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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. Hey'll,

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Speaker 1: welcome to Where to Hunt. I'm your guest host Tony Peterson,

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Speaker 1: and today we're talking with dear researcher Dr John McRoberts

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Speaker 1: about a radio collared buck that walked nearly two hundred miles.

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Speaker 1: Welcome to Wired to Hunt, which is brought to you

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Speaker 1: by First Light. I am your guest host, Tony Peterson.

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Speaker 1: Mark is out of the office this week. He is

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Speaker 1: down I think he said at a Cosplay convention in Tuscaloosa.

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Speaker 1: He said he was gonna be a anime samurai or something. Anyway,

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Speaker 1: I hope Mark's having fun down there. I've got the

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Speaker 1: rains to Where to Hunt today, and I've got a

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Speaker 1: fascinating guest with me. His name is Dr John McRoberts,

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Speaker 1: and he's done all kinds of wildlife research in his life,

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Speaker 1: some really cool international studies, but he's also led some

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Speaker 1: studies here in the States, including a pretty comprehensive two

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Speaker 1: regions study in Missouri that they're still parsing through all

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Speaker 1: the data. But it's the one that came uh that

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Speaker 1: came out recently where this buck that they had Collard

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Speaker 1: walked nearly two hundred miles from his home range as

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Speaker 1: a three and a half year old and kind of

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Speaker 1: set the dear world on fire a little bit, because

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Speaker 1: a lot of us think that dear you know, stick

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Speaker 1: to a core range, they disperse when they're young bucks,

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Speaker 1: and then they find a nice area they like and

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Speaker 1: they hang out there. And this GPS study kind of

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Speaker 1: turn that around on its head and really has has

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Speaker 1: opened up some possibilities that maybe that buck that you

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Speaker 1: think is going to live on your farm forever, he

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Speaker 1: could just lie out and take off. It also kind

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Speaker 1: of explores the possibility that this was an anomalous one

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Speaker 1: off event. Uh. John has so much interesting information. It's

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Speaker 1: always it's always a pleasure to talk to wildlife researchers

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Speaker 1: who are also passionate hunters, and he definitely Uh is

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Speaker 1: that I think you're gonna absolutely love this episode. John,

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Speaker 1: Thank you so much for coming on the podcast. Thank you, Tony,

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Speaker 1: it's a pleasure to be here with you today. So

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Speaker 1: you're you're like a secret celebrity in the hunting industry

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Speaker 1: right now because you're you're you're one of the one

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Speaker 1: of the folks behind this research study that's getting all

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Speaker 1: this this crazy press in the in the white tail space,

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Speaker 1: specifically on this buck who took a crazy journey that

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Speaker 1: you guys tracked. And we're gonna get to that a

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Speaker 1: little later, but let's let's talk a little bit about

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Speaker 1: how you got into wildlife biology and you've you've done

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Speaker 1: some really cool research not just with white tales but

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Speaker 1: but other game animals as well. Where did that come from? Well,

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Speaker 1: like a lot of biologists these days, I got interested

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Speaker 1: in the field because I grew up hunting. I mean

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Speaker 1: from a young age. My dad had me in the

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Speaker 1: duck blind with him, and then that transferred to other

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Speaker 1: hunting opportunities. Growing up in Missouri and we were a

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Speaker 1: family farming. UH had that background going for me and

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Speaker 1: so always access to a spot to hunt. And that

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Speaker 1: interest in hunting blossomed into an undergraduate degree and Fisheries

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Speaker 1: and Wildlife from the University of Missouri. And I was

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Speaker 1: never a great student and grad's grad school was never

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Speaker 1: on my radar, but I was having a lot of

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Speaker 1: fun studying wildlife and helping with different research projects led

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Speaker 1: me to some interesting travels and it just kept kept snowballing.

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Speaker 1: So you wanted to your growing up with your background hunting,

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Speaker 1: hunting and fishing in Missouri, you knew you wanted to

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Speaker 1: be around that, you know, and there's only so many

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Speaker 1: career paths to take, right. You could be a conservation

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Speaker 1: officer maybe, or you can you can get into science.

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Speaker 1: That's kind of kind of it, right, Yeah. And one

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Speaker 1: of the funny things was I didn't even connect the

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Speaker 1: dots when I was high school and younger that you

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Speaker 1: could do this as a career. And so I knew

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Speaker 1: that there were folks working in the in the field,

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Speaker 1: but I didn't know how you got there. And so

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Speaker 1: I started off as a as a biochemistry major in

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Speaker 1: college and then figured out there was a fisheries and

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Speaker 1: wildlife program and slowly started trick laing over into that,

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Speaker 1: into that space. And it was a fantastic life decision.

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Speaker 1: And I've never looked back. And you ended up in

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Speaker 1: a in a position where you get to ask a question.

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Speaker 1: You so something in your experience makes you ask this

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Speaker 1: big question that you that you get to design a

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Speaker 1: study around, to work with some people to design a

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Speaker 1: study around. When you were growing up, was it, you know,

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Speaker 1: were you looking at those green heads coming in or

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Speaker 1: those deer walking through the field, were you thinking Do

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Speaker 1: you feel like you were thinking a little deeper about

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Speaker 1: it than the average hunter, where you're like, why are

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Speaker 1: those deer here? Why do they do this? Or I

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Speaker 1: wouldn't say thinking deeper. All of us as hunters are

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Speaker 1: trying to figure out what the next step is for

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Speaker 1: for those long beards, for those green heads, for whatever

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Speaker 1: we're after. And so, yeah, there are a lot of

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Speaker 1: questions from the deer stand and from the duck blind

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Speaker 1: and as you're walking the fields for pheasants, just trying

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Speaker 1: to figure out why are these species doing what they're doing,

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Speaker 1: Why are they in this location and not the other location?

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Speaker 1: And that was to be, you know, to enjoy hunting

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Speaker 1: a bit more, to understand what made these species make

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Speaker 1: the decisions they made. And so I think that's something

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Speaker 1: that all of us hunters share together, and I had

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Speaker 1: the good fortune to turn that into a career. Yeah,

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Speaker 1: you and I were just chatting before we started here

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Speaker 1: about you've got a little little, tiny baby at home,

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Speaker 1: and I was telling you about my time with raising

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Speaker 1: two babies at the same time time. And I think

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Speaker 1: one thing that kids remind you of, especially when they

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Speaker 1: get to a certain age, is they're they're just curious.

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Speaker 1: They're asking questions all the time. And it's so it

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Speaker 1: makes you think, like how jaded sometimes we can get

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Speaker 1: as adults, or how we can kind of just like

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Speaker 1: make up our mind that you know, X, Y and

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Speaker 1: Z or this way we don't have to think about

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Speaker 1: them anymore. And hunting fishing too, of course, but hunting

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Speaker 1: is sort of a nice conduit to curiosity, Like it

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Speaker 1: keeps you you know, because you're never gonna master it,

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Speaker 1: and it keeps you thinking because you see things out

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Speaker 1: there that you you just naturally have to question because

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Speaker 1: it's something new or something different. I think that's one

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Speaker 1: of the best things about it. I would agree entirely.

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Speaker 1: And it's you're you're right, you don't master it, but

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Speaker 1: you can hopefully get a bit better the more you

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Speaker 1: learn and the more you study. Yeah, and if somebody

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Speaker 1: tells you they've mastered it, there they're full of ship.

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Speaker 1: Feel free to run away. So you go to college,

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Speaker 1: you have this moment where you say, you know this

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Speaker 1: is this is a career. Fisheries and wildlife research is

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Speaker 1: something I could I could get into. Where where do

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Speaker 1: you go from there. Well, the next step was gaining

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Speaker 1: experience and the you know, the average college student in

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Speaker 1: this field would spend a few summers or a year

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Speaker 1: following graduation doing what we'd call technician work, and there

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Speaker 1: you're on a research project. And that is one of

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Speaker 1: the funniest parts of this whole career path because you're

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Speaker 1: getting such a diverse exposure to wildlife research. I uh did,

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Speaker 1: did tech work on blackfooted ferrets in eastern Montana. I

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Speaker 1: spent some time in South Africa, spent some time catching waterfowl,

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Speaker 1: and then right before college, I had an interesting chance

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Speaker 1: to go to Western China and work with the Smithsonian

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Speaker 1: doing panda research, and so spent six months in China

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Speaker 1: and then came back and started grad school at Texas

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Speaker 1: Tech and their wildlife program. What did you do in

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Speaker 1: South Africa? That was a variety of projects. I was

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Speaker 1: helping grad students, and so I did everything from radio

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Speaker 1: tracking leopards, which was you know, maybe the pinnacle, to

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Speaker 1: doing vegetation surveys, doing soil surveys. Spent a lot of

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Speaker 1: time digging in the dirt to get soil profiles, and

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Speaker 1: so everything was related to natural resources to wildlife, and

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Speaker 1: I bounced around among grad students and had a ball,

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Speaker 1: And that was really what solidified my my career path.

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Speaker 1: Was that semester in South Africa. Yeah, I'm going to

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Speaker 1: speculate here that growing up as a young man in Missouri,

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Speaker 1: you probably didn't see yourself in South Africa studying leopards

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Speaker 1: or in China studying pandas. No. I didn't. I didn't.

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Speaker 1: Did you Did you ever have any close calls with leopards? No?

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Speaker 1: I really didn't. I wish I had a good leopard story,

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Speaker 1: but they were They had VHF transmitters on them, and

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Speaker 1: we weren't trying to see how close we could get

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Speaker 1: to these leopards. We were trying to get a point

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Speaker 1: and triangulate and plot a location on their map. But

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Speaker 1: we didn't. We had very clear instructions from the professor

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Speaker 1: in charge not to Yeah, not to push it. Good call,

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Speaker 1: good advice, reasonable advice. Yeah, they I got to go

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Speaker 1: to Africa, South Africa in like I think two thousand

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Speaker 1: and seven, and where we were hunting, they said, you know,

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Speaker 1: we've got a pretty healthy leopard population. And when I

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Speaker 1: went over there, I was like, I would love to

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Speaker 1: see a leopard and I was sitting in a sort

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Speaker 1: of a makeshift blind. It was like they kind of

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Speaker 1: a test out spot first, So it was just it

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Speaker 1: wasn't like the big concrete ones they build that are

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Speaker 1: you know, almost impenetrable, right. And I was sitting there

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Speaker 1: and I had all these kudo just go blowing out

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Speaker 1: of there, and I was like, oh, that that was

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Speaker 1: so weird. I mean they took off like knocking over

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Speaker 1: trees and just crazy. And then I had a leopard

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Speaker 1: call behind me, and I was like, I don't want

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Speaker 1: to see a leopard. When you hear that guttural, just

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Speaker 1: you know, it sounds like a saw going through wood,

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Speaker 1: almost like a rough saw. I mean it's just that's

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Speaker 1: that is an animal that commands respect really quickly. Yeah, yea.

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Speaker 1: Through their vocalizations, they just convey power. Yeah. Yeah, they're

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Speaker 1: no joke. And you know that a lot of the

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Speaker 1: natives that we were around over there, that was what

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Speaker 1: they were most scared of. Well, they they deserve respect,

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Speaker 1: no doubt about that. Yeah. We're gonna get to deer

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Speaker 1: in a second. I gotta ask you. Any everybody looks

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Speaker 1: at pandas like they're these uh, sweet lovable teddy bear

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Speaker 1: type of things. Are they secretly kind of pricks or not.

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Speaker 1: I've never called him a prick before. But they aren't

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Speaker 1: the They aren't the cuddly animal that that they get

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Speaker 1: made out to be. I mean, there they you gotta

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Speaker 1: watch yourself around them. And so where I was working

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Speaker 1: was a breeding research facility looking at reproductive behavior oh

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Speaker 1: pain because of the captive breeding interest in this species.

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Speaker 1: And you didn't get too close to the bars on

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Speaker 1: the cage. I mean, you didn't get in there and

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Speaker 1: give them a big bear hug. They had stories of

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Speaker 1: workers at this facility that had been hurt and a

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Speaker 1: panda yawns and you see those canine teeth and it's

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Speaker 1: it's not the cute, cuddly animal that is made out

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Speaker 1: to be. Now, I did get to play with some

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Speaker 1: of the baby panda cubs and that was that was fun. Yeah,

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Speaker 1: I bet it's so interesting. So you so you kind

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Speaker 1: of you go through this phase in your research career

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Speaker 1: where you're starting out and you kind of get to

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Speaker 1: be a globe trotter, go do some really cool stuff.

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Speaker 1: What do you do when you end up back in

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Speaker 1: the States. Well, I want I got back to the

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Speaker 1: States in uh and started grad school in Texas and

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Speaker 1: was doing lesser prairie chicken research and so lesser prairie

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Speaker 1: chickens are getting a lot of press these days because

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Speaker 1: of listing potential and some some conflict and some disagreement

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Speaker 1: between different industries and conservation groups. And so got back

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Speaker 1: here after China and started doing a research project design

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Speaker 1: a aerial survey technique to find lex lesser prairie chicken

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Speaker 1: breeding areas from helicopters, and so got to spent two

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Speaker 1: years flying at low altitudes in Texas and New Mexico

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Speaker 1: developing the technique to find these birds and and get

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Speaker 1: a better idea of what populations were like. And it

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Speaker 1: was the goal. There was the assumption, you know, that

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Speaker 1: the populations were going down because the land use practices

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Speaker 1: and stuff, and you're trying to figure out what the

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Speaker 1: what the a more accurate way to determine populations to

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Speaker 1: follow them or what well it was to get baseline

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Speaker 1: information because everybody saw the writing on the wall that

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Speaker 1: the populations had been declining for a long time, for

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Speaker 1: decade and decades, and that had a companied land use changes,

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Speaker 1: and there were other factors in play. But to start

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Speaker 1: off with, with these conservation efforts, we needed to know

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Speaker 1: where birds were, where birds were not, and how to

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Speaker 1: survey their range, what was going on with them That

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Speaker 1: was knock in the population down. Um. It was more feature,

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Speaker 1: I mean, it was a variety of things. It was

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Speaker 1: different grazing practices, it was different land use practices putting

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Speaker 1: up features on the landscape like you know, telephone poles

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Speaker 1: for example, that provide purchase for raptors and that would

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Speaker 1: nail them. There were industries expanding in this area of

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Speaker 1: oil and gas ranching, industries that I'm not saying aren't

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Speaker 1: necessary to our our survival, but competition for for habitat,

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Speaker 1: and any conservationist, me, hunter, outdoor enthusiast knows that we

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Speaker 1: don't have the prairies like we used to down there.

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Speaker 1: It was short grass or mixed grass prairies, and like

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Speaker 1: everywhere else, those habitats were becoming more fragmented and prairie

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Speaker 1: grouse like prairie chickens or sharp tails or sage grouse,

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Speaker 1: they need big open spaces and they don't do well

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Speaker 1: with overhead cover, no even the appearance of overhead cover

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Speaker 1: right right now. And so that was an interesting project

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Speaker 1: finished up that and then again was not going to

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Speaker 1: go I was going to get out of school as

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Speaker 1: quick as I could. But I had a wonderful advisor

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Speaker 1: and decided to start a PhD project. Started one looking

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Speaker 1: at mule deer research, and then had the opportunity to

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Speaker 1: go to the Yucatan Peninsula and do some of the

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Speaker 1: first oscillated turkey research down in the Jungles in Mexico.

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Speaker 1: And so spent about four years down there catching oscillated turkeys,

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Speaker 1: putting transmitters on them and tracking their movements, their survival,

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Speaker 1: and learning as much we could about that species. What

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Speaker 1: what what was the meal dear research all about? Well,

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Speaker 1: that was that was looking at at habitat use in

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Speaker 1: New Mexico, and I started on that, but it was

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Speaker 1: only there for about three or four months before this

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Speaker 1: opportunity came up to two doing the turkey research. So

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Speaker 1: another student picked up the mule deers and I headed

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Speaker 1: south of the border. So I didn't get too deep

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Speaker 1: into that. Did you did you get to hunt oscillated

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Speaker 1: when you were down there at all? I did a

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Speaker 1: number of times, right place, at the right time. Do

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Speaker 1: you as a you know you're ensconsin academia in your research,

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Speaker 1: even though even though you're you're a chosen field is

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Speaker 1: wildlife biology, which implies hunting and conservation. Do you do

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Speaker 1: you find any times where you kind of have to

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Speaker 1: hide that, like, oh, I'm doing, you know, oscillated research,

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Speaker 1: but I'm also hunting these things. Or do you just

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Speaker 1: you know, wave it loud and proud. I I don't

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Speaker 1: it and I occasionally find folks who don't agree with it.

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Speaker 1: But there's a real great story with oscillated turkeys specifically

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Speaker 1: that ties in with hunting. And there's an area and

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Speaker 1: I'm gonna name drop the area because I'm sure some

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Speaker 1: of your your listeners have heard of Carlos Cana Cruz

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Speaker 1: or Los Flores. There are two management areas in the

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Speaker 1: state of Campeche and they are they're a commonplace for

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Speaker 1: hunters coming from the US to go down and harvest

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Speaker 1: an oscillated turkey. This area has without question, the most

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Speaker 1: dense population of oscillated turkeys in the world. Now, when

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Speaker 1: I say the world, we only find them in the

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Speaker 1: Yucatan in Mexico and the northern parts of Belize and Guatemala.

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Speaker 1: But the reason why populations are so dense is because

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Speaker 1: it's it's more economically practical for the locals to conserve

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Speaker 1: turkeys and not shoot them for the dinner pot each

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Speaker 1: day and then bring Americans down who are paying three

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Speaker 1: four thousand dollars into this community and you know, have

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Speaker 1: a good hunt. Hunters come through, they win, they get

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Speaker 1: their birds. You've got jobs for people, cooking for people,

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Speaker 1: driving for guides in a hunting guide down there. Is

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Speaker 1: one of the most lucrative business for these local Mayan

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Speaker 1: people in that area and so the reason why there

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Speaker 1: are are so many birds is because there's that conservation

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Speaker 1: and that value in place directly because of sport hunting. Yep. Yeah,

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Speaker 1: it's a there's just such a weird balance with you know.

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Speaker 1: I mean, you could call nobody really needs to go

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Speaker 1: hunt oscillated turkeys. It's like a cool trophy and kind

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Speaker 1: of the you know, the fifth subspecies in the in

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Speaker 1: the Grand Slam or the Super Slam, I guess or

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Speaker 1: whatever it would be for turkeys, but you can't, you know,

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Speaker 1: like trophy hunting is is sort of has a lot

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Speaker 1: of negative connotations throughout the world, and you know that's

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Speaker 1: what we get painted as all the time. But there's

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Speaker 1: you can't divorce those cottage industries and those economic benefits

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Speaker 1: from it like that. You can't. You can hate it

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Speaker 1: all you want, but you can't take away the fact

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Speaker 1: that some of those places that are really really economically challenged,

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Speaker 1: these these little industries rise up around there and the

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Speaker 1: conservation comes with it, and now all of a sudden,

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Speaker 1: it's pretty much win win win, even though not everybody

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Speaker 1: wants to acknowledge that, Yeah, it is the case. It's

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Speaker 1: an interesting hunt. It's a lot different than hunting turkeys

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Speaker 1: back in the US, and that's one of my favorite

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Speaker 1: things to do is turkey hunt. But when you're out

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Speaker 1: there and you've got the chance of seeing a jaguar

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Speaker 1: walk by, and you've got two cans up in the trees,

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Speaker 1: and you've got how our monkeys howling, it's it's a

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Speaker 1: really unique, interesting, well interesting part of the world. And

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Speaker 1: then when there's a hunt to go with it, is

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Speaker 1: anybody making an oscillated decoy? Um, yeah, I've got one

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Speaker 1: right here in my office. They don't work too well.

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Speaker 1: Or yeah, I had one made up thinking that I

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Speaker 1: could use it to help capture turkeys, and oscillated are

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Speaker 1: as wary as as I've ever seen. And I grew

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Speaker 1: up hunting turkeys easterns in Missouri, public land and private land, um,

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Speaker 1: and that can be a wary bird. When you get

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Speaker 1: an old tom but oscillated, it's just takes it to

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Speaker 1: the next level. And I think it's because that so

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Speaker 1: many predators down there in a oftentimes thick environment that

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Speaker 1: they're just they're neurotic. They're on all the time, thinking

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Speaker 1: that something's about to grab them. Is it? Is it

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Speaker 1: any part of that due to Probably I would just

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Speaker 1: assume there probably has been a pretty good history of

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Speaker 1: unregulated hunting of them as well. Yes, And that is

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Speaker 1: a would be a major mortality source of that bird

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Speaker 1: down there, is the subsistence hunting. And you know, it's

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Speaker 1: a poor part of of the country, Mexico specifically, and

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Speaker 1: so it's hard to fault people. I mean, you can't

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Speaker 1: fault them at all, and they need to feed their families.

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Speaker 1: But that is a major mortality source. We had radio

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Speaker 1: marked birds with radios that would end up in town,

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Speaker 1: that would end up hung on a you know, on

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Speaker 1: a t post somewhere along the road. And so the

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Speaker 1: subsistence hunters were definitely definitely taking some birds. Uh. I

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Speaker 1: know I keep saying we're gonna get into white ties.

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Speaker 1: I gotta ask you something else. I swear to God,

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Speaker 1: anybody who's listening, we're gonna get there. So the we

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Speaker 1: we have a little place on a lake in north

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Speaker 1: central Minnesota and they're doing a study on the walleye

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Speaker 1: movements right now because they're the natural reproduction. The take

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Speaker 1: is pretty high. The natural reproduction seems to be pretty low,

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Speaker 1: and a lot of the walleyes seem to be reaching

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Speaker 1: sexual maturity really small, because you know, there's not you're

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Speaker 1: not getting those two females that would kind of be

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Speaker 1: driving the reproduction in a lot of bodies of water.

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Speaker 1: And so they they I don't know how many walleyes

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Speaker 1: are part of this, it seems like a lot of them.

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Speaker 1: But they went up, fisheries went out and I'm assuming

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Speaker 1: the electro shocked um. And then they tagged a ton

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Speaker 1: of them and put transmitters in them, and then they

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Speaker 1: sunk some listening devices throughout the lake to to follow

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Speaker 1: the patterns and see if there's one damn on there

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Speaker 1: where if they go through it, they can't get back up.

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Speaker 1: And so they're like, are we are we losing them down? There,

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Speaker 1: and I get personally frustrated because I know a lot

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Speaker 1: of people fishing up there who are not calling in

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Speaker 1: or or you know, turning in any of the data.

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Speaker 1: They're just knife in those suckers and chucking the transmitters.

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Speaker 1: Like I I'm only assuming here, But it seems like

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Speaker 1: the compliance with with you know, this study from the

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Speaker 1: general population is really low. Have you bumped into that

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Speaker 1: and some of the stuff you've done, I mean, I

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Speaker 1: know that's a different kind of thing. Yeah, and you

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Speaker 1: get a little bit of everything. Um, And we can

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00:21:53,160 --> 00:21:56,040
Speaker 1: talk about this as we move into deer. But as

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Speaker 1: the technology improves, your capability to track wildlife species just

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Speaker 1: keeps improving. And so when I was talking about the turkeys,

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Speaker 1: we had a VHF transmitter that's very high frequency. That's

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Speaker 1: the one that beeps and you hold out your antenna

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00:22:13,359 --> 00:22:16,879
Speaker 1: and try and home in or trying triangulate to find

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Speaker 1: out where that animal is. When we started with this

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Speaker 1: deer project, we're going to discuss it's all GPS based

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Speaker 1: and so, um, it's harder for anybody not to comply

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Speaker 1: because it's all satellite based, and you know, I get

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Speaker 1: more worried about invading people's privacy than them taking a

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00:22:42,240 --> 00:22:46,399
Speaker 1: collar and not reporting it because if they throw it

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00:22:46,440 --> 00:22:49,399
Speaker 1: in the bed of their pickup truck, I still know

390
00:22:49,440 --> 00:22:52,720
Speaker 1: where that collar is. Yeah, I guess you don't. You

391
00:22:52,720 --> 00:22:56,840
Speaker 1: don't think about that that aspect of it. And so

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00:22:57,400 --> 00:23:02,320
Speaker 1: it's it's imperative that that people calling duck bands that

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00:23:02,800 --> 00:23:05,600
Speaker 1: if they harvest a marked animal, it's always helpful to

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00:23:05,680 --> 00:23:10,440
Speaker 1: know that. And we should all be on the same team.

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Speaker 1: I mean, this is this research hasn't done for our

396
00:23:13,280 --> 00:23:19,160
Speaker 1: own ships and giggles. It's for to inform well part

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00:23:19,160 --> 00:23:20,800
Speaker 1: of the part of the reason I asked that, right,

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Speaker 1: I wonder about that. So this this study where this

399
00:23:23,600 --> 00:23:26,719
Speaker 1: buck took this crazy walk and crossed all these rivers

400
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Speaker 1: and highways and interstates. The when you when you read

401
00:23:30,720 --> 00:23:33,719
Speaker 1: the research summary that you guys put out there the paper,

402
00:23:34,320 --> 00:23:37,199
Speaker 1: you know it mentions you know this, this could have

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Speaker 1: serious implications for c w D management. And you know,

404
00:23:41,880 --> 00:23:44,359
Speaker 1: I know, as somebody who's written about CWD a million times,

405
00:23:44,359 --> 00:23:47,720
Speaker 1: like I know, how like man, there's two factions, right,

406
00:23:48,320 --> 00:23:50,159
Speaker 1: and some people don't want to hear it, and so

407
00:23:50,240 --> 00:23:54,159
Speaker 1: I could see it. There's kind of, at least to

408
00:23:54,320 --> 00:23:56,120
Speaker 1: some extent, some people just kind of seem to want

409
00:23:56,119 --> 00:23:57,679
Speaker 1: to stick their heads in the sands say this is

410
00:23:57,720 --> 00:24:00,080
Speaker 1: not an issue and I'm not gonna worry about that.

411
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Speaker 1: And so I could see something like this where a

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00:24:02,600 --> 00:24:05,320
Speaker 1: study goes, hey, this buck walk two d miles, you know,

413
00:24:05,400 --> 00:24:07,359
Speaker 1: in a way we've never seen before, or you know,

414
00:24:07,400 --> 00:24:10,160
Speaker 1: beat the previous record by a hundred miles. I could

415
00:24:10,160 --> 00:24:12,359
Speaker 1: see people kind of being like, I don't you know

416
00:24:12,440 --> 00:24:15,120
Speaker 1: if this is gonna lead the more restrictions around CWD,

417
00:24:15,160 --> 00:24:17,000
Speaker 1: I don't want anything to do with this. You pull

418
00:24:17,040 --> 00:24:19,840
Speaker 1: their funding, whatever do you do you bump in anything

419
00:24:19,880 --> 00:24:24,280
Speaker 1: like that. Yeah, and people will use whatever results you have,

420
00:24:25,400 --> 00:24:30,520
Speaker 1: they'll spin them to align with their their personal values

421
00:24:30,640 --> 00:24:37,760
Speaker 1: or personal objectives. Luckily, most wildlife biologists and the state

422
00:24:37,840 --> 00:24:42,200
Speaker 1: agency we're working with through this research, the Missouri Department

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00:24:42,200 --> 00:24:47,439
Speaker 1: of Conservation, who have been fantastic and very valuable collaborators,

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00:24:47,440 --> 00:24:49,680
Speaker 1: and this whole research has been done hand in hand

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00:24:49,720 --> 00:24:52,639
Speaker 1: with them. But they get it that you manage for

426
00:24:52,720 --> 00:24:55,720
Speaker 1: a population, you don't manage for an individual. And so

427
00:24:56,359 --> 00:25:03,320
Speaker 1: we saw this this amazing uh dispersal, But we're not

428
00:25:03,440 --> 00:25:07,080
Speaker 1: going to or I wouldn't recommend that CWD regulations are

429
00:25:07,160 --> 00:25:10,520
Speaker 1: changed because we saw this and documented it. Once we

430
00:25:10,560 --> 00:25:14,119
Speaker 1: can get into some more of the averages, and you know,

431
00:25:15,200 --> 00:25:17,760
Speaker 1: I can check the figures, but about the deer don't

432
00:25:17,800 --> 00:25:20,640
Speaker 1: move more than five miles. But then you've got something

433
00:25:20,640 --> 00:25:22,320
Speaker 1: that go a little bit further. And then you need

434
00:25:22,359 --> 00:25:26,199
Speaker 1: to be conservative with your recommendations on how to manage

435
00:25:26,200 --> 00:25:31,560
Speaker 1: c w D, but also balance opportunity and be pragmatic

436
00:25:31,640 --> 00:25:35,240
Speaker 1: with with those regulations as well. And so yes, some

437
00:25:35,359 --> 00:25:39,720
Speaker 1: people say, you know two miles put a two hundred

438
00:25:39,760 --> 00:25:43,639
Speaker 1: mile buffer on anything, and well, all of a sudden

439
00:25:43,640 --> 00:25:48,719
Speaker 1: that covers Missouri and the surrounding states. Yeah, I mean

440
00:25:49,040 --> 00:25:53,320
Speaker 1: the question behind that, right is is this a was

441
00:25:53,400 --> 00:25:57,280
Speaker 1: this just a weird, circumstantial thing that led to this

442
00:25:57,320 --> 00:26:00,639
Speaker 1: buck during a hunting season? Um, and you know the

443
00:26:01,480 --> 00:26:04,280
Speaker 1: land break down as far as open ground and little

444
00:26:04,280 --> 00:26:07,800
Speaker 1: little patches of cover, and the way those natural barriers

445
00:26:07,800 --> 00:26:10,040
Speaker 1: are what we thought were natural barriers funneled in one

446
00:26:10,080 --> 00:26:12,480
Speaker 1: way and then the next was this just sort of

447
00:26:12,480 --> 00:26:15,159
Speaker 1: a one off phenomenally or as we do more of

448
00:26:15,160 --> 00:26:17,920
Speaker 1: these GPS studies, is this gonna are we gonna see

449
00:26:18,359 --> 00:26:22,680
Speaker 1: that dispersal range open up a little bit because of this? Well,

450
00:26:22,720 --> 00:26:27,440
Speaker 1: I think I think your last two comments were both correct. One,

451
00:26:27,520 --> 00:26:30,080
Speaker 1: it's an anomaly. I mean, we've got we've had hundreds

452
00:26:30,080 --> 00:26:33,800
Speaker 1: of deer marked with these GPS colors where we can

453
00:26:33,840 --> 00:26:36,159
Speaker 1: find them, and we're not seeing this. We had another

454
00:26:38,680 --> 00:26:43,240
Speaker 1: interesting dispersal, but after this one, the next maximum distance

455
00:26:44,240 --> 00:26:48,080
Speaker 1: was about forty seven miles and so that's that's almost

456
00:26:48,080 --> 00:26:50,880
Speaker 1: a third of what we saw with this, dear. And

457
00:26:51,119 --> 00:26:54,000
Speaker 1: maybe I should should just get in and describe this, dear,

458
00:26:54,080 --> 00:26:56,879
Speaker 1: so we can all be on the same page with

459
00:26:56,920 --> 00:26:58,879
Speaker 1: what we're talking about. Can we can we start with

460
00:26:58,920 --> 00:27:02,080
Speaker 1: what the study was designed for first? Certainly so. There

461
00:27:02,119 --> 00:27:05,719
Speaker 1: was a study that we did University of Missouri, University

462
00:27:05,760 --> 00:27:09,880
Speaker 1: of Montana, and the Missouri Department of Conservation where we

463
00:27:09,920 --> 00:27:15,080
Speaker 1: wanted to look at white tailed deer survival, population recruitment,

464
00:27:16,000 --> 00:27:20,760
Speaker 1: habitat use, and resource selection. And the purpose of this

465
00:27:20,880 --> 00:27:24,720
Speaker 1: study was to be able to manage dear more effectively,

466
00:27:25,359 --> 00:27:28,520
Speaker 1: and so the survival and the recruitment data would be

467
00:27:28,640 --> 00:27:32,159
Speaker 1: used for population models so that the dear biologists in

468
00:27:32,160 --> 00:27:37,080
Speaker 1: the state could tweet different factors that they can use

469
00:27:37,160 --> 00:27:40,840
Speaker 1: to affect deer management and see what the outcome of

470
00:27:40,880 --> 00:27:45,000
Speaker 1: those would be. There was interest among landowners and deer

471
00:27:45,080 --> 00:27:48,959
Speaker 1: hunters to know more about localized management scales, and so

472
00:27:49,000 --> 00:27:51,840
Speaker 1: that's what we were looking at with resource selection and

473
00:27:51,880 --> 00:27:55,520
Speaker 1: with habitat use, and then with those movement questions. C

474
00:27:55,800 --> 00:27:58,760
Speaker 1: w D has been detected in Missouri, and so knowing

475
00:27:58,800 --> 00:28:02,199
Speaker 1: how dear move on the land endscape would help the

476
00:28:02,240 --> 00:28:07,520
Speaker 1: state agency designed the best management practices two try and

477
00:28:07,600 --> 00:28:11,760
Speaker 1: minimize c w D transmission. So it was a big study.

478
00:28:12,040 --> 00:28:15,919
Speaker 1: We had to study areas one in northwest Missouri and

479
00:28:16,000 --> 00:28:19,720
Speaker 1: one in the central Ozarks in southern Missouri. And that

480
00:28:19,760 --> 00:28:22,680
Speaker 1: was because basically we're two different states of Missouri. Northern

481
00:28:22,880 --> 00:28:27,800
Speaker 1: it's more agrarian, glaciated planes agg country, and then you

482
00:28:27,840 --> 00:28:30,600
Speaker 1: get down into the southern part of the state and

483
00:28:30,680 --> 00:28:35,520
Speaker 1: it's it's the Ozarks, it's rocky soils, it's red oak

484
00:28:36,160 --> 00:28:40,040
Speaker 1: dominated communities, some pine down there, and so two very

485
00:28:40,080 --> 00:28:43,760
Speaker 1: different ecosystems, and so we needed to study areas. We

486
00:28:43,960 --> 00:28:48,880
Speaker 1: captured deer using rocket nets and clover traps from January,

487
00:28:48,960 --> 00:28:53,680
Speaker 1: February and March for five years and then would put

488
00:28:53,720 --> 00:28:56,600
Speaker 1: GPS transmitters on the deer and that could give us

489
00:28:56,640 --> 00:29:00,880
Speaker 1: information about where they're moving, what their survivals lie. Habitat

490
00:29:00,920 --> 00:29:03,680
Speaker 1: that they're using. And then in the spring we captured

491
00:29:03,720 --> 00:29:07,120
Speaker 1: fams the young neo Nate fawns day old faons and

492
00:29:07,160 --> 00:29:09,560
Speaker 1: put expandable collars on them so that we could get

493
00:29:09,560 --> 00:29:12,320
Speaker 1: an idea of fallon survival to get back at those

494
00:29:12,800 --> 00:29:17,400
Speaker 1: questions about population recruitment. So this was I mean, this

495
00:29:17,440 --> 00:29:22,760
Speaker 1: was sort of a multifaceted study where you're looking at uh,

496
00:29:22,920 --> 00:29:26,800
Speaker 1: mortality rates, you're looking at dispersal rates, and and kind

497
00:29:26,840 --> 00:29:31,200
Speaker 1: of the overarching theme there is to give the Missouri

498
00:29:31,240 --> 00:29:37,840
Speaker 1: Department of Conservation better data to manage populations around exactly. So,

499
00:29:38,680 --> 00:29:41,680
Speaker 1: and this is this is one thing when I I've

500
00:29:41,720 --> 00:29:44,680
Speaker 1: dealt with state game agencies a lot, with interviews and

501
00:29:44,880 --> 00:29:48,120
Speaker 1: just you know, various things, and I've always felt like

502
00:29:48,800 --> 00:29:51,000
Speaker 1: the missing component to a lot of this stuff was

503
00:29:51,040 --> 00:29:53,360
Speaker 1: the pr And you know, so when you when you

504
00:29:53,360 --> 00:29:56,560
Speaker 1: talk about that, what you're really doing is is using

505
00:29:56,560 --> 00:30:00,280
Speaker 1: science to give the Department of Conservation better to tools

506
00:30:00,360 --> 00:30:03,000
Speaker 1: to manage the deer in a better way, which is

507
00:30:03,080 --> 00:30:07,040
Speaker 1: you know, pretty easy to get behind. But there's always

508
00:30:07,120 --> 00:30:09,680
Speaker 1: been this kind now I shouldn't say always in a

509
00:30:09,680 --> 00:30:13,120
Speaker 1: lot of states, there's been this sort of perception that

510
00:30:13,200 --> 00:30:15,760
Speaker 1: the state agencies are like, you know, we'll throw out

511
00:30:15,760 --> 00:30:19,560
Speaker 1: a million deer number because it's I actually had a

512
00:30:19,560 --> 00:30:21,600
Speaker 1: big game coordinator in Minnesota tell me this one time.

513
00:30:22,000 --> 00:30:24,800
Speaker 1: We we say a million deer because it sounds really good,

514
00:30:25,400 --> 00:30:27,880
Speaker 1: but because we you know, we don't really know. I mean,

515
00:30:28,280 --> 00:30:31,200
Speaker 1: they're not actually counting noses in the woods, right, And

516
00:30:31,520 --> 00:30:33,320
Speaker 1: so there's kind of this perception out there with a

517
00:30:33,320 --> 00:30:35,360
Speaker 1: lot of hunters where it's like the state gaming they

518
00:30:35,400 --> 00:30:37,800
Speaker 1: have no idea how many deer out there, and and

519
00:30:37,800 --> 00:30:40,040
Speaker 1: and to some extent over the years, that's probably been

520
00:30:40,080 --> 00:30:43,760
Speaker 1: a little bit true because of the nature of you know,

521
00:30:43,840 --> 00:30:46,959
Speaker 1: an entire wildlife population in the state. That's a big

522
00:30:47,040 --> 00:30:49,680
Speaker 1: thing to get a handle on. And so if you've

523
00:30:49,760 --> 00:30:52,200
Speaker 1: fallen into that camp and said that this is the

524
00:30:52,240 --> 00:30:54,800
Speaker 1: kind of research that makes you know, you don't get

525
00:30:54,800 --> 00:30:57,480
Speaker 1: to an exact number, but you get better at these

526
00:30:57,480 --> 00:31:01,440
Speaker 1: population modeling through this kind of research, You're exactly right,

527
00:31:01,520 --> 00:31:07,200
Speaker 1: and it's it's to be more informed. And so, like technology,

528
00:31:07,240 --> 00:31:11,760
Speaker 1: the methods to answer those questions on population size or

529
00:31:11,840 --> 00:31:14,720
Speaker 1: growth keep getting better and better. And so we use

530
00:31:14,840 --> 00:31:19,000
Speaker 1: these data that we're collecting put in a statistical model.

531
00:31:19,080 --> 00:31:23,479
Speaker 1: I mean, this isn't me as a biologist designing this tool.

532
00:31:24,160 --> 00:31:28,800
Speaker 1: These are high end statisticians who work with the biologists,

533
00:31:28,800 --> 00:31:32,800
Speaker 1: and so you have biology and play, you have real

534
00:31:32,840 --> 00:31:38,640
Speaker 1: field data, you have statisticians, and everybody brings their strengths

535
00:31:38,680 --> 00:31:42,480
Speaker 1: to the table to develop this tool to manage dear

536
00:31:43,240 --> 00:31:47,200
Speaker 1: and you get better estimates out on what population sizes

537
00:31:47,360 --> 00:31:50,840
Speaker 1: are or growth rates are. But you also get measures

538
00:31:50,840 --> 00:31:56,840
Speaker 1: of confidence around those estimates. And so instead of just saying, Okay,

539
00:31:56,880 --> 00:32:00,080
Speaker 1: a million sounds like a nice, pretty round number, you

540
00:32:00,160 --> 00:32:04,760
Speaker 1: might have this very odd number, very specific number, and

541
00:32:04,800 --> 00:32:07,719
Speaker 1: then a measure of confidence around that number, so you know,

542
00:32:08,480 --> 00:32:12,000
Speaker 1: you know how strong to place your bet. Yeah, and

543
00:32:12,040 --> 00:32:16,680
Speaker 1: I the the idea behind the two separate you know

544
00:32:16,720 --> 00:32:20,520
Speaker 1: study areas is I think that's super important as well.

545
00:32:20,840 --> 00:32:23,320
Speaker 1: You know, anybody who's been in northwestern Missouri versus you know,

546
00:32:23,360 --> 00:32:25,800
Speaker 1: the ozarks nose, it's the same thing here in Minnesota.

547
00:32:25,800 --> 00:32:27,960
Speaker 1: If you're in you know, up by the boundary waters

548
00:32:28,040 --> 00:32:29,840
Speaker 1: versus the southwest corner of the state, you might as

549
00:32:29,840 --> 00:32:32,320
Speaker 1: well be in different states. There's they're so vastly different.

550
00:32:32,320 --> 00:32:36,240
Speaker 1: So when you're talking about predation and fawn mortality rates

551
00:32:36,240 --> 00:32:38,360
Speaker 1: and things like that, they're gonna vary. I would assume

552
00:32:38,480 --> 00:32:42,480
Speaker 1: very so much from those two different areas. It is.

553
00:32:42,640 --> 00:32:45,040
Speaker 1: It is the case, and you can't extrapolate one to another.

554
00:32:45,080 --> 00:32:47,760
Speaker 1: And so to really have a complete study you need

555
00:32:47,800 --> 00:32:50,840
Speaker 1: to have those two questions. I mean, you gave the

556
00:32:50,880 --> 00:32:56,720
Speaker 1: Minnesota example, deer in so many states could could follow

557
00:32:56,760 --> 00:32:59,960
Speaker 1: the same example, and so we needed both of those studies.

558
00:33:00,560 --> 00:33:03,040
Speaker 1: And it's a it's a testament to the Conservation department.

559
00:33:03,120 --> 00:33:06,800
Speaker 1: This research is expensive. I mean getting the collars out there,

560
00:33:07,320 --> 00:33:11,440
Speaker 1: it's a lot of hands on deck to make this work.

561
00:33:11,520 --> 00:33:16,160
Speaker 1: It's a lot of um of resources of a variety

562
00:33:16,160 --> 00:33:20,160
Speaker 1: of types. And so to have one study is a big,

563
00:33:20,800 --> 00:33:23,200
Speaker 1: a big step with with the size of the project

564
00:33:23,280 --> 00:33:26,480
Speaker 1: we were working to have these two. It's it's more

565
00:33:26,520 --> 00:33:29,960
Speaker 1: than double. And this this was this was a five

566
00:33:30,040 --> 00:33:35,160
Speaker 1: year study. Five year study. Okay. And this Buck the Wanderer,

567
00:33:35,920 --> 00:33:40,440
Speaker 1: Uh you captured him. It was a rocket net, right, correct? Yep.

568
00:33:40,560 --> 00:33:44,520
Speaker 1: We captured him in January of two thousand seventeen in

569
00:33:44,640 --> 00:33:48,240
Speaker 1: north west Missouri using a rocket net one evening. And

570
00:33:48,480 --> 00:33:50,960
Speaker 1: how old was he then? Do you think about two

571
00:33:50,960 --> 00:33:53,760
Speaker 1: and a half And we could say that with confidence,

572
00:33:53,840 --> 00:33:56,800
Speaker 1: just with you know, going beyond two and a half

573
00:33:56,800 --> 00:33:59,720
Speaker 1: for an adult is you know, there's some question, but

574
00:33:59,760 --> 00:34:02,040
Speaker 1: still we can feel like we can get pretty close.

575
00:34:02,480 --> 00:34:03,640
Speaker 1: But this one would have been a two and a

576
00:34:03,680 --> 00:34:07,720
Speaker 1: half year old adult buck. When when did he go

577
00:34:07,760 --> 00:34:13,359
Speaker 1: on his excursion? He started moving in early November of

578
00:34:13,400 --> 00:34:18,680
Speaker 1: that year and Missouri's Missouri has a very long bow season,

579
00:34:18,719 --> 00:34:22,799
Speaker 1: as you probably know, September fifteen to January, but then

580
00:34:22,800 --> 00:34:27,680
Speaker 1: a rather concentrated firearms season ten eleven days in mid November,

581
00:34:27,960 --> 00:34:31,760
Speaker 1: and he started moving about a week before that season began,

582
00:34:32,600 --> 00:34:35,839
Speaker 1: and the next twenty two days he was on the move.

583
00:34:36,520 --> 00:34:40,040
Speaker 1: So previous to the you know, we'll get into the

584
00:34:40,040 --> 00:34:43,719
Speaker 1: red movement versus the pressure, the hunting pressure movement, but

585
00:34:43,800 --> 00:34:46,920
Speaker 1: previous to that he had stayed pretty tight. Right, that's correct.

586
00:34:47,239 --> 00:34:50,799
Speaker 1: But what was his home range before that? It was,

587
00:34:51,160 --> 00:34:54,520
Speaker 1: you know, it was nothing out of the ordinary. Um

588
00:34:54,840 --> 00:34:57,680
Speaker 1: the area where he was captured, it was a couple

589
00:34:57,680 --> 00:35:01,480
Speaker 1: of sections of very good a deer habitat. It was

590
00:35:01,560 --> 00:35:03,719
Speaker 1: a ground, there was plenty of timber, there was a

591
00:35:03,760 --> 00:35:08,919
Speaker 1: stream moving through, and so it was where you would

592
00:35:08,920 --> 00:35:11,560
Speaker 1: want to be as a deer. It was hunted, it

593
00:35:11,680 --> 00:35:17,320
Speaker 1: was not pressured. In fact, two biologists owned the property

594
00:35:17,360 --> 00:35:20,440
Speaker 1: where we happened to capture this deer, and so they

595
00:35:20,520 --> 00:35:25,879
Speaker 1: knew management and up until he started moving. Well, from

596
00:35:25,880 --> 00:35:28,000
Speaker 1: the time we caught him and collared him with the

597
00:35:28,000 --> 00:35:31,839
Speaker 1: GPS collar until the time he began this this long

598
00:35:31,840 --> 00:35:49,560
Speaker 1: distance movement, he was a very normal adult book as

599
00:35:49,600 --> 00:35:52,400
Speaker 1: a hunter in this rule do you do? You just

600
00:35:52,440 --> 00:35:54,000
Speaker 1: look at that buck and go. He had no reason

601
00:35:54,040 --> 00:35:57,640
Speaker 1: to leave, knowing knowing where he spent most of his time.

602
00:35:58,400 --> 00:36:01,400
Speaker 1: I as a as a hunter and as a biologist,

603
00:36:01,400 --> 00:36:04,400
Speaker 1: I'd say that, I mean there there. It wasn't like

604
00:36:04,480 --> 00:36:10,080
Speaker 1: it was a density dependent question where there were too

605
00:36:10,080 --> 00:36:13,160
Speaker 1: many deer. It wasn't like there was too much hunting pressure.

606
00:36:13,520 --> 00:36:16,799
Speaker 1: He was in a good spot when he when he

607
00:36:16,880 --> 00:36:21,040
Speaker 1: starts moving. How how often are you seeing these you know,

608
00:36:21,080 --> 00:36:23,560
Speaker 1: GPS readings? Is it? Are you? Are you seeing them

609
00:36:23,560 --> 00:36:25,680
Speaker 1: every day? Or you did you did this happen? You

610
00:36:25,760 --> 00:36:27,480
Speaker 1: check back a couple after a couple of weeks and go,

611
00:36:27,640 --> 00:36:32,480
Speaker 1: holy cow, it was well, it was neither. We had

612
00:36:32,480 --> 00:36:36,080
Speaker 1: so many deer that it, uh, it's not practical for

613
00:36:36,160 --> 00:36:38,560
Speaker 1: me to check on each deer each day, something like that,

614
00:36:38,880 --> 00:36:42,120
Speaker 1: And so we have hundreds of deer locations during the

615
00:36:42,200 --> 00:36:45,520
Speaker 1: hunting season, we're coming in every ninety minutes, so it's

616
00:36:45,600 --> 00:36:48,359
Speaker 1: just a huge amount of data that would come in.

617
00:36:49,239 --> 00:36:54,359
Speaker 1: And so the story of this one was I got

618
00:36:54,400 --> 00:36:58,239
Speaker 1: an email that kind of filtered through a handful of

619
00:36:58,280 --> 00:37:02,399
Speaker 1: folks said do we have deer and a color deer

620
00:37:02,400 --> 00:37:08,879
Speaker 1: in Monroe County? And I said, no, that's way off

621
00:37:08,920 --> 00:37:12,600
Speaker 1: of our study area. And I would have been surprised

622
00:37:12,600 --> 00:37:14,480
Speaker 1: at the number of questions I get, like that somebody

623
00:37:14,480 --> 00:37:16,400
Speaker 1: will catch a fall on and put a dog collar

624
00:37:16,480 --> 00:37:21,520
Speaker 1: on it or something stupid like that, and you know,

625
00:37:21,760 --> 00:37:23,759
Speaker 1: do you have a color deer down here on though?

626
00:37:23,800 --> 00:37:27,200
Speaker 1: And then I get a picture and it's whatever on

627
00:37:27,320 --> 00:37:29,239
Speaker 1: this deer, which is too bad for the deer. I'd

628
00:37:29,280 --> 00:37:32,560
Speaker 1: encourage people not to do that. But anyway, so I

629
00:37:32,600 --> 00:37:36,800
Speaker 1: got this picture and it was a real grainy trail

630
00:37:36,880 --> 00:37:39,879
Speaker 1: camp photo, and I thought, and that looks like one

631
00:37:39,880 --> 00:37:42,920
Speaker 1: of our collars. And then I think, Okay, it's somebody

632
00:37:43,200 --> 00:37:45,640
Speaker 1: playing a joke on me, because this wouldn't be the

633
00:37:45,640 --> 00:37:49,800
Speaker 1: first time within the community that that sort of stuff

634
00:37:49,840 --> 00:37:53,879
Speaker 1: has gone on. So I didn't There wasn't a real

635
00:37:53,920 --> 00:37:55,840
Speaker 1: good way with how these data were structured for me

636
00:37:55,880 --> 00:37:57,840
Speaker 1: to figure out if this was one of our deer.

637
00:37:57,880 --> 00:38:01,320
Speaker 1: Other than looking at this game in camp picture, I

638
00:38:01,360 --> 00:38:03,000
Speaker 1: could see that it was an adult, and so I

639
00:38:03,040 --> 00:38:07,080
Speaker 1: got our list of adult deer one morning when I

640
00:38:07,160 --> 00:38:10,400
Speaker 1: couldn't sleep but about two in the morning, and started

641
00:38:10,440 --> 00:38:13,200
Speaker 1: going through deer by deer by deer looking at locations,

642
00:38:13,640 --> 00:38:17,960
Speaker 1: and I don't know on deer, you know fifteen. I

643
00:38:18,000 --> 00:38:20,240
Speaker 1: opened up these points and they all display on Google

644
00:38:20,239 --> 00:38:23,560
Speaker 1: Earth and thought, holy smoke, and I see this line

645
00:38:23,600 --> 00:38:27,520
Speaker 1: going across northern Missouri. Knew exactly where we captured it,

646
00:38:27,960 --> 00:38:31,200
Speaker 1: and then lo and behold it was where this photo

647
00:38:31,320 --> 00:38:33,799
Speaker 1: had had come from. So we would have figured out

648
00:38:33,800 --> 00:38:37,680
Speaker 1: once we started running all the analyzes, but how this

649
00:38:37,719 --> 00:38:40,920
Speaker 1: one transpired was a bit unusual and was a bit

650
00:38:40,960 --> 00:38:43,440
Speaker 1: of a shock when I drew up all these locations

651
00:38:43,480 --> 00:38:45,600
Speaker 1: for the first time. So when you when you get

652
00:38:45,600 --> 00:38:47,560
Speaker 1: that email with that grainy photo and you look at

653
00:38:47,600 --> 00:38:49,000
Speaker 1: it and you go, man, that could be one of

654
00:38:49,040 --> 00:38:53,080
Speaker 1: our collars, and what what percentage are you buying into?

655
00:38:53,120 --> 00:38:54,600
Speaker 1: Maybe this is one of our d are you? Are

656
00:38:54,600 --> 00:38:58,800
Speaker 1: you at like three? It'd be hard to pay the

657
00:38:58,880 --> 00:39:01,800
Speaker 1: percent I didn't. I thought something was up. I thought

658
00:39:01,840 --> 00:39:07,840
Speaker 1: something was fishy, and you know, it's hard to trust

659
00:39:08,440 --> 00:39:10,520
Speaker 1: what comes in on a trail camera. I mean, we

660
00:39:11,000 --> 00:39:15,200
Speaker 1: get a lot of mountainline photos from around the country

661
00:39:15,200 --> 00:39:19,000
Speaker 1: that end up in you know, who knows where, um,

662
00:39:19,040 --> 00:39:21,440
Speaker 1: and so people just claiming, oh, yeah, here's a photo

663
00:39:21,480 --> 00:39:24,480
Speaker 1: from the back forty So I wasn't quite sure what

664
00:39:24,520 --> 00:39:28,040
Speaker 1: was up, but it looked enough like our collar that

665
00:39:29,360 --> 00:39:32,520
Speaker 1: I was definitely curious. And then lo and behold it

666
00:39:32,600 --> 00:39:36,120
Speaker 1: was our dear that had had moved across the better

667
00:39:36,160 --> 00:39:38,640
Speaker 1: part of north Missouri and at that point he was

668
00:39:38,680 --> 00:39:46,200
Speaker 1: still alive. Correct. What do you do? Then? Uh, share

669
00:39:46,239 --> 00:39:49,279
Speaker 1: the news with with the study collaborators and say, hey,

670
00:39:49,320 --> 00:39:51,560
Speaker 1: look at this. There was nothing that we needed to

671
00:39:51,600 --> 00:39:55,000
Speaker 1: do that here obviously didn't do anything wrong. I was

672
00:39:55,719 --> 00:39:59,680
Speaker 1: initially most surprised that he was still alive after making

673
00:39:59,719 --> 00:40:04,280
Speaker 1: this movement during the firearms season, when we have hundreds

674
00:40:04,280 --> 00:40:08,960
Speaker 1: of thousands of deer hunters in Missouri out there. This

675
00:40:09,040 --> 00:40:12,480
Speaker 1: was a nice deer. Um. It was you know it

676
00:40:13,800 --> 00:40:18,920
Speaker 1: dear that many a hunter would have taken without question. Ye,

677
00:40:19,040 --> 00:40:22,319
Speaker 1: this this buck at that point three and a half, right, yeah, yeah,

678
00:40:22,360 --> 00:40:25,400
Speaker 1: and he's he's walking a couple hundred miles through a

679
00:40:25,480 --> 00:40:28,200
Speaker 1: state during a gun season that's open with like, you know,

680
00:40:28,239 --> 00:40:31,560
Speaker 1: half a million hunters out there and there's seven of

681
00:40:31,560 --> 00:40:35,080
Speaker 1: them that would give him a pass if that. Yeah,

682
00:40:35,320 --> 00:40:38,200
Speaker 1: and so what you see this and you go, this

683
00:40:38,239 --> 00:40:41,760
Speaker 1: is this is real? This buck did this. You start

684
00:40:41,800 --> 00:40:44,280
Speaker 1: to I'm guessing you kind of start to reverse engineer

685
00:40:44,440 --> 00:40:47,600
Speaker 1: his his route and take a look at these you know,

686
00:40:47,680 --> 00:40:49,440
Speaker 1: these things that we kind of thought they're not he's

687
00:40:49,440 --> 00:40:51,279
Speaker 1: not gonna cross or he's not gonna do this. And

688
00:40:51,320 --> 00:40:53,680
Speaker 1: he he broke a lot of rules, didn't he. Well,

689
00:40:53,760 --> 00:40:57,319
Speaker 1: he just he covered a lot of ground. And so

690
00:40:58,040 --> 00:41:01,799
Speaker 1: the data that we had for this deer was a

691
00:41:01,960 --> 00:41:05,319
Speaker 1: GPS point every five hours for most of the year,

692
00:41:05,840 --> 00:41:09,480
Speaker 1: and then ten days before the gun season started the

693
00:41:09,480 --> 00:41:13,720
Speaker 1: firearms season, I up that sampling frequency to every ninety

694
00:41:13,760 --> 00:41:17,319
Speaker 1: minutes so I could see how the hunting pressure might

695
00:41:17,320 --> 00:41:20,480
Speaker 1: affect dear. I did that for all deer, which was

696
00:41:20,520 --> 00:41:23,280
Speaker 1: about the time he started moving. Now those were related.

697
00:41:23,480 --> 00:41:27,120
Speaker 1: This was all just something done laptop to satellite to collar,

698
00:41:27,239 --> 00:41:30,440
Speaker 1: and it wouldn't affect the deer any the change of

699
00:41:30,480 --> 00:41:35,280
Speaker 1: sampling frequency, and so having that resolution is really pretty

700
00:41:35,280 --> 00:41:37,279
Speaker 1: good to know every hour and a half, here's where

701
00:41:37,280 --> 00:41:41,280
Speaker 1: he is. And this was big agg country, and seeing

702
00:41:41,320 --> 00:41:44,200
Speaker 1: how he moved across some of these open egg fields

703
00:41:44,840 --> 00:41:50,319
Speaker 1: was very interesting, seeing how some of the major barriers

704
00:41:50,440 --> 00:41:54,359
Speaker 1: like Interstate thirty five going north south that he had

705
00:41:54,360 --> 00:41:58,759
Speaker 1: to work along that, seeing across you know, the river crossings,

706
00:41:59,480 --> 00:42:02,920
Speaker 1: and then he'd find some good place to hang up

707
00:42:02,960 --> 00:42:05,879
Speaker 1: during the day. Typically timber area wasn't moving a lot

708
00:42:05,960 --> 00:42:09,120
Speaker 1: during the day, and then night would fall and he'd

709
00:42:09,120 --> 00:42:14,359
Speaker 1: be back back moving. Yeah, And I mean it's I

710
00:42:14,360 --> 00:42:17,520
Speaker 1: think a lot of hunters would look at that and go, yeah,

711
00:42:17,800 --> 00:42:20,440
Speaker 1: you know, it makes sense that he a buck during

712
00:42:20,680 --> 00:42:23,400
Speaker 1: the rut in a heavily pressured state when the firearms

713
00:42:23,400 --> 00:42:25,520
Speaker 1: season is open, it's going to kind of hole up

714
00:42:25,560 --> 00:42:28,280
Speaker 1: in the day and whatever, you know, his just chosen

715
00:42:28,320 --> 00:42:29,880
Speaker 1: cover and then at night he's going to cover a

716
00:42:29,880 --> 00:42:31,480
Speaker 1: ton of ground and get his thing done and then

717
00:42:31,560 --> 00:42:34,799
Speaker 1: hold up again. But this buck, and and this is

718
00:42:34,880 --> 00:42:36,520
Speaker 1: kind of seems like what this buck did. But he

719
00:42:36,560 --> 00:42:41,120
Speaker 1: did it, you know, eight miles apart every day for days.

720
00:42:41,200 --> 00:42:44,880
Speaker 1: And it's not like he was not coming across Doze

721
00:42:45,200 --> 00:42:47,280
Speaker 1: at this time. If he was looking for a breeding

722
00:42:47,360 --> 00:42:52,800
Speaker 1: opportunity with the ground he covered, he would have found

723
00:42:52,840 --> 00:43:00,360
Speaker 1: does and so that's him dispersing to find breeding opportunity.

724
00:43:00,600 --> 00:43:03,440
Speaker 1: Doesn't make a whole lot of sense to me. He

725
00:43:03,560 --> 00:43:08,239
Speaker 1: was in good habitat. He wandered through very good habitat,

726
00:43:09,360 --> 00:43:16,719
Speaker 1: And so what motivated this eighty five mile dispersal is

727
00:43:17,440 --> 00:43:22,719
Speaker 1: still the big question. What do you think it was?

728
00:43:25,760 --> 00:43:27,560
Speaker 1: I was afraid you're gonna ask that. No, I've had

729
00:43:27,560 --> 00:43:29,600
Speaker 1: a lot of time to think about this. I think

730
00:43:29,640 --> 00:43:33,480
Speaker 1: this was an individual deer that was just marching to

731
00:43:33,560 --> 00:43:37,560
Speaker 1: his own beat, and a deer hunter would like a

732
00:43:37,560 --> 00:43:41,160
Speaker 1: better answer. I'd like a better answer as a biologist.

733
00:43:41,560 --> 00:43:45,080
Speaker 1: But you think of the things conventional wisdom that would

734
00:43:45,760 --> 00:43:49,120
Speaker 1: drive a deer to disperse. You know, there's age related

735
00:43:49,200 --> 00:43:54,200
Speaker 1: factors where you know, new deer are coming into the population,

736
00:43:55,120 --> 00:43:58,919
Speaker 1: the young, the yearlings, the juvenile deer get pushed out

737
00:43:59,000 --> 00:44:03,799
Speaker 1: by by you know, mom or whatever social pressures are there.

738
00:44:04,719 --> 00:44:08,280
Speaker 1: That wasn't the case here habitat. He was an excellent

739
00:44:08,320 --> 00:44:12,000
Speaker 1: habitat and he really went through good habitat this whole area.

740
00:44:12,040 --> 00:44:14,919
Speaker 1: I mean, this is a place that does well by deer,

741
00:44:15,600 --> 00:44:20,919
Speaker 1: not overcrowded, not undercrowded. It was just in good, good,

742
00:44:20,960 --> 00:44:24,120
Speaker 1: dear habitat. This whole way, and I know this country well,

743
00:44:24,320 --> 00:44:28,520
Speaker 1: and so I think this was a an anomaly of

744
00:44:28,520 --> 00:44:32,680
Speaker 1: a deer that just started moving and maybe got to

745
00:44:32,920 --> 00:44:37,040
Speaker 1: this new area as the breeding season was winding down

746
00:44:37,680 --> 00:44:43,279
Speaker 1: and there was not the you know, some in a

747
00:44:43,440 --> 00:44:48,640
Speaker 1: pressure that was driving him that maybe he didn't even realize. Yeah,

748
00:44:48,680 --> 00:44:51,120
Speaker 1: it's it's a good question, and all of us involved

749
00:44:51,120 --> 00:44:54,520
Speaker 1: at the study are looking for or interested in an answer,

750
00:44:54,560 --> 00:44:57,200
Speaker 1: but we might never know what drove this particular dear

751
00:44:57,320 --> 00:44:59,440
Speaker 1: to do what he did. He may have known that

752
00:44:59,480 --> 00:45:02,520
Speaker 1: it was time to breed, but didn't connect the dots

753
00:45:02,560 --> 00:45:04,879
Speaker 1: on what that meant. And maybe that's one out of

754
00:45:05,520 --> 00:45:11,040
Speaker 1: ten thousand that this happens to. It's it's really interesting

755
00:45:11,120 --> 00:45:14,279
Speaker 1: that the explanation might just be that he's wired to

756
00:45:14,280 --> 00:45:17,680
Speaker 1: be a pioneer and not a settler and he really

757
00:45:17,719 --> 00:45:21,360
Speaker 1: found his legs at three and a half years old,

758
00:45:21,200 --> 00:45:24,520
Speaker 1: do you think. So. I do a lot of work

759
00:45:24,560 --> 00:45:26,920
Speaker 1: in the dog space, the working dog and sporting dog space,

760
00:45:27,400 --> 00:45:30,720
Speaker 1: and there's a new, uh, a new kind of trainer

761
00:45:30,760 --> 00:45:33,839
Speaker 1: out there. I say, like a younger trainer out there.

762
00:45:34,080 --> 00:45:37,080
Speaker 1: And I'm generalizing here, but the focus is is way

763
00:45:37,080 --> 00:45:40,200
Speaker 1: more on assessing your dog as an individual you know,

764
00:45:40,400 --> 00:45:44,400
Speaker 1: not as uh Labrador retriever or not as a GSP

765
00:45:44,600 --> 00:45:45,920
Speaker 1: or what. You know, you can factor that and of

766
00:45:45,920 --> 00:45:48,520
Speaker 1: course that stuff is gonna filter in, but really looking

767
00:45:48,520 --> 00:45:50,120
Speaker 1: at like, how how's the best way to train this

768
00:45:50,280 --> 00:45:53,360
Speaker 1: individual dog of mine through you know, taken into account

769
00:45:53,360 --> 00:45:55,440
Speaker 1: of drive and temperament and the time I have and

770
00:45:55,640 --> 00:45:58,080
Speaker 1: all of that stuff, and you start to realize how

771
00:45:58,120 --> 00:46:01,480
Speaker 1: individualized dogs are. And we we kind of know this

772
00:46:01,520 --> 00:46:04,239
Speaker 1: because we've co evolved with them for twenty years. But

773
00:46:04,320 --> 00:46:07,640
Speaker 1: do you think that there's a chance the more research

774
00:46:07,719 --> 00:46:09,800
Speaker 1: you do like this on deer, especially if you start,

775
00:46:10,239 --> 00:46:13,759
Speaker 1: you know, mixing in uh bucks that are reaching more

776
00:46:13,840 --> 00:46:16,440
Speaker 1: mature age, which are probably like typically a little bit

777
00:46:16,560 --> 00:46:19,759
Speaker 1: underrepresented in a lot of these studies, you'll see just

778
00:46:19,880 --> 00:46:23,239
Speaker 1: these individual tendencies of more dear kind of breakout or

779
00:46:23,280 --> 00:46:26,120
Speaker 1: do you have enough history with research to go. I

780
00:46:26,160 --> 00:46:28,879
Speaker 1: don't think that's going to happen. No, I think we'll

781
00:46:28,880 --> 00:46:31,520
Speaker 1: see it more. I mean, I think this is a rarity,

782
00:46:31,600 --> 00:46:36,000
Speaker 1: or we'd be more familiar with it already. But a

783
00:46:36,080 --> 00:46:38,279
Speaker 1: while back in our conversation you ask if this was

784
00:46:38,280 --> 00:46:40,640
Speaker 1: an anomaly or if we might see this more. I

785
00:46:40,680 --> 00:46:42,680
Speaker 1: think we will see it more because of the technology.

786
00:46:43,040 --> 00:46:47,440
Speaker 1: And so the traditional way to monitor deer for research

787
00:46:47,520 --> 00:46:51,400
Speaker 1: like this would be with that very high frequency color

788
00:46:51,719 --> 00:46:54,040
Speaker 1: that beats and you have to be so close to

789
00:46:54,080 --> 00:46:57,359
Speaker 1: hear it. And so what happens when we have a

790
00:46:57,360 --> 00:47:00,720
Speaker 1: major dispersal. I never would have looked for this deer

791
00:47:00,760 --> 00:47:08,080
Speaker 1: where it was, and so with the VHF collar. You know,

792
00:47:08,239 --> 00:47:11,759
Speaker 1: we biologists are great at coming up with excuses on

793
00:47:11,800 --> 00:47:15,279
Speaker 1: why things don't work, you know, radio failure, hit by

794
00:47:15,320 --> 00:47:18,360
Speaker 1: a car, poached, whatever the case. We can't hear the

795
00:47:18,360 --> 00:47:22,360
Speaker 1: beep anymore. And the farther it goes from its known area,

796
00:47:22,760 --> 00:47:25,440
Speaker 1: the more surveys you would have to do as the

797
00:47:25,440 --> 00:47:28,160
Speaker 1: biologist to figure out where this dear moved. Well, now

798
00:47:28,160 --> 00:47:33,400
Speaker 1: with GPS technology, we get locations delivered us via satellite

799
00:47:33,800 --> 00:47:37,080
Speaker 1: that we pull up on Google Earth or whatever platform

800
00:47:37,120 --> 00:47:40,240
Speaker 1: you're using, and we can see these long distance movements.

801
00:47:40,719 --> 00:47:45,040
Speaker 1: We can see movements where dear go aways and then

802
00:47:45,120 --> 00:47:49,719
Speaker 1: come back, where with VHF technology you just would have thought, well,

803
00:47:49,760 --> 00:47:52,279
Speaker 1: I didn't find that dear today, and then it came

804
00:47:52,320 --> 00:47:54,600
Speaker 1: back and you start tracking it again, and you had

805
00:47:54,680 --> 00:47:57,759
Speaker 1: no idea that it had this this movement where it

806
00:47:57,760 --> 00:48:02,439
Speaker 1: went ten miles and then came back. So not many

807
00:48:02,440 --> 00:48:05,640
Speaker 1: folks are using VHF anymore. Everybody's gone the satellite route.

808
00:48:06,080 --> 00:48:11,600
Speaker 1: And with the migration work out west, with deer research

809
00:48:11,640 --> 00:48:14,360
Speaker 1: in the Midwest. I had an oscillated turkey that I

810
00:48:14,360 --> 00:48:18,080
Speaker 1: put a GPS transmitter on into Yucatan, and right before

811
00:48:18,160 --> 00:48:21,359
Speaker 1: she started to nest, she went twelve miles straight into

812
00:48:21,360 --> 00:48:24,520
Speaker 1: the jungle, nested, and then came back to exactly where

813
00:48:24,560 --> 00:48:26,680
Speaker 1: I caught her, without a hundred yards or where I

814
00:48:26,719 --> 00:48:29,520
Speaker 1: caught her. So had I not had that GPS technology,

815
00:48:29,560 --> 00:48:32,160
Speaker 1: I never would have seen that movement. And so I

816
00:48:32,160 --> 00:48:35,720
Speaker 1: think we'll start seeing more of these interesting long distance

817
00:48:35,760 --> 00:48:40,320
Speaker 1: movements just because we have the capability to track now. Yeah,

818
00:48:40,480 --> 00:48:42,359
Speaker 1: and we should we should kind of clarify this too,

819
00:48:42,400 --> 00:48:45,320
Speaker 1: because I probably I probably keep conflating these two. There's

820
00:48:45,400 --> 00:48:49,239
Speaker 1: dispersal and there's excursions. And you know when you talk

821
00:48:49,280 --> 00:48:52,200
Speaker 1: about dispersal for various reasons, you know, mom kicking the

822
00:48:52,480 --> 00:48:55,239
Speaker 1: youngster out or whatever, because the in breeding. You know,

823
00:48:55,360 --> 00:48:57,719
Speaker 1: there's bucks that are maybe a year and a half

824
00:48:57,719 --> 00:49:01,560
Speaker 1: old ending up, you know, three miles away into what

825
00:49:01,680 --> 00:49:05,120
Speaker 1: will be his home range, and you know, and vice

826
00:49:05,200 --> 00:49:07,799
Speaker 1: versa Buck's coming back into that. And then there's these

827
00:49:07,840 --> 00:49:09,799
Speaker 1: excursions we see and that there was that buck, I

828
00:49:09,800 --> 00:49:12,160
Speaker 1: think it was in Pennsylvania. They did that study where

829
00:49:12,200 --> 00:49:14,919
Speaker 1: that buck made an excursion. He lived on public land

830
00:49:14,960 --> 00:49:18,640
Speaker 1: in a real tight area and early in his life

831
00:49:18,800 --> 00:49:21,279
Speaker 1: made an excursion way, you know, twelve miles away or something,

832
00:49:21,320 --> 00:49:23,200
Speaker 1: and then he went and died there and they were

833
00:49:23,239 --> 00:49:25,319
Speaker 1: kind of like, well, what the hell happened here? Where this?

834
00:49:25,440 --> 00:49:27,840
Speaker 1: This just felt like an area he knew about somehow,

835
00:49:28,320 --> 00:49:30,359
Speaker 1: but you know, only built it into his life like

836
00:49:30,440 --> 00:49:33,680
Speaker 1: basically twice. And so there's there's those two different things.

837
00:49:33,719 --> 00:49:37,760
Speaker 1: Do you think you'll see maybe like a clear picture

838
00:49:37,840 --> 00:49:40,480
Speaker 1: now of those excursions as well? I mean I think

839
00:49:40,520 --> 00:49:43,520
Speaker 1: you'd have to. With this right, we'll see anything that

840
00:49:43,520 --> 00:49:48,000
Speaker 1: that is movement, and so you can adjust these callers

841
00:49:48,040 --> 00:49:51,880
Speaker 1: to take a point every you know, every fifteen minutes

842
00:49:51,920 --> 00:49:55,000
Speaker 1: if you want to. And so depending on the research question,

843
00:49:55,040 --> 00:49:58,440
Speaker 1: as your aunts asking, you'll see how animals move on

844
00:49:58,480 --> 00:50:01,400
Speaker 1: the landscape. You'll see when they go, when they stop,

845
00:50:02,040 --> 00:50:08,000
Speaker 1: and and you know, as with everything technological, it keeps

846
00:50:08,000 --> 00:50:13,040
Speaker 1: getting more refined, more capabilities, lighter weight, better battery life,

847
00:50:13,040 --> 00:50:18,560
Speaker 1: and so the the questions that we will be able

848
00:50:18,600 --> 00:50:21,480
Speaker 1: to ask as biologists will keep getting more and more

849
00:50:21,560 --> 00:50:25,760
Speaker 1: refined and informative. As a deer hunter, does this secretly

850
00:50:25,760 --> 00:50:27,919
Speaker 1: give you hope that at any moment a buck could

851
00:50:27,960 --> 00:50:30,080
Speaker 1: just show up from six counties away and you could

852
00:50:30,120 --> 00:50:36,600
Speaker 1: kill him. It does, It does, But what's more this project.

853
00:50:37,520 --> 00:50:41,360
Speaker 1: I've always been a deer hunter, But after five years

854
00:50:41,400 --> 00:50:45,200
Speaker 1: of doing nothing but deer, after spending hours upon hours

855
00:50:45,239 --> 00:50:49,640
Speaker 1: upon hours catching deer, handling deer, traveling for deer, I've

856
00:50:49,680 --> 00:50:53,319
Speaker 1: taken a break from deer hunting for for just a

857
00:50:53,400 --> 00:50:57,680
Speaker 1: year or two to regathered. Haven't taken a break from hunting,

858
00:50:57,840 --> 00:51:02,440
Speaker 1: but I've I've hit a little bit of dear fatigue

859
00:51:02,480 --> 00:51:05,040
Speaker 1: on waiting for deer to appear. Well, you're you're out

860
00:51:05,040 --> 00:51:07,879
Speaker 1: in Montana, right, m Yeah, you can take a break

861
00:51:07,880 --> 00:51:11,280
Speaker 1: from deer in Montana. Took a took a new position

862
00:51:11,320 --> 00:51:13,680
Speaker 1: at the University of Montana, and so based out of Missoula.

863
00:51:13,760 --> 00:51:16,279
Speaker 1: These days, if you if you live in I don't know,

864
00:51:16,360 --> 00:51:18,400
Speaker 1: Pennsylvania or some of these dates, you don't get a

865
00:51:18,400 --> 00:51:20,440
Speaker 1: break from deer. You take a break from dear. You're

866
00:51:20,440 --> 00:51:23,200
Speaker 1: taking a break from big game hunting just about right,

867
00:51:23,239 --> 00:51:25,200
Speaker 1: and I know I said that, and listeners are going

868
00:51:25,280 --> 00:51:28,560
Speaker 1: to be thinking this guy's crazy because one of the

869
00:51:28,600 --> 00:51:32,399
Speaker 1: neat parts about this job, in this research was being

870
00:51:32,440 --> 00:51:35,400
Speaker 1: able to spend so much time with so many avid,

871
00:51:35,520 --> 00:51:38,719
Speaker 1: passionate deer hunters. And I learned a lot from these

872
00:51:38,760 --> 00:51:42,360
Speaker 1: folks and new things to think about and and so um,

873
00:51:42,400 --> 00:51:45,840
Speaker 1: I will not claim to be in that in that rank,

874
00:51:46,800 --> 00:51:49,560
Speaker 1: but the people who are man more power to him.

875
00:51:50,200 --> 00:51:53,080
Speaker 1: So this this study, you know, obviously everything that the

876
00:51:53,360 --> 00:51:55,279
Speaker 1: thing that gets the most attention is this buck that

877
00:51:55,320 --> 00:51:58,080
Speaker 1: walked the eighty five miles out of out of his

878
00:51:58,200 --> 00:52:00,839
Speaker 1: home range. Was there in anything else in there? Because

879
00:52:00,840 --> 00:52:03,279
Speaker 1: this was this was comprehensive. It was five years and

880
00:52:03,320 --> 00:52:06,800
Speaker 1: two locations and a lot of a lot of deer collared?

881
00:52:06,840 --> 00:52:09,760
Speaker 1: Was there anything else? Like, is there like a one

882
00:52:09,880 --> 00:52:14,120
Speaker 1: b uh, you know, secondary kind of finding that you

883
00:52:14,160 --> 00:52:16,360
Speaker 1: saw or something that happened where you're like, man, that

884
00:52:16,400 --> 00:52:19,920
Speaker 1: was really cool. But it's getting overshadowed by this this wanderer. No,

885
00:52:20,280 --> 00:52:23,720
Speaker 1: but there will be and so uh we this data

886
00:52:23,760 --> 00:52:27,640
Speaker 1: set was hundreds of individuals, thousands of individuals, really millions

887
00:52:27,640 --> 00:52:30,960
Speaker 1: of data points. This was a huge undertaking and we're

888
00:52:31,000 --> 00:52:34,160
Speaker 1: still in the in the stages of analyzing these data,

889
00:52:34,320 --> 00:52:41,000
Speaker 1: and so this particular individual, This was a very comprehensive analysis,

890
00:52:41,080 --> 00:52:43,640
Speaker 1: but it was pretty straightforward because you're reporting on on

891
00:52:43,640 --> 00:52:47,239
Speaker 1: one individual. Over the next couple of years, we're gonna

892
00:52:47,280 --> 00:52:49,759
Speaker 1: have more and more information coming out, and I think,

893
00:52:50,000 --> 00:52:53,160
Speaker 1: I think we've just scratched the surface on the interesting

894
00:52:53,239 --> 00:52:56,760
Speaker 1: findings that will result from this project. Yeah. So this,

895
00:52:56,760 --> 00:52:59,279
Speaker 1: this buck and getting kind of clued into this, this

896
00:53:00,360 --> 00:53:04,320
Speaker 1: uh anomaly of a dispersal is almost like a distraction

897
00:53:04,400 --> 00:53:05,840
Speaker 1: or a little bit of a mission creep on the

898
00:53:05,880 --> 00:53:11,400
Speaker 1: overall project. You know, this, this will probably have the

899
00:53:11,440 --> 00:53:15,080
Speaker 1: most press, press and most exposure from anything we do.

900
00:53:15,480 --> 00:53:18,719
Speaker 1: Now from a management standpoint, this will not be the

901
00:53:18,760 --> 00:53:24,280
Speaker 1: most important, but it'll probably make the best deer camp conversation. Yeah.

902
00:53:24,360 --> 00:53:26,960
Speaker 1: Does that? So let me ask you this, like, personally,

903
00:53:27,000 --> 00:53:29,200
Speaker 1: does that drive you nuts that guys like me focus

904
00:53:29,239 --> 00:53:31,680
Speaker 1: on just this part and then there's so much more

905
00:53:31,760 --> 00:53:35,120
Speaker 1: to the work you're doing. Not at all? I mean,

906
00:53:35,200 --> 00:53:39,960
Speaker 1: I whatever folks UH find interesting, I'm just glad that

907
00:53:39,960 --> 00:53:42,080
Speaker 1: they're interested in the work that we're doing. I Mean,

908
00:53:42,120 --> 00:53:46,439
Speaker 1: the the most frustrating thing is the research that gets

909
00:53:46,480 --> 00:53:49,200
Speaker 1: done that really doesn't mean much, and it's done for

910
00:53:49,239 --> 00:53:53,320
Speaker 1: the sake of publishing a paper or for doing research,

911
00:53:54,160 --> 00:53:56,480
Speaker 1: and so that's that's where I get frustrated. We don't

912
00:53:56,520 --> 00:53:59,000
Speaker 1: have that with this project. And that's the great thing.

913
00:53:59,360 --> 00:54:02,319
Speaker 1: The work that we're doing will be used for management.

914
00:54:02,880 --> 00:54:07,560
Speaker 1: It will be uh available to landowners, to deer hunters,

915
00:54:07,560 --> 00:54:11,399
Speaker 1: to a wildlife enthusiasts of any kind to use what

916
00:54:11,440 --> 00:54:14,560
Speaker 1: we find that might affect their land management or their

917
00:54:14,640 --> 00:54:17,879
Speaker 1: conservation goals or their deer hunting or whatever the case

918
00:54:17,920 --> 00:54:21,720
Speaker 1: will be. So any of it that gains exposure, gains interests,

919
00:54:21,760 --> 00:54:26,640
Speaker 1: can help people is fantastic. Is there This might be

920
00:54:26,640 --> 00:54:28,759
Speaker 1: a little bit of a weird question, but there's you know,

921
00:54:28,760 --> 00:54:31,960
Speaker 1: there's kind of like politics involved in everything every career,

922
00:54:32,000 --> 00:54:34,600
Speaker 1: and especially when you're doing you know, depending where you're

923
00:54:34,600 --> 00:54:36,880
Speaker 1: getting funding and working with the general public or something

924
00:54:36,880 --> 00:54:40,040
Speaker 1: that's you know, directly affects the general public. Is this

925
00:54:40,360 --> 00:54:42,160
Speaker 1: Do you look at this kind of and you kind

926
00:54:42,160 --> 00:54:44,480
Speaker 1: of just said this but as sort of a weird

927
00:54:45,360 --> 00:54:47,919
Speaker 1: just like a nice win to get where it got

928
00:54:47,920 --> 00:54:50,320
Speaker 1: people interested, and it could you could use it in

929
00:54:50,360 --> 00:54:53,120
Speaker 1: the future at least as like a public goodwill thing,

930
00:54:53,160 --> 00:54:55,440
Speaker 1: like somebody finds, you know, like the general hunting population

931
00:54:55,480 --> 00:54:59,120
Speaker 1: finds this super interesting. It might make it easier to

932
00:55:00,160 --> 00:55:02,080
Speaker 1: come up with some other research projects in the future

933
00:55:02,080 --> 00:55:04,520
Speaker 1: and get support for it through you know, the Department

934
00:55:04,560 --> 00:55:06,719
Speaker 1: Conservation or something like that, because of just like one

935
00:55:06,760 --> 00:55:08,480
Speaker 1: little this is like this is like a weird little

936
00:55:08,520 --> 00:55:11,560
Speaker 1: lottery ticket for you, I hope. So, I mean best

937
00:55:11,560 --> 00:55:16,320
Speaker 1: case scenario, very best case scenario is some high school

938
00:55:16,360 --> 00:55:20,720
Speaker 1: or reads this paper, finds it interesting, and then picks

939
00:55:20,719 --> 00:55:24,160
Speaker 1: a path to be a wildlife biologist. And so if

940
00:55:24,160 --> 00:55:27,200
Speaker 1: this gets us any goodwill this one deer that that

941
00:55:27,360 --> 00:55:29,719
Speaker 1: is getting a lot of national press these days, even

942
00:55:29,719 --> 00:55:34,480
Speaker 1: international press. Our our adult book from MISSOURIUS hit some

943
00:55:34,560 --> 00:55:39,960
Speaker 1: international news outlets. Whatever gets people more interested in wildlife conservation.

944
00:55:40,160 --> 00:55:44,400
Speaker 1: Wildlife research um is is excellent and I'll take that

945
00:55:44,480 --> 00:55:49,520
Speaker 1: however I can get it absolutely it's a win. What's

946
00:55:49,600 --> 00:55:52,319
Speaker 1: next then, Man, So you mentioned you've got some white

947
00:55:52,320 --> 00:55:55,080
Speaker 1: tail burnout, and so are you Are you switching gears

948
00:55:55,080 --> 00:55:58,080
Speaker 1: here and and studying something else or do you have

949
00:55:58,200 --> 00:56:00,279
Speaker 1: something you're like, is there is there a white until

950
00:56:00,360 --> 00:56:03,200
Speaker 1: related question you as a research or you're just sitting there.

951
00:56:03,200 --> 00:56:04,799
Speaker 1: It's kind of rallying around the back of your head,

952
00:56:04,840 --> 00:56:06,920
Speaker 1: You're like, Man, I want to address that. Is there

953
00:56:06,920 --> 00:56:09,960
Speaker 1: anything like that? Not at the moment. I mean, with

954
00:56:10,080 --> 00:56:13,040
Speaker 1: this work, we've got we've got the survival questions we're

955
00:56:13,040 --> 00:56:15,600
Speaker 1: still asking, we've got the recruitment questions, we've got the

956
00:56:15,680 --> 00:56:18,920
Speaker 1: habitat use. What I'm most excited about right now with

957
00:56:19,040 --> 00:56:21,760
Speaker 1: these data, and we were just scratching the surface on

958
00:56:21,760 --> 00:56:25,640
Speaker 1: on what we can use this data set for is

959
00:56:25,719 --> 00:56:29,719
Speaker 1: to see this final product of the population model. And

960
00:56:29,760 --> 00:56:32,480
Speaker 1: I think that's gonna be real slick, and I think

961
00:56:32,480 --> 00:56:36,680
Speaker 1: it's gonna make everybody who cares about deer in Missouri

962
00:56:36,760 --> 00:56:38,759
Speaker 1: make their lives better because we're gonna be able to

963
00:56:38,840 --> 00:56:42,319
Speaker 1: manage that much better when we take all this this

964
00:56:42,840 --> 00:56:48,440
Speaker 1: ecological data, land cover data, put the statistics to it,

965
00:56:49,040 --> 00:56:51,279
Speaker 1: and we're going to have a product that will be

966
00:56:51,360 --> 00:56:54,600
Speaker 1: very powerful for for deer management in Missouri. So that's

967
00:56:54,640 --> 00:56:57,160
Speaker 1: what I'm most most excited about now. And I had

968
00:56:57,200 --> 00:56:58,840
Speaker 1: I had a little white tail burned out. I'm not

969
00:56:58,880 --> 00:57:02,840
Speaker 1: burnt out on on the question an answer side of

970
00:57:02,880 --> 00:57:07,160
Speaker 1: it and doing these analyzes, I I just found myself.

971
00:57:07,920 --> 00:57:09,960
Speaker 1: I took one dough this year with my muzzle loader

972
00:57:10,000 --> 00:57:12,600
Speaker 1: because I needed stuff for the freezer. But in the

973
00:57:12,680 --> 00:57:16,680
Speaker 1: November portion of the of the hunting season, it uh,

974
00:57:16,760 --> 00:57:18,960
Speaker 1: it felt like I was back in the rocket net

975
00:57:19,000 --> 00:57:21,800
Speaker 1: blind saying all right, where where's the deer? And I

976
00:57:22,120 --> 00:57:24,600
Speaker 1: spent so many hours with that, I was ready to

977
00:57:24,600 --> 00:57:28,720
Speaker 1: get my GSP and go go find quail instead. Believe me, man,

978
00:57:28,800 --> 00:57:30,760
Speaker 1: you don't have you don't have to explain yourself. I've

979
00:57:30,800 --> 00:57:33,080
Speaker 1: hunted deer for my job for a long time, and

980
00:57:33,160 --> 00:57:36,160
Speaker 1: I really like fishing Smalley's and I really like following

981
00:57:36,200 --> 00:57:40,040
Speaker 1: my dog around for roosters. Like there's a time every

982
00:57:40,080 --> 00:57:42,080
Speaker 1: deer season I hit where I cannot wait to get

983
00:57:42,120 --> 00:57:44,400
Speaker 1: out of the trees and go do something else. Well,

984
00:57:44,720 --> 00:57:46,880
Speaker 1: my dad, Duck hunts hard, and he says his favorite

985
00:57:46,960 --> 00:57:49,920
Speaker 1: day is opening day and his second favorite day is

986
00:57:50,680 --> 00:57:52,760
Speaker 1: the last day of the season, and he doesn't miss

987
00:57:52,800 --> 00:57:57,520
Speaker 1: too many days between. Yeah, it's it's a weird. It's

988
00:57:57,520 --> 00:57:59,800
Speaker 1: a weird place to get into. I think, I think

989
00:57:59,800 --> 00:58:01,880
Speaker 1: this is what a lot of you know this This

990
00:58:01,960 --> 00:58:04,800
Speaker 1: podcast is so so focused on white tails, but hunting

991
00:58:04,840 --> 00:58:07,840
Speaker 1: in general, it's weird to get into that space where

992
00:58:07,880 --> 00:58:10,560
Speaker 1: you know, like this is the thing I just I

993
00:58:10,600 --> 00:58:12,400
Speaker 1: have to go do this, like I live for this,

994
00:58:12,480 --> 00:58:14,920
Speaker 1: I have to go do it. But you also come

995
00:58:14,960 --> 00:58:16,720
Speaker 1: to terms with the fact that there's points where you're

996
00:58:16,760 --> 00:58:20,080
Speaker 1: really gonna hate it and and really but but you

997
00:58:20,160 --> 00:58:22,000
Speaker 1: also get to that spot where you're like, I know,

998
00:58:23,040 --> 00:58:25,000
Speaker 1: even though I want to just smash that alarm and

999
00:58:25,040 --> 00:58:27,000
Speaker 1: not get up today, I know it's going to be

1000
00:58:27,040 --> 00:58:29,280
Speaker 1: worth it every time I do. So you have these

1001
00:58:29,320 --> 00:58:32,120
Speaker 1: just up and down moments, uh huh, and you're exactly right,

1002
00:58:32,120 --> 00:58:34,400
Speaker 1: and then you get out there and the stunts sun

1003
00:58:34,560 --> 00:58:37,600
Speaker 1: starts coming up and you're glad you got up. Do

1004
00:58:37,400 --> 00:58:40,160
Speaker 1: you do you see? So you you mentioned that you

1005
00:58:40,160 --> 00:58:43,640
Speaker 1: know this is obviously a Missouri study and it's going

1006
00:58:43,720 --> 00:58:46,760
Speaker 1: to help the game managers in Missouri at the Department

1007
00:58:46,800 --> 00:58:49,400
Speaker 1: of Conservation balance that check book much better from year

1008
00:58:49,440 --> 00:58:51,520
Speaker 1: to year as far as figuring out, you know, how

1009
00:58:51,600 --> 00:58:53,560
Speaker 1: many deers should be, how many deers should be out there,

1010
00:58:53,560 --> 00:58:55,600
Speaker 1: and how many can we allow to be taken and

1011
00:58:55,680 --> 00:58:57,520
Speaker 1: you know how many you're gonna end up in coyote

1012
00:58:57,520 --> 00:59:01,240
Speaker 1: bellies and all that stuff. Do you have uh you know,

1013
00:59:01,280 --> 00:59:04,120
Speaker 1: are there are there state game agencies? Is does Kansas

1014
00:59:04,120 --> 00:59:05,960
Speaker 1: reach out to you and say, hey, can we like,

1015
00:59:06,320 --> 00:59:08,160
Speaker 1: can we talk to you about this or do other

1016
00:59:08,240 --> 00:59:10,200
Speaker 1: states or is there kind of like individual fife terms

1017
00:59:10,240 --> 00:59:12,880
Speaker 1: where you don't really work together. No, it's a very

1018
00:59:13,160 --> 00:59:15,920
Speaker 1: very collaborative field. And it's a small world. I mean,

1019
00:59:15,920 --> 00:59:17,800
Speaker 1: there's just not that many of us out there, and

1020
00:59:17,840 --> 00:59:19,560
Speaker 1: so you go to the meetings and you see the

1021
00:59:19,600 --> 00:59:23,600
Speaker 1: same faces and you bounce ideas, and there's no you know,

1022
00:59:23,640 --> 00:59:28,600
Speaker 1: there's no territoriality, there's no uh, there's really no competition

1023
00:59:28,720 --> 00:59:32,000
Speaker 1: because everybody's working toward the same goal and helping each

1024
00:59:32,000 --> 00:59:34,880
Speaker 1: other out. And so I mean it goes so far

1025
00:59:34,920 --> 00:59:38,520
Speaker 1: as to share an equipment if somebody gets shorthanded and

1026
00:59:38,560 --> 00:59:41,400
Speaker 1: you need to go help someplace over here or teach

1027
00:59:41,400 --> 00:59:45,280
Speaker 1: a new technique. I mean, with the the learning curve

1028
00:59:45,360 --> 00:59:47,520
Speaker 1: on some of this stuff is very steep, and so

1029
00:59:48,160 --> 00:59:52,800
Speaker 1: I've been people around the country about techniques for capturing

1030
00:59:52,880 --> 00:59:56,400
Speaker 1: gear or how to fit collars correctly, or what to

1031
00:59:56,440 --> 01:00:00,160
Speaker 1: watch out for with you know, programming, GPS, call ours

1032
01:00:00,240 --> 01:00:02,560
Speaker 1: or whatever the case may be. And so it's a

1033
01:00:02,680 --> 01:00:08,040
Speaker 1: very very collaborative community. And that's deer, that's turkeys, that's

1034
01:00:08,720 --> 01:00:11,480
Speaker 1: quay o, that's roosters, that's all of this stuff because

1035
01:00:11,480 --> 01:00:14,360
Speaker 1: we're all we're really all on the same team. Yeah,

1036
01:00:14,480 --> 01:00:16,840
Speaker 1: I suppose with the you know, you mentioned the rocket

1037
01:00:16,880 --> 01:00:18,479
Speaker 1: netting and some of the some of the other ways

1038
01:00:18,480 --> 01:00:21,560
Speaker 1: you capture deer for studies, I suppose you have to

1039
01:00:21,560 --> 01:00:27,000
Speaker 1: be super careful about how that's presented so the general

1040
01:00:27,000 --> 01:00:30,760
Speaker 1: public can digest it and be okay with it. Yeah,

1041
01:00:30,800 --> 01:00:33,800
Speaker 1: you do, and we take steps. I mean, the last

1042
01:00:33,840 --> 01:00:38,720
Speaker 1: thing we want to some you know, capture related mortality,

1043
01:00:39,000 --> 01:00:41,720
Speaker 1: because that's not why we're doing the work. That's not

1044
01:00:41,760 --> 01:00:44,920
Speaker 1: why we got into this field. And so, for example,

1045
01:00:44,960 --> 01:00:48,240
Speaker 1: the rocket nets, there have been many many times that

1046
01:00:48,320 --> 01:00:53,120
Speaker 1: I can think of having deer at the capture site

1047
01:00:53,720 --> 01:00:56,200
Speaker 1: where I can't hit the button on the net to

1048
01:00:56,200 --> 01:00:58,640
Speaker 1: shoot the net because there's too many deer and we

1049
01:00:58,680 --> 01:01:02,240
Speaker 1: don't have enough handlers, or their deer are two packed

1050
01:01:02,240 --> 01:01:06,200
Speaker 1: in close together and they're gonna hurt each other, or

1051
01:01:06,240 --> 01:01:08,000
Speaker 1: you know, they're too close to the net or too

1052
01:01:08,000 --> 01:01:10,600
Speaker 1: close to the rockets or whatever, and so it's not dear,

1053
01:01:10,640 --> 01:01:13,240
Speaker 1: show up and you you shoot things. You know, it's

1054
01:01:13,240 --> 01:01:16,760
Speaker 1: an ethical shot. And so usually with the rocket nets,

1055
01:01:17,080 --> 01:01:22,000
Speaker 1: that's how we are capturing adult books, and oftentimes they

1056
01:01:22,040 --> 01:01:24,240
Speaker 1: travel alone, and so that makes it a bit easier.

1057
01:01:24,920 --> 01:01:27,640
Speaker 1: But it's uh, you know, it's not just go out

1058
01:01:27,680 --> 01:01:29,840
Speaker 1: and take your first shot. You're waiting for things to

1059
01:01:29,880 --> 01:01:34,480
Speaker 1: be right so that that animal safety is ensured. Is

1060
01:01:34,520 --> 01:01:37,760
Speaker 1: there is there some like a similar kind of rush

1061
01:01:37,840 --> 01:01:40,440
Speaker 1: when you start doing that right away? As you know,

1062
01:01:40,440 --> 01:01:42,000
Speaker 1: like when you start hunting in a in a buck

1063
01:01:42,040 --> 01:01:44,720
Speaker 1: walks in and it doesn't wear off. No, it doesn't

1064
01:01:44,720 --> 01:01:48,720
Speaker 1: wear off. It doesn't wear at all. No, But I

1065
01:01:48,720 --> 01:01:52,160
Speaker 1: I can think of, well, you know all hunters know

1066
01:01:52,520 --> 01:01:55,200
Speaker 1: when your heart starts pounding and you're thinking, is this

1067
01:01:55,240 --> 01:01:57,200
Speaker 1: going to come through my shirt? I mean, it's just

1068
01:01:57,240 --> 01:02:00,120
Speaker 1: so it feels like it almost shakes your whole body.

1069
01:02:00,520 --> 01:02:03,400
Speaker 1: I've had those times in the rocket net blind and

1070
01:02:03,480 --> 01:02:06,240
Speaker 1: so you know, it's just circumstances or it's a big

1071
01:02:06,280 --> 01:02:09,000
Speaker 1: buck coming out or whatever the case may be, but

1072
01:02:09,160 --> 01:02:13,200
Speaker 1: it is. It is definitely a thrill. Do you ever miss?

1073
01:02:14,680 --> 01:02:18,920
Speaker 1: I don't. Some of your colleagues do. Some of my

1074
01:02:18,960 --> 01:02:21,600
Speaker 1: employees have missed before. And that's always a you know,

1075
01:02:21,600 --> 01:02:23,720
Speaker 1: when you miss with a forty by sixty ft net,

1076
01:02:25,440 --> 01:02:28,840
Speaker 1: usually you're the one buying beers that night, is I

1077
01:02:29,480 --> 01:02:32,080
Speaker 1: you don't have any like you've never just like filmed

1078
01:02:32,120 --> 01:02:36,800
Speaker 1: that have you somebody just with him? Oh withing, No, No,

1079
01:02:37,320 --> 01:02:40,919
Speaker 1: we don't do much filming just because there's enough other

1080
01:02:41,000 --> 01:02:43,480
Speaker 1: things going on. But we had one of the media

1081
01:02:43,520 --> 01:02:46,600
Speaker 1: folks with the Conservation Department come out and you know,

1082
01:02:46,680 --> 01:02:51,720
Speaker 1: I had to clarify this doesn't happen every time. And

1083
01:02:53,040 --> 01:02:56,680
Speaker 1: you know, it's like stars aligned and a nice ten

1084
01:02:56,760 --> 01:02:59,280
Speaker 1: point walked out about four thirty in the afternoon and

1085
01:02:59,280 --> 01:03:01,880
Speaker 1: it's snowing, and you know, he was on the road

1086
01:03:02,000 --> 01:03:04,920
Speaker 1: with the footage he wanted to share with the public

1087
01:03:05,080 --> 01:03:07,880
Speaker 1: before the sun went down, which rarely happens, and so

1088
01:03:08,200 --> 01:03:13,160
Speaker 1: we've had a few a few lucky spots over the years.

1089
01:03:13,560 --> 01:03:16,680
Speaker 1: I was just thinking with with somebody missing, what with

1090
01:03:16,760 --> 01:03:19,919
Speaker 1: a net like that, what a wonderful representation of buck

1091
01:03:19,960 --> 01:03:23,520
Speaker 1: fever that would be? Yeah, and there you know the

1092
01:03:27,200 --> 01:03:29,400
Speaker 1: it's happened. There's been some buck fever in the rocket

1093
01:03:29,400 --> 01:03:32,880
Speaker 1: net blind before, but it's a it's a very effective

1094
01:03:32,920 --> 01:03:40,280
Speaker 1: tool to catch him. Yeah, that's that's awesome. So you what,

1095
01:03:40,440 --> 01:03:42,600
Speaker 1: what do you want to do? You know, you're a

1096
01:03:42,600 --> 01:03:44,479
Speaker 1: young fellow, you've done a lot of really really cool

1097
01:03:44,520 --> 01:03:47,120
Speaker 1: stuff already. What's kind of like a dream study for you?

1098
01:03:47,160 --> 01:03:49,280
Speaker 1: Doesn't have to be white tails, Like, what's what's something

1099
01:03:49,320 --> 01:03:52,400
Speaker 1: out there that's just like man, I would that that

1100
01:03:52,680 --> 01:03:55,960
Speaker 1: keeps me up at night. I think more about oscillated turkeys,

1101
01:03:56,440 --> 01:04:00,160
Speaker 1: and just because there have been so few, there's been

1102
01:04:00,160 --> 01:04:02,880
Speaker 1: next to no research on there on those that species.

1103
01:04:02,880 --> 01:04:06,520
Speaker 1: And so I was one of the first, maybe the

1104
01:04:06,600 --> 01:04:11,040
Speaker 1: first to put transmitters on wild birds. Other people had

1105
01:04:11,080 --> 01:04:13,160
Speaker 1: caught some birds that were in some protected areas, but

1106
01:04:13,200 --> 01:04:16,920
Speaker 1: it'd be like collar and elk and yellowstone at one

1107
01:04:16,920 --> 01:04:21,640
Speaker 1: of the campgrounds doesn't really represent the wild bird. And

1108
01:04:21,720 --> 01:04:24,760
Speaker 1: that's such an interesting bird and an interesting area and

1109
01:04:24,880 --> 01:04:29,440
Speaker 1: interesting questions to be asked that that would be if

1110
01:04:29,480 --> 01:04:31,640
Speaker 1: somebody gave me a million dollars to design a study,

1111
01:04:31,680 --> 01:04:34,720
Speaker 1: that'd be where I'd go. So is there is there

1112
01:04:34,760 --> 01:04:36,840
Speaker 1: a level just of personal interest because you think it's

1113
01:04:37,040 --> 01:04:39,320
Speaker 1: they're cool and where they live is really really neat,

1114
01:04:39,560 --> 01:04:42,320
Speaker 1: But also there's just sort of a gap in the

1115
01:04:42,320 --> 01:04:45,439
Speaker 1: there's there's a void in the in the knowledge about

1116
01:04:45,480 --> 01:04:49,160
Speaker 1: him out there. Both of those definitely. And then the

1117
01:04:49,200 --> 01:04:52,520
Speaker 1: other thing is there's research like that that could really

1118
01:04:52,520 --> 01:04:57,840
Speaker 1: help people, and that's still used as a food source by,

1119
01:04:58,120 --> 01:05:01,280
Speaker 1: like I said, very poor people. And I don't see

1120
01:05:01,320 --> 01:05:06,200
Speaker 1: that changing. And then there's that international hunting interest in

1121
01:05:06,240 --> 01:05:09,600
Speaker 1: the species, and there is a void and so there's

1122
01:05:09,640 --> 01:05:12,720
Speaker 1: just a lot of reasons that it's appealing, but it's

1123
01:05:12,760 --> 01:05:15,400
Speaker 1: a personal, personal one. Yeah. I mean there's a lot

1124
01:05:15,440 --> 01:05:17,960
Speaker 1: going on there. There. There could be you know, globally,

1125
01:05:18,000 --> 01:05:21,600
Speaker 1: there could be so many kind of parallel situations to

1126
01:05:21,720 --> 01:05:24,680
Speaker 1: that that that that could benefit from this, This particular

1127
01:05:24,720 --> 01:05:27,000
Speaker 1: there might not seem related at all. Is there is

1128
01:05:27,040 --> 01:05:29,800
Speaker 1: there a part of that too? It kind of feels like,

1129
01:05:31,160 --> 01:05:34,560
Speaker 1: you know, white tales, there's there's been so much focus

1130
01:05:34,600 --> 01:05:36,480
Speaker 1: on them for so long, and they're the most popular

1131
01:05:36,520 --> 01:05:39,600
Speaker 1: game species out there, and you know, I would assume

1132
01:05:39,640 --> 01:05:41,920
Speaker 1: they've been studied as far as like game species, they've

1133
01:05:41,960 --> 01:05:45,360
Speaker 1: probably been studied more than anything is in this country anyway.

1134
01:05:45,720 --> 01:05:47,120
Speaker 1: Is there is there a part of that where you're

1135
01:05:47,120 --> 01:05:50,240
Speaker 1: just like, man, there's just less to learn about white

1136
01:05:50,240 --> 01:05:52,920
Speaker 1: tails or not. No, I think it just becomes more

1137
01:05:53,000 --> 01:05:57,960
Speaker 1: nuanced and and um, you know, white tailed research has

1138
01:05:58,040 --> 01:06:02,440
Speaker 1: gone back longer in any other game species research probably,

1139
01:06:03,160 --> 01:06:09,439
Speaker 1: and so you keep getting better tools to ask more

1140
01:06:09,480 --> 01:06:14,440
Speaker 1: difficult and more informed questions. And to me, that's progress.

1141
01:06:14,520 --> 01:06:19,160
Speaker 1: And I agree with you white tail deer are the

1142
01:06:19,160 --> 01:06:24,160
Speaker 1: game species of North America. They probably generate more excitement,

1143
01:06:24,360 --> 01:06:29,440
Speaker 1: generate more dollars for conservation than than other species. And

1144
01:06:29,480 --> 01:06:33,160
Speaker 1: so I I'm glad to see that that research is

1145
01:06:33,440 --> 01:06:35,880
Speaker 1: still going strong and glad to see that people are

1146
01:06:35,920 --> 01:06:40,360
Speaker 1: interested in it. Yeah. So last question here. You mentioned

1147
01:06:40,640 --> 01:06:43,480
Speaker 1: way at the beginning of this when we were talking about,

1148
01:06:43,480 --> 01:06:45,280
Speaker 1: you know, your time in South Africa, your time in

1149
01:06:45,360 --> 01:06:49,520
Speaker 1: China studying prairie chicken LECs and finding those like you

1150
01:06:49,520 --> 01:06:53,640
Speaker 1: you mentioned multiple times habitat and land use and habitat

1151
01:06:53,640 --> 01:06:56,800
Speaker 1: and land use. As you as you get further into

1152
01:06:56,800 --> 01:07:00,000
Speaker 1: your career and do more research on various wildlife species

1153
01:07:00,440 --> 01:07:03,400
Speaker 1: across the world, do you like, how often do you

1154
01:07:03,440 --> 01:07:05,840
Speaker 1: just come back to habitat and go, Man, this is

1155
01:07:05,880 --> 01:07:09,520
Speaker 1: like this is the lynchpin that holds everything together for

1156
01:07:09,800 --> 01:07:13,400
Speaker 1: game population. I know, I know, it's like so dynamically variable,

1157
01:07:13,680 --> 01:07:17,200
Speaker 1: but that's like one constant that just seems to need

1158
01:07:17,320 --> 01:07:20,840
Speaker 1: love all the time. Well, it exactly is. I mean,

1159
01:07:20,840 --> 01:07:24,480
Speaker 1: you need the food, water, the cover, and so you

1160
01:07:24,560 --> 01:07:27,760
Speaker 1: don't have good habitat. Some some species can live anywhere.

1161
01:07:28,040 --> 01:07:32,240
Speaker 1: I mean you look at coyotes, look at white tailed deer,

1162
01:07:32,280 --> 01:07:36,280
Speaker 1: I mean, um, some of these generalists can get by anywhere,

1163
01:07:36,320 --> 01:07:40,760
Speaker 1: but then you get more into the specialist species and

1164
01:07:42,400 --> 01:07:46,240
Speaker 1: the habitat needs become very specific or very large or

1165
01:07:46,320 --> 01:07:48,880
Speaker 1: something that makes it a challenge to have that on

1166
01:07:49,000 --> 01:07:53,920
Speaker 1: the landscape. But without good habitat, you're gonna have nothing. Yeah,

1167
01:07:54,080 --> 01:07:57,240
Speaker 1: and that's you mentioned this great intangible with some game

1168
01:07:57,280 --> 01:08:00,000
Speaker 1: species they just play well with man and some don't.

1169
01:08:00,600 --> 01:08:03,200
Speaker 1: And you know when you when you talked about that

1170
01:08:03,640 --> 01:08:06,800
Speaker 1: the buck that took the huge dispersal and you know,

1171
01:08:06,880 --> 01:08:10,320
Speaker 1: he crossed and he crossed I think it was the

1172
01:08:10,360 --> 01:08:14,960
Speaker 1: Grand River like multiple times, and you think of all

1173
01:08:15,000 --> 01:08:17,240
Speaker 1: these things that you go, that's probably a pretty good

1174
01:08:17,320 --> 01:08:19,360
Speaker 1: natural barrier, and a lot of times he paralleled it, it

1175
01:08:19,320 --> 01:08:22,720
Speaker 1: it seemed like, especially the roads. But eventually it was

1176
01:08:22,760 --> 01:08:25,679
Speaker 1: time to cross. And you you just I would think

1177
01:08:26,280 --> 01:08:28,680
Speaker 1: mule deer other than maybe a migration or elk or

1178
01:08:28,680 --> 01:08:30,559
Speaker 1: some of these other critters that maybe don't play as

1179
01:08:30,600 --> 01:08:32,080
Speaker 1: well with man. You'd think, man, that would be like

1180
01:08:32,120 --> 01:08:37,040
Speaker 1: a really really hard edge for them. They might cross it,

1181
01:08:37,080 --> 01:08:38,719
Speaker 1: but probably not. And then you think about that white

1182
01:08:38,720 --> 01:08:40,920
Speaker 1: tail and you look at his route and go, it

1183
01:08:40,960 --> 01:08:44,880
Speaker 1: didn't seem to really phase him, right, Now he definitely

1184
01:08:44,960 --> 01:08:47,559
Speaker 1: knew it was there. He responded to it, but it

1185
01:08:47,600 --> 01:08:51,320
Speaker 1: wasn't going to stop him. Yeah, yeah, it's wild. It's

1186
01:08:51,320 --> 01:08:54,720
Speaker 1: wild anyway, John, Uh, this was so much fun, man.

1187
01:08:54,800 --> 01:08:57,040
Speaker 1: I really appreciate you coming on. Where can people if

1188
01:08:57,080 --> 01:08:59,559
Speaker 1: they want to geek out on this research, where can

1189
01:08:59,600 --> 01:09:04,600
Speaker 1: they go to find it? Well, the the scientific publication

1190
01:09:04,680 --> 01:09:09,720
Speaker 1: is open access and so google or search however you

1191
01:09:09,720 --> 01:09:15,920
Speaker 1: want because it's in metric for the scientific title ko

1192
01:09:16,200 --> 01:09:18,960
Speaker 1: dispersal by a white tailed Deer and you can get

1193
01:09:18,960 --> 01:09:22,880
Speaker 1: the article. And that's where I'd encourage people to go,

1194
01:09:23,080 --> 01:09:26,280
Speaker 1: just to see what kind of work goes into preparing

1195
01:09:26,360 --> 01:09:32,000
Speaker 1: a peer reviewed scientific publication to find out more. This

1196
01:09:32,120 --> 01:09:36,760
Speaker 1: is um. This is really getting extensive news coverage and

1197
01:09:36,840 --> 01:09:41,000
Speaker 1: so um folks won't have to look for to find this.

1198
01:09:41,840 --> 01:09:45,200
Speaker 1: Now the future research that is coming out, keep an

1199
01:09:45,200 --> 01:09:48,360
Speaker 1: eye on the Missouri Department of Conservation web page and

1200
01:09:48,640 --> 01:09:52,120
Speaker 1: we're going to have everything out and available for the public.

1201
01:09:52,200 --> 01:09:55,720
Speaker 1: And they were the sponsoring agency and so they're going

1202
01:09:55,760 --> 01:09:58,040
Speaker 1: to help get the word out to folks interested in

1203
01:09:58,080 --> 01:10:00,240
Speaker 1: this sort of thing. Yeah, that's there's there's gonna be

1204
01:10:00,280 --> 01:10:02,080
Speaker 1: more coming on this. Let's let's touch on that quick

1205
01:10:02,080 --> 01:10:04,400
Speaker 1: and then we will wrap this up. When you mentioned

1206
01:10:04,400 --> 01:10:10,360
Speaker 1: this paper, this this peer reviewed research paper that's out there. People,

1207
01:10:11,000 --> 01:10:14,000
Speaker 1: they're very quick to make judgments on something like this.

1208
01:10:14,120 --> 01:10:15,880
Speaker 1: If you say, oh, this buck walking and you're you're

1209
01:10:15,920 --> 01:10:18,840
Speaker 1: laughing because you know this, go read. If you're like,

1210
01:10:19,000 --> 01:10:21,080
Speaker 1: I don't believe this is be somebody through that collar

1211
01:10:21,120 --> 01:10:22,120
Speaker 1: in the back of a truck and it drove a

1212
01:10:22,200 --> 01:10:24,200
Speaker 1: hunter in eighty five months, go look at this paper,

1213
01:10:24,200 --> 01:10:27,920
Speaker 1: look at the resources cited, the sources cited, go look

1214
01:10:27,960 --> 01:10:32,760
Speaker 1: at the extensive uh level of evidence in this and

1215
01:10:32,800 --> 01:10:37,280
Speaker 1: then make your judgment call on what happened here. Very

1216
01:10:37,360 --> 01:10:40,240
Speaker 1: I would encourage that, yes, yes you will. You will

1217
01:10:40,280 --> 01:10:43,439
Speaker 1: have a harder time refuting these findings if you actually

1218
01:10:43,439 --> 01:10:44,960
Speaker 1: go give that a read. It's kind of a long

1219
01:10:45,000 --> 01:10:48,240
Speaker 1: one and it's pretty dense, but it'll be worth it. Uh, John,

1220
01:10:48,320 --> 01:10:50,720
Speaker 1: thank you so much. I really appreciate this. It was

1221
01:10:50,760 --> 01:10:54,120
Speaker 1: my pleasure. I enjoyed visiting. That's it for this week, folks.

1222
01:10:54,160 --> 01:10:56,519
Speaker 1: I hope you enjoyed listening to John as much as

1223
01:10:56,560 --> 01:11:01,240
Speaker 1: I love talking to him. Such interesting conversation with deer

1224
01:11:01,280 --> 01:11:04,519
Speaker 1: researcher like that, who has who has so much real

1225
01:11:04,560 --> 01:11:07,800
Speaker 1: world information and also comes from a hunting background. You

1226
01:11:07,880 --> 01:11:11,080
Speaker 1: gotta love that I have been your guest host, Tony Peterson.

1227
01:11:11,120 --> 01:11:12,720
Speaker 1: This is Wired to Hunt, which is brought to you

1228
01:11:12,800 --> 01:11:15,240
Speaker 1: by First Light. As I always thank you, thank you,

1229
01:11:15,320 --> 01:11:17,400
Speaker 1: thank you for listening and checking in. If you want

1230
01:11:17,439 --> 01:11:20,760
Speaker 1: more white tail information, check out our YouTube stuff that

1231
01:11:20,760 --> 01:11:23,439
Speaker 1: we're putting out every week. We've got how to videos,

1232
01:11:23,479 --> 01:11:25,559
Speaker 1: all kinds of neat things there. Check out my Wire

1233
01:11:25,600 --> 01:11:29,479
Speaker 1: to Hunt Foundations podcast as well, and of course go

1234
01:11:29,600 --> 01:11:31,320
Speaker 1: to the meat eater dot com. You're gonna find a

1235
01:11:31,320 --> 01:11:34,080
Speaker 1: whole bunch of articles by some of the top white

1236
01:11:34,120 --> 01:11:37,439
Speaker 1: tail writers in the country.