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Speaker 1: Welcome to the Wired to Hunt podcast, your home for

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Speaker 1: deer hunting news, stories and strategies, and now your host,

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Speaker 1: Mark Kenyon. Welcome to the Wired to Hunt Podcast. I'm

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Speaker 1: your host, Mark Kenyan. This is episode number one thirty

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Speaker 1: nine Tannis Show. We were joined by wildlife biologists Bronson Strickland,

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Speaker 1: and we're talking white tail research. All right, Welcome to

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Speaker 1: the Wired to Hunt podcast, brought to you by Sitka Gear,

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Speaker 1: and today I think is going to be a very

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Speaker 1: interesting episode because joining us here soon is a man

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Speaker 1: by the name of Bronson Strickland. And Bronson is a

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Speaker 1: wildlife biologist and his technically his title is Associate Extension

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Speaker 1: Professor of Wildlife Ecology and Management at Mississippi State University,

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Speaker 1: and he's the co director of Mississippi State's Deer Lab, which,

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Speaker 1: as I understand it is one of the nation's premier

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Speaker 1: dear focused research departments. So with all those impressive titles,

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Speaker 1: Bronson is going to bring with him a fascinating array

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Speaker 1: of insights into the biology and behavior of white tailed deer.

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Speaker 1: And from what I've read and different things I've seen

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Speaker 1: from in the past, he's just been involved in so

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Speaker 1: many different interesting research projects when it comes to white tails,

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Speaker 1: what they do, why they do it, um, and and

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Speaker 1: some interesting insights into what we can take from that

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Speaker 1: as hunters. So so that's gonna be the kind of

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Speaker 1: game plan today. UM. I don't have any particular single agenda.

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Speaker 1: I just want to dig into everything we possibly can

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Speaker 1: from Bronson, everything we can possibly learn. So, uh, Dann,

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Speaker 1: are you up for some science? Dude? I love science.

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Speaker 1: I used to like take a balloon and rub it

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Speaker 1: on my brother's head and then stick it to the wall.

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Speaker 1: That's that's science, static electricity, you know. So I'm I'm

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Speaker 1: up for good. I'm up for science. I love I

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Speaker 1: love the details behind the you know, the animal and

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Speaker 1: what makes them tick. And you know, from a from

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Speaker 1: a hunter standpoint, that should almost be as important. Of

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Speaker 1: knowing how a white tail operates should be more important

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Speaker 1: than you know, like strategy of how to intercept them.

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Speaker 1: I think I think they kind of go hand in hand.

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Speaker 1: But it's it's the foundation, right, You need this foundation

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Speaker 1: both to become a better hunter from execution of a strategy.

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Speaker 1: But then also I think it makes you a better hunter,

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Speaker 1: just maybe not a better quantifiably as like how effectively

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Speaker 1: you can kill it here, but it might make you

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Speaker 1: a more well rounded hunter and just understanding our quarry,

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Speaker 1: understanding these animals, having some insight, and uh, I don't know.

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Speaker 1: I think it's just very interesting too. I mean, I'm

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Speaker 1: I'm fascinated. If I didn't go into what I did

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Speaker 1: so well, I went into marketing and business and stuff

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Speaker 1: and then roundabout way I got to hear. But if

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Speaker 1: I hadn't done that, I think I could have been

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Speaker 1: a wildlife biologist or like a I don't know, I

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Speaker 1: could just sit and watch animals all day and like

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Speaker 1: just take note of what they're doing and why are

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Speaker 1: they doing this? And I mean, I don't think I'm

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Speaker 1: too unique in that either. I mean a lot of

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Speaker 1: hunters we just enjoy watching them right right. Amen, It's uh,

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Speaker 1: I don't know, it's it's gonna be interesting because I

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Speaker 1: have there's so many things that we see. We talk

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Speaker 1: all the time with different guests or just you and me,

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Speaker 1: like we as hunters make all these different assumptions or

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Speaker 1: we identify these patterns or trends, and we say, dear,

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Speaker 1: do this most of the time. So we're gonna just

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Speaker 1: our hunting stretch. You know, maybe it's because of like

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Speaker 1: you know, moon ideas or barre metric pressure things, or

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Speaker 1: we see these different things and people write about articles

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Speaker 1: and we we go base a lot of what we

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Speaker 1: do in October and November and December based on this stuff. Um.

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Speaker 1: But I'd venture to guess at least three quarters of

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Speaker 1: those like recommendations usually aren't backed up by verifiable science. UM.

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Speaker 1: So it's always nice to be able to go and

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Speaker 1: talk to the people that really do have data and

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Speaker 1: really do have you know, quantifiable evidence to say whether

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Speaker 1: or not some of these hunches that we have are

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Speaker 1: actually true. So I'm hoping that Bronson can help us

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Speaker 1: do that. Can he help verify or um, you know,

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Speaker 1: dispel some of these different hunches that we as hunters have,

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Speaker 1: and I think we'll probably have a lot more above

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Speaker 1: and beyond. But that's what I'm particularly treated. I think

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Speaker 1: what everybody should really pay attention to about, you know,

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Speaker 1: the biology of a white tail. You know how they smell,

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Speaker 1: how they see, you know how you know how their

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Speaker 1: body is, you know their senses here, how they hear,

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Speaker 1: and then maybe relate that to some of the products

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Speaker 1: that you buy and say, okay, is this product making

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Speaker 1: a uh you know, just a b S claim or

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Speaker 1: is there a um a biological reasoning why they're why

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Speaker 1: their product works with white tails as well? So that's

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Speaker 1: so that's that's that's the kind of stuff I'm interested in,

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Speaker 1: you know, like, Okay, so does in ozonics or really

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Speaker 1: work or does these these sprays really work to a

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Speaker 1: white tails nose? How sensitive is a white tails nose?

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Speaker 1: What's the best camo pattern? Right? You know, like are

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Speaker 1: all these things? Can can you can you quantify whether

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Speaker 1: or not um a company claims that their product does

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Speaker 1: this if you know, like actually no, because I'll tell

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Speaker 1: you why the biology of a white tales I is

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Speaker 1: blah blah blah blah blah. You know what I mean. Yeah,

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Speaker 1: it is always interesting to get a biological perspective on

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Speaker 1: those things. So is a product aimed to you know,

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Speaker 1: actually make a difference based on known science of a

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Speaker 1: white tail? Or is it is the unique value of

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Speaker 1: this product simply that it's catchy to the hunter's eye,

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Speaker 1: you know, right, it's got something that's gonna make a

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Speaker 1: hunters say, oh, or I want to wear that because

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Speaker 1: it looks cool or whatever. So, yeah, there's there's something

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Speaker 1: to be said about trying to get some science behind it.

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Speaker 1: So I'm pretty I'm pretty excited for this stuff because

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Speaker 1: I grew up watching national geographics with my dad and brother,

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Speaker 1: and uh that was like our Sunday night tradition, pop

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Speaker 1: some popcorn and watch national geographics. And typically it was

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Speaker 1: about some kind of animals, right, and and they would

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Speaker 1: always go into detail about you know, this fish has

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Speaker 1: this extra appendage through you know, through evolution because it

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Speaker 1: does this or does I to do this specifically, so

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Speaker 1: you know, everything poor genetics always fails during you know,

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Speaker 1: through evolution. So the white tail has got to the

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Speaker 1: point it is today because it is it's it's almost perfect.

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Speaker 1: It's in its environment, you know what I mean minus

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Speaker 1: minus hunters like human beings right well, and and AND's

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Speaker 1: and even above and beyond that, the the unique adaptability

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Speaker 1: of the white deer and that it can learn to

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Speaker 1: live with anyones and around humans, I mean, more so

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Speaker 1: than most other large mammals in the world, let alone

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Speaker 1: North America. White tails have adapted to that better than

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Speaker 1: more many many other species. So um, I mean that's

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Speaker 1: one of the you know, you know, and we talked

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Speaker 1: to talk about a lot. But in the world where

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Speaker 1: the human footprint is massive and ever increasing UM. Unfortunately,

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Speaker 1: much to our dismay. You know, we're pushing a lot

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Speaker 1: of species to the brink or pushing them into increasingly

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Speaker 1: small little pockets UM. Fortunately for deer and deer hunters,

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Speaker 1: deer one of those species that can handle that increasing

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Speaker 1: footprint and they can learn to live within it. UM.

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Speaker 1: So that's ah, they found it a nice little evolutionary

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Speaker 1: niche here where they fit in very well as kind

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Speaker 1: of a mutually what it's a symbiotic relationship, I think

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Speaker 1: would be the biological term, right if I'm reminder science

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Speaker 1: class right, where two species UM do better in conjunction.

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Speaker 1: And I think in some cases white tailed deer and

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Speaker 1: humans almost have the type of relationship because in a

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Speaker 1: lot of ways, UM. A lot of things that human

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Speaker 1: development brings to some degree, whether it's agriculture UM or logging,

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Speaker 1: different things, creating edge creating, you know, fresh regrowth. A

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Speaker 1: lot of things that we've done have actually helped white

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Speaker 1: tail populations UM, which is unique compared to a lot

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Speaker 1: of the other animals that our development hasn't. So it's

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Speaker 1: it's one nice silver lining I think in the I

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Speaker 1: know the story of humans impact on wildlife in this country. Um,

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Speaker 1: you know, we've been able to find a good way

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Speaker 1: to make it work with white tails for the for

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Speaker 1: the most part. Right, So right, Yeah, I'm excited about

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Speaker 1: this one. I love science. Is just cool period biology,

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Speaker 1: and you know how things work, why they do what

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Speaker 1: they do, and and all that stuff. Yeah. So I

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Speaker 1: don't have anything too terribly exciting in my neck of

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Speaker 1: the woods to share. Um, so, do we want to

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Speaker 1: jump right into our conversation with Bronson or do you

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Speaker 1: have anything that's a I think note in the lander Dan, No, man,

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Speaker 1: I think we get I think we're gonna need every

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Speaker 1: minute today with this guest, because I have a whole

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Speaker 1: sheet worth of question questions. Perfect, all right, does the

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Speaker 1: does the acorn cruncher really work? Please? Let's lead with that.

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Speaker 1: All right, Well, let's take a quick break for a

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Speaker 1: word from our partners at sick Gear, and then we'll

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Speaker 1: give Bronson call. Alright, So, last week in our Sickest Story,

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Speaker 1: we heard from Jessica de Lorenzo, one of the female

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Speaker 1: hunters involved with the designing and testing of sick as

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Speaker 1: new women's line, And today I wanted just to tell

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Speaker 1: us just a little bit more about what her and

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Speaker 1: the team's work has led to as far as clothing

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Speaker 1: customized for females. I'm one of the really serious process

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Speaker 1: and in the beginning stages we met with fabric designers,

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Speaker 1: product designers and everybody at SICCA and MIA. We really

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Speaker 1: discussed how seams would align, how the layering process would work,

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Speaker 1: and a lot of attention detail went into UM warmth

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Speaker 1: and how females bodies um snooze or keep any heat

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Speaker 1: as opposed to males. So we did a lot of

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Speaker 1: testing UM with like body heat loss, and we found

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Speaker 1: that the females lost their body heat from completely different

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Speaker 1: areas UM at a different rate than men do. So

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Speaker 1: we went through a lot of processes making sure that

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Speaker 1: the prototypes had UM the ground shield technology underneath the

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Speaker 1: spies and the back for a tree stand sitting and

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Speaker 1: on the big game side, I know they get a

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Speaker 1: lot of testing. It's making sure that the fabric's breathed

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Speaker 1: well and they have the UM what do they call

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Speaker 1: utalogen odor control technology, So you weren't gathering any kind

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Speaker 1: of cent or anything. We're working really hard, so that

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Speaker 1: was really important to us as well. So can you

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Speaker 1: elaborate then on you kind of start on it, but

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Speaker 1: could you elaborate on some of the other unique things

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Speaker 1: that have ended up in the final women's line. You

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Speaker 1: know what worse than the final things make this different

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Speaker 1: and unique Compared to the generic sick gear that was

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Speaker 1: available you know in the past, UM well, the most

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Speaker 1: obviously fit, so we made sure that everything was tailored

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Speaker 1: to women's bodies. The way that the garments fit are

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Speaker 1: much different than men's. There's a lot of like stretching

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Speaker 1: materials in it. There's a four way stretch. There's gusting

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Speaker 1: in the jackets for the hip areas on women that

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Speaker 1: isn't in the men's line. UM, the ground shield pathnology

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Speaker 1: for warmth is much different in the women's snatic system

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Speaker 1: it's not offered in the men's um. And that they

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Speaker 1: have pieces for to get our hair out of the way,

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Speaker 1: which was a big deal for us. And another thing

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Speaker 1: all the gloves and accessories where we made sure that

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Speaker 1: they were built on women's um women's forms, so they

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Speaker 1: didn't just um take the men's items inside them smaller.

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Speaker 1: So everything was particularly designed to a female body, which

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Speaker 1: is very important to us. So if you would like

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Speaker 1: to learn more about sick Gears Women's line, or if

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Speaker 1: you'd like to pre order the new women's line, you

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Speaker 1: can visit gear dot com Slash Women's And now back

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Speaker 1: to the show in Bronson Strickland. All right with us?

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Speaker 1: Now is Bronson Strickland. Thanks for joining us. Bronson, Hey,

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Speaker 1: I'm glad to be here. Thanks for having me. Yeah,

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Speaker 1: we just established off air. I wish we were recording,

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Speaker 1: but we just established off air. Given your name and

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Speaker 1: what how Dan feels about it, you are officially a badass, um.

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Speaker 1: So I just want to make sure everybody knows that. Yeah,

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Speaker 1: every everybody that knows me knows that too. When they're

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Speaker 1: all laughing right now. What was it about his name,

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Speaker 1: Dan that just made you feel that way about him?

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Speaker 1: I don't know, maybe your delivery, but Bronson Strickland, you know,

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Speaker 1: like he's an action star or maybe a bodybuilder or

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Speaker 1: it's like, hey, man, did you hear Bronson Strickland just

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Speaker 1: broke the squat record. I could see him being a

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Speaker 1: UFC fighter. Is what UFC fighter? Yes? Yeah, yeah, maybe yeah. Coincidentally,

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Speaker 1: I just retired from from that a few years ago

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Speaker 1: and it started to do this professor gig. So yeah,

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Speaker 1: this this is just a cover up, right, what what

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Speaker 1: do you really do for a loving so on that tonson?

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Speaker 1: What in all reality can you? Can you kick this off?

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Speaker 1: But just tell us a little about yourself, how you

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Speaker 1: got to be here, and what you're doing today. Yeah

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Speaker 1: you bet. Um, I'll give you just a little bit

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Speaker 1: of background about myself. Um, Like a lot of people

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Speaker 1: your listeners, have always been uh fascinated and enamored with

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Speaker 1: the outdoors. Um. As soon as I could begin hunting,

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Speaker 1: I was fortunate enough to raid to be raised in

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Speaker 1: an area where we had about fifty or a hundred acres,

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Speaker 1: And so it began with a bb gun and then

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Speaker 1: a four teen and then a twenty two, and then

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Speaker 1: evolved into deer hunting. And my my story is kind

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Speaker 1: of funny. I remember vividly. I was in eighth grade

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Speaker 1: and I was with my hunting buddy and he said, Hey,

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Speaker 1: I've got this guy this in my boy Scouts group.

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Speaker 1: And his father is a dear biologist. He's actually a

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Speaker 1: professional deer biologist and he works for the University of

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Speaker 1: your Regia. And in eighth grade, when I heard that,

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Speaker 1: I knew that my life was set in front of me,

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Speaker 1: that I cannot possibly fathom another career that would interest

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Speaker 1: me more than being a deer biologist. So from from

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Speaker 1: that point forward, UH got through high school and attended

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Speaker 1: the University of Georgia and got an undergraduate degree in

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Speaker 1: wildlife biology. And after that I went to South Texas

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Speaker 1: UM again because of my fascination with the deer management.

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Speaker 1: The University of Georgia had a cooperative project there with

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Speaker 1: Texas A and in Kingsville in in South Texas, and

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Speaker 1: I got to work on one of these just dream

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Speaker 1: come true ranches sixty thousand acres where UH deer management

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Speaker 1: was the priority, and just learned so much about deer

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Speaker 1: management number one, but deer management in a completely different

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Speaker 1: context in the southeast. And after I graduated there, I

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Speaker 1: came to Mississippi State and I started working here with

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Speaker 1: UM my colleague now UH and co director of the

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Speaker 1: Deer Lab, Steve Dameris, and we started analyzing harvest data

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Speaker 1: collected throughout Mississippi and started looking at spatial trends and

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Speaker 1: trends over time with how the deer heard is responding

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Speaker 1: to the various regulations UH for example, like antler regulations

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Speaker 1: and things like that. UH and Lucky for me. As

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Speaker 1: soon as I graduated in about a year later, a

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Speaker 1: faculty position opened up here in the department. That was

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Speaker 1: in two thousand six, and I've been here ever since.

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Speaker 1: And my job now is that I'm a professor. But

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Speaker 1: my my role is a little bit different. Uh. My

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Speaker 1: appointment is called extension, and extension essentially means outreach. So

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Speaker 1: I do very little teaching in the classroom like my colleagues.

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Speaker 1: My teaching is in the field, so I conduct a

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Speaker 1: lot of seminars and workshops, online training and things like that.

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Speaker 1: That is a sin my my teaching appointment for the

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Speaker 1: State of Mississippi. It sounds like a dream job as

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Speaker 1: far as I can for it is for me, it

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Speaker 1: sure is. I'm very lucky. That's incredible. Now, can you

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Speaker 1: elaborate a little bit more on on what your department does?

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Speaker 1: The Deer lab Um, what's the breadth of that, what's

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Speaker 1: the scope and breadth of your work there? Well, it

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Speaker 1: was established over twenty years ago by by two fellas.

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Speaker 1: You've probably heard of, one guy named Harry Jacobson. He's

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Speaker 1: a long time deer researcher and dear biologists from Mississippi

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Speaker 1: State UH and David Gwen was also here at the time,

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Speaker 1: and they they began the Deer Lab and it's really

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Speaker 1: started a focused effort on applied dear research and and

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Speaker 1: the biggest thing that they did, along with a lot

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Speaker 1: of physiology studies and and age related studies with antlers

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Speaker 1: and so forth, uh, was they started working with Mississippi

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Speaker 1: Hunters in association with our state alife agency, the Mississippi

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Speaker 1: Department of Wilafe, Fisheries and Parks, and started what is

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Speaker 1: called the d MAP program. D MAP stands for the

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Speaker 1: Deer Management Assistance Program and and over the years, excuse me,

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Speaker 1: how this relationship works is that in return for some

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Speaker 1: consultation from a state agency biologists, hunters are required to

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Speaker 1: collect harvest data from from all the deer that are

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Speaker 1: killed on their property, both bucks and does. And we

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Speaker 1: get biological data from dose you know, what what was

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Speaker 1: their age, what was their body weight, what was their

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Speaker 1: lactation status, And from that we can heard, excuse me,

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Speaker 1: we can engage her dynamics in terms of reproduction. And

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Speaker 1: and then also from from the bucks we get of

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Speaker 1: course age, body weight, and antler size, and so from

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Speaker 1: that from having that data set literally statewide and and

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Speaker 1: now we have over three quarters of a million harvest

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Speaker 1: records from Mrs Sippy. Is that we can look at

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Speaker 1: at different trends and and like I said earlier, different

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Speaker 1: impacts regulations have on the demographics of the herd and

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Speaker 1: in terms of antler size and and and things like that.

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Speaker 1: So that was really their brain child. That is what

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Speaker 1: those two guys got started. UM. Steve. Of course, when

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Speaker 1: when Harry Jacobson retired, Steve came on board and his

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Speaker 1: research and my research. We really want everything to be

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Speaker 1: grounded an application, So we have to do nerdy things

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Speaker 1: like other professors, and we have to publish things in

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Speaker 1: scholarly journals. UM. But what we are really interested in

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Speaker 1: is research that will ultimately have an impact for wildlife

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Speaker 1: managers and for hunters. UM. So we've done really applied stuff.

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Speaker 1: Like we finished up research on deer impact on soybean

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Speaker 1: fields in Mississippi to the soybean farmers. UM. We fin

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Speaker 1: sh up. We were talking earlier guys about our our research.

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Speaker 1: We just finished up on UM what we call multiple

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Speaker 1: paternity or or or the reproductive success of different books

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Speaker 1: in the wild. What characteristics of successful bucks have that

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Speaker 1: sire A great number of fauns or not and and

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Speaker 1: all those types of things. UM we worked with UM.

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Speaker 1: You guys have had Jeremy Flynn on the podcast before

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Speaker 1: UM if you might remember him talking about the buck

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Speaker 1: score technology. We worked with Jeremy on that. So again

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Speaker 1: just really applied things that UM we always hope culminates

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Speaker 1: in helping people manage to your either learning about it,

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Speaker 1: learning about the biology, why is this happening, and hopefully

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Speaker 1: steering them in the right direction to manage more efficiently.

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Speaker 1: I've got I guess before. I'm really interested in some

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Speaker 1: of the research you've done, some the findings, but I

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Speaker 1: also really curious about the behind the scenes, like how

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Speaker 1: something like this actually happens, Like what goes into a

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Speaker 1: research study around one of these things. Could you maybe

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Speaker 1: pick an example, UM, and maybe one of the one

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Speaker 1: of the things you just mentioned there where you could

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Speaker 1: actually give us a breakdown of the actual process to

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Speaker 1: you know, determine what's the thing we're going to try

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Speaker 1: to look into, how do we collect the data, collecting

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Speaker 1: that data, analyzing that data. UM, can you walk us

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Speaker 1: through an example of how wildlife biologists in your position

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Speaker 1: does that and then is able to walk away with

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Speaker 1: it from it with applicable and appliable data. Yeah, you

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Speaker 1: bet so. I'll give you an example we are currently

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Speaker 1: going through right now. So UM, we are very fortunate

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Speaker 1: in Mississippi to that our state Wildlife Agency UH, Department

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Speaker 1: of Wilife, Fishers and Parks, they are very engaged in

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Speaker 1: deer research and the deer lab more or less serves

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Speaker 1: as their research arm and so questions that them to

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Speaker 1: us from them, UM just about have to be very

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Speaker 1: applied in nature because this is typically a question that

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Speaker 1: hunters are giving to them. So their their staff of

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Speaker 1: wildlife biologists are being presented with questions and they want

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Speaker 1: some answers. So we work with the staff of of

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Speaker 1: MDWF and P and we literally will will have a meeting. Hey,

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Speaker 1: let's talk about this question. Let's talk about this problem.

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Speaker 1: Is this Is this a question that we can answer

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Speaker 1: with research? If so, how would we go about designing

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Speaker 1: that that project? And then finally we have to develop

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Speaker 1: of course, we have to develop a budget for that,

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Speaker 1: and we determine, hey, do we have an adequate amount

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Speaker 1: of funding UH to answer this question? And so UM,

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Speaker 1: one of the questions most recently in Mississippi, this occurs

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Speaker 1: in a lot of places. Um. But we wanted to

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Speaker 1: simultaneously look at what impact are acorn crops. So like

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Speaker 1: this year there were a lot of deer sightings were down.

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Speaker 1: Uh in some areas, harvests were down. Um, And people

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Speaker 1: want to know what's going on. Is a disease? Uh,

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Speaker 1: We're not seeing as many deer and it may simply

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Speaker 1: be that we had a very very good acorn crop. Simultaneously,

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Speaker 1: we had a big time drought in the southeast and

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Speaker 1: so when a lot of people in November and early

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Speaker 1: December were used to seeing deer on food plots, there

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Speaker 1: was nothing there. You know, there was nothing growing because

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Speaker 1: we had the drought, and so deer sightings were down. UM.

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Speaker 1: The other part, the other interest is like in a

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Speaker 1: lot of places, what is hunting pressure having on deer

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Speaker 1: and specifically on bucks. So what we decided to do

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Speaker 1: was we were gonna put GPS callers on about fifty

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Speaker 1: mature bucks and so we again worked with our state

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Speaker 1: wildlife biologists to find a number of landowners. So we've

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Speaker 1: got about a thirty thousand acre area where all the

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Speaker 1: landowners are very cooperative, The hunting clubs are very cooperative,

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Speaker 1: and they helped us with catching. You're trapping these bucks

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Speaker 1: and putting GPS collars on them, and beginning this late

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Speaker 1: summer and fall, we're gonna be taking a location from

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Speaker 1: from all these bucks about every two to three hours

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Speaker 1: all through the hunting season, and so we're gonna see

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Speaker 1: how they respond to different weather events. We're also going

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Speaker 1: to see how the bucks are going to respond to

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Speaker 1: hunting pressure. So that was had to be a big

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Speaker 1: buy in from the club. Is it's not just we're

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Speaker 1: gonna monitor the deer. We want to monitor the hunters.

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Speaker 1: So we want to know where this hunter went, the

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Speaker 1: route that they took to their stand, the stand they

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Speaker 1: hunted on, and for how long and how they left,

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Speaker 1: and so it's gonna get really complicated. But we're gonna

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Speaker 1: overlay all that material the hunting pressure as well as

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Speaker 1: the buck movements as well as we're gonna be mapping

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Speaker 1: out areas where we have hardwoods and oak trees, gauge

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Speaker 1: what the mast crop is and see if we see

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Speaker 1: more or less movement based on the acorn crops, and

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Speaker 1: just really ultimate goal is try to provide UH deer

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Speaker 1: managers and deer hunters with answers, you know, try to

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Speaker 1: help explain what they're seeing and um and you see

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Speaker 1: a science to answer those questions. So how do you

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Speaker 1: in this type of example, you know, and I am

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Speaker 1: no scientists, I am no analysts, So I am I'm

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Speaker 1: coming from a very amateur standpoint. But as I as

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Speaker 1: I understand these types of things, and as I try

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Speaker 1: to understand types of things, right, when you're trying to

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Speaker 1: establish a uh you know, and as if relationship, you know,

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Speaker 1: a because of a relationship, you're trying to have to

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Speaker 1: isolate a variable. Right you have to say, okay, here's

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Speaker 1: a control, let's measure what's happening in the control. We

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Speaker 1: have to measure then whatever that variable as we're changing,

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Speaker 1: and then the impact. How do you do that in

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Speaker 1: a situation like this where it sounds like you're measuring

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Speaker 1: different many different variables because we're talking about hunting pressure

430
00:25:51,480 --> 00:25:53,800
Speaker 1: as a variable, as I understand, we're talking about possibly

431
00:25:53,800 --> 00:25:56,959
Speaker 1: whether as a variable, We're talking about food sources as

432
00:25:57,000 --> 00:26:00,919
Speaker 1: a variable. I mean, how can you measure the impact

433
00:26:00,920 --> 00:26:03,080
Speaker 1: of any one of those things when they all may

434
00:26:03,119 --> 00:26:07,639
Speaker 1: have an interdependent reaction or um, you know, relationship within

435
00:26:08,000 --> 00:26:12,800
Speaker 1: into each other. That is very very insightful That is

436
00:26:12,840 --> 00:26:16,480
Speaker 1: a very good question, and that is they Uh. That

437
00:26:16,640 --> 00:26:20,320
Speaker 1: is part of every analysis is you have to figure

438
00:26:20,359 --> 00:26:22,760
Speaker 1: out and we have statistical tests for this. But what

439
00:26:22,840 --> 00:26:27,520
Speaker 1: you just described there is are your data independence or

440
00:26:27,600 --> 00:26:31,480
Speaker 1: are they linked or are they associated? So when one

441
00:26:31,560 --> 00:26:36,359
Speaker 1: variable goes up, another variable automatically goes down, and because

442
00:26:36,440 --> 00:26:40,000
Speaker 1: those two variables are correlated, it's hard to distinguish causation.

443
00:26:40,800 --> 00:26:42,159
Speaker 1: Is it because this one went up or the other

444
00:26:42,200 --> 00:26:46,399
Speaker 1: variable went down? But we have statistical techniques. One example

445
00:26:46,480 --> 00:26:49,760
Speaker 1: is called analysis of variance. And when you plug in

446
00:26:49,840 --> 00:26:52,959
Speaker 1: a lot of variables to movements, So it might be uh,

447
00:26:53,200 --> 00:26:56,960
Speaker 1: hunting pressure, some metric of hunting pressure. It might be

448
00:26:57,160 --> 00:27:03,679
Speaker 1: acorn abundance, that might be intogavaria in food plots, etcetera, etcetera. Uh,

449
00:27:03,840 --> 00:27:08,800
Speaker 1: you can see which of those variables are acting independently

450
00:27:09,680 --> 00:27:12,760
Speaker 1: or are acting in concert and then you know you

451
00:27:12,840 --> 00:27:15,080
Speaker 1: kind of account for it once you know which is

452
00:27:15,080 --> 00:27:18,440
Speaker 1: the case, and then you you with the statistical models,

453
00:27:18,480 --> 00:27:23,000
Speaker 1: you can partition the variation. So what proportion of the

454
00:27:23,080 --> 00:27:28,600
Speaker 1: deer movements are responsible for or excuse me, the deer

455
00:27:28,600 --> 00:27:32,760
Speaker 1: are responding based on acorns? You know, thirty percent of

456
00:27:32,760 --> 00:27:38,280
Speaker 1: the movements we can say is is acorn crop hunting pressure? Well,

457
00:27:38,320 --> 00:27:40,560
Speaker 1: based on our model, we can say that fifty of

458
00:27:40,560 --> 00:27:43,439
Speaker 1: the variation that we see in movements are based on

459
00:27:43,560 --> 00:27:46,719
Speaker 1: hunting pressure. So it's really it's almost like a process

460
00:27:46,760 --> 00:27:50,720
Speaker 1: of elimination. So you might start out with six or

461
00:27:50,760 --> 00:27:57,000
Speaker 1: eight or ten different very plausible, biologically reasonable variables and

462
00:27:57,040 --> 00:27:59,879
Speaker 1: then just kind of one by one with with the

463
00:28:00,040 --> 00:28:03,440
Speaker 1: weight of evidence from your data, you either have support

464
00:28:03,480 --> 00:28:07,359
Speaker 1: for some or you eliminate others. Now, are you, in

465
00:28:07,359 --> 00:28:10,879
Speaker 1: this particular instance, are you going into this with a

466
00:28:10,920 --> 00:28:14,040
Speaker 1: set of hypotheses. Do you have hypothesis that you're trying

467
00:28:14,080 --> 00:28:17,600
Speaker 1: to prove false or true? Or are you I mean,

468
00:28:17,600 --> 00:28:19,680
Speaker 1: I guess as I understand the scientific method, right, that's

469
00:28:19,680 --> 00:28:21,639
Speaker 1: how it works. Or are you going into this with

470
00:28:21,680 --> 00:28:25,720
Speaker 1: a blank canvas and saying what are we gonna find out? Well,

471
00:28:25,760 --> 00:28:29,360
Speaker 1: it'll be it'll be a little bit of both. So, UM,

472
00:28:29,520 --> 00:28:34,080
Speaker 1: we can just about guarantee from the beginning, is hunting

473
00:28:34,119 --> 00:28:38,000
Speaker 1: pressure going to impact dear movements in some way? That's

474
00:28:38,040 --> 00:28:41,080
Speaker 1: going to be true or false, and more than likely

475
00:28:41,120 --> 00:28:44,000
Speaker 1: that's going to be true. But we also want to

476
00:28:44,120 --> 00:28:50,800
Speaker 1: then measure the effect. So UM, at what point, UM

477
00:28:50,840 --> 00:28:53,240
Speaker 1: we may have a little bit of hunting intensity, we

478
00:28:53,280 --> 00:28:57,080
Speaker 1: have no measurable change in dear movements, or we may

479
00:28:57,120 --> 00:29:01,240
Speaker 1: have this greater um hunter into in city may then

480
00:29:01,360 --> 00:29:04,719
Speaker 1: impact dear movements. And we did a study like this

481
00:29:04,880 --> 00:29:10,160
Speaker 1: previously in another location. The excuse me, the design was different,

482
00:29:10,880 --> 00:29:16,000
Speaker 1: different setup, but but what we found was that, uh,

483
00:29:16,000 --> 00:29:18,560
Speaker 1: and this is kind of bad news, but it only

484
00:29:18,600 --> 00:29:20,760
Speaker 1: in this study. It only took about three to four

485
00:29:20,840 --> 00:29:25,080
Speaker 1: days for the deer to adapt, So we know going

486
00:29:25,120 --> 00:29:28,480
Speaker 1: in we have some preliminary data that after three or

487
00:29:28,480 --> 00:29:32,000
Speaker 1: four days of hunters being in the woods and tracks

488
00:29:32,040 --> 00:29:35,960
Speaker 1: and human scent and sounds kind of saturating the woods. Uh,

489
00:29:36,000 --> 00:29:38,600
Speaker 1: the deer learned and they responded in about three to

490
00:29:38,640 --> 00:29:42,840
Speaker 1: four days UM. And they changed their movement patterns, and

491
00:29:43,120 --> 00:29:47,560
Speaker 1: it wasn't so much that they changed the total distance

492
00:29:47,640 --> 00:29:50,280
Speaker 1: that they moved in a day. So let's just for

493
00:29:50,360 --> 00:29:52,440
Speaker 1: easy numbers here. Let's say a deer moved a mile

494
00:29:52,520 --> 00:29:57,000
Speaker 1: every day UM after hunting pressure. They still moved a

495
00:29:57,080 --> 00:29:59,680
Speaker 1: mile when you added up dot to dot or point

496
00:29:59,720 --> 00:30:02,800
Speaker 1: to four, but they did it in a more complex manner.

497
00:30:03,320 --> 00:30:05,640
Speaker 1: So they still moved a mile, but they moved a

498
00:30:05,640 --> 00:30:09,840
Speaker 1: mile in a much smaller area, which was most likely

499
00:30:09,920 --> 00:30:14,560
Speaker 1: they were staying to cover. They weren't exposing themselves. That

500
00:30:15,600 --> 00:30:20,040
Speaker 1: makes sense. Did you did you um in this particular instance,

501
00:30:20,120 --> 00:30:24,520
Speaker 1: did you look into you know, the the Oh gosh,

502
00:30:24,520 --> 00:30:26,280
Speaker 1: I don't know how the right way articulate is. But

503
00:30:26,840 --> 00:30:29,600
Speaker 1: did actual core ranges change? And we always hear this

504
00:30:29,680 --> 00:30:31,760
Speaker 1: when as hunters, we talked about you put pressure on

505
00:30:31,840 --> 00:30:33,800
Speaker 1: deer and if you if you put too much pressure,

506
00:30:33,840 --> 00:30:36,680
Speaker 1: they're gonna get out of dodge. And I've always wondered

507
00:30:36,840 --> 00:30:39,760
Speaker 1: some people say, yeah, they'll completely relocate if you're in

508
00:30:39,760 --> 00:30:42,479
Speaker 1: there too much, or no, they'll do as you just

509
00:30:42,600 --> 00:30:45,280
Speaker 1: mentioned there, they will just being cover more or they'll

510
00:30:45,280 --> 00:30:47,640
Speaker 1: be more likely to move it dark, but they're not

511
00:30:47,680 --> 00:30:51,240
Speaker 1: necessarily going to be gone. We're able to see anything

512
00:30:51,280 --> 00:30:54,480
Speaker 1: like that. To what degree that change happened? Is a distance?

513
00:30:54,760 --> 00:30:58,600
Speaker 1: Is it just timing anything like that? Yeah? Well, with

514
00:30:58,600 --> 00:31:00,960
Speaker 1: with our study now, it's not to say that in

515
00:31:01,520 --> 00:31:05,240
Speaker 1: some instance they might completely get out of dodge. Um.

516
00:31:05,280 --> 00:31:08,840
Speaker 1: But at that study with though with those bucks, they

517
00:31:08,920 --> 00:31:12,000
Speaker 1: pretty much stayed within their their home range in their

518
00:31:12,040 --> 00:31:14,800
Speaker 1: core area. They just moved less or they just move

519
00:31:14,880 --> 00:31:19,040
Speaker 1: more UM more complex pattern. So we didn't have any

520
00:31:19,120 --> 00:31:21,720
Speaker 1: just outright shifts of a deer getting up and moving

521
00:31:21,920 --> 00:31:26,440
Speaker 1: moving out and they just didn't move as often. Yeah. Interesting.

522
00:31:27,080 --> 00:31:30,120
Speaker 1: So on the this is we're about to just go

523
00:31:30,280 --> 00:31:33,760
Speaker 1: crazy rapid fire and new Bronson because there's so many

524
00:31:33,840 --> 00:31:37,160
Speaker 1: questions related to this that I'm intrigued and um by.

525
00:31:37,200 --> 00:31:41,120
Speaker 1: But on this topic of of deer movement, We've talked

526
00:31:41,120 --> 00:31:43,360
Speaker 1: about this a ton on the podcast with different guests,

527
00:31:43,400 --> 00:31:46,120
Speaker 1: all the different variables that may or may not influence

528
00:31:46,200 --> 00:31:49,160
Speaker 1: deer movement. Maybe that's the amount of deer movement, Maybe

529
00:31:49,160 --> 00:31:52,320
Speaker 1: that's the amount of daylight movement. Maybe that's just how

530
00:31:52,360 --> 00:31:54,840
Speaker 1: early in the day they move, different things like that.

531
00:31:54,920 --> 00:31:58,440
Speaker 1: So we talk about what different factors could potentially influence

532
00:31:58,480 --> 00:32:01,480
Speaker 1: deer movement in a way that benefits hunters. Um, there's

533
00:32:01,480 --> 00:32:04,280
Speaker 1: so many theories, there's so many different hunches, there's so

534
00:32:04,280 --> 00:32:05,959
Speaker 1: many different things. I think Dan and I and all

535
00:32:06,040 --> 00:32:08,920
Speaker 1: of our listeners have tried putting into place too, you know,

536
00:32:08,960 --> 00:32:11,320
Speaker 1: slam things a little bit in our favor. Um, as

537
00:32:11,320 --> 00:32:13,760
Speaker 1: I understand it, you have participated and have been part

538
00:32:13,800 --> 00:32:17,280
Speaker 1: of some research that has tried to measure some of this.

539
00:32:17,640 --> 00:32:19,640
Speaker 1: A is that is that correct? And be what can

540
00:32:19,640 --> 00:32:22,640
Speaker 1: you tell us based on the research you've done, Yeah,

541
00:32:22,680 --> 00:32:26,440
Speaker 1: that that that's correct. Um. We did to research projects

542
00:32:26,920 --> 00:32:32,200
Speaker 1: related to this. UM. The first was involved again using

543
00:32:32,800 --> 00:32:36,480
Speaker 1: GPS collars. So we were looking at the movements of deer,

544
00:32:36,560 --> 00:32:39,920
Speaker 1: but both does and bucks and over a couple of seasons,

545
00:32:40,520 --> 00:32:47,520
Speaker 1: and we did not find any evidence whatsoever, UM, that

546
00:32:47,640 --> 00:32:51,600
Speaker 1: the moon for example, changed their activity patterns. We didn't

547
00:32:51,600 --> 00:32:55,640
Speaker 1: see any increases in the movement rates. We didn't see

548
00:32:55,680 --> 00:32:59,880
Speaker 1: any changes uh tempoally throughout throughout the day when they

549
00:33:00,000 --> 00:33:03,760
Speaker 1: are moving more, moving less. And I know, if we

550
00:33:03,800 --> 00:33:05,640
Speaker 1: get a lot of scientists, we get a lot of

551
00:33:06,440 --> 00:33:10,400
Speaker 1: negative feedback for that. And UM, because a lot of

552
00:33:10,440 --> 00:33:13,840
Speaker 1: people and I and I really do I trust some people.

553
00:33:13,880 --> 00:33:15,840
Speaker 1: There's some people I really do trust with their camera data.

554
00:33:15,920 --> 00:33:18,400
Speaker 1: They see some they see some trends and and I'm

555
00:33:18,400 --> 00:33:21,400
Speaker 1: not denying that, uh in that area at that time,

556
00:33:21,440 --> 00:33:24,880
Speaker 1: that trend may well have occurred. But UM, over the

557
00:33:24,920 --> 00:33:28,360
Speaker 1: course of you know a number of bucks and does

558
00:33:28,520 --> 00:33:32,040
Speaker 1: and over uh you know, a two year study. UM,

559
00:33:32,120 --> 00:33:36,880
Speaker 1: we just did not see any reliable impact uh of

560
00:33:36,960 --> 00:33:41,120
Speaker 1: the moon of moon phase. UM, we did see some

561
00:33:41,240 --> 00:33:45,560
Speaker 1: changes and uh when we would have temperature changes, so

562
00:33:45,600 --> 00:33:47,239
Speaker 1: when we would get, you know, the front would be

563
00:33:47,240 --> 00:33:51,600
Speaker 1: coming in, we might see some increased movements. But but

564
00:33:51,760 --> 00:33:57,240
Speaker 1: again it wasn't that dramatic. It was always subtle. Um.

565
00:33:57,400 --> 00:34:01,440
Speaker 1: The the other study we did was quick question before

566
00:34:01,440 --> 00:34:07,440
Speaker 1: you get when when you say an increase in movement,

567
00:34:07,520 --> 00:34:10,359
Speaker 1: do you mean total area covered or do you mean

568
00:34:10,880 --> 00:34:15,799
Speaker 1: like observed movement from a tree stand for a trail

569
00:34:15,840 --> 00:34:20,279
Speaker 1: camera during daylight. Yeah, but very very good point. So

570
00:34:20,360 --> 00:34:23,720
Speaker 1: what we typically do is we will break the deer's

571
00:34:23,840 --> 00:34:27,000
Speaker 1: day up into periods. So we might break the day

572
00:34:27,080 --> 00:34:30,560
Speaker 1: up from thirty minutes before sunrise or an hour before

573
00:34:30,600 --> 00:34:34,400
Speaker 1: sunrise till two hours after uh from from nine am

574
00:34:34,480 --> 00:34:37,120
Speaker 1: to twelve, from twelve to three, from three to you

575
00:34:37,160 --> 00:34:41,200
Speaker 1: know dark, we break the day up, and then for

576
00:34:41,320 --> 00:34:45,400
Speaker 1: every single deer and every single day, we calculate, you know,

577
00:34:45,440 --> 00:34:47,840
Speaker 1: what their movement rate was. And rate could be a

578
00:34:47,880 --> 00:34:51,080
Speaker 1: lot of different ways. It could be total distance moved,

579
00:34:51,200 --> 00:34:54,520
Speaker 1: the average distance in between points. But you know, we

580
00:34:54,600 --> 00:35:00,080
Speaker 1: try to totally characterize was that dear moving more that

581
00:35:00,239 --> 00:35:05,000
Speaker 1: normally does? And then we retrospectively, we go back and

582
00:35:05,040 --> 00:35:07,120
Speaker 1: we look at those weather events and we try to

583
00:35:07,239 --> 00:35:10,440
Speaker 1: fit Okay, we had a big change in the moon here,

584
00:35:10,480 --> 00:35:12,799
Speaker 1: and we had a big change in temperature, we had

585
00:35:12,840 --> 00:35:16,399
Speaker 1: a big difference in rain, and we just see if

586
00:35:16,440 --> 00:35:21,000
Speaker 1: any of those environmental variables are responsible for any changes

587
00:35:21,280 --> 00:35:25,239
Speaker 1: in the deer movement patterns, that's how that's how we

588
00:35:25,280 --> 00:35:28,680
Speaker 1: do it here. Then. So then the outcome of that

589
00:35:28,760 --> 00:35:36,200
Speaker 1: particular event was it wasn't inconclusive. It was that those

590
00:35:36,560 --> 00:35:41,239
Speaker 1: weather may increase a small amount. But moon, you have

591
00:35:41,280 --> 00:35:44,520
Speaker 1: found no evidence to support that the moon increases movement.

592
00:35:45,680 --> 00:35:48,880
Speaker 1: We did not in the two different studies. We did not.

593
00:35:50,120 --> 00:35:54,359
Speaker 1: Um So there there's kind of two things about the moon.

594
00:35:54,440 --> 00:35:56,680
Speaker 1: You know, there's the moon and the position of it

595
00:35:56,719 --> 00:36:00,279
Speaker 1: and is affecting just in general, uh movement rates, more

596
00:36:00,320 --> 00:36:04,719
Speaker 1: movement rates in the day versus nighttime, etcetera. The other

597
00:36:05,000 --> 00:36:09,880
Speaker 1: is it is the moon affecting the breeding season? Is

598
00:36:09,920 --> 00:36:13,680
Speaker 1: the moon affecting the rut? And so with this one

599
00:36:13,760 --> 00:36:16,439
Speaker 1: we just you know, we're just totally using does here

600
00:36:17,120 --> 00:36:19,480
Speaker 1: and we we did it from our pen, from our

601
00:36:19,520 --> 00:36:23,120
Speaker 1: captive facility where no, we know when these doughs are

602
00:36:23,120 --> 00:36:25,680
Speaker 1: being bred. We know when these doughs are having their fawns,

603
00:36:25,800 --> 00:36:30,239
Speaker 1: so we can relate you know, their their patterns, conception

604
00:36:30,280 --> 00:36:33,600
Speaker 1: patterns um And then in the wild, we have a

605
00:36:33,680 --> 00:36:37,040
Speaker 1: data set through the Department of Wife Fisheries and Parks

606
00:36:37,080 --> 00:36:43,200
Speaker 1: called our Springtime Herd Health Evaluations where um does are

607
00:36:43,280 --> 00:36:48,359
Speaker 1: killed and and that the doughs are harvested for scientific purposes,

608
00:36:48,960 --> 00:36:52,759
Speaker 1: where we can extract the fetus, and based on the

609
00:36:52,840 --> 00:36:55,800
Speaker 1: size of the fetus, we know when that dough was bred.

610
00:36:56,360 --> 00:36:59,640
Speaker 1: We know when conception happened, the date. And so through

611
00:36:59,680 --> 00:37:02,319
Speaker 1: both of those sources of data, we went back for

612
00:37:02,440 --> 00:37:05,959
Speaker 1: about a decade and we looked at, Okay, the moon

613
00:37:06,120 --> 00:37:08,759
Speaker 1: was this this year, or the moon was that next year,

614
00:37:08,840 --> 00:37:12,440
Speaker 1: the moon was this here, and it was never correlated.

615
00:37:12,880 --> 00:37:18,280
Speaker 1: There was never you never saw changes in conception dates

616
00:37:18,360 --> 00:37:21,960
Speaker 1: for a population track in any way changes in the

617
00:37:22,000 --> 00:37:25,359
Speaker 1: moon that was just was. There was never a relationship.

618
00:37:26,160 --> 00:37:30,640
Speaker 1: Did you guys those ever look into what might influence

619
00:37:30,920 --> 00:37:33,680
Speaker 1: um the breeding dates specifically in the South. I'm just

620
00:37:33,719 --> 00:37:35,360
Speaker 1: curious given the fact that a lot of this, you

621
00:37:35,440 --> 00:37:38,200
Speaker 1: know where you're you're down in Mississippi, you hear so

622
00:37:38,320 --> 00:37:40,759
Speaker 1: much about the wonky timing of the rut down in

623
00:37:40,800 --> 00:37:43,680
Speaker 1: that part of the country. Were you able ever able

624
00:37:43,719 --> 00:37:48,839
Speaker 1: to look into that. Well, what we have found the

625
00:37:48,960 --> 00:37:53,080
Speaker 1: most support for UM that there's a lot mark as

626
00:37:53,120 --> 00:37:55,040
Speaker 1: you mentioned earlier, that you know, there's a lot of

627
00:37:55,120 --> 00:37:58,840
Speaker 1: interacting variables that that can move breeding season forward or

628
00:37:58,880 --> 00:38:02,279
Speaker 1: back a little bit. But typically what we find the

629
00:38:02,360 --> 00:38:07,120
Speaker 1: strongest evidence for is UM. In the Southeast, it is

630
00:38:07,200 --> 00:38:10,799
Speaker 1: so varied, so you'll have even in Mississippi, you'll have

631
00:38:10,880 --> 00:38:13,520
Speaker 1: the rut occurring in some places the beginning of December

632
00:38:14,000 --> 00:38:16,759
Speaker 1: all the way to the middle of February. Where I

633
00:38:16,760 --> 00:38:20,160
Speaker 1: grew up in Georgia latitudinally exactly the same place where

634
00:38:20,160 --> 00:38:23,239
Speaker 1: I'm at now, but it was around Thanksgiving, a whole

635
00:38:23,239 --> 00:38:29,279
Speaker 1: month earlier. So it is usually related to UM, the

636
00:38:29,360 --> 00:38:34,319
Speaker 1: stocking source. So it's a genetic heritage. So as you

637
00:38:34,400 --> 00:38:38,920
Speaker 1: might know, all throughout the Southeast, when deer were restocked

638
00:38:38,920 --> 00:38:42,640
Speaker 1: in the fifties and sixties UM, for example, in Mississippi,

639
00:38:42,680 --> 00:38:45,239
Speaker 1: we had deer from Mexico, deer from Texas, we had

640
00:38:45,280 --> 00:38:49,080
Speaker 1: deer from Wisconsin, dere from Ohio, deer from North Carolina.

641
00:38:49,360 --> 00:38:53,640
Speaker 1: And when we find these pockets of the breeding season

642
00:38:53,719 --> 00:38:56,840
Speaker 1: is a month before or a month after, you know,

643
00:38:56,840 --> 00:38:59,800
Speaker 1: when we see these big changes, it usually can be

644
00:39:00,000 --> 00:39:04,280
Speaker 1: trace back to what was the stocking source for that area.

645
00:39:04,600 --> 00:39:08,960
Speaker 1: So that that is inherited by the mother. A mother

646
00:39:09,239 --> 00:39:13,400
Speaker 1: will pass that along uh to the female pawn. She

647
00:39:13,520 --> 00:39:18,800
Speaker 1: will inherit the breeding date from her mother, so that

648
00:39:18,800 --> 00:39:22,200
Speaker 1: that usually sets the stage, the window, so to speak,

649
00:39:22,719 --> 00:39:24,840
Speaker 1: and then environment can push it and pull it a

650
00:39:24,880 --> 00:39:28,640
Speaker 1: little bit. And and the biggest thing with environment is

651
00:39:28,640 --> 00:39:32,240
Speaker 1: is herd condition. And so if you've got to really

652
00:39:32,560 --> 00:39:35,760
Speaker 1: either the you know, environmental conditions are poor, food conditions

653
00:39:35,760 --> 00:39:37,880
Speaker 1: are poor, or you just have a really really dense

654
00:39:37,920 --> 00:39:40,520
Speaker 1: deer herd, there's a lot of evidence to say that

655
00:39:41,280 --> 00:39:44,640
Speaker 1: UM dose may come into estras on average a little

656
00:39:44,680 --> 00:39:48,359
Speaker 1: bit later. But but the biggest thing that we've seen

657
00:39:48,400 --> 00:39:52,359
Speaker 1: over time is just adult sex ratio when when you

658
00:39:52,400 --> 00:39:57,720
Speaker 1: have a population where the rut is protracted over six

659
00:39:57,760 --> 00:40:01,200
Speaker 1: weeks two months that you usually we don't even see

660
00:40:01,200 --> 00:40:03,799
Speaker 1: that now much anymore, but usually back in in you know,

661
00:40:03,880 --> 00:40:06,640
Speaker 1: twenty years ago, that was because you had such a

662
00:40:06,640 --> 00:40:11,360
Speaker 1: skewed adult sex ratio where too many doves were coming

663
00:40:11,360 --> 00:40:15,239
Speaker 1: into heat simultaneously that there weren't enough adult bucks in

664
00:40:15,280 --> 00:40:18,640
Speaker 1: the population to breed them, and so they would go

665
00:40:18,719 --> 00:40:22,120
Speaker 1: back back into heat twenty eight days later, and so

666
00:40:22,200 --> 00:40:25,160
Speaker 1: you end up with this three month long rut um

667
00:40:25,200 --> 00:40:28,759
Speaker 1: and it was just simply a product of sex ratio H.

668
00:40:29,280 --> 00:40:31,840
Speaker 1: And that's not just specifically in the Southeast. That was

669
00:40:31,880 --> 00:40:34,040
Speaker 1: something that could be the case anywhere in the country

670
00:40:34,080 --> 00:40:38,759
Speaker 1: with that type of sex ratio. Correct, absolutely, yeah, absolutely

671
00:40:39,320 --> 00:40:42,680
Speaker 1: so can you can you UM, and I'm not sure

672
00:40:42,719 --> 00:40:44,920
Speaker 1: if you've looked into this, but can you speak to

673
00:40:45,040 --> 00:40:50,279
Speaker 1: then what the implications are of other management changes as

674
00:40:50,360 --> 00:40:54,319
Speaker 1: hunters we might make. So if we were to know better,

675
00:40:54,360 --> 00:40:56,239
Speaker 1: try to control the dull population so we have a

676
00:40:56,280 --> 00:40:59,920
Speaker 1: closer sex ratio or influence the age structure. Have you

677
00:41:00,080 --> 00:41:02,200
Speaker 1: been able to see how changes like that or any

678
00:41:02,239 --> 00:41:08,080
Speaker 1: others you've looked into then change behavior or UH running

679
00:41:08,080 --> 00:41:13,279
Speaker 1: timing or anything else that we hunters would be interested in. UM.

680
00:41:14,080 --> 00:41:18,560
Speaker 1: The biggest influence would would of course be managing your

681
00:41:18,600 --> 00:41:22,320
Speaker 1: density relative to food. So it's never for for any

682
00:41:22,480 --> 00:41:27,040
Speaker 1: for for reproduction, for producing UH fawns, specifically buck fawns,

683
00:41:27,120 --> 00:41:30,360
Speaker 1: for producing UH you know, Boone and Crockett bucks or

684
00:41:30,360 --> 00:41:33,240
Speaker 1: Pope and Young's. If you have too many deer relative

685
00:41:33,280 --> 00:41:35,480
Speaker 1: to the food supply, you're never gonna win. You're right,

686
00:41:35,520 --> 00:41:39,239
Speaker 1: You're you're always behind. UM. But but the other part

687
00:41:39,320 --> 00:41:41,160
Speaker 1: of that, as well is is of course going to

688
00:41:41,239 --> 00:41:46,200
Speaker 1: be your sex ratio. So you would never want, especially

689
00:41:46,239 --> 00:41:48,600
Speaker 1: mark up in your neck of the woods. You never

690
00:41:48,680 --> 00:41:53,040
Speaker 1: want to do breeding one month later than she had

691
00:41:53,040 --> 00:41:58,240
Speaker 1: evolved to optimally breed in your environment. So getting getting

692
00:41:58,280 --> 00:42:01,200
Speaker 1: fawns on the ground as early as possible where you're

693
00:42:01,280 --> 00:42:05,040
Speaker 1: from is critically important for them to have enough body

694
00:42:05,080 --> 00:42:08,160
Speaker 1: mass to survive the winter. And so if you have

695
00:42:08,239 --> 00:42:10,520
Speaker 1: a skewed sex ratio and your dough was coming in

696
00:42:10,600 --> 00:42:13,360
Speaker 1: on average, you know a month later now you you

697
00:42:13,440 --> 00:42:15,960
Speaker 1: have put that fall on into the disadvantage and and

698
00:42:16,040 --> 00:42:19,239
Speaker 1: survival rates on average are going to be lower. But

699
00:42:19,360 --> 00:42:23,360
Speaker 1: mainly mark that that's primarily it keeping the herd numbers.

700
00:42:23,400 --> 00:42:26,440
Speaker 1: And if you're keeping the herd numbers in check, then

701
00:42:26,480 --> 00:42:28,640
Speaker 1: your sex ratio is probably going to be in check

702
00:42:28,680 --> 00:42:32,040
Speaker 1: as well. Okay, so speaking of them, this topic of

703
00:42:32,200 --> 00:42:35,000
Speaker 1: herd health and things like that, you know, the the

704
00:42:35,080 --> 00:42:38,200
Speaker 1: other factors I think are generally talked about would be

705
00:42:38,320 --> 00:42:42,440
Speaker 1: than genetics and available nutrition when it comes to you know,

706
00:42:42,520 --> 00:42:46,080
Speaker 1: the the results you'll get from a deer herd. Can

707
00:42:46,120 --> 00:42:49,319
Speaker 1: you talk about that? Um, what you've seen, what the

708
00:42:49,360 --> 00:42:52,080
Speaker 1: influence those different things have, What does how does genetics

709
00:42:52,120 --> 00:42:55,919
Speaker 1: influence things, how does nutrition influence things? UM? How does

710
00:42:56,200 --> 00:42:58,480
Speaker 1: any other changes we might have? And maybe an antler

711
00:42:58,520 --> 00:43:00,439
Speaker 1: growth is something you've looked into, and I think because

712
00:43:00,480 --> 00:43:02,640
Speaker 1: there's been a lot of studies around measuring the impact

713
00:43:02,640 --> 00:43:05,040
Speaker 1: of those couple of factors on ant or growth. But

714
00:43:05,360 --> 00:43:08,359
Speaker 1: can you speak to some of those things? Yeah, you bet,

715
00:43:08,480 --> 00:43:13,160
Speaker 1: you bet? UM. One point I like to emphasize is

716
00:43:13,280 --> 00:43:19,920
Speaker 1: um genetics are very important for individuals and nutrition is

717
00:43:20,040 --> 00:43:25,879
Speaker 1: very important for a population. So those work in concert. UM.

718
00:43:25,960 --> 00:43:28,520
Speaker 1: So when you look at UM and some of this

719
00:43:28,600 --> 00:43:31,759
Speaker 1: is could be on our website mark on m thiss

720
00:43:31,800 --> 00:43:33,560
Speaker 1: you deer lab dot com. You might see some of

721
00:43:33,560 --> 00:43:37,560
Speaker 1: these figures, but you need to think about UM. A

722
00:43:37,640 --> 00:43:42,280
Speaker 1: buck heard specifically, an age class is a bell shaped curve,

723
00:43:43,520 --> 00:43:46,440
Speaker 1: meaning that you're gonna have most of most of the

724
00:43:46,480 --> 00:43:49,120
Speaker 1: bucks in your area are going to have an average

725
00:43:49,120 --> 00:43:52,920
Speaker 1: antler size, and there's gonna be very few of them

726
00:43:52,960 --> 00:43:56,319
Speaker 1: that are way below average, and there's gonna be very

727
00:43:56,360 --> 00:43:58,880
Speaker 1: few of them that are way above average, meaning like

728
00:43:58,880 --> 00:44:04,200
Speaker 1: Boone and Crocketts, and so in Mississippi, even when you

729
00:44:04,239 --> 00:44:09,400
Speaker 1: get bucks to maturity, you're only gonna have about fifteen

730
00:44:11,080 --> 00:44:14,120
Speaker 1: of your mature bucks are gonna be what most hunters

731
00:44:14,200 --> 00:44:18,600
Speaker 1: would consider a trophy. Now that's genetics, That is the

732
00:44:18,640 --> 00:44:22,600
Speaker 1: genetics of individuals. It doesn't matter if they had all

733
00:44:22,640 --> 00:44:25,560
Speaker 1: the food they could possibly eat. If if a particular

734
00:44:25,640 --> 00:44:28,680
Speaker 1: buck is programmed to be a hundred and twenty class

735
00:44:28,719 --> 00:44:30,520
Speaker 1: eight pointer, then it's going to grow to be a

736
00:44:30,560 --> 00:44:33,400
Speaker 1: hundred and twenty class eight pointer whether it had you know,

737
00:44:33,560 --> 00:44:36,320
Speaker 1: average food or way above average food. So that is

738
00:44:36,360 --> 00:44:43,400
Speaker 1: how genetics affects individuals. How nutrition affects the population is

739
00:44:43,440 --> 00:44:47,520
Speaker 1: that you move the average up or down based on nutrition.

740
00:44:48,440 --> 00:44:52,480
Speaker 1: So in Mississippi statewide, and then it varies from region

741
00:44:52,480 --> 00:44:56,640
Speaker 1: to region, but statewide, the average mature buck is going

742
00:44:56,680 --> 00:45:00,600
Speaker 1: to score about and when I say mature, I'm talking

743
00:45:00,640 --> 00:45:03,000
Speaker 1: five and a half or grader, it's going to score about.

744
00:45:04,440 --> 00:45:08,279
Speaker 1: Now when you go to a more fertile region, WHI

745
00:45:08,280 --> 00:45:10,640
Speaker 1: should be our farm region, which is you know, going

746
00:45:10,680 --> 00:45:13,480
Speaker 1: to be a characteristic or similar to the Midwest, it's

747
00:45:13,800 --> 00:45:17,280
Speaker 1: it's a region that is loaded with soybeans and corn.

748
00:45:18,080 --> 00:45:22,319
Speaker 1: Now you have moved to that average positively to a

749
00:45:22,360 --> 00:45:24,439
Speaker 1: hundred and thirty five or a hundred and thirty six,

750
00:45:25,640 --> 00:45:28,680
Speaker 1: but you're still gonna have within that good region really

751
00:45:28,719 --> 00:45:34,120
Speaker 1: small bucks, average bucks, and really good bucks. So I

752
00:45:34,200 --> 00:45:36,760
Speaker 1: hope that makes sense. You're you're never gonna be able

753
00:45:37,480 --> 00:45:41,239
Speaker 1: to manage for genetics and a free ranging herd. Genetics

754
00:45:41,280 --> 00:45:43,600
Speaker 1: are they're they're more, they are more or less fixed

755
00:45:43,640 --> 00:45:46,080
Speaker 1: if it's a free ranging herd. But the one thing

756
00:45:46,120 --> 00:45:48,640
Speaker 1: you can do to move the herd to the right

757
00:45:49,000 --> 00:45:52,960
Speaker 1: or to improve it is genetics. Now, let me fill

758
00:45:53,000 --> 00:45:55,879
Speaker 1: you in on a study. This is a ten year

759
00:45:56,000 --> 00:46:01,600
Speaker 1: study that we did with our state Wildlife Agency UM.

760
00:46:01,800 --> 00:46:05,480
Speaker 1: So what I described was was going on in Mississippi.

761
00:46:05,880 --> 00:46:09,600
Speaker 1: So all throughout the eighties and nineties and in early

762
00:46:09,640 --> 00:46:13,160
Speaker 1: two thousand's, you've got a lot of hunters that sea

763
00:46:13,320 --> 00:46:16,440
Speaker 1: bucks that are killed from our ag region going my gosh,

764
00:46:16,480 --> 00:46:19,759
Speaker 1: they are just killing monster bucks over there, and they

765
00:46:19,760 --> 00:46:22,520
Speaker 1: are killing really really you know, I mean, it's very

766
00:46:22,600 --> 00:46:27,720
Speaker 1: much like a Midwestern landscape. Lots of food and really

767
00:46:27,840 --> 00:46:30,080
Speaker 1: big bucks are harvested in Mississippi. There and then you

768
00:46:30,160 --> 00:46:34,080
Speaker 1: had the opposite in the continuum. You have part of

769
00:46:34,080 --> 00:46:39,080
Speaker 1: our state that is mainly UM devoted to forestry, uh

770
00:46:39,280 --> 00:46:43,200
Speaker 1: pine production. Pine forest production, and the pine trees are

771
00:46:43,200 --> 00:46:45,760
Speaker 1: not the bad guy. There's nothing wrong with the pine forest.

772
00:46:46,480 --> 00:46:49,520
Speaker 1: But as a result of the way those forests are managed,

773
00:46:50,000 --> 00:46:53,600
Speaker 1: you've got a landscape that's full of trees, and you

774
00:46:53,640 --> 00:46:56,279
Speaker 1: have a landscape where the trees are capturing all of

775
00:46:56,280 --> 00:47:00,320
Speaker 1: the sunlight, and by default, you're not producing much food

776
00:47:00,320 --> 00:47:03,640
Speaker 1: on the forest floor. So you've got a region that

777
00:47:03,719 --> 00:47:06,920
Speaker 1: doesn't produce much deer food. And then you've got to

778
00:47:06,960 --> 00:47:09,799
Speaker 1: read a region that produces more food than the deer

779
00:47:09,840 --> 00:47:14,799
Speaker 1: can eat. Well. But hunters look at that, and they're

780
00:47:14,840 --> 00:47:17,680
Speaker 1: from the region of the state, the southeastern region on

781
00:47:17,719 --> 00:47:19,759
Speaker 1: our coast, and they say, we want to we want

782
00:47:19,760 --> 00:47:21,400
Speaker 1: to be able to kill some of those big bucks

783
00:47:21,400 --> 00:47:23,760
Speaker 1: in our region like they do over in the Agg region,

784
00:47:23,800 --> 00:47:26,160
Speaker 1: which is what we call the Delta, the Delta region.

785
00:47:27,120 --> 00:47:32,239
Speaker 1: And it started to get a lot of um questioning,

786
00:47:32,320 --> 00:47:34,840
Speaker 1: not really an outcry, but a lot of questions about

787
00:47:35,200 --> 00:47:39,160
Speaker 1: why can't we just move some of those deer from

788
00:47:39,160 --> 00:47:42,000
Speaker 1: the Delta region. Why can't why can't you biologists just

789
00:47:42,080 --> 00:47:45,080
Speaker 1: capture some of those and turn them loose down here

790
00:47:45,080 --> 00:47:47,440
Speaker 1: in the southern part of the state so we can

791
00:47:47,480 --> 00:47:51,959
Speaker 1: have big deer like they have over there. And now

792
00:47:52,560 --> 00:47:55,960
Speaker 1: the biologist my response, and you know, my colleagues response

793
00:47:56,000 --> 00:47:59,680
Speaker 1: initially was, well, heck, if you take a big old buck,

794
00:47:59,760 --> 00:48:02,840
Speaker 1: this destined to be a one fifty class in the

795
00:48:02,880 --> 00:48:04,759
Speaker 1: agg region, and turn him loose in a region that

796
00:48:04,760 --> 00:48:08,160
Speaker 1: has no food, he's probably gonna get really small. You know,

797
00:48:08,200 --> 00:48:10,440
Speaker 1: he's not gonna be able to fulfill his genetic potential

798
00:48:10,760 --> 00:48:13,279
Speaker 1: because he doesn't have any food. But we decided to

799
00:48:13,360 --> 00:48:17,719
Speaker 1: conduct the experiment anyway. And I'm sorry this this is

800
00:48:17,760 --> 00:48:20,279
Speaker 1: a bit long winded here, but I promise in the

801
00:48:20,360 --> 00:48:24,160
Speaker 1: end it'll it'll be worth it. I'm interested. So here's

802
00:48:24,160 --> 00:48:28,280
Speaker 1: what we did. So we went out and captured between

803
00:48:28,440 --> 00:48:33,840
Speaker 1: thirty and forty does pregnant does from three regions, the

804
00:48:33,920 --> 00:48:37,320
Speaker 1: region of high food, a region of medium food, and

805
00:48:37,560 --> 00:48:42,279
Speaker 1: then the region of low food supply. Captured pregnant does,

806
00:48:42,360 --> 00:48:45,200
Speaker 1: brought him back to our dear research facility and let

807
00:48:45,239 --> 00:48:48,560
Speaker 1: them have their fawns. And then once the dough had

808
00:48:48,560 --> 00:48:51,160
Speaker 1: their fawn, and once the dough ween the fawn, the

809
00:48:51,239 --> 00:48:54,080
Speaker 1: dough now she's out of the study, completely cut out

810
00:48:54,080 --> 00:48:57,799
Speaker 1: of the study. And now every one of those fawns,

811
00:48:57,800 --> 00:49:01,200
Speaker 1: both bucks and does, every one of those fawns is

812
00:49:01,360 --> 00:49:07,839
Speaker 1: raised on the exact same diet, exact same nutrition. Now

813
00:49:07,880 --> 00:49:12,160
Speaker 1: What was surprising was that at three years of age

814
00:49:12,160 --> 00:49:15,080
Speaker 1: when we started measuring from these different regions, and I remember,

815
00:49:15,120 --> 00:49:17,279
Speaker 1: they were all kept separate. The deer from the Good

816
00:49:17,320 --> 00:49:19,319
Speaker 1: region we're in a separate fan and the Medium region,

817
00:49:19,400 --> 00:49:22,400
Speaker 1: the Low region, they were all kept separate, and at

818
00:49:22,440 --> 00:49:28,040
Speaker 1: three years of age we didn't see any difference. And

819
00:49:28,520 --> 00:49:32,759
Speaker 1: we're scratching our head. How can it be that this

820
00:49:32,880 --> 00:49:38,279
Speaker 1: buck fawn that after weaning for for three years eight

821
00:49:38,320 --> 00:49:42,440
Speaker 1: to the exact same diet as the fawns from the

822
00:49:42,480 --> 00:49:47,600
Speaker 1: Good region and there's still a disparity of twenty of

823
00:49:47,600 --> 00:49:50,359
Speaker 1: boone and Crockett's score in twenty to thirty pounds of

824
00:49:50,400 --> 00:49:53,640
Speaker 1: body weight. How can that be? So you're saying it

825
00:49:53,680 --> 00:49:55,920
Speaker 1: was the same. They all grew up and they were

826
00:49:56,120 --> 00:49:58,520
Speaker 1: had the same weight, and they had the same antler size.

827
00:50:00,040 --> 00:50:02,480
Speaker 1: They they had the same weight and antler size of

828
00:50:02,520 --> 00:50:06,120
Speaker 1: where they came from, but not across so so within

829
00:50:06,200 --> 00:50:09,360
Speaker 1: your facility though you had they were the bucks that

830
00:50:09,400 --> 00:50:12,360
Speaker 1: came from the great nutrition spot, they still were bigger.

831
00:50:12,440 --> 00:50:14,799
Speaker 1: The bucks that came from a lower nutrition spot, they

832
00:50:14,840 --> 00:50:16,960
Speaker 1: still are smaller, even though they were raised on the

833
00:50:17,000 --> 00:50:22,600
Speaker 1: same amount of food exactly, So little deer yeah, the

834
00:50:23,160 --> 00:50:26,319
Speaker 1: little guys remained a little even though they ate the

835
00:50:26,400 --> 00:50:30,120
Speaker 1: exact same food as the bucks from our ag region did.

836
00:50:31,360 --> 00:50:34,839
Speaker 1: But here's where the magic happened. So we're scratching our

837
00:50:34,840 --> 00:50:37,480
Speaker 1: heads in a wow, and you know, we're sitting and saying, Godly,

838
00:50:38,400 --> 00:50:40,840
Speaker 1: maybe there is some type of you know, genetic effect.

839
00:50:40,840 --> 00:50:44,160
Speaker 1: And now now you know, like maybe it's because they're restocking.

840
00:50:44,840 --> 00:50:48,879
Speaker 1: Maybe maybe you know, maybe just coincidentally, some of these areas, uh,

841
00:50:48,920 --> 00:50:51,279
Speaker 1: in the southern part of the state, we're stocked with

842
00:50:51,280 --> 00:50:54,680
Speaker 1: the deer that you know, just have average smaller antler size.

843
00:50:55,640 --> 00:50:59,920
Speaker 1: Well to keep in mind here, so that first generation,

844
00:51:00,040 --> 00:51:03,479
Speaker 1: the fawns, we measured the bucks, but we also kept

845
00:51:03,480 --> 00:51:07,959
Speaker 1: the dose around. So now those does were also those

846
00:51:07,960 --> 00:51:10,400
Speaker 1: same dough pons that were born with the buck fawns.

847
00:51:10,840 --> 00:51:14,839
Speaker 1: They were raised on good nutrition and then they were

848
00:51:14,880 --> 00:51:19,120
Speaker 1: bred and then we looked at the second generation. And

849
00:51:19,160 --> 00:51:23,080
Speaker 1: so the second generation was where the magic happened. And

850
00:51:23,160 --> 00:51:26,239
Speaker 1: so now those little deer from the southern part of

851
00:51:26,239 --> 00:51:29,200
Speaker 1: the state, the second generation of three and a half

852
00:51:29,280 --> 00:51:33,280
Speaker 1: year old bucks, there was over a thirty pound difference

853
00:51:34,000 --> 00:51:39,000
Speaker 1: in body weight and twenty in difference in boone Crockett's score.

854
00:51:39,840 --> 00:51:43,879
Speaker 1: So they completely compensated and caught up. But it just

855
00:51:44,000 --> 00:51:49,719
Speaker 1: took two generations. So here's what's really really most important

856
00:51:50,640 --> 00:51:54,080
Speaker 1: is it's the mother. And so one of our little

857
00:51:54,120 --> 00:51:57,480
Speaker 1: sound bites, it's not what that buck ate throughout his life,

858
00:51:57,560 --> 00:52:01,960
Speaker 1: it's what his mother ate throughout her life. This what's important.

859
00:52:03,000 --> 00:52:07,799
Speaker 1: And so a mother being nutritionally stressed even when she

860
00:52:07,880 --> 00:52:10,520
Speaker 1: has that fawn, and even when the fawn is weaned.

861
00:52:11,239 --> 00:52:13,720
Speaker 1: Um and there's a term for this, it's called epigenetics.

862
00:52:14,640 --> 00:52:20,160
Speaker 1: But basically it is a process why where certain genes

863
00:52:20,440 --> 00:52:22,960
Speaker 1: do not get expressed. That's a real simple way of

864
00:52:23,000 --> 00:52:25,799
Speaker 1: saying it. But even though those bucks may have had

865
00:52:25,920 --> 00:52:29,600
Speaker 1: genes to be a hundred and forty class dear, because

866
00:52:29,680 --> 00:52:33,360
Speaker 1: that mother went through nutritional stress, it's almost like the

867
00:52:33,400 --> 00:52:37,319
Speaker 1: body was saying times aren't good enough. Yet there's still

868
00:52:37,320 --> 00:52:39,880
Speaker 1: a nutrition problem. You don't want to grow to be

869
00:52:40,000 --> 00:52:42,680
Speaker 1: thirty pounds bigger than average because you're not going to

870
00:52:42,760 --> 00:52:45,800
Speaker 1: have the food to support your body. So they stayed small.

871
00:52:47,120 --> 00:52:50,399
Speaker 1: But when those dough fawns were raised up until three

872
00:52:50,480 --> 00:52:54,120
Speaker 1: years of age on a really really good nutrition, those

873
00:52:54,120 --> 00:52:59,640
Speaker 1: switches were flipped. It was now genetically they're offspring, their

874
00:52:59,719 --> 00:53:05,040
Speaker 1: buck offspring could now truly express their genetic potential. So

875
00:53:05,200 --> 00:53:08,040
Speaker 1: the first year of the study, the first generation, we

876
00:53:08,080 --> 00:53:11,160
Speaker 1: saw no difference. Big deer stay big, or deer from

877
00:53:11,200 --> 00:53:14,520
Speaker 1: the big region stayed, big deer from the little region

878
00:53:14,600 --> 00:53:18,279
Speaker 1: stay small, And in the second region, all the way

879
00:53:18,320 --> 00:53:20,600
Speaker 1: across the board they were the same. The little deer

880
00:53:20,600 --> 00:53:23,000
Speaker 1: called up with a big deer, but it took a

881
00:53:23,040 --> 00:53:27,040
Speaker 1: generation of mom. So mom was the most important part

882
00:53:27,040 --> 00:53:29,600
Speaker 1: of this. So given that reality, then the mom is

883
00:53:29,640 --> 00:53:32,840
Speaker 1: the most important. What's the action item for hunters or

884
00:53:32,880 --> 00:53:36,640
Speaker 1: managers who want to better allow these deer to express

885
00:53:36,680 --> 00:53:39,920
Speaker 1: those genes? Now that we know it's the female that's

886
00:53:39,920 --> 00:53:42,359
Speaker 1: that's most important on that side of things, Is that

887
00:53:42,360 --> 00:53:46,160
Speaker 1: that she just needs optimal nutrition at the point of

888
00:53:46,560 --> 00:53:49,680
Speaker 1: winning the fonds or right when she gives birth, or

889
00:53:50,200 --> 00:53:53,000
Speaker 1: what's what's the takeaway here for us from an action standpoint?

890
00:53:54,920 --> 00:54:00,239
Speaker 1: It everything you said, really, um, it's a mother being

891
00:54:00,239 --> 00:54:03,240
Speaker 1: and raised in an environment that is not food limited

892
00:54:03,560 --> 00:54:07,399
Speaker 1: or not being nutritionally stressed. And so what we see

893
00:54:07,440 --> 00:54:09,920
Speaker 1: with my colleagues that that are in Texas that do

894
00:54:10,160 --> 00:54:13,920
Speaker 1: of course a lot more supplementally feeding than we do

895
00:54:13,960 --> 00:54:18,760
Speaker 1: in Mississippi. But they typically always now the scientifically minded

896
00:54:18,760 --> 00:54:22,040
Speaker 1: ones that are really measuring, they are really measuring how

897
00:54:22,120 --> 00:54:24,040
Speaker 1: much food are we putting out? And am I getting

898
00:54:24,040 --> 00:54:28,719
Speaker 1: a return on my investment? The first return on investment

899
00:54:28,760 --> 00:54:31,680
Speaker 1: they ever see is at least five years away. And

900
00:54:31,719 --> 00:54:33,480
Speaker 1: most of them will say, you're gonna need a good

901
00:54:33,600 --> 00:54:38,440
Speaker 1: decade of a really up in the nutrition before you're

902
00:54:38,440 --> 00:54:42,560
Speaker 1: really gonna start to see big impacts. And and that

903
00:54:42,680 --> 00:54:46,040
Speaker 1: is simply because you need uh that mother, that mother

904
00:54:46,160 --> 00:54:49,439
Speaker 1: needs to be raised on good nutrition. And then when

905
00:54:49,440 --> 00:54:53,280
Speaker 1: she's starting to be a really good fawn producing age

906
00:54:53,600 --> 00:54:55,680
Speaker 1: three years of age, four years of age, five years

907
00:54:55,680 --> 00:54:58,399
Speaker 1: of age, you know she's lived her life with with

908
00:54:58,560 --> 00:55:00,880
Speaker 1: all the foods she can eat, a high quality foods

909
00:55:00,880 --> 00:55:04,160
Speaker 1: always available. And that's when you start seeing the magic happen.

910
00:55:04,600 --> 00:55:07,640
Speaker 1: And that's when you see that that their buck offspring

911
00:55:08,200 --> 00:55:14,239
Speaker 1: start growing above average. It's not does that so does

912
00:55:14,280 --> 00:55:18,200
Speaker 1: that change year to year? So same though one year

913
00:55:18,560 --> 00:55:24,319
Speaker 1: has awesome food, she's healthy, she she is bred. That

914
00:55:24,440 --> 00:55:27,759
Speaker 1: buck is gonna come out. And now let's compare that

915
00:55:27,800 --> 00:55:32,400
Speaker 1: buck that is born. Let's say this year right over

916
00:55:32,400 --> 00:55:37,840
Speaker 1: this winner, terrible winner, right the buck the while the

917
00:55:38,480 --> 00:55:43,120
Speaker 1: buck is in utero, the mom has poor nutrition. It's

918
00:55:42,719 --> 00:55:47,000
Speaker 1: sending signals genetically to this buck saying, hey, we may

919
00:55:47,040 --> 00:55:50,320
Speaker 1: not have enough food, so you don't need big antlers.

920
00:55:50,400 --> 00:55:57,680
Speaker 1: So conditions like nutrition. If you compare those two bucks

921
00:55:57,719 --> 00:56:00,160
Speaker 1: with each other, and and and we're to compe air

922
00:56:00,280 --> 00:56:03,719
Speaker 1: them one year and then compare them the next year,

923
00:56:04,160 --> 00:56:08,640
Speaker 1: are you seeing this same trend where he's going to

924
00:56:08,760 --> 00:56:12,040
Speaker 1: have smaller antlers even though let's say they had the

925
00:56:12,080 --> 00:56:16,920
Speaker 1: same mom and the same dad. Yes, that that will happen,

926
00:56:17,239 --> 00:56:20,759
Speaker 1: not to the degree that we saw this experiment, but

927
00:56:20,840 --> 00:56:23,520
Speaker 1: we've seen that with our our harvest data, with our

928
00:56:23,600 --> 00:56:28,200
Speaker 1: wild data wild deer, free ranging deer. Is that um,

929
00:56:28,320 --> 00:56:34,520
Speaker 1: we can explain anywhere from five to ten of the

930
00:56:34,560 --> 00:56:38,759
Speaker 1: body size and the antler size of bucks at two

931
00:56:38,800 --> 00:56:43,960
Speaker 1: years of age based on the experience, uh of the

932
00:56:44,000 --> 00:56:49,280
Speaker 1: weather or the environmental severity when those bucks were in utero.

933
00:56:50,120 --> 00:56:54,560
Speaker 1: So yes, so that is the trigger. That's the trigger,

934
00:56:54,880 --> 00:57:00,160
Speaker 1: is the But the deer while in utero, the there

935
00:57:00,280 --> 00:57:04,520
Speaker 1: is sending sending signals to that fetus telling it we're

936
00:57:04,520 --> 00:57:07,600
Speaker 1: having we have a good life, or we're going we're struggling.

937
00:57:09,840 --> 00:57:13,960
Speaker 1: I certainly believe so yeah, um, yeah, I wish I

938
00:57:14,000 --> 00:57:16,280
Speaker 1: was more of a physiologist and I can tell you

939
00:57:16,320 --> 00:57:19,560
Speaker 1: more about the pathways by which that happens. But uh,

940
00:57:19,600 --> 00:57:22,080
Speaker 1: that's certainly what it appears to me to be. Yes,

941
00:57:22,640 --> 00:57:25,960
Speaker 1: that that it's critical all the way from um from

942
00:57:26,000 --> 00:57:29,280
Speaker 1: the time of conception to while that fetus is in

943
00:57:29,360 --> 00:57:32,280
Speaker 1: utero and then and then don't forget as well, uh

944
00:57:32,480 --> 00:57:35,880
Speaker 1: even after birth if environmental conditions are really bad, that

945
00:57:35,920 --> 00:57:40,120
Speaker 1: affects of those lactation. And so during those early growth periods,

946
00:57:40,120 --> 00:57:42,440
Speaker 1: those first couple of months or weeks of life, are

947
00:57:42,440 --> 00:57:44,919
Speaker 1: they getting quality milk or not? So all that can

948
00:57:44,960 --> 00:57:49,560
Speaker 1: be can have lingering effects a year or two years later. Yeah,

949
00:57:50,160 --> 00:57:53,040
Speaker 1: so related to this, but a little bit more to

950
00:57:53,040 --> 00:57:55,080
Speaker 1: what you're talking about a second ago. I just want

951
00:57:55,120 --> 00:57:58,240
Speaker 1: to make sure to vary that we clearly articulate, um,

952
00:57:58,320 --> 00:58:00,480
Speaker 1: what I think you mentioned earlier, and that is that

953
00:58:01,920 --> 00:58:04,000
Speaker 1: that we as hunters and correct me if I'm wrong,

954
00:58:04,120 --> 00:58:07,480
Speaker 1: But we as hunters really cannot influence the genetics of

955
00:58:07,480 --> 00:58:10,760
Speaker 1: a population. So you know, fifteen years ago, you watch

956
00:58:10,880 --> 00:58:14,120
Speaker 1: hunting TV shows, even more recently still, really you watch

957
00:58:14,120 --> 00:58:16,880
Speaker 1: a hunting TV show and everyone's talking, well, we gotta

958
00:58:16,960 --> 00:58:19,000
Speaker 1: kill call buck because we want to improve the antlers

959
00:58:19,040 --> 00:58:20,720
Speaker 1: in this area. We're gonna kill this The late point

960
00:58:21,760 --> 00:58:24,200
Speaker 1: all the management is that all bunch of bs or

961
00:58:24,240 --> 00:58:26,360
Speaker 1: is there any is there anything really there? Or is

962
00:58:26,400 --> 00:58:31,480
Speaker 1: that just old school that doesn't really work. It's old school,

963
00:58:31,640 --> 00:58:36,280
Speaker 1: it doesn't really work. But here's the caveat um. If

964
00:58:36,400 --> 00:58:40,480
Speaker 1: it is a free ranging deer herd. Now all you

965
00:58:40,520 --> 00:58:45,120
Speaker 1: guys know that what has been done by deer breeders

966
00:58:45,440 --> 00:58:50,640
Speaker 1: in in a confined environment. So when you can literally

967
00:58:51,720 --> 00:58:55,160
Speaker 1: pick out this when I have a dozed pedigree and

968
00:58:55,240 --> 00:58:58,320
Speaker 1: I know her buck offspring, and then I can match

969
00:58:58,560 --> 00:59:00,720
Speaker 1: that dough with this buck, and I know what his

970
00:59:00,800 --> 00:59:06,000
Speaker 1: antler sizes. Um, when you can manipulate mating like that,

971
00:59:06,440 --> 00:59:11,160
Speaker 1: then yes you can steer genetics, um, you know, to

972
00:59:11,400 --> 00:59:15,360
Speaker 1: a freakish size. And that's like deer breeders of course

973
00:59:15,440 --> 00:59:18,200
Speaker 1: that um. You know, when you when you produce a

974
00:59:18,240 --> 00:59:21,080
Speaker 1: two hundred or three hundred class two year old dear,

975
00:59:21,400 --> 00:59:24,640
Speaker 1: you know you've manipulated genetics. But but that is the

976
00:59:24,640 --> 00:59:29,800
Speaker 1: difference that is only only applicable in offense and even

977
00:59:29,880 --> 00:59:33,000
Speaker 1: within a fence. It's not like it's a free ranging fence.

978
00:59:33,040 --> 00:59:35,520
Speaker 1: So it's not like, hey, we've got ten thousand acres

979
00:59:35,560 --> 00:59:38,840
Speaker 1: under high fence. That is when you are manipulating who

980
00:59:38,960 --> 00:59:42,400
Speaker 1: is breeding who, when you have a free ranging environment,

981
00:59:43,400 --> 00:59:49,520
Speaker 1: you um, it is remarkably inefficient, so inefficient that it

982
00:59:49,680 --> 00:59:53,840
Speaker 1: is not even practical. And and here's the reasons why.

983
00:59:53,880 --> 00:59:56,640
Speaker 1: So if I want to shoot this spike buck, Well,

984
00:59:56,640 --> 01:00:00,080
Speaker 1: first of all, we know that um, I'll speak I

985
01:00:00,080 --> 01:00:02,840
Speaker 1: won't speak for Michigan, but i'll speak for the Southeast.

986
01:00:03,360 --> 01:00:06,760
Speaker 1: Is usually due to birthdate. They're usually just born a

987
01:00:06,760 --> 01:00:10,400
Speaker 1: little bit later. It's usually a temporary environmental condition, is

988
01:00:10,400 --> 01:00:12,360
Speaker 1: why they while they have a spike. So we know

989
01:00:12,520 --> 01:00:16,640
Speaker 1: good and well that most yearling spikes are gonna grow

990
01:00:16,800 --> 01:00:20,000
Speaker 1: into you know, an average sized mature buck if they're

991
01:00:20,000 --> 01:00:25,040
Speaker 1: allowed to live. Um, but what we cannot do. So

992
01:00:25,160 --> 01:00:28,520
Speaker 1: if I'm gonna shoot this buck, hey, uh, he's four

993
01:00:28,640 --> 01:00:31,400
Speaker 1: I think he's four years of age and he only

994
01:00:31,400 --> 01:00:35,040
Speaker 1: has six points. He is definitely lower quality. I'm going

995
01:00:35,080 --> 01:00:39,080
Speaker 1: to remove him out of the herd for genetic purposes. Well,

996
01:00:39,120 --> 01:00:42,600
Speaker 1: I can take him out and we'll get back to this.

997
01:00:42,720 --> 01:00:45,480
Speaker 1: There are some good reasons to take bucks like that out,

998
01:00:45,520 --> 01:00:49,360
Speaker 1: but it's not for genetics. But the reservoir of genetics.

999
01:00:49,480 --> 01:00:53,480
Speaker 1: Fifty of the genetics that goes into a buck is

1000
01:00:53,480 --> 01:00:57,959
Speaker 1: from the mother. We have no way to select if

1001
01:00:58,160 --> 01:01:01,520
Speaker 1: a doe is going to produce big antlered offspring or

1002
01:01:01,600 --> 01:01:05,520
Speaker 1: small antlerd offspring. So that in and of itself, fifty

1003
01:01:05,920 --> 01:01:10,160
Speaker 1: of the equation you you can affect. And the other

1004
01:01:10,200 --> 01:01:13,760
Speaker 1: part about this is even if you could, you have

1005
01:01:13,840 --> 01:01:17,600
Speaker 1: the process of dispersal. So if on my thousand acres

1006
01:01:17,640 --> 01:01:20,440
Speaker 1: or two thousand acres or five acres, I'm doing all

1007
01:01:20,480 --> 01:01:24,480
Speaker 1: this quote, you know, culling to improve genetics. Well, the

1008
01:01:24,560 --> 01:01:27,920
Speaker 1: doose that do get bred and they have their buck fawn,

1009
01:01:28,720 --> 01:01:31,520
Speaker 1: you know, seventy of them are going to disperse off

1010
01:01:31,560 --> 01:01:36,080
Speaker 1: of my property. So there's always there's always a hole

1011
01:01:36,080 --> 01:01:39,880
Speaker 1: in the bucket. Genetics laws, genetics are always going out

1012
01:01:40,160 --> 01:01:43,560
Speaker 1: and genetics are always coming in because your neighbor's yearling

1013
01:01:43,680 --> 01:01:48,080
Speaker 1: bucks are dispersing onto your property. So when we do

1014
01:01:48,160 --> 01:01:53,240
Speaker 1: simulations and we got with livestock, cattle people to run

1015
01:01:53,360 --> 01:01:55,320
Speaker 1: because you know, they can control this type of stuff,

1016
01:01:55,320 --> 01:01:57,960
Speaker 1: and you know it's it's a very reliable system for

1017
01:01:58,000 --> 01:02:01,800
Speaker 1: them because they can control who breeds too. When we

1018
01:02:01,880 --> 01:02:05,400
Speaker 1: when we met with livestock geneticists and and their modelers

1019
01:02:05,480 --> 01:02:09,040
Speaker 1: for Hey, let's let's replicate this system for dear, and

1020
01:02:09,120 --> 01:02:11,640
Speaker 1: we use all sorts of scenarios. We're going to harvest

1021
01:02:11,760 --> 01:02:14,040
Speaker 1: you know, these call bucks, and we're going to harvest

1022
01:02:14,080 --> 01:02:16,760
Speaker 1: at a super super because it has to be intense.

1023
01:02:16,840 --> 01:02:19,640
Speaker 1: If you're gonna do anything, it's got to be really intense.

1024
01:02:20,120 --> 01:02:24,040
Speaker 1: And what we found after running our simulation model for

1025
01:02:24,920 --> 01:02:31,120
Speaker 1: twenty years of super intense selective harvest or culling, we

1026
01:02:31,400 --> 01:02:34,520
Speaker 1: didn't change antler size but maybe an inch or two,

1027
01:02:35,480 --> 01:02:41,080
Speaker 1: not even noticeable. So basically what we're getting at, Basically,

1028
01:02:41,120 --> 01:02:43,360
Speaker 1: what we're getting at is if you have a TV show,

1029
01:02:43,480 --> 01:02:46,840
Speaker 1: please stop saying I'm going to shoot this management buck

1030
01:02:46,960 --> 01:02:50,600
Speaker 1: or this call buck because you sound like an idiot. Yeah,

1031
01:02:51,120 --> 01:02:53,800
Speaker 1: and don't follow it up with, um, I'm gonna get

1032
01:02:53,800 --> 01:02:57,160
Speaker 1: this out of the gene pool, because you can't. Period,

1033
01:02:57,360 --> 01:03:01,320
Speaker 1: you can't. Those genes are embedded than that population's gene

1034
01:03:01,320 --> 01:03:04,280
Speaker 1: pool and they're just being expressed by different individuals from

1035
01:03:04,360 --> 01:03:07,840
Speaker 1: time to time. Now, is there such a thing people

1036
01:03:07,920 --> 01:03:10,200
Speaker 1: have different names they want to use. You can call

1037
01:03:10,240 --> 01:03:12,480
Speaker 1: it a management buck, you can you know what, whatever,

1038
01:03:13,080 --> 01:03:16,800
Speaker 1: I don't like, you know, not cold buck. But now

1039
01:03:16,920 --> 01:03:21,960
Speaker 1: there are very strategic reasons to remove some bucks from

1040
01:03:21,960 --> 01:03:26,440
Speaker 1: the population. But it's not from a genetic standpoint, it's

1041
01:03:26,480 --> 01:03:31,200
Speaker 1: from a food conservation standpoint. So if I'm working with

1042
01:03:31,240 --> 01:03:34,480
Speaker 1: a property or my property, I'm working with hunters and

1043
01:03:34,600 --> 01:03:37,880
Speaker 1: we are managing four you know, mature bucks, older age

1044
01:03:37,880 --> 01:03:40,040
Speaker 1: class bucks, and so we're trying to harvest you know,

1045
01:03:40,120 --> 01:03:42,240
Speaker 1: five year old bucks, six year old bucks, et cetera.

1046
01:03:43,120 --> 01:03:47,680
Speaker 1: And out walks on my food plot. Here, out walks

1047
01:03:47,720 --> 01:03:55,080
Speaker 1: this two forty pound five year old that scores. Now,

1048
01:03:55,120 --> 01:03:58,640
Speaker 1: again that's rare. To remember our bell shaped curve. You know,

1049
01:03:58,800 --> 01:04:02,640
Speaker 1: there's there's very few really really big bucks with extremely

1050
01:04:02,720 --> 01:04:04,960
Speaker 1: large antlers. There's very few that that are five years

1051
01:04:05,000 --> 01:04:08,280
Speaker 1: old with really small antlers. But they're out there. So

1052
01:04:08,360 --> 01:04:11,440
Speaker 1: if I choose to harvest that buck, I'm not doing

1053
01:04:11,560 --> 01:04:15,480
Speaker 1: anything to improve genetics. But what I did do is

1054
01:04:15,520 --> 01:04:22,640
Speaker 1: save two to three tons of deer forage on my property. Now,

1055
01:04:22,640 --> 01:04:26,360
Speaker 1: would do you follow? I do follow you? Now? Let

1056
01:04:26,360 --> 01:04:29,200
Speaker 1: me ask you this, though, I would assume that your

1057
01:04:29,240 --> 01:04:32,480
Speaker 1: impact would be much greater if you're simply trying to

1058
01:04:32,480 --> 01:04:37,040
Speaker 1: remove pressure on food by harvesting a dough or two

1059
01:04:37,080 --> 01:04:39,240
Speaker 1: doughs or whatever it might be compared to that one

1060
01:04:39,240 --> 01:04:41,280
Speaker 1: buck though, right, So I mean, yes, you can take

1061
01:04:41,280 --> 01:04:44,680
Speaker 1: that buck and you can, um, you can justify it

1062
01:04:44,760 --> 01:04:47,200
Speaker 1: by means of reducing competition for food so that the

1063
01:04:47,280 --> 01:04:50,720
Speaker 1: bucks that do have better chances of um, you know,

1064
01:04:50,760 --> 01:04:52,800
Speaker 1: having the right genetics, have a better chance of being

1065
01:04:52,840 --> 01:04:55,320
Speaker 1: able to reach that potential because of available food. You

1066
01:04:55,320 --> 01:04:57,960
Speaker 1: can achieve that just as well or better by harvesting

1067
01:04:57,960 --> 01:05:04,480
Speaker 1: a dough or still harvesting in general. Correct that that

1068
01:05:04,520 --> 01:05:08,440
Speaker 1: would be one strategy. So one strategy might mean I'm

1069
01:05:08,480 --> 01:05:12,320
Speaker 1: going to keep my dear density very very low, and

1070
01:05:12,400 --> 01:05:14,919
Speaker 1: so I'm gonna have just an adequate number of those,

1071
01:05:14,960 --> 01:05:17,280
Speaker 1: I'm going to have an adequate number of fawn recruitment,

1072
01:05:17,280 --> 01:05:21,480
Speaker 1: and I'm going to keep the dear population really really low. Um.

1073
01:05:21,560 --> 01:05:24,080
Speaker 1: And that works. That is very successful. That type of

1074
01:05:24,080 --> 01:05:29,080
Speaker 1: approach will always be successful. But another successful approach, UM

1075
01:05:29,280 --> 01:05:31,800
Speaker 1: is you've got to think about what your currency is,

1076
01:05:32,560 --> 01:05:38,880
Speaker 1: your currency for producing as many trophy bucks as possible

1077
01:05:39,400 --> 01:05:45,040
Speaker 1: as buck fawns I need I want to have. And

1078
01:05:45,080 --> 01:05:46,800
Speaker 1: I'm not guys, I'm not in any way saying you

1079
01:05:46,840 --> 01:05:49,640
Speaker 1: need to have your be overpopulated. I'm not saying that

1080
01:05:49,680 --> 01:05:53,560
Speaker 1: at all, because there's a fine balance here, but there's

1081
01:05:53,640 --> 01:05:57,919
Speaker 1: also merit for I want to carry as many dose

1082
01:05:58,040 --> 01:06:02,880
Speaker 1: as I can without sacrificing food quality, so that I

1083
01:06:02,920 --> 01:06:07,400
Speaker 1: can be pumping out every year buck fawns and recruiting

1084
01:06:07,480 --> 01:06:10,680
Speaker 1: within within my population. I want to be producing as

1085
01:06:10,720 --> 01:06:14,080
Speaker 1: many bucks as possible. And then when I start seeing

1086
01:06:14,120 --> 01:06:16,480
Speaker 1: at three years of age or at four years of age,

1087
01:06:16,720 --> 01:06:20,200
Speaker 1: if I start seeing that, hey, this buck is really

1088
01:06:20,200 --> 01:06:22,880
Speaker 1: not going to turn out to be a trophy, um,

1089
01:06:22,920 --> 01:06:26,160
Speaker 1: I'm just harvesting it. I'm just having fun. I'm harvesting

1090
01:06:26,160 --> 01:06:28,000
Speaker 1: it with my bow or with my gun, or my

1091
01:06:28,120 --> 01:06:32,240
Speaker 1: guests or my friends or whatever, you know. So there

1092
01:06:32,320 --> 01:06:35,840
Speaker 1: is there is a way that you can still manage

1093
01:06:35,920 --> 01:06:40,960
Speaker 1: four mature trophy bucks and still harvest a lot of

1094
01:06:40,960 --> 01:06:43,480
Speaker 1: bucks along the way. You just got to make sure

1095
01:06:43,600 --> 01:06:46,360
Speaker 1: which one is to pass and which one is to harvest.

1096
01:06:46,560 --> 01:06:50,360
Speaker 1: So basically what you're saying is that big mature buck

1097
01:06:50,600 --> 01:06:55,560
Speaker 1: with a small antler is occupying a spot on your farm.

1098
01:06:55,680 --> 01:06:58,520
Speaker 1: You take him out, and what that does it lets

1099
01:06:58,560 --> 01:07:02,560
Speaker 1: the next up and comer give them the opportunity to

1100
01:07:03,120 --> 01:07:06,320
Speaker 1: showcase what they're going to be. And if they don't

1101
01:07:07,160 --> 01:07:10,240
Speaker 1: if they don't produce, you take them out and it's

1102
01:07:10,280 --> 01:07:14,040
Speaker 1: basically cycling through bucks until you find one that is

1103
01:07:14,520 --> 01:07:19,000
Speaker 1: genetically to your liking and you'll be able to Okay, well,

1104
01:07:19,080 --> 01:07:20,720
Speaker 1: he's gonna make the past this year, and I'm gonna

1105
01:07:20,760 --> 01:07:22,040
Speaker 1: let him go to four. I'm gonna let him go

1106
01:07:22,080 --> 01:07:24,760
Speaker 1: to five. I'm gonna let him go to six, and uh,

1107
01:07:24,880 --> 01:07:27,440
Speaker 1: I'm gonna let him reach his full potential because he

1108
01:07:27,480 --> 01:07:32,440
Speaker 1: could be that you know that that trophy buck that

1109
01:07:32,440 --> 01:07:36,480
Speaker 1: that that's exactly right. Um. So we work with properties

1110
01:07:36,520 --> 01:07:40,240
Speaker 1: all the time that that do precisely that, and so

1111
01:07:40,400 --> 01:07:43,200
Speaker 1: that their their goal is to maximize you know, how

1112
01:07:43,200 --> 01:07:46,520
Speaker 1: do I maximize trophy bucks on my property? Well, what

1113
01:07:46,720 --> 01:07:48,720
Speaker 1: one way to do that is I need to produce

1114
01:07:48,720 --> 01:07:51,840
Speaker 1: a lot of bucks, but I also don't need to

1115
01:07:51,960 --> 01:07:54,960
Speaker 1: carry every one of those bucks at least six and

1116
01:07:55,000 --> 01:07:57,760
Speaker 1: a half years of age. By three or four years

1117
01:07:57,800 --> 01:08:00,360
Speaker 1: of age, I can tell I see going to be

1118
01:08:00,400 --> 01:08:03,720
Speaker 1: a contender. You know, is he a keeper? And ones

1119
01:08:03,800 --> 01:08:07,440
Speaker 1: that aren't keepers? This is where you get to hunt,

1120
01:08:08,080 --> 01:08:10,280
Speaker 1: you get to have fun, you still get to harvest

1121
01:08:10,320 --> 01:08:13,320
Speaker 1: a lot of bucks when you're managing for trophies. Now,

1122
01:08:13,720 --> 01:08:16,599
Speaker 1: let me qualify one thing too. You can be in

1123
01:08:16,640 --> 01:08:20,880
Speaker 1: some environments where and I'm thinking specifically my colleagues in

1124
01:08:20,920 --> 01:08:25,240
Speaker 1: South Texas. You know, we take fond recruitment here sometimes

1125
01:08:25,240 --> 01:08:28,040
Speaker 1: for granted, um, and unless you have a you know,

1126
01:08:28,080 --> 01:08:32,000
Speaker 1: a real predator problem, UM, you're gonna have fond recruitment.

1127
01:08:32,080 --> 01:08:34,800
Speaker 1: You're going to be producing bucks. Now if you're in

1128
01:08:34,800 --> 01:08:37,639
Speaker 1: an environment that's really severe, whether it be really severe

1129
01:08:37,640 --> 01:08:40,599
Speaker 1: in terms of arid or if you're in the up

1130
01:08:40,720 --> 01:08:43,760
Speaker 1: of Michigan, some of those severe environments, you know, recruitments

1131
01:08:43,800 --> 01:08:47,000
Speaker 1: not guaranteed, and so you know you have to be

1132
01:08:47,040 --> 01:08:50,640
Speaker 1: a lot more conservative about um, you know what what

1133
01:08:50,720 --> 01:08:53,240
Speaker 1: dear your harvest. At the same time, if if you're

1134
01:08:53,280 --> 01:08:55,720
Speaker 1: in some of those environments, you've gotta be real conservative

1135
01:08:55,720 --> 01:08:58,519
Speaker 1: about your dough harvests. Even though I may have a

1136
01:08:58,600 --> 01:09:02,120
Speaker 1: lot of adult does if my recruitment rate like can

1137
01:09:02,160 --> 01:09:07,040
Speaker 1: happen a lot is only it's very difficult to accumulate

1138
01:09:07,479 --> 01:09:10,200
Speaker 1: a lot of bucks over the years. So you always

1139
01:09:10,200 --> 01:09:12,600
Speaker 1: want to keep what we call the fawn factory. You

1140
01:09:12,640 --> 01:09:15,360
Speaker 1: always want to keep the fawn factory, uh, you know,

1141
01:09:16,000 --> 01:09:20,920
Speaker 1: going as strong as it possibly can. So we've talked

1142
01:09:20,960 --> 01:09:24,960
Speaker 1: a lot about antlers and managing for maybe bigger antlers

1143
01:09:25,080 --> 01:09:27,640
Speaker 1: or healthier dear trophy deer. And and that's not not

1144
01:09:27,720 --> 01:09:31,519
Speaker 1: everyone's cup of tea. Not everyone's interested and necessarily that um,

1145
01:09:31,560 --> 01:09:33,720
Speaker 1: which is which is perfectly fine. There's a whole lot

1146
01:09:33,720 --> 01:09:37,000
Speaker 1: of different flavors and types of deer hunting. We all like, um.

1147
01:09:37,040 --> 01:09:39,639
Speaker 1: But I'm curious when it comes to antlers. You alluded

1148
01:09:39,680 --> 01:09:43,280
Speaker 1: to us earlier before we talked on air here that

1149
01:09:43,320 --> 01:09:45,880
Speaker 1: you can speak a little bit to the to the

1150
01:09:45,920 --> 01:09:50,040
Speaker 1: evolutionary and ecological purpose of antlers from a from a

1151
01:09:50,080 --> 01:09:54,400
Speaker 1: deer perspective. Can you can you talk about that? Yeah, yeah,

1152
01:09:54,439 --> 01:09:55,960
Speaker 1: I'd be happy to do. This is one of the

1153
01:09:56,000 --> 01:10:00,200
Speaker 1: topics near and dear to my heart is um. Um.

1154
01:10:00,360 --> 01:10:04,120
Speaker 1: So when I'm giving presentations, you know, sometimes the hunters,

1155
01:10:04,120 --> 01:10:06,599
Speaker 1: sometimes in the general public, I've I'd always like to

1156
01:10:06,600 --> 01:10:09,519
Speaker 1: to ask the question, um and and it can just

1157
01:10:09,560 --> 01:10:11,599
Speaker 1: be a curiosity for people, But I say, why why

1158
01:10:11,600 --> 01:10:15,000
Speaker 1: the bucks have antlers? What's the purpose of it? And

1159
01:10:15,000 --> 01:10:16,920
Speaker 1: you'll typically get some people of the world er to

1160
01:10:17,040 --> 01:10:23,840
Speaker 1: run off predators, I guess, And now that's really not it. Um, Well, gosh,

1161
01:10:24,200 --> 01:10:27,479
Speaker 1: why why do bucks have antlers? I'm like, Yeah, there's

1162
01:10:27,479 --> 01:10:31,160
Speaker 1: got to be a really important purpose for this. This

1163
01:10:31,320 --> 01:10:37,080
Speaker 1: structure is very physiologically costly. It is so costly that

1164
01:10:37,160 --> 01:10:39,960
Speaker 1: during the antler growing season for a buck that they

1165
01:10:40,040 --> 01:10:45,479
Speaker 1: go through osteoporosis, they can't even garner enough phosphorus and

1166
01:10:45,520 --> 01:10:49,600
Speaker 1: calcium from their diet that they have to mobilize minerals,

1167
01:10:49,680 --> 01:10:52,800
Speaker 1: you know, from their bones to to complete antler growth.

1168
01:10:52,920 --> 01:10:55,679
Speaker 1: It's like, you know, mother nature must think these things

1169
01:10:55,680 --> 01:10:57,680
Speaker 1: are pretty darn important for them to go through that.

1170
01:10:58,560 --> 01:11:02,080
Speaker 1: And and the answer is um bucks have antlers to

1171
01:11:02,120 --> 01:11:07,320
Speaker 1: fight off other bucks. As as simple as that, I'd

1172
01:11:07,320 --> 01:11:09,160
Speaker 1: like to think of, Yes, this to to chase a

1173
01:11:09,200 --> 01:11:12,080
Speaker 1: wolf off or something like that. But but antlers serve

1174
01:11:12,280 --> 01:11:14,880
Speaker 1: as both to two things we call them, and these

1175
01:11:14,880 --> 01:11:18,519
Speaker 1: are the biological sexy terms. There are a signal in

1176
01:11:18,560 --> 01:11:23,760
Speaker 1: their weapon. So antlers serve as a signal first and

1177
01:11:23,800 --> 01:11:29,200
Speaker 1: foremost to other males. So evolutionarily, it is better for

1178
01:11:29,320 --> 01:11:34,680
Speaker 1: me to show off my age and my um dominance

1179
01:11:35,280 --> 01:11:37,600
Speaker 1: and my dominance. From a standpoint, if if I have

1180
01:11:37,640 --> 01:11:39,600
Speaker 1: a really big body, that means I was able to

1181
01:11:39,600 --> 01:11:41,759
Speaker 1: eat a lot of food, I had access the food,

1182
01:11:42,280 --> 01:11:45,639
Speaker 1: um I was able to grow large antlers. Then by

1183
01:11:45,720 --> 01:11:50,240
Speaker 1: them having that signal that tells younger bucks or subordinate bucks,

1184
01:11:50,240 --> 01:11:52,640
Speaker 1: I don't want to mess with him. I'm not going

1185
01:11:52,680 --> 01:11:56,439
Speaker 1: to jeopardize getting my eye put out or getting uh

1186
01:11:56,840 --> 01:11:59,240
Speaker 1: gored in the longer or anything like that in a fight.

1187
01:11:59,360 --> 01:12:02,120
Speaker 1: So it's a signal, So it reduces a lot of

1188
01:12:02,120 --> 01:12:06,200
Speaker 1: the fights. Um. And then secondly, yes, it is a weapon.

1189
01:12:06,600 --> 01:12:09,200
Speaker 1: So it is a what I like to say it

1190
01:12:09,320 --> 01:12:15,759
Speaker 1: is it is a structure for leverage. So go ahead,

1191
01:12:16,080 --> 01:12:20,639
Speaker 1: I'm sorry you continue with what you're saying there. Well,

1192
01:12:21,880 --> 01:12:25,479
Speaker 1: so we we really can't find you know, we we

1193
01:12:25,479 --> 01:12:28,839
Speaker 1: we really can't find where once you reach a certain

1194
01:12:28,920 --> 01:12:34,200
Speaker 1: point in antler size and configuration, we we really can't

1195
01:12:34,200 --> 01:12:38,320
Speaker 1: see that it conveys an advantage to the buck. Let

1196
01:12:38,320 --> 01:12:41,480
Speaker 1: me give you an example. You can have a very

1197
01:12:41,640 --> 01:12:45,519
Speaker 1: very competitive buck. He's five and a half years old. Uh,

1198
01:12:45,520 --> 01:12:49,240
Speaker 1: he's sixteen inches inside and he's an eight pointer and

1199
01:12:49,280 --> 01:12:54,040
Speaker 1: he scores one thirty five. That can be an absolute

1200
01:12:54,120 --> 01:12:59,160
Speaker 1: stud in terms of him being able to fight other bucks.

1201
01:12:59,200 --> 01:13:02,519
Speaker 1: So just cause he squares up with a buck that

1202
01:13:02,560 --> 01:13:05,240
Speaker 1: has ten points in his hundred fifty class or hundred

1203
01:13:05,280 --> 01:13:09,240
Speaker 1: sixty class, it doesn't in any way mean that that

1204
01:13:09,280 --> 01:13:11,679
Speaker 1: one sixty class is going to be a better fighter.

1205
01:13:12,479 --> 01:13:15,439
Speaker 1: So when you lock those antlers together again, it is

1206
01:13:15,479 --> 01:13:19,880
Speaker 1: a tool for that buck to demonstrate his power and

1207
01:13:20,040 --> 01:13:23,439
Speaker 1: his leverage. So because the fights are one, it's a

1208
01:13:23,479 --> 01:13:28,000
Speaker 1: shoven match, right, Yeah, it's not that they just clash

1209
01:13:28,080 --> 01:13:31,280
Speaker 1: and clash. I mean, it's locking up and then who

1210
01:13:31,320 --> 01:13:34,439
Speaker 1: can basically back the other one up and back him

1211
01:13:34,479 --> 01:13:38,160
Speaker 1: up to the point where he runs off. Um, And

1212
01:13:38,160 --> 01:13:41,080
Speaker 1: and so that's typically what we see. And so what

1213
01:13:41,240 --> 01:13:45,080
Speaker 1: does the average mature buck? What's the average antler size

1214
01:13:45,360 --> 01:13:48,360
Speaker 1: and the average configuration of a buck in the state

1215
01:13:48,439 --> 01:13:53,920
Speaker 1: of Mississippi, The average configuration is the eight pointer. The

1216
01:13:54,000 --> 01:13:56,920
Speaker 1: mature bucks are gonna have an eight eight points and

1217
01:13:56,960 --> 01:14:00,000
Speaker 1: then you only get on down to like or fifteen

1218
01:14:00,080 --> 01:14:03,320
Speaker 1: or sent you know, have ten or more points. So

1219
01:14:04,439 --> 01:14:07,080
Speaker 1: the way I'll look at this over mother Nature's time,

1220
01:14:07,720 --> 01:14:11,120
Speaker 1: if if really big, you know, ten and twelve point

1221
01:14:11,120 --> 01:14:16,439
Speaker 1: antlers were really important for breeding success and predominance, we

1222
01:14:16,439 --> 01:14:19,839
Speaker 1: we would see Mother nature taken us that direction. But

1223
01:14:19,840 --> 01:14:24,640
Speaker 1: but really from a dear's perspective, a buck's perspective, Um,

1224
01:14:24,680 --> 01:14:29,320
Speaker 1: what what is adequate is typically an average set of antlers.

1225
01:14:29,439 --> 01:14:33,200
Speaker 1: So here's my question. Then, So if if we understand

1226
01:14:33,200 --> 01:14:36,519
Speaker 1: the antlers are a signal and a weapon, that results

1227
01:14:36,520 --> 01:14:39,600
Speaker 1: in either a buck avoiding more fights because bucks just

1228
01:14:39,640 --> 01:14:42,800
Speaker 1: won't fight him, or he wins those fights because he's

1229
01:14:42,840 --> 01:14:45,880
Speaker 1: able to get through that ordeal, how does that get

1230
01:14:45,960 --> 01:14:50,360
Speaker 1: passed down then to sexual selection? And who how many

1231
01:14:50,360 --> 01:14:52,200
Speaker 1: times he's able to passage In the next down, is

1232
01:14:52,240 --> 01:14:55,920
Speaker 1: it that those antlers are the signal to a female too,

1233
01:14:56,120 --> 01:14:58,760
Speaker 1: or is it that simply by winning the fight he

1234
01:14:58,840 --> 01:15:01,400
Speaker 1: has access to the female, or how does that result

1235
01:15:01,479 --> 01:15:03,400
Speaker 1: in him passing the gen next more, I guess is

1236
01:15:03,439 --> 01:15:08,479
Speaker 1: my question? Okay too? That two things? So uh the

1237
01:15:08,520 --> 01:15:11,720
Speaker 1: short answer is yes, but by him breeding, by him

1238
01:15:11,760 --> 01:15:14,920
Speaker 1: breeding now, um, so he's gonna be passing on his

1239
01:15:14,920 --> 01:15:18,440
Speaker 1: his genetics there. Now, remember that is going to be blended.

1240
01:15:19,400 --> 01:15:23,320
Speaker 1: So even though pop is an eight pointer uh his genes,

1241
01:15:23,479 --> 01:15:26,800
Speaker 1: when when matched with with the those genes, he may

1242
01:15:26,840 --> 01:15:30,559
Speaker 1: produce a six pointer as as offspring, he may produce

1243
01:15:30,600 --> 01:15:33,719
Speaker 1: a one seventy class you know, as offspring. So there's

1244
01:15:33,720 --> 01:15:36,240
Speaker 1: that blending and that randomness that goes on there with

1245
01:15:36,280 --> 01:15:39,880
Speaker 1: that blending, that that that you never know for sure. Yeah,

1246
01:15:39,920 --> 01:15:42,320
Speaker 1: but but the mechanism by which you talked about for

1247
01:15:42,400 --> 01:15:45,639
Speaker 1: sexual selection. Yes, that is that is him breeding. Now

1248
01:15:45,680 --> 01:15:49,640
Speaker 1: you brought up another very very interesting point and one

1249
01:15:49,880 --> 01:15:52,800
Speaker 1: another project we just completed. We have schemed about this

1250
01:15:52,880 --> 01:15:58,080
Speaker 1: for years. Um, we wanted to look at that very choice. Mark,

1251
01:15:58,120 --> 01:16:01,200
Speaker 1: I believe you mentioned about does does the dough Does

1252
01:16:01,240 --> 01:16:03,400
Speaker 1: she have any say in the matter? Her antler is

1253
01:16:03,439 --> 01:16:07,479
Speaker 1: important to her? Well, we know in birds and and

1254
01:16:07,520 --> 01:16:11,720
Speaker 1: things like like that, it's really critically important. You know,

1255
01:16:11,760 --> 01:16:15,720
Speaker 1: the peacock's tail for example, on the best examples is

1256
01:16:15,880 --> 01:16:19,439
Speaker 1: that males really have to show off. So the female

1257
01:16:19,560 --> 01:16:22,759
Speaker 1: is picking. You know, she's gonna pick which mail she wants,

1258
01:16:23,120 --> 01:16:25,040
Speaker 1: and he's usually going to be the brightest and the

1259
01:16:25,120 --> 01:16:29,839
Speaker 1: biggest or you know, he builds the best nest or whatever. Um,

1260
01:16:29,880 --> 01:16:33,280
Speaker 1: But so does something like that occur in dear and

1261
01:16:33,320 --> 01:16:35,160
Speaker 1: we think, gosh, you know, it's it's going to be

1262
01:16:35,200 --> 01:16:38,599
Speaker 1: important to some degree because, um, when we look at

1263
01:16:38,600 --> 01:16:41,479
Speaker 1: this as biologists, we look at who's got all the

1264
01:16:41,600 --> 01:16:44,040
Speaker 1: risk involved here? Well, the dough has a lot of

1265
01:16:44,160 --> 01:16:47,719
Speaker 1: risk involved because the buck, when he completes his breeding,

1266
01:16:47,880 --> 01:16:50,040
Speaker 1: he's out of the picture. He does not help with

1267
01:16:50,120 --> 01:16:53,479
Speaker 1: parental care, He does not stay around and fend for

1268
01:16:53,560 --> 01:16:56,320
Speaker 1: that dough, you know, a few days later, much less

1269
01:16:56,320 --> 01:17:00,360
Speaker 1: a few months later. So this dough is really this

1270
01:17:00,400 --> 01:17:03,599
Speaker 1: is an important decision for her the quality of her offspring.

1271
01:17:04,080 --> 01:17:07,240
Speaker 1: So what we decided to do was to test if

1272
01:17:07,439 --> 01:17:10,960
Speaker 1: female choice can be playing a role in this. And

1273
01:17:11,040 --> 01:17:13,480
Speaker 1: so we designed to study. We met with an engineer

1274
01:17:14,120 --> 01:17:17,479
Speaker 1: on campus. Here we thought we need some way that

1275
01:17:17,520 --> 01:17:24,759
Speaker 1: we can manipulate antlers, and we manipulated this really big

1276
01:17:24,800 --> 01:17:28,280
Speaker 1: set about a hundred and fifty hundred sixty inch set

1277
01:17:28,280 --> 01:17:31,160
Speaker 1: of antlers, and then a really dinky set that the

1278
01:17:31,200 --> 01:17:36,560
Speaker 1: score about nine. And we took equivalent aged and equivalent

1279
01:17:36,800 --> 01:17:43,639
Speaker 1: body sized bucks and had them in two pens. So

1280
01:17:43,680 --> 01:17:46,280
Speaker 1: again let me emphasize, we're controlling for age or the

1281
01:17:46,320 --> 01:17:48,719
Speaker 1: same age. We're controlling for body size. We're not comparing

1282
01:17:49,960 --> 01:17:53,400
Speaker 1: pound buck to a two pound buck, same age, same

1283
01:17:53,439 --> 01:17:58,360
Speaker 1: body size. And then we put an estraus dough in

1284
01:17:58,439 --> 01:18:01,599
Speaker 1: between them. So we have three pens. On the left

1285
01:18:01,640 --> 01:18:03,640
Speaker 1: pin is a one buck with small antlers. On the

1286
01:18:03,680 --> 01:18:06,519
Speaker 1: extreme right hand side as the same age buck with

1287
01:18:06,560 --> 01:18:09,879
Speaker 1: big antlers, and then we have an estrius dough in between.

1288
01:18:10,960 --> 01:18:15,800
Speaker 1: And most of the time the estrous dough would move over.

1289
01:18:15,880 --> 01:18:18,280
Speaker 1: Now when we put her in here. She is in estris,

1290
01:18:18,960 --> 01:18:21,080
Speaker 1: she's in standing heat what we call it. She is

1291
01:18:21,160 --> 01:18:24,320
Speaker 1: ready to breathe, and and most of the time, like

1292
01:18:24,400 --> 01:18:27,439
Speaker 1: sixty to seventy percent of the time, she will hang

1293
01:18:27,560 --> 01:18:31,639
Speaker 1: out or sidle up to the buck with the largest antlers.

1294
01:18:32,240 --> 01:18:37,080
Speaker 1: So there is definitely something going on with her as well.

1295
01:18:37,600 --> 01:18:39,920
Speaker 1: You know, she is definitely using that as a cue.

1296
01:18:40,560 --> 01:18:44,880
Speaker 1: And how is that manifested? That That's that's where we're

1297
01:18:44,880 --> 01:18:47,479
Speaker 1: still scratching our head. How is that manifested? So what

1298
01:18:47,520 --> 01:18:50,120
Speaker 1: I mean by that is, hey, it really doesn't matter

1299
01:18:50,160 --> 01:18:53,439
Speaker 1: what the doe wants. If hey, here's the dominant buck

1300
01:18:53,479 --> 01:18:56,400
Speaker 1: and he's chased everybody off, you know, he's gonna he's

1301
01:18:56,400 --> 01:18:59,360
Speaker 1: gonna do the breeding. Um that brought us back to

1302
01:18:59,400 --> 01:19:02,320
Speaker 1: maybe this more pusiple paternity stuff when you know, meaning

1303
01:19:02,360 --> 01:19:07,160
Speaker 1: that um uh, a dough can breed multiple males. Remember

1304
01:19:07,160 --> 01:19:12,559
Speaker 1: I said of twins have different fathers, So maybe okay,

1305
01:19:13,560 --> 01:19:16,320
Speaker 1: So what we found in another study and again this

1306
01:19:16,360 --> 01:19:18,840
Speaker 1: is free ranging deer, not in our pens. But so

1307
01:19:18,960 --> 01:19:21,680
Speaker 1: this was right that the advent of being able to

1308
01:19:21,720 --> 01:19:25,880
Speaker 1: do some genetic sampling pretty cost effectively. And so we

1309
01:19:25,920 --> 01:19:30,000
Speaker 1: had our study population and we were looking at UM fawns.

1310
01:19:30,360 --> 01:19:33,200
Speaker 1: What we're we're taking genetic samples from everything everything in

1311
01:19:33,200 --> 01:19:36,840
Speaker 1: the herd, capturing does capturing fawns, capturing books. And what

1312
01:19:36,920 --> 01:19:42,120
Speaker 1: we found out is that UM twenty five cent of

1313
01:19:42,360 --> 01:19:48,439
Speaker 1: twin litters had different fathers. So twenty five percent of

1314
01:19:48,439 --> 01:19:50,679
Speaker 1: the time when you see a doe that has twins

1315
01:19:51,240 --> 01:19:55,719
Speaker 1: and in now in Mississippi, UM that that those twins

1316
01:19:55,800 --> 01:19:58,800
Speaker 1: had different fathers. And so it might be with this

1317
01:19:58,920 --> 01:20:02,400
Speaker 1: female choice thing, she might breathe, she might come into

1318
01:20:02,439 --> 01:20:06,160
Speaker 1: heat um with with that bug that is available, you know,

1319
01:20:06,200 --> 01:20:09,160
Speaker 1: that dominant bug that's available. But she may also allow

1320
01:20:09,240 --> 01:20:12,439
Speaker 1: the breeding, breeding a bug that maybe she prefers, you know,

1321
01:20:12,520 --> 01:20:16,280
Speaker 1: exercising her female choice. So we're about to submit a

1322
01:20:16,280 --> 01:20:18,759
Speaker 1: paper on that and we'll see what the scientific community

1323
01:20:18,800 --> 01:20:21,400
Speaker 1: scientific community thinks about it. But but that's our current

1324
01:20:21,439 --> 01:20:24,080
Speaker 1: thinking at this point. Certainly female choice is a part

1325
01:20:24,160 --> 01:20:27,720
Speaker 1: of it, not as important as as Antler's being a

1326
01:20:27,720 --> 01:20:30,200
Speaker 1: weapon for a male. But but they may also serve

1327
01:20:30,240 --> 01:20:33,040
Speaker 1: as a signal of quality to the female as well.

1328
01:20:33,400 --> 01:20:36,800
Speaker 1: So that's not so okay. So what you just said

1329
01:20:36,800 --> 01:20:40,360
Speaker 1: there is that a doe could be pregnant with twins

1330
01:20:40,360 --> 01:20:44,200
Speaker 1: by two separate bucks. So that then brings me to

1331
01:20:44,240 --> 01:20:48,200
Speaker 1: the question of how many I don't know why I

1332
01:20:48,280 --> 01:20:50,519
Speaker 1: thought this, um, but you just kind of all assume

1333
01:20:50,600 --> 01:20:53,880
Speaker 1: that a dope comes into heat, she's bread once or whatever,

1334
01:20:54,000 --> 01:20:57,400
Speaker 1: and then she's done. But how many times can a

1335
01:20:57,439 --> 01:21:01,400
Speaker 1: dobe get bread or tempted to be bred? I guess

1336
01:21:01,760 --> 01:21:04,040
Speaker 1: I mean, well, that just happened throughout as many times

1337
01:21:04,040 --> 01:21:06,480
Speaker 1: as she gets caught, and then just the one fertilization

1338
01:21:06,560 --> 01:21:08,840
Speaker 1: happens or I don't know this, maybe it's an ignorant question,

1339
01:21:09,240 --> 01:21:11,560
Speaker 1: but how many times will that actually happen? Because it

1340
01:21:11,560 --> 01:21:19,519
Speaker 1: sounds at least twice of the time it happened. Um. Well, no,

1341
01:21:19,920 --> 01:21:22,400
Speaker 1: that's a good question, and I don't really have an

1342
01:21:22,400 --> 01:21:26,160
Speaker 1: answer for you. Um. You know, so when when a

1343
01:21:26,240 --> 01:21:29,040
Speaker 1: dough again, we we call it standing heat, so for

1344
01:21:29,240 --> 01:21:32,360
Speaker 1: you know about it can be six hours, sometimes eight hours,

1345
01:21:32,360 --> 01:21:34,639
Speaker 1: maybe a little bit longer. But that's when we think

1346
01:21:34,720 --> 01:21:38,960
Speaker 1: she is receptive. And so we've all seen where a

1347
01:21:39,000 --> 01:21:41,479
Speaker 1: dough was not quite in standing heat. She's running through

1348
01:21:41,520 --> 01:21:43,320
Speaker 1: the woods and the buck is chasener as part of

1349
01:21:43,320 --> 01:21:46,479
Speaker 1: the normal courtship. Um. But then she reaches a point

1350
01:21:46,520 --> 01:21:50,000
Speaker 1: physiologically you know the hormones are right and she is

1351
01:21:50,040 --> 01:21:55,760
Speaker 1: ready to stand. Now. In your typical situation, you have

1352
01:21:55,880 --> 01:21:59,280
Speaker 1: had this one particular buck, whether he's the big dominant

1353
01:21:59,280 --> 01:22:00,920
Speaker 1: buck or heck he was just the lucky one that

1354
01:22:01,000 --> 01:22:03,400
Speaker 1: was available in the area, when she's in standing heat,

1355
01:22:03,840 --> 01:22:06,400
Speaker 1: but he will continue to court her and breed her,

1356
01:22:06,560 --> 01:22:12,920
Speaker 1: usually multiple times. You know, he's gonna copulate multiple times. Um.

1357
01:22:12,960 --> 01:22:16,720
Speaker 1: Now what we don't know with the multiple breeding with

1358
01:22:16,920 --> 01:22:22,479
Speaker 1: different sires. Um, maybe another buck enters the picture. So

1359
01:22:22,560 --> 01:22:26,120
Speaker 1: maybe the old mature buck. Uh, maybe he's meandering along

1360
01:22:26,280 --> 01:22:28,760
Speaker 1: and he detects, oh, you know, hey, there's a dough

1361
01:22:28,760 --> 01:22:31,519
Speaker 1: in estress, and maybe he comes up and runs off

1362
01:22:31,560 --> 01:22:33,840
Speaker 1: the other buck and then he mounts her and breeds

1363
01:22:33,840 --> 01:22:37,720
Speaker 1: her as well. Um, maybe that buck that that is

1364
01:22:37,760 --> 01:22:41,360
Speaker 1: breeding her. Maybe after he's copulated a few times, maybe

1365
01:22:41,439 --> 01:22:43,960
Speaker 1: he gets the scent. Oh hey, there's you know, there's

1366
01:22:43,960 --> 01:22:45,840
Speaker 1: another dough in estress, and I'm gonna start getting on

1367
01:22:45,880 --> 01:22:48,800
Speaker 1: the trail to find her. Don't don't really know, that's

1368
01:22:48,800 --> 01:22:52,400
Speaker 1: an excellent question. We we really don't know. But obviously

1369
01:22:52,439 --> 01:22:55,599
Speaker 1: there's got to be some situations where, you know, where

1370
01:22:55,600 --> 01:22:59,800
Speaker 1: where multiple bucks are are breeding the same dome. Yeah. Interesting. Okay,

1371
01:22:59,800 --> 01:23:02,719
Speaker 1: so here's the next natural question. Then one second, one second,

1372
01:23:02,880 --> 01:23:04,639
Speaker 1: on second, I want to forget what I wasn't ask,

1373
01:23:04,680 --> 01:23:07,160
Speaker 1: but what you got, make sure you write it down

1374
01:23:07,600 --> 01:23:12,080
Speaker 1: right now. Okay, So does does aggression have anything to

1375
01:23:12,200 --> 01:23:16,320
Speaker 1: play in in the breeding? You know there, I've I've

1376
01:23:16,360 --> 01:23:18,000
Speaker 1: spent a lot of time in the tree stand and

1377
01:23:18,000 --> 01:23:21,160
Speaker 1: I've seen some pretty big antlerd bucks and I've seen

1378
01:23:21,560 --> 01:23:25,599
Speaker 1: some big antlerd bucks that will come into rattling. And

1379
01:23:25,680 --> 01:23:29,559
Speaker 1: I've seen some you know, big antler bucks that you know,

1380
01:23:30,160 --> 01:23:33,080
Speaker 1: if your rattle, they're gone right or you know, they

1381
01:23:33,120 --> 01:23:35,200
Speaker 1: they shy away from the fighting. At the same time,

1382
01:23:35,200 --> 01:23:40,639
Speaker 1: I've seen three year old hundred eight pointer, like you've mentioned,

1383
01:23:40,880 --> 01:23:45,160
Speaker 1: kick everybody's ass because he he was the most aggressive.

1384
01:23:45,160 --> 01:23:47,479
Speaker 1: He came into rattles, he came in you know, he

1385
01:23:48,080 --> 01:23:50,479
Speaker 1: was chasing the big mature bucks off because he was

1386
01:23:50,760 --> 01:23:57,280
Speaker 1: basically a badass. So how much does I guess personality

1387
01:23:57,640 --> 01:24:04,120
Speaker 1: play into like the actual selection during breeding. Yeah, it

1388
01:24:04,120 --> 01:24:06,760
Speaker 1: plays a big role. It plays a really big role.

1389
01:24:06,840 --> 01:24:09,639
Speaker 1: So we see disparities like that a lot in our

1390
01:24:10,040 --> 01:24:13,439
Speaker 1: research facility where you would look at this particular buck

1391
01:24:13,479 --> 01:24:15,200
Speaker 1: and you think, man, he's got to be the stud.

1392
01:24:15,200 --> 01:24:17,679
Speaker 1: Look at his body size as antlers. I mean, he's

1393
01:24:17,720 --> 01:24:21,600
Speaker 1: got to be the guy. But for whatever reason, I

1394
01:24:21,640 --> 01:24:23,800
Speaker 1: guess he's a lover not a fighter type thing. Then

1395
01:24:23,840 --> 01:24:25,719
Speaker 1: there will be this. He might be a little bit younger,

1396
01:24:25,760 --> 01:24:29,600
Speaker 1: he might be fifteen pounds less, but he is that

1397
01:24:29,760 --> 01:24:32,800
Speaker 1: kind of guy that is always looking for a fight,

1398
01:24:33,800 --> 01:24:37,680
Speaker 1: always snorting, always aggressive, even does humans. You know, we're

1399
01:24:37,720 --> 01:24:39,960
Speaker 1: bringing him food and every chance he can get, he's

1400
01:24:39,960 --> 01:24:42,120
Speaker 1: gonna lay his ears back and ram up against the

1401
01:24:42,160 --> 01:24:45,000
Speaker 1: fence and try to get at us. And yeah, so

1402
01:24:45,160 --> 01:24:50,240
Speaker 1: speaking to your point, there is absolutely um something to aggression.

1403
01:24:50,600 --> 01:24:55,240
Speaker 1: And how does that evolve over time? Um? You know,

1404
01:24:55,680 --> 01:24:58,680
Speaker 1: um it must balance out. And and something I think

1405
01:24:58,720 --> 01:25:03,960
Speaker 1: about is I under if on the average, maybe these

1406
01:25:04,040 --> 01:25:08,439
Speaker 1: more aggressive bucks they may not live as long. The

1407
01:25:08,920 --> 01:25:12,840
Speaker 1: more you engage in fights, the more risk you take,

1408
01:25:12,960 --> 01:25:15,400
Speaker 1: and sooner or later risk will catch up with you.

1409
01:25:15,960 --> 01:25:19,479
Speaker 1: So maybe you have these two different strategies that are

1410
01:25:19,600 --> 01:25:22,760
Speaker 1: successful for different bucks during different times of the year,

1411
01:25:22,840 --> 01:25:25,880
Speaker 1: different times of their life. Maybe the more docile guy,

1412
01:25:25,920 --> 01:25:27,720
Speaker 1: maybe he's just gonna sit back and say I'm not

1413
01:25:27,760 --> 01:25:30,640
Speaker 1: gonna risk of my life. I'm gonna wait for the

1414
01:25:30,640 --> 01:25:33,519
Speaker 1: opportune time, and it's always worked for me in the past.

1415
01:25:33,600 --> 01:25:35,960
Speaker 1: And I'll find a doughe in heat, and I'll pursue

1416
01:25:36,000 --> 01:25:39,320
Speaker 1: her and and spread my jeans that way. And then

1417
01:25:39,320 --> 01:25:41,880
Speaker 1: you got that other guy. Just we all know that guy,

1418
01:25:41,920 --> 01:25:44,120
Speaker 1: don't we at the bar? The guy looking for the fight?

1419
01:25:45,800 --> 01:25:53,760
Speaker 1: You know, maybe he's that guy. Maybe that Um day,

1420
01:25:53,880 --> 01:25:56,120
Speaker 1: did that? Did that answer your question? Probably not the

1421
01:25:56,120 --> 01:25:58,240
Speaker 1: best answer, but it's the best I could come up with.

1422
01:25:58,320 --> 01:26:01,200
Speaker 1: That's That's what I've always thought. Anyway, that was excellent,

1423
01:26:02,000 --> 01:26:06,600
Speaker 1: So kind of continue on that thread. Can you elaborate

1424
01:26:06,640 --> 01:26:10,840
Speaker 1: on the typical distribution of breeding success across age groups?

1425
01:26:10,880 --> 01:26:12,200
Speaker 1: Because I think I've read some stuff on this in

1426
01:26:12,280 --> 01:26:14,400
Speaker 1: the past. I think a lot of us assume that

1427
01:26:14,520 --> 01:26:17,160
Speaker 1: the big stud, the big old stud breeds all the dose.

1428
01:26:17,520 --> 01:26:20,639
Speaker 1: But I think I read somewhere that's not necessarily the case.

1429
01:26:20,960 --> 01:26:26,880
Speaker 1: Is that true? That that that is indeed the case? Um.

1430
01:26:27,040 --> 01:26:32,640
Speaker 1: The one factor that affects the distribution of breeding is

1431
01:26:32,720 --> 01:26:37,960
Speaker 1: age structure. So let's let me give you some contrasting populations. UM.

1432
01:26:38,040 --> 01:26:41,160
Speaker 1: One of our studies, one of our colleagues, was conducted

1433
01:26:41,479 --> 01:26:45,360
Speaker 1: on the Keen Branch in South Texas, so lightly very

1434
01:26:45,479 --> 01:26:49,400
Speaker 1: very lightly hunted. Heard. Uh, you can easily have half

1435
01:26:49,680 --> 01:26:53,599
Speaker 1: or more of the bucks within the population are mature,

1436
01:26:54,040 --> 01:26:58,760
Speaker 1: so heavily skewed for these very long lived mature books. Now,

1437
01:26:58,800 --> 01:27:04,000
Speaker 1: in those situations, most of the breeding will occur by

1438
01:27:04,040 --> 01:27:08,240
Speaker 1: those mature bucks because there are so many of them

1439
01:27:08,240 --> 01:27:11,040
Speaker 1: in the population. You know that that one up and

1440
01:27:11,080 --> 01:27:14,640
Speaker 1: comer yearling or that one coming two year old, he

1441
01:27:14,800 --> 01:27:18,439
Speaker 1: just cannot compete with a mature buck. So anytime there's

1442
01:27:18,479 --> 01:27:20,960
Speaker 1: a contest, whether a doze in the presence or not,

1443
01:27:21,120 --> 01:27:26,760
Speaker 1: he's gonna lose. When we contrasted that the the reproductive

1444
01:27:26,760 --> 01:27:29,640
Speaker 1: success of mature bucks there, and we compared it to

1445
01:27:30,200 --> 01:27:34,200
Speaker 1: a public land refuge, uh, national wife refuge here in Mississippi,

1446
01:27:34,520 --> 01:27:39,360
Speaker 1: where the age structure is much younger. So in this population,

1447
01:27:39,920 --> 01:27:43,160
Speaker 1: an old buck was three years of age, Okay, so

1448
01:27:43,280 --> 01:27:46,840
Speaker 1: heavy hunting pressure. Uh. First legal bucks were usually shot,

1449
01:27:47,280 --> 01:27:50,200
Speaker 1: and so you would see most of your bucks were

1450
01:27:50,200 --> 01:27:52,760
Speaker 1: either yearlings or two year olds and occasionally find a

1451
01:27:52,840 --> 01:27:55,320
Speaker 1: three year old. Well then yeah, all the breeding was

1452
01:27:55,360 --> 01:27:57,680
Speaker 1: spread across from from yearlings and two year olds in

1453
01:27:57,720 --> 01:28:01,519
Speaker 1: the three year olds. So it is it is the

1454
01:28:01,560 --> 01:28:05,200
Speaker 1: single most factor that effects who's doing the breeding is

1455
01:28:05,240 --> 01:28:10,320
Speaker 1: the composition of your male age classes? How many? How

1456
01:28:10,320 --> 01:28:13,720
Speaker 1: many does will buck? I don't know an average buck

1457
01:28:13,800 --> 01:28:16,639
Speaker 1: or an average mature buck. How many dose will buck

1458
01:28:17,120 --> 01:28:20,640
Speaker 1: impregnate in a given fall? Do you know? That? Is

1459
01:28:20,640 --> 01:28:25,639
Speaker 1: that something that's been checked in free range? I don't

1460
01:28:25,680 --> 01:28:30,120
Speaker 1: think that has ever been adequately It just be so.

1461
01:28:30,120 --> 01:28:32,280
Speaker 1: So you would have to get a genetic sample of

1462
01:28:32,320 --> 01:28:35,519
Speaker 1: just about every deer and you know, a ten square

1463
01:28:35,560 --> 01:28:38,960
Speaker 1: mile area to find that out. But I'm just gonna

1464
01:28:38,960 --> 01:28:42,240
Speaker 1: pull one out of my back pocket here. I would say, um,

1465
01:28:42,360 --> 01:28:48,400
Speaker 1: you could probably say, uh, ten to twenty well, be good.

1466
01:28:48,800 --> 01:28:52,759
Speaker 1: One buck and that's your that's your best scientific guess.

1467
01:28:53,080 --> 01:28:59,519
Speaker 1: Would would breed ten to twenty does well? If you so?

1468
01:28:59,640 --> 01:29:04,080
Speaker 1: If we were talking about um as many possible. So

1469
01:29:04,240 --> 01:29:07,639
Speaker 1: let's say he had no competition fight. You know, it's

1470
01:29:07,680 --> 01:29:11,320
Speaker 1: just I've got this population of dose and over a

1471
01:29:11,439 --> 01:29:15,120
Speaker 1: two week, three week, one month, depending on UM the

1472
01:29:15,160 --> 01:29:19,599
Speaker 1: distribution of your your conception dates. UM. But he's spending

1473
01:29:19,760 --> 01:29:22,559
Speaker 1: one or two days with the dough tending her, breeding her,

1474
01:29:23,120 --> 01:29:25,360
Speaker 1: and then she's out of heat, and then hey, I'm

1475
01:29:25,360 --> 01:29:27,360
Speaker 1: going to locate another dough spend a day with two

1476
01:29:27,400 --> 01:29:31,320
Speaker 1: of her tend her breed her. UM. So essentially you're

1477
01:29:31,479 --> 01:29:35,760
Speaker 1: saying every every two days or so, UM, he would

1478
01:29:35,880 --> 01:29:40,240
Speaker 1: he would breed a dose throughout the right throughout the

1479
01:29:40,240 --> 01:29:47,040
Speaker 1: breeding season. So again, whild guess there wild scientific guests there,

1480
01:29:47,040 --> 01:29:50,000
Speaker 1: but that that would be a maximum, I would think.

1481
01:29:50,479 --> 01:29:53,840
Speaker 1: And then the more bucks that you put into that population, uh,

1482
01:29:54,000 --> 01:29:57,680
Speaker 1: the fewer and fewer opportunities that buck's gonna have. Right,

1483
01:29:57,880 --> 01:30:01,520
Speaker 1: the more competition you have, the less he's gonna bring eaton. Yeah,

1484
01:30:01,760 --> 01:30:07,439
Speaker 1: interesting stuff. Um Dan, you've got a wild list of questions.

1485
01:30:07,520 --> 01:30:08,800
Speaker 1: Do you wanna do you want to take us in

1486
01:30:08,840 --> 01:30:13,840
Speaker 1: the new direction? Yes? I do? All right, I mean,

1487
01:30:13,920 --> 01:30:18,960
Speaker 1: I gotta this this entire list of things that um

1488
01:30:19,040 --> 01:30:21,759
Speaker 1: I have on this paper. Like Mark said, the first question,

1489
01:30:23,680 --> 01:30:27,200
Speaker 1: everybody talks about mature You know, what is a mature buck?

1490
01:30:27,520 --> 01:30:30,679
Speaker 1: From a biot, from a biology um, from a biology stamp.

1491
01:30:30,760 --> 01:30:37,200
Speaker 1: When does a buck reach full maturity? Okay it um?

1492
01:30:38,520 --> 01:30:43,439
Speaker 1: The short answer is five? Okay, five or six will

1493
01:30:43,439 --> 01:30:47,479
Speaker 1: be the short answer. Now, what what we see is

1494
01:30:47,560 --> 01:30:53,440
Speaker 1: depending on where you're at and the environment, the stability

1495
01:30:53,439 --> 01:30:57,320
Speaker 1: of the environment and the food availability, some bucks get

1496
01:30:57,360 --> 01:31:01,280
Speaker 1: there a little bit quicker. So for example, in Mississippi

1497
01:31:01,320 --> 01:31:03,680
Speaker 1: when I was talking about that agg region earlier, our

1498
01:31:03,760 --> 01:31:07,920
Speaker 1: delta region. Um, when a buck is at four or five,

1499
01:31:08,320 --> 01:31:12,200
Speaker 1: they are pretty much at one. I mean from four

1500
01:31:12,280 --> 01:31:15,040
Speaker 1: to five. They may add a few more pounds and

1501
01:31:15,120 --> 01:31:18,799
Speaker 1: a few more inches, but they've gotten there pretty quick.

1502
01:31:19,360 --> 01:31:21,599
Speaker 1: And and sure they can still get a little bit bigger.

1503
01:31:21,680 --> 01:31:23,559
Speaker 1: They can turn into you know, a big old six

1504
01:31:23,640 --> 01:31:26,280
Speaker 1: or seven year old, but we usually don't see a

1505
01:31:26,320 --> 01:31:29,800
Speaker 1: lot of games and antler at that time. Now in

1506
01:31:29,840 --> 01:31:32,360
Speaker 1: our southern part of the state, the environment is not

1507
01:31:32,520 --> 01:31:37,160
Speaker 1: as stable. Uh, food is much more limited. We usually

1508
01:31:37,160 --> 01:31:40,400
Speaker 1: think they're a year behind. And when you plot their

1509
01:31:40,439 --> 01:31:43,920
Speaker 1: growth curves from from harvest data, when you plot them,

1510
01:31:43,960 --> 01:31:47,360
Speaker 1: you'll see that really really clearly, is that you'll see

1511
01:31:47,400 --> 01:31:51,120
Speaker 1: those Agg region bucks, it'll be a curve. They'll they'll

1512
01:31:51,160 --> 01:31:54,439
Speaker 1: grow like crazy until three years of age, and then

1513
01:31:54,479 --> 01:31:57,200
Speaker 1: a little bit more at four, and then hardly anymore

1514
01:31:57,240 --> 01:32:00,479
Speaker 1: at five. Whereas deer in our southern part of the

1515
01:32:00,520 --> 01:32:04,120
Speaker 1: state food limited. You will basically see a linear line

1516
01:32:04,360 --> 01:32:06,960
Speaker 1: one to two, two to three, three to four. They

1517
01:32:07,040 --> 01:32:10,439
Speaker 1: incrementally bigger and bigger and bigger. UM. And so the

1518
01:32:11,080 --> 01:32:13,680
Speaker 1: clock is a little bit slower because resources are a

1519
01:32:13,720 --> 01:32:19,679
Speaker 1: little bit less so, but more biologically, I guess dan

1520
01:32:20,040 --> 01:32:25,800
Speaker 1: is all skeletal growth pretty much stops them. By skeletal growth,

1521
01:32:25,840 --> 01:32:28,880
Speaker 1: we mean the long bones, the scapula. You know that

1522
01:32:29,000 --> 01:32:33,040
Speaker 1: body size is usually fixed by about three and a

1523
01:32:33,080 --> 01:32:35,840
Speaker 1: half or the most four and a half, and then

1524
01:32:35,880 --> 01:32:41,360
Speaker 1: they're just adding body weight after that. So it depends

1525
01:32:41,439 --> 01:32:43,840
Speaker 1: as as as you said earlier, it depends on how

1526
01:32:43,880 --> 01:32:46,439
Speaker 1: you define it. So if you wanted to say when

1527
01:32:46,479 --> 01:32:49,439
Speaker 1: does body mass level out or when does antler growth

1528
01:32:49,640 --> 01:32:57,400
Speaker 1: level out, you'd probably be safe to say five to six. Okay, interesting, gotcha?

1529
01:32:57,880 --> 01:33:00,320
Speaker 1: Is there anything else? And we've we've talked a lot

1530
01:33:00,360 --> 01:33:05,280
Speaker 1: about um, you know, ant or growth and breeding success

1531
01:33:05,360 --> 01:33:07,439
Speaker 1: and things like that. Is there anything related to just

1532
01:33:07,479 --> 01:33:10,640
Speaker 1: the general biology of white tails or things that you've

1533
01:33:10,680 --> 01:33:12,719
Speaker 1: seen when it comes to that that we're getting wrong

1534
01:33:13,040 --> 01:33:16,759
Speaker 1: on average as far as hunters things we believe, um,

1535
01:33:16,800 --> 01:33:18,479
Speaker 1: you know, how we think dear react to things, or

1536
01:33:18,479 --> 01:33:20,800
Speaker 1: how we think dear see us or smell us or

1537
01:33:20,800 --> 01:33:23,200
Speaker 1: anything like that. Is there any like common misconceptions out

1538
01:33:23,200 --> 01:33:26,880
Speaker 1: there that through your research you've proved to be not true.

1539
01:33:29,840 --> 01:33:33,080
Speaker 1: You know, I'll be honest with you, Probably what I

1540
01:33:33,160 --> 01:33:36,960
Speaker 1: hope anyway, is is the most impactful part of our

1541
01:33:37,000 --> 01:33:42,559
Speaker 1: research is UM, it's probably this the ampler genetic stuff.

1542
01:33:42,920 --> 01:33:46,120
Speaker 1: I mean, I think we have proven that so many

1543
01:33:46,160 --> 01:33:51,400
Speaker 1: ways that UM, how to harvest, developing an appropriate harvest

1544
01:33:51,400 --> 01:33:57,559
Speaker 1: strategy for your books, UM, the purpose of harvesting a

1545
01:33:57,600 --> 01:34:01,920
Speaker 1: particular book, And the mistake that we always see, UM,

1546
01:34:01,960 --> 01:34:06,160
Speaker 1: oh my gosh, it's uh heck, eight out of every

1547
01:34:06,439 --> 01:34:09,639
Speaker 1: or nine out of every ten clubs. When when when

1548
01:34:09,680 --> 01:34:11,679
Speaker 1: they come to us and say, man, we just can't

1549
01:34:11,680 --> 01:34:15,160
Speaker 1: get over the top. We've been doing everything. We've been

1550
01:34:15,160 --> 01:34:19,760
Speaker 1: practicing management, we've food plots, we've we've kept our dear

1551
01:34:19,840 --> 01:34:22,479
Speaker 1: heart under control. We've got our dough harvest. But you know,

1552
01:34:22,640 --> 01:34:25,880
Speaker 1: on and on and on, and the problem that we

1553
01:34:26,000 --> 01:34:28,559
Speaker 1: see or or excuse me, the problem that they always

1554
01:34:28,560 --> 01:34:31,080
Speaker 1: have is they'll say, and I'm gonna use a Boone

1555
01:34:31,120 --> 01:34:34,400
Speaker 1: and Crockett score relative to my area. So in my

1556
01:34:34,560 --> 01:34:37,080
Speaker 1: area here in Mississippi, say we get bucks up to

1557
01:34:37,240 --> 01:34:39,360
Speaker 1: you know, four or five years of Asian, we can't

1558
01:34:39,360 --> 01:34:42,400
Speaker 1: get them over a hundred and thirty. We we just

1559
01:34:42,520 --> 01:34:45,599
Speaker 1: cannot break through this hundred and thirty. Why why why

1560
01:34:45,600 --> 01:34:49,120
Speaker 1: aren't we killing one fifty class dear, one sixty class, dear,

1561
01:34:49,800 --> 01:34:51,800
Speaker 1: and we work with them, and we go back and

1562
01:34:51,840 --> 01:34:54,320
Speaker 1: ask the type of bucks that were harvesting that they

1563
01:34:54,360 --> 01:34:56,800
Speaker 1: are harvesting, and we look at their harvest data. And

1564
01:34:56,840 --> 01:35:00,760
Speaker 1: the biggest problem where people shoot themselves in the foot

1565
01:35:00,880 --> 01:35:05,960
Speaker 1: more often than not, is they shoot the wrong bucks

1566
01:35:06,560 --> 01:35:14,200
Speaker 1: when they're middle aged. So, for example, when you see, um,

1567
01:35:14,240 --> 01:35:18,120
Speaker 1: this one hundred and thirty class, dear, oh my god,

1568
01:35:18,160 --> 01:35:20,559
Speaker 1: you know hey, you know hey, his bow season right

1569
01:35:20,720 --> 01:35:23,080
Speaker 1: is archery. I got a new bow of my hands.

1570
01:35:23,080 --> 01:35:26,080
Speaker 1: And at thirty yards away there's this hundred and thirty class, dear.

1571
01:35:26,800 --> 01:35:31,360
Speaker 1: Now at a three year old, dear that scores one thirty,

1572
01:35:31,520 --> 01:35:36,360
Speaker 1: it's probably going to be a one sixty. A three

1573
01:35:36,439 --> 01:35:39,160
Speaker 1: year old one class, it's probably going to grow at

1574
01:35:39,160 --> 01:35:42,640
Speaker 1: maturity to be a one fifty class, etcetera. And so

1575
01:35:42,720 --> 01:35:48,040
Speaker 1: what we see over time is that exceptional bucks are

1576
01:35:48,160 --> 01:35:52,240
Speaker 1: harvested when they're three years of age, and that's why

1577
01:35:52,280 --> 01:35:55,080
Speaker 1: they can never break through to shooting one and one

1578
01:35:55,120 --> 01:36:00,120
Speaker 1: sixty class. So it is the very opposite, remember or

1579
01:36:00,160 --> 01:36:03,360
Speaker 1: earlier we were talking about when you see these older

1580
01:36:03,400 --> 01:36:06,440
Speaker 1: bucks that are obviously not going to be the trophy,

1581
01:36:06,680 --> 01:36:08,600
Speaker 1: not going to be the buck you're really managing for

1582
01:36:08,760 --> 01:36:11,120
Speaker 1: and hoping for, you know, go ahead and harvest it.

1583
01:36:11,280 --> 01:36:13,680
Speaker 1: You know, that's a good source of dear meat for you,

1584
01:36:13,720 --> 01:36:15,920
Speaker 1: and it'll save a lot of fords for other deer.

1585
01:36:16,280 --> 01:36:19,840
Speaker 1: These people are doing just the opposite. So they're seeing

1586
01:36:19,880 --> 01:36:22,760
Speaker 1: that one hundred and thirty one and thirty five in

1587
01:36:23,560 --> 01:36:28,639
Speaker 1: three year old and harvesting it, and they just um

1588
01:36:28,880 --> 01:36:32,880
Speaker 1: eliminated the possibility of them shooting a one fifty or

1589
01:36:32,960 --> 01:36:37,120
Speaker 1: a one sixty two years later, right and keep them

1590
01:36:38,000 --> 01:36:40,320
Speaker 1: You took the up and comer ount and you got

1591
01:36:40,320 --> 01:36:43,519
Speaker 1: to keep this in mind. One sixty and one seventy

1592
01:36:43,520 --> 01:36:48,800
Speaker 1: class bucks are anomalies. They're rare, very very rare. And

1593
01:36:49,080 --> 01:36:51,880
Speaker 1: so you saw that rare buck when it was three

1594
01:36:51,960 --> 01:36:54,280
Speaker 1: years of age and you harvested it two years early.

1595
01:36:55,360 --> 01:36:58,639
Speaker 1: We we have some really uh good hunting clubs along

1596
01:36:58,640 --> 01:37:03,599
Speaker 1: the Mississippi River, very very uh intense agg region, very

1597
01:37:03,600 --> 01:37:06,880
Speaker 1: fertile soil, etcetera. And you're seeing some of these people,

1598
01:37:07,240 --> 01:37:09,680
Speaker 1: some of these hunters killing two year olds or to

1599
01:37:09,760 --> 01:37:12,200
Speaker 1: scoring you know in the one teams and one twinnies,

1600
01:37:14,479 --> 01:37:16,479
Speaker 1: and those are bucks that would have been that would

1601
01:37:16,479 --> 01:37:18,080
Speaker 1: have grown in to be if they had followed your

1602
01:37:18,080 --> 01:37:21,679
Speaker 1: normal growth curves, they would have end up being booming crocketts,

1603
01:37:21,680 --> 01:37:25,960
Speaker 1: but they were harvested too early. So the composition of

1604
01:37:26,000 --> 01:37:30,639
Speaker 1: your buck harvest is so critical. And what I tell

1605
01:37:30,720 --> 01:37:34,920
Speaker 1: hunters in my workshops is the single most valuable asset

1606
01:37:35,000 --> 01:37:39,280
Speaker 1: you have on your property are high quality young bucks.

1607
01:37:39,680 --> 01:37:42,920
Speaker 1: You got to do everything you possibly can to protect

1608
01:37:42,920 --> 01:37:45,519
Speaker 1: those high quality young bucks. And so I know a

1609
01:37:45,560 --> 01:37:47,720
Speaker 1: lot of your listeners do do this. We do. We

1610
01:37:47,800 --> 01:37:51,320
Speaker 1: do it as well as your preseason camera survey. You know,

1611
01:37:51,360 --> 01:37:53,280
Speaker 1: if you're part of a hunting club, especially, we have

1612
01:37:53,320 --> 01:37:57,040
Speaker 1: a collection of people is saturate the woods with those

1613
01:37:57,080 --> 01:38:00,800
Speaker 1: cameras and we identified the up and bummers and say

1614
01:38:00,840 --> 01:38:05,760
Speaker 1: hands off, do not harvest this buck whatsoever. And then

1615
01:38:05,800 --> 01:38:07,599
Speaker 1: by default you might have this other three or four

1616
01:38:07,680 --> 01:38:10,400
Speaker 1: year old this below averages say you know, you can

1617
01:38:10,439 --> 01:38:12,879
Speaker 1: harvest this one, but this is a buck we're protecting.

1618
01:38:13,360 --> 01:38:17,200
Speaker 1: And when you see clubs year after year systematically do that,

1619
01:38:17,600 --> 01:38:20,240
Speaker 1: then that's when they start reaching their potential, and that's

1620
01:38:20,240 --> 01:38:24,720
Speaker 1: when they start harvesting those um really really high quality

1621
01:38:24,960 --> 01:38:28,200
Speaker 1: sixty and above. Interesting makes sense, It comes down that's

1622
01:38:28,240 --> 01:38:31,479
Speaker 1: the single biggest thing I see. Yeah, those levers. We

1623
01:38:31,479 --> 01:38:34,439
Speaker 1: can push as a hunter, you roll as a manager

1624
01:38:34,560 --> 01:38:36,320
Speaker 1: or harun't it. You can push the food lever, you

1625
01:38:36,360 --> 01:38:39,240
Speaker 1: can increase nutrition. But probably that that very most like

1626
01:38:39,240 --> 01:38:42,200
Speaker 1: you said, that most important lever is the trigger. Whether

1627
01:38:42,240 --> 01:38:44,160
Speaker 1: you choose to pull the trigger or not pull the

1628
01:38:44,200 --> 01:38:49,320
Speaker 1: trigger is is the ultimate, the ultimate decision that will

1629
01:38:49,400 --> 01:38:52,200
Speaker 1: lead to results down the road. Now, I want to

1630
01:38:52,280 --> 01:38:54,000
Speaker 1: jump to something else really quick, if you don't mind,

1631
01:38:54,920 --> 01:38:56,920
Speaker 1: I imagine. I imagine down in the part of the

1632
01:38:56,920 --> 01:38:58,600
Speaker 1: country we're at, in the in the southern part of

1633
01:38:58,640 --> 01:39:02,679
Speaker 1: the country, another limiting factor because of its impacts on nutrition,

1634
01:39:02,920 --> 01:39:05,080
Speaker 1: I assume and for to understand, one of those limiting

1635
01:39:05,120 --> 01:39:08,840
Speaker 1: factors might be competition from hogs. And that's something we've

1636
01:39:08,880 --> 01:39:11,439
Speaker 1: never talked about on the podcast before, but I know

1637
01:39:11,479 --> 01:39:14,040
Speaker 1: a lot of people deal with and as I understand it,

1638
01:39:14,080 --> 01:39:16,400
Speaker 1: you've done some researcher looking into that. Can you tell

1639
01:39:16,479 --> 01:39:23,240
Speaker 1: us about what you're doing on that front? Yeah, absolutely so. Um,

1640
01:39:23,520 --> 01:39:25,400
Speaker 1: maybe a little more than you want to know here.

1641
01:39:25,439 --> 01:39:27,439
Speaker 1: But when I took this job in two thousand six,

1642
01:39:27,720 --> 01:39:29,640
Speaker 1: you know, I'm I'm a I'm a dear biologist. For

1643
01:39:29,680 --> 01:39:32,040
Speaker 1: crying out loud, I'm just my my whole program was

1644
01:39:32,080 --> 01:39:34,640
Speaker 1: going to be focused on deer and helping people and

1645
01:39:34,640 --> 01:39:37,120
Speaker 1: and working with food plots and forest management and a

1646
01:39:37,200 --> 01:39:40,760
Speaker 1: whole program. And then I started getting phone calls first

1647
01:39:40,800 --> 01:39:43,640
Speaker 1: week on the job, Um man, I've got hogs on

1648
01:39:43,680 --> 01:39:45,679
Speaker 1: my property. What can I do about these hogs? And

1649
01:39:45,680 --> 01:39:48,439
Speaker 1: and it's like, oh my god. You know, I had

1650
01:39:48,479 --> 01:39:50,840
Speaker 1: lived in Mississippi for a decade and I knew we

1651
01:39:50,880 --> 01:39:52,200
Speaker 1: had a lot of hogs, but I wasn't in a

1652
01:39:52,280 --> 01:39:55,759
Speaker 1: position to where I was interacting with the hunting public

1653
01:39:55,800 --> 01:39:58,320
Speaker 1: and with landowners to how big of an issue it was.

1654
01:39:58,439 --> 01:40:03,160
Speaker 1: So we started develop lopping educational programs, workshops and seminars,

1655
01:40:03,200 --> 01:40:05,720
Speaker 1: and over the years have just met with thousands of

1656
01:40:05,760 --> 01:40:11,680
Speaker 1: people landowners that um are struggling. I mean that absolutely

1657
01:40:12,320 --> 01:40:17,160
Speaker 1: hate hogs from from a hunting club standpoint, from destruction

1658
01:40:17,240 --> 01:40:21,439
Speaker 1: of roads, from destruction of food plots, from competition. I

1659
01:40:21,479 --> 01:40:25,960
Speaker 1: mean this this is anecdotal. I will admit this is anecdotal,

1660
01:40:26,240 --> 01:40:30,040
Speaker 1: but there's enough smoke. I'm beginning to believe there's fire.

1661
01:40:30,720 --> 01:40:34,120
Speaker 1: Is so many hunters that I trust say, once we

1662
01:40:34,200 --> 01:40:36,880
Speaker 1: started getting so many hogs on our property, we just

1663
01:40:36,920 --> 01:40:40,839
Speaker 1: don't see near as many deer anymore, and um, probably

1664
01:40:40,920 --> 01:40:45,599
Speaker 1: due to food competition. And so you guys know this.

1665
01:40:45,960 --> 01:40:48,760
Speaker 1: You don't have to be a biologist if there is

1666
01:40:48,800 --> 01:40:52,320
Speaker 1: a limited resource on your property. So let's use that

1667
01:40:52,520 --> 01:40:55,880
Speaker 1: white oak or that that grove of white oaks, and

1668
01:40:55,920 --> 01:40:57,960
Speaker 1: the white oaks are dropping in this that place where

1669
01:40:58,000 --> 01:41:01,360
Speaker 1: you can always go see deer. And now you've got

1670
01:41:01,360 --> 01:41:05,439
Speaker 1: a sounder of hogs competing with deer for those white oaks.

1671
01:41:05,560 --> 01:41:12,240
Speaker 1: Who's gonna win? I guess they're all always gonna win.

1672
01:41:12,400 --> 01:41:16,120
Speaker 1: A deer is never going to outcompete a hog. A

1673
01:41:16,160 --> 01:41:19,400
Speaker 1: hog is always gonna win. And so we start seeing

1674
01:41:19,400 --> 01:41:23,160
Speaker 1: all these most valuable resources going into a hog's belly

1675
01:41:23,320 --> 01:41:27,200
Speaker 1: rather than to a deer's belly. Um, we're even starting

1676
01:41:27,200 --> 01:41:31,599
Speaker 1: to see with more more kind of real scientific trail

1677
01:41:31,680 --> 01:41:35,559
Speaker 1: camera surveys, with these types of statistical modeling you can

1678
01:41:35,600 --> 01:41:38,840
Speaker 1: do with that. But that in places where you get

1679
01:41:38,880 --> 01:41:41,639
Speaker 1: more photographs of hogs, you get less photographs of deer.

1680
01:41:42,360 --> 01:41:45,280
Speaker 1: I mean statistically that has been proven in a lot

1681
01:41:45,320 --> 01:41:47,519
Speaker 1: of areas. In places where you see a lot of deer,

1682
01:41:47,560 --> 01:41:49,920
Speaker 1: you're not seeing a lot of hogs. So when hogs

1683
01:41:49,920 --> 01:41:52,600
Speaker 1: and deer are on in the same space on the

1684
01:41:52,640 --> 01:41:56,080
Speaker 1: same property or landscape. You know, hogs are having an

1685
01:41:56,160 --> 01:42:00,519
Speaker 1: impact on your deer. Now that's just the hunters, now

1686
01:42:01,600 --> 01:42:05,120
Speaker 1: the farmers. I mean, we were on a property of

1687
01:42:05,479 --> 01:42:08,920
Speaker 1: part of my study area earlier this year. Um. I

1688
01:42:08,920 --> 01:42:12,759
Speaker 1: don't know if y'all have ever seen um hog rooting

1689
01:42:12,760 --> 01:42:15,679
Speaker 1: in the corn field. And most people when I show

1690
01:42:15,720 --> 01:42:18,880
Speaker 1: them a picture or slide, they when I say, hey, hey,

1691
01:42:19,160 --> 01:42:21,040
Speaker 1: take a look at this field. Can you tell that

1692
01:42:21,080 --> 01:42:25,840
Speaker 1: it's been completely destroyed by hog? And they'll say, what

1693
01:42:25,880 --> 01:42:27,599
Speaker 1: do you mean? I don't. I don't see a single

1694
01:42:27,640 --> 01:42:31,559
Speaker 1: stalk of corn. That's exactly right, because of the night.

1695
01:42:31,880 --> 01:42:36,879
Speaker 1: The very night the farmer put that seed in the ground,

1696
01:42:37,600 --> 01:42:41,400
Speaker 1: a hog or hogs went up and down every single

1697
01:42:41,520 --> 01:42:44,719
Speaker 1: row in a twenty acre, thirty acre, forty acre, eighty

1698
01:42:44,760 --> 01:42:50,160
Speaker 1: acre field and rooted up every sea of corn. I mean, yeah,

1699
01:42:50,200 --> 01:42:53,679
Speaker 1: it's precision. You know, we've got precision agg it's precision rooting.

1700
01:42:54,479 --> 01:42:58,040
Speaker 1: They they will literally every single row where that where

1701
01:42:58,080 --> 01:43:00,800
Speaker 1: that planter has cut the ground and put that corn seed,

1702
01:43:01,120 --> 01:43:04,400
Speaker 1: they will root there and just destroyed the whole crop,

1703
01:43:04,640 --> 01:43:08,519
Speaker 1: you know, in one night. So they are a nightmare.

1704
01:43:09,360 --> 01:43:13,840
Speaker 1: And there a nightmare. I've I've I've never met um.

1705
01:43:13,880 --> 01:43:15,320
Speaker 1: And here's the story. I get a lot. I'll be

1706
01:43:15,320 --> 01:43:19,280
Speaker 1: doing a seminar workshop and you'll see the guy or

1707
01:43:19,320 --> 01:43:21,640
Speaker 1: gal or couple will come up and they go cudly.

1708
01:43:22,439 --> 01:43:25,879
Speaker 1: I wish we had been at the seminar five years ago, because,

1709
01:43:26,400 --> 01:43:28,160
Speaker 1: you know, we thought it was gonna be kind of cool.

1710
01:43:28,520 --> 01:43:31,760
Speaker 1: We thought that when hogs came onto our property and

1711
01:43:31,760 --> 01:43:34,240
Speaker 1: and during deer season, we'd shoot a couple of them,

1712
01:43:34,280 --> 01:43:36,799
Speaker 1: and hey, we thought, you know, during the off season,

1713
01:43:36,880 --> 01:43:40,160
Speaker 1: we'd have some fun on our property shooting some more hogs.

1714
01:43:40,360 --> 01:43:43,240
Speaker 1: And then fast forward five years and they can't get

1715
01:43:43,240 --> 01:43:46,280
Speaker 1: a food plot out of the ground. Say, man, I

1716
01:43:46,320 --> 01:43:49,280
Speaker 1: wish we had gotten on top of this a lot earlier.

1717
01:43:49,360 --> 01:43:52,559
Speaker 1: So just make no mistake, this is this is a

1718
01:43:52,600 --> 01:43:56,519
Speaker 1: species you can't play with. Um. You're literally literally you're

1719
01:43:56,560 --> 01:43:59,160
Speaker 1: playing with fire. In terms of their effects. They just

1720
01:43:59,240 --> 01:44:03,000
Speaker 1: caused so much destruction. And sure they are fun to hunt,

1721
01:44:03,240 --> 01:44:06,280
Speaker 1: There's no doubt about it. The hogs are fun to hunt, uh,

1722
01:44:06,320 --> 01:44:09,800
Speaker 1: and they taste good. But but I've never worked with

1723
01:44:09,840 --> 01:44:16,200
Speaker 1: a property that was interested in ducks, deer or turkey.

1724
01:44:16,320 --> 01:44:18,479
Speaker 1: And then ever say to me, I am so glad

1725
01:44:18,520 --> 01:44:22,679
Speaker 1: we have hogs on our property, never, not once, every

1726
01:44:22,680 --> 01:44:26,040
Speaker 1: one of them now is literally spending thousands and thousands

1727
01:44:26,080 --> 01:44:29,160
Speaker 1: of dollars every single year to try to get rid

1728
01:44:29,200 --> 01:44:34,799
Speaker 1: of them. So, um, they're they're just completely bad news

1729
01:44:34,840 --> 01:44:39,720
Speaker 1: and a bad idea. So what's the solution right now?

1730
01:44:41,000 --> 01:44:44,040
Speaker 1: The best thing you can do is um what we

1731
01:44:44,120 --> 01:44:48,840
Speaker 1: call strategic trapping and and you can be you can

1732
01:44:48,920 --> 01:44:52,479
Speaker 1: you can make a lot of progress um with with

1733
01:44:52,560 --> 01:44:56,200
Speaker 1: the right kind of trapping. And it's not just going

1734
01:44:56,280 --> 01:44:58,599
Speaker 1: out and putting up what we call a little cage trap,

1735
01:44:58,680 --> 01:45:01,200
Speaker 1: like a little box trap where you might catch one

1736
01:45:01,280 --> 01:45:03,920
Speaker 1: or two hogs. You have to use a trap that

1737
01:45:03,960 --> 01:45:07,320
Speaker 1: we call a corral trap. And if you go to

1738
01:45:07,320 --> 01:45:12,519
Speaker 1: our website wild pig info dot com, uh, you'll see

1739
01:45:12,520 --> 01:45:16,559
Speaker 1: an example of this. And so these are big uh

1740
01:45:16,720 --> 01:45:18,880
Speaker 1: corral traps. Why don't we call it a corral trap

1741
01:45:18,920 --> 01:45:21,960
Speaker 1: because you're building a corral. You're building a corral anywhere

1742
01:45:22,000 --> 01:45:25,040
Speaker 1: from twenty to forty ft across and you have a

1743
01:45:25,080 --> 01:45:28,559
Speaker 1: big door on it. And what you do is you

1744
01:45:28,800 --> 01:45:32,760
Speaker 1: pre bait these hogs. You get them um addicted so

1745
01:45:32,840 --> 01:45:35,639
Speaker 1: to speak, to coming into your trap to eat your bait.

1746
01:45:35,840 --> 01:45:40,920
Speaker 1: Most often that's corn and you can be really really sophisticated.

1747
01:45:40,960 --> 01:45:44,840
Speaker 1: You know, we use the electronic trap doors, so every

1748
01:45:44,880 --> 01:45:47,519
Speaker 1: time an animal goes into that trap, it trips a

1749
01:45:47,600 --> 01:45:50,599
Speaker 1: sensor and we can look on our smartphone and yep,

1750
01:45:50,720 --> 01:45:52,479
Speaker 1: all right, all the pigs are in the trap. There's

1751
01:45:52,520 --> 01:45:55,599
Speaker 1: fourteen of them, or hey, all nineteen. You know, pigs

1752
01:45:55,640 --> 01:45:57,880
Speaker 1: are in the trap. Drop the door, and a cell

1753
01:45:58,000 --> 01:45:59,960
Speaker 1: signal us into the trap and it drops the door.

1754
01:46:00,280 --> 01:46:03,160
Speaker 1: And so you can be super super efficient, you know,

1755
01:46:03,240 --> 01:46:06,599
Speaker 1: trapping pigs when you're catching them ten to twenty at

1756
01:46:06,600 --> 01:46:09,880
Speaker 1: a time. So right now, that is the most efficient way.

1757
01:46:09,960 --> 01:46:14,000
Speaker 1: Some people love night shooting, and that can certainly play

1758
01:46:14,040 --> 01:46:15,920
Speaker 1: a role, you know, with the night vision gear and

1759
01:46:15,960 --> 01:46:19,920
Speaker 1: all that. Um, but but you're you usually not going

1760
01:46:19,960 --> 01:46:23,439
Speaker 1: to be near as efficient, you know, shooting single animals

1761
01:46:23,479 --> 01:46:26,439
Speaker 1: at the time. Um, you're never gonna be as efficient

1762
01:46:26,479 --> 01:46:30,040
Speaker 1: as when you're using a trapping a good trapping program.

1763
01:46:30,120 --> 01:46:32,240
Speaker 1: And then also, guys, depending on where you're at in

1764
01:46:32,240 --> 01:46:34,600
Speaker 1: the country. You know, it's very popular in Texas, and

1765
01:46:34,640 --> 01:46:36,680
Speaker 1: we do some of this in Mississippi. If you have

1766
01:46:36,760 --> 01:46:41,040
Speaker 1: the landscape forward is aerial gunning and so through our

1767
01:46:41,160 --> 01:46:45,120
Speaker 1: U S Department of Agriculture, our Wildlife Services. UH. Landowners

1768
01:46:45,120 --> 01:46:47,680
Speaker 1: can get a contract with U S d A and

1769
01:46:47,720 --> 01:46:50,519
Speaker 1: they'll bring the helicopter over and they will aerial gun

1770
01:46:50,880 --> 01:46:53,240
Speaker 1: the property, you know, try to kill us as many

1771
01:46:53,240 --> 01:46:56,479
Speaker 1: hogs as they can. So the amount of money, when

1772
01:46:56,520 --> 01:46:59,160
Speaker 1: you think about your tax dollars, but that is your

1773
01:46:59,320 --> 01:47:04,080
Speaker 1: tax dollar they're paying for a helicopter and aerial gunning. UM.

1774
01:47:04,120 --> 01:47:07,519
Speaker 1: So we're just seeing populations explode and now we're having

1775
01:47:07,520 --> 01:47:10,559
Speaker 1: to spend our tax dollars to control them. And so

1776
01:47:11,439 --> 01:47:14,720
Speaker 1: I just would not encourage any of your listeners. You

1777
01:47:14,760 --> 01:47:17,080
Speaker 1: don't want hogs on your property. If you see a hog,

1778
01:47:17,160 --> 01:47:20,519
Speaker 1: started trapping program and get rid of them as soon

1779
01:47:20,560 --> 01:47:23,040
Speaker 1: as possible. And and again we've got all the resources

1780
01:47:23,040 --> 01:47:27,439
Speaker 1: you need on wild pig info dot com. UH. It's

1781
01:47:27,479 --> 01:47:29,840
Speaker 1: a it's a it's an issue in a situation that's

1782
01:47:29,880 --> 01:47:32,760
Speaker 1: just very foreign to me because I've never hunted um

1783
01:47:32,880 --> 01:47:35,599
Speaker 1: or even lived anywhere where hogs have been an issue.

1784
01:47:35,840 --> 01:47:37,639
Speaker 1: So it's, you know, the idea of some of these

1785
01:47:37,680 --> 01:47:39,920
Speaker 1: things sounds like whoa you know to me just in

1786
01:47:39,960 --> 01:47:43,479
Speaker 1: my own personal life. But I totally understand that it's

1787
01:47:43,479 --> 01:47:45,479
Speaker 1: a huge issue in parts of the country. And I

1788
01:47:45,479 --> 01:47:48,200
Speaker 1: think you know, at least when I think about it,

1789
01:47:48,280 --> 01:47:50,840
Speaker 1: I think about, you know, some animals, you know, let's

1790
01:47:50,920 --> 01:47:53,280
Speaker 1: like predators in different parts of the country. At least

1791
01:47:53,280 --> 01:47:57,760
Speaker 1: from my perspective, right, there's a there's a well, there's

1792
01:47:57,800 --> 01:48:00,000
Speaker 1: a time and a place the need to manage those

1793
01:48:00,080 --> 01:48:03,320
Speaker 1: to maintain balance and the population. Um. And I think

1794
01:48:03,320 --> 01:48:05,599
Speaker 1: that they, at least from my perspective, there's a place

1795
01:48:05,600 --> 01:48:07,639
Speaker 1: where predators on the landscape as a as a natural

1796
01:48:07,680 --> 01:48:10,840
Speaker 1: part of that ecosystem. Um. But I think when it

1797
01:48:10,880 --> 01:48:14,280
Speaker 1: comes to hoggs, right, that's an introduced species, is an

1798
01:48:14,320 --> 01:48:18,080
Speaker 1: invasive species, Correct me if I'm wrong. But Hoggs in

1799
01:48:18,120 --> 01:48:20,000
Speaker 1: the form that they're and now, we're not supposed to

1800
01:48:20,040 --> 01:48:22,479
Speaker 1: be down there where they're at now, and because of that,

1801
01:48:22,520 --> 01:48:25,960
Speaker 1: they're they're really screwing up the balance um. And in

1802
01:48:25,960 --> 01:48:29,280
Speaker 1: those cases it seems like, you know, extreme action is necessary, right,

1803
01:48:30,880 --> 01:48:34,479
Speaker 1: You are precisely right, Yeah, you are precisely right. Now,

1804
01:48:34,560 --> 01:48:37,240
Speaker 1: if we were in Germany, we would be approaching this

1805
01:48:37,280 --> 01:48:40,760
Speaker 1: problem completely different as they are in Germany and in

1806
01:48:40,840 --> 01:48:43,400
Speaker 1: Europe and Russia. It's the game species, you know, it's

1807
01:48:43,520 --> 01:48:46,920
Speaker 1: from that area, that's that's that's where that animal is

1808
01:48:46,960 --> 01:48:50,000
Speaker 1: supposed to be, but it's not in North America, and

1809
01:48:50,080 --> 01:48:52,840
Speaker 1: so it can outcompete a lot of our game species.

1810
01:48:53,720 --> 01:48:57,240
Speaker 1: And um, you know, for example, we do diet studies too.

1811
01:48:57,360 --> 01:49:00,600
Speaker 1: We look at what they eat and I guess you

1812
01:49:00,640 --> 01:49:02,599
Speaker 1: have to see it for it really to sink in.

1813
01:49:02,760 --> 01:49:06,280
Speaker 1: But anything that lives on the ground a hog is

1814
01:49:06,320 --> 01:49:10,320
Speaker 1: going to consume. And just think about that. Anything that

1815
01:49:10,439 --> 01:49:14,479
Speaker 1: a pig, any type of animal that resides on the ground,

1816
01:49:14,520 --> 01:49:15,960
Speaker 1: and if a pig can get his mouth on it

1817
01:49:16,000 --> 01:49:18,679
Speaker 1: and eat it, it's going to So when we open

1818
01:49:18,720 --> 01:49:22,160
Speaker 1: them up, we see we see uh, eggs, we see

1819
01:49:22,240 --> 01:49:25,559
Speaker 1: reptile eggs, we see frogs, we see salamanders, we see snakes.

1820
01:49:25,760 --> 01:49:29,799
Speaker 1: We found an armadillo for crying out loud. Um, we've

1821
01:49:30,000 --> 01:49:32,840
Speaker 1: you know, fawns. I mean, what's it gonna do? You know?

1822
01:49:33,400 --> 01:49:35,800
Speaker 1: If you've got a hand turkey nesting and a hog

1823
01:49:35,920 --> 01:49:38,520
Speaker 1: or a sounder of hogs come up, I mean absolutely

1824
01:49:38,560 --> 01:49:40,720
Speaker 1: it's going to consume those eggs. There's no reason to

1825
01:49:40,760 --> 01:49:44,200
Speaker 1: think you wouldn't. So um when you and if you

1826
01:49:44,280 --> 01:49:46,920
Speaker 1: just have one or two hogs across your property, it's

1827
01:49:46,920 --> 01:49:49,439
Speaker 1: not going to be that big of an impact. But

1828
01:49:49,439 --> 01:49:51,439
Speaker 1: but the danger is thinking, hey, I just got a

1829
01:49:51,439 --> 01:49:53,840
Speaker 1: couple of hogs, because five years later, instead of having

1830
01:49:53,840 --> 01:49:56,679
Speaker 1: a couple hogs, you might have thirty hogs or fifty hogs.

1831
01:49:57,240 --> 01:49:59,080
Speaker 1: And then you start noticing, like some of these other

1832
01:49:59,160 --> 01:50:01,559
Speaker 1: hunting clubs man deer quality, we don't we don't see

1833
01:50:01,560 --> 01:50:04,080
Speaker 1: them any dear anymore. Uh, you know, they just start

1834
01:50:04,200 --> 01:50:07,800
Speaker 1: out convening our our native wildlife, and so they just

1835
01:50:07,880 --> 01:50:11,360
Speaker 1: are again. You know, don't take my word for it.

1836
01:50:11,800 --> 01:50:15,760
Speaker 1: Take every single hunting club or landowner I've ever met,

1837
01:50:16,600 --> 01:50:20,920
Speaker 1: um that we're interested again. They are interested in hunting

1838
01:50:20,960 --> 01:50:24,840
Speaker 1: in terms of primarily deer, but also turkey and ducks.

1839
01:50:24,880 --> 01:50:27,400
Speaker 1: They have never said, I'm so glad we have hogs

1840
01:50:27,400 --> 01:50:31,320
Speaker 1: on our property. They always say it, usually with with

1841
01:50:31,800 --> 01:50:35,120
Speaker 1: you know, disgusted on their face, just sickness on their

1842
01:50:35,160 --> 01:50:37,840
Speaker 1: face because they have to devote so much time and

1843
01:50:37,880 --> 01:50:43,120
Speaker 1: money for their control. It definitely sounds like something it's

1844
01:50:43,160 --> 01:50:45,280
Speaker 1: not a fun thing to deal with them. I'm I'm

1845
01:50:45,320 --> 01:50:48,920
Speaker 1: glad that um selfishly, it hasn't hasn't become an issue

1846
01:50:48,960 --> 01:50:50,400
Speaker 1: up in my neck of the woods, and I hope

1847
01:50:50,400 --> 01:50:52,640
Speaker 1: it doesn't. But you keep you here about these occasional,

1848
01:50:53,240 --> 01:50:55,839
Speaker 1: you know, bursts of it in some random areas sometimes,

1849
01:50:56,400 --> 01:50:57,840
Speaker 1: you know, it's it's something that you have to be

1850
01:50:57,880 --> 01:51:00,240
Speaker 1: aware of in a you know, keep your eyes open

1851
01:51:00,280 --> 01:51:03,680
Speaker 1: to it seems like and you've got them all around you,

1852
01:51:04,080 --> 01:51:07,719
Speaker 1: I mean they're they're they're creeping your way. Now here's

1853
01:51:07,760 --> 01:51:10,280
Speaker 1: the fact of the matter. How are those How are

1854
01:51:10,280 --> 01:51:14,320
Speaker 1: those wild hogs getting to you? Mark? I think, well,

1855
01:51:14,600 --> 01:51:17,760
Speaker 1: everything I've heard of, it's been escaping from cap from captivity.

1856
01:51:17,840 --> 01:51:20,320
Speaker 1: So people that have a game farm or fenced and

1857
01:51:20,400 --> 01:51:23,240
Speaker 1: hunting area around them and something getting out. Is that

1858
01:51:23,840 --> 01:51:27,720
Speaker 1: that's right? Yep, yep, that is exactly right there, evening

1859
01:51:27,920 --> 01:51:33,000
Speaker 1: either escaping from a captive facility or people just literally

1860
01:51:33,080 --> 01:51:37,800
Speaker 1: hauling them and turning them loose. Yikes. So that that yeah,

1861
01:51:37,920 --> 01:51:41,080
Speaker 1: So right now with within the pig management community are

1862
01:51:41,120 --> 01:51:44,960
Speaker 1: our biggest issues are trying to catch people in the

1863
01:51:45,000 --> 01:51:49,080
Speaker 1: act of transportation. And so yeah, we we urge anybody

1864
01:51:49,080 --> 01:51:51,360
Speaker 1: if you ever see something suspicious, what in the world

1865
01:51:51,400 --> 01:51:53,280
Speaker 1: are you doing with um it looks like a bunch

1866
01:51:53,320 --> 01:51:55,800
Speaker 1: of wild pigs or wild hogs in the back of

1867
01:51:55,840 --> 01:52:01,280
Speaker 1: somebody strailer called, call a conservation officer or game warden,

1868
01:52:01,720 --> 01:52:03,840
Speaker 1: let them know, let them check these people out, because

1869
01:52:04,120 --> 01:52:07,920
Speaker 1: that's how it happens. Uh, you open a trailer door

1870
01:52:08,040 --> 01:52:10,640
Speaker 1: on a wildlife refuge and and the next thing you know,

1871
01:52:10,720 --> 01:52:13,400
Speaker 1: five years down the road, you got a big problem.

1872
01:52:13,600 --> 01:52:18,439
Speaker 1: So we gotta address the the issue, um, the reason

1873
01:52:18,479 --> 01:52:21,360
Speaker 1: for their expansion. You know that they expand on their

1874
01:52:21,360 --> 01:52:24,800
Speaker 1: own locally, like any wildlife population does. They grow and

1875
01:52:24,840 --> 01:52:28,280
Speaker 1: reproduce and expand. But you know, all of a sudden

1876
01:52:29,080 --> 01:52:33,439
Speaker 1: hog population showing up in Manitoba or showing up in

1877
01:52:33,560 --> 01:52:38,639
Speaker 1: Michigan or Ohio or Indiana, that's from people moving them around. Yeah,

1878
01:52:38,920 --> 01:52:41,080
Speaker 1: definitely got to keep an eye on for that kind

1879
01:52:41,120 --> 01:52:43,759
Speaker 1: of thing. You don't need. There's enough issues and challenges

1880
01:52:43,800 --> 01:52:46,360
Speaker 1: and things popping up naturally not to don't want to

1881
01:52:46,400 --> 01:52:50,320
Speaker 1: deal with anything new added to the to the festival,

1882
01:52:50,479 --> 01:52:55,040
Speaker 1: that's for sure. That's right. So Bronson, we are we

1883
01:52:55,120 --> 01:52:58,439
Speaker 1: are coming up on time here. Um, but I'm curious,

1884
01:52:58,439 --> 01:53:02,479
Speaker 1: do you have any I don't pet issue or passion

1885
01:53:02,520 --> 01:53:05,880
Speaker 1: project or part of your research that we haven't talked

1886
01:53:05,920 --> 01:53:07,600
Speaker 1: about that you really want to make sure we do

1887
01:53:07,720 --> 01:53:12,800
Speaker 1: touch on here in that kind of our final final thoughts? Uh?

1888
01:53:12,840 --> 01:53:16,559
Speaker 1: Sure do? UM. I would just urge people if if

1889
01:53:16,560 --> 01:53:19,240
Speaker 1: they're more interested in the kind of stuff, we do,

1890
01:53:19,320 --> 01:53:23,120
Speaker 1: the research we do um our website, it's M s

1891
01:53:23,200 --> 01:53:26,920
Speaker 1: U Deer Lab dot com. M s U Deer Lab

1892
01:53:26,960 --> 01:53:29,400
Speaker 1: dot com. Go there and check us out, Uh see

1893
01:53:29,400 --> 01:53:32,960
Speaker 1: what we do. And one thing I'm really excited about

1894
01:53:33,040 --> 01:53:37,759
Speaker 1: Mark is we are jumping into the podcast world ourselves.

1895
01:53:37,800 --> 01:53:41,240
Speaker 1: And so yeah, we recorded our first one last week.

1896
01:53:41,280 --> 01:53:43,479
Speaker 1: I'm not a pro like you, so I've I've got

1897
01:53:43,479 --> 01:53:47,840
Speaker 1: a steep learning curve. UM. But it's gonna be the

1898
01:53:48,120 --> 01:53:51,760
Speaker 1: title of it is Dear University or we're gonna go

1899
01:53:51,880 --> 01:53:54,880
Speaker 1: Dear University or Dear you for short. And we'll have

1900
01:53:54,960 --> 01:53:57,640
Speaker 1: all the episodes on our on our website, the M

1901
01:53:57,680 --> 01:54:00,680
Speaker 1: s U. Deer Lab website. But it's it's gonna be

1902
01:54:00,720 --> 01:54:03,680
Speaker 1: all about deer management and dear science. And so what

1903
01:54:03,720 --> 01:54:06,880
Speaker 1: we hope to do with with every episode is um

1904
01:54:07,200 --> 01:54:12,639
Speaker 1: attack something that is would be of interest to both

1905
01:54:12,720 --> 01:54:17,880
Speaker 1: hunters and to wildlife managers. So what's the hot topic

1906
01:54:17,920 --> 01:54:20,360
Speaker 1: with disease? So we'll spend an issue talking about the

1907
01:54:20,840 --> 01:54:23,160
Speaker 1: you know, the physiology of a v HD or blue

1908
01:54:23,200 --> 01:54:26,720
Speaker 1: tongue or chronic wasting disease. We'll talk about antlers, the

1909
01:54:26,760 --> 01:54:30,160
Speaker 1: physiology of antlers and what can you do we'll talk

1910
01:54:30,200 --> 01:54:35,080
Speaker 1: about habitat management, food plot management, but but just topics

1911
01:54:35,120 --> 01:54:38,920
Speaker 1: like that based on research that that we've conducted and

1912
01:54:39,000 --> 01:54:41,600
Speaker 1: we hope to have UM hope to have that up

1913
01:54:41,640 --> 01:54:44,640
Speaker 1: and running in a month or two. So if your

1914
01:54:44,640 --> 01:54:48,720
Speaker 1: listeners don't mind, just checked to our website and um

1915
01:54:48,920 --> 01:54:50,960
Speaker 1: downloaded and give it a listen when we get it

1916
01:54:51,040 --> 01:54:55,000
Speaker 1: up and running. That's awesome. Well, selfishly, I hope I

1917
01:54:55,000 --> 01:54:57,280
Speaker 1: can get you back on this podcast to talk about

1918
01:54:57,280 --> 01:54:58,960
Speaker 1: some of these issues some more too, because I feel

1919
01:54:58,960 --> 01:55:00,720
Speaker 1: like there's so many things we have got to talk about,

1920
01:55:00,720 --> 01:55:02,520
Speaker 1: Like we haven't got to talk about much about habitat

1921
01:55:02,600 --> 01:55:06,000
Speaker 1: or food plots or um gosh, I'm sure there's a

1922
01:55:06,040 --> 01:55:09,120
Speaker 1: disease is another topic that I'd be interested. So we

1923
01:55:09,200 --> 01:55:11,080
Speaker 1: might need to have a part to Bronson, if you're

1924
01:55:11,120 --> 01:55:14,440
Speaker 1: up for it at some point here soon, just just

1925
01:55:14,520 --> 01:55:16,520
Speaker 1: give me a date if I'm available, we'll do it.

1926
01:55:16,800 --> 01:55:20,280
Speaker 1: Be happy to awesome. Well, I'm excited to check out

1927
01:55:20,280 --> 01:55:22,440
Speaker 1: the podcast that you guys are working on when that

1928
01:55:22,480 --> 01:55:26,000
Speaker 1: comes out, and I'll be sure to continue following everything

1929
01:55:26,000 --> 01:55:28,320
Speaker 1: on the website too. I've seen you know, you guys

1930
01:55:28,360 --> 01:55:30,440
Speaker 1: are always sending out different things that are featured in

1931
01:55:30,760 --> 01:55:33,920
Speaker 1: magazines or different places. It seems like you're doing some really,

1932
01:55:34,280 --> 01:55:37,360
Speaker 1: really interesting and helpful work. So so thank you Bronson

1933
01:55:37,400 --> 01:55:39,000
Speaker 1: for the good work you're doing, and thank you for

1934
01:55:39,080 --> 01:55:43,520
Speaker 1: joining us. Thank you very much. I really appreciate the opportunity.

1935
01:55:43,720 --> 01:55:45,720
Speaker 1: It was an honor. Yeah, this is a lot of fun.

1936
01:55:45,720 --> 01:55:48,360
Speaker 1: And and and Dan had to drop off, as he occasionally

1937
01:55:48,360 --> 01:55:51,280
Speaker 1: has to do to attend to family things, but I

1938
01:55:51,280 --> 01:55:52,560
Speaker 1: know he had to. He had a good time too,

1939
01:55:52,600 --> 01:55:58,000
Speaker 1: So thanks Bronson. Yeah, yeah, anytime. Thanks so much. And that,

1940
01:55:58,080 --> 01:56:01,760
Speaker 1: Ladies and Gentlemen, is this week's podcast. I hope you

1941
01:56:01,840 --> 01:56:04,280
Speaker 1: enjoyed that one as much as I did. I found

1942
01:56:04,320 --> 01:56:07,960
Speaker 1: it just fascinating. And I think based on how much

1943
01:56:08,000 --> 01:56:09,320
Speaker 1: I enjoyed this, I think we're gonna have to have

1944
01:56:09,320 --> 01:56:11,440
Speaker 1: Bronson on again, like I said a couple of seconds ago,

1945
01:56:11,880 --> 01:56:14,200
Speaker 1: and and maybe some new bio, just some other faces

1946
01:56:14,240 --> 01:56:18,160
Speaker 1: in this world too, because there's just so there's so

1947
01:56:18,280 --> 01:56:21,520
Speaker 1: much going on here at a deeper level when it

1948
01:56:21,560 --> 01:56:23,920
Speaker 1: comes to what dear do, why they do it, how

1949
01:56:24,000 --> 01:56:26,440
Speaker 1: they do it that I think his deer hunt is

1950
01:56:26,480 --> 01:56:29,040
Speaker 1: just endlessly fascinating. So we're gonna keep on digging into it.

1951
01:56:29,280 --> 01:56:31,040
Speaker 1: And uh and I hope you enjoyed that too, So

1952
01:56:31,480 --> 01:56:33,600
Speaker 1: before we wrap it up, I do want to thank

1953
01:56:33,600 --> 01:56:36,640
Speaker 1: our partners who have helped keep this podcast going. I

1954
01:56:36,640 --> 01:56:38,320
Speaker 1: want to give a big thank you too. Sick of

1955
01:56:38,400 --> 01:56:42,720
Speaker 1: gear Yetie Cooler's Osonics, Redneck Blinds, maybe an optics, white Tail,

1956
01:56:42,720 --> 01:56:46,000
Speaker 1: Institute of North America, Carbon Express and hunt Terra Maps,

1957
01:56:46,400 --> 01:56:50,520
Speaker 1: and of course thank you for listening and thank you

1958
01:56:50,920 --> 01:56:52,640
Speaker 1: for staying while you're to hunt