little bit older than I am, but I've

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Speaker 1: known John for thirty years, forty years, and it's always

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Speaker 1: been bass pro. Yeah, he's sharp dude though. He's a

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Speaker 1: great guy. Neil find of Begger conservationist than he he is.

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Speaker 1: We should have him on, I'd like to. So if

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Speaker 1: you're listening, John, alright, So moving on, then there's Joannice

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Speaker 1: as though dealing poker. Good morning, and then moving over. Yeah,

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Speaker 1: my name is Kyle Lair. I'm the assistant director of

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Speaker 1: Big Game Records at the Boone and Rocket Club. I've

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Speaker 1: been there for a little over five years now, Assistant

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Speaker 1: director of Big Game. So my main duty is to

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Speaker 1: review all the entries that we get in. So we

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Speaker 1: have about four volunteer official measures throughout North America and

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Speaker 1: it's my job to basically make sure that every entry

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Speaker 1: that comes in basically has all its ta s Carlston

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Speaker 1: size dotted and then to make sure that you know

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Speaker 1: to be there they have any questions while they're scoring,

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Speaker 1: or any questions about our based policies and procedures stuff

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Speaker 1: like that, but mainly keep those entries moving through. So uh,

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Speaker 1: there's volunteer scores. We'll explain what this all means in

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Speaker 1: a minute, but plus minus a good a good majority

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Speaker 1: of them. I mean a lot of the people that

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Speaker 1: we train to be official measures want to be, and

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Speaker 1: so there a lot of what they measure though doesn't

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Speaker 1: quite I mean, it's it's pretty rare to actually harvest

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Speaker 1: to Boone and Crockett Club caliber animal. So a lot

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Speaker 1: of guys want to become official measures and they think

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Speaker 1: they're going to measure a ton and they might only

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Speaker 1: get a couple in their lifetime, but they enough to

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Speaker 1: enter the books. Yeah, but they they don't. The ones

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Speaker 1: that don't make it, we don't necessarily see. So they're

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Speaker 1: doing a lot of basically public contact for us. I

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Speaker 1: mean they're they're the people that most of the public

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Speaker 1: is going to see. They won't talk to Justin or

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Speaker 1: I or anyone else, they'll they'll meet our official measures.

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Speaker 1: So they're pretty much ambassadors for us. And and so

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Speaker 1: it's our job to make sure that they know what

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Speaker 1: they're doing and know how to do it. Yeah, my

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Speaker 1: old man was a scorer. I know he did it.

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Speaker 1: He was really active in Pope and Young. But I'm like,

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Speaker 1: I think, I feel like I know he did Commemorative

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Speaker 1: Bucks of Michigan. He scored for Pope and Young, and

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Speaker 1: I'm almost certainly scored for Boone and Crockett. Is that

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Speaker 1: something you can look up past measures? Yeah? Oh yeah, yeah.

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Speaker 1: We keep track of everyone who's been in managing if

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Speaker 1: he's registered in there not we're the hello last name,

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Speaker 1: Yeah right, I did some research. He was not on there,

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Speaker 1: Frank J. Ronell. We did. We did a database transfer

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Speaker 1: in two thousand and Some of the names could have

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Speaker 1: been lost, but generally speak, I mean, everybody brought their

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Speaker 1: stuff over. He had all the forms and maybe he

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Speaker 1: was just maybe he just nod. I know he measured

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Speaker 1: rifle stuff too. Well, you said it. CBM Commemorative Bucks

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Speaker 1: in Michigan is another great scoring organization. We worked with

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Speaker 1: close and a lot of these, so maybe he could

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Speaker 1: have been registered, state registered. They're they're trained to score

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Speaker 1: with our system. They use our system. They just may

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Speaker 1: not have gone through the the process of an actual

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Speaker 1: BNC certification, which I guess I should introduce myself. Sorry,

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Speaker 1: I'm justin Spring. I'm the director of Big Game Records.

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Speaker 1: I've been with BNC a little over ten years. Um

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Speaker 1: started with him at the assistant director, doing what Kyle

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Speaker 1: Um does now with the review of entry, and then

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Speaker 1: when he came on I transitioned into the director UM

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Speaker 1: basically help him out, make sure the pr side of

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Speaker 1: our official majors they're doing the best they can for conservation.

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Speaker 1: That's kind of what I'm tasked with UM, making sure

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Speaker 1: our scoring is uniform, that the the current science that's

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Speaker 1: coming out, we're not rewarding something that's actually a negative.

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Speaker 1: Which we'll get into further later on what we're looking for.

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Speaker 1: But that's that's my normal position with BNC and kind

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Speaker 1: of helping Kyle out and keeping all of our majors

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Speaker 1: doing the same thing for uniformity throughout the system. All right,

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Speaker 1: we get two deep, we should do this. I want

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Speaker 1: I want to bring people up to speed on something

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Speaker 1: cause I feel like a lot more people know. I

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Speaker 1: think that people know about Boon and Crock. They know

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Speaker 1: the scoring system. So let's just catch up on that

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Speaker 1: and what that means. And you know, if you're out

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Speaker 1: and about and you hear someone say that so and

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Speaker 1: so got a one eight white tail, so and so

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Speaker 1: got a four bowl, right, um, black bear, Like, what

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Speaker 1: does that mean? Okay? So the system was originally start

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Speaker 1: the very beginning, they thought wildlife was going extinct and

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Speaker 1: they were trying to find the biggest specimens which would

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Speaker 1: have gone in the National collection the Heads and Horns,

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Speaker 1: which is now what the Wonders of Wildlife, which we

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Speaker 1: still have that collection. But they were trying to find

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Speaker 1: the biggest to show our generation what was once on

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Speaker 1: the North American continent. They thought everything was going extinct.

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Speaker 1: So that was the beginning of our records. UM. You know,

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Speaker 1: through the work of early club members and conservationists and whatnot,

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Speaker 1: things started to turn around. UM. After World War Two,

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Speaker 1: conservation is really taken off. You had funding and everything

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Speaker 1: in place. People no longer wanted to go see dead

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Speaker 1: heads on the wall of the museum, and we had

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Speaker 1: this data set that we'd started. And so in nineteen

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Speaker 1: fifty they said, we need to create an equitable system

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Speaker 1: to evaluate big game and then we can use this

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Speaker 1: system to UM monitor conservation successes and failures across the

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Speaker 1: North American continent. So they brought together UM. There was

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Speaker 1: a couple of people at the time. There was a

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Speaker 1: taxidermist that had his own scoring system, and then there

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Speaker 1: was system UM which it never took off. It was

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Speaker 1: I can I remember the name of it. H Kyle. Well,

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Speaker 1: you had grantil Fits and Jimmy Clark. Jimmy Clark. It

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Speaker 1: was James Clark taxidermy, and so anybody that was a

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Speaker 1: taxidermy client of his would enter their trophies in a

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Speaker 1: competition and he'd do a banquet and give out awards.

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Speaker 1: So he made one. He made his own little system yep,

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Speaker 1: and and it was actually it was pretty well done. Um.

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Speaker 1: And then the the other side of it was the

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Speaker 1: more you know, widely known system. But anyway, these guys

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Speaker 1: come together, Boone and Crockett put together committee of five

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Speaker 1: of them that they kind of sat down and they said, okay,

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Speaker 1: let's let's look at the science of today and what

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Speaker 1: what are the traits that we see in a mature

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Speaker 1: male specimen that comes from an ideal habitat. And so

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Speaker 1: you look at nature, you have bilateral symmetry. Or that's

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Speaker 1: a huge question. Albody asked, why does BNC have deductions? Well,

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Speaker 1: if you look at nature, and if you're looking at something,

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Speaker 1: your eyes drawn to bilateral symmetry, and it's very common.

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Speaker 1: So the first thing they said is we have to

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Speaker 1: have left or right matching. That's a sign of healthy

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Speaker 1: habitat and healthy animal. And so they went through each

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Speaker 1: different category and kind of said, you know what, what

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Speaker 1: are the signs of a mature animal? What is calm

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Speaker 1: and what's caused by some form of stressor And they

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Speaker 1: came together with the system. You know, for moose, for example, Um,

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Speaker 1: we don't measure the length of points because the mature

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Speaker 1: moose has the palmation that gets bigger, and so a

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Speaker 1: very mature bull may not have long points. So they

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Speaker 1: didn't want to reward the length of a point on

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Speaker 1: a moose. Instead, they wanted to reward the palmation, which

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Speaker 1: seemed to be prevalent in the healthiest of the specimens.

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Speaker 1: So that was that was the basis, and then they

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Speaker 1: just took the number of inches of whatever they felt

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Speaker 1: was important, and that gave you a number, you know,

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Speaker 1: that was that was the original thing, is you know,

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Speaker 1: where where does this rank in terms of um when

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Speaker 1: it took a while to get there, yeah, I mean

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Speaker 1: the scoring system started out with length of horn and

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Speaker 1: what they called the girth of the base or the

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Speaker 1: circumference of base, and that was how they got an

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Speaker 1: idea of a sheet or a goat, And so it

331
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Speaker 1: started out kind of rudimentary, and they realized that in

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Speaker 1: order to be scientific there had to be more rigor,

333
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Speaker 1: and there had to be more measurements to get a

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Speaker 1: to allow again the public what they thought the animals

335
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Speaker 1: that were going extinct, to get a picture of what

336
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Speaker 1: that number was. And so if you say that number

337
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Speaker 1: one sixty year a one eight white tail, like you're

338
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Speaker 1: getting a picture in your head of what that is. Yeah,

339
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Speaker 1: well it becomes I think it becomes ingrained for people

340
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Speaker 1: who are exposed to it a lot. I remember when

341
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Speaker 1: I couldn't picture it right, I didn't know what it meant.

342
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Speaker 1: And then gradually over the years now if someone says

343
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Speaker 1: like a mule deer, I get a very specific image

344
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Speaker 1: in my head and it's like I understand it, And

345
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Speaker 1: I tell the o MS and are the official measure

346
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Speaker 1: we call moms and our course, I say, we've we've

347
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Speaker 1: created a system of measurements that you add and subtract

348
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Speaker 1: to get to some number that we've created. Now we

349
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Speaker 1: think that numbers important, and luckily other agencies and scientists

350
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Speaker 1: and stuff have have bawled into that, and the hunters

351
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Speaker 1: as well. I mean, we wouldn't have a database if

352
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Speaker 1: people didn't take the time to have their trophy measure.

353
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Speaker 1: So they've kind of all bawled into it. And so

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Speaker 1: you know, it's just like I sais just a set

355
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Speaker 1: of measurements that are add and subtracted to get to

356
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Speaker 1: the score. And and we say entered in as many

357
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Speaker 1: different things. Were no organizations as there is out there

358
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Speaker 1: because they all have an important mission and they're all

359
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Speaker 1: trying to accomplish something. We're just one of them, and

360
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Speaker 1: we've created this measuring system. And I think it's important

361
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Speaker 1: to to you know, go, you know, turning that timetable

362
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Speaker 1: back a little bit. The purpose that you know, these

363
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Speaker 1: guys didn't really kind of sit down and come up

364
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Speaker 1: with their scoring system because they needed something to do.

365
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Speaker 1: You know, there's a there, there's a there's a precursor

366
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Speaker 1: to all of that. When the club was found in seven,

367
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Speaker 1: they wanted to solve a problem, and that was the

368
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Speaker 1: fact that our wildlife populations were being decimated, our natural resource,

369
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Speaker 1: we're disappearing in the West, and um, you know, they

370
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Speaker 1: wanted to figure out a way to fix it. And

371
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Speaker 1: so you know, they started with the Yellowstone Preservation Act

372
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Speaker 1: in eighteen nine two. Then and when Roosevelt was president,

373
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Speaker 1: they got the National Wildlife Refuge System in the National

374
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Speaker 1: Forest Reserve. Was he involved with Boone and Crockett? He

375
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Speaker 1: was right, he founded the organization. Yeah, but so he

376
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Speaker 1: founded in eight seven. Yep. Okay, yep, yep. There's an

377
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Speaker 1: interesting story behind that too. Why the hell real quick,

378
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Speaker 1: I want to get back to your timeline there. But

379
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Speaker 1: um why did he so? Why Boone and why Crockett? Okay,

380
00:20:35,920 --> 00:20:39,760
Speaker 1: that's good, so I'll be you can come back to that. No, no,

381
00:20:39,920 --> 00:20:44,840
Speaker 1: actually that's a good place to go. Um. So when

382
00:20:45,440 --> 00:20:48,800
Speaker 1: the club was found in eighteen eighty seven, because Roosevelt

383
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Speaker 1: had been out west for three years. This was after

384
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Speaker 1: the death of his wife and his mother. Um. Yeah,

385
00:20:53,960 --> 00:20:57,160
Speaker 1: he was a real MoMA's boy. Yeah. Well, but anyways,

386
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Speaker 1: he was out and he had two ranches in Madora,

387
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Speaker 1: North Dakota, and that's when he wrote around in the

388
00:21:01,560 --> 00:21:04,160
Speaker 1: Dakotas and Wyoming and Montana and Idaho, and he saw

389
00:21:04,320 --> 00:21:09,080
Speaker 1: this you know, decimation I'm talking about, and so he

390
00:21:09,160 --> 00:21:11,360
Speaker 1: was upset about that. When he got back to New

391
00:21:11,440 --> 00:21:14,879
Speaker 1: York City in seven, he called around a group of

392
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Speaker 1: guys that, you know, we're influential in their respective areas

393
00:21:18,640 --> 00:21:25,560
Speaker 1: industry people, scientists, education folks, politicians, and but they were

394
00:21:25,600 --> 00:21:27,960
Speaker 1: all joined by one common threat, and that was that

395
00:21:28,000 --> 00:21:31,200
Speaker 1: they were they were hunters, they were sportsmen, and they

396
00:21:31,240 --> 00:21:36,000
Speaker 1: cared about conservation in the future. So they sat around

397
00:21:36,000 --> 00:21:37,960
Speaker 1: scratched your heads trying to figure out, you know, what

398
00:21:37,960 --> 00:21:41,120
Speaker 1: were they going to call this new organization. And at

399
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Speaker 1: the time, Daniel Boone and Davy Crocker were two most

400
00:21:44,600 --> 00:21:49,000
Speaker 1: prominent names in that space, you know, I mean they

401
00:21:49,040 --> 00:21:53,160
Speaker 1: were the most famous people someone in America. And he said,

402
00:21:53,240 --> 00:21:57,119
Speaker 1: named two hunters at that time. Yes, now he was

403
00:21:57,160 --> 00:22:03,840
Speaker 1: still probably on that list. We'll talk about that a

404
00:22:03,880 --> 00:22:06,040
Speaker 1: little bit, but I mean I think the uh, you know,

405
00:22:06,119 --> 00:22:08,800
Speaker 1: so that was how Buona Crowd came about. But anyway,

406
00:22:08,840 --> 00:22:13,520
Speaker 1: so back to the timeline, you know, um in asseence

407
00:22:13,520 --> 00:22:17,800
Speaker 1: the scoring system was created as justin indicated first of all,

408
00:22:17,840 --> 00:22:22,359
Speaker 1: to preserve a something that would show future generations that

409
00:22:22,520 --> 00:22:25,560
Speaker 1: actually once existed on the North American continent. But then

410
00:22:25,720 --> 00:22:32,199
Speaker 1: as our conservation efforts we're successful, state laws were implemented

411
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Speaker 1: um and our population started recovering and growing. The system

412
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Speaker 1: then became a majoring tool for how do we measure

413
00:22:40,400 --> 00:22:45,000
Speaker 1: the success of everything that we've done and so and

414
00:22:45,080 --> 00:22:47,600
Speaker 1: that's what it is today. You know, that's we're still

415
00:22:48,600 --> 00:22:51,840
Speaker 1: gathering this data. We're still sharing it with with state

416
00:22:51,880 --> 00:22:57,040
Speaker 1: wildlife managers, with academics, with folks that you know, why

417
00:22:57,119 --> 00:23:00,680
Speaker 1: is my ecosystem not working? In somebody else is they're

418
00:23:00,720 --> 00:23:03,800
Speaker 1: getting bigger male specimens and I am. You know, they

419
00:23:03,840 --> 00:23:05,679
Speaker 1: come to us and they ask us those questions and

420
00:23:05,680 --> 00:23:07,480
Speaker 1: then they can we can hook them up with those

421
00:23:07,520 --> 00:23:10,600
Speaker 1: people that are successful widlife managers and to help them out.

422
00:23:11,440 --> 00:23:14,760
Speaker 1: And UM, so there's a there's a distinct purpose there

423
00:23:14,840 --> 00:23:19,359
Speaker 1: besides just county inches and county numbers. Well have you

424
00:23:19,720 --> 00:23:26,000
Speaker 1: what have you guys like when you look at the records? Well,

425
00:23:26,280 --> 00:23:27,919
Speaker 1: I want to ask I need to ask you other

426
00:23:28,000 --> 00:23:32,920
Speaker 1: question about the records because um, explain the cut off?

427
00:23:34,320 --> 00:23:37,239
Speaker 1: Like you guys don't accept something until it's a certain size, right,

428
00:23:37,320 --> 00:23:39,280
Speaker 1: or do you accept all numbers? It's just they don't

429
00:23:39,280 --> 00:23:43,440
Speaker 1: get put into the book. So what we uh, what

430
00:23:43,480 --> 00:23:46,040
Speaker 1: we do have said that my question isn't clear enough

431
00:23:46,080 --> 00:23:48,359
Speaker 1: for people. We've said a threshold that says, if it

432
00:23:48,440 --> 00:23:52,560
Speaker 1: exceeds this particular level, it is worth recording as a

433
00:23:52,640 --> 00:23:57,320
Speaker 1: mature male specimen created by this habitat. If we were

434
00:23:57,359 --> 00:24:00,400
Speaker 1: clearly or if we were purely wanting to just ignized

435
00:24:00,440 --> 00:24:04,119
Speaker 1: the biggest and best. Why would we accept a threshold.

436
00:24:04,280 --> 00:24:06,360
Speaker 1: We'd say, send in your biggest mule deer this year,

437
00:24:06,920 --> 00:24:08,719
Speaker 1: and then we just keep track of what those biggest

438
00:24:08,720 --> 00:24:11,040
Speaker 1: ones were. But that's not what our mission is. We're

439
00:24:11,080 --> 00:24:15,240
Speaker 1: looking at any time a male specimen reaches a certain

440
00:24:15,280 --> 00:24:18,520
Speaker 1: age under certain conditions, this is the expression of its

441
00:24:18,520 --> 00:24:22,239
Speaker 1: secondary sexual characteristic a k antlers or horns. And so

442
00:24:22,320 --> 00:24:24,280
Speaker 1: we want to mark this down because we can't keep

443
00:24:24,280 --> 00:24:26,919
Speaker 1: track of everything taken in the country, So we have

444
00:24:27,000 --> 00:24:29,640
Speaker 1: to take a small small bit of this data set

445
00:24:29,960 --> 00:24:33,960
Speaker 1: and then we can extrapolate it out. Now we can't say, okay, Bozeman,

446
00:24:34,600 --> 00:24:37,560
Speaker 1: whatever Gallatin County, Montana, I say, historically it put out

447
00:24:38,160 --> 00:24:41,000
Speaker 1: ten book mule deer year and then it drops the five.

448
00:24:41,640 --> 00:24:44,000
Speaker 1: Our records can't say, oh, well, this is what happened.

449
00:24:44,040 --> 00:24:45,720
Speaker 1: But we can say, well, for thirty years it put

450
00:24:45,720 --> 00:24:47,760
Speaker 1: out ten and then all of a sudden, at this

451
00:24:47,800 --> 00:24:50,800
Speaker 1: point it dropped to five. What changed? And so that's

452
00:24:50,920 --> 00:24:52,560
Speaker 1: that's why we have a threshold, and we had to

453
00:24:52,600 --> 00:24:55,080
Speaker 1: set it somewhere. You know, when you you wanted to

454
00:24:55,119 --> 00:24:57,600
Speaker 1: say those mature male specimens that reach an age and

455
00:24:57,760 --> 00:25:02,359
Speaker 1: beyond breeding. Um prime breeding basically was the idea of

456
00:25:02,400 --> 00:25:04,320
Speaker 1: going for the super older ones because it have no

457
00:25:04,359 --> 00:25:07,119
Speaker 1: effect on the population. So that's what you wanted to

458
00:25:07,160 --> 00:25:10,520
Speaker 1: concentrate your your harvest pressure, and then we can record

459
00:25:10,560 --> 00:25:12,800
Speaker 1: how often those happened and try to extrapolate that to

460
00:25:12,840 --> 00:25:18,359
Speaker 1: habitat quality. So what story does it tell? UM? And

461
00:25:18,480 --> 00:25:20,000
Speaker 1: if you pick a species like let's say you pick

462
00:25:20,040 --> 00:25:23,720
Speaker 1: whitetail deer, does when you look at the submissions that

463
00:25:23,760 --> 00:25:26,040
Speaker 1: come in, like the number of deer every year, and

464
00:25:26,080 --> 00:25:29,719
Speaker 1: when do they start like diligently keeping records? The current

465
00:25:29,760 --> 00:25:31,840
Speaker 1: system was nineteen fifty That's what I was trying to

466
00:25:31,840 --> 00:25:34,320
Speaker 1: get what it wasn't real clear. We started for one

467
00:25:34,359 --> 00:25:37,680
Speaker 1: reason and then it was was redone in the fifties

468
00:25:37,720 --> 00:25:41,119
Speaker 1: to switch focus to really use as a gauge. So

469
00:25:41,280 --> 00:25:43,760
Speaker 1: nineteen fifties and that's when the current system came Out's

470
00:25:43,800 --> 00:25:45,400
Speaker 1: when the current system came out. So if you look

471
00:25:45,440 --> 00:25:51,160
Speaker 1: at from nineteen fifty two, now, UM do you like?

472
00:25:51,280 --> 00:25:54,200
Speaker 1: Does does the story that the record book entries tell

473
00:25:54,840 --> 00:25:57,080
Speaker 1: line up with the story as we generally understand it?

474
00:25:57,960 --> 00:26:01,080
Speaker 1: That meaning that UM starting I don't know when the

475
00:26:01,080 --> 00:26:04,560
Speaker 1: hell is started in the nineties you know, we just

476
00:26:04,560 --> 00:26:07,119
Speaker 1: started to have a lot bigger deer around the country

477
00:26:07,160 --> 00:26:11,400
Speaker 1: because management practices began to change. White tail deer, Well yeah, sorry,

478
00:26:11,480 --> 00:26:14,199
Speaker 1: white tail deer, because management practices began to change. Do

479
00:26:14,240 --> 00:26:18,840
Speaker 1: you like does your stuff reflect that? Yes? Absolutely? I

480
00:26:18,840 --> 00:26:23,119
Speaker 1: mean we're we're entering more every year and that is

481
00:26:23,119 --> 00:26:26,640
Speaker 1: a testament to the effective wildlife management by our state

482
00:26:26,640 --> 00:26:29,960
Speaker 1: and federal agencies. And um, that's kudos to them, because

483
00:26:30,000 --> 00:26:33,240
Speaker 1: I mean we're taking more big creatures in what what

484
00:26:33,320 --> 00:26:35,680
Speaker 1: are the things that you what are some animals that

485
00:26:35,760 --> 00:26:39,840
Speaker 1: you realize that more and more of them are making

486
00:26:39,840 --> 00:26:42,800
Speaker 1: the books? And what are animals where you're looking to

487
00:26:42,880 --> 00:26:44,919
Speaker 1: be like, man, something's not right because we're just not

488
00:26:44,960 --> 00:26:47,639
Speaker 1: seeing them anymore. Well, I guess before we get to

489
00:26:47,680 --> 00:26:48,960
Speaker 1: that one, Steve, I want to go a little bit

490
00:26:49,000 --> 00:26:51,280
Speaker 1: more into your white tail question because it's a great example.

491
00:26:51,720 --> 00:26:54,560
Speaker 1: So you have the nineties, right, Milo Hanson, the buck

492
00:26:54,600 --> 00:26:57,960
Speaker 1: out of bigger Saskatchewan world record typical white tail. At

493
00:26:58,000 --> 00:27:02,960
Speaker 1: that time, we were seeing more top end quality deer

494
00:27:02,960 --> 00:27:05,719
Speaker 1: than we are white tails as we are now now.

495
00:27:06,880 --> 00:27:09,440
Speaker 1: I would argue that what you saw was an overpopulation

496
00:27:09,480 --> 00:27:12,120
Speaker 1: of deer. We were too successful and then you have

497
00:27:12,359 --> 00:27:15,359
Speaker 1: a density. You know, the species is too dense, so

498
00:27:15,400 --> 00:27:18,639
Speaker 1: you're actually seeing a decline in the available resources to

499
00:27:18,680 --> 00:27:21,080
Speaker 1: go to antlers. Well, we had that big die off

500
00:27:21,119 --> 00:27:23,040
Speaker 1: a few years ago. Remember when e h D hit

501
00:27:23,480 --> 00:27:26,000
Speaker 1: the Upper Midwest would have never been seen in Wisconsin,

502
00:27:26,520 --> 00:27:29,320
Speaker 1: you know, across the across that area, and so there

503
00:27:29,359 --> 00:27:31,040
Speaker 1: was a huge die off of white tails. Well we

504
00:27:31,080 --> 00:27:34,400
Speaker 1: saw a corresponding dropping entries. But what we saw after

505
00:27:34,480 --> 00:27:37,880
Speaker 1: that was you had unexploited resources that that tucker buck

506
00:27:37,920 --> 00:27:41,040
Speaker 1: out of Tennessee, there was some deer that absolutely blew

507
00:27:41,040 --> 00:27:44,080
Speaker 1: the doors off because there was extra resources. And so

508
00:27:44,160 --> 00:27:46,400
Speaker 1: something like that is is what our records are real

509
00:27:46,480 --> 00:27:49,680
Speaker 1: cool about. And sometimes it gets lost because I can't

510
00:27:49,720 --> 00:27:53,040
Speaker 1: look at Missoula County on Mountain lion entries and make

511
00:27:53,359 --> 00:27:58,199
Speaker 1: a hypothesis off of one county because I don't have

512
00:27:58,200 --> 00:28:00,399
Speaker 1: a big enough data set. But when I look at

513
00:28:00,400 --> 00:28:04,440
Speaker 1: the entire Upper Midwest and see a sevent decline, that's

514
00:28:04,440 --> 00:28:07,639
Speaker 1: where our records fit in and doesn't really exist anywhere else.

515
00:28:08,320 --> 00:28:11,240
Speaker 1: So that now to get back to your question on

516
00:28:11,240 --> 00:28:16,080
Speaker 1: the species we're seeing um, caribou, caribooer dismal um. We

517
00:28:16,280 --> 00:28:19,160
Speaker 1: are just not seeing big caribou come in with our

518
00:28:19,440 --> 00:28:22,720
Speaker 1: Yeah we're not. We used to get a few thousand

519
00:28:22,760 --> 00:28:28,240
Speaker 1: every three years. We're lucky to get um d Yeah.

520
00:28:28,800 --> 00:28:32,199
Speaker 1: Now there you know you you look at the the

521
00:28:32,240 --> 00:28:35,280
Speaker 1: barren ground caribou in northern Alaska. They're starting to see

522
00:28:35,280 --> 00:28:37,760
Speaker 1: the populations go up. So I'm hoping that I start

523
00:28:37,800 --> 00:28:41,200
Speaker 1: to see that gradual uptick in our records. Um I

524
00:28:41,280 --> 00:28:43,320
Speaker 1: haven't noticed it yet. You know that's more Kyle now

525
00:28:43,440 --> 00:28:46,560
Speaker 1: doing the day to day review. But yeah, I mean

526
00:28:46,560 --> 00:28:50,560
Speaker 1: cariboo are bad bears. We're seeing We're seeing more and

527
00:28:50,680 --> 00:28:53,720
Speaker 1: bigger bears from all over North America, the South. The

528
00:28:53,720 --> 00:28:57,560
Speaker 1: said black bears, grizzly bears, brown bears. They're doing awesome

529
00:28:57,880 --> 00:29:00,000
Speaker 1: like bears are. So you see a lot of big

530
00:29:00,040 --> 00:29:04,720
Speaker 1: grizzlies coming out of Cannon in Alaska. Yeah, Alaska, Pope

531
00:29:04,720 --> 00:29:08,640
Speaker 1: and Young's world record broke twice, I believe. Yeah. Yeah,

532
00:29:08,680 --> 00:29:12,560
Speaker 1: they're the Commack bear. Yeah that was a brown bear. Ye,

533
00:29:12,920 --> 00:29:15,400
Speaker 1: Chris Commack killed the world record archery brown bear. We

534
00:29:15,440 --> 00:29:17,560
Speaker 1: haven't seen it break ours. But I mean again, you're

535
00:29:17,560 --> 00:29:21,240
Speaker 1: still seeing some of the biggest bears in the world. Um,

536
00:29:21,280 --> 00:29:24,120
Speaker 1: so that could tell you. Yeah, it's interesting because you

537
00:29:24,160 --> 00:29:25,720
Speaker 1: can't tell what it can tell you. On one hand,

538
00:29:25,760 --> 00:29:30,440
Speaker 1: you could look and say, um, hats off to Alaska,

539
00:29:30,680 --> 00:29:32,880
Speaker 1: right right, doing a great job. Other hand, you could

540
00:29:32,920 --> 00:29:38,120
Speaker 1: look and say, maybe they're not offering enough opportunity. But

541
00:29:38,120 --> 00:29:40,240
Speaker 1: I wouldn't really say that there because they're like increasing operation.

542
00:29:40,360 --> 00:29:42,880
Speaker 1: Was gonna say, increase is increased effort resulting in an

543
00:29:43,120 --> 00:29:45,680
Speaker 1: in an uptick in the number of trophies we're seeing. Yeah,

544
00:29:45,680 --> 00:29:47,640
Speaker 1: it's hard to untangle it. So and that that's again

545
00:29:47,680 --> 00:29:49,600
Speaker 1: where you have to look at it on a North

546
00:29:49,640 --> 00:29:52,600
Speaker 1: American scale. If you get too precise, you can kind

547
00:29:52,600 --> 00:29:54,360
Speaker 1: of miss it a little bit. With our data set,

548
00:29:54,400 --> 00:29:57,400
Speaker 1: we're just looking at overall trends, so I can say, like, oh,

549
00:29:57,440 --> 00:29:59,920
Speaker 1: the southeastern United States, we're seeing bears from a bun

550
00:30:00,080 --> 00:30:03,280
Speaker 1: to states that we've never seen before. They're getting very big,

551
00:30:03,320 --> 00:30:05,840
Speaker 1: they're doing healthy. Bears in that part of the country

552
00:30:05,840 --> 00:30:09,760
Speaker 1: are doing good. Kudos to you know, Carolinas and Arkansas

553
00:30:09,800 --> 00:30:13,200
Speaker 1: and all that. So you mentioned mountain lions, so out

554
00:30:13,200 --> 00:30:17,520
Speaker 1: of the let's just say, I mean they're only you know,

555
00:30:17,960 --> 00:30:20,800
Speaker 1: I don't know how many states have thirteen or fourteen

556
00:30:20,840 --> 00:30:24,520
Speaker 1: states have legal mountain lion seasons. Maybe our mountain lions

557
00:30:25,160 --> 00:30:31,200
Speaker 1: up or down, steady, steady, steady. Um. Missoula County, Montana,

558
00:30:31,720 --> 00:30:35,920
Speaker 1: that is the place to get a book skull mountain lion. Yeah,

559
00:30:36,320 --> 00:30:37,960
Speaker 1: just over the border in Idaho. Those are the two

560
00:30:37,960 --> 00:30:41,320
Speaker 1: top counties. They're staying stable. But that makes sense with

561
00:30:41,360 --> 00:30:43,840
Speaker 1: the biology of a mountain lion, because you can't have

562
00:30:43,880 --> 00:30:46,120
Speaker 1: too many big toms because they kill each other. So

563
00:30:46,200 --> 00:30:48,520
Speaker 1: it makes sense that that would be something that's kind

564
00:30:48,560 --> 00:30:52,120
Speaker 1: of a status quo, you know, across their range. From

565
00:30:52,160 --> 00:30:54,320
Speaker 1: what we've seen, it's not likely to be super violatile,

566
00:30:55,280 --> 00:31:03,560
Speaker 1: all right, um, Melier out down, but holding steady and

567
00:31:03,600 --> 00:31:06,680
Speaker 1: showing positives in some areas and not in others. It's

568
00:31:06,720 --> 00:31:08,440
Speaker 1: not as dismal as you want. No, it's not the

569
00:31:08,480 --> 00:31:11,480
Speaker 1: sixties of mule deer harvest. Um. I actually did a

570
00:31:11,920 --> 00:31:14,360
Speaker 1: talk on this one time a Wild Cheep. When I

571
00:31:14,400 --> 00:31:17,440
Speaker 1: first started, you know, I had a philosophy and I

572
00:31:17,520 --> 00:31:19,360
Speaker 1: was wrong, and you know, I thought I knew everything

573
00:31:19,440 --> 00:31:22,000
Speaker 1: back then. But um, there was a lot of people

574
00:31:22,040 --> 00:31:28,000
Speaker 1: that brought up brought up um like predator control when

575
00:31:28,040 --> 00:31:30,760
Speaker 1: they when they were seeing those big mule deer in

576
00:31:30,800 --> 00:31:33,760
Speaker 1: Colorado and these you know, deer getting killed. There was

577
00:31:33,760 --> 00:31:37,400
Speaker 1: a lot of people using coyote getters, and so yeah,

578
00:31:37,440 --> 00:31:39,840
Speaker 1: there was a big peak back in the sixties and seventies.

579
00:31:40,360 --> 00:31:42,440
Speaker 1: But they're not they're not continuing to draw, but we're

580
00:31:42,440 --> 00:31:44,040
Speaker 1: putting a lot of effort into them. They're just kind

581
00:31:44,040 --> 00:31:47,600
Speaker 1: of plugging along. And why is Colorado blowing the doors

582
00:31:47,640 --> 00:31:49,720
Speaker 1: off of everywhere else? Yeah, that's that's the thing I

583
00:31:49,760 --> 00:31:51,680
Speaker 1: wanted to ask. Man. I mean, there's a ton of

584
00:31:51,720 --> 00:31:53,000
Speaker 1: ground I want to cover, but I do want to

585
00:31:53,040 --> 00:31:56,640
Speaker 1: hit that real quick. Is uh you know, we spent

586
00:31:56,720 --> 00:32:04,520
Speaker 1: twenty years scouring uh this eight up and down in sideways, um,

587
00:32:04,520 --> 00:32:07,320
Speaker 1: trying to find like a like a like a big builder,

588
00:32:07,520 --> 00:32:09,760
Speaker 1: you know, and I thought there's something wrong with us.

589
00:32:11,000 --> 00:32:12,880
Speaker 1: Then I started making a handful of trips down to

590
00:32:13,080 --> 00:32:16,880
Speaker 1: Colorado and Idaho, and I'm like, oh, I get it. Yeah,

591
00:32:17,080 --> 00:32:19,440
Speaker 1: it's just you know, it's just different, man. There's some

592
00:32:19,480 --> 00:32:22,080
Speaker 1: places that have a lot of them. Is it like

593
00:32:22,120 --> 00:32:25,680
Speaker 1: in Colorado puts off ten to one compared to oh yeah,

594
00:32:25,760 --> 00:32:27,560
Speaker 1: some other Western state. I don't I don't know the

595
00:32:27,600 --> 00:32:31,200
Speaker 1: exact but it's it's ridiculous, you know what I mean.

596
00:32:31,200 --> 00:32:33,480
Speaker 1: I was I remember what my breakdown was but Colorado

597
00:32:33,480 --> 00:32:35,880
Speaker 1: had put in three hundred and something entries and the

598
00:32:35,920 --> 00:32:38,280
Speaker 1: next close this was like a hundred and one, and

599
00:32:38,320 --> 00:32:41,400
Speaker 1: then it drops them Montana that was like forty you know,

600
00:32:42,520 --> 00:32:47,400
Speaker 1: in the same time period. It's incredible, is there even?

601
00:32:47,640 --> 00:32:49,560
Speaker 1: I got all kinds of theories about why that is.

602
00:32:49,600 --> 00:32:53,440
Speaker 1: But what's your theory about why that is? We let

603
00:32:53,440 --> 00:32:55,520
Speaker 1: people shoot mule there all the way through the right?

604
00:32:56,360 --> 00:32:59,920
Speaker 1: I mean, I think you know, Colorado is an opportunity state.

605
00:33:00,000 --> 00:33:02,200
Speaker 1: It but they've they've seemed to set it up to

606
00:33:02,200 --> 00:33:04,320
Speaker 1: where they're bigger deer protected and a lot of those

607
00:33:04,360 --> 00:33:07,880
Speaker 1: real big deer come from sleeper units, and so I

608
00:33:07,880 --> 00:33:10,240
Speaker 1: think they've got they've got the groceries, they've got the

609
00:33:10,240 --> 00:33:12,800
Speaker 1: age structure. They protect him at the right time. I think.

610
00:33:13,480 --> 00:33:15,800
Speaker 1: I think there's no over the counter deer tag there.

611
00:33:17,400 --> 00:33:21,840
Speaker 1: Still we have to check, but I don't think so.

612
00:33:22,800 --> 00:33:24,760
Speaker 1: I think pretty much everything mule deer has gone to

613
00:33:24,840 --> 00:33:28,560
Speaker 1: draw down there. But either way, you're right, I mean,

614
00:33:28,560 --> 00:33:32,680
Speaker 1: they definitely protect those deers um, but you can they

615
00:33:32,680 --> 00:33:35,160
Speaker 1: still have lots of opportunities and go shooting. Does you

616
00:33:35,200 --> 00:33:38,840
Speaker 1: know for a lot of the seasons, Hey, before I

617
00:33:38,880 --> 00:33:40,480
Speaker 1: know where I don't want to leave the scoring system

618
00:33:40,440 --> 00:33:42,960
Speaker 1: before I can reason a couple of my questions because

619
00:33:42,960 --> 00:33:44,320
Speaker 1: and I feel like we need to make sure that

620
00:33:44,480 --> 00:33:47,880
Speaker 1: everybody understands it. But what I want to start off

621
00:33:47,920 --> 00:33:51,400
Speaker 1: with is, because you mentioned it has changed over the years, right,

622
00:33:51,760 --> 00:33:54,280
Speaker 1: is there a chance that the scoring system will change again?

623
00:33:54,880 --> 00:33:57,120
Speaker 1: Because and I can give you my example why it

624
00:33:57,160 --> 00:34:00,760
Speaker 1: brings up this question is that I remember when I

625
00:34:00,920 --> 00:34:04,400
Speaker 1: just got into, like, look at having to judge bull

626
00:34:04,440 --> 00:34:08,440
Speaker 1: elk as a guide in Arizona for clients, and so

627
00:34:08,480 --> 00:34:11,080
Speaker 1: I was getting like getting into that mindset, you know.

628
00:34:11,880 --> 00:34:14,600
Speaker 1: And at the time, there was that world record elk

629
00:34:14,640 --> 00:34:17,800
Speaker 1: that was forever number one. The was it the Plute

630
00:34:17,880 --> 00:34:21,440
Speaker 1: bowl out of Colorado, Black Canyon, right, And then it

631
00:34:21,480 --> 00:34:24,600
Speaker 1: got bested by belief like an inch or something like that,

632
00:34:24,880 --> 00:34:31,080
Speaker 1: by the White Mountain bull Black Canyon bull Vain And

633
00:34:31,080 --> 00:34:34,080
Speaker 1: it was a And when you look at the two

634
00:34:34,480 --> 00:34:38,800
Speaker 1: compared side by side, the Black Canyon Bowl, just wait

635
00:34:38,880 --> 00:34:42,319
Speaker 1: alone you can it must weigh twenty pounds more than

636
00:34:42,360 --> 00:34:45,120
Speaker 1: this the White Mountain bull. White Mountain bulls long and

637
00:34:45,239 --> 00:34:48,399
Speaker 1: spin ley, and but it had the length right, which

638
00:34:48,440 --> 00:34:51,160
Speaker 1: was awarded by your system. But I would say if

639
00:34:51,160 --> 00:34:53,400
Speaker 1: you saw the two animals on the hoof, probably and

640
00:34:53,440 --> 00:34:55,400
Speaker 1: saw the two sets of antlers, you'd say, well, for

641
00:34:55,480 --> 00:34:57,880
Speaker 1: sure that that the black cannonball is just gonna crush

642
00:34:58,560 --> 00:35:01,640
Speaker 1: the white mountain bull because he's just bigger and batter

643
00:35:01,680 --> 00:35:04,600
Speaker 1: and healthier just by judging the mass and his antlers. No,

644
00:35:04,719 --> 00:35:08,719
Speaker 1: And so it's a great question. And if we were

645
00:35:08,719 --> 00:35:11,480
Speaker 1: to create our scoring system today, I guarantee there'd be

646
00:35:11,560 --> 00:35:14,840
Speaker 1: some differences. But what we did was in nineteen fifty

647
00:35:14,840 --> 00:35:16,560
Speaker 1: we did the best we could and some of those

648
00:35:16,560 --> 00:35:18,880
Speaker 1: things now we kind of have to to hang onto

649
00:35:19,440 --> 00:35:21,279
Speaker 1: or all those data points that we have to this

650
00:35:21,320 --> 00:35:23,640
Speaker 1: point become invalid because you can't go look at it

651
00:35:23,840 --> 00:35:27,040
Speaker 1: right and so on ELK, when you mas your g one,

652
00:35:27,080 --> 00:35:29,200
Speaker 1: you start in the center, you transition to the outside

653
00:35:29,239 --> 00:35:31,480
Speaker 1: and go to the tip. That's the number one thing

654
00:35:31,520 --> 00:35:33,319
Speaker 1: that we see problems with. If we were to do

655
00:35:33,360 --> 00:35:36,080
Speaker 1: it today, I'd say that's not really, in my opinion,

656
00:35:36,080 --> 00:35:40,920
Speaker 1: not necessary. So when you're taking the little show, how

657
00:35:40,960 --> 00:35:42,920
Speaker 1: detailed this is, So when you're taking the length of

658
00:35:42,920 --> 00:35:45,080
Speaker 1: a time, you draw on a baseline on ELK because

659
00:35:45,080 --> 00:35:47,840
Speaker 1: what we're talking about here and generally speaking, on the

660
00:35:47,960 --> 00:35:50,439
Speaker 1: edge of the beam where the time. You know where

661
00:35:50,440 --> 00:35:52,320
Speaker 1: the beam would be had the time not happened, is

662
00:35:52,360 --> 00:35:54,600
Speaker 1: where you started. You follow the center of the time

663
00:35:54,640 --> 00:35:58,640
Speaker 1: along the outside curve on elk on the g one point,

664
00:35:58,640 --> 00:36:01,200
Speaker 1: the first point, just because of the way it sits

665
00:36:01,200 --> 00:36:03,600
Speaker 1: on that base, and I think what it was originally

666
00:36:03,600 --> 00:36:05,279
Speaker 1: they didn't have a lot of curvature to him, and

667
00:36:05,280 --> 00:36:07,680
Speaker 1: these bulls were at the time not as mature as

668
00:36:07,680 --> 00:36:11,080
Speaker 1: what we're seeing now. They started in the very center,

669
00:36:11,320 --> 00:36:13,839
Speaker 1: like if you're holding the elk rack sideways, like looking

670
00:36:13,880 --> 00:36:16,120
Speaker 1: at it from the side. You start in the center

671
00:36:16,160 --> 00:36:19,000
Speaker 1: of the time, it goes to the end, it transitions

672
00:36:19,040 --> 00:36:21,160
Speaker 1: to the bottom edge of the time, and then continues

673
00:36:21,200 --> 00:36:25,000
Speaker 1: around the outside curve, which at the time made sense.

674
00:36:25,719 --> 00:36:28,719
Speaker 1: Now I would say probably doesn't. But that's how every

675
00:36:28,800 --> 00:36:31,640
Speaker 1: g one has been measured, so we can't now say, oh,

676
00:36:31,680 --> 00:36:34,480
Speaker 1: never mind, we were wrong on that particular measurement. I

677
00:36:34,480 --> 00:36:37,319
Speaker 1: got a suggestion for you what you could do too.

678
00:36:37,480 --> 00:36:40,319
Speaker 1: If you needed a pivot, You wouldn't do a hard pivot, right.

679
00:36:40,840 --> 00:36:46,080
Speaker 1: You would continue to measure everything the way you've always

680
00:36:46,120 --> 00:36:49,200
Speaker 1: measured it, so that you can at least compare relative

681
00:36:49,239 --> 00:36:54,160
Speaker 1: to the past. But then you simultaneously do the new way,

682
00:36:54,280 --> 00:36:57,600
Speaker 1: and everything has been measured twice, and then in ten

683
00:36:57,680 --> 00:37:00,759
Speaker 1: years you'll have this decade long body of the perfect way.

684
00:37:01,400 --> 00:37:04,680
Speaker 1: But then you miss the nineties, which the eighties and

685
00:37:04,680 --> 00:37:07,399
Speaker 1: the nineties which is the taking off of in white tail.

686
00:37:07,920 --> 00:37:11,760
Speaker 1: You know, um, this is for the future generations. But

687
00:37:11,760 --> 00:37:13,960
Speaker 1: but you need to see where the data set. You

688
00:37:13,960 --> 00:37:16,480
Speaker 1: don't want to discount thirty years, fifty years, eighties to

689
00:37:16,560 --> 00:37:18,680
Speaker 1: continue to do it. You continue to do it, but

690
00:37:18,719 --> 00:37:20,920
Speaker 1: you also do it the new way. Everybody has a

691
00:37:21,000 --> 00:37:27,400
Speaker 1: measured everything twice. You tell our volunteers that hold on

692
00:37:27,440 --> 00:37:40,680
Speaker 1: those guys, right. The follow up question to that to

693
00:37:40,719 --> 00:37:43,640
Speaker 1: be would be like, would you ever add something like

694
00:37:44,200 --> 00:37:47,439
Speaker 1: the weight of an animal or just to get more

695
00:37:47,520 --> 00:37:50,080
Speaker 1: data points? We we did for a while ask for

696
00:37:50,120 --> 00:37:52,920
Speaker 1: the weight of the rack um for a while the

697
00:37:53,120 --> 00:37:56,279
Speaker 1: program the chairman was a professor at the University of

698
00:37:56,280 --> 00:37:59,239
Speaker 1: Montana and he was looking at rack weights and so

699
00:37:59,320 --> 00:38:01,520
Speaker 1: we actually had a data collection thing on the back

700
00:38:01,560 --> 00:38:04,279
Speaker 1: of the entry or the hunter guiding hunt form that

701
00:38:04,360 --> 00:38:06,560
Speaker 1: asked how much did the rack of your animal way

702
00:38:06,719 --> 00:38:09,600
Speaker 1: or horns? How do you get the skull and everything

703
00:38:09,600 --> 00:38:11,279
Speaker 1: on it? Well? And there was a there was a thing.

704
00:38:11,320 --> 00:38:13,799
Speaker 1: How was it cut whole skull? And so as we

705
00:38:13,840 --> 00:38:15,920
Speaker 1: tried to do it, we found out that that wasn't

706
00:38:15,960 --> 00:38:19,719
Speaker 1: really a great It's right, there was a lot of

707
00:38:19,800 --> 00:38:24,000
Speaker 1: variation there, so we quit asking for that particular. What

708
00:38:24,080 --> 00:38:28,000
Speaker 1: did this rack wag um? You hear about water displacement?

709
00:38:28,000 --> 00:38:30,400
Speaker 1: There gets a lot of that, to your point, is

710
00:38:30,480 --> 00:38:32,560
Speaker 1: really big and they think it should be water displacement.

711
00:38:32,680 --> 00:38:36,520
Speaker 1: So water displacement is again it's another great system that's

712
00:38:36,560 --> 00:38:40,440
Speaker 1: interesting to know, but it doesn't reward symmetry. It basically

713
00:38:40,480 --> 00:38:43,440
Speaker 1: rewards freak traits. And so if you're looking at the

714
00:38:43,440 --> 00:38:45,480
Speaker 1: overall health of the animal, you get something that gets

715
00:38:45,520 --> 00:38:48,680
Speaker 1: a big infection and a giant bulbous you know growth

716
00:38:48,719 --> 00:38:50,719
Speaker 1: off of it. Well, that's going to displace more water.

717
00:38:51,200 --> 00:38:53,880
Speaker 1: But that's not actually a positive trait when you're looking

718
00:38:53,920 --> 00:38:57,160
Speaker 1: at you know, how well did this rack? I mean

719
00:38:57,239 --> 00:38:59,799
Speaker 1: something that means you get hit by a car or

720
00:38:59,719 --> 00:39:02,080
Speaker 1: or bit by a bug and it got infected or

721
00:39:02,120 --> 00:39:04,360
Speaker 1: you know, got in a fight and it got broken

722
00:39:04,360 --> 00:39:07,520
Speaker 1: early on in velvet and created something goofy. So that's

723
00:39:07,560 --> 00:39:11,480
Speaker 1: what what would cause those abnormalities is generally a stressor

724
00:39:11,520 --> 00:39:15,320
Speaker 1: that would be negative only have one of our universities

725
00:39:15,360 --> 00:39:17,799
Speaker 1: looking at that, isn't one of our fellows looking at

726
00:39:17,840 --> 00:39:23,560
Speaker 1: that abnormalities caused by stressors? And yeah, and it's been

727
00:39:23,600 --> 00:39:26,600
Speaker 1: looked at a few times. Um, you know, basically the

728
00:39:26,640 --> 00:39:29,080
Speaker 1: science that they had in the nineteen fifties is still solid.

729
00:39:29,600 --> 00:39:32,520
Speaker 1: There is some some workout that shows like split eye

730
00:39:32,520 --> 00:39:36,560
Speaker 1: guards in certain areas. Perhaps that population got so genetically

731
00:39:36,600 --> 00:39:38,880
Speaker 1: bottleneck that all the white tails from some area have

732
00:39:38,920 --> 00:39:42,280
Speaker 1: a split eye guard. And so you could argue, well, okay,

733
00:39:42,320 --> 00:39:44,480
Speaker 1: that's that's what they all have. Well that was created

734
00:39:44,480 --> 00:39:47,600
Speaker 1: by a stressor, that the population is still bottlenecked, that

735
00:39:47,719 --> 00:39:50,239
Speaker 1: this negative trade is now dominant, that doesn't mean we've

736
00:39:50,320 --> 00:39:53,480
Speaker 1: changed the system to reward a negative trade is dominant

737
00:39:53,520 --> 00:39:56,439
Speaker 1: just because they got bottlenecked. I think that the one

738
00:39:56,440 --> 00:40:02,240
Speaker 1: of the uh primary, one of the primary hunter based

739
00:40:02,280 --> 00:40:06,200
Speaker 1: criticisms of the Boone and Crockett scoring system. We'll get

740
00:40:06,200 --> 00:40:08,880
Speaker 1: into the non hunter based criticism of Boone and crocket

741
00:40:08,880 --> 00:40:11,480
Speaker 1: sport scoring system, but the hunter based one has to

742
00:40:11,520 --> 00:40:16,040
Speaker 1: do with like deductions to the point where a lot

743
00:40:16,080 --> 00:40:20,000
Speaker 1: of people I hang out with they they please don't

744
00:40:20,000 --> 00:40:30,279
Speaker 1: say nets are for fish, but yeah, so you I'll

745
00:40:30,280 --> 00:40:31,920
Speaker 1: take a stack. Let let me take a staff of this.

746
00:40:31,960 --> 00:40:34,799
Speaker 1: You tell me where I get it wrong. You you know,

747
00:40:34,840 --> 00:40:37,520
Speaker 1: you shoot a deer and you take all these different measurements.

748
00:40:37,520 --> 00:40:40,120
Speaker 1: Not like you measure the length of the main beam.

749
00:40:40,400 --> 00:40:43,400
Speaker 1: You measure the length of all the different times. You

750
00:40:43,440 --> 00:40:47,799
Speaker 1: measure a bunch of circumferences. Um, you measure like the

751
00:40:47,880 --> 00:40:51,680
Speaker 1: width the inside spread, so the distance from one antler

752
00:40:51,719 --> 00:40:54,359
Speaker 1: to the other antler at the widest point. And you

753
00:40:54,400 --> 00:40:56,320
Speaker 1: add all these up, and we're supposed to You're supposed

754
00:40:56,360 --> 00:40:59,120
Speaker 1: to do it for each side, left antler, right antler.

755
00:41:00,040 --> 00:41:02,680
Speaker 1: And so let's say you get a um, you get

756
00:41:02,719 --> 00:41:07,839
Speaker 1: a left antler that's seventy, and you get a right

757
00:41:07,920 --> 00:41:13,240
Speaker 1: antler that's sixty. Someone would think, oh, so you shot

758
00:41:13,280 --> 00:41:17,480
Speaker 1: a one thirty white tail. But in fact they penalize

759
00:41:17,520 --> 00:41:22,000
Speaker 1: you or not or reward you. There's like some penalty

760
00:41:23,200 --> 00:41:26,120
Speaker 1: because of the fact that they're different, and that pisces

761
00:41:26,120 --> 00:41:28,319
Speaker 1: people off. Now you say, now you give them what

762
00:41:28,360 --> 00:41:30,520
Speaker 1: I'm telling people. What I'm trying to say, Well, it's

763
00:41:30,520 --> 00:41:33,640
Speaker 1: not a penalty. We're just we're measuring what's out there.

764
00:41:33,840 --> 00:41:36,799
Speaker 1: People look at people have put constructs on it that

765
00:41:36,840 --> 00:41:40,759
Speaker 1: it's some kind of penalty or reward. Um, sure, it's

766
00:41:40,800 --> 00:41:42,879
Speaker 1: just what the animal. You know, it's what it is.

767
00:41:43,760 --> 00:41:45,400
Speaker 1: It's just a set like I said earlier, it's just

768
00:41:45,440 --> 00:41:48,560
Speaker 1: a set of measurements that are added and subtracted. And

769
00:41:48,600 --> 00:41:50,400
Speaker 1: there's other school But the reason people feel like they

770
00:41:50,440 --> 00:41:52,600
Speaker 1: got robbed, I think is because they look at the

771
00:41:52,760 --> 00:41:55,160
Speaker 1: because it's about them. It's not about the antists. It's

772
00:41:55,160 --> 00:41:56,719
Speaker 1: about them. I'm not saying it's not about them, but

773
00:41:56,760 --> 00:41:59,319
Speaker 1: they're looking at the thing. They see the number because

774
00:41:59,320 --> 00:42:02,799
Speaker 1: it's even like a box. It's like net or gros

775
00:42:03,440 --> 00:42:06,160
Speaker 1: so gross is like all the score of the deer.

776
00:42:06,280 --> 00:42:08,640
Speaker 1: And then you do like your deductions. And I think

777
00:42:08,680 --> 00:42:11,000
Speaker 1: people get bummed when they have to look at the

778
00:42:11,000 --> 00:42:14,880
Speaker 1: one number. They feel like it's taxes they're paying, So

779
00:42:15,040 --> 00:42:16,680
Speaker 1: look at the one number, and then they got to

780
00:42:16,680 --> 00:42:18,960
Speaker 1: write the real number down, and I think they'd rather

781
00:42:19,040 --> 00:42:21,560
Speaker 1: just stick with the big number. When someone asks you,

782
00:42:21,560 --> 00:42:23,040
Speaker 1: like how much do you make? I think a lot

783
00:42:23,080 --> 00:42:25,600
Speaker 1: of like so says, well, how much do you make?

784
00:42:25,840 --> 00:42:28,759
Speaker 1: Depending on the audience, you tell your like pretax or

785
00:42:28,800 --> 00:42:32,680
Speaker 1: post tax number, because you know it just it tells

786
00:42:32,680 --> 00:42:35,240
Speaker 1: the narrative. So I think, yeah, it's like being tax

787
00:42:35,280 --> 00:42:37,239
Speaker 1: so like, why can't I have the big number? Why

788
00:42:37,360 --> 00:42:40,040
Speaker 1: I'm just gonna tell everybody the big number and I'm

789
00:42:40,040 --> 00:42:42,200
Speaker 1: not gonna tell him the small number. Tell him whatever

790
00:42:42,239 --> 00:42:45,719
Speaker 1: you want. So when I go to explain that, because

791
00:42:45,840 --> 00:42:47,440
Speaker 1: you're right, we get that question a ton of why

792
00:42:47,480 --> 00:42:53,440
Speaker 1: are you penalizing this? I love that word penalizing. It's

793
00:42:53,480 --> 00:42:54,960
Speaker 1: like a guy comes to your house, like takes the

794
00:42:55,040 --> 00:43:00,359
Speaker 1: points away, throw a flag. No, um, what you're doing?

795
00:43:00,400 --> 00:43:04,440
Speaker 1: What causes those differences? Like what causes a big deduction?

796
00:43:04,800 --> 00:43:06,239
Speaker 1: I think it would help if I think if you

797
00:43:06,239 --> 00:43:08,160
Speaker 1: can answer this question for me because I had written

798
00:43:08,160 --> 00:43:10,760
Speaker 1: it down. I'm glad you brought it. The bilateral symmetry again,

799
00:43:11,200 --> 00:43:16,439
Speaker 1: can you? Is it today accepted amongst wildlife biologists that

800
00:43:16,560 --> 00:43:20,319
Speaker 1: bilateral symmetry is a display of the health of an

801
00:43:20,360 --> 00:43:25,279
Speaker 1: animal generally speaking, yes, yes, they're like I said, That's

802
00:43:25,280 --> 00:43:27,600
Speaker 1: why I tried to get out where they've shown some

803
00:43:27,600 --> 00:43:31,080
Speaker 1: some traits that aren't necessarily perfectly bilateral have been shown.

804
00:43:31,480 --> 00:43:33,799
Speaker 1: But for the most part, you know, and I mean

805
00:43:33,800 --> 00:43:36,640
Speaker 1: look at nature and the whole. A tree that's healthy

806
00:43:36,680 --> 00:43:39,440
Speaker 1: isn't gonna be lopsided to one side. Um, you know,

807
00:43:39,600 --> 00:43:42,760
Speaker 1: anything you want to look at there is bilateral symmetry,

808
00:43:42,840 --> 00:43:45,000
Speaker 1: and so what causes the variation to that as a

809
00:43:45,040 --> 00:43:47,399
Speaker 1: stress or, which is the basis of it. But where

810
00:43:47,400 --> 00:43:50,080
Speaker 1: I was going is, you know what causes a big deduction? Okay,

811
00:43:50,520 --> 00:43:53,960
Speaker 1: well three of the elks times are broken? Well, he

812
00:43:54,040 --> 00:43:55,880
Speaker 1: was just fighting. I shot him late in the season.

813
00:43:55,960 --> 00:43:59,799
Speaker 1: That's not a penalty. Well, what results in excessive fighting

814
00:43:59,840 --> 00:44:03,720
Speaker 1: that breaks times and overpopulation of bulls. So that actually

815
00:44:03,760 --> 00:44:06,040
Speaker 1: leads us to believe that perhaps the bull population in

816
00:44:06,040 --> 00:44:09,000
Speaker 1: that unit could be too high because these stressors are

817
00:44:09,000 --> 00:44:11,320
Speaker 1: being exhibited on this rack, which results in a deduction.

818
00:44:11,920 --> 00:44:14,799
Speaker 1: So if you're talking to your buddies and you're making

819
00:44:14,800 --> 00:44:17,000
Speaker 1: it about you know, well, my dear, was this or gross? This?

820
00:44:17,680 --> 00:44:19,840
Speaker 1: Gross is fine? But when we're looking at the actual

821
00:44:20,080 --> 00:44:22,680
Speaker 1: health of the species and the habitat, that's what the

822
00:44:22,760 --> 00:44:24,880
Speaker 1: nets for. So my go to is, you know, the

823
00:44:24,920 --> 00:44:27,719
Speaker 1: grosses for the hunter, the nets for the animal. Yeah,

824
00:44:27,760 --> 00:44:29,560
Speaker 1: I think that we're larger. Where it comes from is

825
00:44:29,600 --> 00:44:37,320
Speaker 1: I think that people, um people have taken a tool

826
00:44:39,280 --> 00:44:44,640
Speaker 1: created for a purpose and created like a tool created

827
00:44:44,640 --> 00:44:46,759
Speaker 1: by an organization to pursue like the sort of long

828
00:44:46,880 --> 00:44:50,399
Speaker 1: term goal and they don't know about that. They don't

829
00:44:50,400 --> 00:44:53,279
Speaker 1: know about the tool, and I think it's just a

830
00:44:53,320 --> 00:44:58,040
Speaker 1: way to write how to communicate to your friend over

831
00:44:58,080 --> 00:45:02,799
Speaker 1: the phone what it was. And so they don't they

832
00:45:02,840 --> 00:45:08,399
Speaker 1: see imperfections in the tool without knowing or even like

833
00:45:08,400 --> 00:45:10,480
Speaker 1: like why is it that way? And I mean the

834
00:45:10,480 --> 00:45:13,239
Speaker 1: club itself is so much bigger than records and what

835
00:45:13,480 --> 00:45:16,959
Speaker 1: Justin and I run. I mean, we do so much

836
00:45:17,000 --> 00:45:19,840
Speaker 1: more then. I mean, we're known for our record book,

837
00:45:20,239 --> 00:45:22,799
Speaker 1: but we do so much more for conservation and and

838
00:45:22,840 --> 00:45:24,719
Speaker 1: I know Tony can touch on that a lot more,

839
00:45:24,800 --> 00:45:27,520
Speaker 1: but I mean what we do with our university programs

840
00:45:28,000 --> 00:45:34,719
Speaker 1: and you know what we do with UM our associates

841
00:45:34,760 --> 00:45:38,919
Speaker 1: program and our conservation and ethics program. I mean, it's

842
00:45:38,960 --> 00:45:41,799
Speaker 1: so much more than than what Justin and I do

843
00:45:41,920 --> 00:45:44,560
Speaker 1: well roll that out. I mean, we we've gotten welcome

844
00:45:44,560 --> 00:45:46,080
Speaker 1: back to the scoring thing to mix. There's some more

845
00:45:46,320 --> 00:45:48,800
Speaker 1: points about it, but yeah, like give a bigger context

846
00:45:48,840 --> 00:45:51,479
Speaker 1: for what, you know, besides coming up ways for people

847
00:45:51,480 --> 00:45:55,319
Speaker 1: to argue about how big the deer was, what goes

848
00:45:55,320 --> 00:45:57,520
Speaker 1: down over there? Well, so you know really that like

849
00:45:57,560 --> 00:45:59,640
Speaker 1: the records program is about tim percent of our budget

850
00:46:00,520 --> 00:46:04,480
Speaker 1: UM when we were founded the intent was to make

851
00:46:04,560 --> 00:46:09,040
Speaker 1: some course corrections in UM in wildlife health and inhabitat health,

852
00:46:09,480 --> 00:46:11,360
Speaker 1: and the only way to do that was through policy.

853
00:46:12,160 --> 00:46:16,319
Speaker 1: So you know, back then and today, you know, a

854
00:46:16,400 --> 00:46:20,960
Speaker 1: huge chunk of our mission is spent developing conservation policy

855
00:46:21,040 --> 00:46:25,040
Speaker 1: in Washington, d C. And helping the state's develop UH

856
00:46:25,160 --> 00:46:29,759
Speaker 1: policies at the state level. UM. You know, we're very

857
00:46:29,840 --> 00:46:34,760
Speaker 1: much involved with forced health. We're involved with UM wildlife disease.

858
00:46:35,520 --> 00:46:38,640
Speaker 1: You know, we're we're a group that people go to

859
00:46:38,640 --> 00:46:42,600
Speaker 1: to say, Okay, you guys have the knowledge to come

860
00:46:42,640 --> 00:46:44,719
Speaker 1: up with a solution to the problem that we've identified.

861
00:46:45,520 --> 00:46:48,120
Speaker 1: And that's what we do UM, and that is what

862
00:46:48,440 --> 00:46:52,520
Speaker 1: the probably two thirds of our entire mission budget goes towards,

863
00:46:52,719 --> 00:46:56,440
Speaker 1: is that that effort to make sure that we have land,

864
00:46:56,520 --> 00:47:00,120
Speaker 1: that we have public access, that we have when is

865
00:47:00,160 --> 00:47:03,319
Speaker 1: get out there, they have UH animals to look at

866
00:47:03,560 --> 00:47:06,360
Speaker 1: that the landscape that supports those same animals supports all

867
00:47:06,440 --> 00:47:10,560
Speaker 1: kinds of other wildlife like birds and fish. UM. You know,

868
00:47:10,640 --> 00:47:12,680
Speaker 1: the health of the not on the health of wildlife,

869
00:47:12,680 --> 00:47:16,680
Speaker 1: but also the health of the habitat. So UM policy

870
00:47:16,760 --> 00:47:18,600
Speaker 1: is a big part of what we do. How do

871
00:47:18,640 --> 00:47:23,120
Speaker 1: you guys get money anyway we can. But um, we

872
00:47:23,360 --> 00:47:26,800
Speaker 1: you know, our members, you know, we still when Roosevelt founders,

873
00:47:27,840 --> 00:47:29,360
Speaker 1: we have a hundred you know, he founded us with

874
00:47:29,360 --> 00:47:33,040
Speaker 1: a hundred regular members. And again those are guys and

875
00:47:33,120 --> 00:47:36,080
Speaker 1: gals that are influential in their respective fields. But they're

876
00:47:36,120 --> 00:47:39,839
Speaker 1: all joined by the common threat of being hunters and

877
00:47:40,880 --> 00:47:44,440
Speaker 1: um and so when they they are a big part

878
00:47:44,520 --> 00:47:47,120
Speaker 1: of us. You know that they you know, member contributions,

879
00:47:47,200 --> 00:47:51,120
Speaker 1: member dues are a big part of our budget. Our

880
00:47:51,160 --> 00:47:53,879
Speaker 1: corporate partnerships are a big part of our budget. Are

881
00:47:55,040 --> 00:47:57,640
Speaker 1: we have a fairly healthy endowment, and that those interest

882
00:47:57,760 --> 00:48:00,120
Speaker 1: earnings are a big part of our budgets. People that

883
00:48:00,560 --> 00:48:04,400
Speaker 1: die and then do like a not like bequeath to

884
00:48:04,480 --> 00:48:06,920
Speaker 1: a non prom partially, yeah, partially, and then a lot

885
00:48:06,960 --> 00:48:08,799
Speaker 1: of them have just been major gifts from again those

886
00:48:08,880 --> 00:48:11,600
Speaker 1: hundred Readlar members. Because with you know that that those

887
00:48:11,680 --> 00:48:13,680
Speaker 1: the by laws have never changed. There's just the same

888
00:48:13,800 --> 00:48:16,520
Speaker 1: number of Redlar members today as there was, you know,

889
00:48:16,560 --> 00:48:19,600
Speaker 1: a hundred thirty years ago. You know, so you keep

890
00:48:19,600 --> 00:48:22,680
Speaker 1: it at keep it at one hundred. And so we

891
00:48:22,800 --> 00:48:24,880
Speaker 1: have about a hundred and sixty of what we call

892
00:48:25,000 --> 00:48:28,239
Speaker 1: professional members, and those guys and gals are like worker bees.

893
00:48:29,239 --> 00:48:31,720
Speaker 1: Justin and I are professional members. I've been a professional

894
00:48:31,800 --> 00:48:33,759
Speaker 1: member before I ever came to the club. You're not

895
00:48:33,760 --> 00:48:35,919
Speaker 1: a regular member. I'm not a readier member. I don't

896
00:48:35,960 --> 00:48:40,040
Speaker 1: have quite that big a check. But but anyway, I've

897
00:48:40,080 --> 00:48:42,560
Speaker 1: got the fire in the valley. Just not then. But

898
00:48:42,800 --> 00:48:45,920
Speaker 1: um I uh, but you know those in the like

899
00:48:46,080 --> 00:48:50,040
Speaker 1: state agency heads, federal agency heads, a lot of academics,

900
00:48:50,080 --> 00:48:53,960
Speaker 1: a lot of univeriversity professors in forestry and wildlife departments

901
00:48:54,000 --> 00:48:57,440
Speaker 1: in land grant universities across the country are all in

902
00:48:57,520 --> 00:49:01,200
Speaker 1: that mix of professionals where we were which is really

903
00:49:01,200 --> 00:49:04,080
Speaker 1: our knowledge base. You know, when we have a problem

904
00:49:04,080 --> 00:49:06,480
Speaker 1: like cw D is a good example. You know, we're

905
00:49:06,520 --> 00:49:09,279
Speaker 1: front and stater with c w D research trying to

906
00:49:09,320 --> 00:49:11,279
Speaker 1: figure out a way to stop the spread number one.

907
00:49:11,360 --> 00:49:14,239
Speaker 1: Number two, how are we going to fix the problem? Um?

908
00:49:14,320 --> 00:49:16,400
Speaker 1: And you know, we spend a lot of time and energy,

909
00:49:17,200 --> 00:49:22,480
Speaker 1: uh resources, using our professors and our academics that are

910
00:49:22,520 --> 00:49:25,400
Speaker 1: special that that are known all over the country for

911
00:49:25,480 --> 00:49:28,120
Speaker 1: being experts in c w D trying to come up

912
00:49:28,239 --> 00:49:33,840
Speaker 1: with answers to that problem. UM. So you know, in

913
00:49:33,880 --> 00:49:36,040
Speaker 1: the answer to funding, you know, we do have grants

914
00:49:36,080 --> 00:49:37,880
Speaker 1: you know, we have grants, we we we we have

915
00:49:38,080 --> 00:49:42,520
Speaker 1: we we do a lot of matching funding for issues

916
00:49:42,560 --> 00:49:45,600
Speaker 1: like cw D and things like that. But it's UM,

917
00:49:45,680 --> 00:49:48,799
Speaker 1: we're not a huge organization by comparison to a lot

918
00:49:48,840 --> 00:49:50,879
Speaker 1: of others. Do you guys get checked just some people

919
00:49:50,920 --> 00:49:53,400
Speaker 1: to write into check don Yeah, I mean we I

920
00:49:53,440 --> 00:49:56,480
Speaker 1: mean you know that. It's interesting because all of a sudden,

921
00:49:56,520 --> 00:49:58,920
Speaker 1: like on social media, somebody will say, wow, you know,

922
00:49:58,960 --> 00:50:00,800
Speaker 1: you guys are doing a pretty good job there. I

923
00:50:00,840 --> 00:50:03,800
Speaker 1: want to help support you. Yeah, they'll send us some money.

924
00:50:03,800 --> 00:50:07,880
Speaker 1: But I think the main thing is is that unlike

925
00:50:07,920 --> 00:50:12,400
Speaker 1: other groups, we're not relying on events. We don't have chapters,

926
00:50:12,440 --> 00:50:15,640
Speaker 1: we don't have banquets, we don't have. What we are

927
00:50:15,680 --> 00:50:18,640
Speaker 1: relying on is our members and UH and our member

928
00:50:18,719 --> 00:50:24,560
Speaker 1: support and UM. For a long time, there was the

929
00:50:24,560 --> 00:50:27,200
Speaker 1: Budha Crivate Club. You know, we ran silent and deep,

930
00:50:27,760 --> 00:50:30,319
Speaker 1: and you know, nobody really knew what we did. We

931
00:50:30,360 --> 00:50:32,160
Speaker 1: didn't really care that nobody knew what we did. We

932
00:50:32,200 --> 00:50:33,920
Speaker 1: just cared about what the end result was when we

933
00:50:33,960 --> 00:50:37,160
Speaker 1: got the job done. And now you know, we're kind

934
00:50:37,160 --> 00:50:39,040
Speaker 1: of coming out of a cocoon. In the last ten

935
00:50:39,120 --> 00:50:41,120
Speaker 1: years and talking a little bit more about what we do,

936
00:50:41,200 --> 00:50:43,040
Speaker 1: because we want people to be aware of that these

937
00:50:43,560 --> 00:50:45,759
Speaker 1: these issues are not so different than what they were

938
00:50:45,760 --> 00:50:48,279
Speaker 1: a hundred and thirty years ago. There's just different solutions

939
00:50:48,680 --> 00:50:51,320
Speaker 1: to the same types of issues. You know, the threats

940
00:50:51,360 --> 00:50:54,759
Speaker 1: to our public lands, the you know, the threats to

941
00:50:54,760 --> 00:50:57,520
Speaker 1: the health of our wildlife. UM. But at the end

942
00:50:57,560 --> 00:51:00,880
Speaker 1: of the day, if if, if, if it comes, it

943
00:51:00,920 --> 00:51:03,120
Speaker 1: boils down to the hunter and the angler. If you

944
00:51:03,160 --> 00:51:05,719
Speaker 1: don't have those guys out there fishing and hunting, and

945
00:51:05,760 --> 00:51:08,000
Speaker 1: you don't have those licenses getting sold and you don't

946
00:51:08,040 --> 00:51:11,759
Speaker 1: have you know, our state and federal agencies dry up,

947
00:51:12,160 --> 00:51:15,400
Speaker 1: and that funny mechanism is solely dependent on hunters and

948
00:51:15,440 --> 00:51:18,040
Speaker 1: anglers who need fresh water to fish. They've got to

949
00:51:18,080 --> 00:51:20,960
Speaker 1: have public lands and or land access to hunted, and

950
00:51:20,960 --> 00:51:23,960
Speaker 1: they've got to have healthy, healthy wildlife populations to hunt.

951
00:51:24,640 --> 00:51:27,720
Speaker 1: And so we've always tried to be in a position

952
00:51:27,760 --> 00:51:30,920
Speaker 1: we're kind of looking down the road and anticipating what

953
00:51:30,920 --> 00:51:33,399
Speaker 1: what is the next big problem, what is the next

954
00:51:33,440 --> 00:51:40,160
Speaker 1: big threat? And UM, you know, we have huge wildfires now, UM,

955
00:51:40,200 --> 00:51:44,000
Speaker 1: you know across the Western States, and you know, the

956
00:51:44,040 --> 00:51:47,120
Speaker 1: forest health and rangement health UM is not what it

957
00:51:47,160 --> 00:51:50,200
Speaker 1: needs to be. Um, you know, we've got to take

958
00:51:50,320 --> 00:51:54,600
Speaker 1: much more aggressive approach to active forest management and restoration

959
00:51:54,680 --> 00:51:58,600
Speaker 1: and and make sure that you know, we our forests

960
00:51:58,640 --> 00:52:02,279
Speaker 1: aren't tinder boxes. And so you know, those are the

961
00:52:02,320 --> 00:52:03,920
Speaker 1: kind of issues that we look at. And I know

962
00:52:04,000 --> 00:52:06,480
Speaker 1: it's really boring for the average hunter to sit there

963
00:52:06,520 --> 00:52:10,040
Speaker 1: and all this stuff. That's why you know, the records program,

964
00:52:10,080 --> 00:52:13,239
Speaker 1: you know, the concept of fair chase. Those are two

965
00:52:13,280 --> 00:52:16,120
Speaker 1: cornerstones of our organization that I really are public face

966
00:52:16,239 --> 00:52:19,200
Speaker 1: that are somewhat sexier than what it is that we

967
00:52:19,239 --> 00:52:21,520
Speaker 1: actually do behind the scenes to ensure that those two

968
00:52:21,600 --> 00:52:24,319
Speaker 1: things can happen. Yeah, that the fair chas thing is

969
00:52:24,360 --> 00:52:28,520
Speaker 1: interesting because I see there, you know, you're a hunter

970
00:52:28,680 --> 00:52:32,280
Speaker 1: like your brun and Crocker closed a hunter based organization.

971
00:52:33,120 --> 00:52:35,200
Speaker 1: But there's a little bit of a conflict. I can

972
00:52:35,239 --> 00:52:38,360
Speaker 1: imagine where on one hand, if it's just records keeping,

973
00:52:39,800 --> 00:52:42,839
Speaker 1: it would be that um, like it was just specifically

974
00:52:42,840 --> 00:52:46,560
Speaker 1: trying to understand in some geographical area, how many animals

975
00:52:46,560 --> 00:52:50,760
Speaker 1: of a certain size the landscape kicked off put off.

976
00:52:51,520 --> 00:52:55,200
Speaker 1: It would include like animals that were killed by poachers,

977
00:52:55,840 --> 00:52:59,640
Speaker 1: animals that were struck by vehicles, animals just turned up

978
00:52:59,640 --> 00:53:03,120
Speaker 1: dead and someone swimming pool right and be just bring

979
00:53:03,560 --> 00:53:06,880
Speaker 1: come one, come all. But you don't do that because

980
00:53:06,920 --> 00:53:12,800
Speaker 1: there's a cash attached to getting an animal in the

981
00:53:12,840 --> 00:53:15,520
Speaker 1: record book, and so you don't just let anyone through.

982
00:53:16,160 --> 00:53:19,920
Speaker 1: And you have rules and regulations about who can get

983
00:53:19,960 --> 00:53:27,520
Speaker 1: in that are more stringent even than state law. Yeah, like,

984
00:53:27,520 --> 00:53:30,759
Speaker 1: how do you reconcile tholes that because we actually do

985
00:53:30,840 --> 00:53:34,759
Speaker 1: accept found heads. Oh you do. So the picked up

986
00:53:35,000 --> 00:53:36,880
Speaker 1: is what it goes in as or if it was

987
00:53:36,920 --> 00:53:38,960
Speaker 1: an example party taken. We don't feel that's a fair

988
00:53:39,040 --> 00:53:40,680
Speaker 1: chase thing. But a lot of states all well, three

989
00:53:40,719 --> 00:53:43,680
Speaker 1: states at party hunting, we don't list the hunter what

990
00:53:43,800 --> 00:53:46,640
Speaker 1: those rules. Also, you do have a way I got you,

991
00:53:46,760 --> 00:53:49,440
Speaker 1: so you do log the presence of the animal. You

992
00:53:49,480 --> 00:53:53,320
Speaker 1: just don't reward yep. For example, we have a state

993
00:53:53,400 --> 00:53:55,799
Speaker 1: owned heads. So when they say that Buona Crocker won't

994
00:53:55,880 --> 00:53:59,560
Speaker 1: accept it, we won't let you won't accept it. Isn't

995
00:53:59,600 --> 00:54:03,000
Speaker 1: reward well, well, because it still recognizing the animal. In

996
00:54:03,040 --> 00:54:05,120
Speaker 1: some cases we wouldn't accept it if it was from

997
00:54:05,239 --> 00:54:09,160
Speaker 1: a high fence population that's not indicative of the area's

998
00:54:09,239 --> 00:54:11,960
Speaker 1: native wildlife. I never I never understood that didn't know

999
00:54:12,000 --> 00:54:14,279
Speaker 1: that that was true. So you do take it into account, right,

1000
00:54:14,840 --> 00:54:17,120
Speaker 1: We've got we've got it. One of the first deerray

1001
00:54:17,200 --> 00:54:20,480
Speaker 1: process got hung up on a cemetery fence, like they

1002
00:54:20,480 --> 00:54:23,279
Speaker 1: found it hanging their dead in the morning. So yeah,

1003
00:54:23,280 --> 00:54:26,040
Speaker 1: and that's a picked up head um wildlife agencies if

1004
00:54:26,239 --> 00:54:28,200
Speaker 1: you guys want to know about it. If the state

1005
00:54:28,320 --> 00:54:31,920
Speaker 1: confiscates ahead, we never list whoever you know the poacher was,

1006
00:54:32,360 --> 00:54:35,279
Speaker 1: but it's shown as owned by Montana Fish, Wildlife and

1007
00:54:35,360 --> 00:54:37,680
Speaker 1: Parks and where it came from and where it came from. Oh,

1008
00:54:37,719 --> 00:54:40,200
Speaker 1: I see, I didn't realize that, but it is. It

1009
00:54:40,320 --> 00:54:42,480
Speaker 1: is you you bring up because that's not how people like.

1010
00:54:43,080 --> 00:54:44,640
Speaker 1: So much of what I know about Buda Crock Club

1011
00:54:44,719 --> 00:54:47,040
Speaker 1: was just from like shoot the ship with earlier. I

1012
00:54:47,040 --> 00:54:50,120
Speaker 1: don't like what I hear that. That's that's the thing

1013
00:54:50,160 --> 00:54:52,160
Speaker 1: I always heard, is like that, well they don't accept acts,

1014
00:54:52,360 --> 00:54:55,920
Speaker 1: they don't accept this and that. Um, so yeah, I

1015
00:54:55,920 --> 00:54:57,759
Speaker 1: got you. Now we have it. We have a set

1016
00:54:57,760 --> 00:55:00,640
Speaker 1: of rules that are fair chase requirements that you have

1017
00:55:00,800 --> 00:55:03,000
Speaker 1: to stay within to be recognized. And this goes back

1018
00:55:03,040 --> 00:55:06,120
Speaker 1: to the beginning of it, like for you to your

1019
00:55:06,200 --> 00:55:09,080
Speaker 1: name as a fair chase hunter. This deer taken in

1020
00:55:09,120 --> 00:55:11,840
Speaker 1: fair chase by this person. That goes back to the

1021
00:55:11,920 --> 00:55:14,480
Speaker 1: very beginning. There was no rules and so they had

1022
00:55:14,520 --> 00:55:17,799
Speaker 1: to try to switch everybody's opinion on, you know, what

1023
00:55:17,880 --> 00:55:21,240
Speaker 1: was appropriate. So they said to get into our records book,

1024
00:55:21,320 --> 00:55:23,879
Speaker 1: and this is pre nineteen fifty, you know whatever, you

1025
00:55:23,960 --> 00:55:26,720
Speaker 1: must take this in fair chase. And at the time

1026
00:55:26,760 --> 00:55:29,400
Speaker 1: you couldn't jack light, you couldn't crust them in the snow.

1027
00:55:29,880 --> 00:55:32,520
Speaker 1: It was a lot shorter even then you might have

1028
00:55:32,680 --> 00:55:35,680
Speaker 1: legally been. So they in the very beginning they put

1029
00:55:35,719 --> 00:55:39,160
Speaker 1: that on. What happened is the hunter perception started to change.

1030
00:55:39,160 --> 00:55:42,320
Speaker 1: The fair chase took off. Um, you know, state agencies,

1031
00:55:42,400 --> 00:55:45,960
Speaker 1: loophold started the you know, the Wildlife School in Wisconsin,

1032
00:55:46,040 --> 00:55:48,880
Speaker 1: and all of a sudden you had scientifically trained managers

1033
00:55:48,880 --> 00:55:51,200
Speaker 1: that are setting this. But at the time, fair chase

1034
00:55:51,280 --> 00:55:54,720
Speaker 1: was one of necessity to force harvest on the old

1035
00:55:54,760 --> 00:55:58,600
Speaker 1: mature at males, basically forcing trophy hunting in the beginning,

1036
00:55:58,640 --> 00:56:02,240
Speaker 1: which was the sure male harvest that did not affect

1037
00:56:02,280 --> 00:56:04,879
Speaker 1: the population. And that was the whole beginning of fair

1038
00:56:04,960 --> 00:56:07,799
Speaker 1: chase where we continue it today. Again, the reason for

1039
00:56:07,880 --> 00:56:10,480
Speaker 1: fair chase is kind of morphed. We no longer have

1040
00:56:10,600 --> 00:56:14,040
Speaker 1: to follow fair chase to save wildlife populations, but fair

1041
00:56:14,120 --> 00:56:16,800
Speaker 1: chase is integral to what Tony pointed out on hunting

1042
00:56:16,800 --> 00:56:19,640
Speaker 1: and fishing to continue and the funding for wildlife to continue.

1043
00:56:20,160 --> 00:56:28,040
Speaker 1: What is um what's an example of a practice like

1044
00:56:28,080 --> 00:56:31,080
Speaker 1: what would be something that Buona Crockett Club would regard

1045
00:56:31,120 --> 00:56:34,279
Speaker 1: as not fair chase that could be legal somewhere, and

1046
00:56:34,360 --> 00:56:39,680
Speaker 1: what's vice versa like like the opposite example. So the

1047
00:56:39,719 --> 00:56:41,759
Speaker 1: biggest ones that we see is some states allow the

1048
00:56:41,840 --> 00:56:45,800
Speaker 1: usage of radios to talk somebody intoing a game animal Arizona, Oregon,

1049
00:56:45,840 --> 00:56:48,520
Speaker 1: for example. You can, or you could. The last time

1050
00:56:48,520 --> 00:56:51,359
Speaker 1: I looked talk somebody into an animal. We say, if

1051
00:56:51,360 --> 00:56:54,680
Speaker 1: you guide hunters to game with an electronic you know,

1052
00:56:54,760 --> 00:56:58,600
Speaker 1: with use of electronic communications, it's out. Um. You know.

1053
00:56:58,640 --> 00:57:01,480
Speaker 1: Another one that that's probably with the biggest misconception that

1054
00:57:01,520 --> 00:57:06,040
Speaker 1: we have is cellular trail cameras. It's not fair chase

1055
00:57:06,120 --> 00:57:09,440
Speaker 1: to get live images of a game species while you're

1056
00:57:09,480 --> 00:57:12,200
Speaker 1: hunting it. We're working on that final wording on that,

1057
00:57:12,239 --> 00:57:15,520
Speaker 1: but for cellular trail cameras, if I'm sitting I could

1058
00:57:15,560 --> 00:57:18,200
Speaker 1: be sitting here in this podcast and all of a sudden,

1059
00:57:18,200 --> 00:57:20,480
Speaker 1: the bear walks by one of my trail cameras. If

1060
00:57:20,480 --> 00:57:22,920
Speaker 1: it was legal in Montana, I could then go legally

1061
00:57:23,000 --> 00:57:25,360
Speaker 1: hunt that bear in many states, and that is not

1062
00:57:25,480 --> 00:57:28,760
Speaker 1: fair chase. UM. The other side of it, and this

1063
00:57:28,880 --> 00:57:32,600
Speaker 1: is a very contentious topic, is baiting UM Baiting is

1064
00:57:32,640 --> 00:57:37,200
Speaker 1: a general practice, especially for bears, is not a violation

1065
00:57:37,240 --> 00:57:40,400
Speaker 1: of fair chase. Ah. So that's an example something you

1066
00:57:40,440 --> 00:57:43,280
Speaker 1: might not be allowed to do in a state. But

1067
00:57:43,480 --> 00:57:45,680
Speaker 1: you would accept that if you were correct, and that

1068
00:57:45,800 --> 00:57:48,640
Speaker 1: then you can never do something illegal. So you know,

1069
00:57:48,920 --> 00:57:51,040
Speaker 1: if if if it's illegal, debate in your state, you

1070
00:57:51,080 --> 00:57:54,280
Speaker 1: can't do it, it's okay. So you always you always

1071
00:57:54,360 --> 00:57:57,160
Speaker 1: bow to the state. Right If the state is more restrictive,

1072
00:57:57,240 --> 00:58:00,880
Speaker 1: that's fine. But with with the bay eating for bears,

1073
00:58:01,000 --> 00:58:03,800
Speaker 1: you know, for black bears in certain areas, that's the

1074
00:58:03,840 --> 00:58:05,400
Speaker 1: only way that you can get a real good look

1075
00:58:05,440 --> 00:58:07,120
Speaker 1: at a bear and make sure you're not shooting a

1076
00:58:07,160 --> 00:58:09,600
Speaker 1: cub or the wrong bear. Well, then we look at

1077
00:58:09,600 --> 00:58:11,920
Speaker 1: baiting of servants, which is a very hot topic with

1078
00:58:12,040 --> 00:58:14,720
Speaker 1: c w D. You could make a very strong argument

1079
00:58:14,800 --> 00:58:17,000
Speaker 1: right now in that case for servants, baiting may not

1080
00:58:17,080 --> 00:58:20,080
Speaker 1: be fair chase but as an overall practice for baiting,

1081
00:58:20,200 --> 00:58:22,040
Speaker 1: as it's done for bears, we say it is still

1082
00:58:22,080 --> 00:58:24,560
Speaker 1: a fair chase practice. Who is it? Like, you know,

1083
00:58:25,920 --> 00:58:31,080
Speaker 1: Teddy Roosevelt wasn't arguing about what to do about trail cams,

1084
00:58:31,120 --> 00:58:34,520
Speaker 1: like who who is it? Who is it? Who decides

1085
00:58:35,120 --> 00:58:39,120
Speaker 1: it's there for? For the overall club, there's the entire

1086
00:58:39,240 --> 00:58:41,160
Speaker 1: you know, it would go to the there's an ethics

1087
00:58:41,160 --> 00:58:45,080
Speaker 1: committee um for records. The records injury requirements for fair

1088
00:58:45,160 --> 00:58:47,320
Speaker 1: chase would be the records committee. It's a thirty two

1089
00:58:47,320 --> 00:58:50,240
Speaker 1: member committee. And they got to wrestle with emerging technologies,

1090
00:58:50,920 --> 00:58:55,280
Speaker 1: and we do constantly, uh have you have you addressed

1091
00:58:55,360 --> 00:58:58,280
Speaker 1: drones specifically or is that captured onto something else? So

1092
00:58:58,640 --> 00:59:01,160
Speaker 1: it was captured under something else again, it was it

1093
00:59:01,240 --> 00:59:03,840
Speaker 1: was transmitting images of live games to the hunter, because

1094
00:59:03,840 --> 00:59:06,880
Speaker 1: you can't run a drone without seeing the animal. We

1095
00:59:06,960 --> 00:59:10,000
Speaker 1: actually called out drones by name, and the interraffed David

1096
00:59:13,000 --> 00:59:15,320
Speaker 1: about I was going to say about eight years ago,

1097
00:59:16,600 --> 00:59:18,680
Speaker 1: eight or nine years ago when when it really got

1098
00:59:19,160 --> 00:59:21,560
Speaker 1: when he used to drones really kind of picked up pace.

1099
00:59:21,800 --> 00:59:23,200
Speaker 1: You know, people are using them for a lot of

1100
00:59:23,200 --> 00:59:26,480
Speaker 1: different things, and there they got to be pretty popular,

1101
00:59:26,600 --> 00:59:28,920
Speaker 1: and so we did call him out by name. And

1102
00:59:28,960 --> 00:59:30,480
Speaker 1: it might even been longer, might have been closer to

1103
00:59:30,520 --> 00:59:33,000
Speaker 1: ten years ago. It's been a while, yeah, but it was.

1104
00:59:33,320 --> 00:59:35,760
Speaker 1: We were getting asked by state agencies, what's your thoughts

1105
00:59:35,760 --> 00:59:38,760
Speaker 1: on drones and our answer would be, well, it's transmitting

1106
00:59:38,760 --> 00:59:40,960
Speaker 1: an image. But we got asked enough that to help

1107
00:59:41,040 --> 00:59:43,480
Speaker 1: the states were like, we will call it out by name.

1108
00:59:43,560 --> 00:59:45,880
Speaker 1: So there's no doubt that a drone usage to find

1109
00:59:46,320 --> 00:59:49,160
Speaker 1: the scout for or hunt game falls outside of fair chase.

1110
00:59:49,240 --> 00:59:51,720
Speaker 1: What you know, we would deem as the ethical pursuit

1111
00:59:51,720 --> 00:59:57,360
Speaker 1: of the animal that they can escape. Um. I gave

1112
00:59:57,400 --> 00:59:59,680
Speaker 1: you the big complaint that you get from hunters that

1113
00:59:59,800 --> 01:00:03,160
Speaker 1: there almost like similar. We're not not you know, it's

1114
01:00:03,160 --> 01:00:04,480
Speaker 1: not even fair as a complaint because it's just like

1115
01:00:04,520 --> 01:00:09,280
Speaker 1: a misunderstanding, but that that people will argue about their deductions, right,

1116
01:00:09,480 --> 01:00:12,040
Speaker 1: and people complain about that they felt the score should

1117
01:00:12,040 --> 01:00:15,720
Speaker 1: be higher, and like, well, you know those assholes seventy

1118
01:00:15,760 --> 01:00:25,400
Speaker 1: but I got um doug uh so Uh. The other

1119
01:00:25,480 --> 01:00:29,360
Speaker 1: thing is is I think that the numbers This isn't

1120
01:00:29,400 --> 01:00:32,440
Speaker 1: even a criticism that comes from outside. It's like inside

1121
01:00:32,440 --> 01:00:38,480
Speaker 1: and outside hunting. But people look at the fixation on numbers, right,

1122
01:00:39,520 --> 01:00:42,840
Speaker 1: and then and that people feel that that, uh, it's

1123
01:00:43,280 --> 01:00:48,400
Speaker 1: like that that's kind of this objectification of animals might

1124
01:00:48,400 --> 01:00:51,600
Speaker 1: not be a totally right word, but basically they're taking

1125
01:00:51,600 --> 01:00:54,760
Speaker 1: this sort of like living, this beautiful living thing that

1126
01:00:54,880 --> 01:00:57,640
Speaker 1: has a lot of um. There's you know, a huge

1127
01:00:57,640 --> 01:01:01,120
Speaker 1: amount of cultural pride that we take that that that

1128
01:01:01,200 --> 01:01:04,520
Speaker 1: we have these animals around us, and we generally Americans

1129
01:01:04,560 --> 01:01:09,080
Speaker 1: like generally recognize them as gorgeous, were generally supportive of

1130
01:01:09,240 --> 01:01:12,840
Speaker 1: their existence here. There's a lot of like reverence for them.

1131
01:01:12,920 --> 01:01:16,000
Speaker 1: And people feel that when you boil it down and

1132
01:01:16,080 --> 01:01:18,600
Speaker 1: just take the deer and turn it into a number,

1133
01:01:20,440 --> 01:01:23,760
Speaker 1: that it's something that something gets lost. And so you'll

1134
01:01:23,800 --> 01:01:25,640
Speaker 1: hear hunters say this, and a lot of times like

1135
01:01:25,760 --> 01:01:30,880
Speaker 1: newer hunters, um, we'll be prickly about it because they're

1136
01:01:30,920 --> 01:01:33,200
Speaker 1: excited to get a deer and I think, like a

1137
01:01:33,240 --> 01:01:35,800
Speaker 1: deer if I just get be so happy. And they

1138
01:01:35,880 --> 01:01:38,120
Speaker 1: learned that there's kind of like this, Oh, it was

1139
01:01:38,240 --> 01:01:40,960
Speaker 1: just a blank and should never be that? Why and

1140
01:01:41,120 --> 01:01:43,960
Speaker 1: even we recognize it should never be that And you

1141
01:01:44,000 --> 01:01:46,960
Speaker 1: mentioned to it earlier, it's simply a tool that we've

1142
01:01:47,040 --> 01:01:49,960
Speaker 1: created and it's kind of been I don't know, bastardized.

1143
01:01:50,120 --> 01:01:51,680
Speaker 1: I don't think, I don't, but I don't think it's

1144
01:01:51,680 --> 01:01:53,880
Speaker 1: your I don't know that it's your not I don't

1145
01:01:53,920 --> 01:01:56,320
Speaker 1: think it's your fault at all. But it's a thing.

1146
01:01:56,560 --> 01:02:00,240
Speaker 1: And then but people know it's the Boon and Crockett score. Yeah,

1147
01:02:00,760 --> 01:02:03,720
Speaker 1: so you get right, Yeah, yeah, you probably hear about

1148
01:02:03,720 --> 01:02:06,400
Speaker 1: it all the time. Yeah, and yeah, i'd argue that

1149
01:02:06,480 --> 01:02:08,200
Speaker 1: if you know, if you if you adduce it to

1150
01:02:08,280 --> 01:02:11,280
Speaker 1: any single component, it's inappropriate. Well, it's about you again,

1151
01:02:11,320 --> 01:02:13,640
Speaker 1: it's just about the animal. You should never just kill

1152
01:02:13,680 --> 01:02:16,400
Speaker 1: an animal just because of a number. That's ridiculous. You

1153
01:02:16,480 --> 01:02:19,200
Speaker 1: never adduce it to that. Honestly, if you don't care

1154
01:02:19,200 --> 01:02:22,280
Speaker 1: about regulations anything, you're just shooting something for only meat,

1155
01:02:22,320 --> 01:02:24,160
Speaker 1: and you don't care about the experience and the laws.

1156
01:02:24,240 --> 01:02:27,000
Speaker 1: That's wrong. It's the whole experience. You enjoy the meat,

1157
01:02:27,080 --> 01:02:29,560
Speaker 1: You enjoy them ount, you enjoy the number, how it

1158
01:02:29,680 --> 01:02:32,600
Speaker 1: how it affects the biologic data. Make sure that that's

1159
01:02:32,640 --> 01:02:34,640
Speaker 1: still there. It's just another piece of it. You should

1160
01:02:34,680 --> 01:02:37,400
Speaker 1: never make a hunt about one thing. It's about the

1161
01:02:37,440 --> 01:02:41,760
Speaker 1: whole experience and the animal and everything that animal is UM.

1162
01:02:41,880 --> 01:02:45,040
Speaker 1: I know you guys have some thoughts about how the

1163
01:02:45,240 --> 01:02:52,200
Speaker 1: the evolution of the term trophy hunting, that that it's

1164
01:02:52,640 --> 01:02:57,960
Speaker 1: you know, each generation kind of defines it for whatever

1165
01:02:58,040 --> 01:03:01,920
Speaker 1: to to, you know, defines for their moment. How has

1166
01:03:01,960 --> 01:03:06,400
Speaker 1: that drifted? So I can answer a little bit of

1167
01:03:06,400 --> 01:03:09,000
Speaker 1: that because I have been in the space and as

1168
01:03:09,000 --> 01:03:12,560
Speaker 1: I mentioned before, I started in the space as a

1169
01:03:12,600 --> 01:03:16,560
Speaker 1: film producer, UM, and that was the eighties and at

1170
01:03:16,600 --> 01:03:19,000
Speaker 1: that time there's only four of us that we're doing

1171
01:03:19,040 --> 01:03:22,200
Speaker 1: that type of thing. And UM, you know, if you

1172
01:03:22,280 --> 01:03:24,480
Speaker 1: turn the clock back of ways to the days of

1173
01:03:24,800 --> 01:03:28,160
Speaker 1: Kirk Gouty, an American sportsman, you know, those were days

1174
01:03:28,200 --> 01:03:32,600
Speaker 1: when um, you know, it was all about hunting. And

1175
01:03:32,640 --> 01:03:34,680
Speaker 1: then in the in the eighties and the nineties what

1176
01:03:34,880 --> 01:03:40,520
Speaker 1: started happening was as my um, the business I was in,

1177
01:03:41,040 --> 01:03:44,360
Speaker 1: there was more and more video producers that started getting

1178
01:03:44,360 --> 01:03:47,680
Speaker 1: into the game. You know. My feelings were always you know,

1179
01:03:47,720 --> 01:03:50,600
Speaker 1: they told the story, they stole a story about you know,

1180
01:03:50,760 --> 01:03:53,320
Speaker 1: where the animal lived, what the animal eight. You know,

1181
01:03:53,560 --> 01:03:57,280
Speaker 1: we didn't always get an animal, um, but if we did,

1182
01:03:57,280 --> 01:04:01,080
Speaker 1: it was always underfair chase. But later in the in

1183
01:04:01,120 --> 01:04:03,320
Speaker 1: the late eighties and the early nineties, as more and

1184
01:04:03,360 --> 01:04:05,440
Speaker 1: more of these folks started getting into the business, it's

1185
01:04:05,480 --> 01:04:08,280
Speaker 1: all became about to kill. How many kills do you

1186
01:04:08,320 --> 01:04:10,800
Speaker 1: have in your tape? I'd have idn't actually have distributors

1187
01:04:10,800 --> 01:04:15,320
Speaker 1: asked me that questions. And that was prior, prior to um,

1188
01:04:15,360 --> 01:04:18,160
Speaker 1: you know, the outdoor network to determine if it was

1189
01:04:18,200 --> 01:04:21,080
Speaker 1: good or not exactly, because that's what they were selling

1190
01:04:21,080 --> 01:04:23,960
Speaker 1: it on. So all of a sudden and and and

1191
01:04:24,240 --> 01:04:27,600
Speaker 1: the term trophy then became starting to get really polluted

1192
01:04:27,680 --> 01:04:30,360
Speaker 1: because they did your what you're talking about with the

1193
01:04:30,440 --> 01:04:33,040
Speaker 1: numbers thing. You know, the bigger the better, You're not

1194
01:04:33,440 --> 01:04:35,600
Speaker 1: worth squad if you don't kill a big, big animal

1195
01:04:35,640 --> 01:04:41,480
Speaker 1: with big horns or handlers and so um. So it

1196
01:04:41,520 --> 01:04:43,520
Speaker 1: went from the hunting days of Kirk Guity to the

1197
01:04:43,600 --> 01:04:48,520
Speaker 1: killing days of the eighties and nineties, and it really

1198
01:04:48,840 --> 01:04:54,840
Speaker 1: got the term trophy a black eye. Um. And I

1199
01:04:54,880 --> 01:04:57,160
Speaker 1: think the media drove that. I mean, if you look

1200
01:04:57,160 --> 01:05:00,240
Speaker 1: at our outdoor programming now, for the most part, on

1201
01:05:00,240 --> 01:05:02,720
Speaker 1: our outdoor networks, there's still a lot of whack them

1202
01:05:02,720 --> 01:05:05,280
Speaker 1: and stack and stuff going on in those networks. And

1203
01:05:05,400 --> 01:05:07,480
Speaker 1: I think that's not a tribute to the animal. I

1204
01:05:07,480 --> 01:05:09,400
Speaker 1: don't think it's a tribute to our sport of hunting.

1205
01:05:09,840 --> 01:05:12,760
Speaker 1: I don't think you should really call it hunting, say, media,

1206
01:05:12,800 --> 01:05:15,040
Speaker 1: you're talking about our own media. I'm talking about our

1207
01:05:15,040 --> 01:05:18,800
Speaker 1: outdoor media, you know. And I think that what I

1208
01:05:18,840 --> 01:05:22,960
Speaker 1: see now, uh though, is I see hope in the

1209
01:05:23,000 --> 01:05:26,680
Speaker 1: fact that I think, you know, programs like yours for example,

1210
01:05:26,880 --> 01:05:28,840
Speaker 1: you know, you're talking about these things, and you're talking

1211
01:05:28,880 --> 01:05:32,080
Speaker 1: about and you're going to see a migration back now

1212
01:05:32,240 --> 01:05:34,400
Speaker 1: from the kill to the hunt and what it really means.

1213
01:05:34,680 --> 01:05:38,440
Speaker 1: And you know, the local war movement, you know, consuming

1214
01:05:38,480 --> 01:05:42,400
Speaker 1: those animals. Um. Where does your me come from? Um?

1215
01:05:42,480 --> 01:05:45,439
Speaker 1: And you know, so I think that there's there's there.

1216
01:05:45,440 --> 01:05:47,560
Speaker 1: You know, we're starting a course correction there in a

1217
01:05:47,600 --> 01:05:52,200
Speaker 1: positive way. UM. But I think that's really happened to trophy.

1218
01:05:52,240 --> 01:05:56,000
Speaker 1: It was. It was hyped so much in outdoor in

1219
01:05:56,040 --> 01:05:58,720
Speaker 1: the outdoor media there for twenty years that it just

1220
01:05:58,800 --> 01:06:00,760
Speaker 1: kind of took a left turn and now we gotta

1221
01:06:00,760 --> 01:06:05,080
Speaker 1: get it on the right turn again. It's amazing how um,

1222
01:06:05,160 --> 01:06:09,520
Speaker 1: how universal not in honey media, but how universal in

1223
01:06:09,520 --> 01:06:16,360
Speaker 1: the mainstream media, how universal um of a condemnation trophy

1224
01:06:16,400 --> 01:06:20,520
Speaker 1: hunting is. It's like if you use that you don't

1225
01:06:20,520 --> 01:06:24,080
Speaker 1: even need to say anything else. No, you don't. It's

1226
01:06:24,080 --> 01:06:27,440
Speaker 1: like if you see an article trophy hunter, like you

1227
01:06:27,480 --> 01:06:29,600
Speaker 1: don't need to read the rest of the headline even

1228
01:06:30,400 --> 01:06:34,120
Speaker 1: you know that it is bad. We talked about this recently,

1229
01:06:34,120 --> 01:06:40,280
Speaker 1: whereas there's a fundraiser that uh Don Jr. Was going

1230
01:06:40,360 --> 01:06:43,800
Speaker 1: on for blacktail Deer in Ducks. Okay, so when I

1231
01:06:43,800 --> 01:06:46,320
Speaker 1: hear blacktail deer in Ducks, I don't jump into my

1232
01:06:46,360 --> 01:06:49,440
Speaker 1: head like it doesn't sound to me like an evil

1233
01:06:49,520 --> 01:06:52,600
Speaker 1: trophy hunting adventure. But when describing to new As it

1234
01:06:52,680 --> 01:06:57,440
Speaker 1: was leading a trophy hunt, and you have to say, well,

1235
01:06:57,440 --> 01:06:59,560
Speaker 1: i'd have to know a lot more. I don't really

1236
01:06:59,560 --> 01:07:01,560
Speaker 1: know that that what they're doing. It just seems like

1237
01:07:01,560 --> 01:07:03,280
Speaker 1: they're going hunting deer and ducks, Like how do we

1238
01:07:03,320 --> 01:07:05,200
Speaker 1: know that? But someone's like, I know how to make

1239
01:07:05,240 --> 01:07:07,760
Speaker 1: this seem real bad. I will just use the word

1240
01:07:07,800 --> 01:07:10,240
Speaker 1: trophy and then everyone will know that. I mean, it's

1241
01:07:10,280 --> 01:07:12,760
Speaker 1: real bad. I don't know that you're gonna save that work.

1242
01:07:13,600 --> 01:07:16,560
Speaker 1: The term trophy was never intended to be bad um,

1243
01:07:18,000 --> 01:07:20,160
Speaker 1: but unfortunately it has become that way. And I don't

1244
01:07:20,160 --> 01:07:23,000
Speaker 1: think it's salvageable. It's it's just a term that has

1245
01:07:23,040 --> 01:07:27,960
Speaker 1: been beat up so bad and misrepresented um by you know,

1246
01:07:28,440 --> 01:07:31,520
Speaker 1: the media, whether it's the outdoor media or the general

1247
01:07:31,560 --> 01:07:35,520
Speaker 1: mainstream media with the stories you're like you're talking about, um,

1248
01:07:35,560 --> 01:07:38,880
Speaker 1: you know, we need to find some other description for these,

1249
01:07:39,520 --> 01:07:42,480
Speaker 1: for the for the for the mature mail specims that

1250
01:07:42,520 --> 01:07:45,640
Speaker 1: we're taking out of these ecosystems. Yeah, I do. I

1251
01:07:45,720 --> 01:07:47,600
Speaker 1: was just gonna say that. I feel like some reading

1252
01:07:47,920 --> 01:07:51,880
Speaker 1: Jack O'Connor stuff, I always had the idea that like

1253
01:07:51,960 --> 01:07:57,320
Speaker 1: trophy and then the head or the mount were interchangeable,

1254
01:07:57,640 --> 01:08:00,400
Speaker 1: they were synonymous, and that's how that that how where

1255
01:08:00,520 --> 01:08:02,520
Speaker 1: where how they used trophy? Did you would you guys

1256
01:08:02,560 --> 01:08:06,560
Speaker 1: agree you just want the head? No, no, not that

1257
01:08:06,600 --> 01:08:09,040
Speaker 1: you just wanted to head. But that's just like what

1258
01:08:09,200 --> 01:08:12,760
Speaker 1: that piece of the animal was referred to as. Yeah,

1259
01:08:13,240 --> 01:08:16,679
Speaker 1: that is yeah. You know. I ran into a guy

1260
01:08:16,720 --> 01:08:18,240
Speaker 1: one time I was l COHNT ran into a guy

1261
01:08:18,240 --> 01:08:19,920
Speaker 1: on the trail. He's actually a radio I don't want

1262
01:08:19,960 --> 01:08:22,880
Speaker 1: to say his name. He's a radio host. And I

1263
01:08:22,920 --> 01:08:24,600
Speaker 1: was talking to him like that's where I was like,

1264
01:08:24,600 --> 01:08:27,479
Speaker 1: holy shit, you're but anyways, he was talking about a

1265
01:08:27,479 --> 01:08:31,639
Speaker 1: gun and he said he's taken thirty head, which sounds

1266
01:08:31,640 --> 01:08:36,439
Speaker 1: like livestock I think of, but yeah, like thirty trophies. Sure,

1267
01:08:37,200 --> 01:08:39,800
Speaker 1: And again it wasn't a negative connotation back then. It

1268
01:08:39,880 --> 01:08:42,320
Speaker 1: was just like that was like like they had an

1269
01:08:42,360 --> 01:08:45,840
Speaker 1: animal down and there were many different parts of it,

1270
01:08:46,520 --> 01:08:49,240
Speaker 1: and then like the head was you could say it's

1271
01:08:49,280 --> 01:08:52,200
Speaker 1: called it was just also referred to as the trophy.

1272
01:08:52,280 --> 01:08:56,280
Speaker 1: Like I'll grab the back quarter, you grab the trophy. Yeah. Yeah,

1273
01:08:56,720 --> 01:08:58,840
Speaker 1: they had all their trophies with them. Yeah. It came

1274
01:08:58,880 --> 01:09:04,120
Speaker 1: along with I've I haven't abandoned. I have not abandoned

1275
01:09:04,160 --> 01:09:05,640
Speaker 1: the word. And I try to help it out a

1276
01:09:05,680 --> 01:09:09,439
Speaker 1: little bit when I can, because, um, it's funny because

1277
01:09:09,600 --> 01:09:11,519
Speaker 1: uh and talking to people, like talking to people who

1278
01:09:11,560 --> 01:09:14,920
Speaker 1: are sort of kicking the tires on hunting or you know,

1279
01:09:15,520 --> 01:09:17,640
Speaker 1: curious about hunting, They'll they'll really want to push you

1280
01:09:17,720 --> 01:09:21,519
Speaker 1: right away. They'll be like, well, you're not like trophy hunter.

1281
01:09:22,920 --> 01:09:26,960
Speaker 1: I'll be like, well easy now, because I am. You know,

1282
01:09:27,040 --> 01:09:28,760
Speaker 1: I'm a lot of things. And one of the things

1283
01:09:28,800 --> 01:09:30,719
Speaker 1: I am is I have a lot of this ship

1284
01:09:30,800 --> 01:09:32,720
Speaker 1: laying around my house and I really like it a lot.

1285
01:09:33,560 --> 01:09:34,880
Speaker 1: And so I don't know what you call it, what

1286
01:09:34,920 --> 01:09:37,080
Speaker 1: you want to call it. But like if I get

1287
01:09:37,080 --> 01:09:40,559
Speaker 1: a big deer, any deer, If I get a deer,

1288
01:09:40,880 --> 01:09:43,120
Speaker 1: I want the head. I'm never gonna get rid of it.

1289
01:09:43,160 --> 01:09:45,559
Speaker 1: I've never gotten rid of one, unless I like lent

1290
01:09:45,640 --> 01:09:47,320
Speaker 1: one to a friend because he thought it's cool to

1291
01:09:47,360 --> 01:09:49,280
Speaker 1: have in his house. I've never gotten rid of one

1292
01:09:49,280 --> 01:09:53,920
Speaker 1: of my life. So I don't you know this idea,

1293
01:09:53,960 --> 01:09:55,720
Speaker 1: and some people like overdo or they tried to like,

1294
01:09:55,760 --> 01:09:58,800
Speaker 1: I don't even remove the head from the woods. I'm like, dude,

1295
01:09:59,200 --> 01:10:01,559
Speaker 1: now you're just like you're trying to make a comment

1296
01:10:01,600 --> 01:10:04,120
Speaker 1: on things, and it's just ridiculous, Like you would want

1297
01:10:04,160 --> 01:10:06,800
Speaker 1: it and you would have it in your house, but

1298
01:10:07,880 --> 01:10:10,600
Speaker 1: because other people view there's in the way that you

1299
01:10:10,600 --> 01:10:13,880
Speaker 1: don't find acceptable, you will now sacrifice by leaving yours

1300
01:10:13,880 --> 01:10:16,200
Speaker 1: in the woods in order to have like make turn

1301
01:10:16,240 --> 01:10:19,520
Speaker 1: this into like social commentary. One it should be remembrance

1302
01:10:19,520 --> 01:10:22,960
Speaker 1: of your hunt. I mean, I look at my hanging

1303
01:10:23,000 --> 01:10:25,639
Speaker 1: in my you know, in my house, and I remember

1304
01:10:25,680 --> 01:10:29,240
Speaker 1: every single one of those. Yeah, it's a trophy. I'm

1305
01:10:29,280 --> 01:10:31,080
Speaker 1: all four onto something different. I don't know what the

1306
01:10:31,120 --> 01:10:33,559
Speaker 1: hell you're gonna call it mementos. I don't know mentos.

1307
01:10:33,640 --> 01:10:38,080
Speaker 1: My kids would think mentos in a shadow box. Yeah,

1308
01:10:38,120 --> 01:10:41,040
Speaker 1: I love it, so I haven't like walked away from it.

1309
01:10:41,120 --> 01:10:43,599
Speaker 1: But I know that it takes a lot of explaining either.

1310
01:10:43,640 --> 01:10:45,559
Speaker 1: If you explain it like that, people like, oh, yeah,

1311
01:10:45,600 --> 01:10:48,400
Speaker 1: I get that right, Like I don't know. It's like

1312
01:10:48,439 --> 01:10:51,960
Speaker 1: I like all of it. I eat all the meat whatever,

1313
01:10:52,000 --> 01:10:54,320
Speaker 1: I give the damn bones and my dog. I put

1314
01:10:54,320 --> 01:10:56,880
Speaker 1: the antlers on my shelf, like I take the whole thing.

1315
01:10:59,360 --> 01:11:02,679
Speaker 1: I like it all. And I found a nice like, um, well,

1316
01:11:02,720 --> 01:11:06,800
Speaker 1: benefit from doing some so called trophy hunting. I caught

1317
01:11:06,840 --> 01:11:08,720
Speaker 1: a little bit of slack four on that video we

1318
01:11:08,800 --> 01:11:10,800
Speaker 1: posted that meta your Hunts where I was hunting elk

1319
01:11:10,840 --> 01:11:13,400
Speaker 1: in Colorado at a tag that way to you know,

1320
01:11:13,520 --> 01:11:16,400
Speaker 1: fourteen fifteen years to hunt. But I could have very

1321
01:11:16,439 --> 01:11:19,920
Speaker 1: easily filled that tag opening morning and just shot a bowl.

1322
01:11:20,360 --> 01:11:22,479
Speaker 1: But the older I get, man, I want to hunt.

1323
01:11:22,560 --> 01:11:24,680
Speaker 1: I remember like older clients in mine when I was

1324
01:11:24,720 --> 01:11:27,120
Speaker 1: a hunting guy, they should tell me that, Like, man,

1325
01:11:27,200 --> 01:11:29,439
Speaker 1: my perfect season will be if I can kill one

1326
01:11:29,479 --> 01:11:32,360
Speaker 1: on the last evening of the last day of Pennsylvania's

1327
01:11:32,400 --> 01:11:34,040
Speaker 1: deer season. You don't want to sit in my tree

1328
01:11:34,080 --> 01:11:37,479
Speaker 1: stand like sixty days. And I kind of as a

1329
01:11:37,560 --> 01:11:39,880
Speaker 1: mid twenty year old, like I didn't really get it,

1330
01:11:40,000 --> 01:11:42,640
Speaker 1: But now at forty two, I'm like, oh yeah, I

1331
01:11:42,680 --> 01:11:45,280
Speaker 1: get it. I want to hunt every single moment that

1332
01:11:45,360 --> 01:11:47,760
Speaker 1: I have opened to hunt and then hopefully kill on

1333
01:11:47,840 --> 01:11:51,080
Speaker 1: the last day. And if you're looking for something, you know,

1334
01:11:51,439 --> 01:11:53,320
Speaker 1: a trophies festivalor however you want to put it, a

1335
01:11:53,320 --> 01:11:57,200
Speaker 1: bigger buck, it's it helps you do that, right, unless

1336
01:11:57,200 --> 01:11:59,160
Speaker 1: you can get lucky, But most of the time you're

1337
01:11:59,160 --> 01:12:01,800
Speaker 1: gonna end up hunting more. Yeah, there are a lot

1338
01:12:01,800 --> 01:12:04,120
Speaker 1: of trips that will be over instantaneously. And that's another

1339
01:12:04,120 --> 01:12:08,000
Speaker 1: funny thing, is like, so if you're super moral, uh,

1340
01:12:08,160 --> 01:12:11,840
Speaker 1: you shoot the first thing right away, like that's more

1341
01:12:12,000 --> 01:12:15,160
Speaker 1: moral than that. You wander around the hills for five

1342
01:12:15,280 --> 01:12:19,320
Speaker 1: days and then get one like you're you're less moral.

1343
01:12:20,240 --> 01:12:23,600
Speaker 1: It's just it's this heart. So when you see the

1344
01:12:23,680 --> 01:12:28,639
Speaker 1: word destroyed, he just man, just one of those things

1345
01:12:28,640 --> 01:12:32,880
Speaker 1: just makes you cringe. Well, you know I am. I

1346
01:12:32,880 --> 01:12:36,000
Speaker 1: grew up, you know, in a subsistence family, so I

1347
01:12:36,040 --> 01:12:38,599
Speaker 1: never had a beefsteak till I was sixteen years old.

1348
01:12:38,640 --> 01:12:40,799
Speaker 1: But I will tell you it is my perfect hunting

1349
01:12:40,840 --> 01:12:43,880
Speaker 1: season is when I draw a beat egg for a

1350
01:12:43,920 --> 01:12:46,240
Speaker 1: for a cow because I can run out, I can

1351
01:12:46,280 --> 01:12:48,280
Speaker 1: get the cow, and I can put the cow in

1352
01:12:48,320 --> 01:12:50,160
Speaker 1: the freezer and I have no I've got meat for

1353
01:12:50,160 --> 01:12:52,840
Speaker 1: the rest of the year, and then I can actually

1354
01:12:52,880 --> 01:12:55,599
Speaker 1: focus on Okay, I'm i a tag start picking that place,

1355
01:12:55,640 --> 01:12:58,080
Speaker 1: and I'm gonna start seeing if maybe I can be

1356
01:12:58,160 --> 01:13:01,920
Speaker 1: lucky enough to wander into something that is a respectable

1357
01:13:02,560 --> 01:13:05,120
Speaker 1: you know, but your male specimen. I have not gotten

1358
01:13:05,160 --> 01:13:06,920
Speaker 1: that lucky yet to get into the book, I will

1359
01:13:06,920 --> 01:13:09,080
Speaker 1: tell you that. But you know, you have to put

1360
01:13:09,080 --> 01:13:12,840
Speaker 1: yourself in there and just right, and you know there's

1361
01:13:12,880 --> 01:13:15,559
Speaker 1: no back door, but I but you know, I mean that,

1362
01:13:15,720 --> 01:13:17,840
Speaker 1: because then you do maybe you hunt the rest of

1363
01:13:17,880 --> 01:13:20,000
Speaker 1: the season and maybe you gets something, but maybe you don't.

1364
01:13:20,040 --> 01:13:22,759
Speaker 1: But it is all about being out in the woods

1365
01:13:22,760 --> 01:13:25,519
Speaker 1: and and and and the experience as much as anything else.

1366
01:13:25,720 --> 01:13:29,280
Speaker 1: For I think the majority of hunters um, you know.

1367
01:13:29,439 --> 01:13:33,200
Speaker 1: And I bet I think that the unfortunately the plagiarized

1368
01:13:33,479 --> 01:13:36,400
Speaker 1: you know, the trophy piece has been plagiarized, and they're

1369
01:13:36,439 --> 01:13:38,320
Speaker 1: not plagiarize is probably not the wrong word, but it's

1370
01:13:38,360 --> 01:13:43,880
Speaker 1: been misinterpreted and and and it's not um, it's not

1371
01:13:43,920 --> 01:13:46,200
Speaker 1: what it was originally intended to be. It happens to

1372
01:13:46,200 --> 01:13:50,000
Speaker 1: a lot of words though really you know, like now, um,

1373
01:13:50,040 --> 01:13:54,080
Speaker 1: there's a certain way you can throw an inflection on environmentalists,

1374
01:13:54,840 --> 01:13:59,559
Speaker 1: right and you're like, oh, you know an environmentalist like, oh,

1375
01:13:59,680 --> 01:14:05,760
Speaker 1: so someone who wants like major sort of overreach. They

1376
01:14:05,840 --> 01:14:08,760
Speaker 1: ruin all the good times. You know. It's like there's

1377
01:14:08,760 --> 01:14:11,479
Speaker 1: like words that just like you know, trophy hunting. But

1378
01:14:11,560 --> 01:14:13,439
Speaker 1: you can probably stay here list a dozen words that

1379
01:14:13,479 --> 01:14:16,800
Speaker 1: at one point in time had like a positive connotation,

1380
01:14:16,840 --> 01:14:19,240
Speaker 1: but then a version of it runs wild and it

1381
01:14:19,320 --> 01:14:22,800
Speaker 1: just starts to carry with it to the point where

1382
01:14:22,800 --> 01:14:24,600
Speaker 1: you have no one can self identify anymore as a

1383
01:14:24,600 --> 01:14:29,000
Speaker 1: trophy hunter. You gotta do a lot of backflips to

1384
01:14:29,320 --> 01:14:40,880
Speaker 1: in order to land there. You know, there's nothing I

1385
01:14:40,920 --> 01:14:42,840
Speaker 1: want to ask you guys about that We've we've talked

1386
01:14:42,840 --> 01:14:47,160
Speaker 1: about it at least a dozen times, so Buona Crockett Club.

1387
01:14:47,160 --> 01:14:51,720
Speaker 1: When you're looking at the animals that get submitted, um,

1388
01:14:51,800 --> 01:14:53,960
Speaker 1: you break the like you don't just break it up

1389
01:14:54,000 --> 01:14:59,000
Speaker 1: by species, but you break it up by uh geography

1390
01:14:59,200 --> 01:15:04,000
Speaker 1: in some cases, because there's sort of like not hard

1391
01:15:04,080 --> 01:15:08,040
Speaker 1: lines between things. Meaning the example we always bring up

1392
01:15:08,080 --> 01:15:12,360
Speaker 1: is it a deer in California. Say a deer can

1393
01:15:13,040 --> 01:15:19,599
Speaker 1: run across I five and become a different deer like,

1394
01:15:19,880 --> 01:15:22,599
Speaker 1: meaning he could be a record book deer on the

1395
01:15:22,680 --> 01:15:24,960
Speaker 1: west side, he could be a record book blacktail on

1396
01:15:25,000 --> 01:15:28,639
Speaker 1: the west side of I five. Should he run over

1397
01:15:28,680 --> 01:15:31,560
Speaker 1: to the east side of I five, he ceases to

1398
01:15:31,640 --> 01:15:34,160
Speaker 1: be a record book animal. We can actually look into

1399
01:15:34,200 --> 01:15:37,120
Speaker 1: that now, so we can have a DNA test done

1400
01:15:37,160 --> 01:15:40,880
Speaker 1: that will tell us what, you know, if that deer

1401
01:15:40,960 --> 01:15:44,360
Speaker 1: is a blacktail or mulean deer. Has that come up yet?

1402
01:15:44,600 --> 01:15:46,800
Speaker 1: Oh yeah, yeah, we have. We probably have what about

1403
01:15:46,840 --> 01:15:50,599
Speaker 1: two dozen deer right now that are getting DNA because

1404
01:15:50,640 --> 01:15:52,640
Speaker 1: of that. And so what what you're touching on there

1405
01:15:52,680 --> 01:15:54,599
Speaker 1: is if you look at the blacktail range from Washington,

1406
01:15:54,600 --> 01:15:58,760
Speaker 1: Columbia blacktail Washington, Oregon, California, the southern end of the California,

1407
01:15:58,800 --> 01:16:01,320
Speaker 1: we get down to the California, it wasn't a definitive line,

1408
01:16:01,680 --> 01:16:04,640
Speaker 1: whereas that the peak of the Cascades served as a

1409
01:16:04,680 --> 01:16:08,440
Speaker 1: definitive line between mule deer and blacktail, so that historically

1410
01:16:08,520 --> 01:16:11,479
Speaker 1: was not crossable, which created this speciation where there was

1411
01:16:11,520 --> 01:16:14,840
Speaker 1: as particular Columbia as there well south of that line,

1412
01:16:15,439 --> 01:16:18,519
Speaker 1: there's no definitive. They can't get over the cascades type thing.

1413
01:16:18,960 --> 01:16:21,320
Speaker 1: So it's a mixing, that's what's going on there. It's

1414
01:16:21,360 --> 01:16:26,080
Speaker 1: just that the severe, the severe high altitude spine of

1415
01:16:26,160 --> 01:16:29,640
Speaker 1: that range isn't separating blacktails and mule, where historically with

1416
01:16:29,720 --> 01:16:33,800
Speaker 1: different climate conditions it would have. Okay, and so they

1417
01:16:33,800 --> 01:16:35,800
Speaker 1: can So you get down south of that range and

1418
01:16:35,800 --> 01:16:37,880
Speaker 1: they can slip back and forth. And what what we've

1419
01:16:37,920 --> 01:16:40,599
Speaker 1: shown if you look in Oregon are our blacktail boundary

1420
01:16:41,000 --> 01:16:42,840
Speaker 1: is the forest service line. So we pulled it off

1421
01:16:42,880 --> 01:16:46,120
Speaker 1: the top of the range. We made it conservative so

1422
01:16:46,200 --> 01:16:49,240
Speaker 1: there wouldn't be a potential crossover. So now that they

1423
01:16:49,280 --> 01:16:52,439
Speaker 1: identified the loci that are unique to blacktail and mule there,

1424
01:16:52,880 --> 01:16:55,240
Speaker 1: we can take a deer kill just over the border

1425
01:16:55,280 --> 01:16:57,800
Speaker 1: to the east and say, sure enough, it has all

1426
01:16:57,800 --> 01:17:01,120
Speaker 1: the traits of a blacktail. Let's run the genetic genetics

1427
01:17:01,120 --> 01:17:03,879
Speaker 1: on it and see and if it comes back pure blacktail,

1428
01:17:03,920 --> 01:17:06,559
Speaker 1: we will accept it in the category. Who pays for that?

1429
01:17:06,960 --> 01:17:09,840
Speaker 1: The hunter does. If if we have a question on something,

1430
01:17:09,840 --> 01:17:12,240
Speaker 1: if we're not like if it's a top end or whatever,

1431
01:17:12,280 --> 01:17:14,400
Speaker 1: and we're doing it to preserve the records, will pay

1432
01:17:14,439 --> 01:17:16,960
Speaker 1: for it. But if somebody says, I shot this deer.

1433
01:17:16,960 --> 01:17:18,519
Speaker 1: It doesn't make your book, but I know it's a

1434
01:17:18,560 --> 01:17:21,880
Speaker 1: blacktail taken east of your boundary. I can now say, okay,

1435
01:17:21,880 --> 01:17:23,760
Speaker 1: well a hundred and thirty bucks, and once I get

1436
01:17:23,800 --> 01:17:25,719
Speaker 1: ten samples and I can run it for that cost,

1437
01:17:26,280 --> 01:17:28,200
Speaker 1: send me your DNA sample, and then we can come

1438
01:17:28,200 --> 01:17:31,280
Speaker 1: back and say, divinitively, here's your cue value, a que

1439
01:17:31,360 --> 01:17:35,240
Speaker 1: value of point nine or highers pure blacktail. Really, and

1440
01:17:35,240 --> 01:17:37,120
Speaker 1: you've got two dozen right now that people want to

1441
01:17:37,200 --> 01:17:40,000
Speaker 1: challenge that. Man, See, our stuff is all out of date.

1442
01:17:40,000 --> 01:17:41,559
Speaker 1: Can we talk about that that? This is my favorite

1443
01:17:41,560 --> 01:17:46,360
Speaker 1: thing to talk about. The five dear thing man. The

1444
01:17:46,400 --> 01:17:50,000
Speaker 1: I five boundary is actually Roosevelt's elk, and there's no

1445
01:17:50,080 --> 01:17:53,200
Speaker 1: genetic variation between a Roosevelt and the Rocky. It's completely

1446
01:17:53,200 --> 01:17:56,160
Speaker 1: habitat driven. So then it was just an arbitrary line

1447
01:17:56,160 --> 01:17:58,360
Speaker 1: that has to be easily defiable, which is I five.

1448
01:17:58,800 --> 01:18:01,120
Speaker 1: The other one that's really kind of embarrassing is the

1449
01:18:01,120 --> 01:18:04,760
Speaker 1: Canadian moose stop at the Canadian border and become what

1450
01:18:05,280 --> 01:18:09,360
Speaker 1: sheriffs in the Rockies. Yeah, I got you, but what

1451
01:18:09,400 --> 01:18:11,880
Speaker 1: about like in Maine and now those are all Canadians. Okay,

1452
01:18:12,760 --> 01:18:14,760
Speaker 1: So if you really so, there is a there is

1453
01:18:14,800 --> 01:18:18,160
Speaker 1: a hard line there Canadian moose and shiras they can

1454
01:18:18,240 --> 01:18:20,519
Speaker 1: change back. They probably like the border too, because I

1455
01:18:20,520 --> 01:18:23,160
Speaker 1: think it's cut so it's got a good young growth

1456
01:18:23,200 --> 01:18:25,760
Speaker 1: on it. Well, then you have the bears in Alaska.

1457
01:18:26,040 --> 01:18:29,439
Speaker 1: I've had them, like a brown bear, grizzly bear. I've

1458
01:18:29,439 --> 01:18:32,880
Speaker 1: had that come down to yards. No, yeah, yeah, yeah,

1459
01:18:32,920 --> 01:18:35,800
Speaker 1: we'll get a GPS location. I've literally had to come

1460
01:18:35,800 --> 01:18:38,400
Speaker 1: down to yards. I'm on Google Earth and I've got

1461
01:18:38,479 --> 01:18:41,760
Speaker 1: everything up and I've got the coordinates punched in our

1462
01:18:41,880 --> 01:18:44,960
Speaker 1: line runs. Because what is referring to here is that

1463
01:18:45,400 --> 01:18:49,800
Speaker 1: you have barren ground. So grizzly bear. Yeah, in Alaska,

1464
01:18:49,960 --> 01:18:52,240
Speaker 1: like you know in the Rockies ere everybody says grizzly

1465
01:18:52,280 --> 01:18:55,280
Speaker 1: bearon and but a grizzly bear or brown bear are

1466
01:18:55,520 --> 01:18:58,360
Speaker 1: If you went to a taxono, Mr geneticist, they would

1467
01:18:58,400 --> 01:19:00,840
Speaker 1: tell you that grizzly bears and brown bears is the

1468
01:19:00,840 --> 01:19:06,360
Speaker 1: same thing. Brown bears have some coloration differences, tend to

1469
01:19:06,400 --> 01:19:10,479
Speaker 1: get much bigger. Um. Grizzly bears tend to have like

1470
01:19:10,840 --> 01:19:15,720
Speaker 1: lighter colors, more pellage, uh, you know, greater differences in

1471
01:19:15,760 --> 01:19:19,439
Speaker 1: hair color all kinds of things, Um, not nearly as

1472
01:19:19,479 --> 01:19:21,840
Speaker 1: big and in some ways, like a lot of guys

1473
01:19:21,880 --> 01:19:25,280
Speaker 1: in Alaska will tell you, if it has access to salmon,

1474
01:19:25,880 --> 01:19:28,439
Speaker 1: we called a brown bear, If it doesn't have access

1475
01:19:28,439 --> 01:19:31,639
Speaker 1: to salmon, we called a grizzly. But how do you guys,

1476
01:19:32,840 --> 01:19:34,880
Speaker 1: so a lot did you draw line? We did? Yeah,

1477
01:19:34,960 --> 01:19:37,439
Speaker 1: So it's it's kind of the Alaska Range curling up

1478
01:19:37,479 --> 01:19:40,719
Speaker 1: around the spine of the Alaska Rare until it hits

1479
01:19:40,880 --> 01:19:46,679
Speaker 1: one of the longitude lines. So the north the north

1480
01:19:46,960 --> 01:19:50,080
Speaker 1: edge of the Alaska Ranges of Grizzly, the south face

1481
01:19:50,120 --> 01:19:52,400
Speaker 1: of the Alaska Ranges of brown Bear. Correct, And I've

1482
01:19:52,400 --> 01:19:55,000
Speaker 1: had that come down to yards on that range line.

1483
01:19:55,600 --> 01:19:57,519
Speaker 1: So what what it is is you follow the Alaska

1484
01:19:57,600 --> 01:19:59,800
Speaker 1: Range around there's one spot that it's a straight line

1485
01:19:59,840 --> 01:20:02,680
Speaker 1: like Mount Elias I believe, is it, And then it

1486
01:20:02,720 --> 01:20:05,240
Speaker 1: goes a straight line across, then hits the Alaska Range

1487
01:20:05,240 --> 01:20:07,559
Speaker 1: and follows the peak of the Alaska Range, and then

1488
01:20:07,600 --> 01:20:09,479
Speaker 1: once you reach a certain point, then it follows the

1489
01:20:09,560 --> 01:20:13,240
Speaker 1: longitude out to the coast. I'm guessing again this was

1490
01:20:13,479 --> 01:20:16,839
Speaker 1: the fifties, so I was not around. Um, they probably

1491
01:20:16,840 --> 01:20:18,880
Speaker 1: were going with the salmon thing because that's what I've heard.

1492
01:20:18,960 --> 01:20:21,920
Speaker 1: If it's got access to you know, a big salmon run,

1493
01:20:21,920 --> 01:20:23,839
Speaker 1: it's a brown bear. If it doesn't, it's a grizzly.

1494
01:20:24,320 --> 01:20:26,559
Speaker 1: So it would make sense that salmon couldn't cross that

1495
01:20:26,600 --> 01:20:29,080
Speaker 1: Alaska range and you got the Yukon to the north.

1496
01:20:29,680 --> 01:20:32,240
Speaker 1: But I think that's probably what they were trying to do,

1497
01:20:32,360 --> 01:20:35,599
Speaker 1: is that differentiation. They were set down and said, where

1498
01:20:35,640 --> 01:20:38,639
Speaker 1: is that line that's the most easily definable that hunters

1499
01:20:38,640 --> 01:20:41,880
Speaker 1: will understand and makes sense for separating these out so

1500
01:20:41,920 --> 01:20:44,759
Speaker 1: that you can But you know, bears is the worst

1501
01:20:44,760 --> 01:20:47,280
Speaker 1: because all the biggest grizzly bears are killed right on

1502
01:20:47,320 --> 01:20:49,280
Speaker 1: the line that we're probably a brown bear last week.

1503
01:20:49,479 --> 01:20:52,400
Speaker 1: So the the yeah, so the let me guess that

1504
01:20:52,800 --> 01:20:55,400
Speaker 1: the one you're looking at is a guy that feels

1505
01:20:55,439 --> 01:20:59,920
Speaker 1: he killed a tanker grizzly and you're like, well, may

1506
01:21:00,040 --> 01:21:04,280
Speaker 1: it's just a small brown bear where exactly yeah, yeah,

1507
01:21:04,600 --> 01:21:07,719
Speaker 1: or yeah. So all the biggest grizzlies come from the line,

1508
01:21:08,360 --> 01:21:11,960
Speaker 1: not all of them, but it's it's a larger portion

1509
01:21:12,040 --> 01:21:14,120
Speaker 1: of them. You kind of know where a big grizzlies

1510
01:21:14,160 --> 01:21:16,600
Speaker 1: going to be from when you get an entry, is

1511
01:21:16,600 --> 01:21:23,680
Speaker 1: that right? Huh? What other ones are like that, So

1512
01:21:23,760 --> 01:21:26,320
Speaker 1: the moose, the grizzlies, the moose don't seem to follow

1513
01:21:26,400 --> 01:21:28,920
Speaker 1: the Colorado is blowing the doors off a shiris moose

1514
01:21:29,680 --> 01:21:33,120
Speaker 1: you think would be northern Montana, it's not. Washington does well,

1515
01:21:33,160 --> 01:21:35,680
Speaker 1: but so that one. You know, you think that just

1516
01:21:35,800 --> 01:21:38,200
Speaker 1: over the Canadian border would be the place to hunt shiris,

1517
01:21:38,200 --> 01:21:43,160
Speaker 1: but the answer is Colorado. Um. You know blacktail the

1518
01:21:43,320 --> 01:21:47,519
Speaker 1: California area, there's actually a little bit genetic differentiation. So

1519
01:21:47,800 --> 01:21:50,120
Speaker 1: even though that they do potentially have some mulder, it's

1520
01:21:50,120 --> 01:21:52,760
Speaker 1: actually different black tails with the biggest black tails or

1521
01:21:53,080 --> 01:21:55,880
Speaker 1: northern California in southern Oregon. The further north you get,

1522
01:21:56,240 --> 01:21:58,000
Speaker 1: they almost look more like a sit Ka than they

1523
01:21:58,040 --> 01:22:01,400
Speaker 1: really do. Uh, you know what you consider a Columbia

1524
01:22:01,680 --> 01:22:05,200
Speaker 1: and so you know those are Columbia black tails. Yeah,

1525
01:22:05,200 --> 01:22:07,240
Speaker 1: there's big ones that show up in Washington and Oregon.

1526
01:22:07,840 --> 01:22:09,519
Speaker 1: We haven't seen a lot of real big deer in

1527
01:22:09,520 --> 01:22:11,880
Speaker 1: Oregon since the fires of like the sixties when there

1528
01:22:11,960 --> 01:22:15,280
Speaker 1: was some major stand clearing fires. The years following that

1529
01:22:15,360 --> 01:22:18,679
Speaker 1: we saw some gigantic black tails. We haven't seen that,

1530
01:22:19,080 --> 01:22:23,320
Speaker 1: but that you know, Jackson County Applegate type country down

1531
01:22:23,360 --> 01:22:25,920
Speaker 1: there that still puts out some big ones in northern California.

1532
01:22:26,600 --> 01:22:29,880
Speaker 1: If someone wanted to, uh, what's the quickest way to

1533
01:22:29,880 --> 01:22:32,479
Speaker 1: get in the books? What should you be hunting for?

1534
01:22:32,520 --> 01:22:35,040
Speaker 1: If you want to get in the books? White tails

1535
01:22:35,120 --> 01:22:38,559
Speaker 1: is tough, man real dere is tough off of like

1536
01:22:38,720 --> 01:22:41,880
Speaker 1: rough population estimates and whatnot. I kind of calculated out,

1537
01:22:41,920 --> 01:22:43,360
Speaker 1: you know, how many do you have to look at

1538
01:22:43,400 --> 01:22:46,439
Speaker 1: to find a booner and white tails? Like a hundred

1539
01:22:46,479 --> 01:22:48,479
Speaker 1: and eighty thousand white tails you have to look at

1540
01:22:48,560 --> 01:22:53,360
Speaker 1: to find one that's and again this is all, but

1541
01:22:54,560 --> 01:22:56,920
Speaker 1: usually one in a thousand is kind of what you

1542
01:22:57,280 --> 01:23:00,720
Speaker 1: what you think? Um Shires move mouse and most of

1543
01:23:00,720 --> 01:23:04,360
Speaker 1: their ranges forty inches wide and split brows will get

1544
01:23:04,400 --> 01:23:06,680
Speaker 1: you to the minimum. And I feel like if you

1545
01:23:06,680 --> 01:23:09,240
Speaker 1: can get a shire's tag, you could set your sights

1546
01:23:09,240 --> 01:23:11,640
Speaker 1: at BNC and have a decent chance at it. So

1547
01:23:11,680 --> 01:23:14,280
Speaker 1: you gotta look at how many to find one. I

1548
01:23:14,320 --> 01:23:16,080
Speaker 1: don't remember what that one is off the top of

1549
01:23:16,120 --> 01:23:19,600
Speaker 1: my head, say split browns just to brow times. The

1550
01:23:19,600 --> 01:23:22,439
Speaker 1: way our scoring works is you terminate the measurement on

1551
01:23:22,479 --> 01:23:25,160
Speaker 1: the on a moose palm between two qualifying points on

1552
01:23:25,160 --> 01:23:27,840
Speaker 1: a brow if it does not have a qualifying point

1553
01:23:27,880 --> 01:23:29,280
Speaker 1: on a brow. It just comes to the end of

1554
01:23:29,320 --> 01:23:32,479
Speaker 1: the palm. So if you want the mature moose have

1555
01:23:32,520 --> 01:23:35,080
Speaker 1: a split brow. So that's a characteristic. If you want

1556
01:23:35,080 --> 01:23:37,840
Speaker 1: to a good score, your moose has to have split

1557
01:23:37,920 --> 01:23:40,760
Speaker 1: brows are better. But if it's forty wide with a

1558
01:23:40,800 --> 01:23:43,280
Speaker 1: split brow, that generally will put you at minimum score.

1559
01:23:43,439 --> 01:23:46,720
Speaker 1: So that wants to be a two point brow, and

1560
01:23:46,760 --> 01:23:48,560
Speaker 1: that'll split down towards the end, So I'll give you

1561
01:23:48,600 --> 01:23:51,840
Speaker 1: a little bit more length and increase your score. So

1562
01:23:52,439 --> 01:23:58,639
Speaker 1: is it one in twenty? You gotta look at judging

1563
01:23:58,680 --> 01:24:00,360
Speaker 1: from the people I know that have had mo stech,

1564
01:24:00,479 --> 01:24:03,880
Speaker 1: yeah or less. Like you find a mature bullets, it's

1565
01:24:03,960 --> 01:24:06,479
Speaker 1: pretty pretty good. And I mean maybe it's not true

1566
01:24:06,479 --> 01:24:08,919
Speaker 1: and Wyoming or some other states, but my limited experience

1567
01:24:08,920 --> 01:24:11,920
Speaker 1: with schyris yeah, forty inches and split brow is just

1568
01:24:11,960 --> 01:24:16,320
Speaker 1: a good solid bull. You know. Um, earlier we were talking,

1569
01:24:16,360 --> 01:24:20,479
Speaker 1: you mentioned an interesting case where you've had Yeah, because

1570
01:24:21,320 --> 01:24:25,000
Speaker 1: uh Nanny's mountain goats. So males and females are very

1571
01:24:25,080 --> 01:24:30,040
Speaker 1: very similar horns. The females I have sort of like

1572
01:24:30,080 --> 01:24:32,920
Speaker 1: a little dog leg like almost like imperceptible little dog

1573
01:24:33,000 --> 01:24:35,960
Speaker 1: leg in them when they come up narrower. But you've

1574
01:24:36,000 --> 01:24:39,599
Speaker 1: had a female, you had a female mountain goat make

1575
01:24:39,640 --> 01:24:43,000
Speaker 1: the books. Yeah, we we got a fifty picture of

1576
01:24:43,000 --> 01:24:45,559
Speaker 1: fifty and I don't know if I officially dried. Yeah,

1577
01:24:45,560 --> 01:24:47,120
Speaker 1: I don't know that it's been entered yet, but we

1578
01:24:47,200 --> 01:24:49,880
Speaker 1: got preliminary pictures of it. And yeah, I mean things

1579
01:24:49,880 --> 01:24:54,880
Speaker 1: got I bet you eleven twelve inch horns. Yeah, I mean,

1580
01:24:55,080 --> 01:24:58,200
Speaker 1: well it's a Yeah, it's a name that grew insanely

1581
01:24:58,200 --> 01:25:00,760
Speaker 1: long horns. And not a lot of people they're shooting nanny,

1582
01:25:00,880 --> 01:25:04,120
Speaker 1: so there could be you know, a hundred book nanny's

1583
01:25:04,160 --> 01:25:05,720
Speaker 1: that just died of old age. You're still in the

1584
01:25:06,200 --> 01:25:09,920
Speaker 1: oh not not really. We get that too. You know,

1585
01:25:09,960 --> 01:25:13,479
Speaker 1: people always this the biggest ever. Well it's the largest

1586
01:25:13,520 --> 01:25:15,720
Speaker 1: ever taken in fair Chase that was then entered to

1587
01:25:15,720 --> 01:25:18,400
Speaker 1: Boone and Crockett. I mean, you know, so there could

1588
01:25:18,439 --> 01:25:20,240
Speaker 1: be bigger nannies out there, but we have seen one

1589
01:25:20,280 --> 01:25:23,519
Speaker 1: that made our our minimums. But it used to say

1590
01:25:23,520 --> 01:25:26,040
Speaker 1: on the score chart male or female. But after we

1591
01:25:26,080 --> 01:25:27,800
Speaker 1: had that on there for thirty years and never had

1592
01:25:27,840 --> 01:25:30,519
Speaker 1: a female, we did away with him. If here comes

1593
01:25:30,520 --> 01:25:33,400
Speaker 1: a female goat, hey, what is it? When? Uh? I

1594
01:25:33,400 --> 01:25:35,479
Speaker 1: gotta gripe too. I gotta bring up real quickly as

1595
01:25:35,600 --> 01:25:37,280
Speaker 1: you what I'm gonna ask. You know, my grape has

1596
01:25:37,320 --> 01:25:41,160
Speaker 1: to do with my halina. But I want you guys

1597
01:25:41,200 --> 01:25:47,080
Speaker 1: gonna explain what all time means. But I want to

1598
01:25:47,080 --> 01:25:53,160
Speaker 1: give so. I got a gigantic cavelina, I mean a

1599
01:25:53,320 --> 01:25:57,240
Speaker 1: giant I don't remember what it was. I measured it

1600
01:25:57,439 --> 01:26:00,280
Speaker 1: just like when you measure a bear, like it's length

1601
01:26:00,360 --> 01:26:02,680
Speaker 1: and linked fluss with very easy thing to measure. I

1602
01:26:02,720 --> 01:26:05,559
Speaker 1: measured my have Elena with my old man's I still

1603
01:26:05,600 --> 01:26:08,400
Speaker 1: have my old man's measuring calipers, and I still have

1604
01:26:08,520 --> 01:26:12,840
Speaker 1: his genuine I don't know, like nineteen ten. Uh, I

1605
01:26:12,840 --> 01:26:16,880
Speaker 1: don't know what that was, very old. Uh little tape

1606
01:26:16,880 --> 01:26:21,960
Speaker 1: measure with the pope and young Yeah they're emblem a

1607
01:26:22,000 --> 01:26:25,240
Speaker 1: little emblem on it, and his caliper and all that

1608
01:26:25,280 --> 01:26:27,880
Speaker 1: kind of garbage, the cable. I got a lot of stuff.

1609
01:26:28,200 --> 01:26:32,280
Speaker 1: But I measured my have Elena and then went to

1610
01:26:32,360 --> 01:26:34,880
Speaker 1: check and see if it was book. And you guys

1611
01:26:34,960 --> 01:26:40,519
Speaker 1: don't accept Havelna's now our our records committee hasn't hasn't

1612
01:26:40,560 --> 01:26:42,720
Speaker 1: taken that well. I mean they've taken it on, but

1613
01:26:42,760 --> 01:26:45,680
Speaker 1: they haven't decided yet to add that as a category,

1614
01:26:45,880 --> 01:26:49,280
Speaker 1: and gators either, right they had and gators not yet either.

1615
01:26:50,240 --> 01:26:53,840
Speaker 1: I had to go to your competitor, Safari Club. They're

1616
01:26:55,600 --> 01:27:00,040
Speaker 1: competitor there. They're another scoreing organization that I had to

1617
01:27:00,120 --> 01:27:03,599
Speaker 1: go there to validate that I had a giant alena. Yeah.

1618
01:27:03,720 --> 01:27:05,960
Speaker 1: So the the thing, the first thread the first time

1619
01:27:05,960 --> 01:27:09,040
Speaker 1: they looked at the havilina category. In order to have

1620
01:27:09,120 --> 01:27:11,240
Speaker 1: a category, the states that have them have to ask

1621
01:27:11,320 --> 01:27:13,439
Speaker 1: us and it has to be a managed species. And

1622
01:27:13,520 --> 01:27:15,800
Speaker 1: up until like four or five, maybe five or six

1623
01:27:15,880 --> 01:27:19,679
Speaker 1: years ago, now Texas did not manage havelina's. They switched

1624
01:27:19,760 --> 01:27:22,240
Speaker 1: over to where they actually do manage them now and

1625
01:27:22,280 --> 01:27:24,559
Speaker 1: so that was a big hang up. These were just

1626
01:27:24,840 --> 01:27:27,200
Speaker 1: kind of a shoot on site in Texas, So we

1627
01:27:27,240 --> 01:27:30,280
Speaker 1: can't really create a category form well in Texas, Arizona,

1628
01:27:30,360 --> 01:27:34,040
Speaker 1: New Mexico all now manage for him. That by visited

1629
01:27:34,200 --> 01:27:36,559
Speaker 1: and so it is being talked about at both Pope

1630
01:27:36,560 --> 01:27:38,840
Speaker 1: and Young and Boone and Crockett committee levels. Mines out

1631
01:27:38,840 --> 01:27:42,519
Speaker 1: of West Texas, West Tescas nice. So when you get it,

1632
01:27:42,600 --> 01:27:45,600
Speaker 1: let me know. I'm gonna run down with mine. I

1633
01:27:45,680 --> 01:27:48,320
Speaker 1: wanna be the first guy in the books. Man, I

1634
01:27:48,439 --> 01:27:50,160
Speaker 1: got one on the shelf too. That's gonna get give

1635
01:27:50,160 --> 01:27:52,559
Speaker 1: your run. I thinks between you and I will flip

1636
01:27:52,600 --> 01:27:57,000
Speaker 1: for it. Uh. And then so alligators alligator scenes like

1637
01:27:57,360 --> 01:28:01,599
Speaker 1: a stretch that again my understanding that discussion was before

1638
01:28:01,640 --> 01:28:05,000
Speaker 1: I was around, but that very rarely is one taken

1639
01:28:05,000 --> 01:28:10,000
Speaker 1: in fair chase. Oh ban, Yeah, how do you define?

1640
01:28:10,160 --> 01:28:13,280
Speaker 1: So you have to rewrite fair chase. I mean you're

1641
01:28:13,280 --> 01:28:16,799
Speaker 1: you're trapping him or you're catching them. It's a trapped animal.

1642
01:28:17,479 --> 01:28:19,200
Speaker 1: You know that. That's a good point. You have to

1643
01:28:19,240 --> 01:28:20,840
Speaker 1: kind of just go shoot it and run over and

1644
01:28:20,840 --> 01:28:23,559
Speaker 1: grab it right. You can put a hook into it,

1645
01:28:23,600 --> 01:28:26,719
Speaker 1: which at night we're using a spotlight, which is another.

1646
01:28:26,880 --> 01:28:29,360
Speaker 1: So you have to go out in the daytime cold

1647
01:28:29,400 --> 01:28:32,040
Speaker 1: cock it in the head with something and then retrieve

1648
01:28:32,080 --> 01:28:34,200
Speaker 1: it when you take a buddy you don't like with you.

1649
01:28:35,280 --> 01:28:36,920
Speaker 1: I got something that's good to Oh. That's a really

1650
01:28:36,920 --> 01:28:38,439
Speaker 1: good point, man, you have to do it take a

1651
01:28:38,439 --> 01:28:41,439
Speaker 1: lot of work to accept alligators, and there is there

1652
01:28:41,520 --> 01:28:44,679
Speaker 1: is my understanding, as you can in the alligator rut

1653
01:28:44,720 --> 01:28:46,880
Speaker 1: if that's what it's called, they will call him and

1654
01:28:46,920 --> 01:28:48,240
Speaker 1: you can get him in the day with a bow

1655
01:28:48,400 --> 01:28:52,040
Speaker 1: like on land. So there is a very rare situation.

1656
01:28:52,120 --> 01:28:55,360
Speaker 1: You could take a fair chase alligator by our standards now,

1657
01:28:55,400 --> 01:28:58,680
Speaker 1: but it wouldn't really justify a category. Yeah, I'm with you.

1658
01:28:58,720 --> 01:29:03,120
Speaker 1: Would this sample size be big enough to warrant a category.

1659
01:29:03,280 --> 01:29:06,599
Speaker 1: That's a good point. Uh, okay, here's the thing you're

1660
01:29:06,600 --> 01:29:11,280
Speaker 1: here too. I honestly don't know what this means. People

1661
01:29:11,280 --> 01:29:16,559
Speaker 1: will say that it made the all time every day

1662
01:29:16,600 --> 01:29:20,320
Speaker 1: of the week. Basically, what does that mean? The all

1663
01:29:20,439 --> 01:29:23,240
Speaker 1: time books and the not all times. So basically what

1664
01:29:23,280 --> 01:29:25,880
Speaker 1: had happened was is we created this measuring system in

1665
01:29:27,360 --> 01:29:31,240
Speaker 1: we didn't know at the time, you know, what that

1666
01:29:31,280 --> 01:29:33,960
Speaker 1: would look like, how many entries we would get, And

1667
01:29:34,120 --> 01:29:38,280
Speaker 1: so conservation came along. Wildlife populations were doing better, more

1668
01:29:38,280 --> 01:29:42,519
Speaker 1: people were harvesting mature animals, and so our record books

1669
01:29:42,520 --> 01:29:45,400
Speaker 1: started to grow. And now we're like, okay, we've got

1670
01:29:45,439 --> 01:29:48,000
Speaker 1: this data set that keeps growing. You know, what we're

1671
01:29:48,040 --> 01:29:51,879
Speaker 1: doing is working. We're getting to a point now where

1672
01:29:52,920 --> 01:29:56,920
Speaker 1: every six years we'd have to print encyclopedia and we

1673
01:29:56,960 --> 01:30:00,400
Speaker 1: know what happened to encyclopedias. No one's buying encyclopedias more,

1674
01:30:01,040 --> 01:30:06,240
Speaker 1: and so in order to sell this book, basically, in

1675
01:30:06,320 --> 01:30:08,200
Speaker 1: order to have a book that was manageable, that we

1676
01:30:08,240 --> 01:30:11,320
Speaker 1: could print people would buy at a reasonable price, we

1677
01:30:11,400 --> 01:30:15,439
Speaker 1: kind of had to create this other number um for

1678
01:30:15,560 --> 01:30:19,880
Speaker 1: that trophy to or for that to reach. And so

1679
01:30:19,960 --> 01:30:23,719
Speaker 1: basically it was just a way for the Yeah, yeah,

1680
01:30:23,840 --> 01:30:26,880
Speaker 1: it was just a way for us. It is. But

1681
01:30:27,320 --> 01:30:31,200
Speaker 1: any report. So if you know, one of our university

1682
01:30:31,280 --> 01:30:33,840
Speaker 1: programs comes to us or an agency comes to us

1683
01:30:33,840 --> 01:30:37,720
Speaker 1: and wants to know, you know, category wise, what are

1684
01:30:37,720 --> 01:30:40,960
Speaker 1: you guys seeing, it's always off that that entry score

1685
01:30:41,600 --> 01:30:44,679
Speaker 1: for white tail one sixty, you know, for pronghorn eighty,

1686
01:30:44,720 --> 01:30:49,040
Speaker 1: whatever the case may be. All our data is based

1687
01:30:49,040 --> 01:30:52,479
Speaker 1: off of that one sixty number. And so that that

1688
01:30:52,640 --> 01:30:56,960
Speaker 1: other number that we created was basically just a way

1689
01:30:57,000 --> 01:31:02,920
Speaker 1: for us to reasonably publish and sell a book. Uh So,

1690
01:31:03,040 --> 01:31:05,360
Speaker 1: if one sixty for a white tail makes the book,

1691
01:31:05,840 --> 01:31:09,040
Speaker 1: if it's archery, right, an we take any legal means

1692
01:31:09,040 --> 01:31:13,240
Speaker 1: the harvest does not separate it out. So one sixty

1693
01:31:13,320 --> 01:31:16,840
Speaker 1: makes the book, but what makes the all time? But

1694
01:31:16,920 --> 01:31:20,600
Speaker 1: again that's because we'd have if we won sixties identified

1695
01:31:20,680 --> 01:31:23,280
Speaker 1: as a top species that needs to be recorded, it

1696
01:31:23,320 --> 01:31:25,880
Speaker 1: isn't met our criteria. But we can't put all the

1697
01:31:25,920 --> 01:31:29,160
Speaker 1: one sixty plus is from the last eighty years in

1698
01:31:29,240 --> 01:31:31,080
Speaker 1: one book, so that's really where it came from. So

1699
01:31:31,160 --> 01:31:33,559
Speaker 1: we said, you know what, we're gonna made the year book.

1700
01:31:33,720 --> 01:31:36,160
Speaker 1: It's the Awards Book, which is a three year period.

1701
01:31:36,520 --> 01:31:39,240
Speaker 1: Everything entered in that three years goes in the Awards Book.

1702
01:31:39,560 --> 01:31:41,519
Speaker 1: And then we just have an arbitrary number that the

1703
01:31:41,600 --> 01:31:44,479
Speaker 1: very top of the top goes in this all time

1704
01:31:44,479 --> 01:31:46,640
Speaker 1: book once every six years, and people don't want to

1705
01:31:46,680 --> 01:31:49,080
Speaker 1: hear it, but it's currently one seventy. At the eight

1706
01:31:49,080 --> 01:31:50,880
Speaker 1: we're going that number is gonna be one eighty and

1707
01:31:50,920 --> 01:31:54,439
Speaker 1: the foreseeable future because well the one we just the

1708
01:31:54,520 --> 01:31:57,000
Speaker 1: last all Time book we did is now to volume.

1709
01:31:57,720 --> 01:32:00,000
Speaker 1: I'm getting to the point now we're getting so many

1710
01:32:00,160 --> 01:32:02,160
Speaker 1: entries that what we thought was going to keep it

1711
01:32:02,200 --> 01:32:04,960
Speaker 1: at a one volume book has now expanded, which is great.

1712
01:32:05,000 --> 01:32:08,960
Speaker 1: I mean, that's great that there's that many mature animals

1713
01:32:09,000 --> 01:32:11,760
Speaker 1: out there that are species are doing well. I mean,

1714
01:32:11,760 --> 01:32:14,120
Speaker 1: that's a good sign. I'd rather have that than trying

1715
01:32:14,160 --> 01:32:17,120
Speaker 1: to fill pages. Now, do you guys remember my earlier suggestion,

1716
01:32:17,680 --> 01:32:19,639
Speaker 1: I want to give you another one now? Okay, yeah,

1717
01:32:19,640 --> 01:32:21,479
Speaker 1: that the change in its slightly as we go forward,

1718
01:32:22,280 --> 01:32:24,880
Speaker 1: keep doing the old way, but then right the other

1719
01:32:24,920 --> 01:32:30,640
Speaker 1: suggestion is this peel off, forget about the threshold for

1720
01:32:30,680 --> 01:32:35,160
Speaker 1: the all time. It's the top one hundred or it's

1721
01:32:35,200 --> 01:32:39,280
Speaker 1: the top Yeah, we've looked at that. That's been discussed.

1722
01:32:39,680 --> 01:32:42,320
Speaker 1: I like your your thoughts in the right place. Top

1723
01:32:42,360 --> 01:32:46,000
Speaker 1: one died by the Top one book really yeah, like

1724
01:32:46,080 --> 01:32:53,080
Speaker 1: Top one hundred bucks. Yeah, or you could do like

1725
01:32:53,080 --> 01:32:55,439
Speaker 1: like women's magazines and have it be the top ninety

1726
01:32:55,560 --> 01:32:58,599
Speaker 1: nine just because it catches your because then you're like, wow,

1727
01:32:58,600 --> 01:33:01,320
Speaker 1: it must be real if it's ninety and anybody, anybody

1728
01:33:01,320 --> 01:33:05,960
Speaker 1: can do a Top one bucks. You know him? Are

1729
01:33:05,960 --> 01:33:09,200
Speaker 1: you guys behind the Great Rams Books? Is your names

1730
01:33:09,200 --> 01:33:12,600
Speaker 1: in there? Yeah? So we the last two volumes of

1731
01:33:12,640 --> 01:33:15,479
Speaker 1: that we've actually published. B and C Publishes Books, and

1732
01:33:15,520 --> 01:33:18,040
Speaker 1: so Julie Trips our director of publications, and I think

1733
01:33:18,080 --> 01:33:20,880
Speaker 1: it was Great Rams three. We worked with Bob Anderson

1734
01:33:20,880 --> 01:33:22,719
Speaker 1: and so we actually did all the publication and whatnot

1735
01:33:22,720 --> 01:33:25,760
Speaker 1: of three and four. Is there like, is there an

1736
01:33:25,800 --> 01:33:30,560
Speaker 1: equivalent for mulder Um. We did a Mule Deer retrospective.

1737
01:33:31,200 --> 01:33:34,040
Speaker 1: We went back and we took all of our old entries,

1738
01:33:34,040 --> 01:33:36,479
Speaker 1: the stuff that kind of got dropped and maybe never

1739
01:33:36,520 --> 01:33:38,200
Speaker 1: made an Awards book that you wouldn't see. So we

1740
01:33:38,240 --> 01:33:41,240
Speaker 1: did a mule Deer retrospective, and we looked at doing

1741
01:33:41,840 --> 01:33:44,679
Speaker 1: more of a book like that. But man, that sheep

1742
01:33:44,680 --> 01:33:49,599
Speaker 1: worlds just so picturesque and the characters are so defined

1743
01:33:49,640 --> 01:33:51,360
Speaker 1: that it's it's a little bit easier to build a

1744
01:33:51,360 --> 01:33:53,679
Speaker 1: book around sheep hunters. I think the mule deer hunters.

1745
01:33:54,080 --> 01:33:57,120
Speaker 1: There's a there's a book, Idaho's Great as Mule Deer

1746
01:33:58,080 --> 01:34:00,519
Speaker 1: by Ryan Hatfield. I wish it was like greatest Meal

1747
01:34:00,600 --> 01:34:02,680
Speaker 1: here in the West or whatever, but Idaho's greatest Meal.

1748
01:34:02,880 --> 01:34:07,080
Speaker 1: But it's got the stories right. I think that the dude,

1749
01:34:07,800 --> 01:34:09,200
Speaker 1: one of the dudes that has one of the best

1750
01:34:09,360 --> 01:34:12,880
Speaker 1: bucks ever got in Idaho. He um, he was just

1751
01:34:12,960 --> 01:34:17,120
Speaker 1: wrote out on his up behind his hot Yeah, Grover

1752
01:34:17,240 --> 01:34:20,760
Speaker 1: Cleveland or something like that, wrote out to shoot a

1753
01:34:20,840 --> 01:34:24,400
Speaker 1: buck to eat. Yeah, sees a buck, thinks there something

1754
01:34:24,439 --> 01:34:27,720
Speaker 1: wrong with it, shoots it, thinks himself nothing more than

1755
01:34:27,760 --> 01:34:30,400
Speaker 1: it was quote a squirrely looking buck or a screwy

1756
01:34:30,400 --> 01:34:33,800
Speaker 1: looking buck or something. And in thirty years goes by

1757
01:34:33,800 --> 01:34:36,840
Speaker 1: and someone finds it in his garage and like lo

1758
01:34:36,960 --> 01:34:44,960
Speaker 1: and behold, it's the biggest come on, Idah Hatfield. Actually

1759
01:34:44,960 --> 01:34:47,160
Speaker 1: he was. He had my job, but I went to

1760
01:34:47,200 --> 01:34:49,800
Speaker 1: work for BNC. He was the BNC guy that was

1761
01:34:49,880 --> 01:34:52,120
Speaker 1: doing those books. Yeah, it's got the it's got the

1762
01:34:52,120 --> 01:34:54,040
Speaker 1: best jacket of any book. You go look at the

1763
01:34:54,120 --> 01:34:56,840
Speaker 1: Idaho's Greatest Meal dere if you look at the cover

1764
01:34:56,920 --> 01:35:00,680
Speaker 1: of the book is hilarious. Um, so what's next for

1765
01:35:00,680 --> 01:35:05,280
Speaker 1: Bruning Crockett Man? You guys been around for I can't

1766
01:35:05,280 --> 01:35:13,000
Speaker 1: do that kind of thirties there we go? Yeah, yeah,

1767
01:35:13,120 --> 01:35:15,320
Speaker 1: is there like a midlife crisis that happened? Like it

1768
01:35:15,360 --> 01:35:19,840
Speaker 1: happens like what happens now? Do you guys launch new initiatives?

1769
01:35:19,840 --> 01:35:22,080
Speaker 1: Like what should people? Um, if people want to sort

1770
01:35:22,080 --> 01:35:23,880
Speaker 1: of see what's going on with Boone and Crocket, what

1771
01:35:23,920 --> 01:35:26,920
Speaker 1: do they gotta do? Well, you know, obviously go to

1772
01:35:26,920 --> 01:35:29,519
Speaker 1: our website. Um, you know, we try to keep everything

1773
01:35:29,600 --> 01:35:32,519
Speaker 1: updated there. But um, you know, we're going to continue

1774
01:35:33,120 --> 01:35:36,200
Speaker 1: our policy work with a focus on again have a

1775
01:35:36,240 --> 01:35:40,519
Speaker 1: tad health, wildlife health, and public access and because those

1776
01:35:40,520 --> 01:35:44,040
Speaker 1: are things that just that are NonStop that you always

1777
01:35:44,080 --> 01:35:48,320
Speaker 1: have to address and um you know we uh so

1778
01:35:48,600 --> 01:35:51,280
Speaker 1: give me the three guy. I like that wildlife health,

1779
01:35:51,600 --> 01:35:55,759
Speaker 1: tad health, and public access. We are mainly are three

1780
01:35:56,200 --> 01:35:59,400
Speaker 1: UM focusers. You know, that's a good way to sum

1781
01:35:59,439 --> 01:36:02,559
Speaker 1: it up. Yeah, yeah, Ben's and and you know, and

1782
01:36:02,560 --> 01:36:06,400
Speaker 1: as administrations change, UM, you know, we have to change

1783
01:36:06,400 --> 01:36:10,240
Speaker 1: with those things. So we understand, you know, we can

1784
01:36:10,479 --> 01:36:14,439
Speaker 1: deal with what the politics deal are dealt forward, we

1785
01:36:14,479 --> 01:36:15,960
Speaker 1: have to deal with the hand that we're dealt I

1786
01:36:15,960 --> 01:36:20,960
Speaker 1: guess and UM, but you know, we we just keep

1787
01:36:21,479 --> 01:36:24,479
Speaker 1: plug it along and UM and that's really all we

1788
01:36:24,520 --> 01:36:26,920
Speaker 1: can do. And you know, we've had some successes, we've

1789
01:36:26,960 --> 01:36:29,479
Speaker 1: had some failures. We've had you know, one of the

1790
01:36:29,479 --> 01:36:34,120
Speaker 1: most exciting things for a personal perspective, because even before

1791
01:36:35,040 --> 01:36:37,680
Speaker 1: Boone and Crockett that that I and probably about three

1792
01:36:37,800 --> 01:36:40,360
Speaker 1: or four together select individuals have been working on for

1793
01:36:40,400 --> 01:36:43,600
Speaker 1: the past twenty five years. It's full Funding Bill WCF,

1794
01:36:43,680 --> 01:36:47,120
Speaker 1: which is could be with the Great Outdoors America a

1795
01:36:47,240 --> 01:36:50,000
Speaker 1: Great America Outdoors Act right around the corner for the

1796
01:36:50,000 --> 01:36:52,679
Speaker 1: first time in twenty years, I hope. So I feel

1797
01:36:52,720 --> 01:36:54,400
Speaker 1: like it's been around the corner for a long time

1798
01:36:54,479 --> 01:36:56,479
Speaker 1: now it has. Yes, now you know, we got we

1799
01:36:56,560 --> 01:36:59,800
Speaker 1: got full authorization a year ago. UM. Now if we

1800
01:37:00,000 --> 01:37:03,160
Speaker 1: at full permanent funding, UM, you know, that's that's a

1801
01:37:03,160 --> 01:37:06,200
Speaker 1: big deal, and that's been a long uphill fight. But

1802
01:37:06,840 --> 01:37:10,240
Speaker 1: you know, you don't get every you don't get everything done.

1803
01:37:10,720 --> 01:37:12,840
Speaker 1: You know. We we try to do things right, not fast,

1804
01:37:13,000 --> 01:37:14,920
Speaker 1: and so you know, you've got to kind of look

1805
01:37:14,960 --> 01:37:17,519
Speaker 1: at the landscape. Okay, what is that we have twenty

1806
01:37:18,240 --> 01:37:20,080
Speaker 1: things on our policy agenda that we would like to

1807
01:37:20,080 --> 01:37:22,960
Speaker 1: see done. What out of those things can we get

1808
01:37:22,960 --> 01:37:25,439
Speaker 1: done in one particular administration? It might only be six,

1809
01:37:26,240 --> 01:37:28,400
Speaker 1: So we focus on those six things for those four

1810
01:37:28,479 --> 01:37:31,920
Speaker 1: years or those eight years, and and that's what that's

1811
01:37:31,920 --> 01:37:34,800
Speaker 1: what we focus on. UM. You know, for example, right now,

1812
01:37:34,920 --> 01:37:38,559
Speaker 1: Endangered Species Act, Well, you know that's an old act.

1813
01:37:38,600 --> 01:37:40,800
Speaker 1: It was put in for very good reasons, and we

1814
01:37:40,840 --> 01:37:42,439
Speaker 1: need to keep that act, but it does need to

1815
01:37:42,479 --> 01:37:46,559
Speaker 1: be modernized. And so UM, I don't know that we

1816
01:37:46,600 --> 01:37:49,000
Speaker 1: have the appetite in Congress for that right now. So

1817
01:37:49,240 --> 01:37:51,799
Speaker 1: why we don't want to waste our time on that again,

1818
01:37:52,040 --> 01:37:53,639
Speaker 1: you know, we just want to focus on the things

1819
01:37:53,640 --> 01:37:55,519
Speaker 1: we do think we can move the needle on. And

1820
01:37:55,720 --> 01:37:57,479
Speaker 1: but it's always going to fall into one of those

1821
01:37:57,479 --> 01:38:00,679
Speaker 1: three categories. I have a to act health, wildlife, health,

1822
01:38:00,720 --> 01:38:06,280
Speaker 1: republic access. That's interesting, the need to um, the need

1823
01:38:06,320 --> 01:38:08,840
Speaker 1: to adapt that list in order to just be most

1824
01:38:08,840 --> 01:38:12,599
Speaker 1: effective with what with what's going on, to be able

1825
01:38:12,600 --> 01:38:14,439
Speaker 1: to say, well, you know what, this issue is important

1826
01:38:14,439 --> 01:38:16,280
Speaker 1: to us, but now is not the time, but would

1827
01:38:16,280 --> 01:38:19,040
Speaker 1: be a great time for this issue, right and then

1828
01:38:19,040 --> 01:38:21,479
Speaker 1: and then getting the people together to do that, the

1829
01:38:21,600 --> 01:38:26,160
Speaker 1: different conservation organizations out there to help with that, so

1830
01:38:26,240 --> 01:38:29,439
Speaker 1: the Rocky Mountain out Foundation, you know, the Pheasants Group,

1831
01:38:29,479 --> 01:38:32,760
Speaker 1: the Mule Deer Group, and and so being able to

1832
01:38:32,800 --> 01:38:36,120
Speaker 1: bring those people to the table when those issues come

1833
01:38:36,240 --> 01:38:38,960
Speaker 1: up and knowing when to strike. Yeah. Yeah, I think

1834
01:38:39,000 --> 01:38:41,240
Speaker 1: that's an important point that that Kyle brought up, because,

1835
01:38:41,240 --> 01:38:43,640
Speaker 1: you know, one of the things that that our organization

1836
01:38:43,720 --> 01:38:46,559
Speaker 1: is able to do because we don't have a dog

1837
01:38:46,600 --> 01:38:48,760
Speaker 1: in the fight so to speak in terms of you know,

1838
01:38:48,840 --> 01:38:51,799
Speaker 1: events and funding and because we're pretty much eternally funded

1839
01:38:52,320 --> 01:38:55,040
Speaker 1: you know, UM, you know, we can we can facilitate

1840
01:38:55,080 --> 01:38:58,400
Speaker 1: a lot of things. UM facilitate work with among all

1841
01:38:58,400 --> 01:39:02,280
Speaker 1: the different organizations out there. UM twenty years ago this

1842
01:39:02,760 --> 01:39:06,040
Speaker 1: in August, we UH, we had a meeting at our

1843
01:39:06,080 --> 01:39:09,920
Speaker 1: headquarters with UM with the nonprofits at the time that

1844
01:39:09,920 --> 01:39:13,360
Speaker 1: we're involved with sportsman's based conservation. There was seventeen of

1845
01:39:13,400 --> 01:39:17,320
Speaker 1: them represented, including some of the ones that Kyle mentioned,

1846
01:39:17,400 --> 01:39:21,400
Speaker 1: Rocky Mountinel Foundation, the Meal Deer Guys, National Wild Turkey Federation.

1847
01:39:21,439 --> 01:39:24,040
Speaker 1: And you know, the whole objective of that meeting was

1848
01:39:24,120 --> 01:39:27,960
Speaker 1: to you know, conservation wasn't moving at the time very

1849
01:39:28,120 --> 01:39:32,360
Speaker 1: very well. And um, you know, the club saw an

1850
01:39:32,360 --> 01:39:36,240
Speaker 1: opportunity to say, Okay, you know what, we're not succeeding here. Um,

1851
01:39:36,280 --> 01:39:38,479
Speaker 1: and we're all going different directions. We gotta we gotta

1852
01:39:38,600 --> 01:39:40,360
Speaker 1: check our egos at the door. We gotta check our

1853
01:39:40,439 --> 01:39:41,960
Speaker 1: gen as the door. We're gonna sit down and how

1854
01:39:42,000 --> 01:39:44,640
Speaker 1: we can work together to get the job done. And

1855
01:39:44,720 --> 01:39:47,080
Speaker 1: what boiled out of that original meeting in August the

1856
01:39:47,120 --> 01:39:51,960
Speaker 1: two thousand was was an organization of It started out

1857
01:39:51,960 --> 01:39:56,439
Speaker 1: to be seventeen. Now today it's forty five sportsman's based

1858
01:39:56,600 --> 01:39:59,960
Speaker 1: and shooting sports based organizations that represent roughly eight mill

1859
01:40:00,120 --> 01:40:03,040
Speaker 1: and votes. And when there's an issue that comes in

1860
01:40:03,080 --> 01:40:07,080
Speaker 1: front of Congress, we draft letters that all these organizations

1861
01:40:07,120 --> 01:40:10,599
Speaker 1: signed on to that go to our congressional delegations and

1862
01:40:10,640 --> 01:40:12,519
Speaker 1: say we're in favor of this or we're not in

1863
01:40:12,560 --> 01:40:15,960
Speaker 1: favor of that, and um, and they listen. I mean,

1864
01:40:16,000 --> 01:40:18,840
Speaker 1: eight billion votes is nothing to sneeze at. So you know,

1865
01:40:18,880 --> 01:40:21,759
Speaker 1: if you if you look at the sports sporting community overall,

1866
01:40:21,800 --> 01:40:24,400
Speaker 1: I think it's better organized than it ever was at

1867
01:40:24,560 --> 01:40:27,040
Speaker 1: at that policy level, which is another reason why we're

1868
01:40:27,040 --> 01:40:30,519
Speaker 1: actually making some headway in the past. You know a

1869
01:40:30,560 --> 01:40:33,080
Speaker 1: few years that that we hadn't you know that we

1870
01:40:33,080 --> 01:40:36,840
Speaker 1: hadn't been making but um, but you know, that's one

1871
01:40:36,880 --> 01:40:38,479
Speaker 1: of our strengths is to be able to bring those

1872
01:40:38,520 --> 01:40:41,200
Speaker 1: kind of groups together and and and and focus on

1873
01:40:41,240 --> 01:40:46,240
Speaker 1: a common on common ground. We've had uh with Fosberg,

1874
01:40:46,280 --> 01:40:50,160
Speaker 1: who leads the Theodore Roosevelt Conservation Partnership, on the show

1875
01:40:50,200 --> 01:40:56,640
Speaker 1: a couple of times, and um, we had him on recently, well,

1876
01:40:56,680 --> 01:40:58,959
Speaker 1: I guess it's months ago now. We had an interesting

1877
01:40:59,000 --> 01:41:03,439
Speaker 1: point where he's saying that the periods of real bipartisan

1878
01:41:03,520 --> 01:41:10,160
Speaker 1: dysfunction aren't necessarily bad for conservation because when you have

1879
01:41:10,280 --> 01:41:16,320
Speaker 1: such a tumultuous situation in the House and Senate, oftentimes, uh,

1880
01:41:16,400 --> 01:41:19,679
Speaker 1: you can get good pieces of conservation stuff through because

1881
01:41:19,680 --> 01:41:23,200
Speaker 1: they're just looking for a win, like these broad these

1882
01:41:23,240 --> 01:41:26,760
Speaker 1: things that have some pretty broad support when they have

1883
01:41:26,960 --> 01:41:30,000
Speaker 1: nothing going on and everything's acrimonious, it's like a thing

1884
01:41:30,040 --> 01:41:33,479
Speaker 1: that they can pass and so he doesn't feel totally

1885
01:41:33,520 --> 01:41:35,800
Speaker 1: pessimistic about and that's why he's looking at some of

1886
01:41:35,840 --> 01:41:38,320
Speaker 1: the achievements we've had over the last handful of years

1887
01:41:38,880 --> 01:41:42,879
Speaker 1: where they had, um, you know, some good conservation bills,

1888
01:41:43,320 --> 01:41:47,160
Speaker 1: because his view is that it just gives them at

1889
01:41:47,240 --> 01:41:49,840
Speaker 1: least something to kind of agree on and to move

1890
01:41:49,920 --> 01:41:52,719
Speaker 1: through some kind of you know, some common sense conservation work.

1891
01:41:53,479 --> 01:41:55,599
Speaker 1: I don't know if you share that opinion you look

1892
01:41:56,560 --> 01:41:59,280
Speaker 1: at I do. I do, yeah, And TRCP is a

1893
01:41:59,400 --> 01:42:01,759
Speaker 1: very active member of that a w c P American

1894
01:42:01,760 --> 01:42:05,880
Speaker 1: Wildlife Conservation Partner coalition. I just mentioned UM and they

1895
01:42:05,920 --> 01:42:08,680
Speaker 1: were at the first meeting back in two thousands and so.

1896
01:42:09,240 --> 01:42:13,600
Speaker 1: And Whitney's what's right. I mean, I think that the divisiveness,

1897
01:42:13,680 --> 01:42:16,960
Speaker 1: even though it's not pleasant to deal with, and I'm

1898
01:42:17,000 --> 01:42:20,280
Speaker 1: not exactly sure in the overall health of our country

1899
01:42:20,400 --> 01:42:26,240
Speaker 1: it is, it is a good thing, you know, UM,

1900
01:42:26,280 --> 01:42:29,120
Speaker 1: I will tell you for our world it has at times,

1901
01:42:29,120 --> 01:42:32,760
Speaker 1: not all the time, but UM at times worked our

1902
01:42:32,800 --> 01:42:35,400
Speaker 1: advantage now where you could say, like, here's something good

1903
01:42:35,439 --> 01:42:37,559
Speaker 1: you could do. Yeah, there's a lot of people that

1904
01:42:37,640 --> 01:42:40,840
Speaker 1: support this, and or it gets tacked onto some piece

1905
01:42:40,840 --> 01:42:44,360
Speaker 1: of legislation to get you know, somebody's bill pass. You know,

1906
01:42:44,400 --> 01:42:46,640
Speaker 1: I mean it, so yeah, I think you know it.

1907
01:42:48,439 --> 01:42:51,800
Speaker 1: We've tried as a as an outdoor conservation community to

1908
01:42:51,800 --> 01:42:54,519
Speaker 1: take advantage of those types of things to do a

1909
01:42:54,520 --> 01:42:57,280
Speaker 1: good thing, you know, to to turn what is not

1910
01:42:57,800 --> 01:43:00,360
Speaker 1: is not necessarily a great thing into something at least

1911
01:43:00,880 --> 01:43:02,080
Speaker 1: at the end of the day can come out as

1912
01:43:02,080 --> 01:43:08,080
Speaker 1: a good thing. This this conversation came out of his um.

1913
01:43:08,120 --> 01:43:10,439
Speaker 1: In fact, it was towards the it was an early

1914
01:43:10,600 --> 01:43:13,599
Speaker 1: the early part of the new year here, and we

1915
01:43:13,600 --> 01:43:17,240
Speaker 1: were we had put to him like like like thumbs

1916
01:43:17,280 --> 01:43:20,719
Speaker 1: up or thumbs down for conservation, and he had felt

1917
01:43:20,720 --> 01:43:24,840
Speaker 1: that it was a thumbs up year. Absolutely absolutely, we

1918
01:43:24,840 --> 01:43:28,960
Speaker 1: we made some great home runs last year. That's great

1919
01:43:29,000 --> 01:43:31,200
Speaker 1: to hear because you almost think their day. We were

1920
01:43:31,200 --> 01:43:36,360
Speaker 1: talking about, you know, the planes tribes. Uh, some planes

1921
01:43:36,439 --> 01:43:38,320
Speaker 1: tribes used to have the practice of that they would

1922
01:43:38,320 --> 01:43:42,760
Speaker 1: have a robe like a buffalo robe, and basically they

1923
01:43:42,760 --> 01:43:47,760
Speaker 1: would distill down a year into a symbol, you know,

1924
01:43:48,080 --> 01:43:50,000
Speaker 1: and the symbol could be any number of things, like

1925
01:43:50,120 --> 01:43:53,680
Speaker 1: could be like someone being sick, or like something that

1926
01:43:53,800 --> 01:43:55,760
Speaker 1: was like a phenomenal year for hunting or whatever. You'd

1927
01:43:55,760 --> 01:43:57,439
Speaker 1: like make a thing like to wrap it up, you know,

1928
01:43:57,560 --> 01:44:00,080
Speaker 1: And we were just saying, how is shaping up to

1929
01:44:00,160 --> 01:44:04,679
Speaker 1: be like the bullshit year? Yeah, you know it would

1930
01:44:04,720 --> 01:44:07,320
Speaker 1: have been great, would ended on February eight, you know.

1931
01:44:08,800 --> 01:44:13,960
Speaker 1: Unfortunately it didn't. Ye be going great for a minute there. Yeah, yeah,

1932
01:44:14,080 --> 01:44:16,280
Speaker 1: but no, I think twenty was a great year for

1933
01:44:16,280 --> 01:44:20,439
Speaker 1: for contrivation. Sorry. Yeah, nineteen was a great year. We

1934
01:44:20,479 --> 01:44:23,320
Speaker 1: had an ominous bill. We addressed the fire the wildfire

1935
01:44:23,360 --> 01:44:26,599
Speaker 1: funding fixed, you know, UM, trying to give our agency

1936
01:44:26,640 --> 01:44:29,720
Speaker 1: more money to manage our force instead of fight wildfires.

1937
01:44:29,720 --> 01:44:32,040
Speaker 1: And you know that was a great The Farm bill

1938
01:44:32,120 --> 01:44:37,479
Speaker 1: had some great had some great wildlife in habitat health

1939
01:44:37,800 --> 01:44:42,479
Speaker 1: UM language in it uh S forty seven UM Senate

1940
01:44:42,479 --> 01:44:45,360
Speaker 1: Bill forty seven, which had a lot of public lands

1941
01:44:46,200 --> 01:44:48,880
Speaker 1: great things for public lands uh in it. Yeah, we

1942
01:44:48,920 --> 01:44:52,080
Speaker 1: did we did some. We did some good work last year. Shot.

1943
01:44:52,120 --> 01:44:54,120
Speaker 1: I guess if we can get the Great American uh

1944
01:44:54,800 --> 01:44:57,719
Speaker 1: was the Sportsman's Act. What's it called the Great American

1945
01:44:57,720 --> 01:45:00,280
Speaker 1: Outdoors Act? Outdoors and uh and it's a used to

1946
01:45:00,360 --> 01:45:04,040
Speaker 1: be on the docket here within the next couple of weeks.

1947
01:45:04,080 --> 01:45:06,920
Speaker 1: Will know that's great because I'm assuming that once like

1948
01:45:07,000 --> 01:45:14,120
Speaker 1: October hits, nothing's gonna happen well yeah, campaigns, yeah, you know,

1949
01:45:14,320 --> 01:45:16,120
Speaker 1: and and in the past that's kind of what the

1950
01:45:16,120 --> 01:45:18,240
Speaker 1: way it's been. Of course, this is you know, you

1951
01:45:18,360 --> 01:45:20,599
Speaker 1: never know. But I think we it's in our best

1952
01:45:20,680 --> 01:45:23,080
Speaker 1: interest again as a community get as much done as

1953
01:45:23,120 --> 01:45:25,479
Speaker 1: we as quickly as we can at this point because

1954
01:45:25,520 --> 01:45:28,280
Speaker 1: most of the legislation that's on on deck right now

1955
01:45:28,320 --> 01:45:30,600
Speaker 1: has been stuff that's been worked on. So it's not

1956
01:45:30,680 --> 01:45:36,639
Speaker 1: like we're trying to create language. Just finished across first

1957
01:45:37,080 --> 01:45:39,559
Speaker 1: publishing the deal, so if we can get it across

1958
01:45:39,600 --> 01:45:43,360
Speaker 1: the finish line, it'll be great and and that. So

1959
01:45:43,880 --> 01:45:46,599
Speaker 1: to ask your question, I don't think shot. I think

1960
01:45:46,600 --> 01:45:51,280
Speaker 1: it's hanging in the balance. I feel shot well from

1961
01:45:51,520 --> 01:45:54,320
Speaker 1: we're only on the sixth month right, feel like it

1962
01:45:54,439 --> 01:45:58,080
Speaker 1: just needs to go away and start understand I'm the

1963
01:45:58,080 --> 01:46:01,160
Speaker 1: eternal optimist too, but um but now, you know, we

1964
01:46:01,240 --> 01:46:03,960
Speaker 1: still got some some good things going out there. I mean,

1965
01:46:04,000 --> 01:46:07,639
Speaker 1: I think again, the health of our country, the health

1966
01:46:07,680 --> 01:46:11,479
Speaker 1: of our economy. It you know, it's a driver that

1967
01:46:11,720 --> 01:46:16,080
Speaker 1: is gonna resonate with um, you know, with funding for

1968
01:46:16,160 --> 01:46:18,599
Speaker 1: what we want to do in you know, in in

1969
01:46:18,600 --> 01:46:23,000
Speaker 1: in the conservation arena and but by and large the

1970
01:46:23,960 --> 01:46:28,400
Speaker 1: public in general, I think they realized they may not

1971
01:46:28,520 --> 01:46:32,920
Speaker 1: know why, but that you know, our natural resources in

1972
01:46:33,200 --> 01:46:35,559
Speaker 1: our country are probably the most valuable asset that we

1973
01:46:35,600 --> 01:46:38,840
Speaker 1: have and without those, regardless of Wall Street or anything else,

1974
01:46:39,400 --> 01:46:42,360
Speaker 1: nothing's nothing else is going to take and you know,

1975
01:46:42,479 --> 01:46:46,040
Speaker 1: so we've got to maintain the health and viability of

1976
01:46:46,040 --> 01:46:49,040
Speaker 1: our natural resource space. I'm glad to hear that you

1977
01:46:49,040 --> 01:46:51,880
Speaker 1: think that the public recognizes that, because I don't know

1978
01:46:51,920 --> 01:46:56,439
Speaker 1: if I always feel that way. It's well, it goes

1979
01:46:56,479 --> 01:46:58,560
Speaker 1: back to thee the point about the you know, the

1980
01:46:58,920 --> 01:47:02,400
Speaker 1: quote environmental is um. You know, so you know, there's

1981
01:47:02,479 --> 01:47:05,160
Speaker 1: it does It doesn't make any difference exactly what what

1982
01:47:05,280 --> 01:47:07,920
Speaker 1: the hell they think, but they're at least aware of it,

1983
01:47:08,920 --> 01:47:12,760
Speaker 1: and um they may not again, they may not know why,

1984
01:47:13,160 --> 01:47:16,639
Speaker 1: they may not be exactly have all the right reasons,

1985
01:47:16,800 --> 01:47:20,240
Speaker 1: but at the same time, you know, folks are kind

1986
01:47:20,240 --> 01:47:23,160
Speaker 1: of aware of the fact that you know, are you know,

1987
01:47:23,200 --> 01:47:25,040
Speaker 1: it's kind of important that we have a healthy environment

1988
01:47:25,040 --> 01:47:28,960
Speaker 1: to live in, and it's a healthy in Now, there's

1989
01:47:28,960 --> 01:47:31,000
Speaker 1: gonna be people to think we're cutting down too many trees.

1990
01:47:31,000 --> 01:47:33,200
Speaker 1: There's gonna be people that think they're not cutting down enough.

1991
01:47:33,400 --> 01:47:36,400
Speaker 1: You know, science points to right now, we need more

1992
01:47:36,479 --> 01:47:40,000
Speaker 1: active forest management overall. That's not going to be agreeable

1993
01:47:40,040 --> 01:47:43,240
Speaker 1: with with everybody. But the point I'm making is people

1994
01:47:43,240 --> 01:47:46,160
Speaker 1: are noticing it. You know, people are saying, you know,

1995
01:47:46,200 --> 01:47:50,080
Speaker 1: we've got a forest health problem and um, and I

1996
01:47:50,080 --> 01:47:52,920
Speaker 1: think that's a positive thing. Yeah, even though some of

1997
01:47:52,960 --> 01:47:56,360
Speaker 1: the details might become a little contentious. Details are always contentious.

1998
01:47:56,520 --> 01:47:58,479
Speaker 1: The devil's in the details. Yeah. I like to think

1999
01:47:58,520 --> 01:48:06,080
Speaker 1: I have, um, you know, I have a borderline like

2000
01:48:06,240 --> 01:48:10,760
Speaker 1: unconditional love for my country, like a deep patriotism. And

2001
01:48:10,800 --> 01:48:13,360
Speaker 1: it's hard to picture, like, yeah, how would I feel

2002
01:48:13,400 --> 01:48:16,960
Speaker 1: differently if it was Um, would I feel differently if

2003
01:48:17,000 --> 01:48:20,559
Speaker 1: it was like an environmental waste land. They're not gonna

2004
01:48:20,560 --> 01:48:25,519
Speaker 1: help it, right, it's not gonna help. Well, I just

2005
01:48:25,640 --> 01:48:27,639
Speaker 1: I grew up over the hill here in Butte, Montana,

2006
01:48:27,760 --> 01:48:29,960
Speaker 1: which you know, back when I grew up as a kid,

2007
01:48:30,000 --> 01:48:34,480
Speaker 1: was not exactly the most environmentally pleasant place to look at. Um,

2008
01:48:34,520 --> 01:48:36,200
Speaker 1: but I will tell you, you know, they've made great

2009
01:48:36,200 --> 01:48:39,000
Speaker 1: strides and correcting that problem. And and no, you know,

2010
01:48:40,800 --> 01:48:44,439
Speaker 1: throwing fist throwing, but you know, but your point is

2011
01:48:44,479 --> 01:48:46,320
Speaker 1: well taken. I mean, if we don't have our natural

2012
01:48:46,360 --> 01:48:49,280
Speaker 1: resource base. We don't have you know, wild places and

2013
01:48:49,320 --> 01:48:52,479
Speaker 1: wildlife that have it, those wild places to enjoy as

2014
01:48:52,520 --> 01:48:56,280
Speaker 1: a country. Um, you know, I just I don't. I

2015
01:48:56,360 --> 01:49:00,920
Speaker 1: think the patriotism part becomes a lot more difficult, for sure. Yeah,

2016
01:49:00,960 --> 01:49:04,160
Speaker 1: there's a lot to be proud of, right, and it didn't.

2017
01:49:04,160 --> 01:49:05,920
Speaker 1: And the thing I try to more and more point

2018
01:49:05,920 --> 01:49:10,160
Speaker 1: out to people is, um, like growing up, you know,

2019
01:49:10,200 --> 01:49:12,080
Speaker 1: as a little kid or whatever, you you think all

2020
01:49:12,160 --> 01:49:16,960
Speaker 1: the great things and the wildlife and stuffer uh just

2021
01:49:17,080 --> 01:49:22,639
Speaker 1: there like by accident, untouched like untouched by the hands

2022
01:49:22,640 --> 01:49:25,760
Speaker 1: of man, right, and then later you're like, oh no,

2023
01:49:25,920 --> 01:49:30,200
Speaker 1: they got touched there there because people. They're there because

2024
01:49:30,200 --> 01:49:32,800
Speaker 1: people are like decided to have it be there. It

2025
01:49:32,920 --> 01:49:37,160
Speaker 1: was a conscious decision. It was a conscious decision that

2026
01:49:37,360 --> 01:49:40,400
Speaker 1: like cost money and cost time, and it was like,

2027
01:49:40,880 --> 01:49:46,400
Speaker 1: there's nothing accidental, no North American model. It's awesome. They

2028
01:49:46,479 --> 01:49:50,520
Speaker 1: realized that was that it's awesome. They realized that. Well,

2029
01:49:50,240 --> 01:49:53,040
Speaker 1: I realized, but it took me thirty years to realize it. Well,

2030
01:49:53,080 --> 01:49:57,040
Speaker 1: I mean back in the late Yeah, it was realized

2031
01:49:57,080 --> 01:49:59,680
Speaker 1: and that people had the foresight that they didn't just

2032
01:49:59,800 --> 01:50:05,360
Speaker 1: let walk off some species. But but again, I mean,

2033
01:50:05,400 --> 01:50:08,920
Speaker 1: that's just awesome that that a group of people or

2034
01:50:08,960 --> 01:50:11,519
Speaker 1: a collective of people took that on and said we

2035
01:50:11,800 --> 01:50:14,920
Speaker 1: can't let this happen. Yeah, they tried to do like

2036
01:50:15,240 --> 01:50:18,000
Speaker 1: you know, try to do the impossible, or or what

2037
01:50:18,080 --> 01:50:20,439
Speaker 1: other people were. That's the thing I would like to

2038
01:50:20,960 --> 01:50:22,839
Speaker 1: bring up. And it's similar to when Boone and Crockett

2039
01:50:22,880 --> 01:50:25,320
Speaker 1: started out is Um. When I was working on a

2040
01:50:25,320 --> 01:50:29,759
Speaker 1: book American Buffalo. Uh, I talked about that guy Horn today,

2041
01:50:30,160 --> 01:50:34,360
Speaker 1: m and um. They were so sure that animals gone.

2042
01:50:35,280 --> 01:50:37,400
Speaker 1: That's why the National Collection was started. They were so

2043
01:50:37,520 --> 01:50:40,320
Speaker 1: sure it was gone. They dispatched him out by rail

2044
01:50:41,000 --> 01:50:43,639
Speaker 1: on like an emergency order to go shoot a couple

2045
01:50:43,640 --> 01:50:47,040
Speaker 1: and save the skeletons because they wanted they wanted to

2046
01:50:47,040 --> 01:50:49,679
Speaker 1: be able to have like a specimen and they wanted

2047
01:50:49,760 --> 01:50:53,280
Speaker 1: hides and skeletons, and they're like, hurry, yeah, now is

2048
01:50:53,320 --> 01:50:57,800
Speaker 1: your chance. Well that was originally the measuring system too,

2049
01:50:57,920 --> 01:51:00,960
Speaker 1: was so we could kind of picture man. Yeah, he

2050
01:51:01,040 --> 01:51:02,560
Speaker 1: was supposed to go get like as many as he

2051
01:51:02,600 --> 01:51:05,120
Speaker 1: could get so that Sunday people will be like that's

2052
01:51:05,160 --> 01:51:08,240
Speaker 1: what it looked like when they truck one back to

2053
01:51:08,320 --> 01:51:12,240
Speaker 1: the Bronx Zoo and then they had a buffalo sitting

2054
01:51:12,240 --> 01:51:14,720
Speaker 1: there hanging out in New York and Bronx. Yeah. That's

2055
01:51:14,720 --> 01:51:16,960
Speaker 1: one of the great ironies of that story is that

2056
01:51:18,120 --> 01:51:22,000
Speaker 1: later when they tried to one of the projects of

2057
01:51:22,040 --> 01:51:24,439
Speaker 1: bringing them back to the Great Plains, that they had

2058
01:51:24,479 --> 01:51:27,320
Speaker 1: to use as a source herd the Bronx Zoo. Yeah,

2059
01:51:27,400 --> 01:51:30,360
Speaker 1: they're like bringing them. They're bringing bison from the east

2060
01:51:30,400 --> 01:51:36,479
Speaker 1: to the West, which, like that collector mentality um, proved

2061
01:51:36,520 --> 01:51:39,040
Speaker 1: to be pretty valuable. So where are you guys? How

2062
01:51:39,080 --> 01:51:41,040
Speaker 1: do people find you? Guys in the web? Hopefully you

2063
01:51:41,040 --> 01:51:45,680
Speaker 1: got Boone and Crockett domain. Yeah, yeah, yeah, we got

2064
01:51:45,720 --> 01:51:48,080
Speaker 1: on that one. So yeah, just type that in and

2065
01:51:48,160 --> 01:51:50,720
Speaker 1: you'll find us. That's great, man. And if you got

2066
01:51:50,760 --> 01:51:52,639
Speaker 1: you know, next time you're over at your grandpa's barn

2067
01:51:52,640 --> 01:51:55,400
Speaker 1: and you find some crazy deer heading there. Yeah, I

2068
01:51:55,439 --> 01:51:58,640
Speaker 1: call one of our four official measures out there, and

2069
01:51:58,720 --> 01:52:01,200
Speaker 1: you can find those on our webs right, and maybe

2070
01:52:01,200 --> 01:52:04,000
Speaker 1: you'll make the all time, maybe you'll make the top us.

2071
01:52:04,479 --> 01:52:07,680
Speaker 1: It doesn't matter. Justin says a lot of times that

2072
01:52:07,880 --> 01:52:11,040
Speaker 1: you know, we'd much rather see let's take white tail.

2073
01:52:11,080 --> 01:52:13,360
Speaker 1: We'd much rather see a hundred sixty inch white tail

2074
01:52:13,760 --> 01:52:16,679
Speaker 1: out of an area we've never seen it before. Then

2075
01:52:16,720 --> 01:52:20,280
Speaker 1: the next two white tail, because that's that's telling us

2076
01:52:20,320 --> 01:52:24,400
Speaker 1: more about the management that you know, whatever this area

2077
01:52:24,479 --> 01:52:30,439
Speaker 1: was doing is now producing mature animals. So it's more

2078
01:52:30,479 --> 01:52:33,920
Speaker 1: exciting for us to see that animal come in from

2079
01:52:33,960 --> 01:52:36,920
Speaker 1: an area that we haven't might not have previously seen

2080
01:52:37,560 --> 01:52:39,559
Speaker 1: something come in, or we were seeing him from there

2081
01:52:39,600 --> 01:52:41,280
Speaker 1: and then they kind of dropped off and now they're

2082
01:52:41,280 --> 01:52:44,360
Speaker 1: cooking back up. You know what book I really want

2083
01:52:44,360 --> 01:52:46,439
Speaker 1: the more I think about it, this would be a

2084
01:52:46,479 --> 01:52:49,519
Speaker 1: custom book you can make just for me. It'd be

2085
01:52:49,800 --> 01:52:51,439
Speaker 1: just called the Top one hundred, but it's the Top

2086
01:52:51,479 --> 01:52:55,960
Speaker 1: one hundred everything. So it's all of the things, all

2087
01:52:56,000 --> 01:52:59,600
Speaker 1: of the North American game animals. But it's like, and

2088
01:52:59,640 --> 01:53:01,240
Speaker 1: you might you know, I don't know if it winds

2089
01:53:01,280 --> 01:53:04,000
Speaker 1: up being you just have to figure out the ratio, right,

2090
01:53:04,280 --> 01:53:06,719
Speaker 1: so be like probably like you probably want twenty white tails,

2091
01:53:07,720 --> 01:53:11,559
Speaker 1: ten mule deer, one goat, So what about that would

2092
01:53:11,560 --> 01:53:14,280
Speaker 1: be a good But what about a picked up head? Sure?

2093
01:53:14,280 --> 01:53:15,960
Speaker 1: I don't care, I mean, you don't care if there's

2094
01:53:16,000 --> 01:53:21,759
Speaker 1: not a story, I don't want that. The Top hundred,

2095
01:53:21,760 --> 01:53:24,320
Speaker 1: B and C. And it'd be like you know, you

2096
01:53:24,400 --> 01:53:25,760
Speaker 1: just well, how do you want to break it out?

2097
01:53:25,960 --> 01:53:27,360
Speaker 1: Like you don't want to It can't be like fifty

2098
01:53:27,360 --> 01:53:30,080
Speaker 1: mountain goats. That's not gonna be that's not gonna be

2099
01:53:30,160 --> 01:53:33,160
Speaker 1: very exciting. Like oh, mountain goat act. I would like

2100
01:53:33,200 --> 01:53:35,640
Speaker 1: to have two lions in it. The mountain goats are

2101
01:53:35,640 --> 01:53:38,400
Speaker 1: the best stories we have. Nobody kills a good goat

2102
01:53:38,400 --> 01:53:41,599
Speaker 1: that doesn't almost die. Like three dollars sheet, you'd probably

2103
01:53:41,600 --> 01:53:46,760
Speaker 1: have five big horns. This is getting recorded right fills

2104
01:53:46,800 --> 01:53:51,880
Speaker 1: an outline the whole alright, Boone Crocket Club, Thank you

2105
01:53:52,000 --> 01:53:53,559
Speaker 1: very much. Guys, we should have done this a long

2106
01:53:53,560 --> 01:53:58,120
Speaker 1: time ago. Um, next time something weird happens, let us know,

2107
01:53:58,040 --> 01:54:01,559
Speaker 1: you know, I want to have it be that when

2108
01:54:01,600 --> 01:54:05,080
Speaker 1: you get uh that when you have a hard call

2109
01:54:05,160 --> 01:54:10,600
Speaker 1: to make, like like the bear thing or something that, um,

2110
01:54:10,680 --> 01:54:13,240
Speaker 1: we can come down and participate in making the call. Okay,

2111
01:54:13,840 --> 01:54:18,800
Speaker 1: probably like like here's what I do. And then that

2112
01:54:18,840 --> 01:54:21,800
Speaker 1: guy will run into every hunting publication being that damn

2113
01:54:21,840 --> 01:54:25,160
Speaker 1: Ronella metalized me. That's good. It gets us off of

2114
01:54:25,240 --> 01:54:28,240
Speaker 1: us and puts it on someone else. Fill the side

2115
01:54:28,280 --> 01:54:32,960
Speaker 1: will be like Phil, what do you think? Phil also

2116
01:54:33,000 --> 01:54:35,440
Speaker 1: point out that Boone and Crocket Club has a discount

2117
01:54:35,440 --> 01:54:39,800
Speaker 1: code where listeners of the of this pod here you

2118
01:54:39,800 --> 01:54:43,760
Speaker 1: can get ten bucks off a yearly subscription to Fair

2119
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Speaker 1: Chase magazine. You just got to use the code me eater.

2120
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Speaker 1: And so that's for folks listening right now are like,

2121
01:54:50,800 --> 01:54:53,440
Speaker 1: it's kind of interesting, Well go find out just how

2122
01:54:53,520 --> 01:54:57,160
Speaker 1: interesting and get your discount on Boone and Crockett Clubs

2123
01:54:57,560 --> 01:55:01,680
Speaker 1: publication Fair Chase. All right, thanks guys, yea, thanks for

2124
01:55:01,760 --> 01:55:12,320
Speaker 1: having us. It's immediate podcast talking bout things you might

2125
01:55:12,440 --> 01:55:27,000
Speaker 1: want to hear. It's needy podcasts people about things you

2126
01:55:27,080 --> 01:55:36,880
Speaker 1: might want to talk about, hunting and fishing and conservation,

2127
01:55:38,800 --> 01:55:51,640
Speaker 1: and occasionally by white tailed de Go underwear Steve Vernell.

2128
01:55:51,720 --> 01:56:05,360
Speaker 1: He's the whole man. That guy likes to talk, so

2129
01:56:05,480 --> 01:56:10,960
Speaker 1: purely bug mitton underwear less Stevens as a host man

2130
01:56:11,000 --> 01:56:21,120
Speaker 1: that fella loves to talk. But make no mistake about it,

2131
01:56:21,240 --> 01:56:35,360
Speaker 1: my friends, that skinny guy, and he also walks along right.

2132
01:56:35,440 --> 01:56:40,800
Speaker 1: Can a hand in theis? Tell you know, how can

2133
01:56:40,840 --> 01:56:55,840
Speaker 1: you not be a fan killing Yana Germany? Indeed we

2134
01:56:55,880 --> 01:57:07,680
Speaker 1: should all be fans. Okayl's got this conservation podcast in

2135
01:57:07,840 --> 01:57:19,200
Speaker 1: Yanni as the world's most alf of man. There's a

2136
01:57:19,280 --> 01:57:25,600
Speaker 1: lot to talk time in conservation and things we do

2137
01:57:25,840 --> 01:57:37,000
Speaker 1: and don't want to see. Lots to talk about conservation

2138
01:57:37,920 --> 01:57:49,480
Speaker 1: things we do when you don't want to see, all

2139
01:57:49,560 --> 01:57:55,600
Speaker 1: kinds of hunting and fishing techniques and bas by c

2140
01:57:56,000 --> 01:58:09,600
Speaker 1: W porn fingers and other weird injuries, hunting half and

2141
01:58:09,720 --> 01:58:20,240
Speaker 1: hunting fishing down on the farm, porn fingers and ripped

2142
01:58:20,360 --> 01:58:24,720
Speaker 1: nipples and other eye injuries part of hunting fishing, her

2143
01:58:24,800 --> 01:58:34,160
Speaker 1: life on the farm. But if you're lucky like I was,

2144
01:58:37,280 --> 01:58:39,360
Speaker 1: you know what that we just sold that thing right

2145
01:58:39,400 --> 01:58:49,760
Speaker 1: back on. Uh, we're in the hunt in public access

2146
01:58:52,320 --> 01:59:03,040
Speaker 1: two more subjects. You should all care about getting people

2147
01:59:03,040 --> 01:59:07,800
Speaker 1: out hunting public land and access m in the two things.

2148
01:59:07,880 --> 01:59:21,280
Speaker 1: And we should all care about fishing, catch your release.

2149
01:59:23,120 --> 01:59:32,280
Speaker 1: You think it matters to travel? I do so. Welcome

2150
01:59:32,360 --> 01:59:38,720
Speaker 1: to this podcast friends, it's good to have you do.

2151
01:59:45,680 --> 01:59:52,520
Speaker 1: Welcome to the media podcasted people, get some good have

2152
01:59:52,840 --> 02:00:02,160
Speaker 1: you here. Just remember some of these things might seem

2153
02:00:02,200 --> 02:00:21,600
Speaker 1: real simple, but others they're not so clear. Yeah, it's long,

2154
02:00:21,800 --> 02:00:25,600
Speaker 1: is that well? I can cleane that up. Look, you

2155
02:00:25,640 --> 02:00:26,280
Speaker 1: get the idea