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Speaker 1: Welcome to the Wired to Hunt podcast, home of the

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Speaker 1: modern whitetail hunter and now your host, Mark Kenyon. Welcome

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Speaker 1: to the Wire to Hunt podcast. I'm your guest host

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Speaker 1: Tony Peterson, and today I'm speaking with wildlife biologist Michael Chamberlain.

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Speaker 1: All Right, folks, welcome to the Wire to Hunt podcast,

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Speaker 1: which is brought to you by First Light. You might

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Speaker 1: notice that this is not the voice of Mr Mark Kenyon.

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Speaker 1: Mark shot me a text last week and said he

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Speaker 1: was off to a Segue Polo championship where his team,

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Speaker 1: the Swinging Rollers, were bound to bring home the hardware.

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Speaker 1: I don't know. Then he used a bunch of fist

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Speaker 1: bumpy mode geez, which is how he ends all of

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Speaker 1: his text So I guess good luck to Mark with

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Speaker 1: a sport. I'm pretty sure he made up anyway. Today

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Speaker 1: I'm speaking with Dr Michael Chamberlain, who is most well

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Speaker 1: known for his wild turkey research. Seriously, this guy knows

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Speaker 1: more about wild turkey behavior than just about anyone, but

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Speaker 1: he's also studied a bunch of other animals, including white tales.

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Speaker 1: Topics throughout this episode range from currency to b a

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Speaker 1: d researched fond mortality from black bears and other predators,

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Speaker 1: as well as research into mature buck behavior and how

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Speaker 1: they are so good at avoiding you and I throughout

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Speaker 1: the fall. This podcast goes all over the place, but

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Speaker 1: it is just rooted in white tail research and so interesting. Michael,

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Speaker 1: thank you so much for coming on the podcast. Not

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Speaker 1: a problem. I'm glad. I'm glad to be here. You

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Speaker 1: have you been on the Meat Eater podcast. You've been

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Speaker 1: on a whole bunch, and you are You're and I

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Speaker 1: don't think this is crazy to say, but you're most

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Speaker 1: all known for blowing people's minds. As far as turkey research, Yeah,

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Speaker 1: i'd say that's that's probably accurate. Yeah, I'll do a

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Speaker 1: lot of work with turkeys and have been my entar career.

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Speaker 1: I would say if anybody thinks they know a lot

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Speaker 1: about turkeys, they should just find some of the stuff

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Speaker 1: that you've put out and listened to it because it's fascinating.

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Speaker 1: And I know this is a dear podcast, but I

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Speaker 1: want to ask you one thing. This came up um

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Speaker 1: earlier in the spring. I was I was out with

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Speaker 1: one of my ten year old daughters and we were

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Speaker 1: scouting birds and we were just sitting kind of glassing

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Speaker 1: waiting to photograph a little bit. And we where I live,

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Speaker 1: the Mississippi River is only about ten miles away, and

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Speaker 1: the Rum River and a couple of other rivers are

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Speaker 1: really close. So we have a lot of bald eagles,

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Speaker 1: and those bald eagles will fly over those birds, you know,

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Speaker 1: the turkeys out in the field. The turkey's go nuts

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Speaker 1: and they start making a bunch of noise and run

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Speaker 1: for cover. And my daughter asked me, She's like, they must,

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Speaker 1: you know, eagles must eat turkeys once in a while.

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Speaker 1: And I'm like, I have no idea, honey, but I

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Speaker 1: bet I know the guy who's gonna tell me they do. Yeah,

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Speaker 1: and they all, I mean, they'll attack and harass turkeys

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Speaker 1: quite a bit. Um. It's hard to it's hard to

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Speaker 1: quantify predation by eagles, um because most of the a

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Speaker 1: lot of the sites I work on, we don't we

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Speaker 1: don't have bald eagles, and if we do, it's they're

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Speaker 1: they're not common, right, so Um, But they do absolutely

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Speaker 1: occasionally prey on birds, and they certainly they certainly harass birds.

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Speaker 1: I've seen them harass birds several times. Yeah, I mean

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Speaker 1: that's kind of I gave her more of a weasel

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Speaker 1: word answer because I wasn't sure. But we did see

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Speaker 1: an eagle up at the lake one time, grab a

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Speaker 1: grab a goose, which was traumatizing for some of the

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Speaker 1: kids because it was it was pretty rough. But I

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Speaker 1: told her, I was like, you know, if they get

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Speaker 1: the chance, they're gonna take us wipe. Yeah, you know, yeah,

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Speaker 1: the one, the one eagle we know is pretty efficient

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Speaker 1: at killing turkey's as golden eagles. Yeah, they they'll they'll

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Speaker 1: crush a bird and they're so big and they're so fast,

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Speaker 1: and they the way they hunt and you know, the

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Speaker 1: height that they come down from, you know, turkeys don't

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Speaker 1: I mean, they don't stand a chance really. Um by

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Speaker 1: the time the eagles there and the bird, you know,

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Speaker 1: bird realizes, oh you know, they're going you know, over

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Speaker 1: a hundred miles an hour, so it's, uh, it's lights

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Speaker 1: out for the bird. So they'll do a dive like

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Speaker 1: a falcon. They'll get way high and then come down

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Speaker 1: just smoking some Yeah. Sometimes that their flight speed is ridiculous. Um,

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Speaker 1: So from a turkey's perspective, you know what I mean,

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Speaker 1: you know, those turkeys they use their vision to be

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Speaker 1: able to avoid predation. That's the primary line of defense.

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Speaker 1: And if you've got an animal that's above you, you know,

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Speaker 1: that's different than if it's on the same playing field

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Speaker 1: as you. You know, turkeys have that periscope head. And

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Speaker 1: if what I've noticed in areas where turkeys are dealing

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Speaker 1: with a lot of aerial predation or just harassment, they

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Speaker 1: constantly look up. You'll see them, you know, constantly glancing up.

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Speaker 1: Whereas here, like in the southeast where I live, in

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Speaker 1: the in the forested areas, you don't see that a lot.

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Speaker 1: You don't see birds that are constantly glancing up in

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Speaker 1: the air because they're not used to seeing danger from

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Speaker 1: from above. But like I've I've watched Miriam's a number

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Speaker 1: of times out west that they almost constantly glance up

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Speaker 1: in the sky um at times, which to me just

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Speaker 1: says there's a concern, you know, for the bird. They

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Speaker 1: know danger it could be above and not just on

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Speaker 1: the ground. You know. I wonder, I know we're way

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Speaker 1: off topic here, but I wonder if there's a similar

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Speaker 1: parallel between trout that live in you know, maybe maybe

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Speaker 1: a river that meanders through open ground versus you know,

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Speaker 1: maybe a smaller stream in wooded territory if they're more

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Speaker 1: spooky out in the you know, more open where there's

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Speaker 1: a better chance for aerial predation. Yeah, I mean maybe,

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Speaker 1: I mean turkeys are you know, what we've seen is

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Speaker 1: from a predation standpoint, they you know, they they have

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Speaker 1: a suite of things that harass and attack and you know,

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Speaker 1: so they I mean, that's that's one of the reasons

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Speaker 1: turkeys are so weary, is they there's so many things

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Speaker 1: that want to eat them, including us, that they they're

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Speaker 1: hyper vigilant. You know, they're constantly vigilant of the of

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Speaker 1: their surroundings. And um, that's why, I mean, predation is

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Speaker 1: structured the bird that we love, the chase, and the

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Speaker 1: reason they're so fond to hunt in many ways, it's

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Speaker 1: because of the predators that they've interacted with for you know,

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Speaker 1: for eons. Yeah, all right, let's let's I could talk

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Speaker 1: to you for about turkeys for a long time, but

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Speaker 1: we should talk about dear Can you can you just

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Speaker 1: give the listeners, uh, you know, kind of a summary

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Speaker 1: of what where you came from and what you're doing

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Speaker 1: for research right now? Yeah, So I grew up in Virginia.

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Speaker 1: I was a I was a suburban kid got to

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Speaker 1: hunt on the weekends with my dad. Add Um. I

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Speaker 1: went to college at Virginia Tech major and wilife science,

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Speaker 1: and then I ended up getting an opportunity to go

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Speaker 1: to graduate school at Mississippi State. I did my master's

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Speaker 1: research on on wild turkeys there and um volunteered with

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Speaker 1: some with some other students and was able to get

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Speaker 1: involved in dear research actually a little bit. And then

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Speaker 1: I went to UH. I stayed there and did my

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Speaker 1: PhD degree, which also involved turkeys, but also uh dealt

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Speaker 1: with predators. I actually radio mark turkey's at the same

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Speaker 1: time as bobcats, coyotes, foxes, raccoons, and I tracked them

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Speaker 1: all simultaneously to see how they were interacting with each other.

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Speaker 1: And then when I graduated with my doctorate, I was

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Speaker 1: hired at l s U Louisiana State University, and when

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Speaker 1: I went there, I started studying literally everything uh UM.

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Speaker 1: I was fortunate to be supported by agencies that putting

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Speaker 1: me under, you know, on contracts to do research, so

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Speaker 1: I literally I studied. I started studying deer, I continue

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Speaker 1: to study turkeys. I did wolf research, kayak research, raccoon research.

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Speaker 1: I mean just a bunch of different things, and um,

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Speaker 1: but as I've as I've gone through my career, I've

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Speaker 1: been fortunate enough to be able to kind of specialize

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Speaker 1: now and the work I have now is only basically

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Speaker 1: turkey work, except for one large deer project that we

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Speaker 1: we have in Arkansas right now. UM, looking at at

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Speaker 1: c w D and some of the impacts of that

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Speaker 1: disease on that on that population. So so yeah, he's

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Speaker 1: that ongoing right now. Yeah, we're in our second field season.

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Speaker 1: What's the goal of that study. We're actually we're looking

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Speaker 1: at um differences in behavior of CWD positive versus presume

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Speaker 1: negative animals. We're looking to see what in fluences the

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Speaker 1: disease may have on fond survival. We're looking at how

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Speaker 1: these animals are interacting with each other, because we have

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Speaker 1: positive animals that were that are we're GPS monitoring, and

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Speaker 1: we have animals that are negative when we catch them

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Speaker 1: the first time and then at some point they become positive.

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Speaker 1: So we're tracking this to to see how these animals

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Speaker 1: change their behavior so that we can inform the agency

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Speaker 1: as to what the future looks like for you know,

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Speaker 1: how is this disease spread? We know it, we know

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Speaker 1: in a general sense, how it's spread, but what does

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Speaker 1: that look like from a movement perspective. We're estimating deer

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Speaker 1: density in the in the area, trying to tell the

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Speaker 1: agency how many deer out there, uh, And then ultimately

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Speaker 1: what we're going to do is is model what this

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Speaker 1: population is going to look like five years, twenty years,

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Speaker 1: fifty years down the road, so that the agency can

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Speaker 1: understand what the fields going to look like as this

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Speaker 1: disease progresses. Because you know, we all know, you know,

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Speaker 1: in areas where it's prevalent, and it's very prevalent in

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Speaker 1: northwest Arkansas, these the cw D zones tend to get

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Speaker 1: bigger and bigger and bigger. Not in all cases, but

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Speaker 1: in many cases they do, and the agencies have to

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Speaker 1: continue to expand the zones and the and we want

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Speaker 1: to be able to inform the agency what that's going

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Speaker 1: to look like in the future. And so you're just building,

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Speaker 1: I mean, and you're you're taking something that wasn't probably

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Speaker 1: possible that long ago. You're monitoring healthy deer and infected

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Speaker 1: deer and watching how they interact, how many points of

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Speaker 1: contact they have, the density, and just to see like

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Speaker 1: what does this tell us about the future of what

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Speaker 1: this is going to turn into. Yeah, exactly, did I

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Speaker 1: scare you? Um? In some ways? Yes? And I you know,

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Speaker 1: as a deer hunter first and foremost, you know, I've

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Speaker 1: seen what's happened two deer populations and in areas where

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Speaker 1: with a high prevalence. One only need to go to

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Speaker 1: where we're trapping to see the area of the zoom

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Speaker 1: that we're trapping in that has the highest prevalence. Deer

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Speaker 1: density is very very low, um, and there's there's obvious

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Speaker 1: reason for that. This is a you know, a fatal

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Speaker 1: disease every time. So as a deer hunter, that really

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Speaker 1: i'd say it it very much concerns me. You know,

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Speaker 1: I hunt deer and in a number of states, but

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Speaker 1: my you know, the two states that I hunt the

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Speaker 1: most in would be Georgia here where I live in Louisiana,

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Speaker 1: where I've been in a camp for for many many years.

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Speaker 1: And c w D has not been detected yet in Georgia,

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Speaker 1: but it was recently detected in Louisiana, and that changes everything,

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Speaker 1: as you know, when that disease is encountered, Uh, everything

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Speaker 1: changes from that forward, you know, that point forward, um,

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Speaker 1: when you when you talk about there being a lower

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Speaker 1: density in those the high c w D areas. Let's

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Speaker 1: I want to talk about that a little bit because

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Speaker 1: I think it's like very easy for for the general

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Speaker 1: hunting population to kind of be dismissive of CTWD because

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Speaker 1: you go, Okay, well it's not gonna kill it till

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Speaker 1: seven and a half years old or six and a

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Speaker 1: half years old or whatever. And you know, most of

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Speaker 1: the deer we're hunting are way younger than that. But

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Speaker 1: there has to be you know, not only the like

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Speaker 1: you said, it's fatal, but are you seeing you know,

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Speaker 1: CWD infected deer when they hit a certain age, Uh,

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Speaker 1: you know, the fecundity of the of the entire herd

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Speaker 1: drops a little bit. Like I mean, are they producing

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Speaker 1: as many fonds as healthy deer? Probably not right. That's well,

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Speaker 1: that's one thing we're trying to learn. We're trying to

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Speaker 1: figure out what this research, what that looks like. Um.

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Speaker 1: And you know, there's obviously from a logical perspective, you

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Speaker 1: would think, yes that there there would be some impacts.

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Speaker 1: But but that's one thing we're trying to quantify for sure. Yeah,

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Speaker 1: so you you assume that, but you're you're waiting for

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Speaker 1: the data to show you that it's true. Yeah, never

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Speaker 1: assume anything. I can assume it. Um, I've learned my

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Speaker 1: lesson with assumptions. They're fraught with with getting your butt

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Speaker 1: in a sling. So you know, I wait until I

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Speaker 1: have silent information in hand for sure. How many years

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Speaker 1: are you doing this study before you will be able

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Speaker 1: to sort of model out, you know, into the distant future.

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Speaker 1: And we have four field seasons plan, so we're in

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Speaker 1: our second field season now, so we'll trap two more

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Speaker 1: years and then you know, there's a there's a PhD

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Speaker 1: on student on the project now, he'll finish up two

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Speaker 1: years from now, and a post doctoral researcher will come

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Speaker 1: on and kind of wrap the study up and we'll

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Speaker 1: start disseminating information, you know, probably actually next year because

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Speaker 1: the student will have some information that we can start publishing.

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Speaker 1: And uh, I'm really interested in what work. I mean,

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Speaker 1: I've I've kind of get bits of what we're seeing,

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Speaker 1: you know, Um, but it will be really interesting once

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Speaker 1: we have the data crunchton we can explain it to people.

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Speaker 1: Is this the most Uh, I don't know if comprehensive

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Speaker 1: is the right word. Is it the most advanced study

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Speaker 1: on CDWD in that in that capacity that's going on

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Speaker 1: right now in the South. In the South, is somebody

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Speaker 1: up north doing it. There's been you know, there's been

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Speaker 1: ongoing CWD research in other areas for years. Um, it's

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Speaker 1: just not you know, in the southern US, there just

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Speaker 1: hasn't been you know, until very recently. If you just

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Speaker 1: look the number of states that have reported detections in

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Speaker 1: the South within the past few years, it's it's pretty dramatic.

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Speaker 1: So so I think there are some agencies that are

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Speaker 1: looking at this study with a keen eye because we

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Speaker 1: we will not only provide Arkansas Game and Fish with

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Speaker 1: information it's relevant obviously to them, but other states will

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Speaker 1: be able to benefit from the same information, particularly states

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Speaker 1: that are have recently detected CWD and they're trying to

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Speaker 1: understand what what is it that's going to look like,

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Speaker 1: what's it going to look like five years from now,

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Speaker 1: ten years from now. And you know, a couple of

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Speaker 1: the states that have have recently documented detections are also

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Speaker 1: seeing pretty high prevalence rates, you know, like you see

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Speaker 1: in northwest Arkansas. So I think there are a number

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Speaker 1: of states that are looking at it's the study and

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Speaker 1: and hoping that it's going to provide relevant information to them,

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Speaker 1: and that's that's ultimately our goal is to not just

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Speaker 1: benefit the agency that's funding the work, but also other

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Speaker 1: agencies that are dealing with the same problem. Yeah, well this, this,

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Speaker 1: this work that's going on there in Arkansas, is this

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Speaker 1: going to be a way to kind of Uh, there's

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Speaker 1: an argument made often amongst hunters. It's like CWT has

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Speaker 1: always been here, it's always been on the landscape, and

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Speaker 1: we're only just finding it now because we're testing for it.

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Speaker 1: Will this kind of be the nail in the coffin

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Speaker 1: to that argument? Uh? No, No, because, like you and

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Speaker 1: I talked about before we got on air, Uh, you'll

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Speaker 1: never put a nail in any coffin um in our society,

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Speaker 1: I don't think at this point, because there are naysayers

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Speaker 1: regardless of what topic you come up with. Even when

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Speaker 1: science provides answers, there's always the argument that, well, what if,

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Speaker 1: what if? This? What if? That? In science is not

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Speaker 1: absolute and we change things change I mean from a

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Speaker 1: scientific perspective. You know, I've published work myself that discounted

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Speaker 1: work I did previously. That's I mean that's just part

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Speaker 1: of being a scientist. And so I don't I don't

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Speaker 1: ever look at what I do is and think that's

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Speaker 1: that's it. That's we're shutting the door on that because

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Speaker 1: it's just not reality dealing with people and in dealing

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Speaker 1: with natural systems where there's a lot of there's a

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Speaker 1: lot of chaos. You know, we work in outside and

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Speaker 1: there's a lot of things going on and and the

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Speaker 1: you know, in the environment, that creates noise in our data.

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Speaker 1: So we're constantly learning new things or determining that we

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Speaker 1: didn't know what we thought we knew, and that creates skepticism,

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Speaker 1: as you know, with with people in the public, despite

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Speaker 1: the fact that you know, from the start, we're simply

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Speaker 1: trying to provide information under the recognition that sometimes that

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Speaker 1: information contradicts other pieces of information. That's the way science works. Yeah,

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Speaker 1: there's a the problem with the system is science is

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Speaker 1: you know, science in and of itself is to prove

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Speaker 1: things wrong, and it's going to prove previous science wrong.

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Speaker 1: And that gives that gives people sort of an out

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Speaker 1: for taking it seriously because they can look at and goal, you,

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Speaker 1: you did this wrong. For it's like we're just leveling

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Speaker 1: up information a little bit at a time and learning

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Speaker 1: more and more stuff. And it's you know, it is

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Speaker 1: really inexact, but it's it's so interesting to see, you know,

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Speaker 1: when I I'm pretty fortunate to get to talk to

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Speaker 1: people like you quite often who work in you know,

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Speaker 1: dear science and deer research and wildlife biology, and when

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Speaker 1: c w D comes up, there's always a different feel

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Speaker 1: to the conversation with somebody who's studying it versus somebody

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Speaker 1: you know who who is whatever they are in their

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Speaker 1: life and just likes to deer hunt. There's a big

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Speaker 1: gap there, and I don't know, you know, I just

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Speaker 1: look at this issue and I I'm like, man, we

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Speaker 1: better learn as much as we can about it. Yeah,

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Speaker 1: because that's important. It's a polarizing, contentious topic. And I

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Speaker 1: think part of that goes back to the way that

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Speaker 1: agencies typically react to c w D. You know, there

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Speaker 1: have been somebody and sees that liberalized deer harvest. There

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Speaker 1: have been, um, you know, the way that this disease

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Speaker 1: is handled creates in many ways contention, you know, And

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Speaker 1: and I get it. I understand as a deer hunter,

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Speaker 1: I understand, like we were talking about when I when

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Speaker 1: I got a text saying, you know, c w D

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Speaker 1: has been detected in this parish in Louisiana, I thought, oh, damn,

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Speaker 1: you know what I mean, my heart sink. I was like, well,

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Speaker 1: that's it. You know that everything changes today. Um. And

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Speaker 1: I think a lot of hunters feel like like that,

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Speaker 1: and they they're like, oh, you know, the the sky

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Speaker 1: is falling in it. And I get that because it's

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Speaker 1: hard not to think like that given how serious this

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Speaker 1: disease is. Um. If you don't believe that, you just

335
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Speaker 1: have to understand that deer don't survive this disease. I mean,

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Speaker 1: it's it's like Alzheimer's and you know in humans. I

337
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Speaker 1: mean I watched my father deal with Alzheimer's for years

338
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Speaker 1: and it's just unbelievable. So you have this disease that's

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Speaker 1: out there that they do not survive. Period. Uh. Anyone

340
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Speaker 1: that can look at that and not think that's problematic

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Speaker 1: to me, that's that's a that's an odd point of view. Frankly, Well,

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Speaker 1: I mean I think part of the part of that

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Speaker 1: issue is, you know, a lot of the current hunters

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Speaker 1: have only known really pretty good times with white tails,

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Speaker 1: you know, I mean, like We've been just really really lucky,

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Speaker 1: you know. I mean, I'm I'm forty one, and you know,

347
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Speaker 1: I can remember starting hunting when I was twelve in

348
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Speaker 1: Minnesota and there weren't as many deer then, but we

349
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Speaker 1: could still go out and see deer and just you know,

350
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Speaker 1: we it wasn't very long before we had a lot

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Speaker 1: of deer and we could shoot you know, four doors

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Speaker 1: in a season, and it was, you know, kind of

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Speaker 1: unprecedented times. And that's happened in a lot of places,

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Speaker 1: you know, I mean bigwood stuff, and you know, our

355
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Speaker 1: North different story. Uh, but so many, so many people

356
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Speaker 1: out there who are into this don't really know any

357
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Speaker 1: different than those deer are always gonna be there and

358
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Speaker 1: we're always going to have him to hunt, right Yeah.

359
00:21:13,080 --> 00:21:15,719
Speaker 1: I mean, I'm fifty and I remember talking to my

360
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Speaker 1: dad when he was in his sixties and we broth.

361
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Speaker 1: We both grew up in Virginia, and I can remember

362
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Speaker 1: him telling me that it was front page news when

363
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Speaker 1: he was a kid to see a deer track. And

364
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Speaker 1: you know, I was born in ninety one and I

365
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Speaker 1: remember as a as a child how special it was

366
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Speaker 1: to see a deer if you went to hunt. I mean,

367
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Speaker 1: I can remember sitting for days and not seeing an animal,

368
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Speaker 1: and much less killing one. I also remember many many

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Speaker 1: seasons where if I killed one deer, I was that

370
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Speaker 1: was it. I was tickled. And now you know, on

371
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Speaker 1: most weekends I averaged one. I mean, it's between shooting

372
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Speaker 1: dose and killing bucks. It's I mean, shooting a deer

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Speaker 1: is It's just commonplace now. So I've I've seen it

374
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Speaker 1: from both perspectives, and the thought of going back to

375
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Speaker 1: what I grew up doing, which was not seeing dear.

376
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Speaker 1: I don't know how I continued to be a deer hunter, frankly,

377
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Speaker 1: because it was hard to get through a season and

378
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Speaker 1: and I can remember several years killing a deer on

379
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Speaker 1: the last day of a long deer season and just

380
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Speaker 1: being relieved, almost relieved. You know that I did it,

381
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Speaker 1: You know I did it. I finally got one, and

382
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Speaker 1: I hunted a lot. It's not like I went three

383
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Speaker 1: or four times, you know, days and days and days.

384
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Speaker 1: And I think back to if I were trying to

385
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Speaker 1: take a kid and recruit that person into the hunting

386
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Speaker 1: ranks and make them as passionate as I am about

387
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Speaker 1: deer hunting, and they didn't see dear. It's a tough sell. Yep,

388
00:23:01,440 --> 00:23:04,480
Speaker 1: that's a that's a tough sell. So so yeah, I

389
00:23:04,840 --> 00:23:07,600
Speaker 1: see I see it through both lenses. Yeah, I mean

390
00:23:07,640 --> 00:23:12,200
Speaker 1: that that not just the promise or the optimism that

391
00:23:12,240 --> 00:23:16,080
Speaker 1: you'll see, dear is worth so much to a hunter

392
00:23:16,560 --> 00:23:20,040
Speaker 1: and going out, you know, I go hunt, uh the

393
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Speaker 1: big woods in Wisconsin quite a bit. In northern Wisconsin.

394
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Speaker 1: The deer population is low and the predator population is high,

395
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Speaker 1: and it's you know, the same story in a lot

396
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Speaker 1: of places. And you know, it's I might go a

397
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Speaker 1: week without seeing a deer or two weeks without seeing

398
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Speaker 1: a deer, or sometimes I'll go over there and I've

399
00:23:36,840 --> 00:23:38,760
Speaker 1: got a dough tag and I'm like, I'm gonna, you know,

400
00:23:38,840 --> 00:23:40,760
Speaker 1: do in a buck tag. I'll be like, the next

401
00:23:40,800 --> 00:23:42,520
Speaker 1: one that comes down the trail, I'm gonna shoot. And

402
00:23:42,560 --> 00:23:45,920
Speaker 1: it might take me weeks to make that happen. And

403
00:23:46,240 --> 00:23:49,600
Speaker 1: I look at that and I go, Man, if you know,

404
00:23:50,080 --> 00:23:51,879
Speaker 1: there's an awful lot of people who don't have the

405
00:23:51,960 --> 00:23:54,280
Speaker 1: time and the you know, the abilities that I do

406
00:23:54,359 --> 00:23:57,040
Speaker 1: to get out there like that, that would go no way,

407
00:23:57,040 --> 00:24:00,560
Speaker 1: this is so not worth it, because it's just it

408
00:24:00,720 --> 00:24:02,199
Speaker 1: is not that much fun when you go out there

409
00:24:02,240 --> 00:24:05,119
Speaker 1: and you're like, I'm probably not going to see anything today.

410
00:24:05,400 --> 00:24:07,840
Speaker 1: I'm certainly probably not gonna have anything come within range.

411
00:24:08,240 --> 00:24:11,439
Speaker 1: It's a tough thing. Yeah, alas sure is. You know,

412
00:24:12,800 --> 00:24:15,879
Speaker 1: at this point in my hunting career, you know, the

413
00:24:15,920 --> 00:24:19,439
Speaker 1: harvest is is one thing. It's much less important than

414
00:24:19,480 --> 00:24:23,080
Speaker 1: it used to be when I was young. A perfect

415
00:24:23,160 --> 00:24:25,400
Speaker 1: hunt for me, honestly, and this is this is no

416
00:24:25,720 --> 00:24:29,040
Speaker 1: this is no joke. I've said this before. Getting in

417
00:24:29,080 --> 00:24:32,639
Speaker 1: a stand and just being snowed in with deer all afternoon,

418
00:24:33,160 --> 00:24:35,520
Speaker 1: you know, looking over a green field or something and

419
00:24:35,560 --> 00:24:40,120
Speaker 1: just having deer act like deer um and being able

420
00:24:40,160 --> 00:24:42,359
Speaker 1: to get out of the stand in the dark, not

421
00:24:42,520 --> 00:24:45,879
Speaker 1: have to clean a deer, go and have a beer

422
00:24:45,920 --> 00:24:49,320
Speaker 1: and turn the grill on. That's a perfect afternoon of hunting,

423
00:24:49,840 --> 00:24:52,240
Speaker 1: seeing lots of animals and just being able to sit

424
00:24:52,280 --> 00:24:57,320
Speaker 1: there and relax and watch them beat themselves. That's that's

425
00:24:57,359 --> 00:25:00,560
Speaker 1: awesome for me. And I just from remember back when

426
00:25:00,600 --> 00:25:04,520
Speaker 1: I was a kid, I never had that possibility and

427
00:25:04,520 --> 00:25:06,639
Speaker 1: and like I said, it's a wonder that I continued

428
00:25:07,440 --> 00:25:11,040
Speaker 1: to to deer hunt because it was tough, you know,

429
00:25:11,080 --> 00:25:14,600
Speaker 1: and when my when my kids were growing up, you know,

430
00:25:14,680 --> 00:25:17,840
Speaker 1: they were fortunate to have access to two really good properties.

431
00:25:17,840 --> 00:25:20,000
Speaker 1: We're seeing deer. It was just I mean you they

432
00:25:20,000 --> 00:25:22,840
Speaker 1: were going to see deer every single hunt, I mean period,

433
00:25:23,320 --> 00:25:26,159
Speaker 1: and sometimes they'd see a lot of deer. And you know,

434
00:25:26,200 --> 00:25:29,639
Speaker 1: it's it's a lot easier to get a kid hooked

435
00:25:29,680 --> 00:25:34,320
Speaker 1: on the you know, the experience and and let their

436
00:25:34,359 --> 00:25:37,359
Speaker 1: passion flourish if they know they're going to be successful.

437
00:25:38,320 --> 00:25:41,440
Speaker 1: Um And talking about c w D. You know, for instance,

438
00:25:41,600 --> 00:25:43,439
Speaker 1: some of these some of these folks that are hunting

439
00:25:43,440 --> 00:25:47,280
Speaker 1: and in our study area in northwest Arkansas, they they're

440
00:25:47,280 --> 00:25:51,560
Speaker 1: ready to quit. They aren't seeing dear and then when

441
00:25:51,560 --> 00:25:55,120
Speaker 1: they do see deer and they harvest bucks, they're all positive.

442
00:25:55,480 --> 00:25:59,200
Speaker 1: They all they all test positive. And you know that's

443
00:25:59,200 --> 00:26:02,800
Speaker 1: really frustrating. One you you're you're trying to see animals,

444
00:26:02,880 --> 00:26:05,240
Speaker 1: you finally see animals, you kill an animal, and then

445
00:26:06,080 --> 00:26:10,640
Speaker 1: you can't consume it. Um. You know, there have been

446
00:26:10,640 --> 00:26:13,320
Speaker 1: a number of hunters expressed, have expressed to us that

447
00:26:14,200 --> 00:26:16,439
Speaker 1: they're just ready to put their hands up. Uh, and

448
00:26:16,480 --> 00:26:33,600
Speaker 1: I understand that very frustrating. Do you think, I mean,

449
00:26:33,840 --> 00:26:35,280
Speaker 1: I know this is I know, you don't want to

450
00:26:35,400 --> 00:26:38,439
Speaker 1: make assumptions but I'm gonna ask you to anyway. Is

451
00:26:38,520 --> 00:26:40,399
Speaker 1: there is there a way out of the c w

452
00:26:40,560 --> 00:26:43,120
Speaker 1: D thing? Is there is there any way you see

453
00:26:43,160 --> 00:26:45,800
Speaker 1: that this works out, where we figure something out that

454
00:26:46,080 --> 00:26:48,440
Speaker 1: we can kind of end this thing or or lock

455
00:26:48,520 --> 00:26:53,720
Speaker 1: it up a little bit. Um, You're probably asking the

456
00:26:53,760 --> 00:26:59,440
Speaker 1: wrong person. I'm an eternal pessimist, UM, and I don't

457
00:26:59,480 --> 00:27:04,160
Speaker 1: I don't say that lightly. UM. I tend to be very,

458
00:27:04,280 --> 00:27:08,320
Speaker 1: very skeptical of of everything in life these days. And

459
00:27:09,680 --> 00:27:16,280
Speaker 1: this is just a an impossible situation. It really is. UM.

460
00:27:16,320 --> 00:27:19,040
Speaker 1: In areas where the prevalence has gotten as high as

461
00:27:19,080 --> 00:27:24,560
Speaker 1: it is in some areas, it is a tough, tough situation.

462
00:27:25,680 --> 00:27:28,040
Speaker 1: I don't think there's going to be a light bulb

463
00:27:28,119 --> 00:27:31,560
Speaker 1: that turns on. No. I think this is something that

464
00:27:31,600 --> 00:27:34,880
Speaker 1: we're going to have to deal with. I'll be long

465
00:27:34,920 --> 00:27:38,440
Speaker 1: gone and we'll still be dealing with it. Agencies will

466
00:27:38,480 --> 00:27:40,600
Speaker 1: be dealing with it, Hunters will be dealing with it.

467
00:27:41,480 --> 00:27:44,600
Speaker 1: I hope that it is less impactful than I think

468
00:27:44,640 --> 00:27:49,000
Speaker 1: it's going to be. But the pessimist in me UM

469
00:27:49,960 --> 00:27:53,280
Speaker 1: is concerned about what my passion is going to look

470
00:27:53,320 --> 00:28:00,119
Speaker 1: like ten years from now, twenty years yikes. I But

471
00:28:00,160 --> 00:28:03,960
Speaker 1: I get it, man. Uh, let's let's switch topics here

472
00:28:04,000 --> 00:28:07,080
Speaker 1: to something less less heavy. It's morbid, yea, yeah, maybe

473
00:28:07,080 --> 00:28:09,920
Speaker 1: a little more hopeful. Yeah, let's do something that's that's

474
00:28:09,960 --> 00:28:14,919
Speaker 1: more lighthearted. Your research, your dear research, it seems to

475
00:28:15,160 --> 00:28:18,760
Speaker 1: almost all take place, or probably has all taken place, uh,

476
00:28:18,800 --> 00:28:22,160
Speaker 1: in the South and Southeast primarily. Is that just because

477
00:28:22,160 --> 00:28:24,040
Speaker 1: that's where you live and you grew up and you're like, well,

478
00:28:24,119 --> 00:28:27,000
Speaker 1: that's where I want to be, or did you recognize

479
00:28:27,240 --> 00:28:29,119
Speaker 1: a hole in the research game somewhere and go, you know,

480
00:28:29,160 --> 00:28:31,639
Speaker 1: those northern deer have been studied enough, let's figure out

481
00:28:31,720 --> 00:28:35,720
Speaker 1: the southern deer. No, it's just opportunity. I've just had

482
00:28:35,720 --> 00:28:40,280
Speaker 1: the opportunities where I've been housed the work in Arkansas,

483
00:28:41,320 --> 00:28:44,280
Speaker 1: you know, that's the That's the only dear study that

484
00:28:44,360 --> 00:28:47,240
Speaker 1: I've done that I've been involved with that was outside

485
00:28:47,240 --> 00:28:50,560
Speaker 1: of of my home state. I mean, I did the

486
00:28:50,600 --> 00:28:53,320
Speaker 1: work that I sent you that we did in Louisiana.

487
00:28:54,040 --> 00:28:57,320
Speaker 1: I was technically here in Georgia, but you know, I've

488
00:28:57,480 --> 00:28:59,480
Speaker 1: I've spent a lot of time in Louisiana. I'm very

489
00:28:59,480 --> 00:29:02,680
Speaker 1: familiar with with that state and the deer herds that

490
00:29:02,720 --> 00:29:06,440
Speaker 1: are there. So no, it's just been a thing of opportunity. Yeah,

491
00:29:06,560 --> 00:29:10,280
Speaker 1: just just it is what it is. Uh, let's can

492
00:29:10,320 --> 00:29:13,680
Speaker 1: we talk about the fawns and the bears in Louisiana. Yeah,

493
00:29:13,760 --> 00:29:18,160
Speaker 1: because you know, I hunt two places or two states

494
00:29:18,200 --> 00:29:20,520
Speaker 1: with you know, Minnesota where I hunt, has a decent

495
00:29:20,600 --> 00:29:23,200
Speaker 1: bear population in some of the spots, but Wisconsin where

496
00:29:23,200 --> 00:29:26,000
Speaker 1: I hunt, has a real high bear population. And I

497
00:29:26,040 --> 00:29:29,040
Speaker 1: know I'm probably gonna totally miss quote this, but the

498
00:29:29,440 --> 00:29:33,400
Speaker 1: d n R there did a fawn mortality study in

499
00:29:33,560 --> 00:29:35,360
Speaker 1: one of the counties that I hunt quite a bit.

500
00:29:35,440 --> 00:29:38,200
Speaker 1: This was probably eight or ten years ago now, but

501
00:29:38,280 --> 00:29:42,880
Speaker 1: I remember looking at it and the the mortality rate

502
00:29:42,960 --> 00:29:47,920
Speaker 1: for fawns was up to eleven months old. So all

503
00:29:47,920 --> 00:29:50,440
Speaker 1: the fonds that hit the landscape, you know, nine out

504
00:29:50,440 --> 00:29:53,320
Speaker 1: of every town of them died before they made it

505
00:29:53,360 --> 00:29:56,640
Speaker 1: past eleven months, so basically a year. And the number

506
00:29:56,680 --> 00:30:00,520
Speaker 1: one predator was black bears. And you know, this is

507
00:30:00,640 --> 00:30:03,520
Speaker 1: this is an area with wolves, bobcats, coyotes. You know,

508
00:30:03,640 --> 00:30:06,360
Speaker 1: we run trail cameras over there, and it's like a

509
00:30:06,440 --> 00:30:09,200
Speaker 1: one to one ratio of deer to predators. I mean

510
00:30:09,360 --> 00:30:11,320
Speaker 1: that you get, you know, and I know that's totally anecdotal,

511
00:30:11,320 --> 00:30:12,680
Speaker 1: But it's just I just say that because it's a

512
00:30:12,760 --> 00:30:16,920
Speaker 1: it's a predator dense region. Uh. And people when you

513
00:30:16,960 --> 00:30:18,880
Speaker 1: tell people that about the bears, they go, no, way,

514
00:30:19,040 --> 00:30:21,520
Speaker 1: like not when you have wolves and coyotes and stuff.

515
00:30:21,520 --> 00:30:24,000
Speaker 1: And I think people underestimate how good bears are at

516
00:30:24,160 --> 00:30:28,600
Speaker 1: at at snatching up fawns. Yeah, and that that work

517
00:30:28,680 --> 00:30:30,880
Speaker 1: we did in Louisiana that was a that was a

518
00:30:30,920 --> 00:30:34,520
Speaker 1: bottom lane of hardwood forest. Uh. For people that are

519
00:30:34,520 --> 00:30:37,880
Speaker 1: listening that are not familiar with what that means, it's wet,

520
00:30:39,080 --> 00:30:46,840
Speaker 1: wet soils, hardwood, all hardwoods, mature forest interspersed with agricultural

521
00:30:46,880 --> 00:30:53,280
Speaker 1: fields and regenerating forest areas. UM. And it has superproductive

522
00:30:53,280 --> 00:30:55,880
Speaker 1: deer herds. And in that part of the world, the

523
00:30:55,920 --> 00:30:58,720
Speaker 1: deer herds are very very productive. They produce a lot

524
00:30:58,760 --> 00:31:04,120
Speaker 1: of faons. But in that particular study, UM, bears are

525
00:31:04,280 --> 00:31:08,600
Speaker 1: very very common in that part of Louisiana. UM. We

526
00:31:08,640 --> 00:31:12,680
Speaker 1: actually had three predators at eight fawns in that study.

527
00:31:12,960 --> 00:31:16,120
Speaker 1: We lost fawns to bears. Of course, we lost fawns

528
00:31:16,120 --> 00:31:19,680
Speaker 1: to coyotes, and we lost fawns to bobcats. And by far,

529
00:31:20,320 --> 00:31:24,520
Speaker 1: bears were the most common predator of a fawn. They

530
00:31:24,560 --> 00:31:26,920
Speaker 1: took about half of the fawnds that that died were

531
00:31:27,000 --> 00:31:31,400
Speaker 1: killed by bears um and we what we ended up

532
00:31:31,440 --> 00:31:34,760
Speaker 1: seeing is that most of that predation was very very

533
00:31:34,800 --> 00:31:38,160
Speaker 1: early after the fall and hit the ground. You know,

534
00:31:38,240 --> 00:31:42,200
Speaker 1: at that point, Mom's off somewhere, other fawn is is

535
00:31:42,560 --> 00:31:45,640
Speaker 1: hanging out making decisions on its own. And that's something

536
00:31:45,680 --> 00:31:48,520
Speaker 1: a lot of people don't think through is you know,

537
00:31:48,640 --> 00:31:51,400
Speaker 1: she's not standing there saying don't move, don't move. You know,

538
00:31:51,480 --> 00:31:54,240
Speaker 1: those fallings, they get up and they move around on

539
00:31:54,280 --> 00:31:58,560
Speaker 1: their own. They may not move far. And we saw

540
00:31:58,680 --> 00:32:01,400
Speaker 1: that's when the bears got them, was you know, that

541
00:32:01,480 --> 00:32:05,160
Speaker 1: first handful of days after they were born. And a

542
00:32:05,160 --> 00:32:08,920
Speaker 1: lot of the predation occurred in areas where it looked

543
00:32:08,960 --> 00:32:13,000
Speaker 1: to us like bears were actually foraging on other stuff,

544
00:32:14,280 --> 00:32:19,320
Speaker 1: soft mass that was available, fruits, you know, berries and

545
00:32:19,400 --> 00:32:23,200
Speaker 1: that was really good fawning cover. And those dose were

546
00:32:23,280 --> 00:32:25,400
Speaker 1: you know, those were putting fawns in those areas, and

547
00:32:25,440 --> 00:32:28,680
Speaker 1: then bears were just meandering through there. And you know,

548
00:32:28,720 --> 00:32:33,600
Speaker 1: bears have an exceptional sense of smell and it's not

549
00:32:34,000 --> 00:32:36,400
Speaker 1: difficult for a bear to catch a fawn. I mean,

550
00:32:36,440 --> 00:32:39,479
Speaker 1: they're they're walking along. They you know, people think, well

551
00:32:39,520 --> 00:32:42,440
Speaker 1: they're there's big lumbering kind of you know thing. No,

552
00:32:43,200 --> 00:32:46,280
Speaker 1: when they decide they're going to attack something, they go

553
00:32:46,760 --> 00:32:50,840
Speaker 1: and they were they were a primary predator fawns in

554
00:32:50,840 --> 00:32:53,680
Speaker 1: that study. And um, we actually have seen that in

555
00:32:53,800 --> 00:32:57,160
Speaker 1: other work. There's some ongoing work here in Georgia also

556
00:32:57,200 --> 00:33:00,760
Speaker 1: showing that bears are taking fawns as well. So they're

557
00:33:00,880 --> 00:33:03,480
Speaker 1: I mean, they're an efficient faon predator. So I had

558
00:33:03,520 --> 00:33:08,200
Speaker 1: always assumed that, you know, with the bear's nose and

559
00:33:08,560 --> 00:33:11,320
Speaker 1: I had always just assumed they were they were focusing

560
00:33:11,320 --> 00:33:13,280
Speaker 1: in on fawning grounds and they were coursing back and

561
00:33:13,280 --> 00:33:15,720
Speaker 1: forth until they ran across funds. But you're saying that

562
00:33:15,800 --> 00:33:17,960
Speaker 1: a lot of the faun predation from bears is just

563
00:33:18,000 --> 00:33:20,320
Speaker 1: sort of you know, right place at the right time

564
00:33:20,360 --> 00:33:24,080
Speaker 1: for the bear and just an incidental encounter. No, I

565
00:33:24,120 --> 00:33:28,959
Speaker 1: think there's probably truth to both both lines. That you know,

566
00:33:29,400 --> 00:33:34,760
Speaker 1: fawns are that's a rich price source. I mean, if

567
00:33:34,840 --> 00:33:38,480
Speaker 1: you it's a huge protein pack, and you know, bears

568
00:33:39,160 --> 00:33:42,719
Speaker 1: are in the summer, they're breeding, you know, and females

569
00:33:43,360 --> 00:33:46,200
Speaker 1: that have cubs with them are lactating and there I

570
00:33:46,240 --> 00:33:50,200
Speaker 1: mean they need energy. So I think in our case,

571
00:33:50,720 --> 00:33:53,560
Speaker 1: it was a combination of a lot of the good

572
00:33:53,560 --> 00:33:57,680
Speaker 1: fawning cover happened to be in areas that bears were

573
00:33:57,760 --> 00:34:02,400
Speaker 1: also foraging in, and that just happened to put fawns

574
00:34:02,400 --> 00:34:06,120
Speaker 1: in places where they were very likely to be, you know,

575
00:34:06,200 --> 00:34:08,640
Speaker 1: interacting with a with a bear. Of course they're going

576
00:34:08,680 --> 00:34:12,280
Speaker 1: to lose um our, falln survival rate. And that study

577
00:34:12,320 --> 00:34:17,719
Speaker 1: was about twenty seven so you know, more than almost yeah,

578
00:34:18,040 --> 00:34:23,120
Speaker 1: seventy plus percent mortality, which is pretty consistent frankly with

579
00:34:23,200 --> 00:34:25,240
Speaker 1: what we're seeing and a lot of studies in the South.

580
00:34:25,800 --> 00:34:28,279
Speaker 1: You know, it's it's tough being a fawn for sure,

581
00:34:29,120 --> 00:34:32,200
Speaker 1: in these predator rich communities like what you described were.

582
00:34:32,280 --> 00:34:34,839
Speaker 1: You know up north, you know you have a not

583
00:34:34,960 --> 00:34:38,200
Speaker 1: only do you have a lot of different types of

584
00:34:38,239 --> 00:34:41,280
Speaker 1: things that eat fawns, but you have a high density

585
00:34:41,360 --> 00:34:44,399
Speaker 1: of those animals as well. And that's what we saw

586
00:34:44,400 --> 00:34:47,080
Speaker 1: in Louisiana and that study you reference. We not only

587
00:34:47,080 --> 00:34:50,319
Speaker 1: did we have three primary predators, but the density of

588
00:34:50,320 --> 00:34:55,279
Speaker 1: those predators was high. Yeah. Yeah, and up where where

589
00:34:55,320 --> 00:34:58,000
Speaker 1: I was talking, you also have but once every five years,

590
00:34:58,040 --> 00:35:01,680
Speaker 1: you have a winner that's really really working hard, to

591
00:35:01,719 --> 00:35:04,800
Speaker 1: set them set the overall herd back to and I

592
00:35:04,840 --> 00:35:06,719
Speaker 1: have to imagine that has a pretty big effect on

593
00:35:06,760 --> 00:35:08,719
Speaker 1: it as well, as far as you know, maybe having

594
00:35:08,920 --> 00:35:14,040
Speaker 1: singles versus twins, and you know overall fin production as well. Sure. Yeah,

595
00:35:14,160 --> 00:35:16,879
Speaker 1: And and in our part of the world, at least

596
00:35:16,960 --> 00:35:21,359
Speaker 1: in Louisiana, it's obviously it's not a winner issue. As

597
00:35:21,360 --> 00:35:25,080
Speaker 1: far as snow and cold water is is the biggie

598
00:35:25,400 --> 00:35:28,480
Speaker 1: um are super wet springs where we have a lot

599
00:35:28,520 --> 00:35:33,000
Speaker 1: of flooding, and that's been very common the past decade,

600
00:35:33,680 --> 00:35:37,319
Speaker 1: um much more common than it has been historically, and

601
00:35:37,440 --> 00:35:40,839
Speaker 1: those deer herds. Flooding during the spring and summer can

602
00:35:41,000 --> 00:35:45,720
Speaker 1: can really negatively impact falling production UM through a variety

603
00:35:45,719 --> 00:35:49,719
Speaker 1: of mechanisms, ranging from just condition of the dose too.

604
00:35:50,320 --> 00:35:54,759
Speaker 1: You know, those fawns being dropped in areas where either

605
00:35:55,000 --> 00:36:00,719
Speaker 1: there's very little fawning cover and everything's concentrated, separdation rates increase,

606
00:36:01,040 --> 00:36:03,319
Speaker 1: so it's it's not it's not really winter in the

607
00:36:03,760 --> 00:36:05,680
Speaker 1: in that part of the world, it's actually spring and

608
00:36:05,680 --> 00:36:08,840
Speaker 1: summer that can be really problematic for falls. I was

609
00:36:08,920 --> 00:36:10,640
Speaker 1: I was wondering about that when you were talking about

610
00:36:10,680 --> 00:36:14,840
Speaker 1: the water. Is there you know, I had a I

611
00:36:14,880 --> 00:36:16,680
Speaker 1: had a lesson in that when I hunted. I hunted

612
00:36:16,680 --> 00:36:19,080
Speaker 1: white tails in Florida one time, and you know, it

613
00:36:19,120 --> 00:36:22,239
Speaker 1: was it was first week August and it was pouring rain.

614
00:36:22,320 --> 00:36:24,239
Speaker 1: You know that that monsoon rolls in every day at

615
00:36:24,239 --> 00:36:27,080
Speaker 1: three o'clock and you get you know, it feels like

616
00:36:27,160 --> 00:36:28,960
Speaker 1: four inches of rain in an hour and it's done

617
00:36:29,000 --> 00:36:31,799
Speaker 1: and gets super hot. But what it was what made

618
00:36:31,800 --> 00:36:35,480
Speaker 1: it so miserable was the high ground where we were hunting.

619
00:36:35,800 --> 00:36:39,080
Speaker 1: Everything was there, all the bugs, all the snakes, you know.

620
00:36:39,160 --> 00:36:41,480
Speaker 1: And is is there a component of that with the

621
00:36:41,960 --> 00:36:43,840
Speaker 1: that you see in the Louisiana fons too, because I

622
00:36:43,880 --> 00:36:46,520
Speaker 1: have to imagine it probably concentrates ticks and stuff as

623
00:36:46,560 --> 00:36:50,439
Speaker 1: well or not. Oh, it concentrates everything, yeah, I mean,

624
00:36:51,320 --> 00:36:57,839
Speaker 1: and it's just such a cumulative effect of floodwaters like

625
00:36:57,880 --> 00:37:05,120
Speaker 1: that one. You know, mosquitoes are absolutely insane. Um during

626
00:37:05,120 --> 00:37:09,120
Speaker 1: those flood years like that, you've concentrated everything on small

627
00:37:09,160 --> 00:37:14,800
Speaker 1: patches of ground. You have stressed. Everything that's there is stressed.

628
00:37:15,160 --> 00:37:18,000
Speaker 1: So from a deer's perspective, their stress hormones are off

629
00:37:18,000 --> 00:37:22,400
Speaker 1: the charts. Um. You know, at at fawning time, you know,

630
00:37:22,520 --> 00:37:27,359
Speaker 1: does are designed to go be alone and drop those fawns,

631
00:37:27,400 --> 00:37:30,680
Speaker 1: and they space themselves in a way where they're not

632
00:37:30,760 --> 00:37:35,720
Speaker 1: hanging around with other does. And when flood waters concentrate animals,

633
00:37:35,760 --> 00:37:39,359
Speaker 1: they can't do that, so their entire ecology breaks down

634
00:37:39,719 --> 00:37:42,640
Speaker 1: during these flood events. And what we've seen, what I've

635
00:37:42,680 --> 00:37:48,960
Speaker 1: seen anecdotally on my own on my own camp property, uh,

636
00:37:49,120 --> 00:37:51,520
Speaker 1: is you know we want I can tell you right

637
00:37:51,520 --> 00:37:54,320
Speaker 1: out of the box. If we have a prolonged flood

638
00:37:54,360 --> 00:37:57,680
Speaker 1: event in the spring and summer and fawning there is

639
00:37:57,719 --> 00:38:01,400
Speaker 1: in August, late July and August. If we have flooding

640
00:38:01,440 --> 00:38:07,959
Speaker 1: into early in midsummer, our fonds take an absolute hammer. Um,

641
00:38:08,040 --> 00:38:12,280
Speaker 1: it gets really really bad. And you know what you mentioned,

642
00:38:12,360 --> 00:38:15,479
Speaker 1: you know, your winner events up north. What that does,

643
00:38:16,480 --> 00:38:19,120
Speaker 1: other than just kill a bunch of faons that year,

644
00:38:19,719 --> 00:38:25,160
Speaker 1: is that age cohort progresses through time. So in other words, okay,

645
00:38:25,200 --> 00:38:27,799
Speaker 1: well this year we lose, let's just say our fond

646
00:38:27,880 --> 00:38:31,520
Speaker 1: survival was ten. Well, next year we're going to have

647
00:38:31,920 --> 00:38:34,800
Speaker 1: a dramatically lower percentage of year and a half old

648
00:38:35,320 --> 00:38:39,040
Speaker 1: doze and bucks then we're normally supposed to have. And

649
00:38:39,120 --> 00:38:41,120
Speaker 1: now when they get to be two and a half

650
00:38:41,120 --> 00:38:44,120
Speaker 1: and those does start becoming more productive. There's fewer of them,

651
00:38:44,600 --> 00:38:47,879
Speaker 1: and then that age cohort goes through time and by

652
00:38:47,880 --> 00:38:50,520
Speaker 1: the time they reach four or five or six years old,

653
00:38:50,560 --> 00:38:53,040
Speaker 1: you know at the point where at least from a

654
00:38:53,120 --> 00:38:57,719
Speaker 1: quote unquote trophy deer management perspective, you know, you start

655
00:38:57,800 --> 00:39:01,719
Speaker 1: harvesting some of these animals, there aren't any there's very

656
00:39:01,760 --> 00:39:06,080
Speaker 1: few in the population, and so these effects are not

657
00:39:06,200 --> 00:39:08,719
Speaker 1: just a one year effect there. You know, these they

658
00:39:09,800 --> 00:39:13,520
Speaker 1: become a cumulative it's through time and um and that's

659
00:39:13,600 --> 00:39:17,320
Speaker 1: really tough from a management scenario, you know, a management perspective,

660
00:39:17,320 --> 00:39:19,839
Speaker 1: it creates some challenges because you're trying to think out

661
00:39:20,719 --> 00:39:22,920
Speaker 1: what's this hard going to look like years down the

662
00:39:23,000 --> 00:39:26,760
Speaker 1: road that you know up here where I live. The

663
00:39:26,760 --> 00:39:31,000
Speaker 1: the age class situation comes up most often with fish management,

664
00:39:31,440 --> 00:39:34,880
Speaker 1: right like walleyes. Uh, you know, big topic here and

665
00:39:35,680 --> 00:39:38,319
Speaker 1: you know the perch to some extent some of the

666
00:39:38,320 --> 00:39:41,200
Speaker 1: other game fish, but you hear that a lot where

667
00:39:41,880 --> 00:39:45,360
Speaker 1: you know, yeah, a poor spring or a poor spawn sucks.

668
00:39:45,960 --> 00:39:48,200
Speaker 1: But where you really start to get hit is when

669
00:39:48,280 --> 00:39:51,560
Speaker 1: those fish should be like prime spawning age and there's

670
00:39:51,600 --> 00:39:54,759
Speaker 1: a big gap in the year classes there and you

671
00:39:54,840 --> 00:39:57,680
Speaker 1: see it stretch out. It's it's such a good reminder

672
00:39:58,560 --> 00:40:03,080
Speaker 1: of how difficult game management really is when you when

673
00:40:03,080 --> 00:40:05,560
Speaker 1: you talk about just the whims of mother nature and

674
00:40:05,560 --> 00:40:07,880
Speaker 1: the weather that you're gonna get, and you know, you

675
00:40:07,920 --> 00:40:10,200
Speaker 1: get three of those real wet years in a row,

676
00:40:10,800 --> 00:40:14,359
Speaker 1: like the challenge to manage that, you know, three years

677
00:40:14,400 --> 00:40:17,760
Speaker 1: beyond that or five years beyond that. He's really tough.

678
00:40:18,040 --> 00:40:20,399
Speaker 1: And a lot of people have short memories, so they think,

679
00:40:20,400 --> 00:40:22,319
Speaker 1: why don't I have these deer now, or why don't

680
00:40:22,320 --> 00:40:25,160
Speaker 1: I have these walleyes now when you're when you're wrestling

681
00:40:25,200 --> 00:40:29,719
Speaker 1: with what happened, you know, half a decade ago. Yeah, yeah,

682
00:40:29,760 --> 00:40:32,759
Speaker 1: And it's really frustrating for for landowners, you know, and

683
00:40:32,840 --> 00:40:37,040
Speaker 1: managers as well, because you do everything right and then

684
00:40:37,120 --> 00:40:40,719
Speaker 1: mother nature bite you and there's absolutely nothing you can

685
00:40:40,760 --> 00:40:45,040
Speaker 1: do about it. And you know, again, I'm I'm speaking

686
00:40:45,160 --> 00:40:50,799
Speaker 1: specifically about my experiences in Louisiana, but it's um it

687
00:40:50,880 --> 00:40:54,160
Speaker 1: can happen anywhere, you know, like here in my backyard

688
00:40:54,160 --> 00:40:57,160
Speaker 1: this winter, for instance, we had a very poor mass

689
00:40:57,160 --> 00:41:02,680
Speaker 1: crop and what that did was obvious to our dear herd.

690
00:41:03,120 --> 00:41:08,120
Speaker 1: I harvested a buck about it was the second weekend

691
00:41:08,120 --> 00:41:13,759
Speaker 1: of November, no, first week in in November. And he

692
00:41:13,920 --> 00:41:18,399
Speaker 1: was way way underweight compared to what he should have been.

693
00:41:19,400 --> 00:41:21,759
Speaker 1: And he was a six year old. And of course

694
00:41:21,800 --> 00:41:25,800
Speaker 1: I was tickled to kill him. But when I stood

695
00:41:25,800 --> 00:41:27,879
Speaker 1: there and looked at him on the ground, I thought

696
00:41:27,880 --> 00:41:30,520
Speaker 1: to myself, this guy would have been in trouble come

697
00:41:30,640 --> 00:41:33,920
Speaker 1: late winter, um because and and we our body weights

698
00:41:33,920 --> 00:41:37,400
Speaker 1: were down uniformly, they were down, and and that matters,

699
00:41:37,760 --> 00:41:40,360
Speaker 1: you know. So it may not be a catastrophic event

700
00:41:40,440 --> 00:41:44,239
Speaker 1: like a flood, but even these subtle environmental changes they

701
00:41:44,840 --> 00:41:48,480
Speaker 1: change your management because they alter the herd in ways.

702
00:41:48,560 --> 00:41:52,279
Speaker 1: It may not be very dramatic. They're more subtle, you know.

703
00:41:52,360 --> 00:41:56,160
Speaker 1: But a deer going into winter fifteen pounds lighter than

704
00:41:56,200 --> 00:42:00,759
Speaker 1: he should be, that's a problem. Yeah, let's maybe there's

705
00:42:00,760 --> 00:42:04,080
Speaker 1: a weird jump here, but that that's what always amazes

706
00:42:04,120 --> 00:42:07,040
Speaker 1: me when you get to observe, uh, you know, deer

707
00:42:07,080 --> 00:42:09,760
Speaker 1: on a food plot somewhere. You know, my my experience

708
00:42:09,800 --> 00:42:11,520
Speaker 1: is more up here. I've hunted down south a little bit,

709
00:42:11,560 --> 00:42:14,799
Speaker 1: but I hunted up north a lot. And you see

710
00:42:15,160 --> 00:42:16,960
Speaker 1: sometimes if you have like a little kill plot, you'll

711
00:42:17,000 --> 00:42:20,719
Speaker 1: see one great big doll sort of claimant and she's

712
00:42:20,760 --> 00:42:23,040
Speaker 1: fighting everybody that comes in. I mean, she's she's swinging

713
00:42:23,080 --> 00:42:26,960
Speaker 1: hooves at everybody. And it's so it's it's a behavior

714
00:42:27,040 --> 00:42:29,520
Speaker 1: that you like, you're not you don't witness very often, right,

715
00:42:29,760 --> 00:42:31,360
Speaker 1: and you look at it and you go, it's the

716
00:42:31,360 --> 00:42:33,680
Speaker 1: same thing with turkeys, especially fall turkeys. Like you see

717
00:42:33,680 --> 00:42:36,560
Speaker 1: how territorial they get over a food source, and you're like, yeah,

718
00:42:36,560 --> 00:42:39,319
Speaker 1: that's because they need every freaking calorie they can get,

719
00:42:39,760 --> 00:42:41,640
Speaker 1: because if they don't get it, you know, and they

720
00:42:41,680 --> 00:42:44,040
Speaker 1: go into the winter in the wrong condition, it's over

721
00:42:44,080 --> 00:42:47,200
Speaker 1: for them. Yeah. Yeah, I mean it's you know, they're

722
00:42:47,239 --> 00:42:50,440
Speaker 1: wired to reproduce and survive. That's what these animals are

723
00:42:50,440 --> 00:42:57,200
Speaker 1: wired to do. And um, surviving is is calories and predation,

724
00:42:57,480 --> 00:42:59,880
Speaker 1: you know, So you have to forge and you have

725
00:42:59,880 --> 00:43:03,080
Speaker 1: to be successful, and when times get lean, you have

726
00:43:03,120 --> 00:43:05,279
Speaker 1: take matters in your own hands. Like what you just

727
00:43:05,600 --> 00:43:09,120
Speaker 1: you know with turkeys is really obvious because you can

728
00:43:09,160 --> 00:43:12,400
Speaker 1: just see them fight each other constantly when they encounter

729
00:43:12,520 --> 00:43:15,080
Speaker 1: prey in the in the fall and winter, and and

730
00:43:15,160 --> 00:43:18,120
Speaker 1: dear do the same thing. Obviously. What I've noticed is

731
00:43:19,080 --> 00:43:22,359
Speaker 1: on larger you know, on smaller plots, which I try

732
00:43:22,400 --> 00:43:26,720
Speaker 1: not to even plant, but on larger plots, you'll often

733
00:43:26,760 --> 00:43:30,399
Speaker 1: see these matriarchal groups kind of separate themselves. You'll have

734
00:43:30,480 --> 00:43:33,600
Speaker 1: a you know, a doe and and her last year's

735
00:43:33,640 --> 00:43:35,959
Speaker 1: fawn and then this year's fawn. They'll come in over here,

736
00:43:36,000 --> 00:43:38,120
Speaker 1: and another group will come in over on the other side,

737
00:43:38,160 --> 00:43:41,040
Speaker 1: and they'll kind of they'll kind of keep their distance

738
00:43:41,080 --> 00:43:45,800
Speaker 1: from each other in a way that, um, they avoid strife,

739
00:43:46,040 --> 00:43:49,400
Speaker 1: you know, they And that's that's one reason I actually

740
00:43:49,880 --> 00:43:52,719
Speaker 1: try to encourage people that I've worked with that, you know,

741
00:43:52,760 --> 00:43:57,319
Speaker 1: if you have the capability of planning larger plots, do it. Um. One,

742
00:43:57,360 --> 00:44:00,560
Speaker 1: you'll attract more animals, But to you, you end up,

743
00:44:00,640 --> 00:44:03,200
Speaker 1: I've experienced, you end up with a lot calmer animals.

744
00:44:04,160 --> 00:44:06,600
Speaker 1: Um if you can kind of separate everybody and let

745
00:44:06,800 --> 00:44:08,560
Speaker 1: let them do their thing, and it's a lot of

746
00:44:08,560 --> 00:44:10,239
Speaker 1: fun if you can sit there and watch you know,

747
00:44:10,280 --> 00:44:13,680
Speaker 1: four or five different groups, those that are that are foraging,

748
00:44:13,800 --> 00:44:15,560
Speaker 1: rather than one that comes in a tiny, a little

749
00:44:15,600 --> 00:44:18,439
Speaker 1: plot and when her buddy shows up, she runs off,

750
00:44:18,480 --> 00:44:21,120
Speaker 1: and that type of thing, and it's not as enjoyable.

751
00:44:21,520 --> 00:44:24,040
Speaker 1: I've never ever thought about that from the perspective of

752
00:44:24,080 --> 00:44:26,600
Speaker 1: how you could how you could calm your deer down

753
00:44:26,640 --> 00:44:28,520
Speaker 1: a little bit by giving them some more space to

754
00:44:28,560 --> 00:44:32,360
Speaker 1: self segregate and you know, not not be as on

755
00:44:32,520 --> 00:44:34,880
Speaker 1: edge and competitive all the time. Yeah, and you can

756
00:44:34,920 --> 00:44:37,759
Speaker 1: see this, I mean, you you see this. You know,

757
00:44:38,320 --> 00:44:42,480
Speaker 1: there's another contentious topic, but hunting over bait for instance, well,

758
00:44:42,520 --> 00:44:45,320
Speaker 1: trapping over baits the same thing. I mean, we trap

759
00:44:45,400 --> 00:44:49,640
Speaker 1: over We trapped turkeys and deer with bait. And there

760
00:44:49,760 --> 00:44:53,160
Speaker 1: is no question that if you have a food source

761
00:44:53,239 --> 00:44:57,480
Speaker 1: there in times or lean, when one animal decides he

762
00:44:57,600 --> 00:44:59,640
Speaker 1: or she's going to come in there, if they're socially

763
00:44:59,680 --> 00:45:02,760
Speaker 1: dominant it over another animal, they're going to displace that animal.

764
00:45:02,880 --> 00:45:05,680
Speaker 1: That's just the way it is. So when you've got

765
00:45:05,680 --> 00:45:08,480
Speaker 1: a praise source, that's that's local laws and it's just

766
00:45:08,680 --> 00:45:11,959
Speaker 1: right here, that's that's what I'm using, then you should

767
00:45:12,000 --> 00:45:15,120
Speaker 1: expect to see that displacement. Whereas if that praise source

768
00:45:15,239 --> 00:45:19,680
Speaker 1: is stretched across five acres, for instance, that you don't

769
00:45:19,719 --> 00:45:22,560
Speaker 1: see that social strife. That's what I've noticed in my

770
00:45:23,000 --> 00:45:26,360
Speaker 1: in my own hunting experience. Well, you see that with bears,

771
00:45:27,160 --> 00:45:29,400
Speaker 1: you know, when when you're baiting black bears, you see that.

772
00:45:29,480 --> 00:45:32,120
Speaker 1: I mean, I think The best example though, is that

773
00:45:32,200 --> 00:45:35,160
Speaker 1: fall Turkey. If you figure out where you know a

774
00:45:35,200 --> 00:45:38,279
Speaker 1: group of hens and poults are gonna feed every day,

775
00:45:38,520 --> 00:45:40,360
Speaker 1: and you go in there and put one single feeding

776
00:45:40,360 --> 00:45:42,640
Speaker 1: hen decoy in there, he starts squawking away. You see

777
00:45:42,680 --> 00:45:46,239
Speaker 1: hen behavior that's wild, and they they get so in

778
00:45:46,360 --> 00:45:48,439
Speaker 1: and that the groups of tom's doing the fall as well.

779
00:45:48,760 --> 00:45:51,520
Speaker 1: But you see it's it's almost it's more striking when

780
00:45:51,520 --> 00:45:54,040
Speaker 1: you see hens come in like strutting, a chest bumping,

781
00:45:54,120 --> 00:45:56,520
Speaker 1: and you know, I've arrowed birds in the fall that

782
00:45:56,600 --> 00:45:58,600
Speaker 1: go tip over and then the other ones go stand

783
00:45:58,600 --> 00:46:00,560
Speaker 1: out here and stop here, just like the times doing

784
00:46:00,560 --> 00:46:04,879
Speaker 1: the spring, and it's it's just pure dominance. Man. Yeah, yeah,

785
00:46:04,880 --> 00:46:07,880
Speaker 1: it's pretty that's pretty cool behavior. Yeah it is. Let's

786
00:46:07,880 --> 00:46:11,239
Speaker 1: talk I know everybody wants to hear about this. Uh,

787
00:46:11,320 --> 00:46:16,520
Speaker 1: let's talk that Louisiana study on mature buck movement. Yeah.

788
00:46:16,520 --> 00:46:18,799
Speaker 1: So what we did there is that was actually the

789
00:46:18,840 --> 00:46:21,759
Speaker 1: same study site that we did the fawn work that

790
00:46:21,840 --> 00:46:25,440
Speaker 1: where we saw the bare predation, but that other aspect

791
00:46:25,480 --> 00:46:27,319
Speaker 1: of the study as we captured bucks and we put

792
00:46:27,360 --> 00:46:31,319
Speaker 1: GPS collars on them and we collected locations so that

793
00:46:31,360 --> 00:46:34,840
Speaker 1: we could obviously describe, you know, how they were moving.

794
00:46:35,440 --> 00:46:38,359
Speaker 1: And on that study site, we we had the luxury

795
00:46:38,440 --> 00:46:41,920
Speaker 1: or the fourth fortune, I guess, of having an area

796
00:46:42,000 --> 00:46:46,360
Speaker 1: that was that was very very lightly hunted. This was

797
00:46:46,400 --> 00:46:51,960
Speaker 1: public land, uh, a section that was essentially a sanctuary basically, um.

798
00:46:52,080 --> 00:46:55,160
Speaker 1: And then the rest of the area was open access.

799
00:46:55,239 --> 00:46:57,720
Speaker 1: So we were we were able to see how pressure

800
00:46:57,880 --> 00:47:04,120
Speaker 1: influenced buck behave of yours, which was cool and just anecdotally,

801
00:47:04,160 --> 00:47:08,280
Speaker 1: we were able to observe some really cool behaviors. For instance,

802
00:47:08,320 --> 00:47:11,160
Speaker 1: you know bucks that took excursions and we're gone for

803
00:47:11,280 --> 00:47:14,440
Speaker 1: four or five months and then came back. Bucks that

804
00:47:14,520 --> 00:47:18,319
Speaker 1: maintained two different home ranges each year. You know, they

805
00:47:18,360 --> 00:47:20,680
Speaker 1: had a fall winter home range and then they had

806
00:47:20,680 --> 00:47:23,960
Speaker 1: a spring summer home range, and literally they were miles

807
00:47:24,000 --> 00:47:28,600
Speaker 1: apart from each other. So we yeah, we we've had

808
00:47:28,640 --> 00:47:30,960
Speaker 1: some cool data that we got out of that study.

809
00:47:31,000 --> 00:47:34,840
Speaker 1: And you know, as hunters that really you know, the

810
00:47:34,880 --> 00:47:40,080
Speaker 1: fawn predation stuff that's obviously important, but the data on bucks,

811
00:47:40,080 --> 00:47:41,880
Speaker 1: you know, just people eat it up, you know, and

812
00:47:41,920 --> 00:47:43,880
Speaker 1: I get it. I did too. I look at them like,

813
00:47:43,920 --> 00:47:47,879
Speaker 1: oh wow, that's that's really cool. UM. We also saw

814
00:47:47,920 --> 00:47:53,919
Speaker 1: a lot of individual variation in behavior across bucks, irrespective

815
00:47:53,960 --> 00:47:57,680
Speaker 1: of age, and that's something that we're seeing in turkeys

816
00:47:57,719 --> 00:47:59,960
Speaker 1: as an aside, and a lot of studies are seeing

817
00:48:00,000 --> 00:48:02,680
Speaker 1: it with with other species, that there's no such thing

818
00:48:02,719 --> 00:48:06,279
Speaker 1: as an average deer. They all they all have behaviors

819
00:48:06,280 --> 00:48:08,960
Speaker 1: that are that are kind of individual which, you know,

820
00:48:09,080 --> 00:48:13,200
Speaker 1: kind kind of makes sense. Yeah, it makes sense. It's

821
00:48:13,360 --> 00:48:17,960
Speaker 1: intuitively makes perfect sense. It's just harder for us to

822
00:48:17,960 --> 00:48:19,880
Speaker 1: sort of wrap our brain around because we'd like to

823
00:48:19,920 --> 00:48:23,920
Speaker 1: just generalize. Oh sure, yeah, it's like, yeah, of course,

824
00:48:24,120 --> 00:48:27,440
Speaker 1: hey that Yeah, there's only two kinds of bucks. The

825
00:48:27,440 --> 00:48:29,200
Speaker 1: way to kill this one is to do this, and

826
00:48:29,200 --> 00:48:30,879
Speaker 1: the way to kill the other one is to do that,

827
00:48:30,960 --> 00:48:33,319
Speaker 1: And in reality that's not the way they function. So

828
00:48:33,560 --> 00:48:36,480
Speaker 1: with with the the deer that had to home ranges,

829
00:48:36,680 --> 00:48:38,640
Speaker 1: those those bucks where you're saying, okay, they got fall

830
00:48:38,640 --> 00:48:41,000
Speaker 1: winter and they got spring summer. Not not all the

831
00:48:41,000 --> 00:48:44,080
Speaker 1: bucks have that, right, No, that was just a handful.

832
00:48:45,040 --> 00:48:47,680
Speaker 1: What what do you think that is? Why do you

833
00:48:47,680 --> 00:48:50,440
Speaker 1: think some bucks are like I'm gonna light out, you know,

834
00:48:50,960 --> 00:48:53,319
Speaker 1: eight miles away, and I'm gonna spend my springing my

835
00:48:53,360 --> 00:48:55,600
Speaker 1: summer there, and I'm gonna come back in the fall

836
00:48:55,680 --> 00:48:57,680
Speaker 1: of this spot. But some of them are like, no way,

837
00:48:58,160 --> 00:49:03,360
Speaker 1: I honestly have no idea. We we speculated that, you know,

838
00:49:03,520 --> 00:49:08,040
Speaker 1: maybe there was there was some genetic you know, this

839
00:49:08,160 --> 00:49:13,360
Speaker 1: animal was from animals that had made similar behaviors before.

840
00:49:14,520 --> 00:49:17,520
Speaker 1: But who knows? And what was really crazy with those

841
00:49:18,040 --> 00:49:21,479
Speaker 1: the two in particular, I can remember where they went

842
00:49:22,960 --> 00:49:25,560
Speaker 1: when they left to go to a different home range.

843
00:49:25,920 --> 00:49:29,040
Speaker 1: Didn't look anything different than where they left from. Like

844
00:49:29,520 --> 00:49:33,360
Speaker 1: I just, um, it just didn't really make any sense.

845
00:49:33,760 --> 00:49:39,040
Speaker 1: It was almost as if they pulled the you know,

846
00:49:39,160 --> 00:49:43,200
Speaker 1: the the vacation card, if you will, like I'm just

847
00:49:43,239 --> 00:49:47,440
Speaker 1: going to go over here and hang out. And they

848
00:49:47,480 --> 00:49:50,040
Speaker 1: did it multiple years. It's not they didn't just do

849
00:49:50,160 --> 00:49:52,680
Speaker 1: this once. Uh. In fact, one of these bucks he

850
00:49:52,719 --> 00:49:54,919
Speaker 1: did that every year. He went to the exact same

851
00:49:54,960 --> 00:49:57,959
Speaker 1: spot when he left his one home range he went,

852
00:49:58,280 --> 00:50:00,800
Speaker 1: I mean took him about a day and a half

853
00:50:00,960 --> 00:50:03,520
Speaker 1: and he went over to the other range, so he

854
00:50:03,600 --> 00:50:05,800
Speaker 1: knew exactly where he was going, and he walked straight

855
00:50:05,800 --> 00:50:07,839
Speaker 1: over there to that spot and then hung out there

856
00:50:07,840 --> 00:50:13,120
Speaker 1: all spring and summer. Um, no idea why that is there?

857
00:50:13,160 --> 00:50:20,200
Speaker 1: From a you know, an ecological perspective, there there's some

858
00:50:20,280 --> 00:50:23,920
Speaker 1: mechanisms that may be going on there, you know, animals

859
00:50:23,920 --> 00:50:28,560
Speaker 1: that we think maybe they're prospecting for other areas. Maybe

860
00:50:29,320 --> 00:50:31,800
Speaker 1: the fact that bucks did that outside of the rut

861
00:50:31,920 --> 00:50:36,759
Speaker 1: kind of destroys that theory, because what are they prospecting

862
00:50:36,800 --> 00:50:40,000
Speaker 1: if they're not, if they're not taking these excursions during

863
00:50:40,040 --> 00:50:42,399
Speaker 1: the rut, which most of them were not during the rut,

864
00:50:43,160 --> 00:50:48,959
Speaker 1: then seeing does well that that argument shot, you know, well,

865
00:50:49,080 --> 00:50:53,640
Speaker 1: is he going somewhere that has some abundant resource you

866
00:50:53,680 --> 00:50:56,319
Speaker 1: know that he can't get where he was? Well, in

867
00:50:56,360 --> 00:50:59,440
Speaker 1: our case, the answer was no, he left the bottom

868
00:50:59,520 --> 00:51:02,400
Speaker 1: land four that had agriculture and he walked over to

869
00:51:02,480 --> 00:51:06,560
Speaker 1: another bottom land forest that had agriculture. And you know,

870
00:51:07,000 --> 00:51:09,440
Speaker 1: we were kind of dumbfounded, to be honest with you,

871
00:51:09,960 --> 00:51:14,200
Speaker 1: um and you know stuff like that. Honestly, Tony, we'll

872
00:51:14,239 --> 00:51:17,319
Speaker 1: never we'll never know, we'll never understand some of that.

873
00:51:17,600 --> 00:51:20,480
Speaker 1: And that's good. That's I think it's kind of cool,

874
00:51:20,520 --> 00:51:24,040
Speaker 1: as like, you know, I don't honestly know what the

875
00:51:24,080 --> 00:51:26,239
Speaker 1: answer to that is and maybe I never will, And

876
00:51:26,640 --> 00:51:30,160
Speaker 1: that's okay. I think that stuff's fascinating when you so

877
00:51:30,520 --> 00:51:32,480
Speaker 1: when you say that and you go, okay, the the

878
00:51:32,520 --> 00:51:37,320
Speaker 1: dough availability of dose same in both spots, the abundance

879
00:51:37,360 --> 00:51:40,000
Speaker 1: of food same in both spots. So you just think,

880
00:51:40,040 --> 00:51:43,560
Speaker 1: all right, is that a genetic carryover where at one

881
00:51:43,600 --> 00:51:45,840
Speaker 1: point it was beneficial for some deer or some of

882
00:51:45,840 --> 00:51:49,839
Speaker 1: the deer's ancestors to move, you know, to where they

883
00:51:49,880 --> 00:51:53,040
Speaker 1: were born or some some other spot for half of

884
00:51:53,080 --> 00:51:55,719
Speaker 1: the year. And some of them still carry that and

885
00:51:55,760 --> 00:51:57,839
Speaker 1: it's still expressed in their genes, and some of them don't.

886
00:51:57,920 --> 00:51:59,600
Speaker 1: And then you're like, okay, well, if that's the case,

887
00:51:59,680 --> 00:52:01,959
Speaker 1: what is it, like, where did that come from? Maybe

888
00:52:02,000 --> 00:52:04,480
Speaker 1: he was born there, you know, Maybe these bucks were

889
00:52:04,480 --> 00:52:08,399
Speaker 1: born years ago and they you know, and and one

890
00:52:08,400 --> 00:52:10,520
Speaker 1: of these older one of these bucks was was an

891
00:52:10,560 --> 00:52:14,080
Speaker 1: older guy. Um, for all we know, maybe he was

892
00:52:14,200 --> 00:52:18,160
Speaker 1: fawned over eight miles away, and when he dispersed as

893
00:52:18,160 --> 00:52:20,799
Speaker 1: a year and a half old buck, he dispersed to

894
00:52:20,840 --> 00:52:24,160
Speaker 1: where we ended up catching him, and for whatever reason,

895
00:52:24,200 --> 00:52:27,880
Speaker 1: he was just predisposed to go home each each summer

896
00:52:28,400 --> 00:52:30,600
Speaker 1: and go back to an area that he was his

897
00:52:30,719 --> 00:52:34,080
Speaker 1: natal area. The problem is we obviously we would have

898
00:52:34,120 --> 00:52:36,719
Speaker 1: to catch them as fawns and then track them for

899
00:52:36,760 --> 00:52:39,080
Speaker 1: a number of years to be able to determine that

900
00:52:39,200 --> 00:52:41,719
Speaker 1: and you and we couldn't in that study, but that's

901
00:52:41,719 --> 00:52:45,439
Speaker 1: a plausible hypothesis, is that he was he was born there.

902
00:52:59,200 --> 00:53:01,960
Speaker 1: How often in your research in your career do you

903
00:53:02,040 --> 00:53:05,520
Speaker 1: just just have those moments where you go, there, there's

904
00:53:05,600 --> 00:53:09,840
Speaker 1: so much left for us to learn every day, every

905
00:53:09,880 --> 00:53:17,239
Speaker 1: single day. Uh, And that in some ways is is maddening. Uh,

906
00:53:17,280 --> 00:53:20,520
Speaker 1: And in some ways it it's Uh it makes me

907
00:53:20,600 --> 00:53:25,440
Speaker 1: smile because there will be work to do long after

908
00:53:25,480 --> 00:53:32,520
Speaker 1: I'm gone. Um. I would say probably most days I

909
00:53:32,640 --> 00:53:40,040
Speaker 1: think more about, Okay, I just we just learned this. Um, well,

910
00:53:40,080 --> 00:53:45,320
Speaker 1: now that we know that, we now don't know this,

911
00:53:45,320 --> 00:53:49,960
Speaker 1: this and this or I can't explain what we just observed.

912
00:53:51,480 --> 00:53:53,920
Speaker 1: What's it going to take to get an answer to that? Well,

913
00:53:54,040 --> 00:53:56,000
Speaker 1: I'm going to need to do this, this and this

914
00:53:56,120 --> 00:53:58,640
Speaker 1: and this, this and this is going to take two

915
00:53:58,760 --> 00:54:04,120
Speaker 1: years and just every day it's like we just know

916
00:54:04,480 --> 00:54:08,799
Speaker 1: so little, and as we learn things, we create more

917
00:54:08,880 --> 00:54:11,879
Speaker 1: questions than answers in a lot of cases. And that's

918
00:54:11,920 --> 00:54:14,239
Speaker 1: what I see with with with a lot of my

919
00:54:14,320 --> 00:54:17,759
Speaker 1: Turkey work right now, is we just everything we learn

920
00:54:18,560 --> 00:54:22,000
Speaker 1: it's good And you're like, oh, that's that's interesting. I'm

921
00:54:22,120 --> 00:54:25,080
Speaker 1: you know, and then you sit back and think, why

922
00:54:25,239 --> 00:54:29,879
Speaker 1: is that? And that doesn't make any sense? So let

923
00:54:29,880 --> 00:54:31,719
Speaker 1: me think through that. And then I think through it

924
00:54:31,800 --> 00:54:37,040
Speaker 1: for a week or and I come to the conclusion that, uh,

925
00:54:37,200 --> 00:54:40,919
Speaker 1: I don't have an explanation for that, and to get one,

926
00:54:41,000 --> 00:54:44,279
Speaker 1: it's going to take more information. Okay, well I can

927
00:54:44,320 --> 00:54:49,000
Speaker 1: get more information, but that takes time. So you're in

928
00:54:49,000 --> 00:54:52,200
Speaker 1: this catch twenty two where you're constantly learning things and

929
00:54:52,280 --> 00:54:56,000
Speaker 1: questioning things, and you realize the solution to to some

930
00:54:56,080 --> 00:55:00,160
Speaker 1: of these questions is just time and data. And we

931
00:55:00,280 --> 00:55:03,759
Speaker 1: are an instant gratification society. You know. We want answers now,

932
00:55:04,400 --> 00:55:06,919
Speaker 1: we want answers yesterday, and that's just not the way

933
00:55:07,160 --> 00:55:10,440
Speaker 1: my line of work, you know, functions. But what I

934
00:55:10,520 --> 00:55:13,200
Speaker 1: what I love about it when you talk about those

935
00:55:13,280 --> 00:55:15,760
Speaker 1: bucks in the in the you know, the two different

936
00:55:15,760 --> 00:55:18,920
Speaker 1: home ranges, or I had I had John McRoberts on

937
00:55:19,000 --> 00:55:22,360
Speaker 1: here talking about that Missouri buck that that went like

938
00:55:22,400 --> 00:55:24,759
Speaker 1: a hundred eighty miles that they had collared, you know,

939
00:55:25,480 --> 00:55:29,520
Speaker 1: and it makes me it's this is probably sounds super weird,

940
00:55:29,760 --> 00:55:31,600
Speaker 1: but I sort of feel like the deer are just

941
00:55:31,680 --> 00:55:33,600
Speaker 1: throwing up a pair of middle fingers at us and

942
00:55:33,640 --> 00:55:35,920
Speaker 1: They're like, you're never gonna know what we're doing. Like,

943
00:55:35,960 --> 00:55:38,360
Speaker 1: you're never gonna know, no matter how many cell cameras

944
00:55:38,400 --> 00:55:40,920
Speaker 1: you have, no matter how many different ways, like we

945
00:55:40,960 --> 00:55:43,520
Speaker 1: get to tip the odds in our favor where there's

946
00:55:43,520 --> 00:55:46,239
Speaker 1: still so much we're just never gonna know. Yeah, and

947
00:55:46,280 --> 00:55:48,960
Speaker 1: that's the way it probably should be. Yeah, you know,

948
00:55:49,520 --> 00:55:55,759
Speaker 1: I think about you know, dear, I'm a fanatical I'd

949
00:55:55,760 --> 00:55:59,120
Speaker 1: say I'm I'm a passionate turkey hunter. I'm a fanatical

950
00:55:59,160 --> 00:56:02,120
Speaker 1: deer hunter. I mean, when it gets to be September,

951
00:56:02,680 --> 00:56:06,319
Speaker 1: my my mind goes to places that my wife will

952
00:56:06,320 --> 00:56:09,120
Speaker 1: tell you. She's like, oh, you know, Christ, he's he's

953
00:56:09,160 --> 00:56:13,040
Speaker 1: about to go into his crazy place. Um. And I

954
00:56:13,120 --> 00:56:15,279
Speaker 1: just love the deer hunt, and I loved I love

955
00:56:15,320 --> 00:56:20,560
Speaker 1: the challenge of finding a buck or an area that

956
00:56:20,560 --> 00:56:22,879
Speaker 1: that I know I'm got I've got a good chance

957
00:56:22,920 --> 00:56:27,759
Speaker 1: for encountering a mature deer. The the chase to kill

958
00:56:27,840 --> 00:56:31,760
Speaker 1: that animal is is it just fuels me in the fall.

959
00:56:32,080 --> 00:56:33,799
Speaker 1: That's why I get up in the morning. I think

960
00:56:33,880 --> 00:56:38,520
Speaker 1: about it all the time. And I have run across

961
00:56:38,600 --> 00:56:41,520
Speaker 1: bucks as you have that I think are just they're

962
00:56:41,560 --> 00:56:46,360
Speaker 1: not killed, They're just I tried every possible thing across

963
00:56:46,400 --> 00:56:49,280
Speaker 1: a number of years to kill them, and I just didn't.

964
00:56:49,280 --> 00:56:51,680
Speaker 1: Now a lot of times I've been successful and killed

965
00:56:51,760 --> 00:56:54,960
Speaker 1: animals I was after, but I've also gotten my my

966
00:56:55,040 --> 00:56:59,400
Speaker 1: ass kick too. And that's honestly now in my life,

967
00:57:00,520 --> 00:57:04,560
Speaker 1: is as much, if not more fun than harvesting one,

968
00:57:04,719 --> 00:57:08,560
Speaker 1: because they show me that I just know so little.

969
00:57:09,280 --> 00:57:12,040
Speaker 1: You know, even though I know everything, I you know,

970
00:57:12,200 --> 00:57:15,319
Speaker 1: I think I know enough to get him. I did,

971
00:57:15,400 --> 00:57:17,720
Speaker 1: I still didn't. In fact, I give you an example.

972
00:57:18,320 --> 00:57:20,280
Speaker 1: This was at my camp in Louisiana. We had a

973
00:57:20,320 --> 00:57:25,280
Speaker 1: deer on camera. This was years ago, a beautiful, beautiful

974
00:57:25,280 --> 00:57:28,800
Speaker 1: ten point, just a gorgeous hundred you know, hundred sixty animal.

975
00:57:29,840 --> 00:57:32,480
Speaker 1: Had him on camera in October, and man, he was

976
00:57:32,560 --> 00:57:35,680
Speaker 1: my he was my guy. I was like, Okay, I'm

977
00:57:35,720 --> 00:57:38,240
Speaker 1: going to hunt that deer and I'll kill him. It

978
00:57:38,280 --> 00:57:42,480
Speaker 1: may take me all season, but I'll kill him. Nobody

979
00:57:42,520 --> 00:57:47,480
Speaker 1: ever saw that, dear um cameras everywhere, you know, hunters everywhere.

980
00:57:47,560 --> 00:57:50,320
Speaker 1: There's about ten of us that hunt the property routinely.

981
00:57:51,120 --> 00:57:55,560
Speaker 1: A deer was shot twelve miles away, and I thought

982
00:57:55,560 --> 00:57:59,320
Speaker 1: to myself, I wasted a lot of time hunting a ghost.

983
00:57:59,800 --> 00:58:03,040
Speaker 1: He wasn't even there, and I don't know where he

984
00:58:03,080 --> 00:58:05,200
Speaker 1: had been. I don't know how he got to where

985
00:58:05,240 --> 00:58:07,920
Speaker 1: he went, and I don't even know when he did it.

986
00:58:08,000 --> 00:58:10,600
Speaker 1: Maybe he left the day after we got pictures of him.

987
00:58:10,640 --> 00:58:13,280
Speaker 1: For all I know, I was hunting an animal that

988
00:58:13,280 --> 00:58:15,640
Speaker 1: didn't exist, and it made, you know, at the time

989
00:58:15,680 --> 00:58:18,960
Speaker 1: I got you know, I was frustrated. I was like, man,

990
00:58:19,000 --> 00:58:21,600
Speaker 1: I can't believe I waste all this time. And then

991
00:58:21,640 --> 00:58:24,000
Speaker 1: I was like, yeah, you didn't waste a minute, you know.

992
00:58:24,280 --> 00:58:26,400
Speaker 1: You you were out there doing what you love and

993
00:58:26,400 --> 00:58:29,800
Speaker 1: and I saw a lot of things that year. I

994
00:58:29,840 --> 00:58:32,440
Speaker 1: didn't obviously kill him, but I ended up killing a

995
00:58:32,520 --> 00:58:35,720
Speaker 1: really nice buck that year that was about the size

996
00:58:35,760 --> 00:58:39,400
Speaker 1: of that dear um. So I was I had to

997
00:58:39,400 --> 00:58:41,720
Speaker 1: step back and remind myself that I was fortunate to

998
00:58:41,720 --> 00:58:45,640
Speaker 1: be in the game regardless of who I was chasing. Yeah, well,

999
00:58:45,680 --> 00:58:50,840
Speaker 1: and what do you do? I mean, even yeah, when

1000
00:58:50,840 --> 00:58:54,360
Speaker 1: you when you make that decision. I mean the buck

1001
00:58:54,440 --> 00:58:56,520
Speaker 1: that taught me that I used to be really cocky

1002
00:58:56,560 --> 00:58:58,800
Speaker 1: about this and be like, every year is killable, every

1003
00:58:58,880 --> 00:59:01,920
Speaker 1: year is going to give you a chance. And I

1004
00:59:02,000 --> 00:59:04,680
Speaker 1: ran into a buck on a farm in Southeastern Minnesota.

1005
00:59:04,720 --> 00:59:07,160
Speaker 1: I just had permission on it's just dairy farm. They

1006
00:59:07,240 --> 00:59:09,040
Speaker 1: let a lot of people in there. It gets hunted

1007
00:59:09,160 --> 00:59:11,680
Speaker 1: really hard and shotgun season pretty hard in both season,

1008
00:59:11,720 --> 00:59:14,520
Speaker 1: and but you know, hunted a long time and I

1009
00:59:14,560 --> 00:59:18,360
Speaker 1: found this buck that when I when I first saw him,

1010
00:59:18,400 --> 00:59:20,000
Speaker 1: I was glassing with a buddy of mine and he

1011
00:59:20,080 --> 00:59:22,280
Speaker 1: came out with a bachelor group and he was like

1012
00:59:22,320 --> 00:59:26,200
Speaker 1: a one sixty, which is a big, big deer, and

1013
00:59:26,640 --> 00:59:29,600
Speaker 1: he had a really unique look to him. He looked

1014
00:59:29,600 --> 00:59:31,560
Speaker 1: like a mule deer. He was high and tight kind

1015
00:59:31,600 --> 00:59:36,640
Speaker 1: of you know. And I followed that deer for several

1016
00:59:36,720 --> 00:59:40,760
Speaker 1: seasons and hung multiple cameras, multiple stand sets, and that

1017
00:59:40,880 --> 00:59:46,760
Speaker 1: deer what was so frustrating about him was he was

1018
00:59:47,000 --> 00:59:49,400
Speaker 1: very predictable as far as like how he'd come into

1019
00:59:49,400 --> 00:59:50,960
Speaker 1: this valley to bed and how he'd leave it. Like

1020
00:59:50,960 --> 00:59:52,760
Speaker 1: I could get pictures of him all the time, but

1021
00:59:52,800 --> 00:59:55,080
Speaker 1: it would always be an hour after dark and an

1022
00:59:55,080 --> 00:59:57,959
Speaker 1: hour or two before first light, and I never saw

1023
00:59:58,040 --> 01:00:00,240
Speaker 1: him no matter how much I crept in there, I'd

1024
01:00:00,240 --> 01:00:02,640
Speaker 1: never get him on certain cameras. And it was just

1025
01:00:02,720 --> 01:00:07,600
Speaker 1: like this dear knows what he's doing so well that

1026
01:00:07,720 --> 01:00:10,480
Speaker 1: he's just got me beat like, I don't I got

1027
01:00:10,560 --> 01:00:12,800
Speaker 1: I got nothing left, and I think I don't know

1028
01:00:12,920 --> 01:00:15,320
Speaker 1: this for sure. I'm I'm about sure. He got hit

1029
01:00:15,360 --> 01:00:17,120
Speaker 1: by a car after I had chased him for like

1030
01:00:17,160 --> 01:00:19,760
Speaker 1: three years, because he was crossing this county road quite

1031
01:00:19,760 --> 01:00:22,200
Speaker 1: a bit, and I heard the one of the neighboring

1032
01:00:22,280 --> 01:00:24,440
Speaker 1: landowners was in tears when he found a buck on

1033
01:00:24,480 --> 01:00:25,560
Speaker 1: the side of the road, and I think it was

1034
01:00:25,560 --> 01:00:29,000
Speaker 1: probably him, but you know, and you know that that's

1035
01:00:29,080 --> 01:00:31,680
Speaker 1: sort of being facetious, but maybe not, because that's what

1036
01:00:31,920 --> 01:00:34,960
Speaker 1: that's what the story I was given. But anyway, they

1037
01:00:35,840 --> 01:00:37,800
Speaker 1: they do teach you that like you do when you

1038
01:00:37,800 --> 01:00:41,520
Speaker 1: spend enough time out there, you're like, man, yeah, you

1039
01:00:41,520 --> 01:00:43,320
Speaker 1: think you're pretty hot ship and then you started dealing

1040
01:00:43,400 --> 01:00:46,040
Speaker 1: with one. I mean I just as an example, I

1041
01:00:46,080 --> 01:00:48,440
Speaker 1: had a doll one time on a property that I

1042
01:00:48,480 --> 01:00:51,280
Speaker 1: had permission to hunt here in the Twin Cities, and

1043
01:00:51,360 --> 01:00:55,200
Speaker 1: she was just real dominant, real aggressive and big old

1044
01:00:55,200 --> 01:00:57,160
Speaker 1: dough and I was like, you know what, I'm gonna

1045
01:00:57,240 --> 01:00:59,520
Speaker 1: I'm gonna kill that because every time if I make

1046
01:00:59,560 --> 01:01:01,320
Speaker 1: a mistake with her, she busts me and she lets

1047
01:01:01,320 --> 01:01:05,240
Speaker 1: everybody know. And I hunted that dough super hard, and

1048
01:01:05,280 --> 01:01:08,960
Speaker 1: she beat me every time. You know, I have a

1049
01:01:09,000 --> 01:01:12,840
Speaker 1: Partiley Pieball dough on a property here in Georgia that

1050
01:01:13,200 --> 01:01:16,480
Speaker 1: is now five. And I saw her when she was

1051
01:01:16,520 --> 01:01:19,080
Speaker 1: a year and a half and I saw her when

1052
01:01:19,080 --> 01:01:21,600
Speaker 1: she was two and a half and since then, I've

1053
01:01:21,600 --> 01:01:25,000
Speaker 1: tried to kill this dough for two seasons. Now, I

1054
01:01:25,080 --> 01:01:28,600
Speaker 1: have thousands of pictures of her. I've not laid eyes

1055
01:01:28,680 --> 01:01:34,000
Speaker 1: on her except coming back to my truck to go

1056
01:01:34,160 --> 01:01:37,919
Speaker 1: retrieve another animal. She was standing about eight yards from

1057
01:01:37,920 --> 01:01:43,160
Speaker 1: my truck one afternoon. That dough has. The pictures of

1058
01:01:43,160 --> 01:01:47,440
Speaker 1: her are every single night. She has not moved at

1059
01:01:47,480 --> 01:01:49,400
Speaker 1: all from where I saw her when she was a

1060
01:01:49,440 --> 01:01:51,480
Speaker 1: year and a half old. And she's now five, and

1061
01:01:51,520 --> 01:01:53,920
Speaker 1: I've never laid eyes on her. And I've intentionally been

1062
01:01:53,960 --> 01:01:56,560
Speaker 1: trying to kill this dear because I want to tan

1063
01:01:56,640 --> 01:02:00,720
Speaker 1: her high. She's she's beautiful, and that just goes to

1064
01:02:00,760 --> 01:02:03,280
Speaker 1: show you she's living right under my nose and she

1065
01:02:03,480 --> 01:02:05,520
Speaker 1: but she knows what I'm doing. She knows when I

1066
01:02:05,520 --> 01:02:09,000
Speaker 1: get in there, she knows when I show up. She's

1067
01:02:09,040 --> 01:02:12,720
Speaker 1: got some strategy that I don't understand that allows her

1068
01:02:12,960 --> 01:02:16,520
Speaker 1: to figure out when she's being hunted, and she she

1069
01:02:16,560 --> 01:02:20,160
Speaker 1: goes completely nocturnal, whereas before the season I get pictures

1070
01:02:20,160 --> 01:02:22,640
Speaker 1: of her in the daylight all the time, and I

1071
01:02:22,720 --> 01:02:25,480
Speaker 1: never get daylight pictures of her after the season starts. Ever,

1072
01:02:26,280 --> 01:02:28,240
Speaker 1: I had I had a deer in Louisiana. I called

1073
01:02:28,320 --> 01:02:33,320
Speaker 1: him slick that this deer. I hunted him twelve different times.

1074
01:02:33,320 --> 01:02:36,320
Speaker 1: I'm I hunted this animal, and I had pictures of

1075
01:02:36,400 --> 01:02:40,720
Speaker 1: him twice of the twelve hunts. The two pictures I

1076
01:02:40,760 --> 01:02:44,200
Speaker 1: got of him were between hunts when I did not

1077
01:02:44,400 --> 01:02:48,120
Speaker 1: go to hunt him, and both of those times they

1078
01:02:48,120 --> 01:02:50,560
Speaker 1: were like four o'clock in the afternoon. He was on

1079
01:02:50,640 --> 01:02:53,680
Speaker 1: he was walking by the camera. That deer knew exactly

1080
01:02:53,720 --> 01:02:56,200
Speaker 1: what I was doing. And I ended up killing him

1081
01:02:56,240 --> 01:02:59,840
Speaker 1: on the hunt. And the only reason I did is

1082
01:02:59,880 --> 01:03:02,000
Speaker 1: I was I had decided I was going to go

1083
01:03:02,080 --> 01:03:05,880
Speaker 1: in midday, and I had been leaving around ten ish,

1084
01:03:05,880 --> 01:03:09,120
Speaker 1: ten thirty ish and coming back if the wind was right,

1085
01:03:10,040 --> 01:03:12,640
Speaker 1: uh for an afternoon, you know, two o'clock or whatever,

1086
01:03:13,400 --> 01:03:15,560
Speaker 1: because we usually have a lot going on at my

1087
01:03:15,680 --> 01:03:17,560
Speaker 1: camp during the middle of the day. Well, I was

1088
01:03:17,640 --> 01:03:20,800
Speaker 1: by myself, so there was nothing going on. I laid

1089
01:03:20,800 --> 01:03:22,640
Speaker 1: down in the bed to take a you know, try

1090
01:03:22,680 --> 01:03:24,560
Speaker 1: to nap for a little while, and I just couldn't.

1091
01:03:24,760 --> 01:03:27,120
Speaker 1: I just couldn't. It was like twelve fifteen. I was like,

1092
01:03:27,920 --> 01:03:31,880
Speaker 1: the pastor's there, he's there. So I got up, I

1093
01:03:31,960 --> 01:03:35,160
Speaker 1: put my clothes on, and I went out to where

1094
01:03:35,160 --> 01:03:37,360
Speaker 1: I was. I thought about parking, and I was like,

1095
01:03:37,400 --> 01:03:41,959
Speaker 1: you know what, this deers bedding right here. I'm gonna

1096
01:03:41,960 --> 01:03:44,400
Speaker 1: have to walk farther. So I took all my clothes off,

1097
01:03:44,920 --> 01:03:48,680
Speaker 1: down to literally long underwear, put all my stuff in

1098
01:03:48,680 --> 01:03:53,520
Speaker 1: my backpack, and started walking. And no kidding, when I

1099
01:03:53,600 --> 01:03:55,920
Speaker 1: got to the base of the stand, he crossed the

1100
01:03:56,000 --> 01:03:59,720
Speaker 1: right away in front of me, climbed up in the stand,

1101
01:04:00,240 --> 01:04:03,480
Speaker 1: put my clothes on really quick, got situated in Twenty

1102
01:04:03,480 --> 01:04:06,400
Speaker 1: minutes later, I killed him. And the only reason I

1103
01:04:06,480 --> 01:04:09,880
Speaker 1: killed him is he was chasing a dough and I'm

1104
01:04:09,920 --> 01:04:13,160
Speaker 1: convinced that he was. He had been betting to where

1105
01:04:13,320 --> 01:04:16,880
Speaker 1: I could not get to that stand or another stand

1106
01:04:16,960 --> 01:04:20,040
Speaker 1: that's nearby, on either wind. I could not get there

1107
01:04:20,080 --> 01:04:23,360
Speaker 1: without him knowing that I was there. He was betting

1108
01:04:23,360 --> 01:04:27,280
Speaker 1: in a tiny little sliver of cover, and he had

1109
01:04:27,320 --> 01:04:30,200
Speaker 1: a strategy, and that strategy was there's no way to

1110
01:04:30,240 --> 01:04:34,120
Speaker 1: get in here without me knowing that there's danger, and

1111
01:04:34,240 --> 01:04:37,200
Speaker 1: that day he was not there. He was off chasing

1112
01:04:37,200 --> 01:04:40,320
Speaker 1: that dough, and he was a little farther north than

1113
01:04:40,360 --> 01:04:43,440
Speaker 1: he had been the previous twelve hunts. And I'm convinced

1114
01:04:43,440 --> 01:04:45,800
Speaker 1: that's the only reason I killed him. It's just because

1115
01:04:45,840 --> 01:04:48,480
Speaker 1: he made a mistake. He had my number. If he

1116
01:04:48,520 --> 01:04:51,040
Speaker 1: had not, if he had been betted there where he

1117
01:04:51,120 --> 01:04:53,400
Speaker 1: had been, I would never have killed that deer, because

1118
01:04:53,400 --> 01:04:57,000
Speaker 1: there's no way you couldn't get to him. Yeah, I

1119
01:04:57,040 --> 01:04:59,840
Speaker 1: think we I think one of the biggest things we

1120
01:05:00,080 --> 01:05:07,360
Speaker 1: underestimate about deer and their abilities is how how even

1121
01:05:07,400 --> 01:05:10,360
Speaker 1: as good as we think we are, how many clues

1122
01:05:10,400 --> 01:05:13,000
Speaker 1: we give them that we're there and we were there,

1123
01:05:13,480 --> 01:05:15,520
Speaker 1: and how quickly they get in tune to that, you know.

1124
01:05:15,600 --> 01:05:17,240
Speaker 1: And I mean we talk about this all the time,

1125
01:05:17,280 --> 01:05:19,840
Speaker 1: but I think we just really underestimate how good they

1126
01:05:19,880 --> 01:05:24,000
Speaker 1: are at just knowing us so well in our patterns

1127
01:05:24,000 --> 01:05:26,440
Speaker 1: and just going and reacting to them in a way

1128
01:05:27,080 --> 01:05:31,800
Speaker 1: that we really have a hard time beating. Oh sure, yeah, man,

1129
01:05:31,840 --> 01:05:34,080
Speaker 1: we we do so many things to tip our hand,

1130
01:05:34,720 --> 01:05:36,240
Speaker 1: you know. And I do it all the time, and

1131
01:05:36,280 --> 01:05:39,400
Speaker 1: I don't try to. I try to avoid everything, you know.

1132
01:05:39,760 --> 01:05:46,160
Speaker 1: I'm fanatical about scent. Uh my my kids have UM,

1133
01:05:46,520 --> 01:05:48,720
Speaker 1: we'll tell you. You know, when you walk behind me,

1134
01:05:48,760 --> 01:05:51,400
Speaker 1: I'm like, you know, put your foot right where my

1135
01:05:51,480 --> 01:05:54,880
Speaker 1: foot hit. You know. I try to do everything to

1136
01:05:54,920 --> 01:05:57,120
Speaker 1: be as quiet as I can. I don't talk and

1137
01:05:57,160 --> 01:06:00,400
Speaker 1: I stand. I turned my phone off off. I just

1138
01:06:00,480 --> 01:06:04,880
Speaker 1: sit there motionless. Um. I literally was sit in a

1139
01:06:04,960 --> 01:06:08,400
Speaker 1: standing like a statue. I just sit there and I

1140
01:06:08,480 --> 01:06:12,680
Speaker 1: do everything. Yet I had to get to the stand. Well,

1141
01:06:12,720 --> 01:06:15,560
Speaker 1: I just tipped off somebody, whether they blew at you

1142
01:06:15,760 --> 01:06:17,240
Speaker 1: or you saw him or heard him or not, you

1143
01:06:17,280 --> 01:06:20,680
Speaker 1: tipped off somebody. Uh. You know, I had to drive

1144
01:06:20,760 --> 01:06:22,840
Speaker 1: to the you know, I had to drive to where

1145
01:06:22,880 --> 01:06:25,920
Speaker 1: I was I was going to hunt. Well, that gravel

1146
01:06:26,040 --> 01:06:29,280
Speaker 1: roads clicking, clicking, clicking click. You know, there's gravel clicking.

1147
01:06:29,320 --> 01:06:31,680
Speaker 1: There's all these things that we do that are accused

1148
01:06:32,040 --> 01:06:35,880
Speaker 1: to them that tips our hand and there I think

1149
01:06:35,920 --> 01:06:38,480
Speaker 1: there's just some animal. I know, there's just some animals that,

1150
01:06:39,600 --> 01:06:41,640
Speaker 1: uh that you're not going to kill. We had a

1151
01:06:41,640 --> 01:06:45,240
Speaker 1: turkey like this years ago on a study that turkey

1152
01:06:45,280 --> 01:06:48,000
Speaker 1: spent his entire spring season within a hundred yards of

1153
01:06:48,040 --> 01:06:51,200
Speaker 1: the primary access trail used by hunters on that site.

1154
01:06:52,080 --> 01:06:55,600
Speaker 1: Hundreds of people walked by this bird, and I have

1155
01:06:55,680 --> 01:06:58,000
Speaker 1: no idea how he how he did it, but he

1156
01:06:58,040 --> 01:07:01,400
Speaker 1: did it. He just stayed there, he dealt with the pressure.

1157
01:07:01,560 --> 01:07:03,680
Speaker 1: Did he squat down and let us walk by? I

1158
01:07:04,000 --> 01:07:06,080
Speaker 1: have no idea, but all I know is that bird

1159
01:07:06,160 --> 01:07:11,080
Speaker 1: survived in immense pressure and he had a strategy to

1160
01:07:11,160 --> 01:07:13,080
Speaker 1: do it. And deer are the same way. There are

1161
01:07:13,120 --> 01:07:15,400
Speaker 1: just some animals that they have a strategy like that

1162
01:07:15,440 --> 01:07:18,600
Speaker 1: pob all do. I will very likely never kill her,

1163
01:07:18,680 --> 01:07:22,880
Speaker 1: and that's okay, um, but I'm gonna try some different

1164
01:07:22,880 --> 01:07:27,480
Speaker 1: tricks next year and see, but she'll probably win. Well.

1165
01:07:27,520 --> 01:07:29,280
Speaker 1: What's what's really cool about that in the in the

1166
01:07:29,360 --> 01:07:33,080
Speaker 1: in the Turkey too that you're talking about is we

1167
01:07:33,080 --> 01:07:35,280
Speaker 1: we tend to think, Okay, you know, I hunt tons

1168
01:07:35,280 --> 01:07:37,200
Speaker 1: of public land, right like I go, I wait till

1169
01:07:37,240 --> 01:07:39,600
Speaker 1: hunt public land in a bunch of different states every year,

1170
01:07:40,320 --> 01:07:42,680
Speaker 1: and you know, the goal is kind of treated like

1171
01:07:42,680 --> 01:07:45,120
Speaker 1: an elk hunt, right like get to the spot, however

1172
01:07:45,200 --> 01:07:46,800
Speaker 1: far you gotta go, how much work you gotta do

1173
01:07:46,840 --> 01:07:49,000
Speaker 1: to get to the spot. People don't go that's where

1174
01:07:49,040 --> 01:07:53,120
Speaker 1: the deer will be. And sometimes it works, and sometimes

1175
01:07:53,240 --> 01:07:56,040
Speaker 1: you find those concentrations of deer sign right by the

1176
01:07:56,080 --> 01:07:59,120
Speaker 1: parking lot and you start hunting there and you realize

1177
01:07:59,120 --> 01:08:02,280
Speaker 1: like there's to survive strategies, kind of like a bass

1178
01:08:02,280 --> 01:08:04,800
Speaker 1: fish a lot. And you notice some bass, especially bigger

1179
01:08:04,840 --> 01:08:07,160
Speaker 1: beast tend seemed like they're ambushers, right they get in

1180
01:08:07,200 --> 01:08:10,000
Speaker 1: the shadow of that stump, there's waiting for food to come.

1181
01:08:10,000 --> 01:08:11,600
Speaker 1: But then you see these little wolf packs of the

1182
01:08:11,600 --> 01:08:15,120
Speaker 1: fourteen fifteen inches, they're out hunting, you know, like they're

1183
01:08:15,160 --> 01:08:16,800
Speaker 1: they're not just you know what I mean, like, and

1184
01:08:16,840 --> 01:08:19,040
Speaker 1: it's two different strategies. And then you look at the

1185
01:08:19,040 --> 01:08:21,040
Speaker 1: white tails and you go, Okay, some of them are

1186
01:08:21,040 --> 01:08:23,599
Speaker 1: finding those places where people are just ignoring them because

1187
01:08:23,600 --> 01:08:25,400
Speaker 1: they gotta you know, climb up to get them across

1188
01:08:25,439 --> 01:08:27,720
Speaker 1: a river or something. The other ones are just like,

1189
01:08:27,760 --> 01:08:30,080
Speaker 1: you know what, you guys are so predictable. I'm just

1190
01:08:30,120 --> 01:08:32,120
Speaker 1: gonna use that to my advantage. I'm gonna let you

1191
01:08:32,160 --> 01:08:34,000
Speaker 1: walk right by me, and because I know how you're

1192
01:08:34,000 --> 01:08:35,800
Speaker 1: gonna come in, I know when you're gonna come in,

1193
01:08:35,880 --> 01:08:37,880
Speaker 1: I know where you're gonna leave. And they just use

1194
01:08:37,960 --> 01:08:39,600
Speaker 1: that to their advantage until you throw them for a

1195
01:08:39,640 --> 01:08:42,519
Speaker 1: loop or like you said on that one deer, you know,

1196
01:08:42,840 --> 01:08:44,760
Speaker 1: the dough gets the best of him for just a

1197
01:08:44,760 --> 01:08:47,360
Speaker 1: little bit and you get that edge. Yeah. Yeah, And

1198
01:08:47,439 --> 01:08:50,160
Speaker 1: research has shown that, you know, GPS work on bucks

1199
01:08:50,160 --> 01:08:53,480
Speaker 1: has shown that, you know, some of them have certain strategies.

1200
01:08:53,640 --> 01:08:56,160
Speaker 1: For instance, during the rut, you know certain some bucks

1201
01:08:56,200 --> 01:08:58,960
Speaker 1: do a certain thing and others do something else. And

1202
01:09:00,080 --> 01:09:04,040
Speaker 1: but then when you when you look at these behaviors

1203
01:09:04,120 --> 01:09:08,360
Speaker 1: or these strategies, you also see that how one buck

1204
01:09:08,439 --> 01:09:11,439
Speaker 1: does it is not identical to how another buck does it.

1205
01:09:11,680 --> 01:09:14,800
Speaker 1: So they may have the same broader strategy of well

1206
01:09:14,840 --> 01:09:18,080
Speaker 1: maybe i'll move more, I'll course through my range, I'll

1207
01:09:18,120 --> 01:09:20,360
Speaker 1: try to find as many those as I can, But

1208
01:09:20,479 --> 01:09:23,519
Speaker 1: how they do that looks a little different by you know,

1209
01:09:23,600 --> 01:09:28,080
Speaker 1: by individual. Just speaking to that that you know, variation

1210
01:09:28,200 --> 01:09:33,000
Speaker 1: across these guys and and how they behave and um,

1211
01:09:33,040 --> 01:09:35,880
Speaker 1: you know, and again research is showing this on all

1212
01:09:35,960 --> 01:09:41,760
Speaker 1: sorts of species. Now you know these behavioral strategies, elk, wolves, coyotes,

1213
01:09:41,800 --> 01:09:44,800
Speaker 1: I mean, they all have these strategies, but there's a

1214
01:09:44,800 --> 01:09:49,000
Speaker 1: lot of variation within those strategies. Yea, yeah, you kind

1215
01:09:49,000 --> 01:09:51,519
Speaker 1: of have to. I mean, so we we we filmed

1216
01:09:51,520 --> 01:09:54,720
Speaker 1: a project last year called One Week in November and

1217
01:09:54,880 --> 01:09:56,680
Speaker 1: one of the places they hunted as a farm in

1218
01:09:56,840 --> 01:10:00,639
Speaker 1: southwestern Minnesota. It was my first story of south western Wisconsin.

1219
01:10:00,720 --> 01:10:03,320
Speaker 1: It was my first year hunting this place. You know,

1220
01:10:03,320 --> 01:10:05,679
Speaker 1: I scouted it quite a bit, and it's bluffy stuff,

1221
01:10:05,720 --> 01:10:09,559
Speaker 1: so there's really nice pinch points and funnels and you know,

1222
01:10:09,720 --> 01:10:11,880
Speaker 1: pretty this stuff. I grew up hunting, so I was like,

1223
01:10:12,000 --> 01:10:16,000
Speaker 1: you know, I got this. And after hunting there and

1224
01:10:16,120 --> 01:10:18,720
Speaker 1: seeing how some of those bucks crews and how they

1225
01:10:18,840 --> 01:10:21,640
Speaker 1: use the highway and some of these places where I

1226
01:10:21,720 --> 01:10:23,880
Speaker 1: was like, you know, this is gonna be almost a

1227
01:10:23,920 --> 01:10:27,200
Speaker 1: non factor. It just makes it makes you realize when

1228
01:10:27,200 --> 01:10:30,200
Speaker 1: you do that stuff, you're like, man, they even though

1229
01:10:30,360 --> 01:10:33,200
Speaker 1: like there is that individual variability, like you said, with

1230
01:10:33,240 --> 01:10:35,720
Speaker 1: all these deer and all these animals, they just have

1231
01:10:35,840 --> 01:10:38,720
Speaker 1: these tendencies that a lot of times we don't we're

1232
01:10:38,800 --> 01:10:40,680
Speaker 1: either not aware of or we just kind of like

1233
01:10:40,760 --> 01:10:43,040
Speaker 1: downplay it because we want to sit somewhere, like we

1234
01:10:43,080 --> 01:10:44,920
Speaker 1: want to sit where we can see or we you know,

1235
01:10:44,920 --> 01:10:46,400
Speaker 1: like we don't want to be on that hillside right

1236
01:10:46,439 --> 01:10:50,240
Speaker 1: over the county road. And those deer they're playing that

1237
01:10:50,280 --> 01:10:52,760
Speaker 1: game where they know us so well and they know

1238
01:10:52,760 --> 01:10:55,280
Speaker 1: where they're visible from the roads they know where they

1239
01:10:55,320 --> 01:10:57,120
Speaker 1: feed in those fields, or you know, the does are

1240
01:10:57,160 --> 01:10:58,760
Speaker 1: responding to that, and the bucks are responding to that,

1241
01:10:58,800 --> 01:11:01,120
Speaker 1: and they're they're up reading on a level that's so

1242
01:11:01,280 --> 01:11:05,200
Speaker 1: freaking cool that way, Yeah, which makes hunting them fun.

1243
01:11:05,400 --> 01:11:08,120
Speaker 1: That's one of the reasons it's you know, we're from

1244
01:11:08,160 --> 01:11:11,280
Speaker 1: fanatics about doing it is the uncertainty, you know, if

1245
01:11:11,280 --> 01:11:14,920
Speaker 1: it if I kind of see this, and I've talked

1246
01:11:14,960 --> 01:11:18,280
Speaker 1: with other people that I hunt with about this. You know,

1247
01:11:19,040 --> 01:11:22,280
Speaker 1: at some level, we're supposed to lose most of these battles,

1248
01:11:22,920 --> 01:11:25,200
Speaker 1: you know. I mean, we we have so many tools

1249
01:11:25,240 --> 01:11:28,919
Speaker 1: now available to us to help us be more efficient

1250
01:11:29,000 --> 01:11:32,439
Speaker 1: hunters and more effective, but at the end of the day,

1251
01:11:33,080 --> 01:11:35,559
Speaker 1: we're not supposed to win more often than we lose.

1252
01:11:35,640 --> 01:11:39,880
Speaker 1: And and so losing and failing is that's part of hunting.

1253
01:11:39,920 --> 01:11:43,720
Speaker 1: In fact, I believe that's one of the most important

1254
01:11:43,760 --> 01:11:47,680
Speaker 1: aspects of hunting is failure, is failing at at the

1255
01:11:47,760 --> 01:11:50,400
Speaker 1: hunt and learning from the experiences that you have. I've

1256
01:11:50,400 --> 01:11:52,720
Speaker 1: said this on other podcasts. People have asked me, you

1257
01:11:52,720 --> 01:11:55,880
Speaker 1: know what, what's an important take on is like fail

1258
01:11:56,560 --> 01:12:00,080
Speaker 1: failing is a is a good thing. Failing get in

1259
01:12:00,200 --> 01:12:03,840
Speaker 1: your ass kicked over and over and over can be

1260
01:12:03,920 --> 01:12:07,680
Speaker 1: impactful if you let it be, if you let it

1261
01:12:07,840 --> 01:12:12,000
Speaker 1: drive you um to think more, to be more reflective,

1262
01:12:12,080 --> 01:12:14,720
Speaker 1: to be more introspective about what you're doing, how you're

1263
01:12:14,720 --> 01:12:20,639
Speaker 1: doing it. Failure becomes the seed of of passion, I think,

1264
01:12:21,200 --> 01:12:23,639
Speaker 1: and I think that was the case with me. I lost.

1265
01:12:24,520 --> 01:12:27,240
Speaker 1: Like I said, I grew up. I didn't have a

1266
01:12:27,240 --> 01:12:29,240
Speaker 1: lot of animals to hunt that, you know, But I

1267
01:12:29,320 --> 01:12:32,280
Speaker 1: kept going. I kept going. I kept going. Finally, you know,

1268
01:12:32,400 --> 01:12:36,719
Speaker 1: I'd started being more successful, and then I started creating

1269
01:12:36,760 --> 01:12:39,640
Speaker 1: opportunities for myself because I was a better hunter. I was.

1270
01:12:40,240 --> 01:12:42,960
Speaker 1: I was a student of the animal, not just killing

1271
01:12:43,000 --> 01:12:45,920
Speaker 1: the animal, but learning about the animal and their ecology

1272
01:12:45,920 --> 01:12:49,080
Speaker 1: and their behavior. And that made me a better hunter.

1273
01:12:49,120 --> 01:12:52,320
Speaker 1: And then as I became a better hunter, success was

1274
01:12:52,400 --> 01:12:55,679
Speaker 1: not so fleeting that I was concerned about it anymore.

1275
01:12:56,200 --> 01:12:58,439
Speaker 1: I knew I was going to be successful at some point.

1276
01:13:00,080 --> 01:13:02,600
Speaker 1: Joy the experience. Now, get out there and learn as

1277
01:13:02,680 --> 01:13:05,400
Speaker 1: much as you can and just relax and let things

1278
01:13:05,439 --> 01:13:09,120
Speaker 1: come to you. And I've told some quite a few

1279
01:13:09,160 --> 01:13:13,240
Speaker 1: younger hunters that I've interacted with in my life that

1280
01:13:13,240 --> 01:13:16,960
Speaker 1: that's that's my hunting philosophy and the nutshells. Don't push it.

1281
01:13:17,439 --> 01:13:21,519
Speaker 1: Just let things happen as they're going to happen. Be smart,

1282
01:13:21,680 --> 01:13:24,080
Speaker 1: you know, like we're talking about dear the winds wrong,

1283
01:13:24,120 --> 01:13:27,120
Speaker 1: don't hunt him, don't go there. If if you don't

1284
01:13:27,160 --> 01:13:30,360
Speaker 1: feel like you're comfortable accessing a place, don't go in there.

1285
01:13:30,720 --> 01:13:34,120
Speaker 1: Figure out a better way to do it. Um understand

1286
01:13:34,120 --> 01:13:37,160
Speaker 1: that your first hunt is often your best shot. So

1287
01:13:37,560 --> 01:13:40,680
Speaker 1: if you're after a particular animal, sometimes you need that

1288
01:13:40,800 --> 01:13:44,240
Speaker 1: first hunt to be the the odds need to be

1289
01:13:44,240 --> 01:13:47,240
Speaker 1: tipped in your favor that first time after him. So

1290
01:13:47,360 --> 01:13:50,560
Speaker 1: sometimes that takes being patient and not going to a

1291
01:13:51,040 --> 01:13:53,080
Speaker 1: you know, to hunt a certain animal. That's what I've

1292
01:13:53,120 --> 01:13:56,840
Speaker 1: done in my in my career, uh is just try

1293
01:13:56,920 --> 01:14:01,240
Speaker 1: to slow down sometimes, try to try to let the

1294
01:14:01,600 --> 01:14:04,519
Speaker 1: process work out and be patient. And if you have

1295
01:14:04,640 --> 01:14:07,040
Speaker 1: time to hunt, and I'm lucky enough I have, you know,

1296
01:14:07,120 --> 01:14:09,960
Speaker 1: I will often have a few days in a row

1297
01:14:10,080 --> 01:14:11,879
Speaker 1: or a week in a row at at a particular

1298
01:14:11,920 --> 01:14:14,360
Speaker 1: spot that I can hunt. I can I can afford

1299
01:14:14,400 --> 01:14:17,679
Speaker 1: to be patient. When I'm not, That's when I usually

1300
01:14:17,680 --> 01:14:20,240
Speaker 1: screw up when I when I'm not, when I'm trying

1301
01:14:20,240 --> 01:14:22,639
Speaker 1: to force the issue that's when I usually make a mistake.

1302
01:14:22,880 --> 01:14:24,519
Speaker 1: And then I stepped back and go, you know, Mike,

1303
01:14:24,600 --> 01:14:27,360
Speaker 1: damn it, you knew better than that. You did it anyway,

1304
01:14:27,520 --> 01:14:31,320
Speaker 1: you know, so try to try to slow down. If

1305
01:14:31,320 --> 01:14:33,439
Speaker 1: you don't have that happen to you about three thousand

1306
01:14:33,479 --> 01:14:38,519
Speaker 1: times in your career, you'll never be a good deer hunter. Yeah.

1307
01:14:38,560 --> 01:14:42,160
Speaker 1: I did it this this last year too, and um,

1308
01:14:42,200 --> 01:14:45,040
Speaker 1: this past year season was quite a challenge for me.

1309
01:14:45,840 --> 01:14:48,719
Speaker 1: Um in a lot of ways. But but I screwed

1310
01:14:48,800 --> 01:14:51,000
Speaker 1: up one hunt and and I just thought to myself,

1311
01:14:51,040 --> 01:14:54,000
Speaker 1: I was like, what are you doing? Like, what are

1312
01:14:54,040 --> 01:14:56,200
Speaker 1: you doing? Why did you even go to this stand?

1313
01:14:56,760 --> 01:14:59,200
Speaker 1: Like you shouldn't even be sitting here? What were you

1314
01:14:59,280 --> 01:15:02,439
Speaker 1: thinking to do this? And I even had a friend

1315
01:15:02,439 --> 01:15:04,200
Speaker 1: of mine text me and say where you at? And

1316
01:15:04,520 --> 01:15:06,599
Speaker 1: I told him to stand that I was on. He goes,

1317
01:15:06,720 --> 01:15:09,679
Speaker 1: the hell are you doing? And I was like, yeah,

1318
01:15:09,720 --> 01:15:12,240
Speaker 1: I just realized that. You know, He's like, man, that's

1319
01:15:12,320 --> 01:15:15,240
Speaker 1: You're smarter than that. It's just I had it in

1320
01:15:15,320 --> 01:15:17,400
Speaker 1: my head I was going to force the issue and

1321
01:15:17,400 --> 01:15:20,599
Speaker 1: and I have terrible hunt and I probably ended up

1322
01:15:20,680 --> 01:15:23,559
Speaker 1: educating the deer I was after. And as an aside,

1323
01:15:23,680 --> 01:15:26,400
Speaker 1: he wasn't he was not killed last season by anyone,

1324
01:15:27,320 --> 01:15:31,160
Speaker 1: maybe because of me. Well that that's what happens man.

1325
01:15:31,560 --> 01:15:35,160
Speaker 1: Uh so so so many words of wisdom there, Michael.

1326
01:15:35,240 --> 01:15:38,000
Speaker 1: Let's let's wrap this sucker up. Um. I really appreciate

1327
01:15:38,080 --> 01:15:40,599
Speaker 1: you coming on, and I hope you had a nice

1328
01:15:40,600 --> 01:15:43,320
Speaker 1: little break from talking turkeys and talking deer today. But

1329
01:15:43,680 --> 01:15:46,040
Speaker 1: it was a pleasure, men. Yeah, no, I was. I

1330
01:15:46,120 --> 01:15:48,479
Speaker 1: was glad to join. It's it's uh, it's good to

1331
01:15:48,520 --> 01:15:51,719
Speaker 1: talk about deer during the middle of spring turkey season. Uh.

1332
01:15:51,840 --> 01:15:56,439
Speaker 1: I've certainly been talking about turkeys a lot, so I'm sure. Well,

1333
01:15:56,479 --> 01:16:00,400
Speaker 1: thank you. Not a problem, not a problem. That's it

1334
01:16:00,479 --> 01:16:03,200
Speaker 1: for this show, folks. Be sure to tune in next

1335
01:16:03,280 --> 01:16:06,719
Speaker 1: week for more white tail talk. This has been Where

1336
01:16:06,760 --> 01:16:09,479
Speaker 1: to Hunt, and I'm your guest host, Tony Peterson. As always,

1337
01:16:09,520 --> 01:16:12,719
Speaker 1: I can't thank you enough for your support, for listening

1338
01:16:12,720 --> 01:16:15,080
Speaker 1: and for checking out our other white tail content, which

1339
01:16:15,120 --> 01:16:18,120
Speaker 1: is available at the meat eater dot com slash wired,

1340
01:16:18,240 --> 01:16:20,160
Speaker 1: where you can see a bunch of articles by Mark

1341
01:16:20,240 --> 01:16:24,439
Speaker 1: myself and a bunch of white tail addicts, or on

1342
01:16:24,479 --> 01:16:27,280
Speaker 1: our YouTube channel, the wire to Hunt YouTube channel, where

1343
01:16:27,320 --> 01:16:29,519
Speaker 1: we post weekly how to videos.