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

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Speaker 1: bug bitten, and in my case, underwear. Listening past, you

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Speaker 1: can't predict anything brought to you by first Light. When

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Speaker 1: I'm hunting, I need gear that won't quit. First Light builds,

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Speaker 1: no compromise, gear that keeps me in the field longer,

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Speaker 1: no shortcuts, just gear that works. Check it out at

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Speaker 1: first light dot com. That's f I R S T

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Speaker 1: L I t E dot com. Kevin Monteeth is here today,

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Speaker 1: second podcast appearance. You're one of the top favorite UH

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Speaker 1: guests we've ever had. You're just saying that to be nice.

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Speaker 1: I'm telling people love that show, dude. That was a

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Speaker 1: big show. Over the years, we've had some big shows

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Speaker 1: that people really liked and just gen RD a lot

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Speaker 1: of stuff. That was a big show. That was episode one,

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Speaker 1: six two and it was called Landscape of Fear. Now

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Speaker 1: we're all the way up on episode seven five, so

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Speaker 1: lots changed. Randall's hair has grown like that and been

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Speaker 1: cut back. That was just dozens of times since then.

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Speaker 2: Just a few weeks before Karin and I started, I

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Speaker 2: think so.

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Speaker 1: Yeah, Phil was just getting out of high school. Kevin

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Speaker 1: is here, So Kevin is the Professor of Natural Resource

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Speaker 1: Sciences at the University of Wyoming's hob Hob School of

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Speaker 1: Environment and Natural Resources, Department of Zoology and Physiology. He

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Speaker 1: is the leader of the Monteeth Shop. He is here

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Speaker 1: to dispel almost everything you think in fact about deer,

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Speaker 1: about deer, about deer and elk, about antlers. He dispelled

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Speaker 1: it before and he'll dispel it again in a new way.

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Speaker 1: Puzzle other things. All I do now when I'm talking

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Speaker 1: every time, And I had to do this the other day.

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Speaker 1: I had to do this the other day. Who did

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Speaker 1: I do it to? I'm trying to think, Oh, Morgan Potter,

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Speaker 1: a professional hunter in Africa. He was laying on me

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Speaker 1: the whole genetics. Oh, this area doesn't have good deer.

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Speaker 1: This you know, not laying it on me. I don't

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Speaker 1: mean to, like, you know, he's a buddy of mine,

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Speaker 1: the whole, like Letteria doesn't have good genetics. And I

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Speaker 1: had to lay on him the whole. Not so fast.

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Speaker 1: There's a lot more to the picture.

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Speaker 3: To see him stiffen up there.

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Speaker 1: Like like to get into it. Oh not that I said, Yeah,

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Speaker 1: lot goes into it.

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Speaker 3: I think it might have also been a reaction to

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Speaker 3: some of.

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Speaker 1: The listen, this isn't a this isn't a weapon. This

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Speaker 1: is an antique. I'll explain it in a second, because

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Speaker 1: it's relevant to my life right now, relevant to your life. Yeah,

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Speaker 1: not really. For instance, just to just to give you

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Speaker 1: a pre titillation about what's going to happen, Kevin's just

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Speaker 1: showing me a picture of a buck, a mual to

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Speaker 1: your buck. Anyone on the planet. Anyone on the planet

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Speaker 1: would look at this buck and declare it to be

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Speaker 1: a classic year and a half old buck, a classic

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Speaker 1: first rack, like a spiky little buck with little offshoots.

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Speaker 1: You'd be like, that's a year and a half old buck.

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Speaker 1: Every guy would say that. I would tell my kids

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Speaker 1: if they'd be like, is it a big one? If

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Speaker 1: they got it, I'd be like, that's a year and

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Speaker 1: a half old buck. It ain't. It's two and a

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Speaker 1: half years old. At three and a half, you look

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Speaker 1: at it and be like, eah, probably not a year

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Speaker 1: and a half old, but still looks like that. A

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Speaker 1: buck that'll be a runt for the rest of his life.

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Speaker 1: Not because he's from an area with bad genetics. It's

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Speaker 1: because his mom was an piss poor shape when she

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Speaker 1: was pregnant with him. Correct, that's right. We're gonna get

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Speaker 1: into all that and a lot more. Lots of good research,

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Speaker 1: lots of good research. For first tell, we were just

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Speaker 1: talking about how you call your place your outfit, which

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Speaker 1: is kind of funny. You call it the monteeth shop,

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Speaker 1: and we're talking about it in like in the trades.

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Speaker 1: You know, you meet at the shop. I don't care

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Speaker 1: if you're a tree surgeon, whatever, Millwright, you go meet

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Speaker 1: at the shop. Then you go off to your job site.

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Speaker 1: And it's funny that you called your scientific lab your shop. Yeah,

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Speaker 1: because there's work going on there. There's there's work going

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Speaker 1: on there. Yeah, And we definitely catch him flak for

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Speaker 1: it because, yeah, because it doesn't sound official enough, doesn't

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Speaker 1: fit right.

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Speaker 4: Yeah, if you're going to be a I don't know,

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Speaker 4: a scientist or a group that does science, the typical

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Speaker 4: reference is a lab. And I think that's at least

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Speaker 4: in the in the academic world, that's that's the norm.

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Speaker 4: And I think despite working in an academic world, I

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Speaker 4: don't think I've ever I don't think I've ever fit

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Speaker 4: the mold. I don't think I necessarily have a desire

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Speaker 4: to fit the mold, and so in thinking about what

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Speaker 4: it meant for us over time and who we were

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Speaker 4: to be referenced as in that way. To me, what

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Speaker 4: made more sense if we're going to be data generators,

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Speaker 4: hands dirty in the field, and you know, being able

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Speaker 4: to do the work that puts us in as close

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Speaker 4: proximity the animals that we do. To me, what made

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Speaker 4: sense is a shop something that's more that associated more

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Speaker 4: with the trades, or like a mechanic shop for example.

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Speaker 4: I often maybe reference us as like diagnostic mechanics, working

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Speaker 4: to hands dirty in there, working to figure out what's

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Speaker 4: going on with any you know, particular animal population and

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Speaker 4: working to understand them better. So and I think, I

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Speaker 4: don't know, I guess like my upbringing and who I

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Speaker 4: am at heart, it fits a lot better than being

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Speaker 4: referenced as as a lab.

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Speaker 1: So despite the flack that we get for it, we

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Speaker 1: stuck with it. Oh yeah, I wouldn't pay attention to

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Speaker 1: that before. Before I explain this this year knife I'm home. Uh,

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Speaker 1: you got a mon Teeth shop shirt on that has

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Speaker 1: what I thought was a bigfoot guy pumping iron. You

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Speaker 1: did not. You did think that, and it's not that correct. Okay,

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Speaker 1: what is it, Because this is something I hadn't heard of.

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Speaker 1: I'm embarrassed to have not as much as I hear

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Speaker 1: you talk about Beaver as he's got a picture on

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Speaker 1: his shirt of a bigfoot pumping iron lifting a stick,

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Speaker 1: except except it's not. Yeah.

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Speaker 3: It actually reminds me of the the sand creatures in

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Speaker 3: Star Wars when they shake their Yeah, the Tuesking Raiders

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Speaker 3: when they shake their things at one another.

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Speaker 1: That's good. That's good. Thanks. Speaking of which we're trying

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Speaker 1: to I'm trying to license a song right now that

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Speaker 1: has a line. We did a couple of favors for

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Speaker 1: a guy who looked like a Tusk and Raider. M No,

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Speaker 1: we did a couple of favors for some guys who

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Speaker 1: look like Tusk and Raiders.

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Speaker 2: Is based on a true story.

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Speaker 1: I doubt it, Okay, go on.

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Speaker 4: So it's a beaver holding up a stick, and part

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Speaker 4: of that is the artwork was done by one of

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Speaker 4: our former team members, ran In, jacobek In. We just

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Speaker 4: started doing some work on Beaver's here over the past

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Speaker 4: couple of years and sort of yeah became we became

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Speaker 4: aware of this behavior, and it's a behavior often that's

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Speaker 4: very rarely seen in beavers, but there's a scientific paper

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Speaker 4: on it, and if you if you google stick display

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Speaker 4: in beavers, there is a YouTube video demonstrating them doing it.

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Speaker 4: But amidst like territory holders are at the edge kind

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Speaker 4: of the fringes of potential territories. That's sort of antagonistic behavior.

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Speaker 4: They'll they'll pick up a stick and just go up

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Speaker 4: and down and up and down with that, with displaying

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Speaker 4: that stick. And I don't know, I think it's kind

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Speaker 4: of funny too, given that, you know, most of our

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Speaker 4: work is on big ungulates, and we've done a good

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Speaker 4: bit of work on horns and antlers and those sorts

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Speaker 4: of things. Then what they're used as is weapons and

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Speaker 4: forms of intimidation and display. And beavers don't have antlers

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Speaker 4: or horns on their heads, but perhaps picking up a

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Speaker 4: stick it does some.

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Speaker 1: Sort of has some sort of intimidation element. He's getting

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Speaker 1: pumped exactly. No, that's exactly right.

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Speaker 4: Our stickers have that saying underneath it speaks offten and

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Speaker 4: carry a big stick.

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Speaker 1: Yeah, he's either you know, if you really dug into it,

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Speaker 1: is it uh like a fitness display? You know? Is

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Speaker 1: like is it like hey man, like this is a

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Speaker 1: hard thing to pull off, right, what we were at

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Speaker 1: this when I was just in in Africa, We're hanging

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Speaker 1: out these MASSI dudes and they like they have there's

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Speaker 1: this traditional dance in Messiah culture where it's trying like

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Speaker 1: young men and young women. And in this dance there's

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Speaker 1: like a couple. There's like a display, an athletic display

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Speaker 1: of men, which is just a flat footed jump m

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Speaker 1: again and again like a spring like from a flat

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Speaker 1: footed stance that you just jump again and again and

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Speaker 1: again and like get some impressive height, dude. Especially when

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Speaker 1: I when I was like, let me give it away,

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Speaker 1: I was like impressive height. And then the right it's

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Speaker 1: just a asking like what is it all about. It's

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Speaker 1: just like showing game. Yeah, a flat footed jump, you know.

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Speaker 1: And then for the women, it's kind of like a

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Speaker 1: you put this disc around your neck and it's like

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Speaker 1: a I rating move to make the disc shiver. All right,

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Speaker 1: it's just showing what you got. So that beaver could.

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Speaker 4: Be like exactly balance strength, yeah saying I'm gonnahoop you

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Speaker 4: with this.

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Speaker 3: Stick, or it's like this is the kind of stuff

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Speaker 3: we got around here.

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Speaker 1: You know, you know I had a beaver. I had

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Speaker 1: a beaver interaction their day of my my my boys. Uh,

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Speaker 1: his buddy's family. They had Beaver's plugging up their irrigation

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Speaker 1: crop irrigation system. And I'm went over to advise him.

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Speaker 1: I'm trying to get rid of them. And I made

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Speaker 1: some cast or mound sets just I took Beaver's case.

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Speaker 1: It seemed like just one beaver kind of like doing

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Speaker 1: his deal put castor out. He just left. I like,

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Speaker 1: I don't know, I can't prove this. I feel like

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Speaker 1: he had like a oh ship, someone's already here and

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Speaker 1: like left. I'm telling you he left. He's gone. We

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Speaker 1: keep checking he's gone. That's wild. He had these little

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Speaker 1: dams in this, you know, in an irrigation deal. Yeah,

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Speaker 1: he had these little dams. He had a couple of

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Speaker 1: little ship and cast or mounds the size of a

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Speaker 1: cell phone. You know what I mean. We put like

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Speaker 1: a big old cat, two big old caster mounts. Dude,

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Speaker 1: I'm not kidding that dude moved out, which could be

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Speaker 1: like something interesting to develop for like sort of like

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Speaker 1: non lethal non lethal deal is get after him early

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Speaker 1: and just like sorry boys, making sorry boys, there's already

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Speaker 1: a bad mobile in town. Man. You better go find

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Speaker 1: a new spot to hang out. So he and it

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Speaker 1: did him good because he's alive. He lived to tell

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Speaker 1: the tale. Yeah, oh, this knife. So how they do

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Speaker 1: villas in town? Ak Moosey. I screwed up in my

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Speaker 1: head and I didn't think that she would be here.

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Speaker 1: She could be hanging out right now, but I just

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Speaker 1: didn't think about it. I didn't think andy by the

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Speaker 1: time I invited her. We started, We're recording at nine.

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Speaker 1: Occurred to me at eight forty two. Invite her. She

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Speaker 1: wasn't able to make it shooting pistols. But I'm going

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Speaker 1: to have her fix this because she's a leather sower.

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Speaker 1: My old man brought this home from North Africa and whiskey.

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Speaker 1: Whiskey too. That's like gotta be elephant ivory. Yeah, take

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Speaker 1: a look at that. Oh yeah, that's awesome. It's gotta

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Speaker 1: be elephant. I don't know what the hell. I don't

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Speaker 1: know what kind of ivory it is.

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Speaker 5: It looks exactly like some of this stuff that I

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Speaker 5: have from my grandmother that was from China.

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Speaker 1: So from China, mm hmm, it's from North Africa. No,

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Speaker 1: I know.

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Speaker 5: I'm just saying, like a little Confucius statue carved in irony.

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Speaker 1: Oh, I thought, you mean, you're accusing my dad of

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Speaker 1: bringing home some kind of souvenir knockoff defense just like that.

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Speaker 1: Where's the stick? Yeah? He So he was real like

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Speaker 1: whatever it was, he was. I still remember what he

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Speaker 1: told me about it. He was real particular about it though.

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Speaker 1: And this she is a weird leather. Look at that leather.

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Speaker 1: I've had some people say maybe ostrich, but you can't

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Speaker 1: see a lot of times an ostrich leather. You can

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Speaker 1: see there, you can see the follicle for the feather.

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Speaker 1: I need to have it stitch back up. That was

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Speaker 1: unstitched when I was born. Oh so it's been that

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Speaker 1: way it was. So you know what I just did.

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Speaker 1: I took mineral oil. I put that. I took that

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Speaker 1: sheath and put mineral oil a little a couple of

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Speaker 1: tablespoons of mineral oil in a vac bag and stuck

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Speaker 1: that sheath in there and vac sealed it. Which I'm

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Speaker 1: gonna patent as a way to like bring leather back

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Speaker 1: to like picture what I'm talking about. It's good. Patent

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Speaker 1: it so no one else can do it. Let's say

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Speaker 1: check with me. Take like an old whatever old leather belt, whatever,

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Speaker 1: it's kind of played out, Put oil in a bag,

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Speaker 1: vac seal it in that bag. Then let it sit

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Speaker 1: on your work bench for a couple of days and

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Speaker 1: open it up, and that sucker is rejuvenated. Now. Let

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Speaker 1: guy might tell me that there's a reason that that's

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Speaker 1: dumb or not a good idea, but I don't know.

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Speaker 3: Or he might tell you that people are already doing that.

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Speaker 1: You could tell me that. You could tell me, yeah,

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Speaker 1: don't be surprised when he pulled something out and I

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Speaker 1: fell apart. I don't know, but I think I'm onto something.

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Speaker 1: But I need to have it stitch back up. But

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Speaker 1: isn't that something I want to you know, like those

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Speaker 1: antique road show things? Yeah? Yeah, yeah, like what is

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Speaker 1: up with this thing? Randall wanted to know if he

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Speaker 1: came home from the war and it had crowd blood

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Speaker 1: all over it. But I don't know that he uh,

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Speaker 1: I told him already cleaned it all off. Randall's got

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Speaker 1: back from Germany so much to Steve chagrin. Yeah, I

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Speaker 1: don't like that one bit. So you're glad that I'm back. No,

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Speaker 1: I'm glad you're back, But I felt I was nervous

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Speaker 1: the whole time you're over there that things might break

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Speaker 1: out again. Norm McDonald used to talk about that. Yeah,

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Speaker 1: everybody's always worried about like, uh, North Russia. He's like,

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Speaker 1: why are they not worried about the Germans? We've already

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Speaker 1: wars with them? Do you think it's over now?

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Speaker 6: Uh?

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Speaker 1: Yeah? Oh one other quick thing. I don't want to

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Speaker 1: take up too much of our time. But you know

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Speaker 1: how like I'm bad on sports. I'm bad on like

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Speaker 1: just generally very ignorant about athletics. I was getting my haircut.

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Speaker 1: I get my haircut VI I p barbershop, which is great.

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Speaker 1: You gotta call ahead, but it's great. And one thing

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Speaker 1: I like about it is they always playing sports with

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Speaker 1: the volume off, which I was better you know that way.

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Speaker 1: And because it like during the during the Summer Olympics,

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Speaker 1: it was cool because you just watched these people run

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Speaker 1: around the track but you can't hear what's going on.

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Speaker 1: I thought it was very soothing. I'm in there the

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Speaker 1: other day and like the big Old there's a huge

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Speaker 1: TV screen. It's the only thing in there. They they

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Speaker 1: don't really it's not really like mega decorated, but there's

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Speaker 1: a huge TV screen, and so normally you sit, like

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Speaker 1: when you're getting your haircut, you're looking at the screen,

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Speaker 1: but when you're waiting, your backs to the screen. But

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Speaker 1: the other wall is a giant mirror, and I'm just

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Speaker 1: kind of lost in my thoughts half watching a baseball game,

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Speaker 1: but it's in a mirror, okay, which I didn't think about.

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Speaker 1: And every time a guy hits.

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Speaker 3: The ball, he'd.

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Speaker 1: Run the other way, and I'm like, sitting there. I'm

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Speaker 1: honestly guy sitting there, Like when did they change that?

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Speaker 1: I swear, I swear I was almost gonna ask captain

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Speaker 1: the barber. I was almost gonna be like, why do

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Speaker 1: these dudes take off running like the other way that

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Speaker 1: what you normally would run. All of a sudden it

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Speaker 1: occur to me. I'm like, oh, it's reversed.

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Speaker 3: Yeah, well you're probably excited because he thought he's that boy.

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Speaker 3: The prevalency of left handers is really going up in professional.

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Speaker 1: Sports, I felt. I was so. I was like, so,

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Speaker 1: I was so ready to open my mouth and like, hey,

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Speaker 1: why are they? Yeah, could have been majorly embarrassed. Yeah,

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Speaker 1: a couple quick news bits here. Let me look here

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Speaker 1: a minute. Oh, movie's coming out I can't believe I

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Speaker 1: hadn't heard about this before. There's a movie coming out

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Speaker 1: where Bamby goes and seeks revenge. Have you heard about this?

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Speaker 1: It's called The Reckoning bamb be the Reckoning. Well, everybody

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Speaker 1: says Bamby. But isn't it like Bamby's kid. How does

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Speaker 1: it work? No, no, bam Bamby's dad gets killed. Yeah,

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Speaker 1: the mom.

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Speaker 3: Yeah that's who gets killed.

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Speaker 1: Right, No, this is his old man, Yeah there is. Yeah,

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Speaker 1: remember Spencer tried to work up how many inches of antler?

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Speaker 1: Oh yeah, yeah he Bamby and his dad had the

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Speaker 1: buck does get killed.

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Speaker 3: But I feel like Bambi is also an orphan, right,

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Speaker 3: so maybe there's Yeah, they call it in a while

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Speaker 3: to be honest, it's a CE tier creature feature.

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Speaker 1: I guess that means poor graphics.

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Speaker 6: It's it's made by the same studio that made a

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Speaker 6: bunch of Winnie the Pooh horror films as well. It's

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Speaker 6: like as soon as they enter a certain form of

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Speaker 6: public domain, they just go buck wild on these things.

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Speaker 1: Interesting.

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Speaker 5: Yeah, So the story is like it it drinks some

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Speaker 5: toxic something or other and then it becomes monsterized and

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Speaker 5: it has like a million.

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Speaker 1: Just like Ninja Turtles, the grieving deer decides to take

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Speaker 1: a sip of the nefarious chemical that local corporation Wilburg's.

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Speaker 1: It's always there's always a lot of these movies. There's

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Speaker 1: the animal movies. Is usually a bad corporation.

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Speaker 3: There's a strand of anti capitalism and all of these.

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Speaker 1: Oh yeah, my kids in this little My kids in

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Speaker 1: this little song and dance up right now for a week,

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Speaker 1: like just to keeping busy, my little one, just keeping

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Speaker 1: busy before we go to Alaska, you know. And he's

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Speaker 1: coming home singing. They're teaching them a song. It's like

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Speaker 1: a it's like a it's like a flat out like

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Speaker 1: anti capitalist song. He comes home every night and practice.

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Speaker 2: Is it the one from the Loraxe?

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Speaker 3: Is it?

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Speaker 2: Yeah?

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Speaker 6: My kids at that same song and dance club. Yeah,

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Speaker 6: oh what's his name? I've got two I've got two

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Speaker 6: kids in in there right now. I think Yanni's daughters

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Speaker 6: have done it as well.

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Speaker 1: Yeah, so you think your kid and my kid are

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Speaker 1: in the same song and dance.

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Speaker 2: It's very it's very well. He's he's not doing the

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Speaker 2: Lorax song.

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Speaker 1: Comrades.

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Speaker 6: Now there's different age groups and it's not like the

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Speaker 6: lore Ax was a book written decades ago the exact

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Speaker 6: anti capitalist sentiment, and.

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Speaker 1: So it has nothing to do with the Lorax book.

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Speaker 1: It's a guy with corporate attorneys. It's like, has nothing

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Speaker 1: to do with the Lorax book.

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Speaker 6: Well, yeah, but it's a it's about him cutting down

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Speaker 6: the trees to make the needs and and he's draining

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Speaker 6: the resources dry without a thought of the planet or.

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Speaker 1: No, that's not in the song. I invite you to

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Speaker 1: look at the songs.

359
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Speaker 2: Is it the one that says how how bad can

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Speaker 2: I be?

361
00:20:10,640 --> 00:20:14,159
Speaker 1: Yeah, it's like he's like talking about his corporate attorneys

362
00:20:14,160 --> 00:20:18,400
Speaker 1: are denying. Yeah, it's like this whole either way.

363
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Speaker 3: So next week it'll be the International that one land.

364
00:20:26,200 --> 00:20:27,120
Speaker 2: It's over my head.

365
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Speaker 1: I don't get it. What's the joke?

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Speaker 3: That's the old isn't it the old socialist anthem?

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Speaker 1: I don't know. Sorry, Kevin, you got you got stomach

368
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Speaker 1: for one more news bit? Yeah?

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Speaker 7: Yeah, coming Baby movie is coming out where Bambi becomes

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Speaker 7: a fanged, bloodthirsty monster who will stop at nothing to

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Speaker 7: attack his prey.

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Speaker 1: And uh, there is prey humans or we don't know.

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Speaker 3: Assuming there's a lot of blue collar men.

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Speaker 1: Yea, well, driving around in Like there's a whole genre

375
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Speaker 1: of these movies, like like animated animal movies. There's a

376
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Speaker 1: whole genre where the bad guy is a Southern, a

377
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Speaker 1: Southerner who hunts. You know. I've talked about this one

378
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Speaker 1: show a bunch of times. There's a show my kids

379
00:21:33,600 --> 00:21:35,280
Speaker 1: used to like, I I didn't like. I'm watching it

380
00:21:35,920 --> 00:21:39,159
Speaker 1: and there's like a couple of there's like rotating cast

381
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Speaker 1: of bad guys. One of the bad guys was a

382
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Speaker 1: wild Game chef. One of the bad guys was like

383
00:21:45,600 --> 00:21:51,840
Speaker 1: a very yeah. He was a very like effeminate urban nite. Yeah.

384
00:21:52,440 --> 00:22:00,720
Speaker 1: And one was like a Southern chef. H the villains, Okay,

385
00:22:02,080 --> 00:22:04,760
Speaker 1: we've talked about on the show This is news Ish.

386
00:22:04,880 --> 00:22:07,720
Speaker 1: We've talked about on the show and demonstrated in various

387
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Speaker 1: video projects. We've done the process called ek g MA.

388
00:22:11,160 --> 00:22:16,919
Speaker 1: You familiar it's a fish dispatch method. Oh no, uh.

389
00:22:18,160 --> 00:22:22,240
Speaker 1: My first introduction to EKGM was not by it's a

390
00:22:22,320 --> 00:22:25,639
Speaker 1: Japanese word. My first introduction to ekg MA was not

391
00:22:27,520 --> 00:22:30,200
Speaker 1: having anything to do with the Japanese and not having

392
00:22:30,200 --> 00:22:33,439
Speaker 1: anything to do with fish. But it was demonstrated to

393
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Speaker 1: me in South America with a a giant river turtle,

394
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Speaker 1: which is a seighti species. But these native dudes had

395
00:22:46,520 --> 00:22:48,800
Speaker 1: a net and they were using the net to catch fish,

396
00:22:48,920 --> 00:22:51,520
Speaker 1: and this big turtle gets wrapped up in their net,

397
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Speaker 1: a giant river turtle, and we're in a camp together.

398
00:22:56,359 --> 00:22:58,520
Speaker 1: They take the turtle. We weren't able to film this

399
00:22:58,680 --> 00:23:01,879
Speaker 1: because they explaining to us that, like, it's kind of

400
00:23:01,920 --> 00:23:03,560
Speaker 1: a no no, but the turtle got in the nets,

401
00:23:03,560 --> 00:23:05,399
Speaker 1: so there's what could they do about it? Even though

402
00:23:05,400 --> 00:23:08,600
Speaker 1: they could let either way, They go get it. They

403
00:23:08,640 --> 00:23:13,800
Speaker 1: go and cut a switch like a big slender stick

404
00:23:15,560 --> 00:23:18,520
Speaker 1: and peel the bark off it and basically make like

405
00:23:18,560 --> 00:23:20,920
Speaker 1: a what would look like a like a like a

406
00:23:21,200 --> 00:23:26,440
Speaker 1: hot dog roasting skewer, okay, or like a marshmallow stretch mellow,

407
00:23:28,160 --> 00:23:32,360
Speaker 1: and take the turtle and flop cut his head off. Now,

408
00:23:32,440 --> 00:23:34,959
Speaker 1: when I used to process turtles, we would cut their

409
00:23:35,000 --> 00:23:36,760
Speaker 1: head off. Then you hang them up by the tail

410
00:23:37,560 --> 00:23:41,879
Speaker 1: and it would be hours hours before you could pull

411
00:23:41,920 --> 00:23:46,760
Speaker 1: his leg and his leg didn't suck back into his

412
00:23:46,800 --> 00:23:50,159
Speaker 1: shell right, you'd like you'd hold out a pair of

413
00:23:50,200 --> 00:23:52,359
Speaker 1: channel locks. This is how my dad taught me to

414
00:23:52,359 --> 00:23:55,640
Speaker 1: do it. It's kind of gruesome. It's just how a Scofia

415
00:23:55,720 --> 00:23:58,880
Speaker 1: explains it in Legee cue lan Air. But you hold

416
00:23:58,880 --> 00:24:01,240
Speaker 1: out a pair of channel locks, so the turtle grabs

417
00:24:01,240 --> 00:24:06,560
Speaker 1: the channels, grabs onto it, and you pull out and

418
00:24:06,600 --> 00:24:08,760
Speaker 1: decapitate them, just like you're cutting head off a chicken.

419
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Speaker 1: This is just how it was demonstrated me as a child.

420
00:24:10,680 --> 00:24:14,000
Speaker 1: And then you hang the turtle up and eventually you

421
00:24:14,040 --> 00:24:16,080
Speaker 1: pull his leg and it doesn't pull back, and then

422
00:24:16,119 --> 00:24:20,320
Speaker 1: it's time to clean the turtle. These guys in South America,

423
00:24:21,160 --> 00:24:26,280
Speaker 1: they took a machete fuck and then took that long

424
00:24:26,440 --> 00:24:36,960
Speaker 1: skewer and inserted it into the spine and ran that

425
00:24:37,040 --> 00:24:42,359
Speaker 1: skewer all the way down that spine till it was

426
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Speaker 1: in its tail, and that turtle melted, I mean melted.

427
00:24:51,960 --> 00:24:56,000
Speaker 1: It was just like after they cut the head off.

428
00:24:56,119 --> 00:25:00,119
Speaker 1: Immediately immediately cut the head off, ran that skewer yu

429
00:25:00,560 --> 00:25:04,200
Speaker 1: and there wasn't because like a turtle is a I

430
00:25:04,240 --> 00:25:05,359
Speaker 1: don't know what's going on the nerve.

431
00:25:05,320 --> 00:25:07,000
Speaker 3: Like you know, like disrupting all the nerves.

432
00:25:07,040 --> 00:25:09,520
Speaker 1: Yeah, Like think of the expression like running around like

433
00:25:09,520 --> 00:25:11,520
Speaker 1: a chicken when its head cut off, which I demonstrate,

434
00:25:11,520 --> 00:25:13,040
Speaker 1: which I mentioned to my kids all the time when

435
00:25:13,040 --> 00:25:14,879
Speaker 1: we're hunting, Like they'll hit something and it'll kind of

436
00:25:14,880 --> 00:25:16,680
Speaker 1: do a little mad dash and they feel real bad

437
00:25:16,720 --> 00:25:18,240
Speaker 1: for it, and I'll be like, well, you know, think

438
00:25:18,280 --> 00:25:20,800
Speaker 1: about it, like someone cuts the chicken's head off, it

439
00:25:20,880 --> 00:25:23,960
Speaker 1: runs all over hell, right, it's like dead but not dead.

440
00:25:24,600 --> 00:25:30,640
Speaker 1: So it just like it melted the turtle. I never

441
00:25:30,680 --> 00:25:31,199
Speaker 1: seen anything like.

442
00:25:31,240 --> 00:25:31,280
Speaker 4: It.

443
00:25:33,280 --> 00:25:38,040
Speaker 1: Turns out this is a common fish dispatching method in Japan,

444
00:25:38,920 --> 00:25:41,160
Speaker 1: and I've gone with a friend of mine. She was koreaeing,

445
00:25:41,280 --> 00:25:44,679
Speaker 1: but still, uh we would. She would catch a fish

446
00:25:45,359 --> 00:25:48,960
Speaker 1: and immediately cut its tail, just cut through the cut

447
00:25:49,040 --> 00:25:53,679
Speaker 1: through the cut through the tail to sever the backbone

448
00:25:55,160 --> 00:25:57,480
Speaker 1: the spine at the tail point right, and then you

449
00:25:57,560 --> 00:26:00,360
Speaker 1: kind of cock the tail so it's still connected by skin,

450
00:26:00,440 --> 00:26:03,399
Speaker 1: but it's now cocked back, folded over. And then she

451
00:26:03,560 --> 00:26:07,200
Speaker 1: take this brass wire. She had copper wire maybe whatever

452
00:26:07,240 --> 00:26:10,119
Speaker 1: the hell it was, copper wire. She take this copper

453
00:26:10,160 --> 00:26:18,720
Speaker 1: wire and just in that in that spine, you follow

454
00:26:18,840 --> 00:26:22,000
Speaker 1: me the spinal core, they're basically going, and that fish

455
00:26:22,200 --> 00:26:26,560
Speaker 1: just again just is like done. You know, you think

456
00:26:26,600 --> 00:26:29,239
Speaker 1: of a fish flopping for a long time whatever, you know,

457
00:26:29,320 --> 00:26:32,399
Speaker 1: and like and it influences how that fish goes through rigor,

458
00:26:33,720 --> 00:26:35,560
Speaker 1: and it's just like the fish is just dead or

459
00:26:35,600 --> 00:26:36,920
Speaker 1: and dead.

460
00:26:37,520 --> 00:26:37,840
Speaker 3: Uh.

461
00:26:38,640 --> 00:26:45,840
Speaker 1: This guy here, there's this company, Shinkle Systems. They've built

462
00:26:45,840 --> 00:26:50,679
Speaker 1: a robot that uses the Japanese ekg MA method of

463
00:26:50,760 --> 00:26:55,240
Speaker 1: killing fish, which they regard as the most humane way

464
00:26:55,240 --> 00:26:57,680
Speaker 1: to harvest the animal while producing the best quality meat.

465
00:27:00,080 --> 00:27:03,399
Speaker 1: The process involves inserting I forgot this part. It's kind

466
00:27:03,440 --> 00:27:05,560
Speaker 1: of like a ancillary part of the things. You put

467
00:27:05,560 --> 00:27:08,040
Speaker 1: a spike in the fish's brain first. So the process

468
00:27:08,080 --> 00:27:10,440
Speaker 1: involves inserting a spike into the fish's brains. Who that

469
00:27:10,600 --> 00:27:13,800
Speaker 1: or is caught killing it instantly, reducing distress that can

470
00:27:13,840 --> 00:27:19,880
Speaker 1: cause the fish to spoil more quickly. Oh, oh, he's

471
00:27:19,920 --> 00:27:27,800
Speaker 1: not talking about what that's what he's talking about. Not

472
00:27:28,040 --> 00:27:37,879
Speaker 1: running the hold Cranky Randall anyone. Their machine isn't the

473
00:27:37,960 --> 00:27:41,320
Speaker 1: spine reaming machine. Their machine is just a spike to

474
00:27:41,359 --> 00:27:44,560
Speaker 1: the head machine. Do you need a machine that seems

475
00:27:44,760 --> 00:27:46,280
Speaker 1: that seems a lot easier.

476
00:27:45,920 --> 00:27:49,480
Speaker 5: To make it stuns the fish to help humans do

477
00:27:49,640 --> 00:27:50,320
Speaker 5: that method.

478
00:27:51,400 --> 00:27:53,639
Speaker 1: So it's a robot that stuns a fish. Yes, this

479
00:27:53,720 --> 00:27:55,399
Speaker 1: is what you get for talking about news articles you

480
00:27:55,440 --> 00:27:59,000
Speaker 1: haven't read, do you know what I mean? Like people

481
00:27:59,040 --> 00:28:01,159
Speaker 1: are always saying like, why an article this morning? And

482
00:28:01,160 --> 00:28:03,680
Speaker 1: I'm like, I always go like, eh, you know, I

483
00:28:03,720 --> 00:28:09,920
Speaker 1: don't really read it. I mean I saw it. Oh,

484
00:28:09,960 --> 00:28:12,560
Speaker 1: but the guy says a weird thing. So he's talking

485
00:28:12,600 --> 00:28:25,760
Speaker 1: about how he's what huh okay, well making the subsequent

486
00:28:25,800 --> 00:28:34,040
Speaker 1: cuts in seven seconds? Is he reaming the spines or not?

487
00:28:34,920 --> 00:28:37,960
Speaker 5: The human does it? The machine stuns them.

488
00:28:38,040 --> 00:28:47,280
Speaker 1: Shinkles okay. Shinkle's refrigerator size machine called Poseidon is his operational.

489
00:28:46,640 --> 00:28:48,160
Speaker 2: Steve reads news so you don't have to.

490
00:28:49,200 --> 00:28:53,280
Speaker 1: It's operational on three fishing vessels off the US West Coast.

491
00:28:54,160 --> 00:28:57,440
Speaker 1: The fish is inserted into the robot, which then uses

492
00:28:57,560 --> 00:29:02,400
Speaker 1: computer vision to identify the speed. And it's anatomical information

493
00:29:03,200 --> 00:29:08,800
Speaker 1: spiking the brain and making the subsequent cuts in seven seconds.

494
00:29:10,160 --> 00:29:12,640
Speaker 1: It might be that the bad this is a bad

495
00:29:12,680 --> 00:29:14,320
Speaker 1: writer who wrote what I'm reading.

496
00:29:14,960 --> 00:29:21,800
Speaker 3: Clearly subsequent cuts makes me think that it's doing more

497
00:29:21,840 --> 00:29:22,160
Speaker 3: than did.

498
00:29:22,200 --> 00:29:27,600
Speaker 1: It's reem and the spine. This guy raised twenty two

499
00:29:27,600 --> 00:29:33,680
Speaker 1: billion bucks for this thing. You know what really turns

500
00:29:33,720 --> 00:29:35,600
Speaker 1: me off though it is one of his co quotes.

501
00:29:37,120 --> 00:29:40,960
Speaker 1: He's got a quote that I think is indefensible. Where

502
00:29:41,000 --> 00:29:43,760
Speaker 1: is this quote? Oh, here's a quote from this guy

503
00:29:43,960 --> 00:29:47,680
Speaker 1: who builds himself is not builds himself former SpaceX engineer.

504
00:29:47,760 --> 00:29:52,080
Speaker 1: But I don't see SpaceX engineer and think fisheries primarily

505
00:29:52,120 --> 00:29:57,320
Speaker 1: because they're very interested in Mars and there ain't no

506
00:29:57,400 --> 00:29:59,520
Speaker 1: life on Mars. Like I don't want to go there.

507
00:30:00,520 --> 00:30:06,640
Speaker 1: You know. He says, we've been fishing for forty thousand

508
00:30:06,720 --> 00:30:09,680
Speaker 1: years and the tools haven't really changed.

509
00:30:11,800 --> 00:30:13,640
Speaker 3: A little bit of his oversimplification.

510
00:30:14,080 --> 00:30:17,920
Speaker 1: Yeah, are you all aware of a thing called sonar

511
00:30:18,040 --> 00:30:23,360
Speaker 1: guided bottom trawling? I mean, holy cow, he's saying that,

512
00:30:23,480 --> 00:30:26,080
Speaker 1: like nothing's changed in forty thousand years, But now we

513
00:30:26,120 --> 00:30:27,640
Speaker 1: have a robot that can poke a hole in the

514
00:30:27,680 --> 00:30:29,960
Speaker 1: fish's head, and this is like the right this is

515
00:30:30,000 --> 00:30:33,200
Speaker 1: the right direction. I'm like, nothing's changed in forty thousand

516
00:30:33,240 --> 00:30:33,959
Speaker 1: years of fishing.

517
00:30:34,120 --> 00:30:38,920
Speaker 3: Yeah, good lilastics, internal combustion engines.

518
00:30:40,760 --> 00:30:45,480
Speaker 1: Sonar guided bottom trolls which can literally which can literally

519
00:30:45,680 --> 00:30:57,320
Speaker 1: scrape underwater spires. Whatever. There's another big article about lab

520
00:30:57,400 --> 00:30:59,560
Speaker 1: grown meat and fish, which Krinn put in here and

521
00:30:59,560 --> 00:31:01,680
Speaker 1: then out and another back in here again.

522
00:31:02,120 --> 00:31:03,160
Speaker 2: We don't have to cover it.

523
00:31:03,240 --> 00:31:05,000
Speaker 1: No, we're not could cover it. Okay, I don't care

524
00:31:05,000 --> 00:31:05,720
Speaker 1: if everybody eats it.

525
00:31:05,720 --> 00:31:08,080
Speaker 5: I don't want it well, this was more on the

526
00:31:08,200 --> 00:31:11,800
Speaker 5: lab lab grown fish that it's you know, making its uh,

527
00:31:11,880 --> 00:31:15,760
Speaker 5: it's getting into some good restaurants or I don't know

528
00:31:15,760 --> 00:31:19,000
Speaker 5: if restaurants in Portland. And there are seven states that

529
00:31:19,080 --> 00:31:21,600
Speaker 5: made it illegal, and the news bit is that last

530
00:31:21,680 --> 00:31:24,840
Speaker 5: month Texas was the seventh or eighth state.

531
00:31:25,040 --> 00:31:32,560
Speaker 1: Our own, our own great state made it illegal. Florida, Alabama, Arizona, Tennessee, Texas, Indiana, Mississippi, Montana,

532
00:31:32,680 --> 00:31:39,440
Speaker 1: Nebraska have done various things to like prevent or slow

533
00:31:39,480 --> 00:31:44,800
Speaker 1: down or ban cultured cell cultured meat. Texas just did

534
00:31:44,840 --> 00:31:47,800
Speaker 1: it in June. Yeah, I thought they maybe that's a

535
00:31:47,800 --> 00:31:49,600
Speaker 1: little bit bit, that's a little bit big brother for

536
00:31:49,680 --> 00:31:51,920
Speaker 1: my likings. But like I can't get worked up about

537
00:31:51,920 --> 00:31:53,680
Speaker 1: it because that is I'm kind of grossed out by

538
00:31:53,880 --> 00:31:57,200
Speaker 1: by lab grown meat. But krinn By saying good restaurants,

539
00:31:57,400 --> 00:32:00,480
Speaker 1: think about what you're saying. Though, the articles point out

540
00:32:00,560 --> 00:32:07,520
Speaker 1: how a Haitian restaurant in Portland is serving lab grown salmon.

541
00:32:08,560 --> 00:32:11,040
Speaker 1: Now I haven't been to like Haitia recently, but is

542
00:32:11,040 --> 00:32:14,360
Speaker 1: there is there like a lot of big Haiti What

543
00:32:14,400 --> 00:32:16,840
Speaker 1: did I call it? Tells you how long I've.

544
00:32:18,920 --> 00:32:19,280
Speaker 7: There?

545
00:32:20,000 --> 00:32:23,560
Speaker 1: Like salmon is a part of Haitian cuisine.

546
00:32:23,800 --> 00:32:29,640
Speaker 5: A good the chef is, you know, Wan in a

547
00:32:29,760 --> 00:32:32,200
Speaker 5: James Beard Awards, So I think that that puts him

548
00:32:32,240 --> 00:32:33,560
Speaker 5: at some kind of level.

549
00:32:34,280 --> 00:32:36,480
Speaker 1: I've been nominated for James Beard Awards, and I don't

550
00:32:36,480 --> 00:32:40,120
Speaker 1: feel like I'm at any level. Okay, you ready to

551
00:32:40,120 --> 00:32:44,480
Speaker 1: dig in? Sure? All right? How do we start? Because

552
00:32:44,480 --> 00:32:46,800
Speaker 1: you guys, you guys do so much work. Man, Can

553
00:32:46,840 --> 00:32:49,360
Speaker 1: we start with a recap? We do whatever?

554
00:32:49,640 --> 00:32:51,400
Speaker 4: Let me start with the recap. What kind of recap

555
00:32:51,440 --> 00:32:53,960
Speaker 4: do you want? Can you recap for? Can you recap

556
00:32:54,040 --> 00:32:56,720
Speaker 4: for for us and our listeners?

557
00:32:59,560 --> 00:33:01,080
Speaker 1: Just kind of like I'm sure you give the spiel

558
00:33:01,080 --> 00:33:04,160
Speaker 1: all the time, the spiel about when we see an

559
00:33:04,200 --> 00:33:09,320
Speaker 1: area that we declare like bad genetics or Bucks don't

560
00:33:09,360 --> 00:33:12,640
Speaker 1: get big there. You know that area has got big

561
00:33:12,680 --> 00:33:18,040
Speaker 1: Bucks because it's got good genetics. What is that shorthand for?

562
00:33:18,240 --> 00:33:19,520
Speaker 1: You know what I mean? Like like like what are

563
00:33:19,560 --> 00:33:22,840
Speaker 1: some of the things that actually in your mind? And

564
00:33:22,880 --> 00:33:25,400
Speaker 1: it could include genetics, but like what are the sort

565
00:33:25,440 --> 00:33:30,800
Speaker 1: of hidden factors that are driving whether you're seeing big

566
00:33:30,840 --> 00:33:37,000
Speaker 1: giant Bucks and you're part of the state you know, Yeah, well,

567
00:33:39,040 --> 00:33:40,920
Speaker 1: I mean there's not too big of a question. No, No,

568
00:33:40,920 --> 00:33:41,280
Speaker 1: you're good.

569
00:33:41,280 --> 00:33:43,440
Speaker 4: I'm just trying to figure out how to lean into

570
00:33:43,520 --> 00:33:46,800
Speaker 4: the age genetics and nutrition effect anly size. Tell me again,

571
00:33:47,120 --> 00:33:53,280
Speaker 4: age genetics nutrition cut and much of our focus for

572
00:33:53,320 --> 00:33:56,720
Speaker 4: a long time, and certainly within you know, hunting communities

573
00:33:56,720 --> 00:34:01,240
Speaker 4: as well, we tend to off addly and frequently refer

574
00:34:01,320 --> 00:34:02,360
Speaker 4: to genetics.

575
00:34:02,440 --> 00:34:02,600
Speaker 1: Right.

576
00:34:02,800 --> 00:34:04,800
Speaker 4: It's hard to it's hard to open up a magazine

577
00:34:04,800 --> 00:34:09,000
Speaker 4: and see an article written, you know, about some big

578
00:34:09,040 --> 00:34:12,799
Speaker 4: buck or big bowl that somebody harvested, and inevitably it's like, man,

579
00:34:12,840 --> 00:34:16,000
Speaker 4: the genetics in this area are just fantastic. You know,

580
00:34:16,120 --> 00:34:19,239
Speaker 4: sure large antlers, and there's an aspect of that that

581
00:34:19,400 --> 00:34:24,360
Speaker 4: is that is true, like to to grow you know,

582
00:34:24,360 --> 00:34:28,440
Speaker 4: a substantial set of antlers horns, you need to have

583
00:34:28,520 --> 00:34:32,360
Speaker 4: genetics that support that. Right, But we also we also

584
00:34:32,520 --> 00:34:35,680
Speaker 4: get ourselves in this situation, as we frequently refer to that,

585
00:34:35,800 --> 00:34:38,160
Speaker 4: and I think I think where some of that's come

586
00:34:38,200 --> 00:34:42,360
Speaker 4: from is we we've called it our our hornographic culture,

587
00:34:42,640 --> 00:34:47,359
Speaker 4: where we're so fork focused on horns, antlers, their size, Like,

588
00:34:47,920 --> 00:34:50,000
Speaker 4: we're so drawn into that all we can think about

589
00:34:50,120 --> 00:34:52,600
Speaker 4: is males and the crap that's growing on their head.

590
00:34:53,120 --> 00:34:56,200
Speaker 4: And so we've we've referenced that as our our honographic culture.

591
00:34:56,239 --> 00:34:58,160
Speaker 4: And there's elements of that that are good.

592
00:34:58,200 --> 00:35:03,400
Speaker 3: That doesn't all the greatest shirt dude, pornography hornography.

593
00:35:04,239 --> 00:35:05,880
Speaker 1: We got to can you check someone right away for

594
00:35:06,000 --> 00:35:09,640
Speaker 1: someone steals this idea this is going on. It's yeah,

595
00:35:09,640 --> 00:35:13,000
Speaker 1: a buck mounting a dough and it says hornography a

596
00:35:13,120 --> 00:35:17,480
Speaker 1: big buck and proceeds going, and then yeah, and and

597
00:35:17,480 --> 00:35:23,080
Speaker 1: and yes, that's perfect protease, proceeds go directly to me.

598
00:35:27,480 --> 00:35:32,399
Speaker 1: Proceeds shot. Is that fair? Yeah? One hundred You don't

599
00:35:32,400 --> 00:35:34,399
Speaker 1: care if we steal that. I didn't clear that well, yeah,

600
00:35:34,400 --> 00:35:35,360
Speaker 1: but he said.

601
00:35:36,040 --> 00:35:37,160
Speaker 3: I just sort of threw it out there.

602
00:35:37,320 --> 00:35:39,360
Speaker 1: No, no, no, that's how we're gonna do it. I think. No,

603
00:35:39,440 --> 00:35:41,080
Speaker 1: that's how we're definitely gonna do it. We just stole

604
00:35:41,120 --> 00:35:43,600
Speaker 1: it flat out from the guy, and it recorded.

605
00:35:46,719 --> 00:35:50,160
Speaker 4: We've used that term in a scientific paper, in two

606
00:35:50,200 --> 00:35:52,440
Speaker 4: scientific papers. I mean we laid it out in a

607
00:35:52,640 --> 00:35:53,800
Speaker 4: in a scientific.

608
00:35:53,320 --> 00:35:55,600
Speaker 1: Paper, so so it exists. Can we do the thing,

609
00:35:55,600 --> 00:35:57,879
Speaker 1: and then if we do, proceeds go to you guys. Yeah,

610
00:35:57,880 --> 00:35:59,680
Speaker 1: one hundred percent horniography.

611
00:35:59,719 --> 00:36:02,160
Speaker 3: That's I know, I know when I see it.

612
00:36:04,080 --> 00:36:09,439
Speaker 4: Sorry, continue, I just got so damn excited, so I think,

613
00:36:09,480 --> 00:36:11,960
Speaker 4: but I think what that does is in and of course,

614
00:36:12,040 --> 00:36:14,960
Speaker 4: like when we when you take that scenario where we're

615
00:36:14,960 --> 00:36:17,680
Speaker 4: focused on the males, and then we think about like

616
00:36:19,120 --> 00:36:21,759
Speaker 4: what has happened, for example, in places like Texas. We

617
00:36:21,760 --> 00:36:24,120
Speaker 4: we often think like Texas and line breeding and the

618
00:36:24,200 --> 00:36:26,919
Speaker 4: various things that they do to get males to grow

619
00:36:26,960 --> 00:36:31,640
Speaker 4: these just ridiculous sized antlers at a very young age.

620
00:36:31,680 --> 00:36:33,640
Speaker 4: And there is a genetic element to that. But I

621
00:36:33,719 --> 00:36:37,600
Speaker 4: think that's part of what's just translated to us is well,

622
00:36:37,640 --> 00:36:39,799
Speaker 4: of course, if there's big antlers in this area and

623
00:36:39,800 --> 00:36:42,560
Speaker 4: not in this area, there's better genetics over here for antlers,

624
00:36:42,600 --> 00:36:46,120
Speaker 4: then there is over over in this other area. So

625
00:36:47,560 --> 00:36:53,799
Speaker 4: what what we did to better understand what what aspects

626
00:36:53,840 --> 00:36:58,799
Speaker 4: of genetics versus nutrition go into producing large antlers is

627
00:36:58,840 --> 00:37:03,279
Speaker 4: I think it's fortunately like one of the most I

628
00:37:03,280 --> 00:37:05,920
Speaker 4: think elegant experiments that I've ever been able to be

629
00:37:05,960 --> 00:37:07,960
Speaker 4: a part of, and that is some work we did

630
00:37:07,960 --> 00:37:11,360
Speaker 4: in South Dakota. And with that work, we had animals

631
00:37:11,400 --> 00:37:14,239
Speaker 4: in the Black Hills of southwestern South Dakota, which had

632
00:37:14,320 --> 00:37:17,839
Speaker 4: historically been known to grow some impressively large white tail deer.

633
00:37:17,840 --> 00:37:20,400
Speaker 4: A number of Bars and Custer and other places that

634
00:37:20,480 --> 00:37:25,000
Speaker 4: have just stupid big white tail deer, but more recently

635
00:37:25,000 --> 00:37:28,280
Speaker 4: in the past number of decades, they're all very small,

636
00:37:28,480 --> 00:37:32,360
Speaker 4: like we just don't grow big deer in that region anymore,

637
00:37:32,920 --> 00:37:36,319
Speaker 4: versus in eastern South Dakota, where we can grow impressively

638
00:37:36,360 --> 00:37:39,680
Speaker 4: big animals as little as three to four years of age.

639
00:37:39,719 --> 00:37:43,480
Speaker 4: And so the looming question with that, and it presented

640
00:37:43,480 --> 00:37:47,759
Speaker 4: the situation to evaluate what you reference directly, and that is,

641
00:37:49,120 --> 00:37:51,560
Speaker 4: are those animals in southwestern South Dakota and the Black

642
00:37:51,600 --> 00:37:55,000
Speaker 4: Hill is just genetically different than animals from eastern South Dakota.

643
00:37:55,480 --> 00:37:57,480
Speaker 4: So we did what we call a common garden experiment,

644
00:37:58,080 --> 00:38:00,600
Speaker 4: brought those animals into captivity as new born fawns. So

645
00:38:00,600 --> 00:38:02,719
Speaker 4: we captured animals from the Black Hills in eastern South

646
00:38:02,800 --> 00:38:06,160
Speaker 4: Dakota as newborn fawns, fed them, put them on a

647
00:38:06,239 --> 00:38:09,719
Speaker 4: high nutritional plane. So we we bottle raised them as fawns,

648
00:38:09,760 --> 00:38:11,840
Speaker 4: put them on a high nutritional plane, so we basically

649
00:38:11,880 --> 00:38:15,960
Speaker 4: maxed out nutrition. Nutrition wasn't limiting in any way, and

650
00:38:16,000 --> 00:38:17,600
Speaker 4: then raise those animals all the.

651
00:38:17,560 --> 00:38:18,640
Speaker 1: Way up to adulthood.

652
00:38:19,320 --> 00:38:23,360
Speaker 4: And ad adulthood animals from on average, so once they

653
00:38:23,480 --> 00:38:31,240
Speaker 4: hit peak body mass antler size animals from the Black Hills.

654
00:38:30,840 --> 00:38:35,200
Speaker 4: Males from the Black Hills were seventy pounds smaller than

655
00:38:35,200 --> 00:38:38,960
Speaker 4: males from eastern South Dakota and had forty inches of

656
00:38:39,320 --> 00:38:41,760
Speaker 4: less antler than animals from eastern South.

657
00:38:41,560 --> 00:38:42,600
Speaker 1: Because of bad genetics.

658
00:38:42,640 --> 00:38:47,959
Speaker 4: Because of bad genetics, right, and so rate surface level right, Well,

659
00:38:48,200 --> 00:38:50,560
Speaker 4: they've been on good nutrition since the day that they

660
00:38:50,600 --> 00:38:54,759
Speaker 4: were born, and so clearly that that supports a genetic explanation.

661
00:38:54,840 --> 00:38:55,799
Speaker 1: Then at that point.

662
00:38:56,920 --> 00:39:00,400
Speaker 4: Case clothes which then we then took that one step

663
00:39:00,480 --> 00:39:03,640
Speaker 4: further and looked at the next generation of animals born

664
00:39:03,680 --> 00:39:07,279
Speaker 4: in captivity. So we had Black Hills males and females,

665
00:39:07,320 --> 00:39:10,760
Speaker 4: allowed them to breed males and females from eastern South Dakota.

666
00:39:11,360 --> 00:39:13,640
Speaker 4: So no local bucks jumping over the fence, no local

667
00:39:13,680 --> 00:39:16,000
Speaker 4: bucks jumping over the fence. No, we allowed them to

668
00:39:16,080 --> 00:39:18,719
Speaker 4: breed within their groups from all the animals that we'd

669
00:39:18,840 --> 00:39:23,480
Speaker 4: raised since newborns, and then raised that second generation of

670
00:39:23,480 --> 00:39:26,279
Speaker 4: animals all the way up to adulthood. And so once

671
00:39:26,400 --> 00:39:29,359
Speaker 4: that second generation of animals were raised all the way

672
00:39:29,440 --> 00:39:32,920
Speaker 4: up to adulthood, if we just consider the Black Hills animals,

673
00:39:33,280 --> 00:39:36,880
Speaker 4: those sons born to those Black Hills males that I

674
00:39:37,000 --> 00:39:41,840
Speaker 4: was just talking about. Those sons were had thirty inch

675
00:39:42,160 --> 00:39:47,600
Speaker 4: more antlers than their fathers and were fifty pounds heavier

676
00:39:48,120 --> 00:39:52,280
Speaker 4: than their fathers, indicating they made up over seventy percent

677
00:39:52,320 --> 00:39:55,280
Speaker 4: of the difference that occurred between the first generation animals

678
00:39:55,320 --> 00:39:59,400
Speaker 4: that we brought into captivity. We didn't do anything genetically

679
00:40:00,120 --> 00:40:03,680
Speaker 4: genetics from those those two groups of animals. What was

680
00:40:03,840 --> 00:40:08,440
Speaker 4: different was the mothers from the animals that were now

681
00:40:08,440 --> 00:40:11,320
Speaker 4: born in captivity, that second generation of animals, those mothers

682
00:40:11,360 --> 00:40:13,759
Speaker 4: are on a high nutritional plane. Now, if we go

683
00:40:13,800 --> 00:40:17,160
Speaker 4: back to the wild scenario, when we originally got those animals,

684
00:40:17,320 --> 00:40:19,919
Speaker 4: the mothers in the Black Hills were on a poor

685
00:40:19,960 --> 00:40:23,440
Speaker 4: nutritional plane. Ponderosa Pine dominated for us crappy understory, just

686
00:40:23,480 --> 00:40:27,520
Speaker 4: on a poor nutritional plane. So that what that means

687
00:40:27,680 --> 00:40:31,200
Speaker 4: is called the negative maternal effect. What that means is

688
00:40:31,239 --> 00:40:36,080
Speaker 4: that those animals that we originally got as newborns.

689
00:40:35,640 --> 00:40:38,239
Speaker 1: From the Black Hills.

690
00:40:37,280 --> 00:40:41,120
Speaker 4: Carried the nutritional signature of mom who was living in

691
00:40:41,120 --> 00:40:44,200
Speaker 4: the Black Hills, and now our new animals that are

692
00:40:44,239 --> 00:40:49,200
Speaker 4: born in captivity, they're now carrying the nutritional signature of

693
00:40:49,280 --> 00:40:52,120
Speaker 4: the moms that they're born to in captivity and those

694
00:40:52,160 --> 00:40:55,200
Speaker 4: moms are on a high nutritional plane and there's there's

695
00:40:55,239 --> 00:40:58,040
Speaker 4: almost no I mean, when you think about that level

696
00:40:58,040 --> 00:41:01,440
Speaker 4: of difference, I mean, we're talking fifty pounds difference in

697
00:41:01,480 --> 00:41:07,719
Speaker 4: body mass, thirty inches of antler Like, it's it's it's

698
00:41:07,760 --> 00:41:10,480
Speaker 4: pretty wild to think that we could, through some genetic

699
00:41:10,520 --> 00:41:14,240
Speaker 4: manipulation in a natural setting, obtain that level of difference.

700
00:41:14,239 --> 00:41:16,880
Speaker 4: And here we're talking over a single generation of animals,

701
00:41:17,800 --> 00:41:21,600
Speaker 4: those massive increases in both body size and antlers that

702
00:41:21,640 --> 00:41:26,959
Speaker 4: are attributed exclusively to nutrition of mom while those young

703
00:41:27,040 --> 00:41:28,440
Speaker 4: were in utero.

704
00:41:29,840 --> 00:41:30,160
Speaker 1: And so.

705
00:41:31,760 --> 00:41:33,520
Speaker 4: And even like there's been a number of studies that

706
00:41:33,520 --> 00:41:36,080
Speaker 4: have been done in Texas from some colleagues of mind

707
00:41:36,080 --> 00:41:39,920
Speaker 4: down there where they've you know, gone to great links

708
00:41:40,000 --> 00:41:46,080
Speaker 4: to manipulate genetics by within enclosures, by taking you know,

709
00:41:46,160 --> 00:41:49,880
Speaker 4: helicopter catching males and then and then calling males that

710
00:41:49,920 --> 00:41:52,760
Speaker 4: weren't meeting the level of size that they were working

711
00:41:52,800 --> 00:41:57,840
Speaker 4: to obtain and are basically unable to obtain like a

712
00:41:57,960 --> 00:42:02,360
Speaker 4: positive change in antler growth time through that level of

713
00:42:02,800 --> 00:42:07,040
Speaker 4: very directive, very selective calling. So when you think about that,

714
00:42:07,080 --> 00:42:10,280
Speaker 4: when we translate that to like one area to the next.

715
00:42:10,719 --> 00:42:14,920
Speaker 4: Then what that what that can mean is that and

716
00:42:15,000 --> 00:42:18,960
Speaker 4: there's two sides of it. One, each set of these

717
00:42:19,000 --> 00:42:24,120
Speaker 4: animals is adapted to their local environment. Not every environment

718
00:42:24,320 --> 00:42:27,400
Speaker 4: has has or offers supreme nutrition. Right, So if we

719
00:42:27,480 --> 00:42:31,680
Speaker 4: consider placing the arid desert to you know, a migratory

720
00:42:31,719 --> 00:42:34,600
Speaker 4: meald deer populations that's running up into the high alpine

721
00:42:34,640 --> 00:42:38,560
Speaker 4: and you know, eating tall forb, tall forbes all summer long.

722
00:42:39,320 --> 00:42:41,840
Speaker 4: So all these animals and or you know, in the

723
00:42:41,840 --> 00:42:44,200
Speaker 4: arid system, maybe their winters aren't so bad so that

724
00:42:44,320 --> 00:42:46,319
Speaker 4: it doesn't take it much for them to persist through winter,

725
00:42:46,440 --> 00:42:48,760
Speaker 4: versus the animals that are in the more temperate systems

726
00:42:48,800 --> 00:42:51,839
Speaker 4: in the in the mountains, like it's harder for them

727
00:42:51,880 --> 00:42:55,279
Speaker 4: to persist through winter. And so even if you just

728
00:42:55,360 --> 00:42:59,160
Speaker 4: consider that at the surface level, not not all of

729
00:42:59,200 --> 00:43:01,759
Speaker 4: those animals can and just operate and do the same thing.

730
00:43:02,239 --> 00:43:04,520
Speaker 4: The way in which they operate, their way in which

731
00:43:04,560 --> 00:43:08,040
Speaker 4: they obtain and allocate resources has to be adapted to

732
00:43:08,080 --> 00:43:11,439
Speaker 4: that local environment. So there's a side of that that's

733
00:43:11,480 --> 00:43:15,239
Speaker 4: influencing antler size that we see from one place to

734
00:43:15,320 --> 00:43:18,160
Speaker 4: the next, But it's also a reflection of that local

735
00:43:18,160 --> 00:43:21,680
Speaker 4: adaptation to nutrition they have present there, and their ability

736
00:43:21,680 --> 00:43:27,200
Speaker 4: to allocate resources to antler development versus other things. So

737
00:43:27,239 --> 00:43:29,840
Speaker 4: certainly when you consider like males in one region to

738
00:43:29,880 --> 00:43:32,120
Speaker 4: the next, or males in a harvest from one area

739
00:43:32,160 --> 00:43:35,000
Speaker 4: to the next. Like if we're going to compare apples

740
00:43:35,000 --> 00:43:37,960
Speaker 4: to apples and say there's bigger males here, smaller males here,

741
00:43:38,080 --> 00:43:40,600
Speaker 4: we first need to of course consider what age classes

742
00:43:40,640 --> 00:43:43,000
Speaker 4: of males are there. Right, If they're mostly two and

743
00:43:43,080 --> 00:43:46,680
Speaker 4: three year olds versus over here, you know there's less

744
00:43:46,680 --> 00:43:49,600
Speaker 4: harvest and they're mostly being harvested at four to four

745
00:43:49,600 --> 00:43:52,240
Speaker 4: to seven, we're going to see differences in antler size.

746
00:43:52,239 --> 00:43:55,480
Speaker 4: But once we've accounted for that, nutrition in what we

747
00:43:55,520 --> 00:43:57,319
Speaker 4: see from one place to the next is going to

748
00:43:57,320 --> 00:44:00,000
Speaker 4: play a much bigger role than genetics from one place

749
00:44:00,160 --> 00:44:02,920
Speaker 4: to the next, And most of that nutrition and what

750
00:44:03,080 --> 00:44:04,840
Speaker 4: dictates what a male is going to be for the

751
00:44:04,840 --> 00:44:08,080
Speaker 4: rest of his life is mom and Mom's condition, the

752
00:44:08,080 --> 00:44:15,040
Speaker 4: condition you yep, So I think what's fascinating about that

753
00:44:15,120 --> 00:44:16,960
Speaker 4: is then what we see and even as we see

754
00:44:16,960 --> 00:44:18,640
Speaker 4: from one year to the next, well, we can see

755
00:44:18,640 --> 00:44:23,080
Speaker 4: fluctuations in antler size given environmental potential from one year

756
00:44:23,080 --> 00:44:26,239
Speaker 4: to the next food growth, spring condition, summer conditions, winter

757
00:44:26,360 --> 00:44:29,080
Speaker 4: conditions for that matter, from one year to the next.

758
00:44:29,280 --> 00:44:32,239
Speaker 4: The greatest marker of what a male carries with him

759
00:44:33,000 --> 00:44:36,800
Speaker 4: actually came before he was even dropped on the ground.

760
00:44:36,920 --> 00:44:41,799
Speaker 1: Yeah. That's a buddy mine in Wyoming, a guy that's

761
00:44:42,280 --> 00:44:47,080
Speaker 1: very very shrewd on wildlife and like a lifetime observer

762
00:44:47,160 --> 00:44:53,040
Speaker 1: of wildlife he was. I didn't get into it with him,

763
00:44:53,760 --> 00:44:58,200
Speaker 1: but he's talking about, uh, it's a wet spring, right,

764
00:44:58,640 --> 00:45:03,520
Speaker 1: anticipating good antler growth. He's not wrong though, right. I

765
00:45:03,520 --> 00:45:06,520
Speaker 1: mean that's that is that is helpful. But you could

766
00:45:06,560 --> 00:45:08,919
Speaker 1: also like a thing you wouldn't hear from a guy,

767
00:45:09,200 --> 00:45:12,360
Speaker 1: is a guy wouldn't come and say to you two

768
00:45:12,400 --> 00:45:16,960
Speaker 1: springs ago, correct? Would not? I saw a lot of fat,

769
00:45:17,080 --> 00:45:22,920
Speaker 1: does yep? So I'm hoping to draw a tag this

770
00:45:23,040 --> 00:45:27,200
Speaker 1: year in order to reap the benefits of that great

771
00:45:27,280 --> 00:45:30,560
Speaker 1: nutritional season that those dolls were enjoying some time ago.

772
00:45:31,520 --> 00:45:36,000
Speaker 1: It's just not like part of the lingo, correct. Yeah.

773
00:45:36,239 --> 00:45:38,960
Speaker 4: So, which is also important for us to be mindful

774
00:45:38,960 --> 00:45:42,680
Speaker 4: of as hunters and even as conservationists when what we

775
00:45:43,000 --> 00:45:46,440
Speaker 4: what we see today, for example, from yield of males

776
00:45:46,520 --> 00:45:49,719
Speaker 4: or even size of males within a population in many

777
00:45:49,800 --> 00:45:53,840
Speaker 4: instances isn't a reflection of what happened then or what

778
00:45:54,000 --> 00:45:56,040
Speaker 4: happened over the past couple of years. It's actually a

779
00:45:56,080 --> 00:45:59,480
Speaker 4: reflection of what happened maybe five years ago. And it's

780
00:45:59,480 --> 00:46:02,839
Speaker 4: in part, and it's in large part because of the

781
00:46:02,880 --> 00:46:07,560
Speaker 4: conditions that those males experienced from their mom while they

782
00:46:07,560 --> 00:46:10,200
Speaker 4: were while they were in utero, and those and it's

783
00:46:10,239 --> 00:46:12,720
Speaker 4: called it, it's called a cohort effect as well, where

784
00:46:13,080 --> 00:46:16,560
Speaker 4: we may have years where that were just poor years

785
00:46:16,640 --> 00:46:19,640
Speaker 4: or worse winters where moms were struggling in utero and

786
00:46:19,680 --> 00:46:21,879
Speaker 4: they didn't they lack the ability to give their young

787
00:46:21,880 --> 00:46:24,480
Speaker 4: the silver spoon right out of the gate. And so

788
00:46:24,600 --> 00:46:29,360
Speaker 4: that signal even if conditions improve later on in life,

789
00:46:29,400 --> 00:46:31,759
Speaker 4: just like our example with the common garden experiment, even

790
00:46:31,800 --> 00:46:34,840
Speaker 4: if condition for that male gets better after that spring

791
00:46:34,880 --> 00:46:37,120
Speaker 4: and summer, he's still going to carry the mark of

792
00:46:37,320 --> 00:46:41,440
Speaker 4: mom mom's nutritional signature with him for his entire life.

793
00:46:41,520 --> 00:46:45,759
Speaker 4: So when that cohort of males gets to six years old,

794
00:46:45,880 --> 00:46:47,320
Speaker 4: they're going to carry that signature.

795
00:46:47,440 --> 00:46:50,399
Speaker 1: And so especially when we see I wasn't thinking it's

796
00:46:50,440 --> 00:46:52,680
Speaker 1: even more delayed, it's even more delayed and so as

797
00:46:52,680 --> 00:46:56,520
Speaker 1: we consider even ups and downs and fluctuations within populations,

798
00:46:56,960 --> 00:46:59,480
Speaker 1: For example, we see reductions in density and we can

799
00:46:59,520 --> 00:47:01,680
Speaker 1: talk about to with like bad winters, and then we

800
00:47:01,719 --> 00:47:05,239
Speaker 1: see nutritional recovery within a population, meaning we have a

801
00:47:05,239 --> 00:47:07,400
Speaker 1: bunch of fat moms that are able to you know,

802
00:47:07,480 --> 00:47:09,120
Speaker 1: to plug into their offspring.

803
00:47:10,000 --> 00:47:12,880
Speaker 4: I think what's really interesting is oftentimes in periods of

804
00:47:12,960 --> 00:47:16,480
Speaker 4: population growth, will then see you know, in a few

805
00:47:16,520 --> 00:47:19,560
Speaker 4: year lag behind that is when we'll see a number

806
00:47:19,640 --> 00:47:21,719
Speaker 4: of very large males being harvested.

807
00:47:22,160 --> 00:47:24,680
Speaker 1: And it wasn't It wasn't that again was the timeline.

808
00:47:25,239 --> 00:47:28,120
Speaker 4: So if we if we see a dramatic reduction in

809
00:47:28,160 --> 00:47:31,399
Speaker 4: a population, for example, and so that reduction is then

810
00:47:31,440 --> 00:47:34,560
Speaker 4: tied to reduce competition for food, so nutrition, a big

811
00:47:34,640 --> 00:47:38,080
Speaker 4: winter kill yep, so nutrition improofs for everybody else that remains.

812
00:47:38,200 --> 00:47:40,440
Speaker 1: So does a big winter kill yep. Wipes out all

813
00:47:40,480 --> 00:47:43,800
Speaker 1: the deer? Yeah, the twenty percent that are still stand

814
00:47:43,840 --> 00:47:46,640
Speaker 1: and have gravy now yep, because they got all the

815
00:47:46,640 --> 00:47:49,040
Speaker 1: good betting areas, they got the good feeding areas, Yeah,

816
00:47:49,120 --> 00:47:49,840
Speaker 1: all to themselves.

817
00:47:49,840 --> 00:47:53,239
Speaker 4: That's competition for space and food. So nutritionally they're far

818
00:47:53,280 --> 00:47:56,520
Speaker 4: better off, which means they're they're meeting their needs, and

819
00:47:56,560 --> 00:48:00,640
Speaker 4: then those females are able to invest allocate more resources

820
00:48:00,680 --> 00:48:04,719
Speaker 4: to their offspring. So those offspring and we see this,

821
00:48:05,000 --> 00:48:08,239
Speaker 4: see this in our in our work born bager grow

822
00:48:08,320 --> 00:48:12,200
Speaker 4: quickly and then they're carrying that positive nutritional signature with them. Right,

823
00:48:12,600 --> 00:48:16,160
Speaker 4: We're not going to realize that positive nutritional signature from

824
00:48:16,200 --> 00:48:19,160
Speaker 4: a male harvest perspective until maybe four years down the road.

825
00:48:19,280 --> 00:48:21,640
Speaker 1: Right, So that's the thing in my little point I

826
00:48:21,680 --> 00:48:24,120
Speaker 1: was making that it wasn't even accounting for That's correct,

827
00:48:24,160 --> 00:48:26,680
Speaker 1: you'd have to be I'm excited about this year, yeah,

828
00:48:27,760 --> 00:48:31,279
Speaker 1: because four years ago, Yeah, yep, there was some big

829
00:48:31,920 --> 00:48:34,360
Speaker 1: some big fad do and then there's exactly right.

830
00:48:34,280 --> 00:48:40,560
Speaker 3: There's kind of like two like countervailing forces then with

831
00:48:40,600 --> 00:48:44,000
Speaker 3: a bagged winner, because I think like the traditional understanding

832
00:48:44,040 --> 00:48:45,600
Speaker 3: is like there's a bad winner and then you have

833
00:48:45,640 --> 00:48:48,719
Speaker 3: to wait for the bucks to backfill, Like so the

834
00:48:49,040 --> 00:48:51,440
Speaker 3: three year old deer, the old bucks die, the three

835
00:48:51,520 --> 00:48:53,920
Speaker 3: year old deer become four year old deer, become five

836
00:48:53,960 --> 00:48:55,799
Speaker 3: year old deer, and you see that sort of but

837
00:48:55,880 --> 00:49:00,680
Speaker 3: then in a bad winter that those bucks that are

838
00:49:00,719 --> 00:49:02,880
Speaker 3: born after the bad winner are going to have that

839
00:49:02,960 --> 00:49:05,839
Speaker 3: limited potential for the rest of their life, but then

840
00:49:05,920 --> 00:49:10,319
Speaker 3: the bucks born after that are going to have much

841
00:49:10,360 --> 00:49:12,960
Speaker 3: better potential than the ones that died in the winter.

842
00:49:13,080 --> 00:49:16,400
Speaker 3: So there's like a there's like a a stunned generation

843
00:49:17,080 --> 00:49:21,319
Speaker 3: right before that generation that's enjoying the more food at

844
00:49:21,320 --> 00:49:22,319
Speaker 3: the trot that kind of thing.

845
00:49:22,440 --> 00:49:25,319
Speaker 1: So we try to animate what Randall just said, I'll

846
00:49:25,320 --> 00:49:32,359
Speaker 1: get yeah, sure, well, like like I'm not I know,

847
00:49:32,600 --> 00:49:34,240
Speaker 1: because you're a very smart person.

848
00:49:34,320 --> 00:49:34,520
Speaker 3: Thing.

849
00:49:34,560 --> 00:49:36,640
Speaker 1: I know that you're right, I just don't understand what

850
00:49:36,680 --> 00:49:36,960
Speaker 1: you're saying.

851
00:49:37,000 --> 00:49:38,440
Speaker 3: We should have a chalkboard in here.

852
00:49:38,840 --> 00:49:41,840
Speaker 8: That'd be great, no, Like, you know, like the traditional

853
00:49:41,920 --> 00:49:46,320
Speaker 8: I think, like the traditional very simplistic like logic about

854
00:49:46,360 --> 00:49:50,600
Speaker 8: winter kill is that old bucks with worn down teeth

855
00:49:50,640 --> 00:49:54,239
Speaker 8: are going to die and then that unit, if you're

856
00:49:54,239 --> 00:49:56,880
Speaker 8: just talking about like a unit, you're saying, like, okay,

857
00:49:56,880 --> 00:49:58,799
Speaker 8: that unit will be back on its feet by the

858
00:49:58,840 --> 00:49:59,960
Speaker 8: time the younger.

859
00:49:59,680 --> 00:50:04,799
Speaker 3: Bucks get to that age. And so it's sort of

860
00:50:04,800 --> 00:50:09,319
Speaker 3: this like linear like backfilling. But once those bucks get

861
00:50:09,360 --> 00:50:12,080
Speaker 3: to that age, then you have the generation that we're

862
00:50:12,360 --> 00:50:16,360
Speaker 3: still in mom during that bad winter, So that generation

863
00:50:16,520 --> 00:50:20,239
Speaker 3: is going to drop, and then the ones from the

864
00:50:20,280 --> 00:50:23,759
Speaker 3: generations after that then they're going to have even more

865
00:50:23,760 --> 00:50:27,920
Speaker 3: food than anyone. So there's almost like a delayed on set.

866
00:50:28,600 --> 00:50:32,280
Speaker 3: There's like a drop in potential and then a skyrocketing

867
00:50:32,320 --> 00:50:34,560
Speaker 3: potential in terms of those cohorts.

868
00:50:34,560 --> 00:50:36,799
Speaker 1: Does that make you think he's sitting on a publication here.

869
00:50:37,800 --> 00:50:41,920
Speaker 3: I don't think that it's nearly sophisticated enough to make

870
00:50:42,080 --> 00:50:43,720
Speaker 3: make it a montieth shot paper.

871
00:50:44,200 --> 00:50:47,680
Speaker 4: Well, so let's let me I can essentially describe that

872
00:50:47,800 --> 00:50:51,160
Speaker 4: with what we with what we observe with.

873
00:50:52,840 --> 00:50:54,840
Speaker 1: I'm going to get out my markers the second time.

874
00:50:56,080 --> 00:50:58,120
Speaker 4: With what we observed in the Wyoming Range, and so

875
00:50:58,360 --> 00:51:00,080
Speaker 4: we'll say I want to say before I get into that,

876
00:51:00,239 --> 00:51:02,920
Speaker 4: like I mean, so we've been working in the Wyoming

877
00:51:03,040 --> 00:51:06,160
Speaker 4: Range now since twenty thirteen.

878
00:51:05,920 --> 00:51:08,200
Speaker 1: But can I can I a quick question I want

879
00:51:08,200 --> 00:51:10,239
Speaker 1: to hear all about the wild Range. There's one thing

880
00:51:10,239 --> 00:51:12,960
Speaker 1: I want to clarify. Yep, because you kind of got

881
00:51:13,000 --> 00:51:14,080
Speaker 1: at it, but I just want to make sure you

882
00:51:14,080 --> 00:51:18,560
Speaker 1: get at it. Yep. Your job is not producing. Your

883
00:51:18,640 --> 00:51:22,239
Speaker 1: job is your mandate is not to help hunters kill

884
00:51:22,280 --> 00:51:25,960
Speaker 1: a huge box. Nah. Okay, so let's clarify like that.

885
00:51:26,120 --> 00:51:29,200
Speaker 1: I mean, like, like what is your sort of what

886
00:51:29,480 --> 00:51:32,640
Speaker 1: gets you up in the morning. It's not like guaranteeing

887
00:51:32,880 --> 00:51:35,640
Speaker 1: hunters bigger bucks. Yeah no, okay, yeah, yeah, okay.

888
00:51:36,200 --> 00:51:38,440
Speaker 4: Also, let's sidestep and get some of those things and

889
00:51:38,440 --> 00:51:40,600
Speaker 4: then I'll swoop back to our deer work.

890
00:51:40,600 --> 00:51:41,839
Speaker 1: In so.

891
00:51:43,239 --> 00:51:47,759
Speaker 4: Our motto is advancing science and management, one day at

892
00:51:47,800 --> 00:51:50,919
Speaker 4: one data point at a time. And so that even

893
00:51:50,920 --> 00:51:52,960
Speaker 4: ties back to like the reference to our name and

894
00:51:53,000 --> 00:51:57,080
Speaker 4: that our aim is to want advance science, which means

895
00:51:57,080 --> 00:51:59,720
Speaker 4: I'm One way to think about that is our goal

896
00:51:59,760 --> 00:52:02,839
Speaker 4: is to better understand what makes these animals tick, how

897
00:52:02,880 --> 00:52:05,000
Speaker 4: they make a living within the world that they that

898
00:52:05,080 --> 00:52:08,360
Speaker 4: they do, and then take that one from just like

899
00:52:08,400 --> 00:52:11,160
Speaker 4: an ecological standpoint, better understand them, and then too, what

900
00:52:11,200 --> 00:52:13,960
Speaker 4: does it mean for us from a management or conservation perspective.

901
00:52:14,280 --> 00:52:16,520
Speaker 4: The reference to one data point as a time is

902
00:52:17,120 --> 00:52:20,040
Speaker 4: we often joke like the amount of effort will put

903
00:52:20,080 --> 00:52:24,600
Speaker 4: into one single data point because our work, our work

904
00:52:24,680 --> 00:52:27,680
Speaker 4: is typically if we can pull it off, is long

905
00:52:27,760 --> 00:52:30,080
Speaker 4: term and its individual base, and so that is we're

906
00:52:30,080 --> 00:52:34,200
Speaker 4: working to monitor individual animals for as long as we can.

907
00:52:34,440 --> 00:52:36,880
Speaker 4: So a lot of studies like toss a collar on

908
00:52:36,920 --> 00:52:39,160
Speaker 4: an animal, you know, monitor for a year or two

909
00:52:39,160 --> 00:52:42,359
Speaker 4: and that's it. Well, yes, we're using callers, but we're

910
00:52:42,360 --> 00:52:44,600
Speaker 4: monitoring them for as long as we can, and we're

911
00:52:44,640 --> 00:52:47,120
Speaker 4: collecting a whole suite of other information.

912
00:52:46,719 --> 00:52:50,719
Speaker 1: On them, generations of deal we are. Yeah, that's exactly right.

913
00:52:50,760 --> 00:52:56,400
Speaker 4: And so to do that, and so for example, like

914
00:52:56,719 --> 00:53:00,160
Speaker 4: one animal in particular, it may take a lot lot

915
00:53:00,160 --> 00:53:02,640
Speaker 4: of money and a lot of effort on our side

916
00:53:02,640 --> 00:53:06,280
Speaker 4: of things from a field perspective to monitor that one animal,

917
00:53:06,320 --> 00:53:08,319
Speaker 4: to collect the data point from that one animal. But

918
00:53:08,400 --> 00:53:11,839
Speaker 4: sometimes that one animal is such a critical data point

919
00:53:11,840 --> 00:53:14,840
Speaker 4: to better understanding how what makes those animals tick. And

920
00:53:14,880 --> 00:53:17,440
Speaker 4: then if you take a whole bunch of those individual

921
00:53:17,520 --> 00:53:20,000
Speaker 4: data points from animals and you pull them together, it

922
00:53:20,040 --> 00:53:22,520
Speaker 4: allows you know, patterns to emerge for us as to

923
00:53:22,560 --> 00:53:26,040
Speaker 4: what it means for those animals, notably, I mean, I

924
00:53:26,560 --> 00:53:28,279
Speaker 4: get to I'm very fortunate to get to be the

925
00:53:28,320 --> 00:53:30,960
Speaker 4: spokesperson to talk about our work a fair bit, but

926
00:53:31,440 --> 00:53:33,839
Speaker 4: there's so much that happens behind the scenes that I mean,

927
00:53:33,840 --> 00:53:36,719
Speaker 4: I shouldn't get any of the credit our funding sources.

928
00:53:37,440 --> 00:53:39,040
Speaker 1: We can't do what we do for free.

929
00:53:39,880 --> 00:53:42,719
Speaker 4: And so like I'm we're also constantly raising money to

930
00:53:42,800 --> 00:53:45,480
Speaker 4: make to make that possible. But Wyomen Game of Fish

931
00:53:45,520 --> 00:53:48,600
Speaker 4: Commission in particular has had a lot of foresight to

932
00:53:48,640 --> 00:53:51,400
Speaker 4: support us in some of these long term studies. Groups

933
00:53:51,400 --> 00:53:55,200
Speaker 4: like Merely Fanatic Foundation and Wildlife Natural Resource Trust and

934
00:53:55,280 --> 00:53:57,719
Speaker 4: many others that have been the backbone that make it

935
00:53:57,760 --> 00:53:59,440
Speaker 4: possible to do what we do. And then all of

936
00:53:59,480 --> 00:54:02,040
Speaker 4: the fielder and now especially with the wym Game Fish Department,

937
00:54:02,520 --> 00:54:05,480
Speaker 4: our central to our ability to be successful and support

938
00:54:05,560 --> 00:54:09,000
Speaker 4: us in the field. And then I work with an

939
00:54:09,000 --> 00:54:11,840
Speaker 4: amazing team of people in the shop, grad students, research

940
00:54:11,880 --> 00:54:16,920
Speaker 4: scientists that pour their heart and their soul into the

941
00:54:16,960 --> 00:54:20,160
Speaker 4: work we do the data collection that make all this possible.

942
00:54:20,200 --> 00:54:22,239
Speaker 4: So I want to be able to at least give

943
00:54:22,239 --> 00:54:26,080
Speaker 4: credit where credit is due. And then if any if

944
00:54:26,160 --> 00:54:28,080
Speaker 4: I say anything stupid through the course of this, it's

945
00:54:28,120 --> 00:54:28,759
Speaker 4: totally on me.

946
00:54:28,880 --> 00:54:30,359
Speaker 1: All the good things that are said, they should all

947
00:54:30,360 --> 00:54:30,960
Speaker 1: get the credit for.

948
00:54:31,200 --> 00:54:33,480
Speaker 3: And to put a finer point on this, like when

949
00:54:33,480 --> 00:54:36,719
Speaker 3: you say one data point at a time, what you're

950
00:54:36,760 --> 00:54:41,800
Speaker 3: doing is catching animals by the dozen, and every single

951
00:54:41,800 --> 00:54:45,000
Speaker 3: one of them. You're taking temperature, you're measuring fat, you're

952
00:54:45,040 --> 00:54:51,040
Speaker 3: swabbing the nasal passages, you're ultrasounding for pregnancy, you're measuring

953
00:54:51,080 --> 00:54:54,200
Speaker 3: the size of the fetus. You're I know I'm missing

954
00:54:54,200 --> 00:54:59,759
Speaker 3: stuff there, but yeah, like I've gone on collaring or

955
00:54:59,760 --> 00:55:01,560
Speaker 3: I guess to check in and collaring thing, and it's

956
00:55:01,640 --> 00:55:04,919
Speaker 3: I mean, it's hands on and it's very specific. It's

957
00:55:04,920 --> 00:55:07,640
Speaker 3: like how much does this animal weigh on this day

958
00:55:07,680 --> 00:55:11,920
Speaker 3: of the year. So yeah, like I think just to

959
00:55:11,960 --> 00:55:14,120
Speaker 3: bring it to a more concrete level, like you're you're

960
00:55:14,239 --> 00:55:19,399
Speaker 3: measuring deer and handling deer the same deer in some

961
00:55:19,480 --> 00:55:20,360
Speaker 3: cases for years.

962
00:55:20,520 --> 00:55:22,920
Speaker 1: That's great. Yeah, it be like a thing if you

963
00:55:22,960 --> 00:55:26,400
Speaker 1: go back to the landscape of fear and will move

964
00:55:26,480 --> 00:55:28,239
Speaker 1: on from the old times, we'll talk about new ship.

965
00:55:28,320 --> 00:55:29,879
Speaker 1: But if you go back to the landscape of fear,

966
00:55:30,040 --> 00:55:35,000
Speaker 1: you Kevin explained that you put a collar on a dough,

967
00:55:36,600 --> 00:55:39,840
Speaker 1: You put a collar on her fawon. Yep, you track

968
00:55:39,880 --> 00:55:42,200
Speaker 1: her fawn through her whole life, put a collar on

969
00:55:42,280 --> 00:55:45,319
Speaker 1: her phone. You're starting to get a picture of where

970
00:55:45,360 --> 00:55:49,080
Speaker 1: did that dough ever go? Where does the fawn ever go?

971
00:55:49,400 --> 00:55:51,360
Speaker 1: That's right, does the fon go to new places or

972
00:55:51,400 --> 00:55:56,040
Speaker 1: the same places? When? What are what are the movement patterns?

973
00:55:56,120 --> 00:55:59,359
Speaker 1: What are their sort of relative fitnesses? Because you're able

974
00:55:59,400 --> 00:56:03,919
Speaker 1: to look at like how fit was do a what's

975
00:56:03,960 --> 00:56:07,480
Speaker 1: the condition of herfon? What's the condition of herfon? Yep? Right,

976
00:56:07,520 --> 00:56:12,440
Speaker 1: it paints like an amazing picture. Yes, when you compare

977
00:56:12,480 --> 00:56:15,920
Speaker 1: it to a different style of biology, might be like

978
00:56:16,200 --> 00:56:19,319
Speaker 1: we flew it with a helicopter and did a count. Right,

979
00:56:19,360 --> 00:56:21,759
Speaker 1: it's just different. And I'm not dogging on that kind

980
00:56:21,760 --> 00:56:24,080
Speaker 1: of work. There's probably great work, but like to go like, well,

981
00:56:24,160 --> 00:56:28,120
Speaker 1: let's look at like a lineage, like let's look at

982
00:56:28,120 --> 00:56:31,359
Speaker 1: these like specific animals over a long period of time,

983
00:56:31,400 --> 00:56:33,680
Speaker 1: and what can we tell about those specific animals that

984
00:56:33,800 --> 00:56:36,400
Speaker 1: might inform what you're seeing when you fly over a

985
00:56:36,400 --> 00:56:40,279
Speaker 1: helicopter and count. That's right, right, exactly, It's like you're

986
00:56:40,320 --> 00:56:44,080
Speaker 1: adding a really important piece to a that can plug

987
00:56:44,120 --> 00:56:47,879
Speaker 1: in or inform all these other methodologies. Yep, that's exactly right.

988
00:56:47,920 --> 00:56:50,560
Speaker 3: Like when I went with you, I mean it's your

989
00:56:50,560 --> 00:56:55,600
Speaker 3: students are your team members are like twenty thirty people

990
00:56:55,760 --> 00:56:59,480
Speaker 3: all sleeping in the same forest service cabin, like rallying

991
00:56:59,480 --> 00:57:03,120
Speaker 3: out into the field every day, resting, no drinking rule,

992
00:57:03,320 --> 00:57:06,799
Speaker 3: wrestling deer and at night at night like someone's making

993
00:57:06,840 --> 00:57:10,480
Speaker 3: spaghetti and someone's uh whatever you call it. When you

994
00:57:10,520 --> 00:57:12,080
Speaker 3: spin the blood vile around.

995
00:57:12,120 --> 00:57:13,600
Speaker 1: Centerfuge, centerfuge.

996
00:57:13,640 --> 00:57:15,960
Speaker 3: Yeah, Like it's literally for a service cabin and someone's

997
00:57:15,960 --> 00:57:18,040
Speaker 3: making spaghetti and someone's centerfuging meal.

998
00:57:17,880 --> 00:57:19,920
Speaker 1: Deer blood and uh.

999
00:57:19,960 --> 00:57:21,920
Speaker 3: And then everybody wakes up at five in the morning

1000
00:57:22,120 --> 00:57:25,200
Speaker 3: and rallies out again and a long day like in

1001
00:57:25,240 --> 00:57:29,200
Speaker 3: February and Wyoming just like someone's got a clipboard and

1002
00:57:29,240 --> 00:57:32,760
Speaker 3: someone's measuring deer and someone's someone's sampling stuff, and it's

1003
00:57:32,840 --> 00:57:35,920
Speaker 3: just like it's a it's an impressive operation.

1004
00:57:36,240 --> 00:57:36,959
Speaker 1: Yeah.

1005
00:57:37,000 --> 00:57:41,440
Speaker 4: Well, I and I think like even as you describe

1006
00:57:41,440 --> 00:57:45,760
Speaker 4: Steve the ability to see to connect those pieces together,

1007
00:57:46,080 --> 00:57:49,440
Speaker 4: which I hope is like more from like a mechanistic perspective,

1008
00:57:49,560 --> 00:57:50,760
Speaker 4: like how are these things happening?

1009
00:57:50,840 --> 00:57:51,440
Speaker 1: Why are these.

1010
00:57:51,280 --> 00:57:54,600
Speaker 4: Animals doing what they're doing, the sort of the getting

1011
00:57:54,680 --> 00:57:57,080
Speaker 4: under the hood perspective as to what what it makes

1012
00:57:57,120 --> 00:57:59,520
Speaker 4: for each one of those animals to tick, and then

1013
00:57:59,560 --> 00:58:01,280
Speaker 4: as we see ailed that up, what that means for

1014
00:58:01,480 --> 00:58:06,080
Speaker 4: understanding what's going on within the population. And that can't

1015
00:58:06,120 --> 00:58:09,080
Speaker 4: happen with like a two year study, right, We're talking

1016
00:58:09,240 --> 00:58:11,560
Speaker 4: many many years to be able to put that together

1017
00:58:12,400 --> 00:58:14,480
Speaker 4: in some of our goals with our work in the

1018
00:58:14,520 --> 00:58:16,720
Speaker 4: Wyoming range. So we've been fortunate to work there since

1019
00:58:16,720 --> 00:58:17,360
Speaker 4: twenty thirteen.

1020
00:58:17,440 --> 00:58:19,640
Speaker 1: Yeah, let's talk. Let's let's set that up. We'll talk.

1021
00:58:19,640 --> 00:58:21,560
Speaker 1: We're gonna talk about a specific place. That's right. So

1022
00:58:21,600 --> 00:58:24,000
Speaker 1: do you mind what is the Wyoming Range? Where are

1023
00:58:24,000 --> 00:58:24,520
Speaker 1: we talking about?

1024
00:58:24,600 --> 00:58:29,560
Speaker 4: Yeah, so Wyoming Salt River Range, Western Wyoming. And it's uh,

1025
00:58:30,200 --> 00:58:34,520
Speaker 4: it's home to what either is or has been one

1026
00:58:34,560 --> 00:58:37,400
Speaker 4: of the largest meal deer populations in the world, Region G.

1027
00:58:37,800 --> 00:58:40,160
Speaker 1: Region G. Yeah, like you might, and if you hang

1028
00:58:40,200 --> 00:58:43,320
Speaker 1: out on meal deer forums, yep, there's a lot of

1029
00:58:43,360 --> 00:58:46,320
Speaker 1: talk about Region G and a lot of to talk about.

1030
00:58:46,320 --> 00:58:51,160
Speaker 1: Region G is a county speculating about its future, yes,

1031
00:58:51,280 --> 00:58:54,360
Speaker 1: talking about its challenges, anticipating what are going to be

1032
00:58:54,360 --> 00:58:57,439
Speaker 1: the good years the bad years. It's a very Yeah,

1033
00:58:57,560 --> 00:59:01,720
Speaker 1: it is a very discussed unit. One of the reasons

1034
00:59:01,920 --> 00:59:05,600
Speaker 1: that's probably true is because it's like a unit that

1035
00:59:05,720 --> 00:59:10,160
Speaker 1: sort of occupies a distinct geographical feature. Yes, right, It's

1036
00:59:10,200 --> 00:59:12,120
Speaker 1: like when you say Region G, you're kind of talking

1037
00:59:12,160 --> 00:59:16,760
Speaker 1: about like a range, you know. It's very elegant. Yes,

1038
00:59:16,800 --> 00:59:21,120
Speaker 1: and it is a probably in the top three or

1039
00:59:21,160 --> 00:59:27,000
Speaker 1: four mule deer spots that get discussed, like the Kaibab.

1040
00:59:28,920 --> 00:59:31,520
Speaker 1: I'm trying to think of the Henry Mountains in Utah,

1041
00:59:31,640 --> 00:59:34,960
Speaker 1: people like talk a lot about the agent G gets

1042
00:59:35,120 --> 00:59:42,520
Speaker 1: talked about and mourned, celebrated. Yes, yes, for sure, all

1043
00:59:42,600 --> 00:59:42,959
Speaker 1: of those.

1044
00:59:43,360 --> 00:59:48,200
Speaker 4: Yeah, and so that that population I mean is numbered

1045
00:59:48,200 --> 00:59:50,840
Speaker 4: a thought to have numbered, you know, beyond fifty thousand year,

1046
00:59:51,080 --> 00:59:52,600
Speaker 4: you know, once once upon.

1047
00:59:52,440 --> 00:59:53,120
Speaker 1: A time.

1048
00:59:54,480 --> 00:59:57,439
Speaker 4: And then kind of leaning into the ninety two ninety

1049
00:59:57,520 --> 00:59:59,800
Speaker 4: three winter was a time when they were thought to

1050
00:59:59,800 --> 01:00:02,480
Speaker 4: be well over fifty thousand deer within that herd. There

1051
01:00:02,520 --> 01:00:04,400
Speaker 4: was a lot of female harvest that was happening at

1052
01:00:04,400 --> 01:00:07,080
Speaker 4: that point in time to work to bring densities down

1053
01:00:07,160 --> 01:00:09,880
Speaker 4: because they were over over what game inficion define as

1054
01:00:09,920 --> 01:00:12,880
Speaker 4: the herd unit objective. The ninety two ninety three winter

1055
01:00:12,960 --> 01:00:16,440
Speaker 4: hit and the population crashed, and then since then it's

1056
01:00:16,520 --> 01:00:19,680
Speaker 4: kind of fluctuated. I don't know if we whether numbers

1057
01:00:19,720 --> 01:00:21,840
Speaker 4: matter or not, but maybe like thirty to forty thousand

1058
01:00:21,880 --> 01:00:24,200
Speaker 4: is somewhere somewhere in there and on the high end, yeah,

1059
01:00:24,200 --> 01:00:26,800
Speaker 4: and hasn't risen to those levels previously and.

1060
01:00:26,920 --> 01:00:30,120
Speaker 1: What would be a low end since ninety two. So

1061
01:00:30,480 --> 01:00:33,880
Speaker 1: if let's just let's just accept I know you're saying

1062
01:00:33,920 --> 01:00:37,040
Speaker 1: like maybe fifty thousand, Yeah, if we accept like around

1063
01:00:37,120 --> 01:00:40,200
Speaker 1: fifty thousand, ninety two low end since then, yeah, like

1064
01:00:40,720 --> 01:00:45,520
Speaker 1: it hasn't hit that height again. So the new high

1065
01:00:45,680 --> 01:00:48,400
Speaker 1: end since then has been thirty forty.

1066
01:00:48,360 --> 01:00:51,440
Speaker 4: Yeah, probably mid mid thirties. What park has been like

1067
01:00:51,480 --> 01:00:53,680
Speaker 4: a low end the bottom. So I'm going to get

1068
01:00:53,720 --> 01:00:56,280
Speaker 4: to that eleven eleven thousand.

1069
01:00:57,200 --> 01:00:57,600
Speaker 1: Yeah.

1070
01:00:57,640 --> 01:01:01,920
Speaker 4: So we started in twenty thirteen doing the things that

1071
01:01:01,960 --> 01:01:04,400
Speaker 4: we do is Randell describe to work to understand nutritional

1072
01:01:04,480 --> 01:01:08,120
Speaker 4: dynamics in the herd and then simultaneously just better learn

1073
01:01:08,200 --> 01:01:12,240
Speaker 4: more things about deer that we haven't learned previously. From

1074
01:01:12,320 --> 01:01:14,520
Speaker 4: twenty thirteen, and this was out a time when maybe

1075
01:01:14,600 --> 01:01:16,800
Speaker 4: I forget the exact numbers, were probably bouncing around in

1076
01:01:16,800 --> 01:01:19,840
Speaker 4: the upper thirties, maybe around forty thousand. And from twenty

1077
01:01:19,880 --> 01:01:24,040
Speaker 4: thirteen to twenty sixteen, what we saw each year was

1078
01:01:24,120 --> 01:01:27,680
Speaker 4: just a general decline in body fat of females, both

1079
01:01:27,760 --> 01:01:31,959
Speaker 4: in March and in December, so autumn and spring body fat.

1080
01:01:31,960 --> 01:01:34,960
Speaker 4: It just declined from thirteen all the way up until

1081
01:01:35,000 --> 01:01:38,600
Speaker 4: twenty sixteen, and then in twenty sixteen seventeen we had

1082
01:01:38,600 --> 01:01:41,640
Speaker 4: a bad winter. We lost thirty percent thirty percent of

1083
01:01:41,640 --> 01:01:44,920
Speaker 4: our adult females pretty much all of our radio marked fawns,

1084
01:01:45,560 --> 01:01:49,919
Speaker 4: and so in going into the going into that winter,

1085
01:01:50,720 --> 01:01:54,000
Speaker 4: autumn fat at going into that winter was around seven percent.

1086
01:01:54,160 --> 01:01:57,479
Speaker 4: So think remember seven percent body fat. We lost thirty

1087
01:01:57,520 --> 01:02:02,840
Speaker 4: percent that year. Population rebounded began to rebound after that.

1088
01:02:02,880 --> 01:02:05,720
Speaker 4: Body fat of level levels of female shot way up.

1089
01:02:06,120 --> 01:02:08,280
Speaker 4: And then we had another bad winter in eighteen nineteen,

1090
01:02:08,440 --> 01:02:12,600
Speaker 4: lost thirty percent of our adult females again, and twenty nineteen,

1091
01:02:12,880 --> 01:02:16,800
Speaker 4: twenty eighteen nineteen. So the eighteen nineteen winter eight nineteen.

1092
01:02:18,360 --> 01:02:23,480
Speaker 3: Sorry, we're working on a history product now, we're thinking

1093
01:02:23,520 --> 01:02:24,720
Speaker 3: across centuries, right.

1094
01:02:25,040 --> 01:02:26,840
Speaker 4: So say the dates again. I'm sorry I was dumb.

1095
01:02:26,920 --> 01:02:31,720
Speaker 4: So you aad so win eighteen twenty nineteen. Yeah, so

1096
01:02:32,120 --> 01:02:36,320
Speaker 4: winter's overlap calendar years, right, So sixteen seventeen winter, eighteen

1097
01:02:36,360 --> 01:02:39,280
Speaker 4: nineteen winter, two bad winters, roughly thirty percent of adult females.

1098
01:02:39,320 --> 01:02:42,200
Speaker 4: After each one of those years, we see upticks in

1099
01:02:42,280 --> 01:02:45,560
Speaker 4: nutritional condition following that, so body fat of females shoots up.

1100
01:02:45,640 --> 01:02:49,840
Speaker 4: After that, productivity eventually returns. There's always a lag in

1101
01:02:49,880 --> 01:02:51,880
Speaker 4: a year because what ends up happening. This goes back

1102
01:02:51,920 --> 01:02:55,080
Speaker 4: to what you were referencing, Randall. So in those bad

1103
01:02:55,120 --> 01:03:00,880
Speaker 4: winters fawns because they're they're their their metal demands are different.

1104
01:03:00,920 --> 01:03:03,480
Speaker 4: They're some of the smallest animals on the landscape and

1105
01:03:03,600 --> 01:03:05,680
Speaker 4: they don't come into winter with much body fat. And

1106
01:03:05,680 --> 01:03:10,000
Speaker 4: this all comes down to allocation related principles, right, And

1107
01:03:10,040 --> 01:03:13,200
Speaker 4: so as a fawn, the energy you obtain is mostly

1108
01:03:13,240 --> 01:03:16,120
Speaker 4: going to grow. You don't have a bunch of extra

1109
01:03:16,280 --> 01:03:20,120
Speaker 4: energy to put in body fat. Versus females. Adult females

1110
01:03:20,120 --> 01:03:23,640
Speaker 4: are done growing and whatever extra energy they have they

1111
01:03:23,640 --> 01:03:26,920
Speaker 4: can put into body fat.

1112
01:03:27,120 --> 01:03:27,720
Speaker 1: Or males.

1113
01:03:27,800 --> 01:03:30,720
Speaker 4: Part of the reason why we see age related dynamics

1114
01:03:30,720 --> 01:03:33,439
Speaker 4: and antler size or horn growth that we do goes

1115
01:03:33,480 --> 01:03:35,960
Speaker 4: to the same principle up until they reach a symptotic

1116
01:03:36,000 --> 01:03:39,240
Speaker 4: body mass and so are thus basically done growing in

1117
01:03:39,320 --> 01:03:43,840
Speaker 4: body size. We don't see peak antler size until after that,

1118
01:03:44,040 --> 01:03:45,680
Speaker 4: and that's all principle of allocation.

1119
01:03:45,800 --> 01:03:48,280
Speaker 1: Their first prioritizing.

1120
01:03:48,040 --> 01:03:52,080
Speaker 4: Body growth and then so even those age related dynamics,

1121
01:03:52,160 --> 01:03:55,440
Speaker 4: yes it's age, but it's founded in nutritional principles and

1122
01:03:55,480 --> 01:03:58,000
Speaker 4: how the resources they have and how they allocate energy.

1123
01:03:58,040 --> 01:03:59,480
Speaker 4: So even that's driven by nutrition.

1124
01:04:02,480 --> 01:04:04,280
Speaker 1: I want to restate that point for people that are

1125
01:04:04,320 --> 01:04:10,040
Speaker 1: at work and they're only half paying attention yeah. For

1126
01:04:10,200 --> 01:04:12,240
Speaker 1: me driving down the road listening to something. I'm listening

1127
01:04:12,280 --> 01:04:14,960
Speaker 1: to Clay's Water, which an episode right now, which is fascinating.

1128
01:04:15,000 --> 01:04:18,200
Speaker 1: But I only catch half what he's talking. Monks, I'm driving,

1129
01:04:18,440 --> 01:04:21,200
Speaker 1: So if you're driving and you're not, you're only half listening.

1130
01:04:21,480 --> 01:04:25,120
Speaker 1: This is good. When the buck throws his I never

1131
01:04:25,120 --> 01:04:28,040
Speaker 1: thought about this far. When a buck throws his biggest antlers,

1132
01:04:28,720 --> 01:04:32,760
Speaker 1: he's at a point where he's seeing the least amount

1133
01:04:33,040 --> 01:04:36,360
Speaker 1: of body size growth year over year. That's right. So

1134
01:04:36,400 --> 01:04:38,360
Speaker 1: he's like growing his body grown, his body grown, his

1135
01:04:38,360 --> 01:04:40,920
Speaker 1: body grown, his body boom. He's kind of what he is.

1136
01:04:42,000 --> 01:04:45,120
Speaker 1: And then he gets serious about antlers. That's right, exactly,

1137
01:04:45,920 --> 01:04:47,000
Speaker 1: that's right. Yep.

1138
01:04:48,080 --> 01:04:49,680
Speaker 4: So you can get back to work now, all right,

1139
01:04:49,720 --> 01:04:56,360
Speaker 4: all right, you're getting so after after those winters, we

1140
01:04:56,440 --> 01:04:58,760
Speaker 4: see that, we see that up ticking body fat that

1141
01:04:58,880 --> 01:05:02,960
Speaker 4: occurred there after. But during those winters we lose almost

1142
01:05:03,000 --> 01:05:05,760
Speaker 4: all the funds. The females are in poor condition as

1143
01:05:05,760 --> 01:05:08,840
Speaker 4: they exit winter. And then what we see our spikes

1144
01:05:08,880 --> 01:05:12,960
Speaker 4: and stillborns after those bad winters. And yep, and we

1145
01:05:13,000 --> 01:05:17,040
Speaker 4: see suppressed birth mass after those bad winters as well,

1146
01:05:17,120 --> 01:05:20,080
Speaker 4: which again, yes, I'll say this over and over again.

1147
01:05:20,120 --> 01:05:22,680
Speaker 4: But that has nutritional underpinnings as well for a mom.

1148
01:05:23,360 --> 01:05:27,560
Speaker 4: For these long lived iteroparous animals, which just simply means

1149
01:05:27,560 --> 01:05:30,200
Speaker 4: they live a long time and they reproduce multiple times

1150
01:05:30,200 --> 01:05:33,960
Speaker 4: through their lives, their best strategy is to have the

1151
01:05:34,000 --> 01:05:37,360
Speaker 4: opportunity to reproduce multiple times, as opposed to take one

1152
01:05:37,400 --> 01:05:40,560
Speaker 4: instance and pour all into reproduction, because then they're likely

1153
01:05:40,600 --> 01:05:43,120
Speaker 4: to die. If they die, then they lose out on

1154
01:05:43,160 --> 01:05:46,800
Speaker 4: all those other opportunities to reproduce. So what that means

1155
01:05:46,800 --> 01:05:49,760
Speaker 4: for females that are in poor shape, it's better for

1156
01:05:49,800 --> 01:05:53,880
Speaker 4: them to survive and for their offspring to die than

1157
01:05:53,920 --> 01:05:56,000
Speaker 4: it is for them to pour everything they have into

1158
01:05:56,040 --> 01:05:58,400
Speaker 4: their offspring and then compromise their own survival.

1159
01:05:58,480 --> 01:06:01,920
Speaker 1: So that's part of why we see if she lives,

1160
01:06:01,960 --> 01:06:04,040
Speaker 1: she might crank out three more or whatever.

1161
01:06:03,880 --> 01:06:06,280
Speaker 4: Conditional will get better next number of years, and she's

1162
01:06:06,280 --> 01:06:09,160
Speaker 4: gonna have the opportunity to try again. So we see

1163
01:06:09,200 --> 01:06:13,200
Speaker 4: increased still rates of still borning UH suppressed birth mass.

1164
01:06:13,280 --> 01:06:15,360
Speaker 4: Birth mass plays a huge role in whether or not

1165
01:06:15,440 --> 01:06:18,400
Speaker 4: they survive thereafter, and so what that means is you

1166
01:06:18,440 --> 01:06:20,320
Speaker 4: have high rates of still borning. In fact, in some

1167
01:06:20,360 --> 01:06:22,360
Speaker 4: of those years it's like the leading cause of fund

1168
01:06:22,400 --> 01:06:25,080
Speaker 4: mortality after those bad winters, and so.

1169
01:06:25,560 --> 01:06:29,960
Speaker 1: And and that and that, just because like that fetus

1170
01:06:30,680 --> 01:06:34,680
Speaker 1: isn't just it's not getting fed, malnourished, essentially malnourished in

1171
01:06:34,720 --> 01:06:37,560
Speaker 1: the womb dies in the womb of malnourishment. Yeah, that's

1172
01:06:37,600 --> 01:06:40,360
Speaker 1: exactly right. And they're born dead. That's north I never do.

1173
01:06:40,560 --> 01:06:43,640
Speaker 1: It makes total sense. It just never really thought about that.

1174
01:06:43,800 --> 01:06:46,400
Speaker 1: That has never thought about the fact that a dole

1175
01:06:46,440 --> 01:06:50,080
Speaker 1: would like get pregnant but then not have a fun right.

1176
01:06:50,160 --> 01:06:52,160
Speaker 4: Oh yeah, no, for sure. And I think to to

1177
01:06:52,320 --> 01:06:54,280
Speaker 4: sort of sidestep. I think it's also important to point

1178
01:06:54,320 --> 01:06:58,440
Speaker 4: out for deer in particular, both white till deer and mule.

1179
01:06:58,280 --> 01:07:02,080
Speaker 1: Deer, they very early abort, so they will.

1180
01:07:01,920 --> 01:07:05,960
Speaker 4: Typically once they're pregnant, they will carry they will almost

1181
01:07:06,040 --> 01:07:09,040
Speaker 4: always carry it to term, but then they may drop

1182
01:07:09,080 --> 01:07:12,320
Speaker 4: stillborn's for example, in those in those years with those

1183
01:07:12,440 --> 01:07:15,320
Speaker 4: those bad conditions we have when it's gotten really really

1184
01:07:15,360 --> 01:07:18,560
Speaker 4: bad in those bad winters, we have seen a couple

1185
01:07:18,720 --> 01:07:22,120
Speaker 4: of premature abortions that have occurred, but it's very rare

1186
01:07:22,120 --> 01:07:22,760
Speaker 4: and deer.

1187
01:07:22,560 --> 01:07:25,640
Speaker 1: Okay, so she she birds it, she burns at the

1188
01:07:25,720 --> 01:07:26,240
Speaker 1: right time.

1189
01:07:26,400 --> 01:07:29,240
Speaker 4: Yep, but it's still born, it's dead, and so deer

1190
01:07:29,360 --> 01:07:33,440
Speaker 4: strategy is to typically carry the term, so they're almost

1191
01:07:33,480 --> 01:07:38,520
Speaker 4: always pregnant. Twinning is very common, and then they'll typically

1192
01:07:38,560 --> 01:07:41,000
Speaker 4: make those sort of like nutritional decisions of if they

1193
01:07:41,040 --> 01:07:42,960
Speaker 4: can do it once the young have hit the ground.

1194
01:07:43,360 --> 01:07:45,760
Speaker 4: And so that's why we see that versus creators like

1195
01:07:46,360 --> 01:07:50,320
Speaker 4: prong horn or moose for example, where if things get

1196
01:07:50,360 --> 01:07:53,120
Speaker 4: really bad they may just they're more likely to just

1197
01:07:53,160 --> 01:07:56,720
Speaker 4: abort on the way, or for creators like maybe elk

1198
01:07:56,920 --> 01:08:00,960
Speaker 4: well moose, elk sheep, we see nutritional signs is more

1199
01:08:01,000 --> 01:08:03,720
Speaker 4: strongly tied to like how fat they are and whether

1200
01:08:03,800 --> 01:08:05,840
Speaker 4: or not they're going to be pregnant. So those those

1201
01:08:06,400 --> 01:08:11,320
Speaker 4: those things are evident along along the way regardless. What

1202
01:08:11,360 --> 01:08:14,320
Speaker 4: that means is we're almost missing two cohorts of young.

1203
01:08:14,400 --> 01:08:17,040
Speaker 4: In those instances we lost the we lost the fawns

1204
01:08:17,080 --> 01:08:19,360
Speaker 4: in the bad winter. We lose a vast majority of

1205
01:08:19,360 --> 01:08:21,719
Speaker 4: the fawns that are born after the bad winter because

1206
01:08:21,720 --> 01:08:25,280
Speaker 4: of that nutritional suppression that has occurred. So there's basically

1207
01:08:25,320 --> 01:08:27,920
Speaker 4: two cohorts. And so if you think, you know, from

1208
01:08:27,920 --> 01:08:33,320
Speaker 4: a hunting perspective, two cohorts, then that once those cohorts

1209
01:08:33,320 --> 01:08:35,639
Speaker 4: would have been four to six years of age. There's

1210
01:08:35,680 --> 01:08:37,360
Speaker 4: not going to be a lot of animals in those

1211
01:08:37,400 --> 01:08:41,040
Speaker 4: age ranges because few of them actually made it through,

1212
01:08:41,200 --> 01:08:45,040
Speaker 4: and anyone that made it through, especially after the bad winter,

1213
01:08:45,160 --> 01:08:47,560
Speaker 4: when females are in such poor shape, they're going to

1214
01:08:47,600 --> 01:08:50,680
Speaker 4: carry that nutritional signature of mom barely surviving through that

1215
01:08:50,720 --> 01:08:53,320
Speaker 4: bad winter, and so they're going to be small regardless

1216
01:08:53,320 --> 01:08:54,839
Speaker 4: one they once they hit that age.

1217
01:08:55,160 --> 01:08:58,519
Speaker 1: Then, dude, I got it. I'm sorry, man, I got it.

1218
01:08:58,600 --> 01:09:02,639
Speaker 1: I got two questions. Okay. I I have to try

1219
01:09:02,640 --> 01:09:05,960
Speaker 1: to remember where I was. I don't lose track. But no, no, no,

1220
01:09:06,080 --> 01:09:12,360
Speaker 1: I ask your questions. Write them down and write them down. Okay, Okay,

1221
01:09:12,560 --> 01:09:14,800
Speaker 1: now I feel like it just derailed you. I don't know.

1222
01:09:14,840 --> 01:09:18,200
Speaker 1: I just got to write them down. Okay, okay, right now,

1223
01:09:20,040 --> 01:09:24,080
Speaker 1: not joking. I'm joking. Don't me keep going and wait

1224
01:09:24,080 --> 01:09:31,200
Speaker 1: to ride them there? Uh? Okay, right now, the.

1225
01:09:29,720 --> 01:09:35,559
Speaker 6: Lamb, it's almost as compelling as you're reading news stories

1226
01:09:35,560 --> 01:09:36,160
Speaker 6: for the first.

1227
01:09:36,800 --> 01:09:40,080
Speaker 1: Okay, there's off to Phil. Yeah, dude, listen, it's just

1228
01:09:40,120 --> 01:09:42,679
Speaker 1: that what you're telling me is so interesting to me. Okay,

1229
01:09:42,680 --> 01:09:48,360
Speaker 1: we'll continue on, all right, all right, so after after

1230
01:09:48,400 --> 01:09:48,920
Speaker 1: the bad.

1231
01:09:48,720 --> 01:09:53,479
Speaker 4: Winners, there's of course, there's the there's the flushing in well, actually,

1232
01:09:53,520 --> 01:09:56,000
Speaker 4: let me said stuff a bit. After those two bad winners,

1233
01:09:56,000 --> 01:09:57,760
Speaker 4: we had enough data where we can look at what

1234
01:09:58,000 --> 01:10:00,679
Speaker 4: determines whether or not female do you or survive bad winters.

1235
01:10:00,840 --> 01:10:03,960
Speaker 4: And there's a few factors that are critical. Yes, the

1236
01:10:04,000 --> 01:10:06,160
Speaker 4: worse the winter is the lower probability to have to

1237
01:10:06,160 --> 01:10:10,360
Speaker 4: survive the winter. That's intuitive, right. The more the better food,

1238
01:10:10,439 --> 01:10:13,000
Speaker 4: so like sage brush growth on their winter range, and

1239
01:10:13,000 --> 01:10:16,240
Speaker 4: in this in this range in sage brush is critical

1240
01:10:16,840 --> 01:10:20,000
Speaker 4: the core of their diet through winter. So more and

1241
01:10:20,040 --> 01:10:23,120
Speaker 4: better sage brush growth on their winter range helps them survive.

1242
01:10:23,880 --> 01:10:26,840
Speaker 4: Their ability to freely move on their winter ranges and

1243
01:10:26,920 --> 01:10:28,720
Speaker 4: just be able to move and access food on their

1244
01:10:28,760 --> 01:10:29,840
Speaker 4: winter range plays a role.

1245
01:10:30,040 --> 01:10:31,120
Speaker 1: Is that a function of snow?

1246
01:10:31,479 --> 01:10:34,800
Speaker 4: Yeah, in part a function of snow yep. And then

1247
01:10:36,120 --> 01:10:40,080
Speaker 4: we also see very strong age dependent relationships, So old

1248
01:10:40,200 --> 01:10:42,400
Speaker 4: females and so especially in a bad winter, we're going

1249
01:10:42,479 --> 01:10:45,120
Speaker 4: to lose old females and then some of the very

1250
01:10:45,160 --> 01:10:48,439
Speaker 4: youngest ones. But otherwise, like that sort of prime age

1251
01:10:48,520 --> 01:10:51,759
Speaker 4: from say three to seven or so are pretty solid

1252
01:10:51,760 --> 01:10:55,240
Speaker 4: from an age perspective. But the other driving factor, in

1253
01:10:55,280 --> 01:10:58,960
Speaker 4: the most profound factor, is how fat they were going

1254
01:10:59,000 --> 01:11:02,080
Speaker 4: into that winter. Okay, So I think what's important to

1255
01:11:02,120 --> 01:11:05,439
Speaker 4: considering that and the reason why it happens this way

1256
01:11:05,560 --> 01:11:08,519
Speaker 4: is if you consider why animals die on a winter

1257
01:11:08,840 --> 01:11:10,599
Speaker 4: and a bad winter, and you can you can almost

1258
01:11:10,640 --> 01:11:14,000
Speaker 4: translate this to like any sort of like environmental event

1259
01:11:15,040 --> 01:11:19,280
Speaker 4: that challenges them nutritionally, and that's what a bad winter does.

1260
01:11:20,000 --> 01:11:22,479
Speaker 4: And for an animal to persist through the winter, it

1261
01:11:22,560 --> 01:11:25,360
Speaker 4: just needs to meet its energetic requirements through the course

1262
01:11:25,400 --> 01:11:28,040
Speaker 4: of the winter. And so as you pile up snow,

1263
01:11:28,680 --> 01:11:32,000
Speaker 4: that's going to increase their nutritional deficit. Pile up snow

1264
01:11:32,040 --> 01:11:34,960
Speaker 4: and cold, right, so thermal regulate, cost to thermal regulate,

1265
01:11:35,080 --> 01:11:38,920
Speaker 4: maintain body temperature, cost to locomotion, wade through snow, and

1266
01:11:38,960 --> 01:11:42,439
Speaker 4: access food. And it's also restricting food. It's reducing their

1267
01:11:42,439 --> 01:11:43,759
Speaker 4: ability to access the food.

1268
01:11:44,160 --> 01:11:45,120
Speaker 1: So all of those.

1269
01:11:44,920 --> 01:11:48,240
Speaker 4: Things hamper their ability to meet their daily energy requirements.

1270
01:11:48,880 --> 01:11:52,200
Speaker 4: What fat does is it's basically it's as if they

1271
01:11:52,240 --> 01:11:55,840
Speaker 4: packed groceries on their back from summer range. And so

1272
01:11:56,760 --> 01:11:59,799
Speaker 4: if and if that's one of the most profound factors

1273
01:11:59,800 --> 01:12:03,160
Speaker 4: that influences over winter survival, what it means is that

1274
01:12:03,240 --> 01:12:07,440
Speaker 4: over winter survival is largely dictated by what they experienced

1275
01:12:07,479 --> 01:12:09,920
Speaker 4: on a completely different range that could be one hundred

1276
01:12:09,960 --> 01:12:12,640
Speaker 4: miles away, and the food that they had access to

1277
01:12:12,800 --> 01:12:15,240
Speaker 4: there months earlier, and they're bringing that with them to

1278
01:12:15,280 --> 01:12:18,280
Speaker 4: winter range and that's helping ensure their survival over winter.

1279
01:12:18,479 --> 01:12:20,000
Speaker 1: That's a fact you don't consider.

1280
01:12:19,720 --> 01:12:22,360
Speaker 3: Either, not like how on the on a loan and

1281
01:12:22,439 --> 01:12:26,280
Speaker 3: some of these survival reality television shows, the contestants try

1282
01:12:26,280 --> 01:12:28,920
Speaker 3: to just pack on weight before they go out there.

1283
01:12:29,600 --> 01:12:32,639
Speaker 1: Got it, that's my that's mine. They're going to take situation.

1284
01:12:32,960 --> 01:12:35,040
Speaker 3: Yeah, yeah, you got to bring your reserves.

1285
01:12:36,160 --> 01:12:37,240
Speaker 1: Yeah I ain't ready.

1286
01:12:40,479 --> 01:12:45,920
Speaker 4: Okay, So after and I think one one significant piece

1287
01:12:46,000 --> 01:12:48,720
Speaker 4: here is after those bad winters, we see the we

1288
01:12:48,760 --> 01:12:52,080
Speaker 4: see the upticks in in body fat that's tied to

1289
01:12:52,240 --> 01:12:55,479
Speaker 4: that added precipitation. But those upticks in body fat stay

1290
01:12:55,560 --> 01:12:58,360
Speaker 4: even after that first year. It stays for a couple

1291
01:12:58,400 --> 01:13:01,160
Speaker 4: of different reasons. One reason is reduction in density, so

1292
01:13:01,240 --> 01:13:04,040
Speaker 4: fewer miles on the landscape, and that signature is profound

1293
01:13:04,040 --> 01:13:06,559
Speaker 4: within our data, and some many people don't like to

1294
01:13:06,560 --> 01:13:08,760
Speaker 4: hear that, especially when it comes to deer. They may

1295
01:13:08,840 --> 01:13:11,639
Speaker 4: argue like, oh, density doesn't doesn't matter. We don't want

1296
01:13:11,720 --> 01:13:14,800
Speaker 4: to hear that, But it's true. The more miles that

1297
01:13:14,800 --> 01:13:17,120
Speaker 4: are on the landscape, the increase competition for food and

1298
01:13:17,160 --> 01:13:19,719
Speaker 4: that's gonna have that's going to result in lower body fat.

1299
01:13:19,920 --> 01:13:21,160
Speaker 1: And who doesn't want to hear that?

1300
01:13:21,400 --> 01:13:25,920
Speaker 4: It depends well, so it depends upon the context of

1301
01:13:26,000 --> 01:13:28,240
Speaker 4: what it means for who doesn't want to hear that.

1302
01:13:28,640 --> 01:13:33,000
Speaker 4: What the potential implications are of it is that And

1303
01:13:33,120 --> 01:13:34,920
Speaker 4: I guess we'll either get to this now or more

1304
01:13:34,960 --> 01:13:39,080
Speaker 4: so later. But more is not always better. And much

1305
01:13:39,120 --> 01:13:43,559
Speaker 4: of our publics, for example, want more dear. We always

1306
01:13:43,560 --> 01:13:48,360
Speaker 4: want more deer, But more isn't always better when you

1307
01:13:48,400 --> 01:13:51,080
Speaker 4: consider it from a nutritional perspective of what's happening within

1308
01:13:51,120 --> 01:13:54,320
Speaker 4: a population and so and that that has been very

1309
01:13:54,360 --> 01:13:57,840
Speaker 4: evident within within the wyoming range with our data there

1310
01:13:57,880 --> 01:14:03,120
Speaker 4: and so increased precip fewer miles on the landscape, lower competition.

1311
01:14:03,200 --> 01:14:05,760
Speaker 4: What's also interesting, and this this goes all the way

1312
01:14:05,800 --> 01:14:09,040
Speaker 4: back to our conversation of why we see, for example,

1313
01:14:09,040 --> 01:14:12,720
Speaker 4: even different antler capabilities of size from one place to

1314
01:14:12,760 --> 01:14:16,000
Speaker 4: the next from a nutritional perspective, But what we what

1315
01:14:16,040 --> 01:14:19,799
Speaker 4: we see after animals have gone through those bad winters.

1316
01:14:20,520 --> 01:14:23,519
Speaker 4: We evaluated what factors influence their gain and fat over

1317
01:14:23,560 --> 01:14:25,800
Speaker 4: the summer. And it's all the things I mentioned. It's

1318
01:14:25,920 --> 01:14:28,800
Speaker 4: it's habitat, it's pre sip, it's density of density of

1319
01:14:28,800 --> 01:14:32,160
Speaker 4: deer on the landscape, it's age, it's whether or not

1320
01:14:32,200 --> 01:14:35,160
Speaker 4: you recruited, whether or not you lactated and recruited young.

1321
01:14:35,680 --> 01:14:39,080
Speaker 4: But the other thing is if you've experienced a bad

1322
01:14:39,120 --> 01:14:42,760
Speaker 4: winter and so in the past. Yeah, and so the

1323
01:14:42,800 --> 01:14:47,080
Speaker 4: animals that live through bad winters, we what we see

1324
01:14:47,120 --> 01:14:50,080
Speaker 4: within them is this slight shift and how they allocate

1325
01:14:50,160 --> 01:14:52,880
Speaker 4: their their body reserves over time.

1326
01:14:53,000 --> 01:14:54,439
Speaker 1: You're kidding me, No, I'm not.

1327
01:14:54,600 --> 01:14:58,439
Speaker 4: I'm not at all, and to the point where they learn, Yeah,

1328
01:14:58,479 --> 01:15:01,120
Speaker 4: and not to not to like anti promorphized too much

1329
01:15:01,160 --> 01:15:04,680
Speaker 4: because it's like it took it's a physiological component. But

1330
01:15:04,760 --> 01:15:07,840
Speaker 4: what it is is, yeah, it's an adaptation associated with

1331
01:15:07,880 --> 01:15:11,840
Speaker 4: this like subtle preprogramming, pre programming within the animals.

1332
01:15:12,600 --> 01:15:13,800
Speaker 1: That's a slight shift.

1333
01:15:13,840 --> 01:15:17,800
Speaker 4: And we call it ressensitive reproductive allocation where the risk

1334
01:15:19,040 --> 01:15:22,519
Speaker 4: they allocate resources to reproduction in a risk sensitive way, right,

1335
01:15:22,600 --> 01:15:24,360
Speaker 4: and that risk is associated with what they're going to

1336
01:15:24,439 --> 01:15:26,800
Speaker 4: experience there after. And again if they invest too much

1337
01:15:26,800 --> 01:15:31,160
Speaker 4: into reproduction, they're risking their own survival, right, because they

1338
01:15:31,160 --> 01:15:34,240
Speaker 4: need reserves to survive. We have a bad winter that's

1339
01:15:34,320 --> 01:15:37,719
Speaker 4: like life threatening and they barely make it. We see

1340
01:15:37,720 --> 01:15:42,400
Speaker 4: that shift in resensitive allocation to the level where there's

1341
01:15:42,400 --> 01:15:46,360
Speaker 4: slight increases in body fat that we see if animals

1342
01:15:46,680 --> 01:15:49,599
Speaker 4: had experienced bad winter past, even after we've accounted for

1343
01:15:49,720 --> 01:15:51,840
Speaker 4: all all the other factors that are there.

1344
01:15:52,080 --> 01:15:53,559
Speaker 1: And it's not like you know what I said, like

1345
01:15:53,600 --> 01:15:55,880
Speaker 1: they learn. It's not that it's not that they're like,

1346
01:15:56,000 --> 01:15:58,760
Speaker 1: hey man, when when the weather gets bad, I got

1347
01:15:58,760 --> 01:16:01,519
Speaker 1: a little move. Yeah, rog ou don to old man

1348
01:16:01,600 --> 01:16:04,439
Speaker 1: Lawrence's haystat right. It's not that.

1349
01:16:05,160 --> 01:16:08,800
Speaker 4: No, it's it's like it's physiological and I think what

1350
01:16:08,920 --> 01:16:13,200
Speaker 4: can also Maybe that sounds crazy, it's not. There's been

1351
01:16:13,200 --> 01:16:15,240
Speaker 4: some very elegant work and so so that work of

1352
01:16:15,240 --> 01:16:17,880
Speaker 4: mine was led by Taylor Lashar, and we drew from

1353
01:16:17,960 --> 01:16:21,360
Speaker 4: some prior work when we decided to investigate that idea

1354
01:16:21,920 --> 01:16:24,880
Speaker 4: from Board Barton, who was able to take semi domesticated

1355
01:16:24,920 --> 01:16:29,320
Speaker 4: reindeer in Norway and ones that were supplementally fed through

1356
01:16:29,320 --> 01:16:32,160
Speaker 4: the winter and ones that were not, and just by

1357
01:16:32,240 --> 01:16:36,000
Speaker 4: way of shifting them taking the supplementally fed ones and

1358
01:16:36,040 --> 01:16:39,120
Speaker 4: going to a natural pasture, or taking the ones that

1359
01:16:39,840 --> 01:16:43,200
Speaker 4: were from the natural pastor and going to supplemental feeding.

1360
01:16:43,880 --> 01:16:47,880
Speaker 4: Animals carried their signature of the range that they were

1361
01:16:47,920 --> 01:16:51,160
Speaker 4: on and how they allocated resources to reproduction, with the

1362
01:16:51,200 --> 01:16:54,519
Speaker 4: exception of those that went from supplemental feeding to the

1363
01:16:54,640 --> 01:16:58,880
Speaker 4: natural pasture, they instantly dropped how they allocated resources to

1364
01:16:58,960 --> 01:17:03,080
Speaker 4: reproduction because it was the potential it was a potentially

1365
01:17:03,120 --> 01:17:06,800
Speaker 4: compromising their ability to maintain themselves, which is which is

1366
01:17:06,840 --> 01:17:10,440
Speaker 4: the priority. So I liken it to like winter PTSD,

1367
01:17:10,760 --> 01:17:13,800
Speaker 4: these animals had this life threatening experience and there's this

1368
01:17:13,880 --> 01:17:18,120
Speaker 4: subtle shift within their preprogramming and to support that further,

1369
01:17:18,200 --> 01:17:21,360
Speaker 4: and I worked with captive deer for almost a decade.

1370
01:17:22,040 --> 01:17:25,439
Speaker 4: You can have captive white tailed deer, any any captive

1371
01:17:25,640 --> 01:17:29,920
Speaker 4: ungulate for that matter, except for maybe domestic animals, and

1372
01:17:31,600 --> 01:17:34,040
Speaker 4: supplementally feed them the entire year, so they're on the

1373
01:17:34,040 --> 01:17:38,520
Speaker 4: best nutrition all year round, and we still see cycles

1374
01:17:38,560 --> 01:17:41,519
Speaker 4: in their diet how much they eat in their body

1375
01:17:41,520 --> 01:17:45,160
Speaker 4: fat and body masks through the entire year, when technically,

1376
01:17:46,240 --> 01:17:48,760
Speaker 4: like they're supplementally fed, it could just be constant through

1377
01:17:48,760 --> 01:17:51,799
Speaker 4: the whole year, but it's not We see these natural rhythms,

1378
01:17:51,840 --> 01:17:55,920
Speaker 4: and so they voluntarily through the winter reduce metabolic rate,

1379
01:17:56,040 --> 01:17:59,360
Speaker 4: reduce appetite and how much they're eating to go through

1380
01:17:59,439 --> 01:18:03,040
Speaker 4: those sites. And so I think the most I mean,

1381
01:18:03,040 --> 01:18:05,840
Speaker 4: maybe it's like, Okay, that's cool, so deer slightly reprogrammed

1382
01:18:05,840 --> 01:18:08,479
Speaker 4: after a bad winter. But I think what it also

1383
01:18:08,640 --> 01:18:11,880
Speaker 4: tells us is that animals are adapted to the local

1384
01:18:12,000 --> 01:18:15,200
Speaker 4: environment within the ranges, in the conditions that they experience,

1385
01:18:16,520 --> 01:18:19,200
Speaker 4: to the point where even within an environment like that,

1386
01:18:19,360 --> 01:18:23,080
Speaker 4: when we see this huge, huge shock to the system,

1387
01:18:23,600 --> 01:18:27,439
Speaker 4: we see animals adapting accordingly, and so that should help

1388
01:18:27,560 --> 01:18:30,600
Speaker 4: convince us that when we go from one range to

1389
01:18:30,680 --> 01:18:34,240
Speaker 4: the next, these animals are operating in ways that correspond

1390
01:18:34,400 --> 01:18:38,720
Speaker 4: with that range, so that they're locally adapted, which is

1391
01:18:38,720 --> 01:18:40,800
Speaker 4: the same reason we see antlers the way in which

1392
01:18:40,800 --> 01:18:43,799
Speaker 4: we do females doing what they do. For example, females

1393
01:18:43,840 --> 01:18:45,599
Speaker 4: in the Wyoming range are not going to gain fat

1394
01:18:45,640 --> 01:18:49,559
Speaker 4: the same way that female deer in southeast Montana are

1395
01:18:49,560 --> 01:18:52,840
Speaker 4: going to different range, different environmental conditions, they need different

1396
01:18:52,920 --> 01:18:56,400
Speaker 4: things to be able to persist within that environment. So

1397
01:18:56,439 --> 01:18:59,040
Speaker 4: to go one step further, all these things happening with

1398
01:18:59,080 --> 01:19:02,880
Speaker 4: body fat, the reduction in density within the herd. Then

1399
01:19:02,880 --> 01:19:04,719
Speaker 4: we begin to enter into the twenty two to twenty

1400
01:19:04,720 --> 01:19:06,479
Speaker 4: three winter, which this is like, I don't know, one

1401
01:19:06,560 --> 01:19:09,960
Speaker 4: hundred plus year winter, like while this winter that we've

1402
01:19:09,960 --> 01:19:14,479
Speaker 4: seen for decades within that country, we lost seventy percent

1403
01:19:14,479 --> 01:19:17,719
Speaker 4: of our adult females, all of our radio marked fams

1404
01:19:17,880 --> 01:19:21,000
Speaker 4: six all of them just all gone sixty five percent

1405
01:19:21,040 --> 01:19:26,600
Speaker 4: of our adult males. So man, yeah, that dropped the

1406
01:19:26,640 --> 01:19:29,800
Speaker 4: population to eleven roughly eleven thousand animals through the course

1407
01:19:29,800 --> 01:19:33,040
Speaker 4: of that winter. And I mean it was like animals

1408
01:19:33,040 --> 01:19:35,920
Speaker 4: were sort of like got so concentrated on the south

1409
01:19:35,960 --> 01:19:38,880
Speaker 4: facing slope in places where there's like juniper but no food,

1410
01:19:38,920 --> 01:19:41,960
Speaker 4: the hedgeline, the brows line, and the juniper was like

1411
01:19:42,040 --> 01:19:44,240
Speaker 4: almost as tall as me, where they just all they

1412
01:19:44,240 --> 01:19:48,000
Speaker 4: would do is just walk these trails around around juniper,

1413
01:19:48,160 --> 01:19:51,080
Speaker 4: just eating whatever they could, sage brush, any sagebrush that

1414
01:19:51,240 --> 01:19:54,800
Speaker 4: was exposed, like the twigs, everything was eaten to the

1415
01:19:55,040 --> 01:19:57,400
Speaker 4: to the snow line. I mean, it was it was

1416
01:19:58,320 --> 01:20:04,640
Speaker 4: a very sad experiment experience in an incredibly, incredibly devastating

1417
01:20:04,680 --> 01:20:07,880
Speaker 4: winter in that regard for that deer population.

1418
01:20:07,600 --> 01:20:10,640
Speaker 1: I had as it's probably connected to you, but a

1419
01:20:10,680 --> 01:20:14,880
Speaker 1: buddy mine has a property in Wyhoming and he was

1420
01:20:15,160 --> 01:20:17,960
Speaker 1: there were some researches that we're doing little collaring work

1421
01:20:18,000 --> 01:20:20,320
Speaker 1: on his place. Or it feels he might even been

1422
01:20:20,320 --> 01:20:23,280
Speaker 1: talking about your outfit. It's a Western Whoming. Anyways, he

1423
01:20:23,320 --> 01:20:25,519
Speaker 1: was playing. He was telling me how this guy was

1424
01:20:25,560 --> 01:20:30,880
Speaker 1: telling him that during that winter. He's like, it was

1425
01:20:30,960 --> 01:20:35,479
Speaker 1: that there'd be moments, Yes, yeah, there'd be moments when

1426
01:20:35,520 --> 01:20:39,120
Speaker 1: the mortality signals were like bing bing bing bing bing. Yep.

1427
01:20:39,280 --> 01:20:42,640
Speaker 1: So like within this broad time of like really heard

1428
01:20:42,880 --> 01:20:47,560
Speaker 1: of prolonged period of intense hardship, there'd be like killing.

1429
01:20:47,280 --> 01:20:50,519
Speaker 4: Moments, yes, yeah, yeah, And so can you explain that

1430
01:20:50,520 --> 01:20:50,960
Speaker 4: a little bit?

1431
01:20:51,040 --> 01:20:51,320
Speaker 1: Yeah.

1432
01:20:51,360 --> 01:20:54,519
Speaker 4: So again, this goes back to what we also talked

1433
01:20:54,520 --> 01:20:57,599
Speaker 4: about before, and that animals simply need to meet their

1434
01:20:57,720 --> 01:21:04,120
Speaker 4: daily energy requirements and fat helps with that during that winter.

1435
01:21:04,920 --> 01:21:07,160
Speaker 4: And I think this is just a really striking example

1436
01:21:07,240 --> 01:21:10,600
Speaker 4: of this. For females that entered winter, that entered that

1437
01:21:10,640 --> 01:21:15,759
Speaker 4: winter over fifteen percent that entered winter under fifteen percent

1438
01:21:15,840 --> 01:21:21,320
Speaker 4: body fat, we began losing them on February fifteenth, a

1439
01:21:21,960 --> 01:21:26,040
Speaker 4: female deer that entered winter over fifteen percent body fat,

1440
01:21:26,120 --> 01:21:29,240
Speaker 4: we did not start losing them till March fifteenth, a

1441
01:21:29,320 --> 01:21:32,280
Speaker 4: month later, which, like if you could just distill a

1442
01:21:32,280 --> 01:21:35,680
Speaker 4: bunch of data in a very simple manner, that's incredibly

1443
01:21:35,720 --> 01:21:38,840
Speaker 4: telling to just be able to say that. So what

1444
01:21:38,840 --> 01:21:40,920
Speaker 4: that meant is that animals were very much on the

1445
01:21:41,000 --> 01:21:44,920
Speaker 4: nutritional edge and so they're barely getting by. They're barely

1446
01:21:44,920 --> 01:21:48,160
Speaker 4: meeting their daily energy requirements. And so as we would

1447
01:21:48,160 --> 01:21:50,760
Speaker 4: go through that winter, and that winter was very very

1448
01:21:50,800 --> 01:21:54,360
Speaker 4: cold as well, when we'd see storms come in or

1449
01:21:55,560 --> 01:21:59,120
Speaker 4: significant drops in temperature for a couple of days, we

1450
01:21:59,280 --> 01:22:02,519
Speaker 4: just see spikes in mortality. And that's because what it's

1451
01:22:02,560 --> 01:22:05,439
Speaker 4: done is they're barely meeting their daily energy requirements. We

1452
01:22:05,479 --> 01:22:08,720
Speaker 4: see those those drops in temperature, their energy requirements go

1453
01:22:08,920 --> 01:22:13,360
Speaker 4: up because they're needing to meet basal metabolic demands through

1454
01:22:13,479 --> 01:22:15,960
Speaker 4: the winter in that day and they just can't do it,

1455
01:22:16,000 --> 01:22:18,360
Speaker 4: and so they die. And so that's where those spikes

1456
01:22:18,360 --> 01:22:21,360
Speaker 4: and mortality came from during that period of time. Now,

1457
01:22:22,080 --> 01:22:25,400
Speaker 4: what I think is very also very important and incredibly

1458
01:22:25,439 --> 01:22:28,760
Speaker 4: revealing to the work that we've done there specifically with

1459
01:22:28,800 --> 01:22:31,800
Speaker 4: regards to bad winters. And if we go back to

1460
01:22:31,800 --> 01:22:36,040
Speaker 4: the sixteen seventeen winter, the eighteen nineteen winter, the recovery,

1461
01:22:36,160 --> 01:22:40,640
Speaker 4: the recovery in nutritional condition, so body fat that occurred thereafter,

1462
01:22:41,840 --> 01:22:43,880
Speaker 4: and in part again because of the signals of the

1463
01:22:43,880 --> 01:22:46,000
Speaker 4: bad winter, but also the reduction and density on the

1464
01:22:46,080 --> 01:22:50,000
Speaker 4: landscape so fewer mouths to feed. We entered the twenty

1465
01:22:50,040 --> 01:22:53,519
Speaker 4: two to twenty three winter at over twelve percent body fat.

1466
01:22:54,040 --> 01:22:56,360
Speaker 4: So if you remember, do you remember what we entered

1467
01:22:56,400 --> 01:23:00,360
Speaker 4: the sixteen seventeen winter, seven percent, So that's a that's

1468
01:23:00,400 --> 01:23:04,400
Speaker 4: a huge difference. Entered seven percent during the sixteen seventeen winner,

1469
01:23:04,400 --> 01:23:07,400
Speaker 4: we lost thirty percent of our females. Twenty two twenty

1470
01:23:07,400 --> 01:23:09,760
Speaker 4: three winter way way worse than the sixteen seventeen winter.

1471
01:23:10,880 --> 01:23:13,599
Speaker 4: We ran Taylor ran our survival models for the twenty

1472
01:23:13,640 --> 01:23:17,320
Speaker 4: two to twenty three winter based on a seven percent

1473
01:23:17,479 --> 01:23:20,080
Speaker 4: body fat level. So had we started at seven percent

1474
01:23:20,200 --> 01:23:22,639
Speaker 4: going into the twenty two twenty three winter, we would

1475
01:23:22,640 --> 01:23:25,479
Speaker 4: have lost over ninety percent of our deer population. No

1476
01:23:25,600 --> 01:23:29,320
Speaker 4: kid meaning yeah, yeah, we go back to sixteen seventeen

1477
01:23:29,400 --> 01:23:33,280
Speaker 4: eighteen nineteen. The reductions in deer density, the recovery and

1478
01:23:33,320 --> 01:23:38,519
Speaker 4: body fat that occurred thereafter those winters saved us in

1479
01:23:38,520 --> 01:23:41,040
Speaker 4: the twenty two to twenty three winter. Had we come

1480
01:23:41,040 --> 01:23:43,040
Speaker 4: into the twenty two twenty three winter at those lower

1481
01:23:43,120 --> 01:23:46,519
Speaker 4: levels that we were experiencing previously, prior to those former

1482
01:23:46,560 --> 01:23:51,559
Speaker 4: bad winters, we probably would have lost essentially everything. Meaning

1483
01:23:51,600 --> 01:23:53,920
Speaker 4: if we go back to the more is not always

1484
01:23:53,960 --> 01:23:58,760
Speaker 4: better perspective, especially in that I mean, that's the very

1485
01:23:58,840 --> 01:24:02,639
Speaker 4: clear demonstration of more is not necessarily always better from

1486
01:24:02,680 --> 01:24:03,920
Speaker 4: a nutritional perspective.

1487
01:24:03,920 --> 01:24:04,960
Speaker 1: In that way.

1488
01:24:05,840 --> 01:24:08,880
Speaker 3: Yeah, so you if you had more deer going into

1489
01:24:10,000 --> 01:24:14,080
Speaker 3: that winner, it's not the case that more deer would die,

1490
01:24:14,120 --> 01:24:17,839
Speaker 3: but it would be proportional. You'd actually have a higher proportion.

1491
01:24:18,040 --> 01:24:19,720
Speaker 3: If you had more deer on the landscape, you'd have

1492
01:24:19,720 --> 01:24:21,960
Speaker 3: a higher proportion that would die. That's correct than if

1493
01:24:21,960 --> 01:24:23,360
Speaker 3: you had fewer deer going into it.

1494
01:24:23,439 --> 01:24:29,439
Speaker 1: Yeah, that's exactly right. Yeah. Hmm, God's great, man, I

1495
01:24:29,479 --> 01:24:33,439
Speaker 1: mean it's bad. It's interesting when I say great, I

1496
01:24:33,439 --> 01:24:35,360
Speaker 1: mean it's great. What happened to the deer saying it's

1497
01:24:35,400 --> 01:24:39,560
Speaker 1: a great Oh yeah, yeah, with the input of information,

1498
01:24:39,640 --> 01:24:41,160
Speaker 1: I don't know, just like understanding.

1499
01:24:41,640 --> 01:24:43,840
Speaker 4: Yeah, there's a lot of power in that. Just like

1500
01:24:43,880 --> 01:24:46,920
Speaker 4: that scenario and how it played out for us. I mean,

1501
01:24:46,960 --> 01:24:52,519
Speaker 4: the winters, the winter's basically presented to us a huge

1502
01:24:52,680 --> 01:24:57,240
Speaker 4: experiment or treatment effect essentially based on like the severe

1503
01:24:57,520 --> 01:25:00,519
Speaker 4: the severe nutritional limitation and then the massive reduction and

1504
01:25:00,720 --> 01:25:01,680
Speaker 4: see that occurred with that.

1505
01:25:02,400 --> 01:25:04,519
Speaker 1: Unfortunately, we were doing the work that we were when

1506
01:25:04,560 --> 01:25:05,160
Speaker 1: we were doing it.

1507
01:25:05,200 --> 01:25:08,760
Speaker 4: Otherwise we'd still just we'd still be talking about like

1508
01:25:09,400 --> 01:25:11,160
Speaker 4: how bad winters are, and they are bad, but what

1509
01:25:11,160 --> 01:25:13,439
Speaker 4: does it mean for the population? And even this notion

1510
01:25:13,520 --> 01:25:16,799
Speaker 4: that like you know what happens on a winter range,

1511
01:25:16,880 --> 01:25:19,800
Speaker 4: we see it, right, It's very evident to us these

1512
01:25:19,840 --> 01:25:23,320
Speaker 4: animals die on winter range. But at the same time,

1513
01:25:23,400 --> 01:25:26,479
Speaker 4: one of the major reasons why they're dying is because

1514
01:25:26,520 --> 01:25:28,559
Speaker 4: of what they experienced during the summer and if whether

1515
01:25:28,640 --> 01:25:30,800
Speaker 4: or not they brought enough reserves with them from summer range.

1516
01:25:31,560 --> 01:25:33,120
Speaker 3: And I just thought about, sorry, go ahead, I was

1517
01:25:33,120 --> 01:25:35,599
Speaker 3: gonna say, and all that stuff would be invisible unless

1518
01:25:35,640 --> 01:25:39,880
Speaker 3: you had those very individual, that's correct data points, right,

1519
01:25:40,000 --> 01:25:43,120
Speaker 3: Like you're not going to pick that up from not

1520
01:25:43,240 --> 01:25:45,360
Speaker 3: to poke at the helicopter people, but you're not going

1521
01:25:45,360 --> 01:25:47,880
Speaker 3: to pick that up from an aerial survey, like it's

1522
01:25:47,920 --> 01:25:52,960
Speaker 3: a very intimate knowledge of specific animals.

1523
01:25:53,080 --> 01:25:54,320
Speaker 1: Yeah, and you're going to I mean, you're going to

1524
01:25:54,360 --> 01:25:55,840
Speaker 1: see the level of loss.

1525
01:25:55,680 --> 01:25:58,000
Speaker 3: Right, but you're not going to see the underlying.

1526
01:25:57,760 --> 01:25:59,920
Speaker 4: No, and to be able, you know, for our ability

1527
01:25:59,920 --> 01:26:01,680
Speaker 4: to be able to do that, Like we know how

1528
01:26:01,720 --> 01:26:04,000
Speaker 4: old each animal is, we know the fact that they

1529
01:26:04,040 --> 01:26:06,120
Speaker 4: brought with them this from summer range, we know where

1530
01:26:06,120 --> 01:26:08,360
Speaker 4: they lived on summer range, and we can connect and

1531
01:26:08,400 --> 01:26:11,599
Speaker 4: build all those pieces together, which also not only allows

1532
01:26:11,680 --> 01:26:13,360
Speaker 4: us to tell those stories, but tell a number of

1533
01:26:13,360 --> 01:26:16,920
Speaker 4: our other stories associated with how.

1534
01:26:16,800 --> 01:26:19,800
Speaker 1: Animals learn to migrate. What does it mean for them?

1535
01:26:19,880 --> 01:26:22,040
Speaker 4: What does it mean for the reproductive chronology and their

1536
01:26:22,040 --> 01:26:24,160
Speaker 4: cycle through the year, and those sorts of things. Unless

1537
01:26:24,200 --> 01:26:27,400
Speaker 4: we have those repeated samples and the things that we're measuring,

1538
01:26:28,080 --> 01:26:30,439
Speaker 4: we lack the ability to be able to paint that picture.

1539
01:26:31,040 --> 01:26:33,320
Speaker 4: And so that deer that you talked about at the beginning,

1540
01:26:34,160 --> 01:26:37,840
Speaker 4: that really small male, he was born. He was born

1541
01:26:37,840 --> 01:26:40,719
Speaker 4: in twenty seventeen, so he was born after the sixteen

1542
01:26:40,800 --> 01:26:44,000
Speaker 4: seventeen winter. And yeah, all day long, in fact, I

1543
01:26:44,000 --> 01:26:47,200
Speaker 4: mean even as for the potential of antler growth that

1544
01:26:47,240 --> 01:26:50,080
Speaker 4: exists within the Wyoming range, I mean.

1545
01:26:50,760 --> 01:26:51,479
Speaker 1: All day long.

1546
01:26:51,520 --> 01:26:54,160
Speaker 4: When he was three years old, I mean, I would

1547
01:26:54,200 --> 01:26:55,559
Speaker 4: have said, and I think that's kind of what you're

1548
01:26:55,600 --> 01:26:57,679
Speaker 4: referencing to Steve. I would have called him he's either

1549
01:26:57,800 --> 01:26:59,719
Speaker 4: a large yearling or a small.

1550
01:26:59,479 --> 01:27:01,519
Speaker 1: Two year old. I would have said it in a

1551
01:27:01,560 --> 01:27:05,240
Speaker 1: declarative fashion. And he's three, and he's three three and

1552
01:27:05,280 --> 01:27:08,000
Speaker 1: a half years old, and I would have also said,

1553
01:27:08,680 --> 01:27:11,480
Speaker 1: I can tell you what he is now. My abilities

1554
01:27:11,520 --> 01:27:13,400
Speaker 1: fall apart when they get up to be like four

1555
01:27:13,439 --> 01:27:15,360
Speaker 1: or five six years old, but one hundred percent what

1556
01:27:15,400 --> 01:27:16,400
Speaker 1: you're looking at right.

1557
01:27:17,960 --> 01:27:24,000
Speaker 4: Yeah, absolutely absolutely, Okay, can is it time now?

1558
01:27:24,360 --> 01:27:26,559
Speaker 1: Because this is the benefit of being the host, get

1559
01:27:26,600 --> 01:27:30,320
Speaker 1: the indulgent a series of very fast questions. I want

1560
01:27:30,360 --> 01:27:36,639
Speaker 1: to more information, Okay, hot tip. Yeah, you should start

1561
01:27:36,640 --> 01:27:42,839
Speaker 1: a tag service where it's predictive modeling and you sell

1562
01:27:42,880 --> 01:27:46,360
Speaker 1: to people tips.

1563
01:27:46,960 --> 01:27:52,960
Speaker 4: Be like, I would be looking very seriously X in

1564
01:27:54,000 --> 01:27:57,680
Speaker 4: four years. I think meat eaters should just hire us

1565
01:27:57,680 --> 01:28:00,240
Speaker 4: and meat eater should offer that service thing.

1566
01:28:00,439 --> 01:28:07,479
Speaker 1: Yeah, it's called the Monteith Labs or monte Text Meat Eater. Okay,

1567
01:28:07,560 --> 01:28:10,760
Speaker 1: we'll do that. Okay, okay, not a quick question, yeap.

1568
01:28:12,439 --> 01:28:14,000
Speaker 1: Theod Amy and my boy are driving down the road,

1569
01:28:14,080 --> 01:28:17,280
Speaker 1: my older boy, and there's all these sirens and paramedics

1570
01:28:17,360 --> 01:28:20,920
Speaker 1: running around, and we stopped well shy by take my

1571
01:28:20,960 --> 01:28:23,599
Speaker 1: binoculars and look, and I see something quite startling. There's

1572
01:28:23,600 --> 01:28:29,160
Speaker 1: a wrecked motorcycle. There's seven firefighters and paramedics working on

1573
01:28:29,240 --> 01:28:33,200
Speaker 1: a man who's like splayed out on the road. And

1574
01:28:33,240 --> 01:28:38,240
Speaker 1: there's a demolished fawn. I don't know, no idea the

1575
01:28:38,240 --> 01:28:40,920
Speaker 1: guy lived or not tried to miss a fawn hit

1576
01:28:40,960 --> 01:28:45,760
Speaker 1: The fawn was a mess on the road. Two days later,

1577
01:28:47,000 --> 01:28:51,519
Speaker 1: we drove down that road and there's a dough still

1578
01:28:51,560 --> 01:28:56,200
Speaker 1: standing there, like one hundred yards away, but looking agitated.

1579
01:28:57,120 --> 01:28:59,920
Speaker 1: Three o'clock in the afternoon, she's still on her feet,

1580
01:29:00,040 --> 01:29:05,080
Speaker 1: looking like agitated, down in the ditch. When you with

1581
01:29:05,160 --> 01:29:08,599
Speaker 1: your are you able to ever able to pull out

1582
01:29:08,960 --> 01:29:14,160
Speaker 1: of your data sets? What that relationship's like? Like she

1583
01:29:14,240 --> 01:29:17,599
Speaker 1: has a stillborn? Does she still hang around? Does she

1584
01:29:17,640 --> 01:29:19,920
Speaker 1: immediately walk away? Do you know what I mean? When

1585
01:29:19,920 --> 01:29:23,479
Speaker 1: she loses a fond what is the sort of there's

1586
01:29:23,479 --> 01:29:27,200
Speaker 1: no way to measure grief. Yeah, but like when does

1587
01:29:27,240 --> 01:29:31,240
Speaker 1: she go like huh, I guess it's time to move on? Yeah?

1588
01:29:31,400 --> 01:29:34,519
Speaker 4: Interesting question and highly variable from one individual to the next.

1589
01:29:34,920 --> 01:29:39,960
Speaker 4: In most instances with stillborns, though we can suspect even

1590
01:29:40,080 --> 01:29:42,800
Speaker 4: just via their GPS data around the birth event, whether

1591
01:29:42,880 --> 01:29:45,120
Speaker 4: or not they had stillborns, because they tend to peace.

1592
01:29:44,920 --> 01:29:48,080
Speaker 1: Out pretty quickly. She knows what happened. Yeah, that's exactly right.

1593
01:29:48,720 --> 01:29:51,920
Speaker 4: And also many females like they if they lose one

1594
01:29:51,920 --> 01:29:56,120
Speaker 4: of their young later on to whatever reason, rarely do

1595
01:29:56,160 --> 01:29:56,880
Speaker 4: they stick around.

1596
01:29:57,640 --> 01:29:58,559
Speaker 1: They will often leave.

1597
01:29:58,880 --> 01:30:01,920
Speaker 4: However, there are a handful of females that are just

1598
01:30:03,439 --> 01:30:08,400
Speaker 4: their motherly nature, whatever that is, may may be there

1599
01:30:08,439 --> 01:30:12,360
Speaker 4: and even defending the carcass. Still when when we arrive

1600
01:30:13,960 --> 01:30:17,840
Speaker 4: very typically within twenty four hours by the time when

1601
01:30:17,960 --> 01:30:22,040
Speaker 4: we're working to get in there, but quicker than that, Yeah, yeah,

1602
01:30:22,080 --> 01:30:23,599
Speaker 4: they're gone by the time when we get in there.

1603
01:30:23,800 --> 01:30:29,800
Speaker 1: Yep, got it, yep, exactly. Uh, here's an observation when

1604
01:30:29,840 --> 01:30:34,120
Speaker 1: you're talking about this this thing of like gaming out,

1605
01:30:34,160 --> 01:30:36,640
Speaker 1: how much energy am I going to put into this offspring?

1606
01:30:37,560 --> 01:30:42,000
Speaker 1: Uh it's implied, but but just to point to an

1607
01:30:42,000 --> 01:30:45,200
Speaker 1: example of something entirely different, Like think about a Pacific

1608
01:30:45,240 --> 01:30:52,120
Speaker 1: salmon when they go they're going, Yeah, I mean, John,

1609
01:30:52,120 --> 01:30:54,519
Speaker 1: I mean like like kings can kind of delay, a

1610
01:30:54,600 --> 01:30:57,160
Speaker 1: king salmon can delay. He doesn't have to stay to

1611
01:30:57,200 --> 01:31:01,000
Speaker 1: a set schedule, you know, he can whatever his body

1612
01:31:01,040 --> 01:31:03,360
Speaker 1: will make a decision like, nope, not going to go.

1613
01:31:03,400 --> 01:31:05,080
Speaker 1: That's why you get huge kings, right, that's why you

1614
01:31:05,120 --> 01:31:07,600
Speaker 1: get you could get a seventy pound king because for

1615
01:31:07,640 --> 01:31:10,479
Speaker 1: whatever reason, for a number of years it didn't click

1616
01:31:10,520 --> 01:31:12,920
Speaker 1: on and it didn't run. But these other sam of

1617
01:31:12,960 --> 01:31:16,920
Speaker 1: around a set schedule. It's like I'm going, buddy, I

1618
01:31:16,920 --> 01:31:20,479
Speaker 1: hope the river's right. I hope the river's right because

1619
01:31:20,479 --> 01:31:28,720
Speaker 1: it's happening. Question if I took a deer, If I

1620
01:31:28,960 --> 01:31:33,400
Speaker 1: took a buck from Iowa, Okay one hundred and eighty

1621
01:31:33,400 --> 01:31:37,240
Speaker 1: inch white tail from Iowa two hundred plus pounds, one

1622
01:31:37,280 --> 01:31:39,160
Speaker 1: hundred and eighty inch handlers, and I brought him down

1623
01:31:39,200 --> 01:31:43,000
Speaker 1: and put them in Who's dear country? And Sonora, does

1624
01:31:43,000 --> 01:31:43,679
Speaker 1: he just die?

1625
01:31:44,479 --> 01:31:47,120
Speaker 4: Cus deer country? And scenario probably die. He just dies

1626
01:31:47,400 --> 01:31:53,360
Speaker 4: because his pattern Cou's deer. Did you say I'll fight

1627
01:31:53,479 --> 01:31:54,479
Speaker 4: you over it?

1628
01:31:57,479 --> 01:32:02,320
Speaker 1: Because his groove his like sister, the yeah just isn't

1629
01:32:02,960 --> 01:32:07,920
Speaker 1: going to translate. That's right, Okay, that's right. Yep. Uh.

1630
01:32:08,439 --> 01:32:11,559
Speaker 1: This one's controversial to what you need to answer. Yeah,

1631
01:32:11,600 --> 01:32:15,200
Speaker 1: they look in your face. I just wonder about like

1632
01:32:16,040 --> 01:32:18,320
Speaker 1: is it even possible, Like what if you went and

1633
01:32:18,320 --> 01:32:21,720
Speaker 1: looked at human fitness, human physical fitness. Does some of

1634
01:32:21,760 --> 01:32:23,679
Speaker 1: the stuff is some of the stuff relevant. People don't

1635
01:32:23,720 --> 01:32:25,160
Speaker 1: like to do that kind of stuff, but.

1636
01:32:26,120 --> 01:32:29,360
Speaker 4: Yeah, I mean there's there's a substantial amount. And of

1637
01:32:29,360 --> 01:32:32,960
Speaker 4: course I don't nutrition. Oh yeah, okay, there's lots of

1638
01:32:33,000 --> 01:32:40,679
Speaker 4: evidence within human human medicine associated with maternal nutrition. Yes,

1639
01:32:40,800 --> 01:32:44,280
Speaker 4: that ties that translates over a generation. Yeah, absolutely, God,

1640
01:32:44,400 --> 01:32:46,439
Speaker 4: that's all my questions and humans as well.

1641
01:32:47,160 --> 01:32:48,479
Speaker 1: Okay, that's all my questions.

1642
01:32:50,640 --> 01:32:53,960
Speaker 4: Maybe a couple a couple other things related related to

1643
01:32:54,600 --> 01:32:59,040
Speaker 4: some of these ideas. One of them, okay, So the

1644
01:32:59,080 --> 01:33:01,679
Speaker 4: other thing that we saw after a couple of those

1645
01:33:01,680 --> 01:33:05,320
Speaker 4: bad winters is on winter range, we tried to keep

1646
01:33:05,320 --> 01:33:07,800
Speaker 4: our same animal distribution over time, roughly, so if we

1647
01:33:07,840 --> 01:33:09,799
Speaker 4: lose animals, we work to go back in and recatch

1648
01:33:09,840 --> 01:33:11,880
Speaker 4: adult females from that same general area.

1649
01:33:11,720 --> 01:33:12,920
Speaker 1: To keep the same number cooking.

1650
01:33:13,120 --> 01:33:16,160
Speaker 4: Yep, yeah, exactly, roughly seventy adult females is part of

1651
01:33:16,160 --> 01:33:18,519
Speaker 4: that is what is with that study. As we were

1652
01:33:18,560 --> 01:33:20,040
Speaker 4: going back to some of the areas where we had

1653
01:33:20,080 --> 01:33:23,040
Speaker 4: routinely caught deer in the past and wanted to replace them,

1654
01:33:23,080 --> 01:33:26,280
Speaker 4: we couldn't find any. There was none in there. After

1655
01:33:26,320 --> 01:33:28,960
Speaker 4: those two bad winters, and so it got us scratching

1656
01:33:29,000 --> 01:33:31,960
Speaker 4: our heads. Okay, what does that mean? Did those bad

1657
01:33:32,000 --> 01:33:34,519
Speaker 4: winters just cause animals to like, yeah, I'm not living

1658
01:33:34,520 --> 01:33:37,040
Speaker 4: here anymore and shift out of that country when they

1659
01:33:37,040 --> 01:33:40,320
Speaker 4: were pushed by the bad winter because of snow conditions.

1660
01:33:40,360 --> 01:33:42,040
Speaker 4: Did they then find a new place that was better

1661
01:33:42,040 --> 01:33:44,600
Speaker 4: and then stay there or did they just die and

1662
01:33:44,600 --> 01:33:47,680
Speaker 4: they're not there anymore? So we evaluated that question. And

1663
01:33:47,720 --> 01:33:51,600
Speaker 4: while certainly there are signals, you know, animals can be

1664
01:33:51,600 --> 01:33:54,880
Speaker 4: displaced a bit by a bad winter, which also interesting though,

1665
01:33:55,120 --> 01:34:00,000
Speaker 4: is a stronger signal of that displacement is if animals

1666
01:34:00,120 --> 01:34:03,240
Speaker 4: had a bad winter the prior year, and so again

1667
01:34:03,280 --> 01:34:06,559
Speaker 4: it's like this win this winter PTSD. The shift from

1668
01:34:06,600 --> 01:34:09,479
Speaker 4: where they had lived previously tends to occur after the

1669
01:34:09,520 --> 01:34:12,840
Speaker 4: bad winter, as opposed to during the bad winter. So

1670
01:34:12,880 --> 01:34:16,120
Speaker 4: it's like the memory of the bad winter results in

1671
01:34:16,160 --> 01:34:20,000
Speaker 4: a shift from when they would normally winter because because Meal.

1672
01:34:21,560 --> 01:34:23,519
Speaker 1: And they're like, but I'm sticking it out, but next

1673
01:34:23,560 --> 01:34:25,040
Speaker 1: year I'm going somewhere else.

1674
01:34:25,080 --> 01:34:28,040
Speaker 4: That's that's exactly right, because Meal they're incredibly faithful. They're

1675
01:34:28,040 --> 01:34:30,200
Speaker 4: incredibly faithful to the winter range, to their summer range,

1676
01:34:30,240 --> 01:34:32,120
Speaker 4: into their migratory route do the same thing year after

1677
01:34:32,200 --> 01:34:36,000
Speaker 4: year as adults, and so they they they they shift

1678
01:34:36,200 --> 01:34:39,760
Speaker 4: the winter PTSD shift after the bad winters. But what

1679
01:34:39,800 --> 01:34:45,360
Speaker 4: we learned from that work in particular, was that the

1680
01:34:45,439 --> 01:34:48,280
Speaker 4: reason why we ended up with these vacant holes on

1681
01:34:48,439 --> 01:34:52,840
Speaker 4: winter range was because animals died and we lost them.

1682
01:34:53,720 --> 01:34:56,559
Speaker 4: And remember, one of the key factors that influences whether

1683
01:34:56,640 --> 01:34:58,160
Speaker 4: or not they live or die through winter is how

1684
01:34:58,200 --> 01:35:00,599
Speaker 4: fat they were. So what that means means is what

1685
01:35:00,640 --> 01:35:04,400
Speaker 4: we observe on a winter range with regards to population distribution.

1686
01:35:04,520 --> 01:35:08,599
Speaker 4: For example, Yes, it's influenced by what happens there during winter,

1687
01:35:09,040 --> 01:35:12,120
Speaker 4: but it again is dictated by what happens during summer.

1688
01:35:12,680 --> 01:35:16,080
Speaker 4: So summer nutrition, where animals go and their summer nutrition

1689
01:35:16,760 --> 01:35:19,639
Speaker 4: plays a huge influence in winter distribution of animals.

1690
01:35:20,040 --> 01:35:22,600
Speaker 1: But they're making like that, they're making a mistake. What

1691
01:35:22,600 --> 01:35:25,600
Speaker 1: do you mean they're making a mistake. They're not not

1692
01:35:25,720 --> 01:35:31,479
Speaker 1: the hack on them, They're not thinking it through. They shouldn't. Okay,

1693
01:35:31,680 --> 01:35:35,120
Speaker 1: what's the what's the thinking they're blaming? What would you do? No, No,

1694
01:35:36,320 --> 01:35:41,799
Speaker 1: I'm not I'm joking. I'm not joking. Yeah, it's summer range.

1695
01:35:42,000 --> 01:35:44,280
Speaker 1: It's like it has a poor experience on summer range,

1696
01:35:44,840 --> 01:35:48,360
Speaker 1: goes into a season with low body fat six percent,

1697
01:35:49,479 --> 01:35:53,280
Speaker 1: goes to winter range, has a terrible winter on winter range.

1698
01:35:55,040 --> 01:35:59,600
Speaker 1: Its response could possibly be I need to find a

1699
01:35:59,640 --> 01:36:04,639
Speaker 1: new summer range. Oh yeah, but it's blaming the winter range.

1700
01:36:06,000 --> 01:36:10,080
Speaker 4: You did this on purpose, beautiful segue. So they don't

1701
01:36:10,080 --> 01:36:14,360
Speaker 4: find a new summer range. These animals are These animals

1702
01:36:14,400 --> 01:36:18,040
Speaker 4: are incredibly faithful to their world as adults, and so

1703
01:36:18,080 --> 01:36:20,280
Speaker 4: they don't go find a new summer range. They just

1704
01:36:20,360 --> 01:36:22,320
Speaker 4: they go back up and do the same thing year

1705
01:36:22,520 --> 01:36:27,960
Speaker 4: after year. So then what that then relates to, I

1706
01:36:27,960 --> 01:36:29,479
Speaker 4: have two things I need to get to make sure

1707
01:36:29,520 --> 01:36:32,960
Speaker 4: I get to. One is reproductive chronology. The other the

1708
01:36:33,000 --> 01:36:36,040
Speaker 4: other is what it means across generation of animals. So

1709
01:36:36,880 --> 01:36:39,599
Speaker 4: what that means in that scenario and how that plays

1710
01:36:39,640 --> 01:36:41,200
Speaker 4: out is why did that animal end up with that

1711
01:36:41,240 --> 01:36:42,760
Speaker 4: summer range and why did it end up with that

1712
01:36:42,840 --> 01:36:45,840
Speaker 4: migratory route. We know and have known for a bit

1713
01:36:45,920 --> 01:36:49,599
Speaker 4: now that once once they have their routes as adults,

1714
01:36:49,800 --> 01:36:53,040
Speaker 4: that it's functionally a trade of the animal that it possesses,

1715
01:36:53,280 --> 01:36:55,519
Speaker 4: kind of like even even saying that this animal is

1716
01:36:55,640 --> 01:36:59,160
Speaker 4: really dark colored, Well, this animal has a forty five

1717
01:36:59,200 --> 01:37:02,719
Speaker 4: mile migration at Summer's in Deer Creek and it winters

1718
01:37:02,800 --> 01:37:05,640
Speaker 4: down in Nugget Canyon. Like it's practically a trade of

1719
01:37:05,640 --> 01:37:08,800
Speaker 4: the animal because that's what it does. So then the

1720
01:37:08,880 --> 01:37:11,160
Speaker 4: question is how did it get there? Why did that

1721
01:37:11,200 --> 01:37:13,200
Speaker 4: animal end up with that? And so the other thing

1722
01:37:13,200 --> 01:37:16,639
Speaker 4: we've been working on doing is understanding the only way

1723
01:37:16,640 --> 01:37:18,240
Speaker 4: to get to that is to start from day one

1724
01:37:18,479 --> 01:37:20,880
Speaker 4: from an animal when the day it was born, and

1725
01:37:20,920 --> 01:37:23,800
Speaker 4: then follow it alongside mom as you were describing early on.

1726
01:37:24,479 --> 01:37:26,280
Speaker 4: And so we've been working to do that to test

1727
01:37:26,320 --> 01:37:29,200
Speaker 4: this question of ontogeny migration or like how do they

1728
01:37:29,240 --> 01:37:32,439
Speaker 4: learn to migrate? Where does it come from? And so,

1729
01:37:33,280 --> 01:37:35,080
Speaker 4: which is also very hard when you have so many

1730
01:37:35,080 --> 01:37:37,439
Speaker 4: bad winters because it wipes out all your young animals

1731
01:37:37,439 --> 01:37:40,479
Speaker 4: that you've been trying to monitor alongside mom. But we've

1732
01:37:40,479 --> 01:37:42,880
Speaker 4: at least gotten to the point despite the bad winters

1733
01:37:43,920 --> 01:37:47,040
Speaker 4: that we've had sixteen mother daughter pairs that we've been

1734
01:37:47,080 --> 01:37:49,680
Speaker 4: able to monitor from daughters growing up to be three

1735
01:37:49,760 --> 01:37:52,439
Speaker 4: years of age to the point where they're reproductively active.

1736
01:37:53,479 --> 01:37:59,160
Speaker 4: Of those sixteen, eleven of them adopted Mom's migratory route,

1737
01:37:59,360 --> 01:38:01,280
Speaker 4: so they basic do the exact.

1738
01:38:00,960 --> 01:38:02,080
Speaker 1: Same thing that mom does.

1739
01:38:03,720 --> 01:38:06,519
Speaker 4: Five however, and this sort of gets back to what

1740
01:38:06,560 --> 01:38:09,320
Speaker 4: you were alluding to. Five however, changed things up and

1741
01:38:09,360 --> 01:38:15,479
Speaker 4: did something differently. What's interesting in that is that those

1742
01:38:16,720 --> 01:38:20,040
Speaker 4: there's almost there's not even really like a continuous gradient

1743
01:38:20,120 --> 01:38:24,160
Speaker 4: of like this one definitely did like rate on top

1744
01:38:24,200 --> 01:38:27,400
Speaker 4: of Mom's route, and then you know, weekends, weekends, weekends,

1745
01:38:27,479 --> 01:38:29,840
Speaker 4: And then we have some that basically don't adopt Mom's route.

1746
01:38:29,880 --> 01:38:32,519
Speaker 4: It's either like you do the vast majority of it,

1747
01:38:32,720 --> 01:38:35,800
Speaker 4: or you don't at all got it. And so there

1748
01:38:35,880 --> 01:38:39,720
Speaker 4: are a handful of young females that disperse and do

1749
01:38:40,200 --> 01:38:44,240
Speaker 4: something differently. They find a different migratory route, and so

1750
01:38:44,320 --> 01:38:46,559
Speaker 4: there are a number of them that do things differently,

1751
01:38:46,640 --> 01:38:51,600
Speaker 4: but not as adults. Adults is pretty fixed. The young animals,

1752
01:38:51,640 --> 01:38:55,280
Speaker 4: a few do it, but the ones that do change

1753
01:38:55,320 --> 01:38:58,000
Speaker 4: it seems like whatever they do during their yearling year,

1754
01:38:58,560 --> 01:39:00,640
Speaker 4: so when they turn one year of eight is what

1755
01:39:00,680 --> 01:39:03,000
Speaker 4: they then end up doing for the rest of their lives.

1756
01:39:03,040 --> 01:39:06,320
Speaker 4: Like that's what establishes it. What's also really interesting and

1757
01:39:06,439 --> 01:39:09,000
Speaker 4: is not what I would have expected, which again, our

1758
01:39:09,040 --> 01:39:10,479
Speaker 4: sample size is only sixteen.

1759
01:39:10,560 --> 01:39:12,120
Speaker 1: We have more to go here to be able to

1760
01:39:12,120 --> 01:39:12,920
Speaker 1: get there to do this.

1761
01:39:14,560 --> 01:39:19,320
Speaker 4: But the ones that adopted a different migratory route, much

1762
01:39:19,400 --> 01:39:24,960
Speaker 4: to my surprise, they largely clung to what where they

1763
01:39:25,040 --> 01:39:28,320
Speaker 4: lived on summer range, but then migrated to a different

1764
01:39:28,360 --> 01:39:30,720
Speaker 4: winter range. And I would have thought it would have

1765
01:39:30,720 --> 01:39:33,160
Speaker 4: been the opposite, especially with regards to what you just said,

1766
01:39:33,200 --> 01:39:35,880
Speaker 4: we'll go somewhere else to summer. The majority of them

1767
01:39:35,920 --> 01:39:39,680
Speaker 4: are still residing close to their natal range where they

1768
01:39:39,680 --> 01:39:42,320
Speaker 4: were born, but they adopt a different migratory route and

1769
01:39:42,320 --> 01:39:45,759
Speaker 4: go to a different winter range, which I'm not entirely

1770
01:39:45,800 --> 01:39:48,280
Speaker 4: sure what to make of that yet, but that's that's

1771
01:39:48,280 --> 01:39:48,880
Speaker 4: what we've seen.

1772
01:39:52,040 --> 01:39:55,599
Speaker 3: So you're saying, at sort of the yearlink stage, that's

1773
01:39:55,640 --> 01:39:57,719
Speaker 3: the pattern that they adopt for the rest of their lives.

1774
01:39:57,840 --> 01:39:58,519
Speaker 1: Are they.

1775
01:40:00,000 --> 01:40:03,120
Speaker 3: Is there any correlation to like if the mother dies

1776
01:40:03,640 --> 01:40:07,280
Speaker 3: before they turn one, you know, like yep, are more

1777
01:40:07,280 --> 01:40:11,520
Speaker 3: of those deer likely to be these sort of pathfinders

1778
01:40:11,680 --> 01:40:14,120
Speaker 3: or what's the relationship there?

1779
01:40:14,280 --> 01:40:16,920
Speaker 4: Very good question, which we're only looking at sixteen at

1780
01:40:16,920 --> 01:40:20,559
Speaker 4: the moment, But in looking at those sixteen there seems

1781
01:40:20,560 --> 01:40:23,280
Speaker 4: to be no relationship to whether or not mom dies

1782
01:40:23,520 --> 01:40:26,559
Speaker 4: or mom lives. That seems to not play a role

1783
01:40:26,600 --> 01:40:30,519
Speaker 4: at least as of as of yet. But to then

1784
01:40:30,640 --> 01:40:33,479
Speaker 4: take that to the level of like why we see

1785
01:40:33,479 --> 01:40:36,360
Speaker 4: deer where we do, how they get there, all of

1786
01:40:36,400 --> 01:40:41,240
Speaker 4: these pieces with which determines why animals live where they

1787
01:40:41,240 --> 01:40:45,800
Speaker 4: do and the success that they have for deer anyway,

1788
01:40:45,920 --> 01:40:48,360
Speaker 4: it all starts such early in life. It's a cross

1789
01:40:48,400 --> 01:40:50,800
Speaker 4: generational process. Clearly that we need to be able to

1790
01:40:50,840 --> 01:40:55,400
Speaker 4: document document from them. And one, maintaining migration is clearly

1791
01:40:55,439 --> 01:40:58,719
Speaker 4: something that translates across generation as young learned from mom.

1792
01:40:59,320 --> 01:41:01,920
Speaker 4: But two and so that means we're you know, we're

1793
01:41:01,960 --> 01:41:06,400
Speaker 4: also conserving memory on a landscape. But two also I

1794
01:41:06,439 --> 01:41:10,040
Speaker 4: think the few animals that did something different maybe is

1795
01:41:10,160 --> 01:41:13,920
Speaker 4: like a glimmer of hope that you know, when things

1796
01:41:13,920 --> 01:41:18,320
Speaker 4: are lost. So if we lose certain migratory routes, how

1797
01:41:18,360 --> 01:41:20,519
Speaker 4: do we get them back? Is the question, because if

1798
01:41:20,560 --> 01:41:22,880
Speaker 4: everybody's faithful to what they know, then nobody's going to

1799
01:41:22,920 --> 01:41:25,760
Speaker 4: go back there. And I firmly believe with what we've

1800
01:41:25,800 --> 01:41:27,880
Speaker 4: been able to learn about meal deer over the past

1801
01:41:28,400 --> 01:41:33,920
Speaker 4: fifteen or so years, that something that is hampering us,

1802
01:41:34,000 --> 01:41:36,000
Speaker 4: or has hampered us from the past to the present

1803
01:41:36,760 --> 01:41:40,320
Speaker 4: is when we've lost certain animals across the landscape that

1804
01:41:40,400 --> 01:41:43,000
Speaker 4: migrated into certain places before. I mean, there's you know,

1805
01:41:43,560 --> 01:41:45,120
Speaker 4: many people can say, man, there used to be deer

1806
01:41:45,120 --> 01:41:46,800
Speaker 4: all over this ridgeline, and I just don't see deer

1807
01:41:46,800 --> 01:41:50,040
Speaker 4: here anymore. And they may reference a number of reasons

1808
01:41:50,040 --> 01:41:52,120
Speaker 4: why they think that is. They may reference it as

1809
01:41:52,120 --> 01:41:54,080
Speaker 4: an elkhole. Now it's full of elk, or whatever the

1810
01:41:54,080 --> 01:41:58,479
Speaker 4: case may be. But the reason why they potentially don't

1811
01:41:58,479 --> 01:42:00,800
Speaker 4: come back is because the only way to get that

1812
01:42:00,960 --> 01:42:04,760
Speaker 4: backfilled is for animals to be pioneers and venture out

1813
01:42:04,760 --> 01:42:06,920
Speaker 4: into new range, which adults tend not to do. And

1814
01:42:06,960 --> 01:42:10,080
Speaker 4: so we're relying on the small proportion a few yearlings

1815
01:42:10,080 --> 01:42:14,120
Speaker 4: to potentially regain that space. And if we lose this

1816
01:42:14,200 --> 01:42:16,479
Speaker 4: is that you know, vacant space Randall. We've you know,

1817
01:42:16,600 --> 01:42:19,960
Speaker 4: talked about a bunch as well. If we lose occupancy

1818
01:42:20,000 --> 01:42:23,000
Speaker 4: of those places on the landscape over time, for whatever reason,

1819
01:42:23,720 --> 01:42:26,600
Speaker 4: we lose memory to those places. What that's doing is

1820
01:42:26,600 --> 01:42:29,400
Speaker 4: it's functionally reducing the carrying capacity of the range. If

1821
01:42:29,439 --> 01:42:32,120
Speaker 4: you don't have animals using it, it technically doesn't matter,

1822
01:42:32,560 --> 01:42:35,120
Speaker 4: and maybe viable habitat that's there, but for it to

1823
01:42:35,160 --> 01:42:38,400
Speaker 4: matter to the population, somebody needs to be using it

1824
01:42:38,439 --> 01:42:41,360
Speaker 4: and integrating that food into the into the population.

1825
01:42:41,560 --> 01:42:43,680
Speaker 1: That's so interesting because you look at places like that

1826
01:42:43,840 --> 01:42:45,760
Speaker 1: and you're like, what is it about it that that's

1827
01:42:45,800 --> 01:42:47,760
Speaker 1: not Why don't they like it? That's right?

1828
01:42:47,840 --> 01:42:50,000
Speaker 4: Yeah, so that's what we think. Yeah, why aren't they

1829
01:42:50,000 --> 01:42:52,800
Speaker 4: going here? Well, nobody knows to go there, and so

1830
01:42:53,479 --> 01:42:55,040
Speaker 4: how do you get them? How do you get them

1831
01:42:55,080 --> 01:42:55,519
Speaker 4: back in there?

1832
01:42:55,479 --> 01:42:59,200
Speaker 1: You know, salm and there's a sorry randall and salmon.

1833
01:43:00,479 --> 01:43:03,880
Speaker 1: Some percentage of a run I always say they screw up,

1834
01:43:04,200 --> 01:43:07,280
Speaker 1: but it's some some percentage of it. Run doesn't go

1835
01:43:07,360 --> 01:43:13,640
Speaker 1: back to the natal stream. They screw up end up

1836
01:43:13,640 --> 01:43:17,280
Speaker 1: somewhere else. But think about the implications of it. Yeah,

1837
01:43:17,720 --> 01:43:20,800
Speaker 1: because river systems change. I mean, there could be a

1838
01:43:20,880 --> 01:43:24,680
Speaker 1: river that doesn't have suitable spawning gravel and then a

1839
01:43:24,720 --> 01:43:29,000
Speaker 1: flood whatever landslide on something and all of a sudden boom,

1840
01:43:29,080 --> 01:43:32,880
Speaker 1: it's great, some number of fish, some fish is gonna

1841
01:43:32,960 --> 01:43:36,200
Speaker 1: like some number are going to screw up and go

1842
01:43:36,360 --> 01:43:38,760
Speaker 1: up that thing and then it could be could be

1843
01:43:38,800 --> 01:43:41,639
Speaker 1: something gold mine. It's a portfolio, just place a sweet

1844
01:43:41,680 --> 01:43:44,600
Speaker 1: portfolio effect. And also you create a new right and

1845
01:43:44,960 --> 01:43:47,120
Speaker 1: think about like the thing I always want to think

1846
01:43:47,120 --> 01:43:55,320
Speaker 1: about that too, is like as as climate changes, Yeah,

1847
01:43:55,320 --> 01:43:57,160
Speaker 1: there's a lot of salmon rivers that would be great

1848
01:43:57,160 --> 01:44:00,960
Speaker 1: salmon rivers, but there too far. North's too cold, right,

1849
01:44:01,200 --> 01:44:04,800
Speaker 1: So as because you had that dispersal mechanism, things could

1850
01:44:04,840 --> 01:44:07,800
Speaker 1: just suddenly become kind of right, that's right, and some

1851
01:44:07,880 --> 01:44:10,120
Speaker 1: fish is going to take a left when he should

1852
01:44:10,120 --> 01:44:13,280
Speaker 1: have taken it right and wind up finding a new exactly.

1853
01:44:13,360 --> 01:44:17,320
Speaker 1: It takes two but you follow me, yeah, just yeah, yeah, yeah, absolutely.

1854
01:44:19,240 --> 01:44:22,519
Speaker 1: I got a technical question for you if I see it.

1855
01:44:22,600 --> 01:44:26,280
Speaker 1: Let's say a fellow sees a nice buck on the

1856
01:44:26,479 --> 01:44:30,679
Speaker 1: last day of season, Say it's November night of fellas,

1857
01:44:30,680 --> 01:44:33,599
Speaker 1: he's a nice buck on November twenty fourth, so you know,

1858
01:44:34,800 --> 01:44:37,759
Speaker 1: no one got him. He could die from other stuff.

1859
01:44:38,439 --> 01:44:42,719
Speaker 1: But the next year, on November twenty fourth, in your mind,

1860
01:44:43,000 --> 01:44:45,479
Speaker 1: how far is that dear from where he was the

1861
01:44:45,560 --> 01:44:50,360
Speaker 1: year before, based on your based on your research, all

1862
01:44:50,400 --> 01:44:55,400
Speaker 1: depends it does. Yeah, but he's already an adult, he's

1863
01:44:55,439 --> 01:44:56,160
Speaker 1: already set in his.

1864
01:44:56,120 --> 01:45:00,120
Speaker 4: Ways so best. So we're working on understanding those aspects

1865
01:45:00,120 --> 01:45:03,479
Speaker 4: of males as well. Male dispersal from their natal range,

1866
01:45:04,040 --> 01:45:06,400
Speaker 4: how faithful they are to their seasonal ranges as well,

1867
01:45:06,479 --> 01:45:09,759
Speaker 4: and yeah, I mean in general, as you as you noted,

1868
01:45:10,040 --> 01:45:12,640
Speaker 4: they're living within their within their range, right, and so

1869
01:45:12,800 --> 01:45:15,840
Speaker 4: I think that the certainly not that far from there. He

1870
01:45:15,920 --> 01:45:18,960
Speaker 4: lived up in that country, right. The only wild card

1871
01:45:19,000 --> 01:45:22,479
Speaker 4: within that is during a hunting season, as males get

1872
01:45:22,640 --> 01:45:24,920
Speaker 4: potentially pushed around, he may have been in a non

1873
01:45:24,960 --> 01:45:27,680
Speaker 4: normal place than what he often is the one year

1874
01:45:27,720 --> 01:45:31,160
Speaker 4: that he's seen versus versus the next year. But certainly, yeah,

1875
01:45:31,160 --> 01:45:33,479
Speaker 4: I could have generally he was in a spy he

1876
01:45:33,520 --> 01:45:36,880
Speaker 4: don't want to be. Yeah, yeah, yeah, that's that's exactly right.

1877
01:45:36,920 --> 01:45:38,920
Speaker 4: But otherwise he you know, he lives in that country,

1878
01:45:39,040 --> 01:45:40,679
Speaker 4: so yeah, they're there.

1879
01:45:41,200 --> 01:45:45,839
Speaker 3: Yeah, when you're talking about re restoring a lost migration

1880
01:45:46,120 --> 01:45:49,000
Speaker 3: corridor on this habitat, like because you're it's a it

1881
01:45:49,040 --> 01:45:53,880
Speaker 3: would be a yearling of yearling with some wander lust

1882
01:45:53,920 --> 01:45:57,439
Speaker 3: that takes off down a new ridge or whatever else.

1883
01:45:58,520 --> 01:46:01,240
Speaker 3: There aren't going to be any other deal that then

1884
01:46:01,479 --> 01:46:03,960
Speaker 3: convert to that range. It's all going to come from

1885
01:46:03,960 --> 01:46:06,040
Speaker 3: that deer's offspring, correct.

1886
01:46:05,800 --> 01:46:06,479
Speaker 1: Most likely.

1887
01:46:06,760 --> 01:46:10,719
Speaker 3: Yeah, So it's a long to cross generation you're talking about,

1888
01:46:10,760 --> 01:46:14,400
Speaker 3: but multiple multiple generations before you have like a herd

1889
01:46:14,400 --> 01:46:15,679
Speaker 3: of deer using that.

1890
01:46:15,439 --> 01:46:19,120
Speaker 4: That's right, which reinforces the importance of maintaining what you

1891
01:46:19,240 --> 01:46:23,240
Speaker 4: have because you are maintaining and protecting memory on the landscape.

1892
01:46:23,280 --> 01:46:26,439
Speaker 4: And if once we lose it, especially for a such

1893
01:46:26,479 --> 01:46:29,200
Speaker 4: a faithful animal like a deer, we don't we don't

1894
01:46:29,240 --> 01:46:31,519
Speaker 4: necessarily get it back, just like that versus elk, where

1895
01:46:31,520 --> 01:46:33,920
Speaker 4: they'll just go go anywhere, they'll take advantage of whatever

1896
01:46:33,960 --> 01:46:36,600
Speaker 4: happens to be there. But for deer, their memory is

1897
01:46:36,640 --> 01:46:38,880
Speaker 4: like their fence around their world of what they do.

1898
01:46:38,920 --> 01:46:42,720
Speaker 4: And I think another thing that I think, I think

1899
01:46:42,720 --> 01:46:45,640
Speaker 4: this is just super interesting and I think speaks to

1900
01:46:46,200 --> 01:46:50,920
Speaker 4: the level of intimacy between animal, female deer and their

1901
01:46:51,120 --> 01:46:56,040
Speaker 4: environment is that if you consider mule deer, who all

1902
01:46:56,040 --> 01:46:59,679
Speaker 4: share a common winter range, Okay, some migrate ten miles,

1903
01:46:59,680 --> 01:47:02,040
Speaker 4: some grade one hundred and twenty miles. The one that

1904
01:47:02,040 --> 01:47:04,439
Speaker 4: my rates one hundred and twenty miles is typically often

1905
01:47:04,520 --> 01:47:07,040
Speaker 4: going to be going to higher elevation, potentially traversing a

1906
01:47:07,080 --> 01:47:09,960
Speaker 4: lot of country, dealing with snow conditions and spring stuff

1907
01:47:10,000 --> 01:47:13,439
Speaker 4: like that in an ideal world. And for meal deer,

1908
01:47:13,479 --> 01:47:15,280
Speaker 4: they want to go back and give birth to the

1909
01:47:15,360 --> 01:47:17,879
Speaker 4: range they typically give birth in. We've literally had female

1910
01:47:17,880 --> 01:47:20,519
Speaker 4: deer give birth in the exact same bedsite one year

1911
01:47:20,560 --> 01:47:22,960
Speaker 4: after the next. I mean, they're very faithful to their

1912
01:47:23,439 --> 01:47:26,720
Speaker 4: birth sits. So then the question is, well, what does

1913
01:47:26,760 --> 01:47:30,960
Speaker 4: that mean for them to time birth to coincide with

1914
01:47:31,000 --> 01:47:33,320
Speaker 4: when food is available on their summer range and for

1915
01:47:33,360 --> 01:47:36,439
Speaker 4: their ability to get to their summer range to give birth.

1916
01:47:37,160 --> 01:47:40,440
Speaker 4: And so we looked at that in as we talked earlier,

1917
01:47:40,800 --> 01:47:43,080
Speaker 4: we not only see animals on the day they're bone,

1918
01:47:43,120 --> 01:47:45,240
Speaker 4: but we also see them in utero. So when we

1919
01:47:45,400 --> 01:47:49,120
Speaker 4: recatch females in March, we see the fetuses in utero

1920
01:47:49,160 --> 01:47:51,640
Speaker 4: and we measure their eye diameter, which gives us an

1921
01:47:51,640 --> 01:47:54,920
Speaker 4: indication of how far along in gestation they are. So

1922
01:47:54,960 --> 01:47:57,880
Speaker 4: we've been able to put those pieces together to look

1923
01:47:57,920 --> 01:48:02,040
Speaker 4: at what factors influence eye diameter in March, so that

1924
01:48:02,240 --> 01:48:06,760
Speaker 4: gestational progression, what determines where they are in March, and

1925
01:48:06,760 --> 01:48:09,320
Speaker 4: then what does that mean thereafter Because one of the

1926
01:48:09,360 --> 01:48:12,960
Speaker 4: strongest signals that determines when animals give birth is how

1927
01:48:13,000 --> 01:48:14,800
Speaker 4: far along they were in gestation in March, So we

1928
01:48:14,800 --> 01:48:17,480
Speaker 4: can use fetal eye diameter. In fact, we use it

1929
01:48:17,520 --> 01:48:20,800
Speaker 4: to help guide our planning in the spring when we're

1930
01:48:20,840 --> 01:48:24,200
Speaker 4: going to catch mule, deer fonds or bigcorn sheep lambs.

1931
01:48:24,240 --> 01:48:26,840
Speaker 4: The anticipation of who's going to give birth when we

1932
01:48:26,920 --> 01:48:29,040
Speaker 4: use fetal eye diameter that we measured in March.

1933
01:48:29,160 --> 01:48:32,760
Speaker 1: But that's like fine tuning within a pretty narrow window.

1934
01:48:32,439 --> 01:48:35,840
Speaker 4: Though to some degree for sheep it can be pretty broad.

1935
01:48:36,280 --> 01:48:39,639
Speaker 4: But for deer, yes, it's fine tuning within a narrow window.

1936
01:48:40,680 --> 01:48:43,599
Speaker 4: That said what we so we then looked at so

1937
01:48:43,680 --> 01:48:45,960
Speaker 4: since fetal eye diameter plays such a major role in

1938
01:48:46,000 --> 01:48:48,200
Speaker 4: when they give birth, we looked at factors that influence

1939
01:48:48,200 --> 01:48:51,200
Speaker 4: fetal eye diameter. Uh and I think one of the

1940
01:48:51,240 --> 01:48:55,280
Speaker 4: most fascinating aspects that influence fetal eye diameter is how

1941
01:48:55,439 --> 01:49:00,879
Speaker 4: far they migrate. So animals that migrate further have smaller

1942
01:49:00,920 --> 01:49:05,120
Speaker 4: eye diameter in March than those that migrate short distances,

1943
01:49:06,200 --> 01:49:08,599
Speaker 4: if that may. So the ones that migrate short distances

1944
01:49:08,600 --> 01:49:11,599
Speaker 4: are further along in gestation in March, which means they're

1945
01:49:11,640 --> 01:49:16,160
Speaker 4: going to give birth sooner to be that's right. That's right,

1946
01:49:16,200 --> 01:49:18,519
Speaker 4: and greenup is going to happen faster on that range

1947
01:49:18,560 --> 01:49:20,360
Speaker 4: than the range that's way up in the mountains. So

1948
01:49:22,080 --> 01:49:25,839
Speaker 4: in it the influence is roughly for every ten miles

1949
01:49:25,880 --> 01:49:30,680
Speaker 4: migration migrated. Based on our models, for every ten miles migrated,

1950
01:49:31,280 --> 01:49:35,400
Speaker 4: that's one day behind in gestation. So for an animal

1951
01:49:35,439 --> 01:49:38,080
Speaker 4: that basically hardly migrates to an animal that migrates one

1952
01:49:38,160 --> 01:49:42,559
Speaker 4: hundred miles, can be ten days expected differences based on

1953
01:49:42,560 --> 01:49:44,599
Speaker 4: their feetal eye diameter from when they're going to give birth.

1954
01:49:44,600 --> 01:49:46,559
Speaker 4: That animal that migrates one hundred miles is going to

1955
01:49:47,240 --> 01:49:50,960
Speaker 4: give birth ten days later. Now to take that one

1956
01:49:50,960 --> 01:49:54,320
Speaker 4: step further, and this happened, I forget the exact year.

1957
01:49:54,400 --> 01:49:58,040
Speaker 4: It is twenty twenty. On June second, I was with

1958
01:49:58,080 --> 01:50:00,360
Speaker 4: one of my team members, Ran and Jacobak and we

1959
01:50:00,479 --> 01:50:06,040
Speaker 4: went into deer ninety six, up into where she gave

1960
01:50:06,080 --> 01:50:09,920
Speaker 4: birth that morning, collared her twin fawns, and then we

1961
01:50:10,000 --> 01:50:11,840
Speaker 4: went on and did other work the rest of the day,

1962
01:50:12,040 --> 01:50:14,799
Speaker 4: and then we got a birth notification from a vagineal

1963
01:50:14,840 --> 01:50:17,879
Speaker 4: implant transmitter later that evening. We were about two hours

1964
01:50:17,920 --> 01:50:21,040
Speaker 4: before before dark and we're looking at it and the

1965
01:50:21,120 --> 01:50:23,960
Speaker 4: animal that we just got a birth notification from was

1966
01:50:23,960 --> 01:50:26,320
Speaker 4: about three hundred yards down from where we had just

1967
01:50:26,400 --> 01:50:29,439
Speaker 4: been that morning collaring twin fonns from ninety six, And

1968
01:50:29,479 --> 01:50:31,360
Speaker 4: I'm like, man, wouldn't be cool to hustle in there

1969
01:50:31,400 --> 01:50:33,360
Speaker 4: to see if we can call her. Like, two animals

1970
01:50:33,360 --> 01:50:34,840
Speaker 4: give birth on the same day that live in the

1971
01:50:34,880 --> 01:50:37,479
Speaker 4: same place, So we hustled in there and then collared

1972
01:50:37,479 --> 01:50:39,679
Speaker 4: the single fond from that female on that same day.

1973
01:50:39,720 --> 01:50:42,599
Speaker 4: So which makes sense, right, animals living in this same

1974
01:50:42,680 --> 01:50:46,080
Speaker 4: area giving birth on the same day. So ninety six

1975
01:50:46,160 --> 01:50:48,120
Speaker 4: was the one that was up the ridge, and then

1976
01:50:48,120 --> 01:50:50,000
Speaker 4: it was MFO that was down.

1977
01:50:49,840 --> 01:50:50,639
Speaker 1: Below the ridge.

1978
01:50:50,720 --> 01:50:57,320
Speaker 4: Now we had three years earlier, in spring of twenty seventeen,

1979
01:50:58,120 --> 01:51:02,280
Speaker 4: we had collared mfoh who had just been born to

1980
01:51:02,479 --> 01:51:07,960
Speaker 4: ninety six in that same place on June third, So I.

1981
01:51:07,920 --> 01:51:10,800
Speaker 3: Got lost, mom and grandma are giving mom and daughter.

1982
01:51:10,960 --> 01:51:14,080
Speaker 4: So MFO who we went in that afternoon to call

1983
01:51:14,080 --> 01:51:16,960
Speaker 4: her fone was ninety six's.

1984
01:51:16,600 --> 01:51:18,519
Speaker 1: Daughter from three years prior.

1985
01:51:18,720 --> 01:51:23,519
Speaker 4: Oh, really really ninety six's daughter from three years prior,

1986
01:51:24,200 --> 01:51:27,360
Speaker 4: giving birth on the exact same day on June second, and.

1987
01:51:28,640 --> 01:51:29,720
Speaker 1: Three hundred yards down the hill.

1988
01:51:29,800 --> 01:51:33,600
Speaker 4: Yeah, exactly same place, same birth date. And also she

1989
01:51:33,680 --> 01:51:37,680
Speaker 4: had been born one day difference in the calendar three

1990
01:51:37,960 --> 01:51:46,160
Speaker 4: years prior. So and while that's like seemingly an anecdote,

1991
01:51:46,760 --> 01:51:49,760
Speaker 4: we've seen it and had it happen multiple times. Within

1992
01:51:49,800 --> 01:51:53,280
Speaker 4: these family groups, which indicates to us that not only

1993
01:51:53,520 --> 01:51:56,880
Speaker 4: are is space on the landscape, migratory routes on the

1994
01:51:56,960 --> 01:52:01,719
Speaker 4: landscape inherited across generational time, but even their reproductive synchrony,

1995
01:52:01,720 --> 01:52:05,000
Speaker 4: the reproductive chronology is inherited across generational time. Like that's

1996
01:52:05,040 --> 01:52:07,320
Speaker 4: how in sync they are with their environment. And now

1997
01:52:07,320 --> 01:52:10,080
Speaker 4: I think what's also equally facinating if you consider how

1998
01:52:10,120 --> 01:52:13,560
Speaker 4: the mule deer rut happens, not all of it, but

1999
01:52:13,600 --> 01:52:15,080
Speaker 4: a lot of it happens down to winter range, right,

2000
01:52:15,120 --> 01:52:17,280
Speaker 4: they migrate down to winter range, and so on winter range,

2001
01:52:17,280 --> 01:52:19,880
Speaker 4: we have all these animals with all these different migratory

2002
01:52:19,960 --> 01:52:22,000
Speaker 4: tactics that are showing up on winter range, and they're

2003
01:52:22,040 --> 01:52:25,439
Speaker 4: all rutting in this same place. And then but what

2004
01:52:25,479 --> 01:52:28,840
Speaker 4: it means is what's happening is Gladys over here, who

2005
01:52:28,880 --> 01:52:32,799
Speaker 4: migrates ten miles, she you know, she comes into Estris

2006
01:52:32,880 --> 01:52:37,400
Speaker 4: on November twentieth. But then Jennifer over here, who migrates

2007
01:52:37,439 --> 01:52:40,320
Speaker 4: one hundred miles, she doesn't come into Estris until ten

2008
01:52:40,360 --> 01:52:45,760
Speaker 4: days later on December first. Right, even though they're on

2009
01:52:45,800 --> 01:52:48,920
Speaker 4: the same winter range in the same place, their estra

2010
01:52:49,040 --> 01:52:52,240
Speaker 4: cycle is tied to actually where they go during the

2011
01:52:52,240 --> 01:52:54,639
Speaker 4: summer and it has virtually nothing to do with where

2012
01:52:54,640 --> 01:52:56,320
Speaker 4: they're at on that day on winter range.

2013
01:52:57,720 --> 01:53:09,120
Speaker 1: Damn. Yeah. Besides seeing a lot of stuff that dudes say,

2014
01:53:11,880 --> 01:53:16,360
Speaker 1: do you see things that state game agencies are doing

2015
01:53:17,200 --> 01:53:20,040
Speaker 1: or the way they're thinking about these issues? And management?

2016
01:53:20,080 --> 01:53:22,400
Speaker 1: Do you see things that you're like that just doesn't

2017
01:53:22,640 --> 01:53:26,400
Speaker 1: make any sense. You're you're wanting me to like step

2018
01:53:26,400 --> 01:53:29,360
Speaker 1: on a landline? Yeah, I would like that.

2019
01:53:29,439 --> 01:53:32,600
Speaker 3: How far? How long does it take for the management

2020
01:53:32,640 --> 01:53:33,880
Speaker 3: to catch up with the science?

2021
01:53:34,240 --> 01:53:37,759
Speaker 1: And are you a communication with everybody? Well?

2022
01:53:37,960 --> 01:53:42,240
Speaker 4: So, no matter there's one challenge with all of this, right,

2023
01:53:42,320 --> 01:53:45,439
Speaker 4: no matter what, we're all we're all people, and we've

2024
01:53:45,520 --> 01:53:48,719
Speaker 4: all we've all done things a number of different ways,

2025
01:53:48,760 --> 01:53:51,200
Speaker 4: and we've just accepted them to be true over time

2026
01:53:51,800 --> 01:53:55,519
Speaker 4: without necessarily pausing and saying, wait, why do I think that?

2027
01:53:55,800 --> 01:53:58,160
Speaker 4: Where did that come from? What evidence support it?

2028
01:53:58,240 --> 01:54:00,519
Speaker 1: And it just do I think the screaming my kids

2029
01:54:00,520 --> 01:54:04,080
Speaker 1: all the time? Yeah? Yeah, is helping. That's exactly right,

2030
01:54:04,120 --> 01:54:04,680
Speaker 1: and help with me.

2031
01:54:04,800 --> 01:54:08,920
Speaker 4: I mean, so a fascinating example that like when I

2032
01:54:08,960 --> 01:54:11,040
Speaker 4: grew up in I mean grew up in northeastern South

2033
01:54:11,120 --> 01:54:14,160
Speaker 4: Dakota and we hunted deer every year, white tail deer

2034
01:54:14,200 --> 01:54:17,960
Speaker 4: every year, just locally there to feed our family. And

2035
01:54:18,120 --> 01:54:20,639
Speaker 4: my dad and my grandpa always used to say, like, man,

2036
01:54:20,720 --> 01:54:22,360
Speaker 4: the deer fat this year. I guess we're going to

2037
01:54:22,400 --> 01:54:24,880
Speaker 4: have a bad winter. Like they would say that.

2038
01:54:24,840 --> 01:54:25,679
Speaker 1: Over and over again.

2039
01:54:26,200 --> 01:54:29,599
Speaker 4: And I mean I remember as a kid thinking, wait,

2040
01:54:30,360 --> 01:54:32,120
Speaker 4: how do they know it's going to be a bad winter?

2041
01:54:32,600 --> 01:54:35,080
Speaker 4: How do they know to prepare for a bad winter?

2042
01:54:35,760 --> 01:54:37,880
Speaker 4: And the thing is we know now like they don't

2043
01:54:38,200 --> 01:54:41,440
Speaker 4: like that that is is technically not the case, although

2044
01:54:42,160 --> 01:54:45,040
Speaker 4: if you consider it relative to local adaptation and then

2045
01:54:45,040 --> 01:54:47,240
Speaker 4: the example that we just talked about with this pre

2046
01:54:47,280 --> 01:54:50,480
Speaker 4: programming and allocation of reserves after experiencing a bad winter,

2047
01:54:50,840 --> 01:54:54,040
Speaker 4: there's simultaneously some truth to that because it's tied to

2048
01:54:54,120 --> 01:54:57,880
Speaker 4: what animals have experience. Maybe less so of knowing they're

2049
01:54:57,880 --> 01:55:00,120
Speaker 4: going to experience something, but it's it's a social it

2050
01:55:00,320 --> 01:55:02,400
Speaker 4: was happened in the past, and so I think there's

2051
01:55:02,440 --> 01:55:06,640
Speaker 4: just many things that we operate within that we just

2052
01:55:06,720 --> 01:55:08,920
Speaker 4: we generally don't question. This is just how we do things,

2053
01:55:08,920 --> 01:55:11,440
Speaker 4: and so that's that's where we go. That's where we go.

2054
01:55:11,560 --> 01:55:16,000
Speaker 4: And so there's there's always there's always some you know

2055
01:55:16,120 --> 01:55:20,320
Speaker 4: some challenges in rethinking where we have been and being

2056
01:55:20,360 --> 01:55:22,879
Speaker 4: willing to reconsider new information.

2057
01:55:23,000 --> 01:55:23,640
Speaker 1: I think that's hard.

2058
01:55:23,720 --> 01:55:29,240
Speaker 4: That's hard for anybody, being diplomatic probably, And the thing you.

2059
01:55:29,680 --> 01:55:37,000
Speaker 1: Sometimes look at management strategies and management practices and think

2060
01:55:37,040 --> 01:55:39,200
Speaker 1: to yourself, it just doesn't make any sense.

2061
01:55:40,440 --> 01:55:46,400
Speaker 4: Well, sure, okay, sure, But I can also like there

2062
01:55:46,440 --> 01:55:49,760
Speaker 4: there's also and I mean with full respect for my

2063
01:55:50,080 --> 01:55:53,560
Speaker 4: for my colleagues with management agencies, like, we're also stuck

2064
01:55:53,600 --> 01:55:57,760
Speaker 4: in this difficult position where it's not necessarily difficult position,

2065
01:55:57,840 --> 01:56:02,080
Speaker 4: but they manage a while resources in trust right fundamental

2066
01:56:02,160 --> 01:56:04,680
Speaker 4: aspect of the North American model. We're all owners of it,

2067
01:56:05,560 --> 01:56:07,560
Speaker 4: but it's managing it as a trust on behalf of

2068
01:56:07,560 --> 01:56:10,000
Speaker 4: the public. And so what that means is they don't

2069
01:56:10,000 --> 01:56:11,680
Speaker 4: get to just sit in a room and just say, well,

2070
01:56:11,680 --> 01:56:13,760
Speaker 4: here's what we're going to do because biologically this is

2071
01:56:13,800 --> 01:56:14,480
Speaker 4: the best thing to do.

2072
01:56:15,040 --> 01:56:16,560
Speaker 1: They also have to consider.

2073
01:56:16,200 --> 01:56:19,680
Speaker 4: What the public wants, social pressures, and so it can

2074
01:56:19,680 --> 01:56:23,240
Speaker 4: be an incredibly difficult balance at times. And so oftentimes

2075
01:56:23,240 --> 01:56:25,680
Speaker 4: I view it as, yes, we need to as we

2076
01:56:25,800 --> 01:56:27,879
Speaker 4: learn things, what can that mean for us in updating

2077
01:56:27,880 --> 01:56:31,400
Speaker 4: how we think about various management practices? For sure, that's

2078
01:56:31,480 --> 01:56:34,560
Speaker 4: like maybe that's like, you know, baseline level of what

2079
01:56:34,600 --> 01:56:37,240
Speaker 4: does it mean for us as we consider management practices,

2080
01:56:37,840 --> 01:56:41,040
Speaker 4: But then we don't you know, to then take that

2081
01:56:41,120 --> 01:56:44,600
Speaker 4: the next step. We don't necessarily get the opportunity to say, hey,

2082
01:56:44,600 --> 01:56:47,360
Speaker 4: we're going to change this. We need to be able

2083
01:56:47,400 --> 01:56:49,800
Speaker 4: to bring that information to the public, which is also

2084
01:56:50,280 --> 01:56:53,600
Speaker 4: what's so incredibly valuable, like of your platform here in

2085
01:56:53,720 --> 01:56:56,640
Speaker 4: ability to be able to speak about these things is like,

2086
01:56:56,840 --> 01:56:59,800
Speaker 4: what's critical is that we can bring this information to

2087
01:56:59,840 --> 01:57:02,520
Speaker 4: the public because there are a lot of things that

2088
01:57:02,560 --> 01:57:05,440
Speaker 4: were just stuck on that have happened for a really

2089
01:57:05,680 --> 01:57:08,320
Speaker 4: really long time that oh, this is a solution, this

2090
01:57:08,400 --> 01:57:10,000
Speaker 4: is this is what we need to do. But it's

2091
01:57:10,000 --> 01:57:12,800
Speaker 4: not as simple as that. So being able to communicate

2092
01:57:12,840 --> 01:57:15,760
Speaker 4: it and in meaningful ways that connects with people and

2093
01:57:15,800 --> 01:57:20,080
Speaker 4: maybe change their perspective on things is sort of like

2094
01:57:20,160 --> 01:57:23,880
Speaker 4: ground zero for helping institute management change.

2095
01:57:24,600 --> 01:57:31,760
Speaker 1: Would you ever have the time and appetite to take

2096
01:57:31,880 --> 01:57:38,960
Speaker 1: your data sets from your box and take a look

2097
01:57:39,000 --> 01:57:43,480
Speaker 1: at lunar phase stuff with the rent? Does this interest

2098
01:57:43,520 --> 01:57:43,840
Speaker 1: you at all?

2099
01:57:44,040 --> 01:57:47,959
Speaker 4: Well, so I have just that with movement, Yeah, yeah, movement,

2100
01:57:48,160 --> 01:57:51,000
Speaker 4: we certainly could we absolutely could. And you're talking from

2101
01:57:51,080 --> 01:57:53,240
Speaker 4: like a ret perspective because there's so much focus on

2102
01:57:53,800 --> 01:57:55,320
Speaker 4: or are you talking like during the hunting season.

2103
01:57:55,480 --> 01:57:57,560
Speaker 1: Just the idea, just it would be the I mean,

2104
01:57:57,600 --> 01:57:59,360
Speaker 1: you could go down a million rabbit holes, which is

2105
01:57:59,440 --> 01:58:03,680
Speaker 1: generally that deer are moving at different times, moving in

2106
01:58:03,720 --> 01:58:08,760
Speaker 1: different places, different feeding patterns. According to Moonface, we absolutely could,

2107
01:58:09,080 --> 01:58:11,200
Speaker 1: and you could take because you have so much data,

2108
01:58:11,440 --> 01:58:13,760
Speaker 1: someone could go in and look at what you got

2109
01:58:13,800 --> 01:58:16,440
Speaker 1: and map it out to twenty eight day lunar cycles

2110
01:58:16,600 --> 01:58:21,200
Speaker 1: and say like, uh lo and behold, they do seem

2111
01:58:21,240 --> 01:58:24,520
Speaker 1: to be they do seem to behave differently according to

2112
01:58:24,560 --> 01:58:26,400
Speaker 1: the lunar or you'd be like, we can't find it.

2113
01:58:26,960 --> 01:58:28,760
Speaker 1: You know, that would be that would be very helpful

2114
01:58:28,800 --> 01:58:31,560
Speaker 1: to me. But yes, thank you for doing that. So

2115
01:58:31,680 --> 01:58:33,960
Speaker 1: we need to with our T shirt proceeds. Should we

2116
01:58:34,040 --> 01:58:39,440
Speaker 1: like in dow a absolutely like research center for studies.

2117
01:58:41,400 --> 01:58:44,000
Speaker 1: There is a center for I'm just taking an intern

2118
01:58:44,360 --> 01:58:45,240
Speaker 1: crunching some numbers.

2119
01:58:45,400 --> 01:58:47,360
Speaker 5: I already talked to someone the other day about that

2120
01:58:47,440 --> 01:58:51,960
Speaker 5: we might have them on the show. Totally debunks that.

2121
01:58:52,880 --> 01:58:55,600
Speaker 1: He debunked, Well, maybe he can. Can you have to

2122
01:58:55,880 --> 01:58:57,680
Speaker 1: lend your data sets out is that.

2123
01:58:57,640 --> 01:59:03,200
Speaker 4: Anything in collaboration. Yeah, it all depends, but in collaboration. Yeah. Uh,

2124
01:59:03,240 --> 01:59:07,840
Speaker 4: here's my next question for you. Okay, what what things.

2125
01:59:07,560 --> 01:59:10,280
Speaker 1: Are you that you haven't been able to do besides

2126
01:59:10,360 --> 01:59:13,800
Speaker 1: lunar phase work? Like what do you what do you

2127
01:59:13,880 --> 01:59:16,640
Speaker 1: want to be doing, Jimmy, Like what do you want

2128
01:59:16,680 --> 01:59:19,160
Speaker 1: to be doing next? Like what are the what things

2129
01:59:19,200 --> 01:59:23,360
Speaker 1: do you look at? And you're like, uh, I'm inspired

2130
01:59:23,440 --> 01:59:25,240
Speaker 1: by it and I think I can see a way

2131
01:59:25,240 --> 01:59:27,520
Speaker 1: that we might find some answers, like with this deer

2132
01:59:27,560 --> 01:59:30,120
Speaker 1: work in particular, kind of any kind of your work. Yeah,

2133
01:59:30,200 --> 01:59:32,840
Speaker 1: you know, I mean like you got to have fifty ideas, right,

2134
01:59:32,880 --> 01:59:34,240
Speaker 1: but then in the end you can only do one

2135
01:59:34,280 --> 01:59:35,760
Speaker 1: or two at a time or whatever. I don't know

2136
01:59:35,800 --> 01:59:37,560
Speaker 1: what the numbers are, Yeah for sure.

2137
01:59:37,640 --> 01:59:38,920
Speaker 4: Well, I mean one of the one of the things

2138
01:59:38,920 --> 01:59:41,360
Speaker 4: which we talked about a little bit is we really

2139
01:59:41,360 --> 01:59:43,440
Speaker 4: need to be able to evaluate the rose pedal hypothesis,

2140
01:59:43,440 --> 01:59:45,800
Speaker 4: which I think we've talked maybe a little bit more so.

2141
01:59:46,720 --> 01:59:50,480
Speaker 4: The rose pedal hypothesis ties to uh, deer in particular

2142
01:59:50,560 --> 01:59:54,240
Speaker 4: about how they occupy space on a landscape, and the

2143
01:59:54,360 --> 01:59:57,360
Speaker 4: reference to a rose is that you have a matriarchal

2144
01:59:57,400 --> 02:00:00,480
Speaker 4: female that forms maybe the central pedal on the rose,

2145
02:00:00,520 --> 02:00:02,560
Speaker 4: and then you have her daughters that set up shop

2146
02:00:02,600 --> 02:00:04,800
Speaker 4: around her, and then her granddaughters, and so what you

2147
02:00:04,880 --> 02:00:08,320
Speaker 4: end up with is this natural lineal line of related

2148
02:00:08,360 --> 02:00:11,680
Speaker 4: females that occupy space on the landscape. They've evaluated this

2149
02:00:11,720 --> 02:00:14,960
Speaker 4: in white tail deer. It's where the idea came from initially.

2150
02:00:15,720 --> 02:00:20,160
Speaker 4: So for white tail deer, it's been used to implement

2151
02:00:20,200 --> 02:00:24,840
Speaker 4: management practices to reduce agricultural depredation related issues. So we're

2152
02:00:24,880 --> 02:00:26,760
Speaker 4: deer getting into crops and that sort of thing. And

2153
02:00:26,800 --> 02:00:31,240
Speaker 4: the idea is that just reducing removing a handful of

2154
02:00:31,240 --> 02:00:33,760
Speaker 4: deer from the area won't alleviate the problem because you

2155
02:00:33,800 --> 02:00:35,840
Speaker 4: still have other females that are living there, and then

2156
02:00:35,880 --> 02:00:38,200
Speaker 4: their daughters and granddaughters are just going to repopulate it.

2157
02:00:38,200 --> 02:00:40,000
Speaker 4: So the notion is, if you want no deer there,

2158
02:00:40,480 --> 02:00:42,760
Speaker 4: you need to wipe out the entire rows and then

2159
02:00:42,800 --> 02:00:45,040
Speaker 4: you'll create vacant space and there won't be any deer

2160
02:00:45,040 --> 02:00:47,600
Speaker 4: there and your problem will go away. Well, so as

2161
02:00:47,640 --> 02:00:51,680
Speaker 4: I've thought about one, migration, you know, migration is central

2162
02:00:51,800 --> 02:00:56,400
Speaker 4: to our ability to maintain larger bus populations. And then

2163
02:00:56,560 --> 02:00:59,680
Speaker 4: like as adult deer are incredibly faithful to their environment

2164
02:01:00,080 --> 02:01:01,520
Speaker 4: or does it come from anyway, And so we talked

2165
02:01:01,560 --> 02:01:04,400
Speaker 4: about ontouching migration, but then taking that one step further,

2166
02:01:05,120 --> 02:01:07,960
Speaker 4: what does that mean for how females occupy space on

2167
02:01:08,000 --> 02:01:10,760
Speaker 4: a landscape in particular their natal range, Like, do they

2168
02:01:10,880 --> 02:01:15,640
Speaker 4: even though they're they're migratory, do they similarly adopt space

2169
02:01:15,680 --> 02:01:17,880
Speaker 4: for mom on their summer range? So do we end

2170
02:01:17,960 --> 02:01:21,840
Speaker 4: up with these then matrilineal lines of occupied space on

2171
02:01:22,640 --> 02:01:24,880
Speaker 4: a summer range? And that's what determines why we have

2172
02:01:25,000 --> 02:01:28,680
Speaker 4: animals where we do. And that again, which we we

2173
02:01:28,760 --> 02:01:31,120
Speaker 4: kind of alluded to a bit, but that's central to

2174
02:01:31,520 --> 02:01:34,640
Speaker 4: our ability to maintain an abundance of deer because if

2175
02:01:34,680 --> 02:01:37,880
Speaker 4: we lose roses, we create vacant space and.

2176
02:01:37,920 --> 02:01:40,760
Speaker 1: There they're not there anymore. And we need we need like.

2177
02:01:40,720 --> 02:01:43,800
Speaker 4: A firm answer of that to understand what that means

2178
02:01:43,840 --> 02:01:47,160
Speaker 4: across generational time to one both be able to look

2179
02:01:47,200 --> 02:01:49,800
Speaker 4: forward to what it can mean for management conservation now,

2180
02:01:49,840 --> 02:01:51,160
Speaker 4: but also I think.

2181
02:01:50,960 --> 02:01:53,440
Speaker 1: What's equally as of value is to be able.

2182
02:01:53,280 --> 02:01:55,800
Speaker 4: To look backwards in time to what it means means

2183
02:01:55,880 --> 02:01:57,560
Speaker 4: for us in the past from what we've seen how

2184
02:01:57,560 --> 02:01:59,680
Speaker 4: the landscape has changed and what we see today and

2185
02:02:00,440 --> 02:02:03,600
Speaker 4: relevant to even just the Wyoming range. When we lose

2186
02:02:03,600 --> 02:02:07,800
Speaker 4: seventy percent of our deer population, we inevitably lost roses

2187
02:02:07,840 --> 02:02:09,960
Speaker 4: on the landscape. I mean we had family groups of

2188
02:02:09,960 --> 02:02:12,160
Speaker 4: our radio marke deer that were just completely wiped out.

2189
02:02:12,240 --> 02:02:15,320
Speaker 4: So at that level of loss, like it inevitably happened.

2190
02:02:15,320 --> 02:02:17,840
Speaker 4: So what does that mean for that vacant space over time?

2191
02:02:17,880 --> 02:02:20,040
Speaker 1: And if you like when you when you're somewhere when

2192
02:02:20,080 --> 02:02:22,400
Speaker 1: you're glassing, and you like hunt same place over a

2193
02:02:22,440 --> 02:02:24,400
Speaker 1: few years and you're like, there's always like a little

2194
02:02:24,400 --> 02:02:28,480
Speaker 1: pocket of activity yep in whatever, a little spot yep,

2195
02:02:28,560 --> 02:02:32,440
Speaker 1: you could just yep, gone gone. Why aren't they there anymore?

2196
02:02:32,480 --> 02:02:33,280
Speaker 1: What happened? You know?

2197
02:02:33,360 --> 02:02:35,520
Speaker 4: Did they get pushed out of there? Well no, necessarily,

2198
02:02:35,560 --> 02:02:36,840
Speaker 4: they probably just all died, And so.

2199
02:02:37,240 --> 02:02:39,600
Speaker 1: No one's got no one's backfield, that's right.

2200
02:02:39,640 --> 02:02:41,520
Speaker 4: And so what that could mean, you know, we've had

2201
02:02:41,680 --> 02:02:43,200
Speaker 4: we've had a number of which there's a lot of

2202
02:02:43,240 --> 02:02:45,040
Speaker 4: challenges that mild your face. But if you think this

2203
02:02:45,120 --> 02:02:48,760
Speaker 4: applies to basically like any population of ungulate, if if

2204
02:02:48,880 --> 02:02:52,960
Speaker 4: animals are not using space, then a population may grow

2205
02:02:53,000 --> 02:02:55,120
Speaker 4: back up but reach a new abundance where they don't

2206
02:02:55,120 --> 02:02:58,360
Speaker 4: grow anymore, because if they're not using all that other

2207
02:02:58,440 --> 02:03:01,400
Speaker 4: forage and space on the landscape, it's not functionally part

2208
02:03:01,400 --> 02:03:04,000
Speaker 4: of their caring capacity. It's like they it's like having

2209
02:03:04,040 --> 02:03:06,600
Speaker 4: a pasture, running cattle on a pasture. And you have

2210
02:03:06,640 --> 02:03:09,000
Speaker 4: a thousand acre pasture and you split it down the

2211
02:03:09,000 --> 02:03:11,720
Speaker 4: middle with a fence and you run cattle on the

2212
02:03:11,760 --> 02:03:13,600
Speaker 4: southern half of it. Are you going to run the

2213
02:03:13,680 --> 02:03:16,120
Speaker 4: number of cattle that you have for the thousand acre pasture?

2214
02:03:16,440 --> 02:03:17,800
Speaker 4: Are you going to run the number of cattle that

2215
02:03:17,840 --> 02:03:19,560
Speaker 4: you have for the five hundred acres that they have

2216
02:03:19,640 --> 02:03:21,240
Speaker 4: access to? And you're going to run it on the

2217
02:03:21,240 --> 02:03:24,120
Speaker 4: five hundred acres not the thousand. And so when it

2218
02:03:24,160 --> 02:03:27,480
Speaker 4: comes to even as we think about, you know, nutrition

2219
02:03:27,680 --> 02:03:30,760
Speaker 4: density within any population, and you know, I hear this

2220
02:03:30,840 --> 02:03:33,080
Speaker 4: a lot like, man, how could nutrition be limiting on

2221
02:03:33,080 --> 02:03:33,680
Speaker 4: summer range?

2222
02:03:33,760 --> 02:03:34,880
Speaker 1: Look at these mountains.

2223
02:03:34,920 --> 02:03:39,800
Speaker 4: There's some food everywhere. There's no way that that could matter. Again,

2224
02:03:39,920 --> 02:03:42,560
Speaker 4: you like, you have to remember what it means for

2225
02:03:42,680 --> 02:03:45,040
Speaker 4: the animals themselves and the spaces that they live and

2226
02:03:45,120 --> 02:03:47,760
Speaker 4: occupy on that landscape, because they just don't go willy

2227
02:03:47,840 --> 02:03:50,560
Speaker 4: nilly wander around unless you're an elk and.

2228
02:03:51,000 --> 02:03:53,720
Speaker 1: Just access all of all of those foods.

2229
02:03:54,000 --> 02:03:58,040
Speaker 3: So you're talking about I mean basically habitat loss, like

2230
02:03:58,080 --> 02:04:01,360
Speaker 3: you were losing meal to your habitat, even though the

2231
02:04:01,360 --> 02:04:02,440
Speaker 3: habitat looks the same.

2232
02:04:02,560 --> 02:04:04,800
Speaker 1: Yeah, it's exactly right. Yeah, it's way a good way

2233
02:04:04,800 --> 02:04:07,280
Speaker 1: of putting it. Yeah. Yeah, it's so interesting. You think

2234
02:04:07,320 --> 02:04:11,080
Speaker 1: about like spots, You're like, why are they not here? Yeah? Yeah,

2235
02:04:11,320 --> 02:04:12,880
Speaker 1: and when are they going to come back? Man? You

2236
02:04:12,920 --> 02:04:17,880
Speaker 1: know where maybe you're already doing work here, you know where.

2237
02:04:17,880 --> 02:04:24,680
Speaker 1: I feel like there's so much personal bias applied to

2238
02:04:26,200 --> 02:04:33,520
Speaker 1: any conversation around ungilate density and predation. Right, you got

2239
02:04:33,520 --> 02:04:37,120
Speaker 1: people who the first thing they want to do anytime

2240
02:04:37,160 --> 02:04:39,000
Speaker 1: there's no deer around, they want to tell you about

2241
02:04:39,040 --> 02:04:41,720
Speaker 1: the predators. You got people who basically want to tell

2242
02:04:41,760 --> 02:04:45,840
Speaker 1: you that predators egrass makes no difference.

2243
02:04:45,920 --> 02:04:46,000
Speaker 5: Right.

2244
02:04:46,080 --> 02:04:49,880
Speaker 1: You got these two camps. They're generally not talking about

2245
02:04:50,520 --> 02:04:52,680
Speaker 1: what they regard to be the truth. They're talking about

2246
02:04:52,720 --> 02:04:55,920
Speaker 1: what they wish was the truth, or how they've been

2247
02:04:55,960 --> 02:04:58,480
Speaker 1: brought up or trained. Right, the two these two wildly

2248
02:04:58,840 --> 02:05:05,960
Speaker 1: different perspectives. Do you have, Like are you do you

2249
02:05:06,040 --> 02:05:11,360
Speaker 1: consider are there ways to be to maybe freshen up

2250
02:05:11,400 --> 02:05:16,200
Speaker 1: that conversation about what is going on? Yeah, what do Kyle, like,

2251
02:05:16,240 --> 02:05:19,120
Speaker 1: what do coyote numbers mean for deer populations over time?

2252
02:05:19,160 --> 02:05:21,960
Speaker 1: What do coyote numbers mean for distribution? Yeah? Right? Do

2253
02:05:22,000 --> 02:05:24,000
Speaker 1: you wait on this? Yeah?

2254
02:05:24,160 --> 02:05:28,840
Speaker 4: Yeah, So I actually sent some of my initial predator

2255
02:05:29,320 --> 02:05:32,760
Speaker 4: related work with as it translates to on your population

2256
02:05:32,960 --> 02:05:35,520
Speaker 4: was actually some of the work I did on meal

2257
02:05:35,600 --> 02:05:37,200
Speaker 4: deer in the Sierra Nevada and California.

2258
02:05:38,560 --> 02:05:40,520
Speaker 1: And then I read that work, did you Yeah?

2259
02:05:40,680 --> 02:05:44,880
Speaker 4: I didn't know as you Oh yeah, really, I did

2260
02:05:44,920 --> 02:05:47,280
Speaker 4: a whole bunch of work on nutrition predation on deer

2261
02:05:47,320 --> 02:05:48,360
Speaker 4: in the eastern Sierra.

2262
02:05:48,480 --> 02:05:50,560
Speaker 1: Oh you're you know what, you know what? You're right,

2263
02:05:50,600 --> 02:05:52,840
Speaker 1: because you're even not not. I mean, you know you're right.

2264
02:05:53,840 --> 02:05:56,880
Speaker 1: But I think I saw you now that you're saying this.

2265
02:05:57,280 --> 02:06:01,040
Speaker 1: I saw you cited. Your work is cited in the

2266
02:06:01,040 --> 02:06:05,200
Speaker 1: bear California's bear Management proposal. You're here in Nevada, works

2267
02:06:05,200 --> 02:06:07,760
Speaker 1: outside of there is I just read that and left

2268
02:06:07,800 --> 02:06:10,800
Speaker 1: my mind. Yeah, yeah, yeah, okay, I saw your name there. Yeah.

2269
02:06:10,840 --> 02:06:13,560
Speaker 4: So, and then of course we've done a number of

2270
02:06:14,000 --> 02:06:17,200
Speaker 4: good bit of predation related work in Wyoming as well.

2271
02:06:17,440 --> 02:06:20,800
Speaker 4: The to sort of like, look at a big picture

2272
02:06:20,840 --> 02:06:24,200
Speaker 4: relative to the two camps that that you reference.

2273
02:06:26,480 --> 02:06:32,280
Speaker 1: In which camp is right. Well, here's the thing. The

2274
02:06:32,320 --> 02:06:36,360
Speaker 1: problem is neither camp is right. I had a suspicions, yeah,

2275
02:06:35,560 --> 02:06:37,680
Speaker 1: and anybody.

2276
02:06:37,520 --> 02:06:41,480
Speaker 4: The problem is is the moment anybody wants to say

2277
02:06:42,280 --> 02:06:49,080
Speaker 4: predators don't matter because all mortality is compensatory, or predators matter. Basically,

2278
02:06:49,160 --> 02:06:51,720
Speaker 4: if a canine is on the landscape and it's killing

2279
02:06:51,720 --> 02:06:54,040
Speaker 4: a deer, if that canine wasn't there, we'd have the

2280
02:06:54,080 --> 02:06:58,800
Speaker 4: deer back. Both of those camps are wrong. The fact

2281
02:06:58,800 --> 02:07:03,080
Speaker 4: that there's even camps is wrong, and it's wrong in

2282
02:07:03,160 --> 02:07:05,120
Speaker 4: part because of the way the way in which we

2283
02:07:05,200 --> 02:07:07,360
Speaker 4: think about it, and and part of the challenge has

2284
02:07:07,360 --> 02:07:10,760
Speaker 4: been like how do we understand what predation means within populations?

2285
02:07:10,840 --> 02:07:15,640
Speaker 4: And where we have to go back to, regardless first

2286
02:07:16,600 --> 02:07:22,000
Speaker 4: is is where what makes what makes a population and

2287
02:07:22,040 --> 02:07:25,080
Speaker 4: it and what makes a population is everything we've talked about,

2288
02:07:25,520 --> 02:07:27,880
Speaker 4: and it's food and access to the landscape. And so

2289
02:07:28,200 --> 02:07:32,000
Speaker 4: the bottom line is unless animals have access to the

2290
02:07:32,040 --> 02:07:35,400
Speaker 4: food in their basic requirements food, water, cover than it

2291
02:07:35,600 --> 02:07:38,000
Speaker 4: than it doesn't matter. And so for example, if we're

2292
02:07:38,040 --> 02:07:42,200
Speaker 4: if we're in a place where animals are at the

2293
02:07:42,240 --> 02:07:45,920
Speaker 4: capacity of their landscape no matter what. Basically, any any

2294
02:07:45,960 --> 02:07:47,920
Speaker 4: younger of the population is going to attempt to grow

2295
02:07:48,000 --> 02:07:50,400
Speaker 4: more young each year than they can actually sustain within

2296
02:07:50,440 --> 02:07:52,480
Speaker 4: the population typically, and so what that means is a

2297
02:07:52,560 --> 02:07:57,600
Speaker 4: number need to die and hence compensatory and if we're

2298
02:07:57,760 --> 02:08:02,640
Speaker 4: at that point, one dies and alleviates some level of

2299
02:08:02,680 --> 02:08:05,400
Speaker 4: competition for the others that remain, and so we end

2300
02:08:05,480 --> 02:08:06,440
Speaker 4: up with this feedback.

2301
02:08:07,840 --> 02:08:08,640
Speaker 1: So that's real.

2302
02:08:10,280 --> 02:08:14,160
Speaker 4: And also we can have this situation where predators can

2303
02:08:14,240 --> 02:08:17,520
Speaker 4: play a role, as in, we have the capacity to

2304
02:08:17,640 --> 02:08:20,680
Speaker 4: grow more animals based on the food that is here,

2305
02:08:20,960 --> 02:08:25,400
Speaker 4: but predators are keeping it from growing. So we first

2306
02:08:25,440 --> 02:08:28,000
Speaker 4: have to consider food and we can quibble and argue, oh,

2307
02:08:28,040 --> 02:08:31,400
Speaker 4: this level of coyote population, this level of lion population,

2308
02:08:32,000 --> 02:08:33,720
Speaker 4: whatever the case may be. At the end of the day,

2309
02:08:33,760 --> 02:08:36,040
Speaker 4: you can't interpret anything from that. It actually doesn't mean

2310
02:08:36,120 --> 02:08:38,880
Speaker 4: anything because even for those predators, like they may have

2311
02:08:38,920 --> 02:08:42,840
Speaker 4: alternative prey that's available to them. Where it matters how

2312
02:08:42,880 --> 02:08:46,480
Speaker 4: we understand how predators affect a prey population is actually

2313
02:08:46,480 --> 02:08:49,600
Speaker 4: to understand the prey population and the nutrition and capacity

2314
02:08:49,600 --> 02:08:52,879
Speaker 4: they have to grow before we can actually interpret it.

2315
02:08:52,600 --> 02:08:56,000
Speaker 4: What the predators matter or mean at all. And the

2316
02:08:56,040 --> 02:09:00,120
Speaker 4: other reason why I say both camps are wrong or

2317
02:09:00,200 --> 02:09:02,760
Speaker 4: that the very notion, like the moment you hear somebody

2318
02:09:03,640 --> 02:09:06,720
Speaker 4: toss something into the camp, you already know they're wrong.

2319
02:09:06,920 --> 02:09:10,880
Speaker 4: And it's because we can be in a situation in

2320
02:09:10,960 --> 02:09:15,000
Speaker 4: one single population and during one period of time predators

2321
02:09:15,040 --> 02:09:18,440
Speaker 4: are limiting growth, and during another period of time they

2322
02:09:18,480 --> 02:09:21,560
Speaker 4: don't matter at all because they're so nutritionally limited that

2323
02:09:21,640 --> 02:09:24,480
Speaker 4: they make no difference. And so you have yes here

2324
02:09:24,600 --> 02:09:27,919
Speaker 4: and you have no over here. Yeah, and then imagine

2325
02:09:27,920 --> 02:09:30,240
Speaker 4: the whole gradient in between. And I've worked in two

2326
02:09:30,360 --> 02:09:34,200
Speaker 4: systems now as an example, in the Sierras in the

2327
02:09:34,240 --> 02:09:38,840
Speaker 4: Eastern Sierra, where predation mattered for one migratory segment didn't

2328
02:09:38,880 --> 02:09:41,960
Speaker 4: matter at all for the other. And then also looking

2329
02:09:42,000 --> 02:09:43,960
Speaker 4: at it from a time period of there was a

2330
02:09:44,040 --> 02:09:47,040
Speaker 4: window of time where predation mattered and then a window

2331
02:09:47,040 --> 02:09:48,920
Speaker 4: of time where it didn't matter at all. And I've

2332
02:09:48,920 --> 02:09:51,360
Speaker 4: seen we've seen the same thing in Wyoming as well,

2333
02:09:51,520 --> 02:09:54,560
Speaker 4: same picture within the Wyoming range. Prior to the bad winners,

2334
02:09:55,440 --> 02:09:58,560
Speaker 4: predators really didn't matter because we didn't have the nutritional

2335
02:09:58,600 --> 02:10:01,120
Speaker 4: capacity to grow more deer in anyway we were there,

2336
02:10:01,400 --> 02:10:03,680
Speaker 4: so losing some of the predators really was going to

2337
02:10:03,680 --> 02:10:06,120
Speaker 4: be a washing didn't matter. Now that we've dropped to

2338
02:10:06,200 --> 02:10:10,000
Speaker 4: the level that we have now and our females are fat, robust,

2339
02:10:10,120 --> 02:10:13,280
Speaker 4: we have the capacity to grow. Now we're in a

2340
02:10:13,360 --> 02:10:16,520
Speaker 4: different situation where predators can have a limiting factor and

2341
02:10:16,600 --> 02:10:19,880
Speaker 4: reduce the ability for the population to grow. But then still,

2342
02:10:19,920 --> 02:10:21,080
Speaker 4: at the end of the day, well, what does that

2343
02:10:21,200 --> 02:10:24,280
Speaker 4: mean for us? Should we control predators? Should we have

2344
02:10:24,360 --> 02:10:27,880
Speaker 4: controlled predators over here? Should we not? Like it's the

2345
02:10:27,960 --> 02:10:29,959
Speaker 4: sort of like. It all depends what are the objectives.

2346
02:10:30,040 --> 02:10:32,120
Speaker 4: Just because your remove predators from a system doesn't mean

2347
02:10:32,120 --> 02:10:34,960
Speaker 4: you're going to get more more dear pack, Yeah.

2348
02:10:35,200 --> 02:10:36,920
Speaker 1: Dude, you should be the only guest we ever have

2349
02:10:37,080 --> 02:10:41,200
Speaker 1: on you'd get so bored. No, it's be a weekly

2350
02:10:41,280 --> 02:10:46,560
Speaker 1: show where I ask you questions. I like it. I

2351
02:10:46,760 --> 02:10:48,640
Speaker 1: like it because we're never going to get through it all.

2352
02:10:48,760 --> 02:10:51,080
Speaker 1: We got to wrap up, Randall, got to do this

2353
02:10:51,120 --> 02:10:52,160
Speaker 1: whole stupid thing we're doing.

2354
02:10:54,120 --> 02:10:54,840
Speaker 2: It's the audio.

2355
02:10:55,680 --> 02:11:00,360
Speaker 3: We haven't even talked about. Kevin's fascinating other interests tax

2356
02:11:00,440 --> 02:11:01,880
Speaker 3: dermist hunter.

2357
02:11:03,120 --> 02:11:08,080
Speaker 4: Maybe maybe we should there layers, I know, let's not

2358
02:11:08,200 --> 02:11:09,760
Speaker 4: wait five seven years again.

2359
02:11:10,080 --> 02:11:11,640
Speaker 1: I don't know how whenever it was we did, we

2360
02:11:11,720 --> 02:11:13,800
Speaker 1: got to schedule the next round because we got to

2361
02:11:13,800 --> 02:11:16,840
Speaker 1: wrap up. Dude, it's really like, you guys are doing

2362
02:11:17,000 --> 02:11:20,760
Speaker 1: super cool work. I wasn't trying to hack on fishing

2363
02:11:20,800 --> 02:11:23,120
Speaker 1: game agencies, man, I was just trying to express this.

2364
02:11:23,240 --> 02:11:25,720
Speaker 1: I was trying to express this, like like I just

2365
02:11:25,760 --> 02:11:28,000
Speaker 1: hope there's not a lot of bottlenecks and and and

2366
02:11:28,360 --> 02:11:35,080
Speaker 1: inefficiencies and and and like being able to take the

2367
02:11:35,160 --> 02:11:38,680
Speaker 1: research you're doing, and and and being able to go

2368
02:11:38,800 --> 02:11:42,320
Speaker 1: and say like I can't tell you anything, but there's

2369
02:11:42,360 --> 02:11:46,520
Speaker 1: a I'm it really looks like this is going on.

2370
02:11:46,960 --> 02:11:52,320
Speaker 1: Are we considering this? I mean like, like, is the flow? No?

2371
02:11:52,440 --> 02:11:55,000
Speaker 1: I get this is the flow? Good? And I'm only

2372
02:11:55,080 --> 02:11:58,880
Speaker 1: saying that because, like you know, I've spent my whole

2373
02:11:58,960 --> 02:12:03,640
Speaker 1: life talking about wildlife, right, thinking about wildlife, talking about wildlife.

2374
02:12:03,680 --> 02:12:05,760
Speaker 1: I hang out with peopleho are obsessed with wildlife, and

2375
02:12:05,960 --> 02:12:11,000
Speaker 1: we routinely traffic and stuff that just isn't backed up,

2376
02:12:11,600 --> 02:12:13,760
Speaker 1: and we say it like fact absolutely, and the guys

2377
02:12:13,800 --> 02:12:18,080
Speaker 1: that are doing management are the are my guys are peers.

2378
02:12:18,680 --> 02:12:24,680
Speaker 1: It's like people are operating under assumptions that like aren't

2379
02:12:24,720 --> 02:12:26,640
Speaker 1: as accurate, you know, and sort of looking at like

2380
02:12:27,120 --> 02:12:29,000
Speaker 1: like looking at lower deer numbers and we're like, wow,

2381
02:12:29,040 --> 02:12:31,160
Speaker 1: the reason it's that way is because of whatever. And

2382
02:12:31,240 --> 02:12:33,200
Speaker 1: then you go look at the man, dude, it's just

2383
02:12:33,320 --> 02:12:37,240
Speaker 1: a different There's there's more to what's going on. It's

2384
02:12:37,280 --> 02:12:40,960
Speaker 1: a longer story. It's not like you can't just everything

2385
02:12:41,040 --> 02:12:44,200
Speaker 1: isn't just looking at what just happened. Correct, Absolutely, you

2386
02:12:44,320 --> 02:12:47,640
Speaker 1: know you got you gotta dig back. Oftentimes you got

2387
02:12:47,680 --> 02:12:50,440
Speaker 1: to dig back right now and so and trying to

2388
02:12:50,480 --> 02:12:53,920
Speaker 1: correct a situation, you might be correcting the wrong inputs. Yes,

2389
02:12:54,160 --> 02:12:56,360
Speaker 1: oh absolutely, right, yep, for sure.

2390
02:12:56,560 --> 02:12:58,480
Speaker 4: And and I think the other the other side of

2391
02:12:58,560 --> 02:13:01,480
Speaker 4: that as well is you know a number of things

2392
02:13:01,480 --> 02:13:03,440
Speaker 4: that we talked about, some of the things we can't

2393
02:13:03,480 --> 02:13:06,560
Speaker 4: necessarily change. We can't necessarily change the weather and those

2394
02:13:06,600 --> 02:13:08,880
Speaker 4: sorts of things, but which I think can become a

2395
02:13:08,960 --> 02:13:12,000
Speaker 4: bit dissatisfying sometimes when like pre sip or snowpack or

2396
02:13:12,040 --> 02:13:15,160
Speaker 4: those sorts of things are such play such a driving role.

2397
02:13:15,240 --> 02:13:18,160
Speaker 4: We can manage density and other things like that to

2398
02:13:18,240 --> 02:13:21,160
Speaker 4: help moderate those things. But I also think what's really

2399
02:13:21,200 --> 02:13:25,840
Speaker 4: important is our ability to simply manage our expectations as well,

2400
02:13:26,320 --> 02:13:29,160
Speaker 4: what does it mean for us? Which is kind of dissatisfying,

2401
02:13:29,400 --> 02:13:31,400
Speaker 4: right when you can't say we'll do this because I

2402
02:13:31,480 --> 02:13:34,720
Speaker 4: want this. Well, maybe we can't get that, but here's

2403
02:13:34,720 --> 02:13:36,520
Speaker 4: the reasons why, and we need you to understand that

2404
02:13:36,640 --> 02:13:38,400
Speaker 4: so we can all be we can all be at

2405
02:13:38,440 --> 02:13:40,720
Speaker 4: the same table with the same information, be like, Okay,

2406
02:13:40,760 --> 02:13:42,880
Speaker 4: here's what we're wrestling with. What is this going to

2407
02:13:42,920 --> 02:13:45,920
Speaker 4: mean for us going forward with whatever the situation may

2408
02:13:46,080 --> 02:13:48,720
Speaker 4: may be, from how we use habitat to our presence

2409
02:13:48,760 --> 02:13:51,880
Speaker 4: on the landscape, to how we manage hunting seasons.

2410
02:13:52,640 --> 02:13:58,680
Speaker 1: Yeah, yeah, love it. M Now you got to come

2411
02:13:58,760 --> 02:14:03,720
Speaker 1: back on happily. No, you're not too terribly far away. Now,

2412
02:14:03,920 --> 02:14:06,520
Speaker 1: last time we went to you, remember you did you did?

2413
02:14:06,640 --> 02:14:09,080
Speaker 1: We have to hang out for a bit. Yeah, yeah,

2414
02:14:09,360 --> 02:14:18,560
Speaker 1: it's great. Hmmm, we gotta go a little. Me and

2415
02:14:18,640 --> 02:14:22,200
Speaker 1: Randall's thing is actually kind of interesting. I don't want

2416
02:14:22,240 --> 02:14:27,400
Speaker 1: to talk bad about it. I just it's very similar

2417
02:14:27,400 --> 02:14:30,000
Speaker 1: in a way, trying to find out what happened, what happened?

2418
02:14:30,040 --> 02:14:31,520
Speaker 1: What did happen? I know, I feel like you were

2419
02:14:31,680 --> 02:14:32,320
Speaker 1: very into it.

2420
02:14:32,640 --> 02:14:34,680
Speaker 3: I feel like you were very into that project up

2421
02:14:34,760 --> 02:14:37,840
Speaker 3: until this morning and now that it's dimmed, you know

2422
02:14:38,240 --> 02:14:38,720
Speaker 3: it's dimmed.

2423
02:14:38,840 --> 02:14:42,600
Speaker 1: We're doing very similar work. Yeah, I like it. We're

2424
02:14:42,640 --> 02:14:44,840
Speaker 1: looking at wow, yeah, like what did happen all the

2425
02:14:44,880 --> 02:14:48,600
Speaker 1: buffalo nice? And why? And what little micro things might

2426
02:14:48,640 --> 02:14:50,320
Speaker 1: have been different that would have led to a different outcome.

2427
02:14:50,400 --> 02:14:52,320
Speaker 1: We toy with all this mm hmm.

2428
02:14:53,120 --> 02:14:56,720
Speaker 3: We toy with all this, good man, And but we

2429
02:14:56,800 --> 02:14:58,000
Speaker 3: don't have the data that you have.

2430
02:14:58,920 --> 02:15:01,480
Speaker 1: It's just differ kind of work. Yeah, and we dispel,

2431
02:15:01,640 --> 02:15:04,760
Speaker 1: we dispel things. So yeah, it's important to be like, well,

2432
02:15:04,800 --> 02:15:09,680
Speaker 1: what happened was this? Oh yeah kind of but also

2433
02:15:09,800 --> 02:15:13,600
Speaker 1: this nice you know, I look forward to that. Bison

2434
02:15:13,640 --> 02:15:16,000
Speaker 1: are fascinating. Yeah, I think you'll. I think you'll dig it.

2435
02:15:16,120 --> 02:15:19,400
Speaker 1: It's got all the all the normal gross stuff that

2436
02:15:19,440 --> 02:15:21,720
Speaker 1: we like to put in there too. Of course I

2437
02:15:21,720 --> 02:15:23,800
Speaker 1: would anticipate nothing. Yes, we like it to be that

2438
02:15:23,840 --> 02:15:25,600
Speaker 1: you get kind of grossed out, but also you learn

2439
02:15:25,640 --> 02:15:31,400
Speaker 1: something you have to have that flare wouldn't come from you. Yeah,

2440
02:15:31,400 --> 02:15:33,320
Speaker 1: you gotta have that that cade and something you being

2441
02:15:33,400 --> 02:15:38,400
Speaker 1: like oh, then you're like, oh, can we clip that

2442
02:15:38,560 --> 02:15:45,600
Speaker 1: person sounding back? Splice that in, yeah, back and forth.

2443
02:15:45,760 --> 02:15:57,680
Speaker 1: All right, thank you for coming on the monteeth shop,

2444
02:15:58,320 --> 02:16:04,240
Speaker 1: University of Wyoming. Send your resumes. You're looking for resumes

2445
02:16:04,320 --> 02:16:08,760
Speaker 1: right now? Oh man, you drowning the resumes. Yeah, don't

2446
02:16:08,800 --> 02:16:11,600
Speaker 1: send your resu I'm always looking for supporters, if anybody's interested.

2447
02:16:11,640 --> 02:16:14,160
Speaker 1: If you're looking to support something great, and I think

2448
02:16:14,200 --> 02:16:16,680
Speaker 1: it was demonstrated here today. If you're looking to support

2449
02:16:17,280 --> 02:16:22,080
Speaker 1: if you love wildlife, if you love the West, if

2450
02:16:22,120 --> 02:16:25,520
Speaker 1: you love wildlife, if you love these like majestic landscapes,

2451
02:16:25,640 --> 02:16:30,760
Speaker 1: and you really want to put your money, your conservation

2452
02:16:31,000 --> 02:16:37,200
Speaker 1: dollars to knowledge, this is a great place to put them.

2453
02:16:37,400 --> 02:16:42,040
Speaker 1: If you want to drive knowledge. It's not lobbying, which

2454
02:16:42,120 --> 02:16:46,840
Speaker 1: is important. It's not like on the ground, on the

2455
02:16:46,920 --> 02:16:51,520
Speaker 1: ground habitat improvement, rolling up old fences, getting rid of junipers,

2456
02:16:51,720 --> 02:16:55,120
Speaker 1: which is important. It's not that. If you want to

2457
02:16:55,280 --> 02:16:59,960
Speaker 1: contribute to acquisition of knowledge that can then be extended

2458
02:17:00,120 --> 02:17:04,560
Speaker 1: out into management. Uh, I say the Montieth lab is

2459
02:17:04,600 --> 02:17:09,320
Speaker 1: money well spent shop. What was that shop? Oh? What?

2460
02:17:09,480 --> 02:17:11,080
Speaker 1: I just call it lab? Well that's what they ought

2461
02:17:11,120 --> 02:17:15,039
Speaker 1: to call it. Yeah, you think about it. I think

2462
02:17:15,080 --> 02:17:18,959
Speaker 1: that's where we started. More I think about it, it.

2463
02:17:18,959 --> 02:17:21,120
Speaker 3: Doesn't bode well for a recording session later.

2464
02:17:23,360 --> 02:17:27,120
Speaker 1: The Montee Shop damn it. Thank you, Kevin my pleasure.

2465
02:17:27,200 --> 02:17:27,880
Speaker 1: Thank you for having me.