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

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

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

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Speaker 1: your host, Mark Kenyon. This episode number one, and today

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Speaker 1: we're joined by Dwayne Diefenbach, a wildlife biologist and the

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Speaker 1: leader of the penn State Dear four Study, and we're

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Speaker 1: discussing Antler point restrictions, how dear research is conducted, lessons

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Speaker 1: learned from monitoring deer movements, dear home range insights, and

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Speaker 1: much much more. Hello, and welcome to the Wired to

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Speaker 1: Hunt podcast, brought to you by Sitka Gear. Today in

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Speaker 1: the show, we are going to be joined by Dwayne Diefenbach.

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Speaker 1: He is an agjunct professor of Wildlife Ecology at penn

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Speaker 1: State University in the leader of the penn State Dear

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Speaker 1: Force Study and the Pennsylvania Cooperative Fish and Wildlife Research Unit. UM.

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Speaker 1: That's that's a whole lot of things as far as titles,

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Speaker 1: but I think in short, what it means is that

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Speaker 1: Dwayne spends an enormous amount of time studying and researching

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Speaker 1: and monitoring and learning about white tailed deer and their

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Speaker 1: behavior movements. Impacts all sorts of stuff, and I think

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Speaker 1: it's it's a lot of stuff that that I and

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Speaker 1: Dan and I'm guessing all of you are probably gonna

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Speaker 1: be really interested in. So today, with the two thousand

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Speaker 1: seventeen hunting season kind of coming near to a close

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Speaker 1: for many of us, I kind of thought we can

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Speaker 1: take a step back from the hunting stuff and take

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Speaker 1: a little time to get back to the basics and

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Speaker 1: simply talk about and learn about these critters that we

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Speaker 1: love so much. So that's my thoughts leading into this one.

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Speaker 1: Dan Um, what do you think about that game plan?

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Speaker 1: I like it, man, I tell you what. I I've

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Speaker 1: never done any type of official research before, so this

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Speaker 1: kind of stuff triggers me. I guess I really like

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Speaker 1: to learn about numbers. Like That's what I do for

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Speaker 1: my my real job, right, I I look at spreadsheets

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Speaker 1: all day. I run audits and and that kind of thing.

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Speaker 1: So when you know, in order to do proper research,

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Speaker 1: you have to do those things. So I'm I guess

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Speaker 1: I'm kind of what I'm what I'm getting at is

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Speaker 1: I'm kind of a nerd for statistics. That's good. That's good,

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Speaker 1: and it's funny you bring up what you do. We've

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Speaker 1: been friends for many years now, we've hosted this podcast,

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Speaker 1: done podcast episodes together for for three or four years now.

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Speaker 1: I still don't really understand what you're doing. What what

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Speaker 1: I imagined? Dan? Um? Have you ever seen the movie

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Speaker 1: Off the Space? Uh? Yeah, So I imagined two things.

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Speaker 1: Either you're like one of the guys who just is

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Speaker 1: really angry about his boss constantly telling him to put

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Speaker 1: the cover sheet on the TPS report. You're either that

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Speaker 1: guy or you're the guy I think his name is now,

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Speaker 1: not Peter, I can't remember what his name is. But

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Speaker 1: the two bobs coming in, they're interviewing, so what exactly

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Speaker 1: would you say you do here? And then he's like,

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Speaker 1: I've told you I'm a people person. I think that

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Speaker 1: might be you, Dan, Yeah, it could be. It could

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Speaker 1: be I'm that guy. I'm the guy who just takes

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Speaker 1: one giant stamp and I stamped a paper and then

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Speaker 1: I put it onto another pile, and then stamp a

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Speaker 1: paper and then I put it onto another pile. So okay,

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Speaker 1: that helps, that makes sense, And then that that helps

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Speaker 1: paint to paint a clear picture for me. Now, I

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Speaker 1: don't use a computer at all. My job oh man. So, um,

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Speaker 1: anything we need to cover in the life of Dan

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Speaker 1: before we get to our main show here, Dwayne, I

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Speaker 1: don't you know, as far as I want to tell

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Speaker 1: you a story, but I don't think I can that

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Speaker 1: it's not R rated because it's about my daughter. But

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Speaker 1: but it just as a reminder that I have to

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Speaker 1: watch my mouth around the kids and what I say.

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Speaker 1: I'll just tell it. I'll just tell it and you

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Speaker 1: can edit it out and it parts out if you want. Okay.

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Speaker 1: So the other day at school, warned listeners, Yeah, right, right.

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Speaker 1: So the other day at school, um, or, my wife

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Speaker 1: gets a notification I think from the teacher says, well,

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Speaker 1: your your daughter used foul language at to another student,

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Speaker 1: and and my wife's like, what happened? You know what happened?

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Speaker 1: And like, in a way, I'm kind of proud of

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Speaker 1: her because she used it the right way. But but

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Speaker 1: but she says, another kid started crying over something real

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Speaker 1: simple and petty, and she called that kid a pussy.

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Speaker 1: And then she went on to explain that if you

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Speaker 1: cry over stuff like that, like she goes, are you hurt? No,

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Speaker 1: then you're you know, and then you know, like if

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Speaker 1: you're crying because of nothing. That's what you are. And

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Speaker 1: and I I guess I. My wife's like, you have

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Speaker 1: got to watch your mouth around the kids, so I, uh,

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Speaker 1: I gotta. By the way, I am not gonna win

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Speaker 1: any awards for like best dad and never Okay, So

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Speaker 1: I don't think this is news to anyone dance. But

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Speaker 1: there's that one person who said they're a better father

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Speaker 1: because of you, So right, hold hold that close to

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Speaker 1: your heart. Yeah. But but other than that, you know,

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Speaker 1: just starting to think about late season, man, I think here,

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Speaker 1: I mean, life is just so crazy right now with

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Speaker 1: the holiday, the holidays coming up. But hope I can

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Speaker 1: get some trail cameras out, go to some of my properties,

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Speaker 1: try to find some of these food sources and uh,

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Speaker 1: you know, start that whole game. So have you assuming

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Speaker 1: based on what you just said that then you've not

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Speaker 1: been out at all in December yet? No? The uh

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Speaker 1: the Iowa shotgun seasons just ended on Sunday. Yeah, so

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Speaker 1: you're thinking now, as I understand it, though, you have

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Speaker 1: to hunt with a firearm now, right since you filled

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Speaker 1: your bow tag. Nope, Um, I can use a primitive

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Speaker 1: weapon during that muzzle late muzzloader season so I can

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Speaker 1: continue to use my bow during muzzleloader you know, the

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Speaker 1: late muzzleloader season, and that goes from now until the

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Speaker 1: end of the season. Um so, And I think there's

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Speaker 1: a new law in Iowa, like I can use a

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Speaker 1: crossbow now I can in this in replace of a muzzleloader,

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Speaker 1: I can use a handgun. Uh So I won't be

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Speaker 1: doing any of that, but uh there is an opportunity

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Speaker 1: for me to go out and harvest a second buck

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Speaker 1: if I if I find one worth shooting. Cool, that's exciting.

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Speaker 1: I hope you do. I hope you get out there. Man.

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Speaker 1: Are you done done? Now? Are you done? Oh? No, no,

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Speaker 1: unfortunately not. I will tell you what though, I'm just

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Speaker 1: kinda I am worn down, like I am ready for

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Speaker 1: I'm ready for this season to be done. I think

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Speaker 1: I just I just need kind of a fresh start.

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Speaker 1: I think, Um you know, since we last chatted, I

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Speaker 1: think I had told you, well maybe we didn't talk

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Speaker 1: about it at all, and we talked all about my

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Speaker 1: late season hunts for Holyfield. Well after the last the

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Speaker 1: last time I talked with you, or I think even

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Speaker 1: on this podcast, we chatted about going down to Ohio

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Speaker 1: and the last thing we knew you saw a buck

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Speaker 1: potentially worth shooting, but you were going to make a

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Speaker 1: move that night. No, we talked. We talked since then

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Speaker 1: because I talked about you about how the next day

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Speaker 1: I moved and then he showed up. I got there late. Remember, Um,

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Speaker 1: Remember I forgot all my hunting clothes at my hotel. Okay,

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Speaker 1: So I got into the stop spot late. And when

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Speaker 1: I got to the spot I wanted to hunt, there

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Speaker 1: was already doze feeding there, so I couldn't hunt there.

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Speaker 1: But the big bucks stepped out right where I wanted

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Speaker 1: to be. The next day, and the next day I

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Speaker 1: got to the spot I wanted to be in, the

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Speaker 1: big buck came out where it was the night before. Um.

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Speaker 1: So that was the end of that Ohio trip. Okay,

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Speaker 1: So now you're back in Michigan, right, So yes. Then

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Speaker 1: I got back to Michigan and a cold front was

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Speaker 1: hitting a couple of days later. So my game plan

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Speaker 1: had been to stay out on the Michigan Main Michigan

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Speaker 1: property where Holy Field is until that cold front hit.

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Speaker 1: So the front hit and I started hunting. I started

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Speaker 1: bouncing around. Um and over the course of I don't know,

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Speaker 1: I think that front hit on the fifth of December,

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Speaker 1: maybe something like that. And between the fifth of December

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Speaker 1: and the December I think I hunted maybe six times.

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Speaker 1: Um when there was good missions, Goodwin direction, all that

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Speaker 1: kind of stuff. Um. And you know the long story

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Speaker 1: short on that is, I never saw holy Field. I

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Speaker 1: never saw any bucks at all. Um. Got into some

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Speaker 1: good spots, had good weather, etcetera, etcetera, but just nothing

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Speaker 1: but doze. So that was disappointing. So last year holy

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Speaker 1: Field did kind of the same thing, right, He made

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Speaker 1: it through gun season, showed up one or two more times,

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Speaker 1: but then went away for a while. Or was he

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Speaker 1: consistent all at late season? Last year? He was pretty

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Speaker 1: consistent last December. Um, I only saw him. Well, I

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Speaker 1: saw him a few times in person in December, including

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Speaker 1: that time I had him, you know, in front of

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Speaker 1: me at sixty five yards um. But he was on

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Speaker 1: trail camera ton Um. He was all over the cameras

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Speaker 1: last year during the month of December. I have not

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Speaker 1: gotten a single trail camera picture of him yet this

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Speaker 1: month or in November. I don't have a single picture

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Speaker 1: of him since October. Holy Field. Wait, I thought he

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Speaker 1: made it through the gun season. Oh he did. I've

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Speaker 1: seen him. People have seen it trail camera pictures. Yeah. So,

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Speaker 1: so I saw him, you know, twelve times in November

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Speaker 1: and then UM got a sighting of him the day

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Speaker 1: after gun season. UM, so you know, visually was confirming

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Speaker 1: he was alive. But no pictures at all. He hasn't

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Speaker 1: been on the trail cameras at all, which is weird. Um.

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Speaker 1: But uh but yeah, so I hunted you know during

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Speaker 1: this time period, haven't seen him, no pictures. Um. So

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Speaker 1: then I said, all right, time to I'm not gonna

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Speaker 1: keep beating my head against a wall if he's not

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Speaker 1: moving daylight or anything. So then last weekend I went

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Speaker 1: to Ohio and hunted in Ohio Friday, Friday, Saturday, Sunday. Um.

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Speaker 1: I thought I'd tried to catch they had a little

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Speaker 1: two day guns season going on, and no luck there either. Um.

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Speaker 1: I saw some deer um one night, I think it

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Speaker 1: was Saturday night in particular. I thought I felt pretty

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Speaker 1: good about it. Um got set up in the spot

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Speaker 1: near where I had seen this big buck um the

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Speaker 1: trip before, and UM just started seeing all sorts of deer,

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Speaker 1: more dear than I ever see on this property. UM

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Speaker 1: ended up seeing maybe seven or eight does and I

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Speaker 1: think six different bucks, um, and these does all came

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Speaker 1: piling off these ridges and they all came by within

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Speaker 1: shooting range on me. And these bucks were chasing does.

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Speaker 1: They're they're bumping does around that. I think there was

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Speaker 1: a young faun that maybe was coming into estrus. Finally,

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Speaker 1: you know that kind of second rut you get these

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Speaker 1: late maturity funds. Um. Because it was nuts, like, I

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Speaker 1: haven't seen anything like that on this property in a

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Speaker 1: long time. UM. The issue was that these bucks were

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Speaker 1: all young bucks, two year olds and year and a

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Speaker 1: half olds, um. But it got me excited. I was like, man,

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Speaker 1: if there's you know, a fall here and all these

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Speaker 1: bucks are kyrageting one air, A couple of bucks were fighting, um,

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Speaker 1: all this stuff going on, I was like, man, maybe

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Speaker 1: maybe this would be the time the Big Boys shows.

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Speaker 1: But he didn't. UM. I guess the only good news

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Speaker 1: that there were pictures. There was a good amount of

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Speaker 1: trout camera activity recently with three different mature bucks still

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Speaker 1: in the area, UM, somewhat consistently. You know, all shown

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Speaker 1: up a few times a week. UM. So there's bucks there,

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Speaker 1: but they were not moving in daylight when I was there.

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Speaker 1: So three days there no luck. Came back home and uh,

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Speaker 1: I just pulled trail cameras. Again. I haven't checked pictures

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Speaker 1: in like ten days or two weeks or something like that,

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Speaker 1: and again nothing on camera. Um, just a few pictures

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Speaker 1: of that one three year old I was calling survivor

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Speaker 1: um and some year and a half old. And that's

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Speaker 1: basically it. So I think things are kind of I

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Speaker 1: think things are kind of coming to a close here

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Speaker 1: the two thousand seventeen season. Um, pretty soon here, in

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Speaker 1: a few days, I probably gotta switch over to shooting

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Speaker 1: some doughs because I just have to kill some doughs

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Speaker 1: out here. That was gonna be My question is when

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Speaker 1: when do you give up and then and on Holy

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Speaker 1: Field and then go out and just try to fill

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Speaker 1: the freezer. Yeah, I think I'm I'm a few days

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Speaker 1: away from doing that. Um. So pretty soon here it's

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Speaker 1: gonna be time to just get that done. Um and

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Speaker 1: then uh, you know, move on to two thousand eighteen

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Speaker 1: and whatever that might hold. So, man, it's crazy how

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Speaker 1: different our seasons have been when you think about it,

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Speaker 1: because you're getting to the point where you say, man,

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Speaker 1: you're worn down because you've been hunting so hard you know,

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Speaker 1: throughout almost the entire from September all the way until now.

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Speaker 1: And I'm I'm to the point where, Man, I cannot

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Speaker 1: wait to get out and sit a late season hunt,

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Speaker 1: even if it means forty degrees in December, you know

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Speaker 1: what I mean? Like I I feel like my season

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Speaker 1: was over too soon, very very different seasons. I think

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Speaker 1: you hunted ten days and saw like six bucks over

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Speaker 1: one fifty and I hunted. I hunted like a hundred

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Speaker 1: fifty days and saw six x over ten inches something

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Speaker 1: like that. Um, it's been a it's been a season, man,

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Speaker 1: I'll tell you that. But next episode, I think we do.

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Speaker 1: I want to do like a two thousand and seventeen analysis,

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Speaker 1: like review the season, go through in detail. Pretty soon.

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Speaker 1: I want to sit down, like think through all the

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Speaker 1: things that I thought coming into the season, all the

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Speaker 1: things I've learned after the season, all the mistakes I made. Um,

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Speaker 1: I want to do like a really comprehensive review for

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Speaker 1: myself and then and I thought maybe that'd be helpful

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Speaker 1: to share that here, So keep that, keep that in

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Speaker 1: mind for yourself, Dan for our next one. Um, so

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Speaker 1: we can both do that perfect. But I guess it

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Speaker 1: is now time for us to wrap this up because

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Speaker 1: it's it's time to get Dwayne on the phone. So

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Speaker 1: let's take a quick break here for our sick coust

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Speaker 1: story and then we'll call Dwayne for this week's sick

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Speaker 1: of story. We're joined by photographer Caleb Boyd who tells

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Speaker 1: us about a hunt of a life time that never

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Speaker 1: ended with the harvest. So I've been fortunate enough to

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Speaker 1: travel and photograph around the world on commercial and documentary hunts,

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Speaker 1: and one of my most memorable hunting experiences was capturing

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Speaker 1: a hunt for a U. S. Marshal that was retired

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Speaker 1: and it was on a brown bear hunt out of

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Speaker 1: good News Bay, Alaska. This ten day hunt turned into

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Speaker 1: a fourteen day crazy rain, freezing, snowing, um out of

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Speaker 1: tents and glassing for ten to twelve hours a day,

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Speaker 1: and it was unbelievable weather where we saw wolves, wolverine, bears, moose,

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Speaker 1: you name it. And it was on the frozen tundra.

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Speaker 1: So each night was windy and raining and it was

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Speaker 1: it was a long time, So fourteen days out in

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Speaker 1: the field, and it was one of the coolest experiences

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Speaker 1: of my life being able to see all these animals

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Speaker 1: and yet not take an animal, and so even though

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Speaker 1: the hunt was unsuccessful, the experience was an adventure of

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Speaker 1: a lifetime. On Caleb's hunt, he was wearing Sitka's Mountain

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Speaker 1: pants and jet Stream vests. If you'd like to create

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Speaker 1: a stick of story of your own, or to learn

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Speaker 1: more about Sitka's technical hunting apparel, visit sitka gear dot com.

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Speaker 1: Alright with us now on the show is Dwayne Diefenbach.

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Speaker 1: Welcome to the show, Dwayne. Oh, thank you, glad to

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Speaker 1: be here. Yeah, and uh I was. I was mentioning

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Speaker 1: to Dan Um earlier that I've been following your work

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Speaker 1: through the Dear Forest Study over on your blog for

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Speaker 1: a handful of years now, Um, and I've just been

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Speaker 1: pretty fascinated with the things you've been sharing, the little

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Speaker 1: updates in regards to what you're doing and how you're doing,

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Speaker 1: and different different insights you've pulled from that study. So

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Speaker 1: so for a while I've wanted to try to have

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Speaker 1: you on the show. I'm excited that we can finally

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Speaker 1: do this, So so first off, thank you, and secondly, Um,

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Speaker 1: for those though that maybe aren't familiar with who you

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Speaker 1: are and the work you're doing, can you just kind

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Speaker 1: of give us a quick overview of of what it

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Speaker 1: is you do and how you got to this point. Sure, UM, so,

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Speaker 1: I I'm the leader of the Pennsylvania Cooperative Fish and

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Speaker 1: Wildlife Research Unit, which is located at penn State University.

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Speaker 1: I'm actually an employee of the U S Geological Survey UM,

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Speaker 1: but because I'm stationed at Penn State UM, I'm also

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Speaker 1: on the adjunct faculty, so it gives me a unique

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Speaker 1: position where UM I do research. UM I also do

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Speaker 1: a little bit of teaching. I mentor graduate students, and

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Speaker 1: one of the more important parts of my job job

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Speaker 1: is to work collaboratively with our state UH natural resource

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Speaker 1: agencies and so UM. For the past seventeen years, I've

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Speaker 1: been doing deer research with the Pennsylvania Game Commission, and

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Speaker 1: a few years ago UM I actually also started a

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Speaker 1: joint project with both the Game Commission and our Pennsylvania

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Speaker 1: Bureau of Forestry. So the Bureau Forestry manages about two

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Speaker 1: million acres in Pennsylvania. UM and both agencies, of course

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Speaker 1: UM have a vested interest in managing deer um the

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Speaker 1: best they can, and so both agencies are working with

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Speaker 1: me now on on our current deer research so so

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Speaker 1: why did you want to get involved the dear, dear

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Speaker 1: research at the beginning? You know, seventeen years ago, when

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Speaker 1: you start with this focus area, I have to laugh

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Speaker 1: because most of my career I've avoided deer. UM. I've

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Speaker 1: done work on waterfowl and UH. I reintroduced bobcats to

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Speaker 1: an island and have done various research projects and and

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Speaker 1: quite frankly, I never was that excited about having to

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Speaker 1: do research with deer. But of course when I started

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Speaker 1: in this position, UM, deer are very important to the

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Speaker 1: Game Commission, and right around two thousand, UM they decided

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Speaker 1: that they wanted to do some research because for years

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Speaker 1: they really hadn't done any research on deer, and UM

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Speaker 1: they were thinking about implementing some major changes. UM. They

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Speaker 1: ended up implementing Antler point restrictions in Pennsylvania, UM to

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Speaker 1: create an older age structure in the buck population. And

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Speaker 1: so along with that, they knew that they needed to

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Speaker 1: do research. And so UM that's where I came in.

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Speaker 1: And I guess I haven't looked back. It's actually, despite

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Speaker 1: my trepidation, UM, it's actually been very rewarding because the

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Speaker 1: Pennsylvania Game Commission has provided a lot of resources towards

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Speaker 1: the dear research. So we've been able to do some

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Speaker 1: really interesting work and address just some basic questions about

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Speaker 1: why dear disperse and how they disperse, and and and

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Speaker 1: things like that. So it's it's actually been a fun ride,

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Speaker 1: I imagine. So so, speaking of those early issues there,

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Speaker 1: you mentioned, you know how there's a lot of work

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Speaker 1: being done around the Antler point restrictions when those were introduced. Um,

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Speaker 1: did you get involved in analyzing, you know, the health

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Speaker 1: of the herd or anything along those lines before and

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Speaker 1: after those were introduced. Yeah, that was a big project

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Speaker 1: from two thousand two to two thousand and five. We

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Speaker 1: actually we radio colored over five deer um and because

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Speaker 1: we wanted to look at what harvest rates were before

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Speaker 1: Antler point restrictions were implemented, we wanted to know what

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Speaker 1: harvest rates were afterwards. And we wanted to know whether

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Speaker 1: um deer that weren't harvested the first year because their

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Speaker 1: racks were too small, whether they survived and were actually

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Speaker 1: available to be harvested in the second year. So, you know,

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Speaker 1: those are questions that people didn't really know the answer to,

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Speaker 1: and um, we said, well, if we're going to make

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Speaker 1: these changes, we need to know what actually happens. So

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Speaker 1: we looked at the biological side of things. UM. At

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Speaker 1: the same time, probably more importantly, we wanted to know

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Speaker 1: what hunters thought about Antler Point restrictions before they were implemented,

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Speaker 1: during and after. And so it was a complete evaluation

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Speaker 1: of both the social side of of what we call

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Speaker 1: a p RS or Antler Point restrictions as well as

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Speaker 1: the biological effects. So, of course now I need to

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Speaker 1: ask in particular because the whole issue of a PRS

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Speaker 1: have been a hot topic in my home state of Michigan. UM.

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Speaker 1: We've instituted a p RS in one corner of the state. UM,

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Speaker 1: and then it's been a it's been a hotly contested

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Speaker 1: debate over the last year or so a couple of

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Speaker 1: years about whether that should be institute across the rest

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Speaker 1: of the state. UM. Do you recall what do you

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Speaker 1: guys end up finding out as far as the social

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Speaker 1: and biological answers to those questions you just post work. Well,

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Speaker 1: the bottom line is, from a biological standpoint, Antler point

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Speaker 1: restrictions work. UM. In Pennsylvania, we found that hunters were,

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Speaker 1: you know, obeyed the law. UM. They UM, there was

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Speaker 1: you know some um, there was very little illegal killing

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Speaker 1: of deer. There was a little bit of mistaken kill

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Speaker 1: um um. But for the most part, UM we increased

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Speaker 1: the number of older aged bucks by like so UM.

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Speaker 1: It was a success from the standpoint of creating an

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Speaker 1: older age structure of bucks. UM. From the from the

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Speaker 1: social side, it was pretty interesting. What we found was

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Speaker 1: that hunters that supported well, I would say basically, hunters

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Speaker 1: made up their mind before we implemented Antler point restrictions.

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Speaker 1: Either they said they were going to work beforehand and

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Speaker 1: supported them, or they didn't, and after Antler point restrictions

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Speaker 1: it really did change their mind. H And how has

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Speaker 1: that study continued on today? I mean, are people still

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Speaker 1: tracking the social um side of that at all? Because

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Speaker 1: I'm curious to hear you now that's been a significant

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Speaker 1: number of years since if viewpoints have changed at all?

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Speaker 1: Do you know that? Um? Well, the Game Commission are

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Speaker 1: our research has concluded, But the Game Commission does do

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Speaker 1: UM surveys from time to time every couple of years.

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Speaker 1: And I think that what we found in our study

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Speaker 1: is that before Antler point restrictions were implemented, about six

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Speaker 1: hunters supported them, and by the time we got done it.

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Speaker 1: It was basically the same, and I think that pretty

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Speaker 1: much holds true today. Interesting, it sounds like from the

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Speaker 1: things I've heard, it was quite a contentious issue there

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Speaker 1: for you guys as well. UM, I heard it got

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Speaker 1: a little bit, oh a little feisty at some of

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Speaker 1: these different town hall meetings and little heated As far

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Speaker 1: as the road show that I know that, I think

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Speaker 1: it was Gary Alt who who was at the time,

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Speaker 1: I think was kind of trying to communicate with the

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Speaker 1: hunting population about why this is a good idea, and UM,

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Speaker 1: it sounded like that was quite an interesting time to

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Speaker 1: be in Pennsylvania. Well, the critical component this and Gary

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Speaker 1: Alt went all over the state for a couple of years,

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Speaker 1: meeting with holding you know, tens, if not hundreds of

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Speaker 1: public meetings, and eventually meeting literally with tens of thousands

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Speaker 1: of sportsmen in Pennsylvania. UM. And what he generally found

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Speaker 1: is overwhelming support of the people who attended those meetings.

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Speaker 1: And UM, but I think that education. UM. Other people,

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Speaker 1: not just us, have pointed out that if you're going

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Speaker 1: to make changes like this, you really need to do

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Speaker 1: your homework up front and inform hunters of what the

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Speaker 1: changes are going to be, what the expectation is, what

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Speaker 1: they can expect to see um and that's really important

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Speaker 1: to making sure it's a success. And I think that

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Speaker 1: has a lot to do with why it was successful

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Speaker 1: in Pennsylvania and that the majority of hunters supported it

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Speaker 1: before they were implemented. I got a quick question in

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Speaker 1: regards to like the logistics of this research and how

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Speaker 1: it was implemented. What like, how how long did this

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Speaker 1: research take? How how did you go about collecting all

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Speaker 1: that data? Well, we did surveys before and after every

428
00:26:41,960 --> 00:26:47,359
Speaker 1: hunting season, so in two thousand and two UM, which

429
00:26:47,440 --> 00:26:54,479
Speaker 1: was basically um uh sort of a pre treatment, right,

430
00:26:54,560 --> 00:26:58,600
Speaker 1: because you had all these bucks out there and then

431
00:26:58,640 --> 00:27:01,960
Speaker 1: suddenly many of them were not going to be legal

432
00:27:02,000 --> 00:27:06,040
Speaker 1: to harvest, and there weren't that many older aged bucks.

433
00:27:06,040 --> 00:27:09,760
Speaker 1: Only about of our population was two and a half

434
00:27:09,880 --> 00:27:12,920
Speaker 1: years or older. On the odds of having a four

435
00:27:13,000 --> 00:27:15,160
Speaker 1: and a half year old buck in the population were

436
00:27:15,240 --> 00:27:20,639
Speaker 1: probably on the order of one to two UM. So

437
00:27:21,040 --> 00:27:24,639
Speaker 1: what we did was in the winter before a p

438
00:27:24,840 --> 00:27:28,320
Speaker 1: r S were implemented, we caught as many deer as

439
00:27:28,359 --> 00:27:32,159
Speaker 1: we could um mail deer and put radio collars on

440
00:27:32,200 --> 00:27:35,320
Speaker 1: them so that we could monitor their harvest rates and

441
00:27:35,400 --> 00:27:39,119
Speaker 1: survival rates. Um. And then as I mentioned, right before

442
00:27:39,200 --> 00:27:43,280
Speaker 1: that first hunting season, we sent out a survey to

443
00:27:43,359 --> 00:27:46,439
Speaker 1: a random sample of hunters, and then we did another

444
00:27:46,560 --> 00:27:49,439
Speaker 1: survey right after the end. And then of course the

445
00:27:49,520 --> 00:27:53,560
Speaker 1: next winter we captured and radio collared more dear and

446
00:27:53,560 --> 00:28:01,240
Speaker 1: we did that for three years. So you captured and

447
00:28:01,480 --> 00:28:06,480
Speaker 1: collared five deer, right, I forget the actual numbers, but

448
00:28:06,520 --> 00:28:08,800
Speaker 1: it was between five and six hundred deer I believe.

449
00:28:09,160 --> 00:28:11,439
Speaker 1: So so how did that happen? I mean, was that

450
00:28:11,480 --> 00:28:13,840
Speaker 1: it throughout different parts of the state or was that

451
00:28:13,880 --> 00:28:18,240
Speaker 1: in one general area? Was it like, did it take

452
00:28:18,359 --> 00:28:22,439
Speaker 1: one weekend or two areas one? Because Pennsylvania, we the

453
00:28:22,520 --> 00:28:26,040
Speaker 1: a p rs are divided into a three point area

454
00:28:26,080 --> 00:28:29,840
Speaker 1: in a four point area because there Western Pennsylvania has

455
00:28:29,920 --> 00:28:34,600
Speaker 1: better habitat and a lot more yearlings um will have

456
00:28:35,880 --> 00:28:38,640
Speaker 1: uh four you know, will be eight points in their

457
00:28:38,640 --> 00:28:40,680
Speaker 1: first year, or a lot of them will be six

458
00:28:40,760 --> 00:28:45,280
Speaker 1: points and some will even be eight points. Um. So

459
00:28:45,280 --> 00:28:47,560
Speaker 1: so in the Western PA they had to have four

460
00:28:47,640 --> 00:28:50,760
Speaker 1: points on a side. So we had one study area

461
00:28:50,800 --> 00:28:52,920
Speaker 1: out in western part of the state, and then we

462
00:28:52,960 --> 00:28:55,920
Speaker 1: had another study area in the central part of the

463
00:28:55,960 --> 00:28:59,520
Speaker 1: state where we had a three point rule. Um, so

464
00:28:59,560 --> 00:29:03,040
Speaker 1: we had to study areas had cruise capturing deer in

465
00:29:03,120 --> 00:29:06,880
Speaker 1: both areas for three years. Okay, so that was over.

466
00:29:07,240 --> 00:29:11,280
Speaker 1: It was five over a three year period. Yes, okay, okay,

467
00:29:11,320 --> 00:29:14,160
Speaker 1: I got you. And I don't know if you even

468
00:29:14,200 --> 00:29:17,520
Speaker 1: have the information or can answer this, But how much

469
00:29:19,040 --> 00:29:30,200
Speaker 1: did this undertaking cost? Uh? Well, let's see, that's already

470
00:29:30,240 --> 00:29:35,720
Speaker 1: about ten years ago. But I would say, um, over

471
00:29:35,760 --> 00:29:40,640
Speaker 1: the three years, pool boy, well over a million dollars.

472
00:29:44,080 --> 00:29:50,960
Speaker 1: Quentin Endeavor, I would imagine absolutely. Her research is not cheap.

473
00:29:51,640 --> 00:29:55,240
Speaker 1: It's very We figure it concess about a thousand dollars

474
00:29:55,240 --> 00:29:59,560
Speaker 1: for every deer that we capture. Wow, can you not

475
00:29:59,680 --> 00:30:03,000
Speaker 1: the wrong this topic? Um? Can you outline for us

476
00:30:03,400 --> 00:30:07,480
Speaker 1: actually how you go about a deer capture, whether be

477
00:30:07,640 --> 00:30:10,000
Speaker 1: for a study like you did back in two, two, three,

478
00:30:10,120 --> 00:30:12,520
Speaker 1: four and five, or you know, eventually I want to

479
00:30:12,560 --> 00:30:14,960
Speaker 1: move into the current research being done. I'm kind of

480
00:30:14,960 --> 00:30:17,480
Speaker 1: curious to hear about actually how you're getting your hands

481
00:30:17,480 --> 00:30:20,280
Speaker 1: on these deer and putting collars or whatever it might

482
00:30:20,280 --> 00:30:24,400
Speaker 1: be on them. How does that actually happen for you guys? Well,

483
00:30:24,440 --> 00:30:29,080
Speaker 1: we've tried lots of different things. We've used helicopters, UM,

484
00:30:29,800 --> 00:30:35,200
Speaker 1: dart guns. We have clover traps, which are basically just

485
00:30:35,280 --> 00:30:40,400
Speaker 1: a walk in trap. UM. We've used rocket nets where

486
00:30:40,640 --> 00:30:45,040
Speaker 1: rockets fire a net over bait UM. And we also

487
00:30:45,160 --> 00:30:49,040
Speaker 1: use drop nets, which are even larger nets that are

488
00:30:50,640 --> 00:30:53,080
Speaker 1: set up with a trigger that the deer walk under

489
00:30:53,120 --> 00:30:57,160
Speaker 1: the net net drops down on them. What we've found

490
00:30:57,600 --> 00:31:04,960
Speaker 1: is that UM helicopters are not effective. UM. Well, they

491
00:31:05,000 --> 00:31:08,239
Speaker 1: can be, but there they're really not practical in the

492
00:31:08,240 --> 00:31:15,240
Speaker 1: Eastern US with our forests um UH dart guns are

493
00:31:15,600 --> 00:31:21,240
Speaker 1: really time consuming and we've given up on trying to

494
00:31:21,360 --> 00:31:27,240
Speaker 1: dart free ranging deer UM. So basically we're we in

495
00:31:27,280 --> 00:31:30,680
Speaker 1: our current research project on the Deer forest study, we

496
00:31:30,840 --> 00:31:35,480
Speaker 1: just use rocket nets and clover traps. UM. If we're

497
00:31:35,600 --> 00:31:38,560
Speaker 1: in an area that has a lot more open areas

498
00:31:38,640 --> 00:31:42,840
Speaker 1: and and higher densities of deer, we might use drop

499
00:31:42,920 --> 00:31:44,840
Speaker 1: nets because we can catch a lot of deer at

500
00:31:44,840 --> 00:31:47,479
Speaker 1: one time, But for the most part, we just use

501
00:31:47,560 --> 00:31:50,760
Speaker 1: the clover traps and rocket nets. So you get a

502
00:31:50,800 --> 00:31:54,920
Speaker 1: deer netted and then so there's a deer on the

503
00:31:54,960 --> 00:31:57,360
Speaker 1: ground with a net around it. I gotta imagine that

504
00:31:57,400 --> 00:32:02,160
Speaker 1: deer is struggling. Um, what happens next? Imagine someone runs

505
00:32:02,240 --> 00:32:04,320
Speaker 1: up to it, grabs it, tries to cover its head

506
00:32:04,400 --> 00:32:08,280
Speaker 1: or something. Can you walk me through that? Sure? If

507
00:32:08,320 --> 00:32:11,600
Speaker 1: we catch deer with the rocket net, usually we're catching

508
00:32:11,640 --> 00:32:17,600
Speaker 1: two or three deer, but we might catch five, six, seven, eight, um.

509
00:32:17,760 --> 00:32:21,520
Speaker 1: So those deer tangled up in the net, um, we

510
00:32:22,440 --> 00:32:26,000
Speaker 1: have a crew that will restrain them, and we also

511
00:32:26,160 --> 00:32:29,680
Speaker 1: sedate them because you know, if we catch three or

512
00:32:29,720 --> 00:32:33,200
Speaker 1: four deer, it's going to take Even if they were sedated,

513
00:32:33,280 --> 00:32:36,680
Speaker 1: it might take ten twenty minutes to get them untangled

514
00:32:36,720 --> 00:32:38,920
Speaker 1: from the net. So we have to sedate the deer.

515
00:32:39,880 --> 00:32:43,239
Speaker 1: We always blindfold deer when we handle them because with

516
00:32:43,320 --> 00:32:46,720
Speaker 1: them not being able to see, that reduces some of

517
00:32:46,720 --> 00:32:52,880
Speaker 1: the stimulation and their calmer UM. And so will sedate them,

518
00:32:53,080 --> 00:32:55,960
Speaker 1: untangle them out of the net. At that point, we

519
00:32:56,080 --> 00:32:58,920
Speaker 1: can you know, tag them, caller them if we have

520
00:32:59,000 --> 00:33:04,320
Speaker 1: to collect any sand upples, will do that at that time, um,

521
00:33:04,400 --> 00:33:08,480
Speaker 1: and then then we can give them a reversal drug

522
00:33:08,600 --> 00:33:13,120
Speaker 1: for the sedative, and we have to basically monitor to

523
00:33:13,160 --> 00:33:17,280
Speaker 1: them until they're able to get up and move on

524
00:33:17,320 --> 00:33:21,120
Speaker 1: their own. If we capture a deer in a clover

525
00:33:21,240 --> 00:33:26,320
Speaker 1: trap UM. We generally don't like to sedate them because

526
00:33:26,960 --> 00:33:30,080
Speaker 1: if we have a well trained crew, they can go

527
00:33:30,320 --> 00:33:34,600
Speaker 1: in um restrain the deer, fit the caller, and and

528
00:33:34,840 --> 00:33:37,320
Speaker 1: ear tag it and collect whatever data we need to

529
00:33:37,360 --> 00:33:41,760
Speaker 1: collect in in about three to five minutes. So there's

530
00:33:41,800 --> 00:33:45,320
Speaker 1: really no need to sedate them because the stresses UM

531
00:33:45,680 --> 00:33:48,840
Speaker 1: very very short term. And uh and if you don't

532
00:33:48,880 --> 00:33:51,520
Speaker 1: have to sedate and animal, it's it's a lot better

533
00:33:51,600 --> 00:33:56,360
Speaker 1: because less things to go wrong. Yeah, so you're leading,

534
00:33:56,760 --> 00:33:59,520
Speaker 1: you know these studies. Now, do you still get to

535
00:33:59,560 --> 00:34:01,680
Speaker 1: do any few would work like that today? Or do

536
00:34:01,720 --> 00:34:03,880
Speaker 1: you have grad students or or or crew as you

537
00:34:03,880 --> 00:34:06,680
Speaker 1: mentioned doing all that stuff? Now? I only go out

538
00:34:06,720 --> 00:34:12,560
Speaker 1: if it's nice weather. Smart man, I ask because I'm wondering,

539
00:34:12,600 --> 00:34:15,200
Speaker 1: you know, is like that is this kind of thing?

540
00:34:15,400 --> 00:34:17,200
Speaker 1: Is this the really fun part when you're in the

541
00:34:17,239 --> 00:34:20,680
Speaker 1: field hands on, actually like up close to these animals.

542
00:34:20,920 --> 00:34:24,200
Speaker 1: Is that what you love about it? Or is your

543
00:34:24,239 --> 00:34:27,719
Speaker 1: passion more so in just understanding from far out and

544
00:34:27,800 --> 00:34:32,399
Speaker 1: analyzing and seeing the big picture. Oh, I guess I'm

545
00:34:32,440 --> 00:34:35,319
Speaker 1: a little bit of a different wildlife biologist in that

546
00:34:35,719 --> 00:34:40,920
Speaker 1: I really like the data UM to see the patterns

547
00:34:41,280 --> 00:34:46,840
Speaker 1: so there's different rewards. I mean obviously, um, capturing deers

548
00:34:47,080 --> 00:34:50,800
Speaker 1: I I call it capture and release hunting, right because

549
00:34:50,880 --> 00:34:55,160
Speaker 1: you're you're trying to outsmart these animals, get them to

550
00:34:55,200 --> 00:34:57,640
Speaker 1: walk into your trap or or in front of the

551
00:34:57,840 --> 00:35:01,319
Speaker 1: rocket net and capture the them. So you have to

552
00:35:01,360 --> 00:35:05,160
Speaker 1: figure out what their patterns are, what they're doing, um,

553
00:35:05,280 --> 00:35:07,760
Speaker 1: how can you get them to you know, come into

554
00:35:07,800 --> 00:35:11,480
Speaker 1: the bait um and then of course handling the animal.

555
00:35:12,080 --> 00:35:14,440
Speaker 1: You know, it's always exciting, I mean who gets to

556
00:35:14,840 --> 00:35:19,520
Speaker 1: hold a live white tailed deer? Um. But but the

557
00:35:19,600 --> 00:35:24,640
Speaker 1: more rewarding aspect, at least for me, is uh is

558
00:35:24,640 --> 00:35:27,920
Speaker 1: when you when you get all these data. Um, when

559
00:35:27,960 --> 00:35:30,920
Speaker 1: all the data are collected and you can analyze it,

560
00:35:30,960 --> 00:35:33,680
Speaker 1: then you can start to see patterns, and then then

561
00:35:33,719 --> 00:35:38,520
Speaker 1: you get to understand, um, things that you otherwise wouldn't

562
00:35:38,520 --> 00:35:43,640
Speaker 1: know just from watching a deer or handling a deer. UM.

563
00:35:43,640 --> 00:35:47,120
Speaker 1: When you have you know, five deer data from five

564
00:35:47,520 --> 00:35:50,960
Speaker 1: deer over three years, then you can really get to

565
00:35:51,040 --> 00:35:55,400
Speaker 1: understand about what's going on with these animals. Before we

566
00:35:55,480 --> 00:35:58,400
Speaker 1: get into this is going to lead, Mark's got some

567
00:35:58,440 --> 00:36:01,080
Speaker 1: more questions for you about the the studies and what

568
00:36:01,280 --> 00:36:04,000
Speaker 1: the findings were. But I have a real quick question

569
00:36:04,040 --> 00:36:11,359
Speaker 1: about what happens when a hunter shoots or kills one

570
00:36:11,400 --> 00:36:14,080
Speaker 1: of these tagged animals. What happens next? Do they have

571
00:36:14,160 --> 00:36:17,359
Speaker 1: specific instructions that they are supposed to follow, how does

572
00:36:17,400 --> 00:36:22,400
Speaker 1: that get educated out to them? And whatnot? Well, Um,

573
00:36:22,440 --> 00:36:26,920
Speaker 1: every deer it's ear tagged and the radio collar has

574
00:36:27,120 --> 00:36:30,719
Speaker 1: UM has wording on there with a toll free number,

575
00:36:30,760 --> 00:36:33,600
Speaker 1: and it also indicates that there's a hundred dollar reward

576
00:36:34,080 --> 00:36:37,160
Speaker 1: if they report the recovery of that caller. I mean,

577
00:36:37,560 --> 00:36:40,200
Speaker 1: you know, it could be someone just walking in the woods,

578
00:36:41,160 --> 00:36:44,000
Speaker 1: could be a road killed deer. Um. Most of them

579
00:36:44,000 --> 00:36:48,720
Speaker 1: are you know, most mortality is hunter harvest. So most

580
00:36:48,719 --> 00:36:50,919
Speaker 1: of the time it's a hunter who harvested the deer

581
00:36:50,920 --> 00:36:54,040
Speaker 1: and it's reporting it to us. So they just call

582
00:36:54,160 --> 00:36:59,239
Speaker 1: a toll free number. Um, we uh, you know, get

583
00:36:59,239 --> 00:37:03,120
Speaker 1: the information from them, and uh and if we need

584
00:37:03,160 --> 00:37:08,840
Speaker 1: to recover the caller. Interesting And Dan, you were you

585
00:37:08,880 --> 00:37:10,800
Speaker 1: were spot on you know me, Well, now you know

586
00:37:10,840 --> 00:37:14,759
Speaker 1: where I'm trying to go with things. Um, because yes,

587
00:37:14,880 --> 00:37:18,080
Speaker 1: that was where I wanted to direct things next. Um, Dwayne,

588
00:37:18,480 --> 00:37:22,360
Speaker 1: because as you mentioned, you really enjoy the patterns and

589
00:37:22,480 --> 00:37:26,200
Speaker 1: understanding the high level data that kind of illuminates bigger

590
00:37:26,239 --> 00:37:29,680
Speaker 1: picture behaviors. UM. And from what I've seen following from

591
00:37:29,680 --> 00:37:32,360
Speaker 1: a distance, this recent work you've been doing with the

592
00:37:32,400 --> 00:37:35,080
Speaker 1: Deer Forest Study has given you an opportunity to see

593
00:37:35,080 --> 00:37:37,080
Speaker 1: some of those things that I think US hunters might

594
00:37:37,120 --> 00:37:40,440
Speaker 1: be particularly interested in as well. So can you can

595
00:37:40,480 --> 00:37:43,120
Speaker 1: you fill us in real quick on what this current

596
00:37:43,120 --> 00:37:45,960
Speaker 1: project as you've been working on this dear force study?

597
00:37:46,000 --> 00:37:48,279
Speaker 1: What is it? What have you been looking looking at?

598
00:37:48,440 --> 00:37:50,719
Speaker 1: Where are the goal has been so far? Um? And

599
00:37:50,719 --> 00:37:52,520
Speaker 1: then from there I've got some questions about some of

600
00:37:52,560 --> 00:37:56,800
Speaker 1: the things you've learned. Sure, so the Deer Forest Study

601
00:37:56,960 --> 00:38:00,959
Speaker 1: really should be called the Forest Deer Study, But here

602
00:38:01,000 --> 00:38:03,640
Speaker 1: are more charismatic and it rolls off the tongue better

603
00:38:03,680 --> 00:38:07,319
Speaker 1: if you call it the Dear Forest Study. UM. And

604
00:38:07,440 --> 00:38:13,480
Speaker 1: really um. In Pennsylvania, we've been doing a series of projects. UM.

605
00:38:13,520 --> 00:38:16,480
Speaker 1: What we do, what I do is in working with

606
00:38:16,520 --> 00:38:19,400
Speaker 1: the game commission, as we sit down and say, okay,

607
00:38:19,600 --> 00:38:22,439
Speaker 1: we have to make a decision here about managing dear

608
00:38:22,640 --> 00:38:25,799
Speaker 1: What is it that we don't know that we would

609
00:38:25,840 --> 00:38:28,440
Speaker 1: really what's the most important thing that we don't know

610
00:38:28,560 --> 00:38:31,360
Speaker 1: that we'd like to know, and that's how we focused

611
00:38:31,360 --> 00:38:34,480
Speaker 1: our research. So we've looked at you know, the effects

612
00:38:34,480 --> 00:38:36,960
Speaker 1: of a p RS. After that, we moved on to

613
00:38:37,800 --> 00:38:41,120
Speaker 1: female survival and harvest rates and how that relates to

614
00:38:41,160 --> 00:38:45,960
Speaker 1: how we model populations and UM. And we've answered other

615
00:38:46,000 --> 00:38:49,080
Speaker 1: things along the way about dispersal and all sorts of stuff.

616
00:38:49,120 --> 00:38:54,839
Speaker 1: But right now we're at the point where UM, you know,

617
00:38:54,880 --> 00:38:57,760
Speaker 1: the game commission is making decisions and in their decision

618
00:38:57,840 --> 00:39:02,000
Speaker 1: model UM, the biolo just need to make recommendations to

619
00:39:02,080 --> 00:39:04,680
Speaker 1: the board of Commissioners who make the decisions about what

620
00:39:04,760 --> 00:39:09,280
Speaker 1: to do. And we have a sort of a decision

621
00:39:09,320 --> 00:39:13,200
Speaker 1: model that the biologists walt work through and they're looking

622
00:39:13,239 --> 00:39:19,959
Speaker 1: at deer population trends and habitat conditions UM, as well

623
00:39:20,000 --> 00:39:25,040
Speaker 1: as what UM, what hunters or society desires. Those are

624
00:39:25,040 --> 00:39:28,480
Speaker 1: the three things that go into it. And what we've

625
00:39:29,920 --> 00:39:33,880
Speaker 1: what's happened is we know a lot about deer in Pennsylvania.

626
00:39:33,960 --> 00:39:36,880
Speaker 1: Now now we we have a pretty good handle on

627
00:39:36,920 --> 00:39:40,200
Speaker 1: how we model numbers and look at trends and populations,

628
00:39:40,760 --> 00:39:44,759
Speaker 1: survival and harvest rates UM. What we don't have a

629
00:39:44,800 --> 00:39:49,759
Speaker 1: good handle on is is the forest conditions and how

630
00:39:49,800 --> 00:39:53,880
Speaker 1: those conditions are being affected by deer browsing. We know

631
00:39:54,400 --> 00:39:58,319
Speaker 1: there's been lots and lots of research that deer can

632
00:39:58,400 --> 00:40:03,080
Speaker 1: have a tremendous effect on the environment. UM. They have

633
00:40:03,400 --> 00:40:07,640
Speaker 1: preferred species of plants that they prefer to eat, you know,

634
00:40:07,760 --> 00:40:10,479
Speaker 1: just like you and I would prefer ice cream over

635
00:40:10,480 --> 00:40:15,879
Speaker 1: Brussels sprouts. You know, they prefer trillium and Indian cucumber

636
00:40:16,160 --> 00:40:22,680
Speaker 1: over um, you know, something as untasty as mountain laurel

637
00:40:22,800 --> 00:40:26,120
Speaker 1: or rhododendron. So they can you know, if you have

638
00:40:26,200 --> 00:40:28,120
Speaker 1: a lot of deer on the landscape, they can have

639
00:40:28,200 --> 00:40:31,680
Speaker 1: a real impact. And and so we know they can

640
00:40:31,719 --> 00:40:38,000
Speaker 1: have an impact. But the question is, UM, what are

641
00:40:38,040 --> 00:40:41,440
Speaker 1: the desired habitat conditions and what is it that we

642
00:40:41,480 --> 00:40:45,520
Speaker 1: need to monitor to say, Okay, deer are not really

643
00:40:45,560 --> 00:40:49,520
Speaker 1: having an impact. We're happy with the forest conditions as

644
00:40:49,560 --> 00:40:52,720
Speaker 1: they are. And so that's the focus of this research.

645
00:40:52,800 --> 00:40:57,240
Speaker 1: We've got four study areas UM. Each study areas about

646
00:40:58,280 --> 00:41:04,520
Speaker 1: oh anywhere from twenty five to forty square miles. And

647
00:41:05,000 --> 00:41:09,040
Speaker 1: what our goal is is too in each of these areas,

648
00:41:09,120 --> 00:41:13,600
Speaker 1: UM either stabilize or change dear populations and at the

649
00:41:13,680 --> 00:41:21,160
Speaker 1: same time monitor vegetation and see UM and hopefully learn

650
00:41:21,200 --> 00:41:25,120
Speaker 1: a couple of things. A what things about the habitat

651
00:41:25,440 --> 00:41:29,520
Speaker 1: on the vegetation should we be measuring um? How does

652
00:41:29,560 --> 00:41:34,680
Speaker 1: the habitat respond to changes in dear density UM? And

653
00:41:35,200 --> 00:41:41,399
Speaker 1: also how do hunters respond to the actions that we're

654
00:41:41,400 --> 00:41:45,040
Speaker 1: taking Because we're using hunters as a management tool to

655
00:41:45,120 --> 00:41:51,120
Speaker 1: manipulate deer numbers UM, and so they're a very important tool,

656
00:41:51,160 --> 00:41:54,280
Speaker 1: a crucial tool to the game commission. So we also

657
00:41:54,320 --> 00:41:58,960
Speaker 1: want to better understand how hunters hunt on these areas.

658
00:41:59,000 --> 00:42:03,000
Speaker 1: So so this is a rather rudimentary question, but can

659
00:42:03,040 --> 00:42:08,040
Speaker 1: you explain for us why this kind of leg of

660
00:42:08,080 --> 00:42:11,560
Speaker 1: the stool matters? You mentioned, you know, hunters and deer

661
00:42:11,600 --> 00:42:18,480
Speaker 1: and then habitat. Why does understanding the proper habitat health matter?

662
00:42:18,719 --> 00:42:20,440
Speaker 1: Why do we want to make sure that deer in

663
00:42:20,480 --> 00:42:23,399
Speaker 1: habitat are in balance um. I think this is something

664
00:42:23,440 --> 00:42:25,720
Speaker 1: that a lot of people understand, but maybe some people

665
00:42:25,840 --> 00:42:28,400
Speaker 1: might say, well, I think I think it might be

666
00:42:28,400 --> 00:42:30,640
Speaker 1: fair to say that a lot of guys UM, A

667
00:42:30,640 --> 00:42:34,000
Speaker 1: lot of hunters just want more dear, right, if more

668
00:42:34,080 --> 00:42:37,440
Speaker 1: dear means better hunting? I think at some level, if

669
00:42:37,480 --> 00:42:39,879
Speaker 1: you at a very simple level, you might assume more

670
00:42:39,920 --> 00:42:43,280
Speaker 1: dear means better hunting. Um, why might that be wrong?

671
00:42:43,320 --> 00:42:48,280
Speaker 1: And why do you think having the proper balance matters? Well,

672
00:42:48,320 --> 00:42:52,400
Speaker 1: first of all, let me a couple of things before

673
00:42:52,440 --> 00:42:57,160
Speaker 1: I dive into that. First of all, UM, you know this,

674
00:42:58,000 --> 00:43:04,560
Speaker 1: all of this is driven by human society's values. Hunters

675
00:43:04,600 --> 00:43:08,560
Speaker 1: obviously are a big part of this, but um, deer

676
00:43:08,680 --> 00:43:12,520
Speaker 1: are a public resource and so the state agencies have

677
00:43:12,640 --> 00:43:16,239
Speaker 1: to be responsible to not just hunters. Um. Of course

678
00:43:16,320 --> 00:43:18,200
Speaker 1: hunters are a big part of it, but they also

679
00:43:18,239 --> 00:43:23,160
Speaker 1: have to look at um that all stakeholders and all

680
00:43:23,239 --> 00:43:26,680
Speaker 1: people that are influenced by deer, whether it's because of

681
00:43:26,680 --> 00:43:33,000
Speaker 1: a vehicle collision or damage to their um landscaping or

682
00:43:33,440 --> 00:43:41,080
Speaker 1: industrial timber companies who are having problems with regenerating forest. Um. Society,

683
00:43:41,160 --> 00:43:44,440
Speaker 1: the Game Commission has developed a deer management plan, working

684
00:43:44,440 --> 00:43:48,680
Speaker 1: with Society to set some objectives, goals and objectives for

685
00:43:48,800 --> 00:43:53,920
Speaker 1: managing deer. And so based on those objectives, they're trying

686
00:43:53,960 --> 00:43:57,640
Speaker 1: to make the best decision possible. And I would say,

687
00:43:57,680 --> 00:44:01,680
Speaker 1: from a hunting perspective, why would you not want to

688
00:44:01,760 --> 00:44:07,440
Speaker 1: just maximize deer? Well, mm hmm, um. Probably for the

689
00:44:07,480 --> 00:44:10,360
Speaker 1: main reason is that you're not going to have the

690
00:44:10,440 --> 00:44:14,480
Speaker 1: deer that you want. Um, if you don't take into

691
00:44:14,560 --> 00:44:18,880
Speaker 1: account habitat, I mean a farmer doesn't just say, well,

692
00:44:19,360 --> 00:44:21,800
Speaker 1: I've got twenty acres and I want to raise a

693
00:44:21,880 --> 00:44:24,720
Speaker 1: hundred and fifty cattle on those twenty acres. You can't

694
00:44:24,760 --> 00:44:27,719
Speaker 1: do it. Um. He's gonna he might be able to

695
00:44:27,800 --> 00:44:32,080
Speaker 1: keep a hundred cows alive on twenty acres, but it's

696
00:44:32,080 --> 00:44:33,759
Speaker 1: going to be a mud pit and he's gonna have

697
00:44:33,800 --> 00:44:35,520
Speaker 1: to bring in a lot of hay. And he could

698
00:44:35,560 --> 00:44:38,239
Speaker 1: do it. And the same thing goes with deer. We

699
00:44:38,280 --> 00:44:43,560
Speaker 1: could have a lot more deer in Pennsylvania, but is

700
00:44:43,600 --> 00:44:48,040
Speaker 1: it the deer that you want? Um? And uh. And

701
00:44:48,120 --> 00:44:53,400
Speaker 1: so having a trying to have some sort of balance

702
00:44:53,480 --> 00:44:59,200
Speaker 1: between habitat quality and deer numbers. UM. If you can

703
00:44:59,520 --> 00:45:04,360
Speaker 1: seek balance, you're going to maximize potentially the size of deer,

704
00:45:04,440 --> 00:45:08,880
Speaker 1: the reproductive rates of deer, um, and generally have a

705
00:45:08,880 --> 00:45:13,040
Speaker 1: healthy deer population, which you know, I would think would

706
00:45:13,120 --> 00:45:18,400
Speaker 1: be lead to you know, happier hunters in terms of

707
00:45:18,960 --> 00:45:24,520
Speaker 1: more higher quality hunting experience. Yeah, yeah, I certainly agree

708
00:45:24,560 --> 00:45:28,120
Speaker 1: with that. UM. And now I'm kind of rewinding the

709
00:45:28,120 --> 00:45:30,440
Speaker 1: clock a little bit. But in some of your earlier work.

710
00:45:30,960 --> 00:45:33,359
Speaker 1: You mentioned that some of your research, some of your

711
00:45:33,400 --> 00:45:35,279
Speaker 1: projects have related to some of these things as far

712
00:45:35,320 --> 00:45:39,480
Speaker 1: as modeling populations. UM, is that something you could speak to,

713
00:45:39,640 --> 00:45:42,920
Speaker 1: which is how do how does the Game Fish Commission

714
00:45:43,080 --> 00:45:46,880
Speaker 1: and or researchers they work with determine that balance or

715
00:45:46,920 --> 00:45:49,080
Speaker 1: how what are you guys looking at to help figure

716
00:45:49,080 --> 00:45:52,799
Speaker 1: out what that proper balance point is? Um? Is that

717
00:45:52,840 --> 00:45:58,319
Speaker 1: something you've got some experience with? Well? UM, I think

718
00:45:58,400 --> 00:46:01,720
Speaker 1: this current project is trying to get a better idea

719
00:46:02,640 --> 00:46:07,759
Speaker 1: of what that balances between habitat and deer numbers. UM.

720
00:46:07,800 --> 00:46:11,880
Speaker 1: In you know, in you know, back up a second,

721
00:46:12,120 --> 00:46:17,800
Speaker 1: quite frankly, UM, deer management North America. That travesty is

722
00:46:17,800 --> 00:46:22,600
Speaker 1: is that any wildlife management is traditionally been called a

723
00:46:22,680 --> 00:46:26,520
Speaker 1: three legged stool. As you mentioned earlier, there's the species

724
00:46:26,520 --> 00:46:30,160
Speaker 1: you're managing, there's the habitat that species depend on, and

725
00:46:30,200 --> 00:46:34,480
Speaker 1: there's people. If you look at deer management in UH

726
00:46:34,640 --> 00:46:39,240
Speaker 1: in North America, for the most part, they they address

727
00:46:39,320 --> 00:46:44,239
Speaker 1: the concerns of people, and they address deer numbers, but

728
00:46:44,480 --> 00:46:48,600
Speaker 1: they don't address habitat. In fact, Pennsylvania is the only

729
00:46:48,680 --> 00:46:54,680
Speaker 1: state in in the northeastern US that explicitly has some

730
00:46:55,400 --> 00:47:03,160
Speaker 1: goals and objectives relating to habitat conditions um, and so um,

731
00:47:03,200 --> 00:47:06,279
Speaker 1: you know, and we've only recently implemented that. I mean

732
00:47:06,320 --> 00:47:09,759
Speaker 1: we've been um, well we've had to have had some

733
00:47:09,800 --> 00:47:14,200
Speaker 1: goals and excuse me in the past. But but Pennsylvania's,

734
00:47:14,640 --> 00:47:18,200
Speaker 1: you know, is a rare situation and that we're trying

735
00:47:18,200 --> 00:47:25,880
Speaker 1: to explicitly incorporate habitat conditions in while making deer management decisions. So. UM,

736
00:47:26,960 --> 00:47:30,280
Speaker 1: so just getting deer counts and estimates of deer numbers

737
00:47:30,320 --> 00:47:34,760
Speaker 1: and trends is not so difficult. There's some different methods

738
00:47:34,800 --> 00:47:39,520
Speaker 1: out there just based on you know, using hunter harvest data. Um.

739
00:47:39,560 --> 00:47:42,640
Speaker 1: That it's I mean, it's expensive. I mean the agency

740
00:47:42,680 --> 00:47:46,239
Speaker 1: puts a lot of personnel, time and effort into collecting

741
00:47:46,280 --> 00:47:51,360
Speaker 1: those data. But it can be done. Um. The hard

742
00:47:51,440 --> 00:47:56,399
Speaker 1: part is figuring out that balance. And that's where this

743
00:47:56,440 --> 00:47:59,160
Speaker 1: project is coming in. So I really, I really see

744
00:47:59,160 --> 00:48:03,280
Speaker 1: it as UM, this current project is being cutting edge

745
00:48:03,920 --> 00:48:08,919
Speaker 1: um potentially being well not just potentially, but being will

746
00:48:08,960 --> 00:48:12,360
Speaker 1: be very informative for the Game Commission, who has to

747
00:48:12,400 --> 00:48:16,880
Speaker 1: make manage management decisions statewide, as well as the Bureau

748
00:48:16,920 --> 00:48:19,919
Speaker 1: of Forestry that has to make decisions on the two

749
00:48:19,960 --> 00:48:24,439
Speaker 1: million acres that they're responsible for. So, so, have you

750
00:48:24,920 --> 00:48:29,120
Speaker 1: to this point yet been able to UM achieve any

751
00:48:29,120 --> 00:48:31,640
Speaker 1: of the goals that you have kind of laid out

752
00:48:31,640 --> 00:48:35,160
Speaker 1: for this study yet or have there been any UM

753
00:48:35,160 --> 00:48:39,040
Speaker 1: takeaways yet that are actionable or is that still some

754
00:48:39,080 --> 00:48:42,200
Speaker 1: time to come. Well, you know, when we started this,

755
00:48:42,320 --> 00:48:44,960
Speaker 1: we knew that this was going to be have to

756
00:48:45,000 --> 00:48:49,959
Speaker 1: be a long term project. And that's because, um, dear

757
00:48:50,200 --> 00:48:54,640
Speaker 1: easy there there's more dear born every year. Um. You know,

758
00:48:54,719 --> 00:48:59,359
Speaker 1: a buck. Average age of a buck is um, it's

759
00:48:59,400 --> 00:49:01,440
Speaker 1: still a year and a half old. Most of the

760
00:49:01,480 --> 00:49:04,200
Speaker 1: books are year and a half old. But you have

761
00:49:04,280 --> 00:49:06,799
Speaker 1: a high turnover and so you can learn a lot

762
00:49:06,840 --> 00:49:12,400
Speaker 1: in just a few years monitoring deer vegetation. Right, the

763
00:49:13,239 --> 00:49:16,600
Speaker 1: if you're going to grow a stand of oak um,

764
00:49:16,640 --> 00:49:21,399
Speaker 1: that's going to take at least eighty years. So so

765
00:49:21,480 --> 00:49:25,600
Speaker 1: we're talking about a completely different time frame or perspective

766
00:49:25,719 --> 00:49:30,200
Speaker 1: than what biologists usually think about. Now, foresters think about

767
00:49:30,280 --> 00:49:35,359
Speaker 1: eighty year time frames, but biologists are usually thinking about two, three,

768
00:49:35,560 --> 00:49:38,680
Speaker 1: four or five years. Well, now, in two, three, or

769
00:49:38,719 --> 00:49:42,320
Speaker 1: four or five years, you might have an oak seedling

770
00:49:42,400 --> 00:49:48,440
Speaker 1: go from six inches to three ft tall UM, and

771
00:49:48,520 --> 00:49:51,759
Speaker 1: so to be able to monitor vegetation change is going

772
00:49:51,800 --> 00:49:55,120
Speaker 1: to take what and we've already found this out at

773
00:49:55,200 --> 00:49:58,720
Speaker 1: least five years and probably ten years before we start

774
00:49:58,760 --> 00:50:02,200
Speaker 1: to get a good handle on on the vegetation response

775
00:50:02,280 --> 00:50:06,640
Speaker 1: to the changes we make in deer numbers. How fragile

776
00:50:07,040 --> 00:50:13,560
Speaker 1: is that balancing act as far as uh population versus

777
00:50:13,600 --> 00:50:21,040
Speaker 1: habitat um, I don't think it's too fragile. I mean,

778
00:50:21,520 --> 00:50:25,960
Speaker 1: we've been trying to manage or mismanaged deer for over

779
00:50:26,000 --> 00:50:30,640
Speaker 1: a hundred years, UM, and we still have forests. It's

780
00:50:30,760 --> 00:50:34,759
Speaker 1: just is it the forest that you want? Um. We

781
00:50:34,800 --> 00:50:38,479
Speaker 1: have lots of places in Pennsylvania that you know back

782
00:50:38,480 --> 00:50:42,600
Speaker 1: in the eighties, UM, we had very high deer densities,

783
00:50:42,640 --> 00:50:46,840
Speaker 1: and so basically black cherry was very successful at growing,

784
00:50:46,960 --> 00:50:53,360
Speaker 1: but not so much sugar maple or or other species. So, UM,

785
00:50:53,480 --> 00:50:57,040
Speaker 1: the forest will survive, but there will be some effects

786
00:50:57,120 --> 00:51:00,799
Speaker 1: and deer populations aren't going to crash because as of

787
00:51:00,840 --> 00:51:05,719
Speaker 1: these things. UM. I think what we are is the

788
00:51:05,760 --> 00:51:09,880
Speaker 1: wildlife profession is maturing, and we're at a point where

789
00:51:10,680 --> 00:51:13,239
Speaker 1: we have a lot of the basic tools. Now we're

790
00:51:13,280 --> 00:51:16,040
Speaker 1: trying to learn how to fine tune those and put

791
00:51:16,040 --> 00:51:19,200
Speaker 1: a sharper point on those tools and then getting a

792
00:51:19,200 --> 00:51:25,640
Speaker 1: little bit more specific who makes those decisions on how

793
00:51:26,160 --> 00:51:31,279
Speaker 1: this this habitat should be and what is considered a

794
00:51:31,320 --> 00:51:38,000
Speaker 1: good habitat. Yeah, so that's a difficult thing. UM. What

795
00:51:38,120 --> 00:51:43,120
Speaker 1: the Game Commission has done is looked at um UH

796
00:51:44,239 --> 00:51:47,040
Speaker 1: data that the U. S. Forest Service collects as part

797
00:51:47,040 --> 00:51:52,520
Speaker 1: of the National Program, and UM and forest ecologists have

798
00:51:52,640 --> 00:52:00,600
Speaker 1: identified UM some certain minimum level of tree reach generation

799
00:52:00,719 --> 00:52:03,560
Speaker 1: what we call it advanced regeneration. So if you have

800
00:52:03,640 --> 00:52:08,280
Speaker 1: a forest out there, there's big trees obviously, like oaks

801
00:52:08,320 --> 00:52:12,000
Speaker 1: that are dropping acorns, and those acorns do lead to

802
00:52:12,560 --> 00:52:15,520
Speaker 1: seedlings that pop up in the understory, and that's called

803
00:52:15,760 --> 00:52:18,759
Speaker 1: advanced regeneration because if you go in and cut down

804
00:52:18,760 --> 00:52:22,319
Speaker 1: those large oaks, those small seedlings that are on the

805
00:52:22,360 --> 00:52:25,560
Speaker 1: ground or we're going to pop up and replace those

806
00:52:25,680 --> 00:52:32,080
Speaker 1: large trees that you removed. So there are some UM standards,

807
00:52:32,080 --> 00:52:36,160
Speaker 1: depending on the forest type and the habitat of UM

808
00:52:36,200 --> 00:52:39,200
Speaker 1: sort of some minimum conditions that you'd want to see

809
00:52:39,880 --> 00:52:43,839
Speaker 1: UM in terms of advance regeneration of seedlings. And so

810
00:52:43,920 --> 00:52:48,680
Speaker 1: that's what the Game Commission is using right now. UM. However,

811
00:52:49,960 --> 00:52:53,400
Speaker 1: there's more than just trees out in the forest and

812
00:52:53,760 --> 00:52:58,000
Speaker 1: um and and agencies like the Bureau of Forestry are

813
00:52:58,040 --> 00:53:02,400
Speaker 1: not just responsible for tree is but their their mandate

814
00:53:02,640 --> 00:53:05,880
Speaker 1: is to look at the plant community as a whole.

815
00:53:06,440 --> 00:53:11,040
Speaker 1: And so there's lots of understory species um uh, like

816
00:53:11,239 --> 00:53:18,239
Speaker 1: trillium and indian cucumber and vibe um uh uh, some

817
00:53:18,360 --> 00:53:21,680
Speaker 1: hobble bush, which is a viburnum. In fact, hobble bush

818
00:53:21,719 --> 00:53:25,640
Speaker 1: is extremely rare in Pennsylvania because it's highly preferred by deer,

819
00:53:26,320 --> 00:53:28,600
Speaker 1: and with the high deer densities that we've had in

820
00:53:28,600 --> 00:53:33,439
Speaker 1: the past, it's disappeared in a lot of places um so. So,

821
00:53:34,160 --> 00:53:38,560
Speaker 1: but those those other things, like those understory plants, we

822
00:53:38,640 --> 00:53:41,680
Speaker 1: really don't know a lot. We people have done research

823
00:53:41,719 --> 00:53:47,600
Speaker 1: and they say, yeah, dear love trillium, dear love indian cucumber, um.

824
00:53:47,640 --> 00:53:52,239
Speaker 1: But what we don't know is what percent of the

825
00:53:52,320 --> 00:53:56,439
Speaker 1: landscape out there should have indian cucumber, how common should

826
00:53:56,480 --> 00:53:59,640
Speaker 1: it be? And so we don't even have any idea

827
00:53:59,760 --> 00:54:02,080
Speaker 1: what that should be. And that's part of what we're

828
00:54:02,080 --> 00:54:05,520
Speaker 1: trying to address with this research is get some insights

829
00:54:05,560 --> 00:54:12,359
Speaker 1: into um what level of UM deer numbers would lead

830
00:54:12,400 --> 00:54:16,799
Speaker 1: to UM. You know a plant diversity of plants in

831
00:54:16,880 --> 00:54:23,719
Speaker 1: the understory community that would be acceptable to society. All right,

832
00:54:23,800 --> 00:54:26,360
Speaker 1: let's pause here for a moment to thank our partners

833
00:54:26,520 --> 00:54:30,920
Speaker 1: at White Tailed Properties. This week with white Tail Properties,

834
00:54:31,080 --> 00:54:33,680
Speaker 1: we are joined by Dave Skinner ad Land Specialists out

835
00:54:33,680 --> 00:54:36,080
Speaker 1: of Kentucky, and Davey is going to be telling us

836
00:54:36,080 --> 00:54:38,759
Speaker 1: about what to look for when buying a property with

837
00:54:38,840 --> 00:54:43,160
Speaker 1: intentions of early season white tail hunting. Yeah, in Kentucky

838
00:54:43,160 --> 00:54:45,640
Speaker 1: at season comes in in September, and if you're looking

839
00:54:46,120 --> 00:54:49,799
Speaker 1: specifically for that early season hunt, some things keep them

840
00:54:49,840 --> 00:54:53,920
Speaker 1: on one just like late season food sources are keen um.

841
00:54:53,960 --> 00:54:56,680
Speaker 1: And there's two food sources in Kentucky that trump everything

842
00:54:56,760 --> 00:55:01,160
Speaker 1: else in September, soy beans and acorns. Um. If I'm

843
00:55:01,360 --> 00:55:03,719
Speaker 1: looking to han specifically that early season, I want to

844
00:55:03,760 --> 00:55:06,080
Speaker 1: make certain I either have solwy things on the property

845
00:55:06,160 --> 00:55:09,239
Speaker 1: or Jason to the property. More than anything, though eight

846
00:55:09,280 --> 00:55:11,440
Speaker 1: onns are always number one. I want to know that

847
00:55:11,480 --> 00:55:15,239
Speaker 1: there's white oak treats on the property. Um becose one.

848
00:55:15,280 --> 00:55:18,080
Speaker 1: Those eight mores are following. That's where the deal will be.

849
00:55:19,200 --> 00:55:21,000
Speaker 1: If you'd like to learn more and to see the

850
00:55:21,000 --> 00:55:24,200
Speaker 1: properties that Dave currently has listed for sale visit white

851
00:55:24,239 --> 00:55:28,640
Speaker 1: tail properties dot com backslash skinner that's s k I

852
00:55:29,160 --> 00:55:34,600
Speaker 1: N N E er. So a lot of the a

853
00:55:34,640 --> 00:55:37,120
Speaker 1: lot of the big picture take away from the study

854
00:55:37,200 --> 00:55:39,200
Speaker 1: could be years out. It sounds like this is a

855
00:55:39,239 --> 00:55:42,759
Speaker 1: long term thing, UM, But I do know from the

856
00:55:42,800 --> 00:55:45,920
Speaker 1: things I've read on your website and different presentations that

857
00:55:45,960 --> 00:55:49,720
Speaker 1: I've seen online, UM, that that you guys are learning

858
00:55:49,760 --> 00:55:53,719
Speaker 1: plenty in the interim as far as dear behavior, just

859
00:55:53,840 --> 00:55:56,560
Speaker 1: given the the interesting insights you guys are able to

860
00:55:56,640 --> 00:55:59,279
Speaker 1: gain by the fact simply that you have you know,

861
00:55:59,680 --> 00:56:03,600
Speaker 1: uh collared and are tracking so many different deer. Right,

862
00:56:04,400 --> 00:56:07,279
Speaker 1: what what are some of the most interesting things that

863
00:56:07,320 --> 00:56:13,240
Speaker 1: you guys have learned so far on that front? Yes, so, UM.

864
00:56:13,280 --> 00:56:17,400
Speaker 1: So we call her dear UM in this study because

865
00:56:17,840 --> 00:56:20,799
Speaker 1: we do need to keep track of numbers and get

866
00:56:20,800 --> 00:56:24,239
Speaker 1: an idea of how many deer out there, what the

867
00:56:24,280 --> 00:56:31,480
Speaker 1: harvest rates are, UM and UM. And we've been using

868
00:56:31,560 --> 00:56:37,480
Speaker 1: these GPS satellite callers because they're really they're expensive, but

869
00:56:37,560 --> 00:56:42,800
Speaker 1: they're also very cost effective because UM, instead of having

870
00:56:42,840 --> 00:56:46,399
Speaker 1: a truck and multiple multiple trucks and technicians running around

871
00:56:46,400 --> 00:56:50,600
Speaker 1: the woods with antenna's trying to track dear UM. With

872
00:56:50,719 --> 00:56:53,440
Speaker 1: these satellite GPS callers, I can sit at my desk

873
00:56:53,520 --> 00:56:56,839
Speaker 1: and get hundreds of times more data than I ever

874
00:56:56,920 --> 00:57:02,799
Speaker 1: could with an army of field technique scions. So so

875
00:57:02,880 --> 00:57:06,440
Speaker 1: those callers have just been sort of a side benefit

876
00:57:06,520 --> 00:57:10,000
Speaker 1: of this project UM that have provided a lot of

877
00:57:10,040 --> 00:57:15,080
Speaker 1: insights into how dear move UM in different times of

878
00:57:15,120 --> 00:57:20,280
Speaker 1: the year UM, in particular how they respond to hunting UM,

879
00:57:20,320 --> 00:57:23,600
Speaker 1: which is interesting for us because, like I said, part

880
00:57:23,600 --> 00:57:27,520
Speaker 1: of this study is to learn about how hunters hunt UM,

881
00:57:27,640 --> 00:57:29,800
Speaker 1: what the harvest rates are that we see on these

882
00:57:29,840 --> 00:57:34,080
Speaker 1: study areas. In particular, if we're trying to manage or

883
00:57:34,120 --> 00:57:37,520
Speaker 1: manipulate deer numbers, UM, you need to do that through

884
00:57:37,560 --> 00:57:41,080
Speaker 1: Antler West harvest. So how these deer respond to hunting

885
00:57:41,200 --> 00:57:43,560
Speaker 1: is for me has been the most fascinating part of

886
00:57:43,560 --> 00:57:47,040
Speaker 1: this project. Yeah, so so tell us about that. What

887
00:57:47,120 --> 00:57:49,520
Speaker 1: have you seen as far as a year in a

888
00:57:49,560 --> 00:57:52,080
Speaker 1: life of a deer maybe maybe buck if you've got

889
00:57:52,160 --> 00:57:54,919
Speaker 1: a box in particular, UM, and then how does hunting

890
00:57:54,960 --> 00:57:58,280
Speaker 1: pressure impact that. I've sure I've seen some interesting things

891
00:57:58,280 --> 00:58:01,160
Speaker 1: you guys have posted as far as you know, actually

892
00:58:01,240 --> 00:58:04,400
Speaker 1: looking at one specific buck and learning about a specific

893
00:58:04,440 --> 00:58:07,000
Speaker 1: bucks travel patterns throughout the year, and that was pretty

894
00:58:07,000 --> 00:58:12,760
Speaker 1: fascinated by that. Yeah, Actually, if you indulge me for

895
00:58:12,800 --> 00:58:16,520
Speaker 1: a second, back up a little bit about ten years ago,

896
00:58:16,840 --> 00:58:20,200
Speaker 1: So I had a student who, um, we didn't have

897
00:58:20,280 --> 00:58:23,320
Speaker 1: satellite callers at that time, but we were interested in

898
00:58:24,040 --> 00:58:29,480
Speaker 1: in deer harvest rates and how where a deer lived

899
00:58:29,520 --> 00:58:32,640
Speaker 1: on the landscape, how that might influence its probability of

900
00:58:32,680 --> 00:58:36,840
Speaker 1: being harvested. And so what he did was he captured

901
00:58:36,880 --> 00:58:39,880
Speaker 1: a bunch of deer radio collared them, and then we

902
00:58:39,960 --> 00:58:43,800
Speaker 1: also did some aerial surveys and we're able to map

903
00:58:44,000 --> 00:58:46,960
Speaker 1: hunter density across his study area where he had all

904
00:58:47,000 --> 00:58:52,560
Speaker 1: these collar deer, and we found real differences in in

905
00:58:52,680 --> 00:58:58,240
Speaker 1: how hunters were distributed across the landscape. And actually, um,

906
00:58:58,400 --> 00:59:00,680
Speaker 1: what was really interesting is what he found as there

907
00:59:00,760 --> 00:59:04,240
Speaker 1: was sort of this sweet spot that if you you

908
00:59:04,280 --> 00:59:09,000
Speaker 1: were most deer was most likely to be harvested if

909
00:59:09,080 --> 00:59:14,120
Speaker 1: it was oh about a third of a mile from

910
00:59:14,120 --> 00:59:18,920
Speaker 1: a road. Um, if if you were real close, if

911
00:59:18,920 --> 00:59:21,840
Speaker 1: a deer was real close to the road, you know,

912
00:59:21,920 --> 00:59:27,240
Speaker 1: its home range was real close to the road, Um,

913
00:59:27,280 --> 00:59:29,800
Speaker 1: it would have you have a very high probability of

914
00:59:29,800 --> 00:59:34,760
Speaker 1: being harvested. And then as you got further away from

915
00:59:34,800 --> 00:59:39,320
Speaker 1: that road, that probability being harvested would decline. But if

916
00:59:39,360 --> 00:59:42,720
Speaker 1: you looked at like hunters success, the sweet spot wasn't

917
00:59:42,800 --> 00:59:46,360
Speaker 1: right next to the road because that's where all the

918
00:59:46,440 --> 00:59:50,080
Speaker 1: hunters were. The sweet spot was a little bit further

919
00:59:50,120 --> 00:59:53,960
Speaker 1: away from the road where there weren't quite as many hunters, um.

920
00:59:54,080 --> 00:59:57,680
Speaker 1: But you still had enough hunters that you were moving

921
00:59:57,720 --> 01:00:01,680
Speaker 1: deer around, and those deer ended up getting hard harvested anyway.

922
01:00:01,720 --> 01:00:06,400
Speaker 1: So that was really intriguing. Um. But it was kind

923
01:00:06,440 --> 01:00:10,640
Speaker 1: of a black box type of thing because we knew

924
01:00:10,640 --> 01:00:13,440
Speaker 1: where deer were sort of on the landscape, and we

925
01:00:13,520 --> 01:00:16,640
Speaker 1: knew where hunters were, but we didn't have any real

926
01:00:16,720 --> 01:00:20,000
Speaker 1: time data. Right. All we could say is, yeah, this

927
01:00:20,080 --> 01:00:23,680
Speaker 1: is where this deer spent its time and it got harvested.

928
01:00:23,720 --> 01:00:25,880
Speaker 1: And this is where this deer spent most of its

929
01:00:25,880 --> 01:00:30,040
Speaker 1: time and it didn't get harvested. Well, now, what we

930
01:00:30,080 --> 01:00:34,240
Speaker 1: can do with these satellite callers is actually watch these

931
01:00:34,280 --> 01:00:38,080
Speaker 1: deer move in real time. And in Pennsylvania we have

932
01:00:38,120 --> 01:00:41,040
Speaker 1: to keep in mind we've got like three quarters of

933
01:00:41,080 --> 01:00:46,440
Speaker 1: a million deer hunters um. Uh. Most of those are

934
01:00:46,520 --> 01:00:51,440
Speaker 1: out during our twelve day rifle season. So there's intensive

935
01:00:51,760 --> 01:00:57,640
Speaker 1: hunting pressure. And so how do those deer respond, especially

936
01:00:57,640 --> 01:01:00,880
Speaker 1: on public lands because there's lots of con entional wisdom

937
01:01:00,920 --> 01:01:03,720
Speaker 1: out there. Oh, they run off the public land and

938
01:01:03,720 --> 01:01:07,640
Speaker 1: they spend all their time on private land, or you know,

939
01:01:07,720 --> 01:01:12,080
Speaker 1: they go nocternal and they don't move during the day. UM.

940
01:01:12,200 --> 01:01:17,760
Speaker 1: So what actually happens and what we found is that

941
01:01:17,760 --> 01:01:21,840
Speaker 1: that that conventional wisdom isn't all correct at what it's

942
01:01:21,840 --> 01:01:25,640
Speaker 1: correcked up to be UM. And and insights that sort

943
01:01:25,640 --> 01:01:29,280
Speaker 1: of explained what we saw with that earlier study. And

944
01:01:29,360 --> 01:01:36,960
Speaker 1: so these dear um respond very much to the hunting pressure. UM.

945
01:01:37,000 --> 01:01:42,160
Speaker 1: They respond. Before the dear season opens, UM, I give

946
01:01:42,160 --> 01:01:46,240
Speaker 1: a lot of talks and show show hunters movies, UM.

947
01:01:46,280 --> 01:01:50,000
Speaker 1: And I asked them, so, what happens on Saturday and

948
01:01:50,040 --> 01:01:52,439
Speaker 1: Sunday because we always have a Monday opener for dear

949
01:01:52,520 --> 01:01:58,800
Speaker 1: season And they said, well, Um, Saturday and Sunday, everyone's

950
01:01:58,840 --> 01:02:02,600
Speaker 1: going out to their dear stand and I said, yeah, exactly,

951
01:02:02,640 --> 01:02:06,320
Speaker 1: and and that just triggers the behavior and the deer

952
01:02:06,360 --> 01:02:11,360
Speaker 1: and they know something's up. And so UM, come Monday

953
01:02:11,360 --> 01:02:16,360
Speaker 1: morning by two to four o'clock in the morning, though

954
01:02:16,480 --> 01:02:19,360
Speaker 1: a lot of those deer in a hiding spot that

955
01:02:19,480 --> 01:02:24,600
Speaker 1: they've discovered, and they just sit tight until the middle

956
01:02:24,600 --> 01:02:27,240
Speaker 1: of the day and then they might start moving in

957
01:02:27,240 --> 01:02:35,240
Speaker 1: the afternoon. Um, but they respond to that hunting pressure. Um,

958
01:02:35,280 --> 01:02:38,560
Speaker 1: that's really really interesting. And then there's lots of different aspects.

959
01:02:38,600 --> 01:02:42,720
Speaker 1: But I'm rambling on here and I you must have

960
01:02:42,760 --> 01:02:45,640
Speaker 1: another question. Yeah, so so let's talk about this, um,

961
01:02:45,720 --> 01:02:49,920
Speaker 1: this immediate reaction to you know, opening day hunting pressure.

962
01:02:50,000 --> 01:02:51,960
Speaker 1: You mentioned that some of them go into a hiding

963
01:02:52,000 --> 01:02:54,960
Speaker 1: spot of some sort. Can you elaborate on specifically what

964
01:02:55,080 --> 01:02:58,880
Speaker 1: you found? Um? Were these dear you know, you mentioned

965
01:02:58,880 --> 01:03:02,040
Speaker 1: that some people think these deer's runoff public land or

966
01:03:02,160 --> 01:03:04,960
Speaker 1: some of these deer go completely nocturnal. Um, can you

967
01:03:05,000 --> 01:03:07,240
Speaker 1: elaborate on those two points? So did you see that

968
01:03:07,320 --> 01:03:09,880
Speaker 1: these deer did they stay in the same general areas

969
01:03:09,880 --> 01:03:12,480
Speaker 1: pressure but just stop moving as much? Did they change

970
01:03:12,480 --> 01:03:14,520
Speaker 1: the time of day they moved in more? I'd love

971
01:03:14,560 --> 01:03:19,280
Speaker 1: to hear the details of that. Yeah. So um uh

972
01:03:19,320 --> 01:03:22,360
Speaker 1: in these study areas where we are, which is you know,

973
01:03:24,160 --> 01:03:29,919
Speaker 1: ninety plus percent forested, um, the deer. The average home

974
01:03:30,040 --> 01:03:32,480
Speaker 1: range of a deer is about a square mile. A

975
01:03:32,520 --> 01:03:36,320
Speaker 1: male and a female deer. Except during the rut, male

976
01:03:36,400 --> 01:03:40,040
Speaker 1: home ranges expand to two some of them up to

977
01:03:40,160 --> 01:03:43,800
Speaker 1: three four square miles. But if you exclude the rut,

978
01:03:43,880 --> 01:03:47,400
Speaker 1: the home range of these deers about a square mile UM.

979
01:03:47,480 --> 01:03:50,600
Speaker 1: During the during the rifle season, they don't leave their

980
01:03:50,640 --> 01:03:56,720
Speaker 1: home range. Um, they've the ones that are still alive.

981
01:03:56,720 --> 01:04:01,800
Speaker 1: Of course, we're only callering adult deer that are at

982
01:04:01,880 --> 01:04:04,040
Speaker 1: least a year and a half old. So in there

983
01:04:04,040 --> 01:04:07,520
Speaker 1: the first hunting season that they're watching them, they've already

984
01:04:07,520 --> 01:04:12,560
Speaker 1: survived two hunting seasons. We're monitoring them during their third

985
01:04:12,640 --> 01:04:15,280
Speaker 1: hunting season, so they're two and a half years old

986
01:04:15,320 --> 01:04:20,120
Speaker 1: at least summer older. Um. So what those deer do

987
01:04:20,560 --> 01:04:25,800
Speaker 1: is they've through by just getting lucky. Have found out

988
01:04:25,840 --> 01:04:28,600
Speaker 1: that if I go to this spot in my home range,

989
01:04:29,200 --> 01:04:32,680
Speaker 1: I am unlikely to be disturbed and I'm just gonna

990
01:04:32,760 --> 01:04:37,480
Speaker 1: hunker down there until this hunting season is over with.

991
01:04:38,200 --> 01:04:42,400
Speaker 1: And so where I said a home range of um

992
01:04:42,440 --> 01:04:45,800
Speaker 1: a deer is about a square mile when you look

993
01:04:45,920 --> 01:04:49,840
Speaker 1: during that two week hunting season, UM, it averages about

994
01:04:49,840 --> 01:04:54,680
Speaker 1: a hundred acres. Interesting, did you did you look at

995
01:04:54,720 --> 01:04:57,760
Speaker 1: this at all? Or did you did you separate out

996
01:04:57,840 --> 01:05:00,720
Speaker 1: any of this data by age class. Oh could you

997
01:05:00,760 --> 01:05:04,600
Speaker 1: see with a four and a half old buck or

998
01:05:04,680 --> 01:05:07,680
Speaker 1: older they moved X and a two and a half

999
01:05:07,760 --> 01:05:10,440
Speaker 1: yearld though moved. Why did you look at anything along

1000
01:05:10,480 --> 01:05:16,800
Speaker 1: on those lines? UM? No, we haven't, because first of all,

1001
01:05:16,880 --> 01:05:22,120
Speaker 1: we don't age. We only age our dear as UM juveniles,

1002
01:05:22,200 --> 01:05:25,439
Speaker 1: which is less than a year old or adults over

1003
01:05:25,480 --> 01:05:29,000
Speaker 1: a year old UM. And the reason we do that

1004
01:05:29,200 --> 01:05:32,880
Speaker 1: is that it's just too difficult and we don't really

1005
01:05:32,920 --> 01:05:37,360
Speaker 1: need to know how old it is UM other than

1006
01:05:37,440 --> 01:05:41,200
Speaker 1: juvenile adult and adult UM. And the other thing I

1007
01:05:41,240 --> 01:05:44,400
Speaker 1: can say is that looking at dear that we've followed

1008
01:05:44,440 --> 01:05:49,440
Speaker 1: for two three years, UM, their behavior does doesn't change.

1009
01:05:51,200 --> 01:05:58,720
Speaker 1: What about broken down by sex UM? Well, both males

1010
01:05:58,720 --> 01:06:02,760
Speaker 1: and females have a high spot during that intensive hunting

1011
01:06:02,800 --> 01:06:10,160
Speaker 1: period UM, although bucks from what I've seen UM are

1012
01:06:10,200 --> 01:06:14,600
Speaker 1: a little bit different and that they tend to like UM.

1013
01:06:14,640 --> 01:06:16,840
Speaker 1: If they're going to hide out, it's gonna be on

1014
01:06:17,000 --> 01:06:21,840
Speaker 1: a on a ridge or um on a crust of

1015
01:06:22,480 --> 01:06:25,000
Speaker 1: So we've got two different you know, I should need

1016
01:06:25,040 --> 01:06:27,600
Speaker 1: to back up a little bit. Two of our study

1017
01:06:27,600 --> 01:06:31,040
Speaker 1: areas are in central Pennsylvania, which is what's called in

1018
01:06:31,080 --> 01:06:34,320
Speaker 1: the ridge and Valley region. So we have these long

1019
01:06:34,480 --> 01:06:38,280
Speaker 1: linear ridges and valleys that go and sort of a

1020
01:06:38,320 --> 01:06:43,560
Speaker 1: southwest and northeast direction. In our northern study area, it's

1021
01:06:43,640 --> 01:06:47,360
Speaker 1: up on what's called the Allegheny Plateau, and this is

1022
01:06:47,520 --> 01:06:50,080
Speaker 1: it looks very mountainous, but really what it is is

1023
01:06:50,160 --> 01:06:54,480
Speaker 1: it's a flat plane that eroded, so the mountaintops are flat,

1024
01:06:54,920 --> 01:06:57,960
Speaker 1: and then you have the steep drainages where erosion has

1025
01:06:58,000 --> 01:07:02,960
Speaker 1: occurred over the millennia, and so in both of these places,

1026
01:07:02,960 --> 01:07:06,840
Speaker 1: the bucks will sit either on the on the edge

1027
01:07:06,840 --> 01:07:12,120
Speaker 1: of one of these steep um ravines or on the

1028
01:07:12,160 --> 01:07:15,600
Speaker 1: top of a ridge, and that's their preferred hiding spot,

1029
01:07:16,680 --> 01:07:20,600
Speaker 1: usually in a place where if it's flat to the west,

1030
01:07:21,360 --> 01:07:24,640
Speaker 1: they can smell anything that's coming from the prevailing winds.

1031
01:07:25,520 --> 01:07:29,520
Speaker 1: And on the eastern side, there's no way you'd sneak

1032
01:07:29,600 --> 01:07:33,120
Speaker 1: up on them because it's you know, very steep slopes,

1033
01:07:33,880 --> 01:07:36,480
Speaker 1: and so if they do get disturbed, they can just

1034
01:07:36,640 --> 01:07:43,000
Speaker 1: jump off that point and heckn in thirty seconds to

1035
01:07:43,040 --> 01:07:47,000
Speaker 1: a minute, they would be hundreds of feet below you

1036
01:07:47,120 --> 01:07:51,760
Speaker 1: in elevation and you know, half a mile away. Now,

1037
01:07:51,800 --> 01:07:56,480
Speaker 1: the dough the females, they tend to have a similar

1038
01:07:56,600 --> 01:08:01,720
Speaker 1: hiding spot, but not It's just seems to be an

1039
01:08:01,760 --> 01:08:05,320
Speaker 1: area that they run to where they didn't get disturbed.

1040
01:08:06,880 --> 01:08:09,480
Speaker 1: I love that you were able to see this with

1041
01:08:09,560 --> 01:08:12,600
Speaker 1: your studies and these these collar deer because because what

1042
01:08:12,680 --> 01:08:15,200
Speaker 1: you described there as far as these typical hideouts, you know,

1043
01:08:15,520 --> 01:08:19,960
Speaker 1: it is right in line with what so many hunters

1044
01:08:20,240 --> 01:08:23,479
Speaker 1: have seen as far as where you know, these bucks

1045
01:08:23,520 --> 01:08:27,960
Speaker 1: tend to like to bed, you know so often from observation, UM,

1046
01:08:28,360 --> 01:08:31,400
Speaker 1: you'll find that these mature bucks especially find a great

1047
01:08:31,439 --> 01:08:33,680
Speaker 1: betting spot or hiding spots you might call it, you know,

1048
01:08:33,840 --> 01:08:36,599
Speaker 1: during a pressure time of year, up on a ridge

1049
01:08:36,640 --> 01:08:38,439
Speaker 1: where they can see down ahead of them, they can

1050
01:08:38,479 --> 01:08:41,200
Speaker 1: smell behind them. And it makes sense from a from

1051
01:08:41,200 --> 01:08:43,439
Speaker 1: a rational kind of logic standpoint if you just think,

1052
01:08:43,479 --> 01:08:45,760
Speaker 1: how would a deer be able to survive? Um, But

1053
01:08:45,840 --> 01:08:49,080
Speaker 1: it's really neat to see the actual data, UM prove

1054
01:08:49,160 --> 01:08:53,000
Speaker 1: that too. UM. Yeah, I just posted a blog today,

1055
01:08:53,479 --> 01:08:57,639
Speaker 1: UM where it's a buck that we've followed for three years,

1056
01:08:58,840 --> 01:09:02,120
Speaker 1: and I show some of these spots where he hit

1057
01:09:02,200 --> 01:09:05,080
Speaker 1: out on these ridges and some of them are right

1058
01:09:05,120 --> 01:09:09,439
Speaker 1: next to a road, but they're steep, you know, there

1059
01:09:09,439 --> 01:09:14,960
Speaker 1: are you know, if you for every um two feet

1060
01:09:15,040 --> 01:09:17,800
Speaker 1: that you go horizontally, you need to go a foot up.

1061
01:09:18,560 --> 01:09:22,759
Speaker 1: So they're very steep and and not many people, certainly

1062
01:09:22,800 --> 01:09:26,400
Speaker 1: not me, are going to be trapesing around those areas,

1063
01:09:26,400 --> 01:09:32,800
Speaker 1: and they they discover them and that's where they sit. Now.

1064
01:09:33,800 --> 01:09:36,559
Speaker 1: You mentioned, I think you said there was two of

1065
01:09:36,600 --> 01:09:39,439
Speaker 1: your study locations in the ridge and valley location. There's

1066
01:09:39,479 --> 01:09:43,400
Speaker 1: one study location on the plateau. Um. Did you say

1067
01:09:43,439 --> 01:09:45,880
Speaker 1: there's four total study areas. Was that right? Well, two

1068
01:09:45,880 --> 01:09:47,680
Speaker 1: are up on the plateau or two are in the

1069
01:09:47,760 --> 01:09:50,240
Speaker 1: ridge and valley? Okay, okay. I was gonna ask if

1070
01:09:50,240 --> 01:09:52,080
Speaker 1: you had a study group that was more in a

1071
01:09:52,120 --> 01:09:55,040
Speaker 1: flat land type scenario. But it sounds like you don't write.

1072
01:09:56,960 --> 01:09:58,600
Speaker 1: Lots of people have asked, you know what, we're going

1073
01:09:58,680 --> 01:10:03,840
Speaker 1: to study deer and more, you know, egg um situations

1074
01:10:03,880 --> 01:10:07,880
Speaker 1: and probably not for a while because that's even more

1075
01:10:07,960 --> 01:10:16,280
Speaker 1: complicated habitat situation um and but and there. Yeah, so yeah,

1076
01:10:16,320 --> 01:10:18,600
Speaker 1: I don't have a lot of insights in those situations.

1077
01:10:18,640 --> 01:10:23,680
Speaker 1: These are just large tracts of public land um predominantly

1078
01:10:23,760 --> 01:10:28,320
Speaker 1: forceded so you know, so that's sort of the topography issues.

1079
01:10:28,800 --> 01:10:32,040
Speaker 1: The other one is the the idea about deer you know,

1080
01:10:32,120 --> 01:10:35,599
Speaker 1: going on private land and so on the bound. We've

1081
01:10:35,640 --> 01:10:39,800
Speaker 1: got deer that are that we've captured that spend time

1082
01:10:39,840 --> 01:10:44,920
Speaker 1: on public land and private land, and quite frankly, most

1083
01:10:44,960 --> 01:10:49,960
Speaker 1: of them they're hiding spot is on public land. Um,

1084
01:10:50,080 --> 01:10:53,840
Speaker 1: they might go down into the private land is more

1085
01:10:53,920 --> 01:10:58,920
Speaker 1: likely to have some crops and food resources. But um,

1086
01:10:58,960 --> 01:11:01,759
Speaker 1: like I said, these or don't leave their home range.

1087
01:11:02,360 --> 01:11:04,559
Speaker 1: I mean, they'd be nuts to leave your home range. Right.

1088
01:11:04,600 --> 01:11:07,160
Speaker 1: They know it like the back of their hand, so

1089
01:11:07,200 --> 01:11:09,800
Speaker 1: they know where to go. They know you know where

1090
01:11:09,840 --> 01:11:13,080
Speaker 1: the danger is. Um, So they're going to stay within

1091
01:11:13,160 --> 01:11:17,920
Speaker 1: their home range. They're just seeking out spots that they've discovered.

1092
01:11:17,960 --> 01:11:23,160
Speaker 1: They're not going to get disturbed. What about any patterns

1093
01:11:23,800 --> 01:11:32,320
Speaker 1: that show once a deer maybe population or uh individual

1094
01:11:32,479 --> 01:11:37,719
Speaker 1: deer has been spooked or bumped pressured, how how long

1095
01:11:38,479 --> 01:11:43,760
Speaker 1: before it returns back to its normal pattern? Yeah, that

1096
01:11:43,800 --> 01:11:45,960
Speaker 1: would be a great question. But that's a really difficult

1097
01:11:45,960 --> 01:11:54,519
Speaker 1: one to answer because we haven't radio colored any hunters. Yea, Um,

1098
01:11:54,560 --> 01:12:00,720
Speaker 1: but I can tell you that, um, uh, you know,

1099
01:12:00,840 --> 01:12:03,479
Speaker 1: like we've got I've got One of our blog post

1100
01:12:03,600 --> 01:12:07,519
Speaker 1: is about a dough I call her Hillside Dough because

1101
01:12:07,560 --> 01:12:10,160
Speaker 1: she had this spot that was in her home range.

1102
01:12:10,160 --> 01:12:13,000
Speaker 1: It was the steepest spot in her home range, on

1103
01:12:13,040 --> 01:12:16,120
Speaker 1: the side of a ridge, and that's where she hid

1104
01:12:17,040 --> 01:12:20,559
Speaker 1: and she would go there every day. Um. And there

1105
01:12:20,680 --> 01:12:26,280
Speaker 1: was one Saturday where so you know, like early in

1106
01:12:26,320 --> 01:12:30,120
Speaker 1: the hunting season, the first week or so, there's a

1107
01:12:30,120 --> 01:12:33,519
Speaker 1: lot a lot of guys are just sitting, um, and

1108
01:12:33,560 --> 01:12:36,480
Speaker 1: then later in the week they'll start to put on drives.

1109
01:12:36,560 --> 01:12:40,760
Speaker 1: And so especially that first Saturday and the second week,

1110
01:12:41,320 --> 01:12:45,080
Speaker 1: and there was one day where you could see where

1111
01:12:45,120 --> 01:12:47,599
Speaker 1: she was in her hiding spot and she got kicked

1112
01:12:47,600 --> 01:12:51,760
Speaker 1: out and she made this big loop around the edge

1113
01:12:51,800 --> 01:12:55,800
Speaker 1: of her home range that she didn't normally do. But

1114
01:12:55,880 --> 01:12:58,679
Speaker 1: the next day, you know, and so then that night

1115
01:12:58,880 --> 01:13:01,760
Speaker 1: she was out doing a normal thing, and the next

1116
01:13:01,800 --> 01:13:04,320
Speaker 1: morning she went back up to that same hiding spot.

1117
01:13:05,160 --> 01:13:08,880
Speaker 1: M So my guess is that even if you bumped

1118
01:13:08,880 --> 01:13:12,720
Speaker 1: a deer out of that hiding spot, odds are it's

1119
01:13:12,760 --> 01:13:16,400
Speaker 1: going to go back to it. Yeah, it makes sense.

1120
01:13:16,400 --> 01:13:20,559
Speaker 1: As you said, Um, it makes the most sense from

1121
01:13:20,560 --> 01:13:23,040
Speaker 1: a survival standpoint to stick to an area, you know,

1122
01:13:23,800 --> 01:13:26,120
Speaker 1: like the back of your hand, versus going somewhere brand

1123
01:13:26,160 --> 01:13:28,360
Speaker 1: new when faced with danger. You know, if I was

1124
01:13:28,360 --> 01:13:31,759
Speaker 1: faced with danger, I would probably run into my bedroom

1125
01:13:31,800 --> 01:13:34,559
Speaker 1: and locked the door. Um, I wouldn't just go running

1126
01:13:34,560 --> 01:13:37,960
Speaker 1: willy nilly somewhere brand new. Um. So that you know

1127
01:13:38,000 --> 01:13:41,360
Speaker 1: that makes sense? Um. Now, some other things I've seen

1128
01:13:41,400 --> 01:13:44,800
Speaker 1: you guys right about in the past related to some

1129
01:13:44,960 --> 01:13:47,519
Speaker 1: factors that you guys took a look at, some outside

1130
01:13:47,560 --> 01:13:49,200
Speaker 1: factors you took a look at to see how they

1131
01:13:49,240 --> 01:13:51,720
Speaker 1: may or may not impact deer movement. And you know,

1132
01:13:52,280 --> 01:13:54,840
Speaker 1: a lot of our listeners, myself and Dan included, we

1133
01:13:54,920 --> 01:13:58,680
Speaker 1: kind of obsess over what types of things might impact

1134
01:13:58,920 --> 01:14:01,800
Speaker 1: dear as far as how often they move or how

1135
01:14:01,800 --> 01:14:04,000
Speaker 1: early in the day they might move, you know, like,

1136
01:14:04,400 --> 01:14:07,920
Speaker 1: does the moon impact when dear move? Does certain weather

1137
01:14:08,000 --> 01:14:11,000
Speaker 1: factors like temperature or barre metric pressure or anything like

1138
01:14:11,040 --> 01:14:15,040
Speaker 1: that impact when and how much dear move? Um? Can

1139
01:14:15,080 --> 01:14:17,400
Speaker 1: you speak to that? Have there been anything's you've looked

1140
01:14:17,400 --> 01:14:21,439
Speaker 1: at it on those lines and what have you learned? Yes? So,

1141
01:14:21,800 --> 01:14:26,800
Speaker 1: um so. Obviously the moon's a big one. Um. You

1142
01:14:26,920 --> 01:14:31,880
Speaker 1: hear a lot about um about the moon affecting the

1143
01:14:31,920 --> 01:14:37,759
Speaker 1: timing of the rut um, weather influencing the rut um weather,

1144
01:14:37,840 --> 01:14:44,000
Speaker 1: and moon just influencing dear movements in general. UM. So

1145
01:14:44,240 --> 01:14:47,519
Speaker 1: you know, I wouldn't say we have the final answer

1146
01:14:47,640 --> 01:14:50,360
Speaker 1: on this because a lot of people over the years

1147
01:14:50,360 --> 01:14:53,280
Speaker 1: have looked at try to look at the influence of

1148
01:14:53,360 --> 01:14:58,280
Speaker 1: moon on movements and that sort of thing, UM, and

1149
01:14:58,280 --> 01:15:00,960
Speaker 1: and the results are sort of equivocal. When you go

1150
01:15:01,040 --> 01:15:03,880
Speaker 1: in the literature, some people said, yeah, there is an

1151
01:15:03,880 --> 01:15:07,120
Speaker 1: effect of the moon on movements, and other people say, no,

1152
01:15:07,280 --> 01:15:11,720
Speaker 1: I couldn't find any effect. UM. But I think with

1153
01:15:11,800 --> 01:15:14,439
Speaker 1: these as more and more of these satellite collars are

1154
01:15:14,479 --> 01:15:17,960
Speaker 1: being used and we can get much more detailed information

1155
01:15:18,000 --> 01:15:21,439
Speaker 1: on their movements than we could UM in the past.

1156
01:15:21,520 --> 01:15:26,640
Speaker 1: With with the older collorum technology, we'll be able to

1157
01:15:26,680 --> 01:15:29,320
Speaker 1: get at this. But from what we've been finding in

1158
01:15:29,400 --> 01:15:33,600
Speaker 1: our study is we can find very little influence of

1159
01:15:33,640 --> 01:15:39,920
Speaker 1: the moon on anything. UM. The Game Commission UM looked

1160
01:15:39,920 --> 01:15:45,240
Speaker 1: at road killed females, thousands of them over UH seven

1161
01:15:45,280 --> 01:15:50,519
Speaker 1: year period and UM basically there's no relationship between the

1162
01:15:50,520 --> 01:15:55,840
Speaker 1: moon and the timing of when females get pregnant. UM.

1163
01:15:55,880 --> 01:15:59,680
Speaker 1: We've looked at the effects of I had an undergraduate

1164
01:15:59,680 --> 01:16:03,599
Speaker 1: student this summer looking at to see if UM. So,

1165
01:16:03,680 --> 01:16:07,080
Speaker 1: for example, in a full moon, you might expect deer

1166
01:16:07,200 --> 01:16:10,559
Speaker 1: might move more at night and then to feed, and

1167
01:16:10,560 --> 01:16:15,040
Speaker 1: then they wouldn't have to move during the day. And Um,

1168
01:16:15,160 --> 01:16:21,840
Speaker 1: she did find an effect statistically, but from a biological standpoint,

1169
01:16:21,880 --> 01:16:25,160
Speaker 1: I mean, the difference in movement was like you and

1170
01:16:25,200 --> 01:16:27,880
Speaker 1: I making an extra trip to the bathroom or something

1171
01:16:27,960 --> 01:16:32,040
Speaker 1: during the day. So she could find no effect of

1172
01:16:32,040 --> 01:16:37,240
Speaker 1: of moon conditions on on deer movements. Um. She also

1173
01:16:37,320 --> 01:16:45,720
Speaker 1: looked at rain and wind. Um, there's some influence there. Um.

1174
01:16:45,760 --> 01:16:49,559
Speaker 1: I I think it's kind of funny that we've looked

1175
01:16:49,600 --> 01:16:52,640
Speaker 1: at this a couple of different times, and the suggestion

1176
01:16:52,760 --> 01:16:57,400
Speaker 1: is that males are kind of whimps. Um. If it's raining,

1177
01:16:57,680 --> 01:17:03,920
Speaker 1: they're less likely to move, but females don't seem to care. Um.

1178
01:17:03,960 --> 01:17:10,240
Speaker 1: And then with wind, Um, there isn't much effect. Um.

1179
01:17:10,280 --> 01:17:13,519
Speaker 1: If it's really calm, they're less likely to be moving.

1180
01:17:13,560 --> 01:17:15,439
Speaker 1: If there's a little bit of wind, they'll move a

1181
01:17:15,439 --> 01:17:19,840
Speaker 1: little bit more. Um. Some evidence to suggest that when

1182
01:17:19,880 --> 01:17:24,519
Speaker 1: you get very windy conditions they'll move more. But again, um,

1183
01:17:24,520 --> 01:17:27,240
Speaker 1: we've a lot of this work. We've limited to the

1184
01:17:27,280 --> 01:17:31,800
Speaker 1: month of October because that's our archery season and deer

1185
01:17:31,800 --> 01:17:35,680
Speaker 1: aren't really affected by the hunting. That's going on at

1186
01:17:35,680 --> 01:17:38,720
Speaker 1: that time of year. Um. But on the other hand,

1187
01:17:38,760 --> 01:17:41,040
Speaker 1: it's a beautiful time of year and you don't get

1188
01:17:41,040 --> 01:17:43,240
Speaker 1: a lot of wind, you don't get a lot of rain,

1189
01:17:44,000 --> 01:17:47,200
Speaker 1: So we really can't say, you know, a whole lot

1190
01:17:47,240 --> 01:17:50,479
Speaker 1: about how that influences their move you know, I can

1191
01:17:50,520 --> 01:17:52,680
Speaker 1: say for October there doesn't seem to be a lot

1192
01:17:52,720 --> 01:17:56,040
Speaker 1: of effect, but you don't get hurricanes in October and

1193
01:17:56,080 --> 01:18:02,360
Speaker 1: things like that, right, Right, So the it sounds like

1194
01:18:02,439 --> 01:18:04,960
Speaker 1: from what you guys are seeing in your studies, and

1195
01:18:05,000 --> 01:18:08,080
Speaker 1: this is probably something that we as deer hunters would

1196
01:18:08,160 --> 01:18:12,240
Speaker 1: say is true to just anecdotally, nothing impacts a deer

1197
01:18:12,280 --> 01:18:16,200
Speaker 1: movement as much as hunting pressure. Right. If there's anything

1198
01:18:16,240 --> 01:18:22,679
Speaker 1: that has a really significant impact, it's that. Would you agree, Yeah, yeah,

1199
01:18:22,720 --> 01:18:26,080
Speaker 1: that's I mean, you don't need to you don't need

1200
01:18:26,120 --> 01:18:29,639
Speaker 1: statistics to see that when you look at these movies

1201
01:18:29,680 --> 01:18:32,360
Speaker 1: that I posted on the blog about what deer doing

1202
01:18:32,439 --> 01:18:36,840
Speaker 1: during that that during our rifle season with the intensive

1203
01:18:36,880 --> 01:18:40,760
Speaker 1: hunting pressure. People have asked me about archery and I've

1204
01:18:40,800 --> 01:18:45,560
Speaker 1: looked at deer movements that time of year and you can't, um,

1205
01:18:45,680 --> 01:18:48,439
Speaker 1: you can't tell anything. And then of course in between

1206
01:18:48,479 --> 01:18:54,479
Speaker 1: that's the rut and the rut you know, they're they're

1207
01:18:54,520 --> 01:18:59,000
Speaker 1: just in the rut and they're going seven. Yeah. Have

1208
01:18:59,120 --> 01:19:03,400
Speaker 1: you seen any patterns as far as buck behavior movement

1209
01:19:03,479 --> 01:19:06,680
Speaker 1: during the rut? I remember seeing one study I do

1210
01:19:06,760 --> 01:19:10,200
Speaker 1: not remember where this study came out of UM, where

1211
01:19:10,200 --> 01:19:13,639
Speaker 1: they had seen that UM, with these types of GPS

1212
01:19:13,720 --> 01:19:16,519
Speaker 1: or satellite collars, they could actually see that many bucks

1213
01:19:17,120 --> 01:19:20,559
Speaker 1: would go to kind of focal points within their home

1214
01:19:20,680 --> 01:19:23,479
Speaker 1: range during the rut, two to three or four different

1215
01:19:23,479 --> 01:19:27,719
Speaker 1: focal points, these likely being doe hotspots or doe betting

1216
01:19:27,760 --> 01:19:29,840
Speaker 1: years that they would check, you know, every twenty four

1217
01:19:29,840 --> 01:19:34,080
Speaker 1: hours or so many hours throughout that running time period. UM.

1218
01:19:34,360 --> 01:19:36,120
Speaker 1: Did you see anything like that in your study or

1219
01:19:36,160 --> 01:19:38,439
Speaker 1: if you, have you seen anything else as far as

1220
01:19:38,479 --> 01:19:42,360
Speaker 1: dear behavior buck behavior during the rut through your own studies? Yeah,

1221
01:19:42,360 --> 01:19:45,679
Speaker 1: that's a really great question. I read that paper, UM.

1222
01:19:45,720 --> 01:19:52,320
Speaker 1: That work was done in Texas and UM, and so I,

1223
01:19:52,720 --> 01:19:55,080
Speaker 1: you know, I looked at a lot of our you know,

1224
01:19:55,120 --> 01:19:58,480
Speaker 1: because I've made these movies to look at these deer movements,

1225
01:19:59,160 --> 01:20:02,680
Speaker 1: and I could not discern a pattern. And then I

1226
01:20:02,760 --> 01:20:06,680
Speaker 1: read this paper and I looked through that and I

1227
01:20:06,760 --> 01:20:12,360
Speaker 1: can honestly say that I, you know, just visually looking

1228
01:20:12,400 --> 01:20:16,439
Speaker 1: at movements can see nothing like that UM. In fact,

1229
01:20:16,560 --> 01:20:19,719
Speaker 1: my students and I have been talking and we want

1230
01:20:19,760 --> 01:20:25,519
Speaker 1: to investigate that UM a little more quantitatively. And one

1231
01:20:25,560 --> 01:20:27,760
Speaker 1: of the things we're going to do is that we

1232
01:20:27,880 --> 01:20:33,519
Speaker 1: do have UM in certain situations, we have both males

1233
01:20:33,560 --> 01:20:37,040
Speaker 1: and females in the same area, and so we can

1234
01:20:37,080 --> 01:20:41,080
Speaker 1: look at both of their movements, and in fact, UM

1235
01:20:41,200 --> 01:20:44,960
Speaker 1: we can see when a breeding event probably occurred, because

1236
01:20:45,000 --> 01:20:48,519
Speaker 1: these satellite colors are very accurate. You know, they're within

1237
01:20:48,600 --> 01:20:52,080
Speaker 1: tens of meters, and you can see like these this

1238
01:20:52,320 --> 01:20:56,639
Speaker 1: male and a female UM will have overlapping home ranges,

1239
01:20:56,680 --> 01:20:59,080
Speaker 1: and the mail will be moving around, and then suddenly

1240
01:21:00,040 --> 01:21:04,120
Speaker 1: those two animals will be right next to each other

1241
01:21:04,240 --> 01:21:06,760
Speaker 1: for twelve to twenty four hours, so you know that

1242
01:21:06,760 --> 01:21:10,800
Speaker 1: that's probably a male tending a female. And so what

1243
01:21:10,840 --> 01:21:15,400
Speaker 1: we'd like to do is take those situations where we

1244
01:21:15,479 --> 01:21:18,040
Speaker 1: know there's a male and a female, a male tending

1245
01:21:18,080 --> 01:21:22,200
Speaker 1: a female, and seeing if we can detect patterns of

1246
01:21:22,320 --> 01:21:25,840
Speaker 1: movement that are different from other parts of the rut,

1247
01:21:25,880 --> 01:21:28,760
Speaker 1: and see if we can actually distinguish that. But I

1248
01:21:28,800 --> 01:21:32,240
Speaker 1: can honestly say that I have not been able to

1249
01:21:32,320 --> 01:21:40,040
Speaker 1: detect any pattern of males making regular movements UM supposedly, UM,

1250
01:21:40,080 --> 01:21:44,240
Speaker 1: you know, trying to check out females. Instead, what I

1251
01:21:44,360 --> 01:21:50,200
Speaker 1: see in our study areas as males UM basically doubling,

1252
01:21:50,320 --> 01:21:55,080
Speaker 1: tripling their home range area and just moving constantly seven,

1253
01:21:55,600 --> 01:21:59,080
Speaker 1: just going back and forth across their study area as

1254
01:21:59,120 --> 01:22:03,960
Speaker 1: quickly as they and constantly. UM, my guess is searching

1255
01:22:04,000 --> 01:22:08,160
Speaker 1: for females. In addition, if you look at home ranges

1256
01:22:08,200 --> 01:22:11,559
Speaker 1: of females during this time of year when they're most

1257
01:22:11,640 --> 01:22:17,240
Speaker 1: likely to be um inestrius um, their home range actually shrinks.

1258
01:22:17,840 --> 01:22:19,639
Speaker 1: And that makes a lot of sense. Right, if you're

1259
01:22:19,680 --> 01:22:21,639
Speaker 1: lost in the woods and you want to be found,

1260
01:22:22,320 --> 01:22:24,559
Speaker 1: you sit still. So if a female's out there and

1261
01:22:24,600 --> 01:22:27,000
Speaker 1: she wants to be found by a male, don't move

1262
01:22:27,040 --> 01:22:30,000
Speaker 1: around a lot. If you want to find a female,

1263
01:22:30,200 --> 01:22:32,360
Speaker 1: move around a lot. And so that's what we see,

1264
01:22:32,400 --> 01:22:35,599
Speaker 1: the males moving around a lot and the females actually

1265
01:22:35,640 --> 01:22:40,640
Speaker 1: reducing their movements. What what percentage? And I don't know,

1266
01:22:41,160 --> 01:22:44,760
Speaker 1: well maybe maybe you do know this. Um. You mentioned

1267
01:22:45,320 --> 01:22:47,720
Speaker 1: that during the rut, it seems like these bucks are

1268
01:22:47,760 --> 01:22:52,559
Speaker 1: just moving all over the place seven. Um. Is that

1269
01:22:52,720 --> 01:22:55,880
Speaker 1: literal or what percentage? If you've if you've tracked this

1270
01:22:55,960 --> 01:22:58,600
Speaker 1: to to this level of detail, what percentage of the

1271
01:22:58,680 --> 01:23:01,519
Speaker 1: day during the rut is a luck actually like betted

1272
01:23:01,560 --> 01:23:04,320
Speaker 1: down versus up and moving? And then what does that

1273
01:23:04,360 --> 01:23:06,800
Speaker 1: look like, you know, in a period of the year

1274
01:23:06,880 --> 01:23:10,200
Speaker 1: that is not during the run, um betted versus on

1275
01:23:10,280 --> 01:23:17,000
Speaker 1: his feet moving? Yes, um, No, you know the in Pennsylvania, um,

1276
01:23:17,680 --> 01:23:24,760
Speaker 1: half the females are bred by around the November so UM.

1277
01:23:24,880 --> 01:23:32,639
Speaker 1: So that um. First that you've got like a two

1278
01:23:32,760 --> 01:23:40,240
Speaker 1: or three week period in November. Um that that most

1279
01:23:40,280 --> 01:23:45,559
Speaker 1: of the breeding occurs and um and during that time period,

1280
01:23:46,160 --> 01:23:51,120
Speaker 1: a lot of the males basically do not have do

1281
01:23:51,200 --> 01:23:54,879
Speaker 1: not exhibit that crepuscular behavior, you know, where they're active

1282
01:23:54,920 --> 01:23:59,400
Speaker 1: in the around sunrise and active around sunset and middle

1283
01:23:59,400 --> 01:24:01,760
Speaker 1: of the day and middle of the night they're less

1284
01:24:01,800 --> 01:24:05,800
Speaker 1: likely to be active. That just disappears they're just going

1285
01:24:06,560 --> 01:24:13,280
Speaker 1: seven um and and yeah, that's what that's what they're doing.

1286
01:24:14,720 --> 01:24:18,400
Speaker 1: And then outside of the rut then they fall back

1287
01:24:18,439 --> 01:24:22,120
Speaker 1: to that as you mentioned, cris carepascular behavior where they're

1288
01:24:22,120 --> 01:24:25,880
Speaker 1: most active at dawn and dusk, um, not so much

1289
01:24:25,880 --> 01:24:27,439
Speaker 1: in millian night, not so much in the middle of

1290
01:24:27,479 --> 01:24:29,519
Speaker 1: the day. But it doesn't sound like you guys have

1291
01:24:29,640 --> 01:24:33,960
Speaker 1: ever looked. Actually, you know, on average a buck spends

1292
01:24:34,120 --> 01:24:37,960
Speaker 1: six of his day moving around of his day Betted,

1293
01:24:38,000 --> 01:24:41,000
Speaker 1: I'm just kind of curious of that particular number. I've

1294
01:24:41,000 --> 01:24:46,320
Speaker 1: never actually seen anywhere. Yeah, jeez, I think I did

1295
01:24:46,320 --> 01:24:49,639
Speaker 1: a blog post about this a little bit ago. Um.

1296
01:24:49,680 --> 01:24:54,280
Speaker 1: This one buck that I followed, um during the peak

1297
01:24:54,320 --> 01:24:58,040
Speaker 1: of the rut, except for maybe four to five am,

1298
01:24:58,120 --> 01:25:01,080
Speaker 1: when it looked like he took a rest every day.

1299
01:25:01,439 --> 01:25:06,120
Speaker 1: The rest of the time he was just going yikes.

1300
01:25:08,120 --> 01:25:12,759
Speaker 1: What about wounded deer? Have you like, tracked a deer

1301
01:25:13,280 --> 01:25:17,960
Speaker 1: that was known to be wounded by a hunter and

1302
01:25:18,160 --> 01:25:21,880
Speaker 1: followed his patterns you know, maybe not a critical wound

1303
01:25:22,479 --> 01:25:31,720
Speaker 1: and how he acted after being shot. Um? Not really. Um.

1304
01:25:31,720 --> 01:25:34,640
Speaker 1: You know, we catch these deer and they disappear. We

1305
01:25:34,760 --> 01:25:41,200
Speaker 1: do know one that um uh got hit by a

1306
01:25:41,200 --> 01:25:49,040
Speaker 1: car um and survived. Um. But I haven't to be honest,

1307
01:25:49,080 --> 01:25:54,720
Speaker 1: I haven't looked in detail to see what his movements were. UM.

1308
01:25:54,800 --> 01:26:02,839
Speaker 1: We you know, we don't see a lot a crippling loss. Um.

1309
01:26:02,880 --> 01:26:05,240
Speaker 1: You know where a hunter shot a deer and then

1310
01:26:05,280 --> 01:26:10,679
Speaker 1: it died and it wasn't recovered. Um. We don't see

1311
01:26:10,720 --> 01:26:16,040
Speaker 1: a lot of I mean, in the seventeen years I've

1312
01:26:16,040 --> 01:26:18,280
Speaker 1: been studying deer in Pennsylvania, we don't see a lot

1313
01:26:18,320 --> 01:26:24,880
Speaker 1: of illegal harvest. Um. So yeah, I don't have a

1314
01:26:24,880 --> 01:26:28,759
Speaker 1: lot of insights on that. Okay, we're gonna take one

1315
01:26:28,920 --> 01:26:32,599
Speaker 1: final break here for word from our partners at Matthew's Archery.

1316
01:26:33,040 --> 01:26:36,040
Speaker 1: And as you've heard over the past few weeks, Matthews

1317
01:26:36,080 --> 01:26:39,559
Speaker 1: has released their new triacts both and today we've got

1318
01:26:39,600 --> 01:26:43,280
Speaker 1: Matthews design engineer Marque has to talk about one part

1319
01:26:43,320 --> 01:26:46,479
Speaker 1: of the technology and that bow, the new three D damping.

1320
01:26:47,400 --> 01:26:50,120
Speaker 1: If you can imagine your point of contact, which is

1321
01:26:50,160 --> 01:26:53,360
Speaker 1: your grip on the bow that's zero zero. You can

1322
01:26:53,439 --> 01:26:56,120
Speaker 1: turn your bow in three directions from that position, so

1323
01:26:56,400 --> 01:26:59,559
Speaker 1: you can imagine forward to back, you imagine left or right,

1324
01:26:59,640 --> 01:27:02,479
Speaker 1: and then you can imagine that twisting motion all that

1325
01:27:02,560 --> 01:27:05,120
Speaker 1: happens from the grip. And if you can maximize the

1326
01:27:05,160 --> 01:27:08,600
Speaker 1: effectiveness of the damper in all three of those directions,

1327
01:27:08,600 --> 01:27:12,439
Speaker 1: which we we have done well um within the past,

1328
01:27:12,479 --> 01:27:14,920
Speaker 1: but as our risers get more and more eye shaped,

1329
01:27:15,120 --> 01:27:19,240
Speaker 1: which is a really stable shooting platform, the damper inherently

1330
01:27:19,280 --> 01:27:23,240
Speaker 1: got underneath the grip and you lost that twisting benefit.

1331
01:27:23,800 --> 01:27:25,320
Speaker 1: So by moving it out in front of the riser,

1332
01:27:25,360 --> 01:27:27,479
Speaker 1: you get the benefit of the eye shaped riser, which

1333
01:27:27,520 --> 01:27:30,599
Speaker 1: is very stable, like I said, and you now have

1334
01:27:30,920 --> 01:27:34,760
Speaker 1: a system that is working in all the directions that

1335
01:27:34,800 --> 01:27:38,080
Speaker 1: it should and it really dampens the vibration. Well, what

1336
01:27:38,280 --> 01:27:42,400
Speaker 1: little bit that we have from your perspective, Why does

1337
01:27:42,439 --> 01:27:44,880
Speaker 1: that matter in a hunting situation? That? Why does it

1338
01:27:44,960 --> 01:27:48,960
Speaker 1: matter to to eliminate even more sound, even more vibration? Sure, well,

1339
01:27:48,960 --> 01:27:51,719
Speaker 1: it's two things. One, it's just the experience of shooting.

1340
01:27:52,040 --> 01:27:55,120
Speaker 1: Uh No, one wants a buzzy bow whenever they shoot.

1341
01:27:55,200 --> 01:27:59,000
Speaker 1: It's the experience of that. It's letting the arrow go

1342
01:27:59,520 --> 01:28:03,440
Speaker 1: and just nothing. You don't feel anything but too. Obviously,

1343
01:28:03,479 --> 01:28:07,160
Speaker 1: in a hunting situation, your target is not a film target.

1344
01:28:07,200 --> 01:28:10,920
Speaker 1: It can move. Being as stealthy as possible is a

1345
01:28:10,920 --> 01:28:14,720
Speaker 1: goal that has always been around. And the quieter you

1346
01:28:14,760 --> 01:28:18,000
Speaker 1: can be, the more accurate your shot is going to be.

1347
01:28:18,160 --> 01:28:21,120
Speaker 1: And that's not necessarily because you're shooting better, but it's

1348
01:28:21,120 --> 01:28:24,559
Speaker 1: because the target is not reacting as fast or at all.

1349
01:28:25,400 --> 01:28:27,800
Speaker 1: If you'd like to learn more about the Matthews Try

1350
01:28:27,880 --> 01:28:31,400
Speaker 1: acts and their three D damping technology, you can visit

1351
01:28:31,439 --> 01:28:35,479
Speaker 1: Matthews Inc. Dot com. Here's another question as we're as

1352
01:28:35,479 --> 01:28:40,439
Speaker 1: we're looking into these specific examples out of curiosity. Another

1353
01:28:40,520 --> 01:28:44,000
Speaker 1: thing that I feel like, well two, here's two things

1354
01:28:44,040 --> 01:28:48,599
Speaker 1: so too common. Um oh, old wives, tales of sorts.

1355
01:28:48,640 --> 01:28:51,720
Speaker 1: Maybe that we like to talk about a lot. One

1356
01:28:51,840 --> 01:28:55,360
Speaker 1: being uh, the October lull. A lot of people like

1357
01:28:55,520 --> 01:28:58,879
Speaker 1: to say that they believe dear just don't move, especially

1358
01:28:58,920 --> 01:29:01,880
Speaker 1: bucks don't move very much in mid October. Have you

1359
01:29:01,880 --> 01:29:03,280
Speaker 1: looked at this at all? Have you been able to

1360
01:29:03,320 --> 01:29:07,920
Speaker 1: see if there's actually any quantifiable decrease in movement UM

1361
01:29:08,000 --> 01:29:10,599
Speaker 1: during mid October. From everything I've read and seen, that

1362
01:29:10,720 --> 01:29:12,920
Speaker 1: is not the case. But have you guys looked into

1363
01:29:12,920 --> 01:29:17,880
Speaker 1: that in your own study? Um? Yeah, Well I'll be

1364
01:29:17,920 --> 01:29:22,200
Speaker 1: honest here, Until until this summer, I didn't even know

1365
01:29:22,240 --> 01:29:27,760
Speaker 1: what the October lull was UM. And I think a

1366
01:29:27,840 --> 01:29:32,439
Speaker 1: lot of it is from people looking at harvest data

1367
01:29:33,000 --> 01:29:36,559
Speaker 1: and UM and making inferences from the harvest data the

1368
01:29:36,600 --> 01:29:40,280
Speaker 1: fact that a lot of deer get shot in archery

1369
01:29:40,360 --> 01:29:42,800
Speaker 1: early in October and then it tapers off and then

1370
01:29:42,800 --> 01:29:48,080
Speaker 1: it picks up again and in in November and so

1371
01:29:48,400 --> 01:29:54,920
Speaker 1: um so, and that's more of a hunter behavior issue

1372
01:29:54,920 --> 01:29:58,280
Speaker 1: than a deer issue, because you need to look at

1373
01:29:59,000 --> 01:30:02,360
Speaker 1: the number of people that are out hunting. UM. So,

1374
01:30:02,439 --> 01:30:05,200
Speaker 1: for example, I know my my neighbor is a big

1375
01:30:05,280 --> 01:30:08,080
Speaker 1: archery hunter, and he's out there the first couple of weeks,

1376
01:30:09,080 --> 01:30:11,880
Speaker 1: but then he's like, well, you know, this was fun.

1377
01:30:11,960 --> 01:30:14,559
Speaker 1: I got it out of my system, but I'm really

1378
01:30:14,560 --> 01:30:17,439
Speaker 1: going to wait till November one because that's when that

1379
01:30:17,640 --> 01:30:20,400
Speaker 1: I know that rut's going to start kicking in. And

1380
01:30:20,439 --> 01:30:22,800
Speaker 1: so I suspect a lot of this idea of the

1381
01:30:22,880 --> 01:30:29,599
Speaker 1: October lull is more an effect of hunter behavior and

1382
01:30:29,720 --> 01:30:36,960
Speaker 1: hunter effort UM I have not seen any evidence of

1383
01:30:38,080 --> 01:30:42,760
Speaker 1: in October lull. Yeah, well, I think that's that's what

1384
01:30:42,880 --> 01:30:45,320
Speaker 1: most of the other studies that I have have read

1385
01:30:45,360 --> 01:30:48,920
Speaker 1: and seen seemed to indicate as well, that that there's

1386
01:30:48,960 --> 01:30:51,960
Speaker 1: not actually any real decrease and dear movement, but but

1387
01:30:52,080 --> 01:30:56,120
Speaker 1: certainly um hunter activity and other things that that might

1388
01:30:56,240 --> 01:31:00,679
Speaker 1: change what we see as hunters UM and might change

1389
01:31:00,720 --> 01:31:03,479
Speaker 1: a little bit of where they spend time, but probably

1390
01:31:03,520 --> 01:31:06,000
Speaker 1: not the quantity of time they move around. So so

1391
01:31:06,000 --> 01:31:07,720
Speaker 1: it's interesting to see that you guys haven't seen that

1392
01:31:07,840 --> 01:31:10,160
Speaker 1: there's there's a lot of things going in on and

1393
01:31:10,400 --> 01:31:15,360
Speaker 1: on in October. I mean food source sources are maturing.

1394
01:31:15,560 --> 01:31:18,040
Speaker 1: So you know, if you're an area with a lot

1395
01:31:18,080 --> 01:31:21,160
Speaker 1: of oaks and it's a spotty year, that could cause

1396
01:31:21,479 --> 01:31:25,920
Speaker 1: changes and dear movements. UM. You know, people ask me

1397
01:31:26,000 --> 01:31:30,519
Speaker 1: about how you know food influences them. Unfortunately I don't

1398
01:31:30,560 --> 01:31:32,680
Speaker 1: have a map. If I had a map of the

1399
01:31:33,520 --> 01:31:36,960
Speaker 1: you know, oak production across our study areas, I could

1400
01:31:37,000 --> 01:31:41,200
Speaker 1: answer that question, but I don't. UM. But you know,

1401
01:31:41,240 --> 01:31:46,320
Speaker 1: there's a lot of different things in the ruts kicking in. Um.

1402
01:31:46,360 --> 01:31:50,880
Speaker 1: You know that last week in October, probably of our

1403
01:31:51,400 --> 01:31:54,200
Speaker 1: females are bred by the end of October, so that

1404
01:31:54,280 --> 01:31:57,639
Speaker 1: last week and in October you will see some males

1405
01:31:57,720 --> 01:32:01,360
Speaker 1: that will exhibit you know that those behavior movements, those

1406
01:32:01,400 --> 01:32:05,760
Speaker 1: real intensive movements. UM. Yes, So there's a lot of

1407
01:32:05,800 --> 01:32:09,840
Speaker 1: stuff going on that that I think more of it

1408
01:32:09,920 --> 01:32:13,240
Speaker 1: is the excitement of the early October opener of archery

1409
01:32:13,520 --> 01:32:16,960
Speaker 1: and then not much is happening until the rut kicks

1410
01:32:16,960 --> 01:32:21,040
Speaker 1: in at the end of October. So, so is there

1411
01:32:21,080 --> 01:32:24,400
Speaker 1: anything else, Dwayne that we haven't touched on that that

1412
01:32:24,520 --> 01:32:27,719
Speaker 1: hunters need to know that you guys have learned from

1413
01:32:27,760 --> 01:32:30,040
Speaker 1: this study to this point yet any big takeaways you

1414
01:32:30,040 --> 01:32:32,559
Speaker 1: guys have had yet that that you wish more hunters

1415
01:32:32,560 --> 01:32:36,880
Speaker 1: were aware of, or um that we would find helpful. Well,

1416
01:32:36,920 --> 01:32:39,200
Speaker 1: I guess a few things. I mean, I'm not sure

1417
01:32:39,240 --> 01:32:44,200
Speaker 1: if to think about, you know, especially during the you know,

1418
01:32:44,360 --> 01:32:48,800
Speaker 1: during that rifle season. UM. So if there was any advice,

1419
01:32:49,160 --> 01:32:54,320
Speaker 1: um um I would give hunters is a, UM, don't

1420
01:32:54,360 --> 01:32:59,160
Speaker 1: give up on the afternoon hunt. Um. What we've found

1421
01:32:59,200 --> 01:33:02,400
Speaker 1: is that when you're sitting in the early morning, those

1422
01:33:02,479 --> 01:33:05,320
Speaker 1: deer are already in their hiding spot and they're sitting

1423
01:33:05,360 --> 01:33:08,639
Speaker 1: to um. Of course, a lot of deer shot then.

1424
01:33:08,760 --> 01:33:11,680
Speaker 1: But you know, these bucks that we're following, which are

1425
01:33:11,680 --> 01:33:13,360
Speaker 1: two and a half, three and a half, four and

1426
01:33:13,360 --> 01:33:16,240
Speaker 1: a half years old, a lot of them are just

1427
01:33:16,400 --> 01:33:22,680
Speaker 1: sitting when you're sitting. But by lunchtime, um, a lot

1428
01:33:22,760 --> 01:33:27,360
Speaker 1: of them have started making their movements. Um. And and

1429
01:33:27,439 --> 01:33:30,920
Speaker 1: so I think you, you know, pack a lunch and

1430
01:33:32,040 --> 01:33:36,200
Speaker 1: don't give up on the afternoon hunt. UM. The other

1431
01:33:36,280 --> 01:33:42,120
Speaker 1: thing is knowing where these big bucks or older bucks hide,

1432
01:33:43,120 --> 01:33:46,840
Speaker 1: you're not going to sneak up on them, so you

1433
01:33:46,920 --> 01:33:51,320
Speaker 1: might want to rethink the tree stand and um, you know,

1434
01:33:51,439 --> 01:33:55,280
Speaker 1: deer drives are probably going to be effective. Now you know,

1435
01:33:56,640 --> 01:33:58,720
Speaker 1: you may not be the guy who gets it, but

1436
01:34:00,120 --> 01:34:02,439
Speaker 1: that deer is more likely to be harvested. I think

1437
01:34:02,439 --> 01:34:07,000
Speaker 1: if hunters worked cooperatively. Yeah, that's that's probably the two

1438
01:34:07,000 --> 01:34:11,400
Speaker 1: big takeaways that that I would say that you know

1439
01:34:11,640 --> 01:34:14,320
Speaker 1: that at least popped in my head as I've been

1440
01:34:14,360 --> 01:34:17,960
Speaker 1: looking at these deer and thinking, man, how would you

1441
01:34:18,040 --> 01:34:20,839
Speaker 1: ever get to this guy? Because he's got the perfect

1442
01:34:20,920 --> 01:34:24,280
Speaker 1: hiding spot right right. These these bucks that make it

1443
01:34:24,320 --> 01:34:28,080
Speaker 1: to those older age classes, they make it for a reason, right,

1444
01:34:28,280 --> 01:34:30,880
Speaker 1: They've made it to four or five or whatever because

1445
01:34:30,920 --> 01:34:33,400
Speaker 1: they were able to find these little hidy holes that

1446
01:34:33,520 --> 01:34:37,120
Speaker 1: give them an advantage to to avoid suckers like us

1447
01:34:37,120 --> 01:34:40,160
Speaker 1: that go out there looking for him. So it makes

1448
01:34:40,160 --> 01:34:43,240
Speaker 1: sense and it definitely makes us work work for our

1449
01:34:43,360 --> 01:34:46,000
Speaker 1: venice and no doubt about that. Uh, Dan, do you

1450
01:34:46,000 --> 01:34:47,679
Speaker 1: have it? Do you have a final question or anything

1451
01:34:47,680 --> 01:34:50,080
Speaker 1: before we wrap things up? Yeah, I just have one

1452
01:34:50,160 --> 01:34:57,080
Speaker 1: question in regards to decision making by let's say your research.

1453
01:34:57,479 --> 01:35:03,160
Speaker 1: All right, We've we see example in other states that

1454
01:35:03,920 --> 01:35:07,040
Speaker 1: people have made a decision that hunting is not a

1455
01:35:07,080 --> 01:35:12,280
Speaker 1: good way or the people don't like hunting, so they

1456
01:35:12,320 --> 01:35:15,080
Speaker 1: pass laws that band hunting and then maybe they capture

1457
01:35:15,120 --> 01:35:19,240
Speaker 1: deer and do castration, you know they castrated or uh

1458
01:35:19,320 --> 01:35:23,360
Speaker 1: certain like in Canada they've outlawed certain types of bear

1459
01:35:23,439 --> 01:35:29,040
Speaker 1: hunting because of emotion over logic. Do you, I guess

1460
01:35:29,080 --> 01:35:35,599
Speaker 1: what are your thoughts on when research and science are

1461
01:35:35,720 --> 01:35:41,560
Speaker 1: trumped when it comes to decision making based off of emotions.

1462
01:35:41,560 --> 01:35:48,400
Speaker 1: Help help me here, UM, So your question is about

1463
01:35:48,520 --> 01:35:51,920
Speaker 1: how do you get Do you get frustrated? Maybe when

1464
01:35:52,000 --> 01:35:56,040
Speaker 1: some of the uh, the I guess organizations that you

1465
01:35:56,120 --> 01:35:59,479
Speaker 1: work with maybe don't make a decision. You know, the

1466
01:35:59,800 --> 01:36:02,760
Speaker 1: re search or the science says yes, you should do this,

1467
01:36:03,160 --> 01:36:07,960
Speaker 1: but maybe public opinion or emotion trumps that and maybe

1468
01:36:07,960 --> 01:36:16,480
Speaker 1: a different decision is made. UM. Well, it only frustrates

1469
01:36:16,560 --> 01:36:22,479
Speaker 1: me UM when clear goals and objectives have been set

1470
01:36:23,479 --> 01:36:30,360
Speaker 1: um and identified, and biologists have taken those goals and

1471
01:36:30,439 --> 01:36:35,000
Speaker 1: objective collected data and said, if this is your desired

1472
01:36:35,040 --> 01:36:38,880
Speaker 1: goal or objective, Um, this is what you need to do,

1473
01:36:39,960 --> 01:36:44,880
Speaker 1: and that is ignored. UM. That's frustrating because you know,

1474
01:36:45,000 --> 01:36:49,639
Speaker 1: as scientists or managers, we're not We're not the ones

1475
01:36:49,680 --> 01:36:54,719
Speaker 1: who say this is this is what the condition should be. UM,

1476
01:36:54,760 --> 01:36:58,960
Speaker 1: that society's decision so as long as society says, hey,

1477
01:36:59,000 --> 01:37:04,040
Speaker 1: this is what we want, aren't wildlife biologists are perfectly

1478
01:37:04,080 --> 01:37:08,960
Speaker 1: happy going out to collect data and to make a

1479
01:37:08,960 --> 01:37:12,720
Speaker 1: recommendation saying Okay, if this is your goal, um, this

1480
01:37:12,800 --> 01:37:16,839
Speaker 1: is where you are, and this would be this action

1481
01:37:16,960 --> 01:37:19,280
Speaker 1: would help you get to where you want to be.

1482
01:37:20,320 --> 01:37:27,760
Speaker 1: And when that's ignored, that's frustrating. And that happens. That happens,

1483
01:37:28,560 --> 01:37:31,120
Speaker 1: you know, not that's not just an anti hunter thing.

1484
01:37:31,160 --> 01:37:35,960
Speaker 1: I mean, when you know an agency has goals and

1485
01:37:36,000 --> 01:37:40,479
Speaker 1: objectives for dear densities and they say, this is where

1486
01:37:40,479 --> 01:37:42,439
Speaker 1: you want to be and this is what you need

1487
01:37:42,479 --> 01:37:46,160
Speaker 1: to do, and then the decision is to, for example,

1488
01:37:46,280 --> 01:37:49,880
Speaker 1: not harvest as many deer as what the biologists recommends,

1489
01:37:50,000 --> 01:37:56,600
Speaker 1: or completely ignore their recommendation. That's really frustrating. Yeah, you

1490
01:37:56,640 --> 01:37:59,679
Speaker 1: can definitely see that being the case. And it certainly

1491
01:37:59,680 --> 01:38:02,519
Speaker 1: does seem like to your point, Dan, that these types

1492
01:38:02,560 --> 01:38:06,120
Speaker 1: of things certainly are happening, and you hear from from

1493
01:38:06,120 --> 01:38:08,960
Speaker 1: other people involved in the management side of things that yes,

1494
01:38:09,000 --> 01:38:13,720
Speaker 1: science based decision making definitely does get trumped by politics

1495
01:38:13,880 --> 01:38:19,160
Speaker 1: or um certain segments of of of the society being

1496
01:38:19,200 --> 01:38:22,040
Speaker 1: really noisy and the grease here the squeaky wheel getting

1497
01:38:22,040 --> 01:38:25,280
Speaker 1: the grease sometimes, and that to something I think we

1498
01:38:25,360 --> 01:38:28,519
Speaker 1: all need to stay vigilant about and uh, pay attention

1499
01:38:28,520 --> 01:38:32,639
Speaker 1: to what's happening, and um, you know, as best as possible,

1500
01:38:33,000 --> 01:38:35,280
Speaker 1: keep people like you out there, Dwayne, who can help,

1501
01:38:35,880 --> 01:38:39,280
Speaker 1: who can help our managers understand what's happening, and UM,

1502
01:38:39,400 --> 01:38:41,799
Speaker 1: make sure we're managing these places as best as possible.

1503
01:38:42,200 --> 01:38:44,760
Speaker 1: And from everything I've seen, it certainly seems like you

1504
01:38:44,760 --> 01:38:48,120
Speaker 1: guys are doing that well. You know, everything in life

1505
01:38:48,160 --> 01:38:52,240
Speaker 1: is about relationships and trust, and so one of my

1506
01:38:52,320 --> 01:38:56,000
Speaker 1: objectives with the blog and this research is too, well,

1507
01:38:56,040 --> 01:38:59,160
Speaker 1: the research will help inform management, but the blog is

1508
01:38:59,200 --> 01:39:03,479
Speaker 1: to share in form nation and to hopefully show as

1509
01:39:03,520 --> 01:39:10,080
Speaker 1: many people hunters and non hunters alike, UM, that we

1510
01:39:10,160 --> 01:39:15,599
Speaker 1: do have information that UM, that we have a skilled

1511
01:39:15,600 --> 01:39:20,040
Speaker 1: set of managers and researchers in Pennsylvania, and that we're

1512
01:39:20,080 --> 01:39:22,480
Speaker 1: trying to do the best we can with the resources

1513
01:39:22,560 --> 01:39:26,720
Speaker 1: we have to help make better decisions for a deer

1514
01:39:26,760 --> 01:39:30,519
Speaker 1: and for us. Yeah, and and for anyone out there

1515
01:39:30,560 --> 01:39:33,000
Speaker 1: listening who has not yet seen the blog that you

1516
01:39:33,120 --> 01:39:35,680
Speaker 1: just mentioned, UM, I highly recommend checking it out and

1517
01:39:35,720 --> 01:39:39,360
Speaker 1: following along because, as I mentioned already. You guys. From

1518
01:39:39,360 --> 01:39:42,160
Speaker 1: everything I've seen over several years now that I've been following,

1519
01:39:42,160 --> 01:39:45,760
Speaker 1: you are posting some really interesting things, um, from that

1520
01:39:45,920 --> 01:39:48,880
Speaker 1: research standpoint, from a data driven standpoint that we don't

1521
01:39:48,920 --> 01:39:54,080
Speaker 1: see a ton of in the popular deer hunting media. Um. So, Dwayne,

1522
01:39:54,160 --> 01:39:57,000
Speaker 1: where can people go if they want to follow that

1523
01:39:57,040 --> 01:39:59,680
Speaker 1: blog or follow your study in more detail? Where can

1524
01:39:59,720 --> 01:40:03,559
Speaker 1: they find that stuff online? Yeah? Just um, the the

1525
01:40:03,880 --> 01:40:10,080
Speaker 1: r r L is um ecosystems dot PSU, dot E

1526
01:40:10,240 --> 01:40:16,080
Speaker 1: d you slash dear. Okay, we will link to that

1527
01:40:16,840 --> 01:40:19,639
Speaker 1: on the website on wired Hunt. Um if you can't

1528
01:40:19,680 --> 01:40:21,920
Speaker 1: remember that you r L. And I'm guessing also if

1529
01:40:21,960 --> 01:40:24,840
Speaker 1: someone just google dear four study, that probably find it

1530
01:40:24,840 --> 01:40:27,240
Speaker 1: that way too as well. Right, Yeah, I'm pretty sure

1531
01:40:27,240 --> 01:40:31,639
Speaker 1: they would. You gotta love the power of Google. Well, we'll, Dwayne,

1532
01:40:31,680 --> 01:40:33,559
Speaker 1: anything else you want to touch on before we wrap

1533
01:40:33,600 --> 01:40:36,840
Speaker 1: this up. No, thank you very much for the opportunity

1534
01:40:36,960 --> 01:40:41,080
Speaker 1: to share some of our findings and spread the word

1535
01:40:41,080 --> 01:40:44,240
Speaker 1: about the research we're doing here. Yeah. Absolutely, and thank

1536
01:40:44,280 --> 01:40:46,960
Speaker 1: you Dwyane. You know one final quick thing I noticed

1537
01:40:47,080 --> 01:40:50,599
Speaker 1: one once when I was on your website. There's a

1538
01:40:50,600 --> 01:40:53,280
Speaker 1: a link or a portion of the website dedicated to

1539
01:40:53,439 --> 01:40:56,439
Speaker 1: getting hunters involved and actually sharing some of their data

1540
01:40:56,520 --> 01:40:59,080
Speaker 1: with you. Um. Is that right? And is that something

1541
01:40:59,120 --> 01:41:01,760
Speaker 1: you need? Do you need more hunters participating to help

1542
01:41:02,040 --> 01:41:06,599
Speaker 1: in any way? Yeah? So, um so if hunters are

1543
01:41:06,680 --> 01:41:11,080
Speaker 1: hunting on our study areas, um, there, it's posted with

1544
01:41:11,200 --> 01:41:14,320
Speaker 1: signs UM and there's a toll free number to call

1545
01:41:14,600 --> 01:41:17,439
Speaker 1: and they can just call us and give us their

1546
01:41:17,520 --> 01:41:21,760
Speaker 1: license number and and then at the end of the

1547
01:41:21,800 --> 01:41:26,600
Speaker 1: season we send them a survey. Okay, and uh, and

1548
01:41:26,640 --> 01:41:28,639
Speaker 1: maybe we can get some of these guys to throw

1549
01:41:28,680 --> 01:41:32,479
Speaker 1: some radio collars on two right to help with your study. Yeah,

1550
01:41:32,560 --> 01:41:35,400
Speaker 1: well you can, you know, you get those apps, you know,

1551
01:41:35,640 --> 01:41:39,880
Speaker 1: Matt my Walk and stuff, and yeah, that would be cool,

1552
01:41:40,000 --> 01:41:44,320
Speaker 1: but we're not there yet. Yeah. I was in all seriousness.

1553
01:41:44,360 --> 01:41:46,960
Speaker 1: I was thinking, like, there's there's gotta be a way

1554
01:41:47,040 --> 01:41:49,840
Speaker 1: to do something like that, to to get a number

1555
01:41:49,880 --> 01:41:52,360
Speaker 1: of hunters to have an app that tracks their location

1556
01:41:52,400 --> 01:41:55,280
Speaker 1: that you could use in conjunction with radio color Deer.

1557
01:41:55,800 --> 01:41:57,800
Speaker 1: You know, someday would be really neat to see a

1558
01:41:57,840 --> 01:42:01,160
Speaker 1: study like that. Um. And I'll just try to take

1559
01:42:01,200 --> 01:42:02,880
Speaker 1: credit for it right here that that we were the

1560
01:42:02,880 --> 01:42:05,880
Speaker 1: first ones come for that idea. Right, No, you weren't

1561
01:42:05,920 --> 01:42:11,680
Speaker 1: because we actually did something. But um, but that was

1562
01:42:11,720 --> 01:42:14,000
Speaker 1: back when you you know you all we had were

1563
01:42:14,520 --> 01:42:17,200
Speaker 1: handheld GPS units and it was a lot of work,

1564
01:42:17,320 --> 01:42:20,200
Speaker 1: you know, out in the field talk getting hunters to

1565
01:42:20,240 --> 01:42:23,760
Speaker 1: wear units and getting them back and stuff. But yeah,

1566
01:42:23,960 --> 01:42:26,479
Speaker 1: pretty soon if we can convince people to run the

1567
01:42:26,520 --> 01:42:29,000
Speaker 1: app on their phone and then email it to us,

1568
01:42:29,520 --> 01:42:32,080
Speaker 1: yeah we could do some of that stuff. We're pretty

1569
01:42:32,160 --> 01:42:37,960
Speaker 1: much there possibly. Yeah, fascinating stuff. So all right, well, Dwayne,

1570
01:42:37,960 --> 01:42:40,280
Speaker 1: we're gonna wrap this up and just want to thank

1571
01:42:40,320 --> 01:42:42,760
Speaker 1: you again. We really appreciate the time and I look

1572
01:42:42,760 --> 01:42:44,960
Speaker 1: forward to continue to follow along with the blog and

1573
01:42:45,280 --> 01:42:47,679
Speaker 1: seeing what kind of other interesting stuff you guys find out.

1574
01:42:48,400 --> 01:42:51,960
Speaker 1: All right, thank you, and that will do it for

1575
01:42:52,040 --> 01:42:56,559
Speaker 1: us today. Definitely hope you enjoyed this one. Before we go,

1576
01:42:56,720 --> 01:42:58,320
Speaker 1: just want to let you know that we will be

1577
01:42:58,439 --> 01:43:02,320
Speaker 1: off next week, taking little time to spend with our

1578
01:43:02,360 --> 01:43:05,840
Speaker 1: families and friends and enjoy the holidays. So if you're

1579
01:43:05,880 --> 01:43:08,240
Speaker 1: board next week, go ahead and listen to some past

1580
01:43:08,320 --> 01:43:12,320
Speaker 1: episodes if you'd like. There's plenty of those three Wire

1581
01:43:12,360 --> 01:43:15,280
Speaker 1: Done podcasts. You can go back and listen to sixty

1582
01:43:15,320 --> 01:43:18,519
Speaker 1: two Wild podcasts. You can go back and listen to

1583
01:43:18,560 --> 01:43:22,479
Speaker 1: another twenty one White Tailed Q and A episode, so

1584
01:43:22,880 --> 01:43:26,200
Speaker 1: you definitely have plenty to dig into. Um So, in

1585
01:43:26,200 --> 01:43:28,639
Speaker 1: the meantime that we do want to thank our partners

1586
01:43:28,680 --> 01:43:32,559
Speaker 1: who have made this podcast possible all year. We really

1587
01:43:33,000 --> 01:43:36,840
Speaker 1: really do appreciate these companies lending a hand to help

1588
01:43:36,920 --> 01:43:38,840
Speaker 1: us create this thing. You know, we we put this

1589
01:43:38,880 --> 01:43:41,240
Speaker 1: thing out there, but it does take time and energy

1590
01:43:41,400 --> 01:43:45,080
Speaker 1: and funding. So big things to sit a gear YETI Cooler's,

1591
01:43:45,120 --> 01:43:48,679
Speaker 1: Matthew's Archery, Maven Optics, the White Tailed Institute of North America,

1592
01:43:49,040 --> 01:43:52,479
Speaker 1: Trophy Ridge and Hunt Terra Maps, And finally, of course,

1593
01:43:52,600 --> 01:43:56,080
Speaker 1: thank you all for listening. I appreciate you taking time

1594
01:43:56,080 --> 01:43:59,000
Speaker 1: to spend with us really all year, all season. The

1595
01:43:59,040 --> 01:44:01,639
Speaker 1: fact that you've been falling along with our stories, supporting us,

1596
01:44:01,760 --> 01:44:05,880
Speaker 1: commenting and sending messages of encouragement, sharing your stories, that's

1597
01:44:05,920 --> 01:44:08,720
Speaker 1: that's just incredible. It's it's been an awesome yeor and

1598
01:44:09,080 --> 01:44:12,160
Speaker 1: we just appreciate you being here along for the ride. Also,

1599
01:44:12,240 --> 01:44:15,240
Speaker 1: just want to wish you all a very merry Christmas,

1600
01:44:15,240 --> 01:44:18,880
Speaker 1: Happy Holidays, Happy New Year. I hope you enjoyed these

1601
01:44:18,880 --> 01:44:22,280
Speaker 1: coming days and weeks with those you love, friends and family.

1602
01:44:22,680 --> 01:44:24,479
Speaker 1: Hopefully get out in the tree stand a little bit

1603
01:44:24,479 --> 01:44:26,880
Speaker 1: more to enjoy these final hunts of the year. And

1604
01:44:27,000 --> 01:44:30,760
Speaker 1: until next time, and until next year, I hope you'll

1605
01:44:30,800 --> 01:44:32,479
Speaker 1: stay wired to hung