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Speaker 1: Welcome to the Wired to Hunt podcast, your guide to

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Speaker 1: the whitetail woods, presented by First Light, creating proven versatile

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Speaker 1: hunting apparel for the stand, saddle or blind. First Light

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Speaker 1: Go Farther, Stay Longer, and now your host, Mark Kenyon.

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Speaker 2: Welcome to the Wired to Hunt podcast. This week on

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Speaker 2: the show, we are reviewing what science and in the

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Speaker 2: field research studies can tell us about the whitetail rut

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Speaker 2: and how to hunt it. All right, welcome back to

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Speaker 2: the Wired Hunt podcast, brought to you by First Light

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Speaker 2: and their Camera for Conservation Initiative, and today we are

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Speaker 2: getting ready for the super bowl of the white tail season,

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Speaker 2: the rut. We are just about their, folks. If you're

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Speaker 2: listening to this right when this episode drops, we are

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Speaker 2: in that la third of October. The pre rut is

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Speaker 2: ramping up across most parts of the country and the

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Speaker 2: rut is just ahead of us. We're all very excited

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Speaker 2: about it. We are all amped up about it. Our

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Speaker 2: rut cations are about to start, and over the next

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Speaker 2: two weeks we are going out more than that. Really,

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Speaker 2: the next three four weeks probably we're going to be

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Speaker 2: discussing all things rut. But today I want to start

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Speaker 2: with a scientific perspective. Next week a little teaser here,

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Speaker 2: I'm going to be reviewing and doing a November kind

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Speaker 2: of decoding November breakdown, similar to what I did a

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Speaker 2: few weeks ago for October. So we're going to hear

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Speaker 2: from a number of different experts on how they approach

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Speaker 2: hunting during the rut. Today, I want to take a

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Speaker 2: step back and not just talk tactics and actually talk science.

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Speaker 2: So what do the studies show. What does the research

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Speaker 2: show about the breeding phase of the year for whitetail deer,

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Speaker 2: About behavior during this phase of the year, About how

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Speaker 2: deer utilize their territories, how do they move, how do

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Speaker 2: they interact? What does all of this mean for us

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Speaker 2: as hunters? How do you time the rut? How do

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Speaker 2: you predict the rut? Can you predict the rut? All

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Speaker 2: of this and much much more will be discussed today.

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Speaker 2: I have two different guests joining me. The first guest

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Speaker 2: is Dwayne Diefenbach. He is a affiliate professor of Wildlife

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Speaker 2: Ecology at Penn State University and the lead on a

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Speaker 2: more than ten year study now through Penn State University

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Speaker 2: called the Deer Forest Study, in which they have been

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Speaker 2: radio coloring GPS, coloring bucks and does in several different

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Speaker 2: sites across Pennsylvania and studying their movements and impacts on

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Speaker 2: the landscape and much much more. But for the purposes

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Speaker 2: of our discussion today, Dwayne and his team have analyzed,

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Speaker 2: looked at, and parsed out a ton of data related

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Speaker 2: to how deer moved during the rut, how they behave

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Speaker 2: during the rut, how their movements change, you know, day

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Speaker 2: by day, week by week. They have a really interesting

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Speaker 2: rut tracker on their website which you can go back

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Speaker 2: and look at more than ten years of data and

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Speaker 2: the averages of that data to see you know, cumulative

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Speaker 2: distance traveled and you know when those you know, movements

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Speaker 2: start changing and ramping up and all that kind of stuff.

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Speaker 2: So today, the first portion of this podcast is my

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Speaker 2: conversation with Dwayne about what he's learned about the Whitetail rut,

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Speaker 2: what their studies have shown, you know, comparing and contrasting

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Speaker 2: some conventional wisdom about the rut with what his studies

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Speaker 2: have shown as well. Very interesting stuff. That's part one

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Speaker 2: of this episode. Part two of this episode is actually

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Speaker 2: a little bit of a time machine. We're going to

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Speaker 2: go back more than ten years ago to a previous

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Speaker 2: episode that I recorded on this very same topic. We

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Speaker 2: did a similar take a science of the whitetail rut

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Speaker 2: type of episode, but this was way back in twenty fourteen,

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Speaker 2: back before most of you were probably listening, and it

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Speaker 2: was a pretty darn great episode. I really enjoyed it.

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Speaker 2: My guest for that one was my pal Matt Ross,

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Speaker 2: who is now the Senior Director of Conservation for the

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Speaker 2: National Deer Association. Back then, it was the QTMA he

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Speaker 2: worked for, and I don't know what his title was

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Speaker 2: back then, but he's smarter, older, and more senior in

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Speaker 2: the organization now, so we'll give him the title of

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Speaker 2: senior director today in twenty twenty five. But what he

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Speaker 2: shared then is still really interesting today. So I went

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Speaker 2: back and listened to that episode, and I pulled out

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Speaker 2: several excerpts from that conversation that I thought would be

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Speaker 2: relevant to our conversation up today. So part one Dwayne,

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Speaker 2: Part two, Matt Ross. Both conversations are about the science

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Speaker 2: of the white tail rut, what the research shows, and

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Speaker 2: how we as hunters can kind of take what that

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Speaker 2: research tells us and overlay it on top of our

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Speaker 2: own personal lived hunting experience, and you're gonna hear a

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Speaker 2: lot about this, a lot of this discussion around well,

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Speaker 2: the research says this, but as a hunter, I see that.

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Speaker 2: How do you make sense of that? How do you

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Speaker 2: take those two data sets, you know, your anecdotal experience

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Speaker 2: and what the research says, and how do you bring

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Speaker 2: those two things together to put together a plan that

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Speaker 2: makes sense. Especially in part two of this where we

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Speaker 2: are talking with Matt, you're gonna hear us kind of

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Speaker 2: wrestling with that challenge. I think it is a valuable

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Speaker 2: wrestling match of sorts to think through all of this

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Speaker 2: to say, how do I use this data but then

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Speaker 2: also my intuitions as a hunter to make sure I

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Speaker 2: make the best of the white tail rut. That's what

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Speaker 2: I'm hoping we can do here today with this conversation.

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Speaker 2: That's why I'm excited to bring it to you today.

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Speaker 2: So one more heads up for the second portion of

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Speaker 2: this conversation with Matt, we don't have video, so I

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Speaker 2: apologize to those of you watching this on YouTube. Back

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Speaker 2: in twenty fourteen, we were not recording video. Also, back

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Speaker 2: in twenty fourteen, I was a lot younger, maybe less articulate,

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Speaker 2: So forgive me if my questions aren't so good, if

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Speaker 2: the quality of the interviewing isn't quite so good. But

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Speaker 2: I will say we got a little humor in this

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Speaker 2: excerpt because my buddy Dan Johnson was co hosting with

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Speaker 2: me back then, so you will see a little Dan

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Speaker 2: Johnson cameo in the Matt Ross excerpt as well. So,

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Speaker 2: without any further ado, I've been rambling too long as

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Speaker 2: I do, Let's get to my chat with Dwayne Diefenbach

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Speaker 2: from Penn State University, and then an excerpt from my

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Speaker 2: chat with Matt Ross from way back in twenty fourteen,

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Speaker 2: all about the science of the whitetail rut. All right

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Speaker 2: with me now on the line is Dwayne Diefenbach. Dwayne,

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Speaker 2: thank you so much for joining me.

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Speaker 3: Oh, it's a pleasure to be here.

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Speaker 2: We chatted. I don't know if you remember this, but

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Speaker 2: we chatted on this podcast. It did not seem this

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Speaker 2: long ago, but I went back and checked. It was

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Speaker 2: twenty seventeen, so about eight years ago we last had

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Speaker 2: a conversation about your work and the Deer Force study,

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Speaker 2: and I know you guys have been hard at it

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Speaker 2: ever since. Uncovering many other secrets and mysteries and discoveries

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Speaker 2: when it comes to the world of interactions between deer

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Speaker 2: and the ecosystem around them. But I wonder this, I'm

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Speaker 2: just gonna rip off the band aid. There's gonna be

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Speaker 2: no niceties here. There's gonna be no beating around the bush.

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Speaker 2: If you were to look back over the course of

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Speaker 2: this study. If I'm right, I think you guys started

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Speaker 2: the deer for study in twenty thirteen, right, yeah, okay,

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Speaker 2: And then you just mentioned to me that you've been

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Speaker 2: studying whitetail deer for even longer than that, for twenty

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Speaker 2: five or so years. What would you say, since beginning

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Speaker 2: this set of studies, what would you say is the

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Speaker 2: single most fascinating discovery or insight that you've uncovered about

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Speaker 2: the whitetail rut since beginning this inquiry.

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Speaker 3: Well, I guess it would be the work that we

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Speaker 3: did in the early two thousands where Pennsylvania implemented Antler

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Speaker 3: point restrictions. So we greatly increase the number of males

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Speaker 3: in the population, and we also, because of high deer densities,

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Speaker 3: reduce the population overall by about twenty three percent. So

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Speaker 3: we had fewer deer, fewer females, more older males, and

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Speaker 3: nothing changed in recruitment in the population.

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Speaker 2: What do you make of that?

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Speaker 3: Well, I make it. What do I make of it?

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Speaker 3: It's I think what it is is white tail deer

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Speaker 3: are so productive, especially in Pennsylvania, we're kind of at

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Speaker 3: the sweet spot. We don't have severe winners. We've got

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Speaker 3: fairly product high productivity in our forests and food resources,

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Speaker 3: so fairly high recruitment that that you can that white

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Speaker 3: tail deer populations can sustain real changes to their sex

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Speaker 3: and age structure and it has little effect on recruitment.

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Speaker 2: So one of the pieces of conventional knowledge that I

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Speaker 2: that I've picked up over the years, and that's been

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Speaker 2: discussed much throughout the world of deer management, is this

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Speaker 2: idea that a unbalanced, aid structured deer herd with a

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Speaker 2: disproportionate number of young bucks and many more bucks than

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Speaker 2: dough So maybe this is what Pennsylvania used to be

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Speaker 2: like that in that type of scenario, the belief was

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Speaker 2: that you might have a more spread out rut and

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Speaker 2: a rut that's less frenzied and less uh, you know,

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Speaker 2: there's not as much competition for breeding rights, And so

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Speaker 2: the idea was, if you have a deer herd like that,

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Speaker 2: the rut is going to be less concentrated, less exciting

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Speaker 2: for a hunter. The alternative was, if you had a

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Speaker 2: better managed deer herd with a more balanced a structure,

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Speaker 2: a more balanced sex structure, that you would have a

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Speaker 2: more intense run. Did you look at anything like that?

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Speaker 2: Is has there been any change when it? Is it

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Speaker 2: a little bit more concentrated? Is it a little bit

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Speaker 2: more intense from a visible perspective? Or or truly is

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Speaker 2: there zero difference?

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Speaker 3: We failed to detect a different So, and I'll let

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Speaker 3: me explain a little bit.

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Speaker 4: Uh.

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Speaker 3: Pennsylvania before and after implementing Antler point restrictions would check

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Speaker 3: roadkilled females to look at the number of embryos. So

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Speaker 3: when you do that, you can measure the size of

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Speaker 3: the embryo and you can then calculate when that female

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Speaker 3: became pregnant, and you can look at that by the

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Speaker 3: age structure of the of the females. You know, we

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Speaker 3: would expect older females to probably breed earlier because they're

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Speaker 3: in better physical condition. We would also expect them to

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Speaker 3: have more offspring, you know, year and a half old,

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Speaker 3: or excuse me, females that got pregnant as a fawn

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Speaker 3: and we're giving birth as a one year old. You

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Speaker 3: would expect that to be a fairly low percentage, and

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Speaker 3: it varies widely in Pennsylvania because in our northern tier

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Speaker 3: we have lower quality habitat and very few of those

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Speaker 3: fawns get pregnant, whereas in the southern tier it can

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Speaker 3: be up to fifty percent of them. So you would yeah,

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Speaker 3: so this is kind of complicated, but hopefully I can

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Speaker 3: make a story that makes sense here. So, so we've

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Speaker 3: we tracked deer before and after we reduce the population,

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Speaker 3: so and there were more older bucks, so you could

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Speaker 3: make we could make some predictions.

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Speaker 2: Right.

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Speaker 3: First of all, we could predict that if what you

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Speaker 3: were saying is true, the conventional wisdom that we would

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Speaker 3: see less variants in the in when females got pregnant,

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Speaker 3: so you would expect that to read to decline over

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Speaker 3: time as we reduce the population, had more older bucks

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Speaker 3: in the population.

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Speaker 2: A smushed in bell curve, right, yes, exactly.

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Speaker 3: And then and then the other thing you might predict, well,

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Speaker 3: maybe it's a little bit earlier because they're getting bread, right, away, right,

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Speaker 3: And and also you might expect more young animals eventually

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Speaker 3: if you reduce the population and there's more food out there.

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Speaker 3: So what limits you know fawns is they have to

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Speaker 3: hit a certain weight before they will go into estros.

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Speaker 3: And so if there's more food out there because you

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Speaker 3: have fewer deer, you might expect the percentage of funds

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Speaker 3: to increase that get pregnant. And so we were able

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Speaker 3: to test that and it nothing, right, The variability didn't change,

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Speaker 3: The timing didn't change. Nothing. It's November thirteenth every year.

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Speaker 3: You can just set your calendar to it.

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Speaker 2: Okay, So let's cover our bases, kind of working backwards

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Speaker 2: from what you just stated, which is that the peak

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Speaker 2: of breeding that you found in Pennsylvania is November thirteenth,

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Speaker 2: year after year. Despite that, everyone loves to theorize about

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Speaker 2: all the different other things that might impact the rut.

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Speaker 2: So you have been studying and tracking collar deer specifically

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Speaker 2: in the Deer study for I guess twelve years now,

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Speaker 2: thirteen years now, let's just walk through a few of

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Speaker 2: the things that folks typically like to say might impact

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Speaker 2: the rut. I know what your answer will be, but

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Speaker 2: let's just dive into it and see if there's any

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Speaker 2: maybe small discrepancies. The moon is popularly pointed to as

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Speaker 2: an influence on the rut. I believe you've done pretty

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Speaker 2: extensive work comparing and contrasting all of your data to

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Speaker 2: possible moon phases. Can you tell me what you saw there?

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Speaker 3: Yeah, that it doesn't affect it at all. I mean,

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Speaker 3: there's no relationship. It's a flat line. It doesn't matter

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Speaker 3: what the day is of the peak. The half the

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Speaker 3: females are bred by November thirteenth every year. So but

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Speaker 3: let me back up a little bit because I know

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Speaker 3: you're going to ask what the next question is going

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Speaker 3: to be, probably, but we have.

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Speaker 4: To do it.

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Speaker 3: I think we have to talk a little bit about

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Speaker 3: the ecology of wait tail deer, so please so and

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Speaker 3: actually an evolutionary ecology standpoint. So why why do deer

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Speaker 3: get pregnant in the fall?

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Speaker 2: You know?

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Speaker 5: Why?

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Speaker 3: Is that date? Well, female gets pregnant and that that

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Speaker 3: fawn is going to be in gestation for about two

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Speaker 3: hundred and ten days. So that means that if you

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Speaker 3: add two hundred and ten days to November thirteen, that's

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Speaker 3: basically Memorial Day. So what's the advantage of being born

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Speaker 3: in Memorial Day versus April first or July second. Well,

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Speaker 3: the advantage is from a long term perspective, is that

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Speaker 3: fawns that are born on that date are born as

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Speaker 3: early as they possibly can to be as big as

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Speaker 3: they can going into winter, and they're born as late

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Speaker 3: as they possibly can while minimizing the risk of being

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Speaker 3: dying from exposure to harsh weather conditions. And so really,

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Speaker 3: fawns funds or wait tail deer have evolved to give

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Speaker 3: that peak birth in the spring to maximize the survival

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Speaker 3: of fawns in surviving the next winter. Well, what's the

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Speaker 3: best thing that's going to tell you? What can you

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Speaker 3: rely on in November every year to tell you that

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Speaker 3: you should get pregnant now in order to give birth

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Speaker 3: at the optimal time in the spring.

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Speaker 2: And I believe the answer is the answer the the

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Speaker 2: number of pumpkin pies sold at Costco and how that

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Speaker 2: rises out.

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Speaker 3: It would be if that's if that's related to day

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Speaker 3: length and that people buy more pumpkin pies. So so

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Speaker 3: there's day length is the trigger and people have proven

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Speaker 3: it so as days get shorter, that triggers hormonal changes

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Speaker 3: in wait till deer. And and because that's the best

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Speaker 3: predictor of when the best time is to get pregnant,

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Speaker 3: to give birth at the best time of year. So yeah,

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Speaker 3: because otherwise I don't know of any other predictor that's

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Speaker 3: going to tell you when's the best time to give

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Speaker 3: put that phone on on the ground.

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Speaker 2: So so follow up question to this because yes, yes,

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Speaker 2: so so photo period as you described has been well

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Speaker 2: proven to be that consistent factor. That lead is too consistent. Uh,

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Speaker 2: you know, bonder up. Some people would say, and I

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Speaker 2: just simply don't understand the physiology enough myself to dispute it.

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Speaker 2: But some people would say that why couldn't the moon,

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Speaker 2: the light from the moon somehow influence deer, just as

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Speaker 2: light from the sun influences deer. Why why is that

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Speaker 2: not the case? And I simply just don't understand the

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Speaker 2: biology enough to answer that myself.

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Speaker 3: Well, a couple of things. I mean, you know, it's

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Speaker 3: a reasonable hypothesis because there's lots of things that are

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Speaker 3: affected by the moon, especially if you relate live in

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Speaker 3: the ocean. But but I'd say two things that complicate that.

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Speaker 3: One is moonlight. Despite the fact that you can read

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Speaker 3: a newspaper in the in the middle on a bright,

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Speaker 3: you know, full moon. I do not believe that that

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Speaker 3: is enough light energy to trigger hormonal changes in deer.

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Speaker 3: You really need that day length is what's going to

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Speaker 3: cause it. Second of all, weather could screw that all up, right.

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Speaker 3: You can have a week of rainy weather and it

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Speaker 3: might be the full moon.

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Speaker 2: And so.

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Speaker 3: Why would you rely on something that or actually they're

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Speaker 3: not relyingt you're saying it could change it, but it

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Speaker 3: could you know, be messed up by just random weather.

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Speaker 2: Well, and it must be deeply programmed in a deer

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Speaker 2: because because the same thing could hypothetically happen with weather,

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Speaker 2: I mean, sorry, with with photo period. Right, So what

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Speaker 2: you just described, we might have ten days of cloudiness

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Speaker 2: where it feels very dark and they are not actually

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Speaker 2: feeling the typical effects of changing sunlight, right, But it

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Speaker 2: must be this circadium rhythm, this internal clock that is

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Speaker 2: built into them over years and years and years in

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Speaker 2: which their body has adapted to what the typical daylight

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Speaker 2: changes would be, so that even if they do have

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Speaker 2: a ten day stretch where it's not what it should

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Speaker 2: be in late October or early November, the body is

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Speaker 2: programmed to still have that is that is that?

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Speaker 3: And I think you know that day length is acting

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Speaker 3: over months, it's not for weeks, so yeah, it's acting

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Speaker 3: at a different timescale, so there's less variability that can

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Speaker 3: be introduced into it.

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Speaker 5: You know.

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Speaker 3: It's kind of a subtle point and in when you

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Speaker 3: think about deer, and it's easy to talk about decisions

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Speaker 3: that they make, but it's really you know, we it's

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Speaker 3: a tough concept. I don't know if we want to

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Speaker 3: get into it here, but we talk about approximate and

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Speaker 3: ultimate reasons for doing things. And the ultimate reason and

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Speaker 3: that the deer get pregnant in November is because of

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Speaker 3: that fawn survival. But there are the approximate queue is

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Speaker 3: that day length, right, that's what's causing it. But the

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Speaker 3: evolutionary advantage of giving birth in May is the ultimate

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Speaker 3: reason why that behavior or physiological response shifts. And I

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Speaker 3: would say that, you know, to point out I'm sure

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Speaker 3: you're going to talk about predators or I don't know

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Speaker 3: when you want to get into that, but it could

337
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Speaker 3: be another thing. The reason why they all give birth

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Speaker 3: at the same time that's been hypothesized is that if

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Speaker 3: you dump all these fawns out at the same time,

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Speaker 3: there's too much food out there and some of them

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Speaker 3: the predators can't eat them all before before the deer

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Speaker 3: get big enough, the fawns get big enough to evade predators,

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Speaker 3: And so that's another hypothesis, but that really and I've

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Speaker 3: published research with colleagues where you can show that, yes,

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Speaker 3: there is some evidence that fawns that are born out

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Speaker 3: at the tails, the ones that are born really early

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Speaker 3: and really late, are more likely to die. But if

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Speaker 3: that was truly driving that behavior, the peak and birth

349
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Speaker 3: would exist in other parts of the white tailed deer's range.

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Speaker 3: And when you go down to South America, when you

351
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Speaker 3: get close to the equator and the day is almost

352
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Speaker 3: twelve hours every day of the year, deer give birth.

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Speaker 3: Other things drive when deer give birth, like rainfall. Even

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Speaker 3: in Texas, rainfall drives the timing of birth more than predators.

355
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Speaker 3: So you know, that's another hypothesis that yes, it has

356
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Speaker 3: some effect, but we can look at other parts of

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Speaker 3: the deer's range and see that no, that can't explain

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Speaker 3: that pattern and not reproduction.

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Speaker 2: So, as you mentioned, it's it's well established that the

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Speaker 2: timing of breeding is consistent, relatively consistent, but on a

361
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Speaker 2: bell curve. So there's a peak where most give you know,

362
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Speaker 2: most most conceived, and then of course there's tails on

363
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Speaker 2: either side. But as you mentioned and as every hunter knows,

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Speaker 2: every single year, we as individuals experience a different kind

365
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Speaker 2: of activity during the run, right, And so you have

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Speaker 2: two different things we have here. We're measuring the date

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Speaker 2: of conception, the date of peak breeding versus running activity,

368
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Speaker 2: which would be you know, as hunters, we're thinking deer chasing,

369
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Speaker 2: running around, looking for does fighting, doing all that kind

370
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Speaker 2: of stuff that's typically what many of us hope to

371
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Speaker 2: see during the rut. I'm curious in your tracking, whether

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Speaker 2: it be through the things you guys have documented through

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Speaker 2: the RUT tracker, where you're tracking you know, average distance

374
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Speaker 2: or absolute distance traveled or any other metric. What about

375
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Speaker 2: what some people have talked about with maybe the moon

376
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Speaker 2: doesn't impact breeding, but might the moon, or might a

377
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Speaker 2: cold front, or might some other factor uncover running activity

378
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Speaker 2: or enhance running activity. So some theorize if you have

379
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Speaker 2: a certain moon, you will see more of this daylight activity,

380
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Speaker 2: while if you have a certain other moon, more of

381
00:24:46,840 --> 00:24:49,480
Speaker 2: the running activity just happens after dark because they're moving

382
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Speaker 2: less in daylight or let's insert cold front in there.

383
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Speaker 2: Some folks say, well, if you you know, all things

384
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Speaker 2: being equal and November first, sure there's going to be

385
00:24:59,080 --> 00:25:01,680
Speaker 2: a lot of running activity. But if it's eighty degrees,

386
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Speaker 2: a lot of that running activity is probably happening after dark.

387
00:25:04,960 --> 00:25:07,520
Speaker 2: But if it's forty degrees, a lot of that running

388
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Speaker 2: activity is happening in the daylight. And that makes a

389
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Speaker 2: big difference on a hunter's experience. Has your data or

390
00:25:15,040 --> 00:25:17,399
Speaker 2: observations validated any of that?

391
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Speaker 3: Maybe sort of? I don't know. How's that.

392
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Speaker 2: Love it?

393
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Speaker 3: So it's it's kind of tough, right, I agree. So

394
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Speaker 3: a lot of my data is big picture, like the

395
00:25:36,720 --> 00:25:40,200
Speaker 3: study that I said where we monitored you know, timing

396
00:25:40,280 --> 00:25:46,320
Speaker 3: of births or pregnancy was over six years data statewide.

397
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Speaker 3: You know, we found no changes, no variation across the

398
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Speaker 3: state or anything. And so so I'm looking at the

399
00:25:56,280 --> 00:25:59,760
Speaker 3: big picture in the average, and the average is oftentimes

400
00:26:01,280 --> 00:26:04,840
Speaker 3: or it's misleading, right, So there's it doesn't capture the

401
00:26:04,920 --> 00:26:09,280
Speaker 3: variability it's out there, and I really think that it's

402
00:26:11,320 --> 00:26:14,760
Speaker 3: it's low, like there could be lots of variation locally.

403
00:26:14,920 --> 00:26:17,320
Speaker 3: So for example, one of the things you talked about

404
00:26:17,400 --> 00:26:22,439
Speaker 3: that bell curve. Well, yeah, it's a bell curve with

405
00:26:22,600 --> 00:26:26,680
Speaker 3: a tail out to the right. So the breeding picks

406
00:26:26,760 --> 00:26:30,679
Speaker 3: up really quick in early October, but it dribbles on

407
00:26:31,160 --> 00:26:35,840
Speaker 3: into December. But what you find in December is that

408
00:26:36,000 --> 00:26:38,560
Speaker 3: many of those deer there to getting bred in December

409
00:26:38,600 --> 00:26:43,560
Speaker 3: are fawns that are just getting big enough to come

410
00:26:43,600 --> 00:26:48,600
Speaker 3: into estrus. And and so you part of the reason

411
00:26:48,640 --> 00:26:52,159
Speaker 3: people so much, very see much, so much variability and

412
00:26:52,280 --> 00:26:55,280
Speaker 3: might claim that there's a second runt right there's that

413
00:26:55,400 --> 00:26:58,760
Speaker 3: all these females got missed and now thirty days later

414
00:26:58,840 --> 00:27:03,040
Speaker 3: they're coming back into right into estra's again that that

415
00:27:03,320 --> 00:27:06,040
Speaker 3: isn't really happening. I think you know, some of that

416
00:27:06,160 --> 00:27:10,000
Speaker 3: variability is like, yeah, all the adult females are bred,

417
00:27:10,080 --> 00:27:13,240
Speaker 3: and then suddenly you've got a few fawns that hit

418
00:27:13,320 --> 00:27:16,880
Speaker 3: that body mass and they're in estra. So there's lots

419
00:27:16,880 --> 00:27:22,200
Speaker 3: of local variability that you know, hunting on your same

420
00:27:22,280 --> 00:27:26,680
Speaker 3: property year after year, you're going to see different levels

421
00:27:26,720 --> 00:27:30,879
Speaker 3: of rut activity in the bucks. And the reason I

422
00:27:30,920 --> 00:27:34,920
Speaker 3: say I sort of don't have data is that if

423
00:27:34,960 --> 00:27:39,040
Speaker 3: you really could track, what you would need to know

424
00:27:39,520 --> 00:27:41,840
Speaker 3: is if you could put a collar on a buck

425
00:27:42,840 --> 00:27:46,040
Speaker 3: and know, okay, this buck is now tending a dough

426
00:27:46,960 --> 00:27:51,440
Speaker 3: and this buck is not tending a dough. And what

427
00:27:51,520 --> 00:27:55,359
Speaker 3: we found is that you can't tell you have to

428
00:27:55,400 --> 00:27:58,560
Speaker 3: have the female. So we've had situations where we had

429
00:27:58,600 --> 00:28:01,480
Speaker 3: both a buck and a dough rate collared and we

430
00:28:01,560 --> 00:28:04,400
Speaker 3: could see that twenty four hour twelve to twenty four

431
00:28:04,440 --> 00:28:08,960
Speaker 3: hour tending period because they would just be lockstep together

432
00:28:09,240 --> 00:28:13,639
Speaker 3: that whole time. And but if all you have is

433
00:28:13,760 --> 00:28:18,080
Speaker 3: tracking the buck, you cannot detect those types of movements.

434
00:28:18,359 --> 00:28:22,080
Speaker 3: So so what I'm saying is that I don't really

435
00:28:22,160 --> 00:28:27,159
Speaker 3: have the data to look at what causes the variability

436
00:28:27,160 --> 00:28:30,000
Speaker 3: in a buck's behavior. All I can do is look

437
00:28:30,040 --> 00:28:33,480
Speaker 3: at this buck and say, all right, here's he's going

438
00:28:33,520 --> 00:28:37,840
Speaker 3: one mile per day in early October and it and

439
00:28:37,960 --> 00:28:40,480
Speaker 3: by you know, the first week in November, he's doing

440
00:28:40,560 --> 00:28:45,640
Speaker 3: three to five miles a day. So that's my caveat.

441
00:28:47,160 --> 00:28:52,480
Speaker 2: A quick aside here, you just intrigued me. In these

442
00:28:52,520 --> 00:28:55,120
Speaker 2: cases where you have been able to document a buck

443
00:28:55,160 --> 00:28:58,320
Speaker 2: and doll pair together and actually have both of them collared.

444
00:29:00,680 --> 00:29:03,400
Speaker 2: What have you seen a lot of us hypothesize. A

445
00:29:03,400 --> 00:29:05,040
Speaker 2: lot of people tell you what a buck and to

446
00:29:05,120 --> 00:29:07,880
Speaker 2: do a buck and a doe do when they are

447
00:29:08,320 --> 00:29:12,120
Speaker 2: tending and together. But were you guys able to pull

448
00:29:12,160 --> 00:29:15,160
Speaker 2: out any specific insights as far as you know, how

449
00:29:15,200 --> 00:29:17,840
Speaker 2: far they traveled on average, how much time they spent

450
00:29:17,920 --> 00:29:20,920
Speaker 2: together on average, anything anything else that might be of interest,

451
00:29:20,920 --> 00:29:24,320
Speaker 2: because I think there's there's a lot of theories. I'm

452
00:29:24,360 --> 00:29:25,200
Speaker 2: not sure how much is it.

453
00:29:25,320 --> 00:29:29,760
Speaker 3: So nothing nothing jumps out in terms of movement, because,

454
00:29:29,800 --> 00:29:33,880
Speaker 3: like I said, we followed this buck and the movements

455
00:29:33,920 --> 00:29:38,240
Speaker 3: while he's tending a dough aren't any different than when

456
00:29:38,280 --> 00:29:41,880
Speaker 3: he's just walking around, or we couldn't distinguish We couldn't

457
00:29:41,880 --> 00:29:46,320
Speaker 3: distinguish that, Oh, it's very particular what happens while he's

458
00:29:46,400 --> 00:29:52,000
Speaker 3: tending the dough. So nothing there that you know that

459
00:29:52,080 --> 00:29:56,680
Speaker 3: I could point to to say it's what's unique about it.

460
00:29:58,360 --> 00:30:02,120
Speaker 2: So so what about that variability you mentioned? How like

461
00:30:02,200 --> 00:30:07,880
Speaker 2: there is some variability? And for example, I went back

462
00:30:07,920 --> 00:30:10,080
Speaker 2: and looked through some of the data you guys have

463
00:30:10,560 --> 00:30:14,040
Speaker 2: collected and shared, and something I noticed is that you

464
00:30:14,080 --> 00:30:18,240
Speaker 2: guys have been tracking average cumulative distance traveled by bucks

465
00:30:18,280 --> 00:30:21,479
Speaker 2: during the rut for a number of years, and there

466
00:30:21,520 --> 00:30:25,600
Speaker 2: have been some years where it's dramatically different. For example,

467
00:30:25,680 --> 00:30:28,800
Speaker 2: in twenty fifteen and correct me if I got these wrong,

468
00:30:28,840 --> 00:30:30,560
Speaker 2: but I believe these are the correct numbers pulled from

469
00:30:30,600 --> 00:30:34,520
Speaker 2: your website. In twenty fifteen, the average cumulative distance traveled

470
00:30:34,680 --> 00:30:39,360
Speaker 2: by Bucks in November was eighty eight miles. In twenty

471
00:30:39,400 --> 00:30:45,200
Speaker 2: twenty three, same month, average cumulative distance traveled it was

472
00:30:45,240 --> 00:30:48,640
Speaker 2: one hundred and fifty miles, so nearly double the amount

473
00:30:48,680 --> 00:30:52,160
Speaker 2: of travel during the November of twenty three versus the

474
00:30:52,200 --> 00:30:57,239
Speaker 2: November of twenty fifteen. Can you speak to anything that

475
00:30:57,320 --> 00:31:00,320
Speaker 2: might help us understand why twenty fifteen and it's so

476
00:31:00,400 --> 00:31:03,280
Speaker 2: little travel, twenty twenty three had so much. And I

477
00:31:03,320 --> 00:31:05,920
Speaker 2: gotta believe there's other years that have been you know,

478
00:31:06,080 --> 00:31:09,640
Speaker 2: bouncing here and there and everywhere. As you have collared

479
00:31:09,680 --> 00:31:12,480
Speaker 2: and studied all these deer, that makes me think there's

480
00:31:12,520 --> 00:31:13,200
Speaker 2: something happening.

481
00:31:14,400 --> 00:31:14,640
Speaker 5: Yeah.

482
00:31:14,800 --> 00:31:19,800
Speaker 3: Part of it is you know the sample, right, they're

483
00:31:19,880 --> 00:31:25,000
Speaker 3: different deer every year, and so we know that there's

484
00:31:25,720 --> 00:31:30,720
Speaker 3: huge variability among deer. Like some guys, some of these

485
00:31:30,760 --> 00:31:35,080
Speaker 3: Bucks will have home ranges of five square miles and

486
00:31:35,160 --> 00:31:37,560
Speaker 3: some might only have two and a half square miles.

487
00:31:38,120 --> 00:31:41,560
Speaker 3: And we don't have huge sample sizes, right. We try

488
00:31:42,280 --> 00:31:50,760
Speaker 3: to collar across our four study areas about oh ten

489
00:31:51,080 --> 00:31:54,160
Speaker 3: bucks or so, so it's not a huge sample size.

490
00:31:54,200 --> 00:31:57,400
Speaker 3: So part of that variability from year to year is

491
00:31:57,480 --> 00:31:59,960
Speaker 3: just the sample of deer that we have to capture.

492
00:32:02,160 --> 00:32:09,760
Speaker 3: I do think there must be uh. I would think

493
00:32:09,840 --> 00:32:15,080
Speaker 3: that there are some other factors that could influence that movement,

494
00:32:15,760 --> 00:32:21,320
Speaker 3: like food availability. In fact, this year is gonna is

495
00:32:21,360 --> 00:32:25,080
Speaker 3: already looking to be an extreme year for white tail

496
00:32:25,440 --> 00:32:30,680
Speaker 3: dough doughs in that they're for the first two weeks

497
00:32:30,680 --> 00:32:34,040
Speaker 3: in October they are averaging movements of about half a

498
00:32:34,080 --> 00:32:39,720
Speaker 3: mile a day, and and and the long term average

499
00:32:39,760 --> 00:32:43,520
Speaker 3: is about a mile and even like all of the

500
00:32:43,600 --> 00:32:53,200
Speaker 3: deer are below average. Yeah, and now the perfect thing

501
00:32:53,360 --> 00:32:56,960
Speaker 3: is like, this is what's so difficult about studying deers.

502
00:32:57,000 --> 00:32:59,640
Speaker 3: We have no control on a lot of things. So

503
00:33:00,040 --> 00:33:02,760
Speaker 3: you've had a drought, and we also have a bumper

504
00:33:02,800 --> 00:33:05,840
Speaker 3: acorn crop. So is the fact that there's lots of

505
00:33:05,880 --> 00:33:10,200
Speaker 3: acorns or is it because it's a drought? Right, I

506
00:33:10,320 --> 00:33:13,800
Speaker 3: have no idea. If I had to put money on it,

507
00:33:13,840 --> 00:33:16,960
Speaker 3: I would say acorns because you know, some of the

508
00:33:17,040 --> 00:33:20,400
Speaker 3: reports and I'm sure people reading our blog will write

509
00:33:20,400 --> 00:33:23,560
Speaker 3: in you know, people have already commented that you know,

510
00:33:23,600 --> 00:33:26,240
Speaker 3: it's like walking on marbles out there in places. And

511
00:33:26,280 --> 00:33:30,720
Speaker 3: if that's the case, then your does aren't going to move,

512
00:33:31,040 --> 00:33:33,960
Speaker 3: They're going to move very little. So the question is

513
00:33:34,320 --> 00:33:37,800
Speaker 3: does a buck have to move more to find more

514
00:33:37,880 --> 00:33:40,480
Speaker 3: dough or does he not have to go as far

515
00:33:40,640 --> 00:33:43,719
Speaker 3: to find dough because they're easier to find because they

516
00:33:43,720 --> 00:33:48,560
Speaker 3: don't move as much. You know, because I've always you

517
00:33:48,600 --> 00:33:52,200
Speaker 3: can you can see either no change in dough movements

518
00:33:52,240 --> 00:33:56,960
Speaker 3: during the rut or actually a slight reduction. And you know,

519
00:33:57,040 --> 00:33:59,080
Speaker 3: that could be like I say, if you're lost in

520
00:33:59,120 --> 00:34:01,520
Speaker 3: the woods, would you're supposed to do You're supposed to

521
00:34:01,800 --> 00:34:05,640
Speaker 3: not move because you're more likely to be rescued, and

522
00:34:05,720 --> 00:34:09,160
Speaker 3: so that could be the behavior of white tailed deer.

523
00:34:10,480 --> 00:34:14,920
Speaker 3: So anyway, there's the short The short answer is I

524
00:34:14,920 --> 00:34:18,480
Speaker 3: don't really know, but I know there are things that

525
00:34:18,520 --> 00:34:24,719
Speaker 3: are influencing their movements, and I can't really parse out

526
00:34:25,360 --> 00:34:28,720
Speaker 3: the difference between my different samples because I have different

527
00:34:28,719 --> 00:34:32,759
Speaker 3: bucks every year, or you know, or what factor is

528
00:34:32,800 --> 00:34:37,839
Speaker 3: actually driving because because I don't even well, there are

529
00:34:37,880 --> 00:34:41,120
Speaker 3: some measures of acorns, but acorns can also be very

530
00:34:41,160 --> 00:34:47,200
Speaker 3: spotty and you know, spatially distribute on the landscape. So yeah,

531
00:34:47,320 --> 00:34:51,920
Speaker 3: it's it's the challenge of studying creatures in the wild

532
00:34:52,000 --> 00:34:55,960
Speaker 3: is is we have You know, radio callers give us

533
00:34:56,000 --> 00:35:01,160
Speaker 3: great insights that we wouldn't others see. But until we

534
00:35:01,239 --> 00:35:04,520
Speaker 3: have cameras on those collars and see what they see,

535
00:35:05,320 --> 00:35:06,640
Speaker 3: there's a lot that we're missing.

536
00:35:07,719 --> 00:35:11,080
Speaker 2: What do you think would surprise people? You guys have

537
00:35:11,160 --> 00:35:13,759
Speaker 2: shared these videos on occasion on your blog where you

538
00:35:13,880 --> 00:35:16,400
Speaker 2: show the actual path, the travel path that some of

539
00:35:16,440 --> 00:35:20,000
Speaker 2: these deer taking. If the general public were to sit

540
00:35:20,080 --> 00:35:22,719
Speaker 2: down and watch the travel of every one of these

541
00:35:22,760 --> 00:35:25,600
Speaker 2: bucks that you have collared over the years during the rut,

542
00:35:26,160 --> 00:35:28,400
Speaker 2: is there anything that you think would surprise people?

543
00:35:31,200 --> 00:35:35,320
Speaker 3: Well, I think the interesting thing is when you watch

544
00:35:35,360 --> 00:35:40,800
Speaker 3: our movies, it looks like they suddenly speed up because

545
00:35:42,239 --> 00:35:47,120
Speaker 3: and in a sense they do, but it's they're not.

546
00:35:47,480 --> 00:35:51,000
Speaker 3: It's just that they're probably walking in more of a

547
00:35:51,040 --> 00:35:55,280
Speaker 3: straight line distance between locations. Because so our radio callers

548
00:35:55,320 --> 00:35:57,640
Speaker 3: for the readers, if you don't know, the way they

549
00:35:57,680 --> 00:36:01,120
Speaker 3: work is we program them and tell them, Okay, get

550
00:36:01,160 --> 00:36:03,880
Speaker 3: me a location once an hour, or get me a

551
00:36:03,960 --> 00:36:07,360
Speaker 3: location once every thirty minutes. Whatever you want, you can do.

552
00:36:08,440 --> 00:36:10,759
Speaker 3: It just depends on how good your battery life is,

553
00:36:12,120 --> 00:36:16,240
Speaker 3: and so it looks like these deer are just cuttsing

554
00:36:16,320 --> 00:36:19,080
Speaker 3: around and then suddenly they go boom boom boom, like

555
00:36:19,120 --> 00:36:22,160
Speaker 3: a you know, a paddle ball, you know, a ball

556
00:36:22,200 --> 00:36:27,600
Speaker 3: on us on a rubber band. But those bucks really

557
00:36:28,400 --> 00:36:32,239
Speaker 3: most of the time they are walking under half a

558
00:36:32,280 --> 00:36:37,839
Speaker 3: mile an hour, So they aren't walking faster, They're just

559
00:36:38,440 --> 00:36:44,120
Speaker 3: they're just walking twenty four to seven. And and so

560
00:36:44,280 --> 00:36:47,719
Speaker 3: that to me was I mean, I guess from my perspective,

561
00:36:47,760 --> 00:36:51,359
Speaker 3: that's what I found the most interesting was, you know,

562
00:36:51,440 --> 00:36:54,759
Speaker 3: slow and steady wins the race, and those guys are

563
00:36:54,800 --> 00:36:58,400
Speaker 3: just plodding along. But they're just going twenty four to

564
00:36:58,440 --> 00:37:02,640
Speaker 3: seven if you look at their activity patterns. Yeah, maybe

565
00:37:02,640 --> 00:37:04,960
Speaker 3: it dips down in the you know, four o'clock in

566
00:37:05,000 --> 00:37:07,760
Speaker 3: the morning or something, but basically they are just going

567
00:37:08,560 --> 00:37:09,600
Speaker 3: twenty four to seven.

568
00:37:10,719 --> 00:37:14,560
Speaker 2: So so are you are you saying that your data

569
00:37:14,840 --> 00:37:19,640
Speaker 2: validates the idea that many popularized that at least during

570
00:37:19,640 --> 00:37:22,560
Speaker 2: the rut you should be hunting even during the middle

571
00:37:22,600 --> 00:37:24,520
Speaker 2: of the day, because those bucks will still be on

572
00:37:24,560 --> 00:37:25,879
Speaker 2: their feet and moving quite a bit.

573
00:37:27,520 --> 00:37:30,680
Speaker 3: I think so, although they still show up on my

574
00:37:30,800 --> 00:37:34,160
Speaker 3: game cameras only at night, So I have no idea.

575
00:37:34,800 --> 00:37:37,680
Speaker 3: I have no idea why I don't see them. But

576
00:37:37,680 --> 00:37:40,960
Speaker 3: but again, you know where I live, it's a mixture

577
00:37:40,960 --> 00:37:44,680
Speaker 3: of forests and field, right, so they could be sticking

578
00:37:44,719 --> 00:37:47,239
Speaker 3: to the woods. And my game cameras are always on

579
00:37:47,280 --> 00:37:51,640
Speaker 3: the edges of a field, you know, on a scrape

580
00:37:51,760 --> 00:37:55,080
Speaker 3: or you know that sort of thing. But you know,

581
00:37:55,160 --> 00:37:58,240
Speaker 3: we we study, you know, the deer for studies in

582
00:37:58,480 --> 00:38:01,000
Speaker 3: what we call the Big Woods of Pennsylvania. So these

583
00:38:01,000 --> 00:38:05,960
Speaker 3: are just contiguous tracks. They're ninety five percent forested over

584
00:38:06,120 --> 00:38:11,160
Speaker 3: twenty twenty five square miles. So in that context, we

585
00:38:11,360 --> 00:38:15,560
Speaker 3: just basically see these bucks walking around twenty four to

586
00:38:15,560 --> 00:38:19,719
Speaker 3: seven And you know, so you know, one time, I think,

587
00:38:19,800 --> 00:38:24,239
Speaker 3: I you know, tried to figure the calculations and this

588
00:38:24,320 --> 00:38:27,680
Speaker 3: one buck he started and ended up in the same

589
00:38:27,760 --> 00:38:32,040
Speaker 3: place within twelve hours. And over those twelve hours he

590
00:38:32,120 --> 00:38:35,080
Speaker 3: went up and over three ridges, like almost a mile

591
00:38:35,120 --> 00:38:39,160
Speaker 3: in elevation change and did I don't know how many

592
00:38:39,320 --> 00:38:42,560
Speaker 3: two or three miles in that loop. And that was

593
00:38:42,760 --> 00:38:48,239
Speaker 3: just you know, just walking. I mean, you know, so

594
00:38:48,320 --> 00:38:51,319
Speaker 3: in a month time they're ended up walking most most

595
00:38:51,360 --> 00:38:54,040
Speaker 3: of them are doing over one hundred miles. Some are

596
00:38:54,080 --> 00:38:58,919
Speaker 3: over one hundred and fifty miles. So yeah, it's it's

597
00:38:58,960 --> 00:39:01,960
Speaker 3: amazing that we have the technology now to document that,

598
00:39:02,200 --> 00:39:06,759
Speaker 3: and it explains so many things like why does this

599
00:39:06,880 --> 00:39:11,239
Speaker 3: buck show up that I've never seen all summer long? Yeah,

600
00:39:11,239 --> 00:39:14,520
Speaker 3: because their home ranges are, you know, tripling.

601
00:39:17,280 --> 00:39:21,319
Speaker 2: You guys have a really interesting page of Elbow on

602
00:39:21,360 --> 00:39:26,280
Speaker 2: your website, your rut tracker that shows both this year's

603
00:39:26,600 --> 00:39:31,200
Speaker 2: average movement I believe it's cumulative distance or average distance,

604
00:39:32,400 --> 00:39:34,960
Speaker 2: and then you show what this year's data is compared

605
00:39:35,000 --> 00:39:38,719
Speaker 2: to the average from the previous ten years. Could you

606
00:39:38,800 --> 00:39:43,880
Speaker 2: walk folks through what generally that timeline of movement looks

607
00:39:44,000 --> 00:39:46,520
Speaker 2: like when you see it really picking up, when it

608
00:39:46,680 --> 00:39:49,440
Speaker 2: kind of exponentially hockey curves up, and then what that

609
00:39:49,480 --> 00:39:52,359
Speaker 2: bell curve looks like just from a date I think

610
00:39:52,400 --> 00:39:54,080
Speaker 2: I think a lot of folks have a general idea

611
00:39:54,080 --> 00:39:56,719
Speaker 2: of when these time periods are, but I think it'll

612
00:39:56,760 --> 00:39:58,680
Speaker 2: be interesting just to hear from you on exactly what

613
00:39:58,800 --> 00:40:00,680
Speaker 2: that date range that you've document it is.

614
00:40:00,920 --> 00:40:01,120
Speaker 5: Yeah.

615
00:40:01,640 --> 00:40:07,920
Speaker 6: Yeah, So what I've been doing is just calculating the

616
00:40:08,200 --> 00:40:12,239
Speaker 6: distance that each deer travels every day, and then we

617
00:40:12,280 --> 00:40:15,480
Speaker 6: can average that to show the average distance that a

618
00:40:15,560 --> 00:40:20,120
Speaker 6: deer moved and I think our locations are hourly at

619
00:40:20,120 --> 00:40:22,080
Speaker 6: this point, and.

620
00:40:23,680 --> 00:40:30,279
Speaker 3: So the bucks and doze in the first half of

621
00:40:30,320 --> 00:40:34,160
Speaker 3: October and moving about a mile per day, and it's

622
00:40:34,200 --> 00:40:38,000
Speaker 3: not until the third week in October that that actually

623
00:40:38,040 --> 00:40:42,400
Speaker 3: starts to increase. It gets going in the fourth week,

624
00:40:42,560 --> 00:40:47,840
Speaker 3: and by the first week in November, it's that hockey

625
00:40:47,920 --> 00:40:52,880
Speaker 3: curve has almost got you up to the peak. And yeah,

626
00:40:52,920 --> 00:40:56,400
Speaker 3: and so that goes through into the middle of November,

627
00:40:57,120 --> 00:41:02,279
Speaker 3: and then you know the rut our hunting seasons in

628
00:41:02,320 --> 00:41:09,440
Speaker 3: Pennsylvania are start the Saturday after Thanksgiving and the rifle

629
00:41:09,480 --> 00:41:13,680
Speaker 3: season where most of the deer harvested, so we still

630
00:41:13,719 --> 00:41:17,680
Speaker 3: have a lot of or actually not a lot, but

631
00:41:17,840 --> 00:41:21,319
Speaker 3: so most of the deer. Half the deer bred by

632
00:41:21,320 --> 00:41:25,879
Speaker 3: November thirteenth, but a week later, you know it's going

633
00:41:25,960 --> 00:41:29,719
Speaker 3: to be eighty percent of the deer are bred. And

634
00:41:29,760 --> 00:41:34,120
Speaker 3: by the time that rifle season opens, you know, there's

635
00:41:34,480 --> 00:41:38,160
Speaker 3: one out of five dough that could get pregnant. Is

636
00:41:38,800 --> 00:41:43,920
Speaker 3: you know in estris so and the rifle season, because

637
00:41:43,920 --> 00:41:47,560
Speaker 3: there's so many hunters out there, just changes deer behavior

638
00:41:47,960 --> 00:41:51,480
Speaker 3: and so if you're just looking at those averages, it

639
00:41:51,560 --> 00:41:54,200
Speaker 3: just kind of all falls apart. And even the females

640
00:41:54,200 --> 00:41:56,880
Speaker 3: start moving a lot because of all the hunter activity

641
00:41:56,880 --> 00:42:01,640
Speaker 3: in the woods. But yeah, from you know, starting this

642
00:42:01,840 --> 00:42:07,359
Speaker 3: coming week and by the end of October, things those

643
00:42:07,480 --> 00:42:10,960
Speaker 3: bucks are really moving. And then for the next two

644
00:42:11,040 --> 00:42:14,960
Speaker 3: weeks in November, lots of movement, and then it quickly

645
00:42:15,000 --> 00:42:15,600
Speaker 3: falls off.

646
00:42:26,880 --> 00:42:33,399
Speaker 2: So there is a commonly discussed phase of the rut

647
00:42:33,640 --> 00:42:36,279
Speaker 2: that a lot of folks refer to as lockdown, which

648
00:42:36,320 --> 00:42:40,040
Speaker 2: would be when we hit peak breeding. The idea being

649
00:42:40,080 --> 00:42:44,719
Speaker 2: that when the most does are available to breed, you're

650
00:42:44,719 --> 00:42:47,239
Speaker 2: gonna have a reduction in deer movement because there's gonna

651
00:42:47,239 --> 00:42:49,320
Speaker 2: be a lot of bucks locked down, a lot of dos,

652
00:42:49,480 --> 00:42:52,719
Speaker 2: and those pairings will not move as much, and then

653
00:42:52,719 --> 00:42:55,680
Speaker 2: there won't be as many solo bucks cruising around searching.

654
00:42:55,680 --> 00:42:58,399
Speaker 2: And so the idea being that, man, when lockdown hits,

655
00:42:58,400 --> 00:43:00,000
Speaker 2: which for a lot of people is the middle of November,

656
00:43:00,440 --> 00:43:03,879
Speaker 2: you're gonna have some slower hunts. When I look at

657
00:43:03,880 --> 00:43:07,080
Speaker 2: your data, when I look at this rut tracker, it

658
00:43:07,200 --> 00:43:09,560
Speaker 2: kind of does show that because it peaks, you know,

659
00:43:09,640 --> 00:43:13,600
Speaker 2: as you just described, you see movement on average peaking

660
00:43:13,680 --> 00:43:16,439
Speaker 2: around that first couple weeks of November, but then right

661
00:43:16,480 --> 00:43:22,000
Speaker 2: around you know, November seventeenth, sixteenth, right around that ballpark,

662
00:43:22,360 --> 00:43:25,880
Speaker 2: it does start to kind of precipitously decline and then

663
00:43:25,920 --> 00:43:28,680
Speaker 2: a plateaus again for that like third week of November,

664
00:43:28,719 --> 00:43:31,040
Speaker 2: and then as you mentioned, continues to trickle down from there.

665
00:43:31,600 --> 00:43:34,440
Speaker 2: Does that, in your view, validate the theory that I

666
00:43:34,560 --> 00:43:36,440
Speaker 2: just described or how would you interpret that?

667
00:43:38,960 --> 00:43:47,480
Speaker 3: I I don't think so, because from from that first

668
00:43:47,560 --> 00:43:53,520
Speaker 3: week in November, that's when the bulk of females are

669
00:43:53,560 --> 00:43:59,239
Speaker 3: being bred. So if it's because there's so many females

670
00:43:59,280 --> 00:44:03,880
Speaker 3: available to be bred, is reducing movements, why don't you

671
00:44:04,040 --> 00:44:09,879
Speaker 3: see that during the first two weeks in November. And

672
00:44:10,160 --> 00:44:12,359
Speaker 3: the other thing is, like I said, when we've had

673
00:44:12,400 --> 00:44:17,880
Speaker 3: males and females where we've had both of them and

674
00:44:18,000 --> 00:44:23,320
Speaker 3: can see that that male is tending that female, there's

675
00:44:23,440 --> 00:44:28,759
Speaker 3: no evidence that they don't move. He's just following her around.

676
00:44:30,680 --> 00:44:33,799
Speaker 3: And you know, they're not like elk where they have

677
00:44:33,920 --> 00:44:37,040
Speaker 3: a harem that they're trying to keep in one place

678
00:44:37,080 --> 00:44:41,280
Speaker 3: and all together. He's just trying to follow that female

679
00:44:41,320 --> 00:44:45,480
Speaker 3: and breed with her, you know, at the best opportunity

680
00:44:45,520 --> 00:44:52,160
Speaker 3: and fend off other competitors. So when we know that

681
00:44:52,239 --> 00:44:56,200
Speaker 3: a buck is with a female. We have no evidence

682
00:44:56,280 --> 00:44:57,960
Speaker 3: that they don't move as much.

683
00:45:00,600 --> 00:45:04,799
Speaker 2: Okay, so here here's another one, another theory. I guess

684
00:45:05,000 --> 00:45:06,680
Speaker 2: I'm throwing a lot of theories. That you were throwing

685
00:45:06,680 --> 00:45:08,319
Speaker 2: a lot of spaghetti at the wall, not a lot

686
00:45:08,400 --> 00:45:11,000
Speaker 2: sticking on the wall. Dwayne, I'll tell you that, but

687
00:45:12,360 --> 00:45:15,359
Speaker 2: you can't deny the science. Here's another one, and I'll

688
00:45:15,360 --> 00:45:17,680
Speaker 2: see what you have to say. And then, actually, this

689
00:45:17,719 --> 00:45:20,520
Speaker 2: one's not necessarily a theory. This is actually a study.

690
00:45:22,600 --> 00:45:24,360
Speaker 2: There used to be an idea that you could not

691
00:45:24,480 --> 00:45:27,640
Speaker 2: pattern bucks during the rut, that they are completely random

692
00:45:27,920 --> 00:45:32,480
Speaker 2: and completely unpredictable. A study relatively recently came out that

693
00:45:32,600 --> 00:45:38,680
Speaker 2: described bucks using certain focal areas a disproportionate amount of

694
00:45:38,680 --> 00:45:41,440
Speaker 2: time during the rut. That there were, for many bucks,

695
00:45:41,920 --> 00:45:44,919
Speaker 2: certain places that they spent more time than others during

696
00:45:44,960 --> 00:45:49,440
Speaker 2: their rut. Have you seen anything like that in your data.

697
00:45:53,360 --> 00:45:53,440
Speaker 7: No.

698
00:45:53,719 --> 00:46:02,880
Speaker 3: In fact, we tried to replicate that. So you would

699
00:46:02,920 --> 00:46:07,560
Speaker 3: expect that those focal areas might occur because they're coming

700
00:46:07,600 --> 00:46:10,680
Speaker 3: back to check on a female to see if she's

701
00:46:10,880 --> 00:46:16,680
Speaker 3: in heat or not. And when we had known females

702
00:46:16,719 --> 00:46:23,520
Speaker 3: and males, we could find no relationship between focal areas

703
00:46:23,520 --> 00:46:28,120
Speaker 3: by just looking at where the buck went and whether

704
00:46:28,200 --> 00:46:35,439
Speaker 3: they actually had a mating event. So I mean, they

705
00:46:35,480 --> 00:46:38,279
Speaker 3: may have focal areas, but I think those could be

706
00:46:38,320 --> 00:46:42,560
Speaker 3: explained by environmental factors too. I mean, is there something

707
00:46:42,600 --> 00:46:46,240
Speaker 3: about that site that they go back to for food

708
00:46:46,480 --> 00:46:51,920
Speaker 3: or shade or who knows what. So I would say

709
00:46:51,960 --> 00:46:55,600
Speaker 3: that the there's a lot to be learned. I'm not

710
00:46:55,640 --> 00:47:02,280
Speaker 3: going to discount it, but we tried to to, you know, say, okay,

711
00:47:02,360 --> 00:47:05,439
Speaker 3: if this is what's going on, does that match up

712
00:47:06,040 --> 00:47:09,600
Speaker 3: in situations where we know this buck is following this

713
00:47:09,719 --> 00:47:14,839
Speaker 3: female and we couldn't We couldn't get that to match up.

714
00:47:15,640 --> 00:47:19,640
Speaker 3: But you know, with better technology we might be able

715
00:47:19,640 --> 00:47:22,440
Speaker 3: to answer those questions. Because I was just at the

716
00:47:23,239 --> 00:47:26,000
Speaker 3: at a meeting and talking to some of the reps

717
00:47:26,000 --> 00:47:30,040
Speaker 3: for some of these companies, and they're coming out with

718
00:47:30,239 --> 00:47:34,800
Speaker 3: radio callers now that they can get a GPS location

719
00:47:35,080 --> 00:47:38,839
Speaker 3: like every thirty minutes, but then the onboard caller has

720
00:47:38,840 --> 00:47:45,440
Speaker 3: an accelerometer and a and a magnetometer, so they can

721
00:47:45,800 --> 00:47:49,200
Speaker 3: they can actually predict the movements that a deer spends

722
00:47:49,280 --> 00:47:52,359
Speaker 3: in between those two locations that you get, and that

723
00:47:52,440 --> 00:47:55,400
Speaker 3: could be a game changer because now we kind of

724
00:47:55,400 --> 00:47:59,760
Speaker 3: have to interpolate what that deer is doing between location

725
00:48:00,239 --> 00:48:03,680
Speaker 3: and location x plus one, and this time they could

726
00:48:03,760 --> 00:48:07,840
Speaker 3: potentially draw an exact path where that deer goes. So

727
00:48:08,520 --> 00:48:11,800
Speaker 3: I'm not gonna discount it, but I'm gonna say right now,

728
00:48:11,920 --> 00:48:14,400
Speaker 3: I don't have any evidence that would support that.

729
00:48:16,239 --> 00:48:23,200
Speaker 2: So one other kind of local variable, then one idea

730
00:48:23,239 --> 00:48:28,040
Speaker 2: be here. There might be actual environmental you know, focus areas.

731
00:48:28,640 --> 00:48:30,480
Speaker 2: I think you kind of alluded to this earlier, but

732
00:48:30,520 --> 00:48:36,279
Speaker 2: I want to ask more specifically, what about localized variations

733
00:48:36,320 --> 00:48:39,680
Speaker 2: and timing a little bit. So I know, in general,

734
00:48:39,840 --> 00:48:45,960
Speaker 2: we have this you know, pretty hard evolutionary biological queue

735
00:48:46,000 --> 00:48:48,960
Speaker 2: for when the peak of breeding should happen, but we

736
00:48:49,040 --> 00:48:53,600
Speaker 2: frequently hear about how there might be localized situations where

737
00:48:53,600 --> 00:48:55,000
Speaker 2: it might be a little bit over here, a little

738
00:48:55,000 --> 00:48:57,600
Speaker 2: bit over there, Like for example, you hear the hear

739
00:48:57,640 --> 00:48:59,480
Speaker 2: the story like, well, there's a Doe family group that

740
00:48:59,560 --> 00:49:02,120
Speaker 2: always to come in early in my neck of the woods.

741
00:49:02,640 --> 00:49:07,040
Speaker 2: And honestly, I can say that anecdotally, I do have

742
00:49:07,120 --> 00:49:09,440
Speaker 2: a place that I hunt that it sure seems like

743
00:49:09,800 --> 00:49:13,400
Speaker 2: every single year for fifteen years now, it sure seems

744
00:49:13,400 --> 00:49:16,920
Speaker 2: like the last week of October is like when everything

745
00:49:17,080 --> 00:49:20,480
Speaker 2: breaks loose, But everywhere else I go, it's it's, you know,

746
00:49:20,520 --> 00:49:22,960
Speaker 2: the typical first two weeks of November, but it feels

747
00:49:22,960 --> 00:49:26,000
Speaker 2: like November seventh for me on October twenty fifth in

748
00:49:26,040 --> 00:49:30,799
Speaker 2: this little zone. Has your data shown that that might be?

749
00:49:30,880 --> 00:49:32,480
Speaker 2: Is that a possible thing. Do you have one of

750
00:49:32,520 --> 00:49:34,760
Speaker 2: your study sites where it's a couple of days earlier

751
00:49:34,760 --> 00:49:37,560
Speaker 2: on average some years or most years or anything like that.

752
00:49:38,400 --> 00:49:43,360
Speaker 3: Well, I guess two things to say. One from my data,

753
00:49:43,960 --> 00:49:48,680
Speaker 3: so northern Pennsylvania spring green up is like might be

754
00:49:48,760 --> 00:49:54,279
Speaker 3: a week or so later than southern Pennsylvania. But we

755
00:49:54,400 --> 00:49:58,240
Speaker 3: really see no spatial variation in the timing of birth.

756
00:49:58,840 --> 00:50:02,360
Speaker 3: And in fact, you know, if you look at maps

757
00:50:02,600 --> 00:50:06,080
Speaker 3: of the timing of breeding across for the white tail

758
00:50:06,120 --> 00:50:10,000
Speaker 3: deer across most of North Americas until you get like

759
00:50:10,120 --> 00:50:15,080
Speaker 3: south of the Carolinas, it's all November. And I'm sure

760
00:50:15,120 --> 00:50:18,200
Speaker 3: it varies a little bit, but it's basically it's November

761
00:50:18,600 --> 00:50:26,839
Speaker 3: sometime in November. But with your respect to like local variation, well,

762
00:50:27,000 --> 00:50:31,320
Speaker 3: you know, when a female goes into estrus is really

763
00:50:31,360 --> 00:50:37,759
Speaker 3: strongly related to her physical condition, and there's research to

764
00:50:37,920 --> 00:50:42,200
Speaker 3: show that a female that's in good condition is going

765
00:50:42,280 --> 00:50:48,520
Speaker 3: to give birth to offspring in better condition, so you

766
00:50:48,719 --> 00:50:52,360
Speaker 3: could have some and they're also right, in better condition,

767
00:50:52,440 --> 00:50:56,800
Speaker 3: they're going to produce more offspring. And so I could

768
00:50:56,840 --> 00:51:00,560
Speaker 3: see in an area that you have, you know, some

769
00:51:00,760 --> 00:51:05,520
Speaker 3: females that have are in better condition come into heat

770
00:51:06,160 --> 00:51:12,040
Speaker 3: and there's right, variation is a good thing when it

771
00:51:12,080 --> 00:51:15,760
Speaker 3: comes to evolution because you never know when the future

772
00:51:15,840 --> 00:51:18,640
Speaker 3: is going to change, and if it does change, certain

773
00:51:18,680 --> 00:51:22,200
Speaker 3: individuals are going to be you know, they didn't know

774
00:51:22,320 --> 00:51:25,960
Speaker 3: about know it, but they are preadapted. So I could

775
00:51:26,040 --> 00:51:29,920
Speaker 3: say that there could be physiological there could be genetic reasons.

776
00:51:31,000 --> 00:51:37,919
Speaker 3: There potentially could be local food resource reasons, but I mean,

777
00:51:38,080 --> 00:51:40,680
Speaker 3: I don't think I could ever collect enough data to

778
00:51:40,800 --> 00:51:44,120
Speaker 3: actually parse that out, and it would have to involve

779
00:51:44,200 --> 00:51:49,040
Speaker 3: some sort of experimentation. I mean, our genetic tools are

780
00:51:49,040 --> 00:51:55,360
Speaker 3: getting better these days. So and I think that deer

781
00:51:55,400 --> 00:52:01,360
Speaker 3: research should be focusing on stress level deer, which is

782
00:52:01,360 --> 00:52:05,520
Speaker 3: an indicator of physiological condition, because some of our research

783
00:52:05,520 --> 00:52:08,719
Speaker 3: has shown that that stress is a better predictor of

784
00:52:08,800 --> 00:52:13,360
Speaker 3: fond survival than how many predators are on the landscape.

785
00:52:13,440 --> 00:52:19,480
Speaker 3: So yes, I could envision local variation in the related

786
00:52:19,520 --> 00:52:24,600
Speaker 3: to you know, either intrinsic characteristics of the white tailed

787
00:52:24,600 --> 00:52:30,040
Speaker 3: deer in that area or external factors that are influencing it.

788
00:52:30,120 --> 00:52:33,880
Speaker 3: But yeah, I haven't been able to collect enough data

789
00:52:33,960 --> 00:52:37,000
Speaker 3: yet to tackle that. Interesting.

790
00:52:37,960 --> 00:52:41,880
Speaker 2: Okay, so let's let's tie a bow on this, Dwayne.

791
00:52:41,960 --> 00:52:48,120
Speaker 2: Let's imagine that I were to set one relatively new hunter,

792
00:52:48,200 --> 00:52:50,880
Speaker 2: Let's say, someone who's been deer hunting for a handful

793
00:52:50,920 --> 00:52:55,520
Speaker 2: of years, but they're not a salty old fifty years

794
00:52:55,560 --> 00:52:59,719
Speaker 2: in kind of guy. And this person is trying to

795
00:52:59,760 --> 00:53:04,200
Speaker 2: have more hunting success during the rut. And they came

796
00:53:04,239 --> 00:53:07,760
Speaker 2: to you and said, Dwayne, what would be the three

797
00:53:08,160 --> 00:53:12,279
Speaker 2: most useful insights from your study of the white tail

798
00:53:12,360 --> 00:53:15,080
Speaker 2: rut that could help me as a hunter this year?

799
00:53:16,360 --> 00:53:18,920
Speaker 2: What would those three things be? So, the three most

800
00:53:19,080 --> 00:53:23,360
Speaker 2: useful observations or insights from your studies that could be

801
00:53:23,360 --> 00:53:26,520
Speaker 2: applied for hunters during the rut?

802
00:53:26,920 --> 00:53:28,880
Speaker 3: Well, first of all, you're supposed to ask me that

803
00:53:29,000 --> 00:53:34,919
Speaker 3: question beforehand, so I have time to prepare. So ah,

804
00:53:36,280 --> 00:53:39,359
Speaker 3: so uh, what would you do?

805
00:53:43,600 --> 00:53:43,839
Speaker 2: I guess?

806
00:53:43,960 --> 00:53:47,520
Speaker 3: So one thing I would say is so, game cameras

807
00:53:47,560 --> 00:53:51,160
Speaker 3: are awesome, right, we know so much more about what's

808
00:53:51,200 --> 00:53:56,200
Speaker 3: in the woods out there. I would say, if you

809
00:53:56,520 --> 00:54:01,000
Speaker 3: see a buck in September and early October on your

810
00:54:01,040 --> 00:54:06,319
Speaker 3: game camera, odds are you're in the core area of

811
00:54:06,400 --> 00:54:11,319
Speaker 3: that buck. And what you could do is and I know,

812
00:54:12,480 --> 00:54:15,480
Speaker 3: you know people that have harvestered our bucks and talk

813
00:54:15,600 --> 00:54:18,520
Speaker 3: to them. They got lucky and they had a bunch

814
00:54:18,560 --> 00:54:22,880
Speaker 3: of cameras out that basically encompassed his core area, so

815
00:54:23,000 --> 00:54:26,320
Speaker 3: they knew that that's where I need to focus my time.

816
00:54:27,600 --> 00:54:31,239
Speaker 3: That's probably the biggest thing that I know. Otherwise, I mean,

817
00:54:31,480 --> 00:54:34,800
Speaker 3: I've stared at and made lots of movies of bucks,

818
00:54:35,360 --> 00:54:40,040
Speaker 3: and you know, I'm not as smart as Ai. I guess,

819
00:54:40,120 --> 00:54:43,920
Speaker 3: but I cannot find a pattern too, you know interview

820
00:54:44,000 --> 00:54:46,440
Speaker 3: Does does he go up this draw? Does he go

821
00:54:46,520 --> 00:54:49,319
Speaker 3: up and down this draw instead of this one? No,

822
00:54:49,440 --> 00:54:54,000
Speaker 3: I've never been able to to pattern a buck when

823
00:54:54,040 --> 00:54:56,959
Speaker 3: I've had these bucks, you know, radio.

824
00:54:56,640 --> 00:54:58,800
Speaker 2: College, even with radio Calordetta.

825
00:54:58,880 --> 00:55:03,799
Speaker 3: So yeah, so I don't think I can come up

826
00:55:03,840 --> 00:55:06,400
Speaker 3: with three because you didn't give me enough advance warning.

827
00:55:06,520 --> 00:55:09,600
Speaker 3: But one, Yeah, go ahead.

828
00:55:10,040 --> 00:55:13,160
Speaker 2: I was just going to say, as I listened to you,

829
00:55:13,840 --> 00:55:16,600
Speaker 2: and as I have gone back and read through the

830
00:55:16,640 --> 00:55:20,600
Speaker 2: many different articles that you guys have published and the

831
00:55:20,680 --> 00:55:24,959
Speaker 2: data you've shared with the public in a certain way.

832
00:55:26,400 --> 00:55:29,040
Speaker 2: One of the main takeaways I'm getting from this is

833
00:55:30,000 --> 00:55:33,400
Speaker 2: keep it simple, stupid, you know, as a hunter, because

834
00:55:33,400 --> 00:55:36,400
Speaker 2: what you're telling me, and what this data seems to show,

835
00:55:37,160 --> 00:55:39,960
Speaker 2: is that deer are deer, and they're going to do

836
00:55:40,080 --> 00:55:43,719
Speaker 2: basically the same thing every single year, with a little

837
00:55:43,760 --> 00:55:46,960
Speaker 2: bit of regional variability, a little bit of localized variability,

838
00:55:47,000 --> 00:55:50,440
Speaker 2: but in general, the data seems to show that the

839
00:55:50,520 --> 00:55:54,800
Speaker 2: rut is consistent every year, that movement's going to generally

840
00:55:54,880 --> 00:55:58,040
Speaker 2: increase at the same time of year that it's generally

841
00:55:58,080 --> 00:56:02,000
Speaker 2: difficult to pattern them. That generally, all these weather factors

842
00:56:02,000 --> 00:56:04,640
Speaker 2: that vary from day to day today don't have a

843
00:56:04,840 --> 00:56:09,319
Speaker 2: net statistically significant impact on any of that. So rather

844
00:56:09,360 --> 00:56:11,520
Speaker 2: than all of us racking our brains every day all

845
00:56:11,600 --> 00:56:14,520
Speaker 2: day trying to discover some silver bullet that's going to

846
00:56:14,560 --> 00:56:16,200
Speaker 2: tell us that, oh, you should hunt this day and

847
00:56:16,239 --> 00:56:19,200
Speaker 2: not that day, maybe we just simply need to know

848
00:56:19,239 --> 00:56:23,680
Speaker 2: that during the rut, which there's a very simple calendar

849
00:56:23,719 --> 00:56:26,040
Speaker 2: which can show you when those periods of the rut

850
00:56:26,040 --> 00:56:28,680
Speaker 2: are as you describe late October into the first couple

851
00:56:28,719 --> 00:56:31,759
Speaker 2: weeks of November and slowly tailing down from there. During

852
00:56:31,760 --> 00:56:34,239
Speaker 2: that time period, be in the woods as much as

853
00:56:34,320 --> 00:56:37,120
Speaker 2: you possibly can. Is it just that simple? Thank you?

854
00:56:37,239 --> 00:56:40,719
Speaker 3: Thank you for my number two reason. Yes, explaining my

855
00:56:40,840 --> 00:56:43,200
Speaker 3: number two reason. Yes, you just got to be out

856
00:56:43,239 --> 00:56:46,399
Speaker 3: there in the woods. And if you need to plan

857
00:56:46,520 --> 00:56:52,399
Speaker 3: your vacation a year ahead of time, easy, pasy right,

858
00:56:52,480 --> 00:56:55,040
Speaker 3: it's going to be the first two weeks in November.

859
00:56:55,239 --> 00:56:59,560
Speaker 3: Well in Pennsylvania, you know, the real excitement is the

860
00:56:59,560 --> 00:57:01,000
Speaker 3: first two weeks in November.

861
00:57:02,200 --> 00:57:07,319
Speaker 2: Yeah, Dwyane. Is there anything else that you wish folks

862
00:57:07,400 --> 00:57:10,200
Speaker 2: knew about your study that's been ongoing for so many

863
00:57:10,280 --> 00:57:12,000
Speaker 2: years now or where it's head. Is there any final

864
00:57:12,000 --> 00:57:13,160
Speaker 2: thought you want to leave folks with.

865
00:57:13,640 --> 00:57:17,360
Speaker 3: Well, the project is actually wrapping up. It'll be done

866
00:57:17,680 --> 00:57:23,040
Speaker 3: June of twenty twenty six, and so this will be

867
00:57:23,200 --> 00:57:27,600
Speaker 3: the last year that we'll be collecting factor telemetry data

868
00:57:27,600 --> 00:57:32,640
Speaker 3: will be ending in February. So we have, you know,

869
00:57:32,720 --> 00:57:35,320
Speaker 3: the twenty twenty five RUT tracker, so you can go

870
00:57:35,360 --> 00:57:41,000
Speaker 3: and see the averages, will show you some of the variation.

871
00:57:41,720 --> 00:57:43,960
Speaker 3: And I think what we're going to do this year is,

872
00:57:45,400 --> 00:57:47,560
Speaker 3: I guess kind of like the Big Fat Bear week

873
00:57:48,000 --> 00:57:50,720
Speaker 3: I can't. I can't show you the actual movements of

874
00:57:50,840 --> 00:57:54,520
Speaker 3: deer this year just because you know the ethical issues

875
00:57:54,560 --> 00:57:57,600
Speaker 3: of you know, this deer is radio collared, it's available

876
00:57:57,600 --> 00:58:00,400
Speaker 3: to be harvested. I don't want to share you know,

877
00:58:00,640 --> 00:58:03,880
Speaker 3: current location information, but we're going to go back through

878
00:58:03,920 --> 00:58:09,040
Speaker 3: our archives and find deer that have really interesting movements

879
00:58:09,320 --> 00:58:13,000
Speaker 3: and share that on the blog for folks if they

880
00:58:13,000 --> 00:58:15,200
Speaker 3: want to come see that. So I think I think

881
00:58:15,240 --> 00:58:20,200
Speaker 3: that'll be fun and we'll, you know, update on how

882
00:58:20,240 --> 00:58:24,760
Speaker 3: things go this year and then kind of share what

883
00:58:25,000 --> 00:58:27,720
Speaker 3: some of these bucks that we're not following them anymore

884
00:58:27,800 --> 00:58:29,560
Speaker 3: and I don't even know if they're alive or not,

885
00:58:29,800 --> 00:58:32,959
Speaker 3: but you can see what they actually did during the rut.

886
00:58:34,000 --> 00:58:37,440
Speaker 2: Very interesting. Well, I have been following the blog for

887
00:58:37,560 --> 00:58:41,360
Speaker 2: a long time now. I've found it to be really

888
00:58:41,400 --> 00:58:43,600
Speaker 2: fascinating over the years. I appreciate all the work that

889
00:58:43,640 --> 00:58:46,479
Speaker 2: you guys have been putting into it and certainly would

890
00:58:46,520 --> 00:58:48,840
Speaker 2: encourage anyone listening to go check it out. Am I

891
00:58:48,920 --> 00:58:51,880
Speaker 2: right that the URL that'll take you to the main

892
00:58:51,960 --> 00:58:55,280
Speaker 2: homepage at lease is that deer dot p SU dot edu.

893
00:58:55,680 --> 00:58:56,120
Speaker 3: Correct.

894
00:58:56,720 --> 00:58:59,840
Speaker 2: Perfect, All right, Well, Dwyane, thanks for all your work

895
00:58:59,880 --> 00:59:01,120
Speaker 2: and thanks for this chat.

896
00:59:01,560 --> 00:59:03,240
Speaker 3: Oh, you're welcome, it's fine.

897
00:59:05,360 --> 00:59:09,480
Speaker 2: And now are excerpts from my twenty fourteen conversation with

898
00:59:09,560 --> 00:59:12,439
Speaker 2: Matt Ross about the science of the white tail run.

899
00:59:16,320 --> 00:59:17,960
Speaker 4: Well hunters want to know about the rut is how

900
00:59:18,040 --> 00:59:18,840
Speaker 4: can it kill something?

901
00:59:18,920 --> 00:59:19,120
Speaker 6: Right?

902
00:59:19,480 --> 00:59:22,640
Speaker 4: When should I be out there? Yes, for the most part,

903
00:59:23,520 --> 00:59:26,760
Speaker 4: it's consistent year to year. You can pick the first

904
00:59:26,880 --> 00:59:29,560
Speaker 4: or second and in some cases third week in November

905
00:59:30,080 --> 00:59:31,960
Speaker 4: and take time off and go out there and hunt,

906
00:59:32,040 --> 00:59:35,200
Speaker 4: and you're going to see some activity. It's a bomber

907
00:59:35,200 --> 00:59:36,960
Speaker 4: when you're out there and you're not seeing much, and

908
00:59:37,000 --> 00:59:40,200
Speaker 4: that is impacted by other things. There are other influences

909
00:59:41,080 --> 00:59:43,440
Speaker 4: that can change that. And again this goes back to

910
00:59:43,480 --> 00:59:47,040
Speaker 4: the research where you know, I can tell you, you know,

911
00:59:47,040 --> 00:59:49,640
Speaker 4: in terms of weather and things, what the research says.

912
00:59:49,920 --> 00:59:51,760
Speaker 4: You know, my gut tells me some of that stuff

913
00:59:51,840 --> 00:59:53,959
Speaker 4: is not you know, there's something that we haven't found

914
00:59:53,960 --> 00:59:56,320
Speaker 4: out yet. And I'll just tell you for the most part,

915
00:59:56,360 --> 00:59:59,760
Speaker 4: there hasn't been any research that says weather and I'm

916
00:59:59,760 --> 01:00:03,560
Speaker 4: talking everything from barometric pressure to rain events, to temperature

917
01:00:03,640 --> 01:00:07,760
Speaker 4: drops to all this stuff. As we're talking about collar deer,

918
01:00:08,000 --> 01:00:11,080
Speaker 4: hundreds of collar deer in some cases in some of

919
01:00:11,080 --> 01:00:14,520
Speaker 4: these studies and have not seen a correlation to a

920
01:00:14,560 --> 01:00:17,880
Speaker 4: weather change almost every variable you can think of with

921
01:00:18,000 --> 01:00:21,800
Speaker 4: weather and see any difference in deer activity. Again, we

922
01:00:21,800 --> 01:00:24,360
Speaker 4: don't know. We don't have cameras on these deer. We

923
01:00:24,360 --> 01:00:26,280
Speaker 4: don't know if they're actually breeding, but we can actually

924
01:00:26,280 --> 01:00:29,320
Speaker 4: monitor activity, how much they're moving in a day or

925
01:00:29,360 --> 01:00:32,560
Speaker 4: a twenty four hour period, or how long those distances are,

926
01:00:32,600 --> 01:00:34,840
Speaker 4: and there hasn't been any. My gut tells me there's

927
01:00:34,840 --> 01:00:39,360
Speaker 4: something weather related out there, and I still want to

928
01:00:39,400 --> 01:00:42,640
Speaker 4: plan when I'm hunting based on some of that. But

929
01:00:42,680 --> 01:00:44,880
Speaker 4: the neat thing is I can go out there and

930
01:00:44,920 --> 01:00:47,400
Speaker 4: sit out there and see a frenzy of activity. I

931
01:00:47,440 --> 01:00:49,720
Speaker 4: get of a buddy two counties over that I'm texting

932
01:00:49,760 --> 01:00:54,439
Speaker 4: that's seeing completely something different, and that's property specific. It's

933
01:00:54,480 --> 01:00:57,240
Speaker 4: even the deer herd specific to that property. Based on

934
01:00:58,000 --> 01:01:01,120
Speaker 4: those deer. I mean, maybe the the doze on the

935
01:01:01,120 --> 01:01:04,560
Speaker 4: property I'm on are all synchronized and they're all coming

936
01:01:04,560 --> 01:01:07,240
Speaker 4: into estress around the same time or just before, or

937
01:01:07,600 --> 01:01:11,560
Speaker 4: maybe there's those handful of early breeding events that are

938
01:01:11,560 --> 01:01:14,160
Speaker 4: happening that are making all bucks so crazy. I mean,

939
01:01:14,240 --> 01:01:16,439
Speaker 4: it's so site specific and one of the cool things

940
01:01:16,440 --> 01:01:20,200
Speaker 4: that we've done at QTMA is partnered with some other

941
01:01:20,840 --> 01:01:26,440
Speaker 4: organizations Sitka, Cabelas and others with powder Hook and developed

942
01:01:26,440 --> 01:01:28,640
Speaker 4: an app to track some of that stuff, to create

943
01:01:28,680 --> 01:01:32,520
Speaker 4: a heat map of daytime activity where you can just

944
01:01:32,680 --> 01:01:36,000
Speaker 4: log in your observations of what you're seeing or the

945
01:01:36,040 --> 01:01:39,040
Speaker 4: deer you're killing, and they take all that into account

946
01:01:39,280 --> 01:01:41,720
Speaker 4: and create that. It's a really, really neat thing. So

947
01:01:42,000 --> 01:01:43,560
Speaker 4: when it comes down to the rut, I mean, why

948
01:01:43,560 --> 01:01:45,480
Speaker 4: do people want to talk about it. I want to

949
01:01:45,480 --> 01:01:47,320
Speaker 4: talk about it because they want to figure out how

950
01:01:47,400 --> 01:01:49,360
Speaker 4: they can best be successful to go out there and

951
01:01:49,400 --> 01:01:52,840
Speaker 4: show it a deer specifically a buck. One of the

952
01:01:52,880 --> 01:01:54,600
Speaker 4: best things that I can offer to you is take

953
01:01:54,640 --> 01:01:57,600
Speaker 4: this science and use it to the best of your ability.

954
01:01:57,640 --> 01:01:59,880
Speaker 4: I mean, but at the same time, a lot of

955
01:01:59,880 --> 01:02:02,840
Speaker 4: it has to be site specific, and some of the

956
01:02:02,880 --> 01:02:04,600
Speaker 4: stuff that you get to talk about on your show

957
01:02:05,360 --> 01:02:07,960
Speaker 4: is you as a hunter, you as a lease or

958
01:02:08,000 --> 01:02:11,919
Speaker 4: a landowner, just keeping tabs on that deer herd, either

959
01:02:11,920 --> 01:02:15,840
Speaker 4: through trail cameras or individual box and tracking them throughout

960
01:02:15,840 --> 01:02:17,520
Speaker 4: the year and getting a sense of when stuff is

961
01:02:17,520 --> 01:02:20,800
Speaker 4: happening and trying to be ready for when it happens

962
01:02:20,840 --> 01:02:23,720
Speaker 4: within the window of when the bigger science says, you

963
01:02:23,720 --> 01:02:25,040
Speaker 4: know what, there's about a two to two and a

964
01:02:25,080 --> 01:02:27,280
Speaker 4: half week window when I should be out there, and

965
01:02:27,320 --> 01:02:29,439
Speaker 4: then just try to target when you need to be out.

966
01:02:40,520 --> 01:02:42,160
Speaker 2: Yeah, that makes a lot of sense, and I think it's,

967
01:02:42,480 --> 01:02:45,720
Speaker 2: you know, right in line with what you said is

968
01:02:45,800 --> 01:02:48,560
Speaker 2: looking at this the high level scientific data, but then

969
01:02:48,600 --> 01:02:52,720
Speaker 2: to your point, understanding the site specific uniqueness of your

970
01:02:52,760 --> 01:02:56,200
Speaker 2: property and the situation at hand. And that brings me

971
01:02:56,280 --> 01:02:59,520
Speaker 2: to something that I that I am equally fascinated by

972
01:02:59,520 --> 01:03:02,720
Speaker 2: that I know you looked into, which is actual buck

973
01:03:02,840 --> 01:03:05,520
Speaker 2: behavior during the rut. I know there's been a number

974
01:03:05,560 --> 01:03:08,880
Speaker 2: of GPS studies that have looked into this. Two things

975
01:03:08,880 --> 01:03:11,760
Speaker 2: specifically that I have found interesting about behavior that these

976
01:03:11,760 --> 01:03:14,920
Speaker 2: studies have shown are the phenomena of how they relate

977
01:03:14,960 --> 01:03:18,240
Speaker 2: to focal points and then also this other phenomena of

978
01:03:18,760 --> 01:03:21,479
Speaker 2: taking these excursions. Could you share with us a little

979
01:03:21,520 --> 01:03:24,400
Speaker 2: bit about what these studies have found about buck behavior

980
01:03:24,600 --> 01:03:27,520
Speaker 2: during the rut related to those two things.

981
01:03:27,680 --> 01:03:30,640
Speaker 4: Yeah, no problem. Let me talk about the focal points first.

982
01:03:31,400 --> 01:03:33,920
Speaker 4: Aaron Foley, who's a research at Texas A and M,

983
01:03:33,960 --> 01:03:36,080
Speaker 4: and a lot of his co authors looked at this.

984
01:03:36,240 --> 01:03:39,320
Speaker 4: There's a multiple year study looking at a bunch of

985
01:03:39,360 --> 01:03:43,880
Speaker 4: different things related to bucks and their use of space,

986
01:03:43,920 --> 01:03:47,240
Speaker 4: and again out of Texas and for people that aren't

987
01:03:47,520 --> 01:03:50,920
Speaker 4: qtm A members. This was actually a feature article and

988
01:03:51,000 --> 01:03:53,280
Speaker 4: Quality White Tails that's our publication that comes out every

989
01:03:53,280 --> 01:03:58,280
Speaker 4: other month with the last issue. Really really interesting stuff

990
01:03:58,280 --> 01:04:02,439
Speaker 4: in terms of how bucks, you again, their home range

991
01:04:02,520 --> 01:04:06,400
Speaker 4: or their core areas over during the rut and how

992
01:04:06,440 --> 01:04:10,000
Speaker 4: that changes. And for the most part, we've always and

993
01:04:10,400 --> 01:04:13,760
Speaker 4: the research does point to bucks meeting individuals. You know,

994
01:04:13,840 --> 01:04:16,240
Speaker 4: some are up on their feet a lot and they

995
01:04:16,280 --> 01:04:18,680
Speaker 4: move a lot, you know, day or night. Some don't

996
01:04:18,720 --> 01:04:21,280
Speaker 4: move that very much. Some have large home ranges, some

997
01:04:21,320 --> 01:04:23,760
Speaker 4: have very small home ranges, and there's combinations of all

998
01:04:23,800 --> 01:04:27,480
Speaker 4: four of those, and it really varies based on the

999
01:04:27,560 --> 01:04:32,480
Speaker 4: individual buck. When it comes to the focal points. What

1000
01:04:32,640 --> 01:04:35,160
Speaker 4: Mark is asking about is this was one of the

1001
01:04:35,200 --> 01:04:41,000
Speaker 4: first studies that actually showed spatial memory, meaning a buck remembering,

1002
01:04:42,200 --> 01:04:45,840
Speaker 4: if you will, where dough groups are and returning to

1003
01:04:45,920 --> 01:04:51,520
Speaker 4: those places on a fairly consistent basis. What the researchers

1004
01:04:51,520 --> 01:04:54,640
Speaker 4: found there was about every twenty to twenty eight hours

1005
01:04:55,120 --> 01:04:59,440
Speaker 4: they had both Bucks and does collared and they were

1006
01:04:59,480 --> 01:05:05,560
Speaker 4: able to document multiple bocks visiting what the research is

1007
01:05:05,600 --> 01:05:09,400
Speaker 4: called focal areas. There would be somewhere between one to

1008
01:05:09,480 --> 01:05:12,480
Speaker 4: three focal areas within that BUX home range. So if

1009
01:05:12,520 --> 01:05:15,440
Speaker 4: a buck was traveling, you know, one thousand acres, that's

1010
01:05:15,480 --> 01:05:19,360
Speaker 4: what their home range is. I'm just that's a just

1011
01:05:19,400 --> 01:05:22,600
Speaker 4: a random number I'm coming up with. They might have

1012
01:05:22,680 --> 01:05:27,919
Speaker 4: a core area throughout most of the year of five

1013
01:05:27,960 --> 01:05:29,880
Speaker 4: to ten percent of that and that's basically what the

1014
01:05:29,960 --> 01:05:33,520
Speaker 4: research shows. You know, between five to ten percent of

1015
01:05:33,520 --> 01:05:35,240
Speaker 4: a bus home range will be its core area, and

1016
01:05:35,280 --> 01:05:37,080
Speaker 4: it might not be one spot, it might be one

1017
01:05:37,160 --> 01:05:42,760
Speaker 4: or two. During the run, their home range expands, sometimes

1018
01:05:42,800 --> 01:05:46,840
Speaker 4: excessively three four times of size, and they are using

1019
01:05:47,040 --> 01:05:52,000
Speaker 4: more of that space. So the home range I defined

1020
01:05:52,000 --> 01:05:54,560
Speaker 4: it earlier as the space a buck is ninety to

1021
01:05:54,640 --> 01:05:57,240
Speaker 4: ninety five percent of the time the core area has

1022
01:05:57,480 --> 01:05:59,560
Speaker 4: many hunters call them, it's like their bedroom. They're there

1023
01:05:59,600 --> 01:06:02,360
Speaker 4: fifty time. Half the time you'll find the buck there.

1024
01:06:03,400 --> 01:06:06,680
Speaker 4: During the rut bucks, he's less of their core area

1025
01:06:07,200 --> 01:06:11,200
Speaker 4: less often, they're not in that fifty percent space as much,

1026
01:06:11,520 --> 01:06:15,160
Speaker 4: and they're they're using way more of that ninety ninety

1027
01:06:15,480 --> 01:06:17,280
Speaker 4: five percent of their home range. They're out there a

1028
01:06:17,280 --> 01:06:19,480
Speaker 4: lot more than the user, so they're shifting where they

1029
01:06:19,520 --> 01:06:24,080
Speaker 4: are in their home range. The really interesting thing though,

1030
01:06:24,200 --> 01:06:28,720
Speaker 4: is these researchers found that these bucks are not doing

1031
01:06:28,720 --> 01:06:32,880
Speaker 4: it randomly. They are picking these focal areas. They're you know,

1032
01:06:32,880 --> 01:06:36,560
Speaker 4: a handful of them, usually three or four of them

1033
01:06:36,600 --> 01:06:40,240
Speaker 4: within the buck, within that buck's home range, where he's

1034
01:06:40,960 --> 01:06:44,360
Speaker 4: concentrating on it, spending some time there, leaving it, going

1035
01:06:44,400 --> 01:06:47,760
Speaker 4: to another one, leaving it, going to another one, leaving

1036
01:06:47,800 --> 01:06:50,280
Speaker 4: it going to another one, and returning to the original one.

1037
01:06:50,600 --> 01:06:54,040
Speaker 4: Every twenty twenty eight hours, that buck is returning to

1038
01:06:54,080 --> 01:06:58,720
Speaker 4: one of those spots. And with multiple bucks collared, they

1039
01:06:58,720 --> 01:07:01,720
Speaker 4: were able to see this on the landscape where there

1040
01:07:01,840 --> 01:07:05,440
Speaker 4: was and they also had those collared that there were

1041
01:07:06,560 --> 01:07:10,120
Speaker 4: spatial memory where bucks were returning to these spots, saying,

1042
01:07:10,560 --> 01:07:13,000
Speaker 4: you know, there's a dough group there and you'd have

1043
01:07:13,080 --> 01:07:15,520
Speaker 4: one or two or three bucks returning to that spot

1044
01:07:15,680 --> 01:07:18,080
Speaker 4: at different periods to find them. So that gives a

1045
01:07:18,120 --> 01:07:21,760
Speaker 4: lot of confirmation to you know, the whole adage. You know,

1046
01:07:21,800 --> 01:07:24,840
Speaker 4: if you hunt where the dos are, you'll see bucks

1047
01:07:25,280 --> 01:07:28,840
Speaker 4: or along those lines. Yeah, that is true. I mean

1048
01:07:28,880 --> 01:07:31,240
Speaker 4: during the rut, bucks are trying to find these does

1049
01:07:31,280 --> 01:07:34,880
Speaker 4: they're checking the receptiveness possibly and returning and that research

1050
01:07:34,960 --> 01:07:37,400
Speaker 4: is ongoing, but that that is something that's really interesting.

1051
01:07:38,000 --> 01:07:42,960
Speaker 4: The other thing that Mark asked about was about excursions,

1052
01:07:43,040 --> 01:07:46,000
Speaker 4: and that's something else I've been able to look in

1053
01:07:46,120 --> 01:07:49,280
Speaker 4: depth at. And one of the things that we found

1054
01:07:49,360 --> 01:07:53,479
Speaker 4: with excursions is that they happened year round. By far,

1055
01:07:53,520 --> 01:07:57,520
Speaker 4: they're more rut related fall time excursions in there are spring,

1056
01:07:58,160 --> 01:08:01,680
Speaker 4: but there has been documented cases from Pennsylvania all the

1057
01:08:01,720 --> 01:08:06,080
Speaker 4: way down to Louisiana and everywhere in between, from agricultural

1058
01:08:06,200 --> 01:08:12,000
Speaker 4: environments like Iowa and Maryland to heavily forested environments. But

1059
01:08:12,080 --> 01:08:16,040
Speaker 4: bucks are making these excursions, and what they are is

1060
01:08:16,280 --> 01:08:19,400
Speaker 4: within that home range where a buck is ninety to

1061
01:08:19,479 --> 01:08:24,679
Speaker 4: ninety five percent of time, they might spend take sometimes one,

1062
01:08:24,840 --> 01:08:30,240
Speaker 4: sometimes multiple events where they leave that space. They're gone

1063
01:08:30,280 --> 01:08:32,439
Speaker 4: for a very short period of time, usually a day

1064
01:08:32,680 --> 01:08:38,479
Speaker 4: for thirty six hours, and they return quickly. There's by

1065
01:08:38,600 --> 01:08:42,200
Speaker 4: far more rut related excursions that are happening, almost probably

1066
01:08:42,200 --> 01:08:46,640
Speaker 4: a three to one, and it's generally about half of

1067
01:08:46,760 --> 01:08:50,840
Speaker 4: bucks make them and all the ones that do go

1068
01:08:51,960 --> 01:08:55,559
Speaker 4: the majority do it multiple times. It's almost going back

1069
01:08:55,560 --> 01:08:58,800
Speaker 4: to that individuality where you know, if a buck's got

1070
01:08:58,800 --> 01:09:01,320
Speaker 4: the propensity to do that, he might do that. So

1071
01:09:01,680 --> 01:09:04,559
Speaker 4: it lends a lot of credibility to the hunter that

1072
01:09:04,680 --> 01:09:06,519
Speaker 4: he's a buck show up on his trail camera, or

1073
01:09:06,520 --> 01:09:08,760
Speaker 4: you're sitting there and his deer comes cruising through that

1074
01:09:08,800 --> 01:09:13,400
Speaker 4: you've never seen before, and you know you miss your

1075
01:09:13,479 --> 01:09:15,439
Speaker 4: chance at it and you never see that deer again.

1076
01:09:16,080 --> 01:09:18,160
Speaker 4: That could be a buck that was on an excursion.

1077
01:09:19,240 --> 01:09:22,120
Speaker 4: Or likewise, if you've been following a deer and you

1078
01:09:22,200 --> 01:09:25,800
Speaker 4: have great documentation of that buck even out of the

1079
01:09:25,840 --> 01:09:29,280
Speaker 4: summer getting into pre ra or even getting into you know,

1080
01:09:29,280 --> 01:09:30,880
Speaker 4: in the next couple of weeks, you're seeing this deer

1081
01:09:30,920 --> 01:09:33,880
Speaker 4: on camera and then all of a sudden, poof, that

1082
01:09:33,960 --> 01:09:37,280
Speaker 4: deer's gone. He may have actually made one of those

1083
01:09:37,280 --> 01:09:40,360
Speaker 4: excursions and the ones that this isn't confirmed, but the

1084
01:09:40,360 --> 01:09:44,400
Speaker 4: ones that are revelated or assumed obviously to be in

1085
01:09:44,439 --> 01:09:47,040
Speaker 4: search of does there might not be enough receptive dos

1086
01:09:47,080 --> 01:09:49,639
Speaker 4: in his home range, she's checked all his focal areas

1087
01:09:50,000 --> 01:09:53,200
Speaker 4: and he's going elsewhere, or you know, very likely the

1088
01:09:53,280 --> 01:09:56,400
Speaker 4: case he's on a dough that's not quite receptive and

1089
01:09:56,439 --> 01:09:59,639
Speaker 4: she takes him outside of his home range. There's actually

1090
01:09:59,640 --> 01:10:03,120
Speaker 4: been one documented case of a booty calling deer where

1091
01:10:04,000 --> 01:10:08,080
Speaker 4: there was a doll that was collared and she left

1092
01:10:08,120 --> 01:10:10,439
Speaker 4: her home range and he left his where they were

1093
01:10:10,479 --> 01:10:13,240
Speaker 4: both ninety nine, and they overlapped a little bit and

1094
01:10:13,240 --> 01:10:17,160
Speaker 4: they rendezvous and they were together for a day or so,

1095
01:10:17,320 --> 01:10:19,479
Speaker 4: and that was in Tennessee, and they went back to

1096
01:10:19,520 --> 01:10:20,799
Speaker 4: their respective home ranges.

1097
01:10:22,920 --> 01:10:24,519
Speaker 5: Yeah for a college bar.

1098
01:10:25,439 --> 01:10:30,200
Speaker 4: Yeah yeah. So there's all this interesting stuff going out there.

1099
01:10:30,880 --> 01:10:32,720
Speaker 4: And that's probably also you know, when one of those

1100
01:10:32,720 --> 01:10:36,840
Speaker 4: guys that you know, you see a social media post

1101
01:10:36,960 --> 01:10:39,160
Speaker 4: or somebody shoots a buck that looks a lot like

1102
01:10:39,680 --> 01:10:41,360
Speaker 4: the buck you've been following, and it's a couple of

1103
01:10:41,400 --> 01:10:43,200
Speaker 4: miles away, I mean, it could very well be the

1104
01:10:43,240 --> 01:10:46,360
Speaker 4: same deer and Bill travel anywhere between one to five miles.

1105
01:10:46,720 --> 01:10:50,080
Speaker 4: That's the average distance in these excursions.

1106
01:10:50,080 --> 01:10:55,960
Speaker 7: So the question, I'm sorry in regards to the annual patterning, then,

1107
01:10:57,240 --> 01:10:59,200
Speaker 7: me and Mark have been talking a lot about annual

1108
01:10:59,240 --> 01:11:02,439
Speaker 7: patterning patterning the past couple of weeks and trying to,

1109
01:11:02,600 --> 01:11:05,759
Speaker 7: you know, maybe hunt where we got a trail camera

1110
01:11:05,800 --> 01:11:07,559
Speaker 7: picture of a deer the previous year.

1111
01:11:08,120 --> 01:11:09,760
Speaker 5: Are these excursions or.

1112
01:11:09,720 --> 01:11:15,040
Speaker 7: Focal points like annual, like on the second week of October,

1113
01:11:15,080 --> 01:11:16,880
Speaker 7: you can expect the deer to do the same thing.

1114
01:11:18,439 --> 01:11:21,240
Speaker 4: Now, there's not a lot of there's not a lot

1115
01:11:21,240 --> 01:11:24,800
Speaker 4: of reasons to say that they are continuous in the

1116
01:11:24,840 --> 01:11:28,840
Speaker 4: same place or direction. Some of the research does show

1117
01:11:31,240 --> 01:11:34,000
Speaker 4: some weird stuff where they might have a lot of

1118
01:11:33,760 --> 01:11:36,200
Speaker 4: the collar deer going in the same way, and I

1119
01:11:36,200 --> 01:11:37,920
Speaker 4: think some of that has to do with the terrain

1120
01:11:37,960 --> 01:11:42,040
Speaker 4: and the landscape in those cases, I don't know of

1121
01:11:42,160 --> 01:11:44,160
Speaker 4: anything dan that has said that, you know, you can

1122
01:11:44,200 --> 01:11:46,360
Speaker 4: count that on that deer leaving and then coming back.

1123
01:11:46,720 --> 01:11:49,280
Speaker 4: I wouldn't be surprised if something like that was the case.

1124
01:11:49,400 --> 01:11:52,599
Speaker 4: But again, going down to the individuality of a deer,

1125
01:11:54,040 --> 01:11:56,920
Speaker 4: you know, you might have some that are likely to

1126
01:11:56,960 --> 01:11:58,759
Speaker 4: do that and other ones that are way more random.

1127
01:11:58,800 --> 01:12:00,920
Speaker 4: They just pick up and leave because their brain told

1128
01:12:01,000 --> 01:12:03,680
Speaker 4: them to. The reason I wouldn't be surprised if that

1129
01:12:03,800 --> 01:12:06,040
Speaker 4: happened was, you know, there's a lot of there's a

1130
01:12:06,080 --> 01:12:09,879
Speaker 4: lot of habitual behavior with deer. That's how they survive. Obviously,

1131
01:12:09,920 --> 01:12:13,080
Speaker 4: they know how to how to do something, and even

1132
01:12:13,120 --> 01:12:15,639
Speaker 4: coming down to like when they drop their antlers, you know,

1133
01:12:15,439 --> 01:12:18,360
Speaker 4: you've seen before in some of the research. You know,

1134
01:12:18,640 --> 01:12:21,400
Speaker 4: bucks can drop antlers within a day or two of

1135
01:12:21,439 --> 01:12:23,719
Speaker 4: when they did in the last year. So it wouldn't

1136
01:12:23,720 --> 01:12:27,920
Speaker 4: shock me if somebody had had a collar buck and

1137
01:12:27,960 --> 01:12:30,880
Speaker 4: they showed that these deer were doing the same thing

1138
01:12:31,520 --> 01:12:33,719
Speaker 4: year and then you you're out around the same time.

1139
01:12:34,400 --> 01:12:37,120
Speaker 4: I just don't remember or recall any of the research

1140
01:12:37,160 --> 01:12:40,400
Speaker 4: showing that, and that's probably because the bucks would have

1141
01:12:40,439 --> 01:12:42,880
Speaker 4: to be collared for multiple years, and a lot of

1142
01:12:42,880 --> 01:12:45,160
Speaker 4: these collars don't have the longevity of that. I mean,

1143
01:12:45,160 --> 01:12:49,400
Speaker 4: they're very expensive, but they usually only last year. Sometimes

1144
01:12:49,400 --> 01:12:51,120
Speaker 4: they only last a couple of months, believe or not.

1145
01:12:51,200 --> 01:12:56,000
Speaker 4: But so that hasn't been documented to my knowledge. But

1146
01:12:56,200 --> 01:12:59,040
Speaker 4: I'm I'm a huge fan of what you're asking. Yeah,

1147
01:12:59,200 --> 01:13:01,559
Speaker 4: I mean, I'm I'm kind of in a little bit

1148
01:13:01,560 --> 01:13:03,599
Speaker 4: different stage. I was talking to a friend the other

1149
01:13:03,680 --> 01:13:06,320
Speaker 4: day about the stages of hunting. You know, you're supposed

1150
01:13:06,320 --> 01:13:09,040
Speaker 4: to go through the shooter stage, and then the limiting

1151
01:13:09,080 --> 01:13:12,120
Speaker 4: out stage, and then the trophy stage, and then it

1152
01:13:12,160 --> 01:13:15,800
Speaker 4: goes on to I think the fourth one is the

1153
01:13:15,880 --> 01:13:18,719
Speaker 4: type of tactic you use, and then finally sportsman stage

1154
01:13:18,720 --> 01:13:21,720
Speaker 4: where you're just enjoying the experience. I'm somewhere in the

1155
01:13:21,760 --> 01:13:22,120
Speaker 4: middle of that.

1156
01:13:22,160 --> 01:13:22,519
Speaker 3: I don't think.

1157
01:13:22,520 --> 01:13:26,639
Speaker 4: I think there's something missing for the young father who's

1158
01:13:26,640 --> 01:13:29,479
Speaker 4: got a toddler and a preschooler and flies around the

1159
01:13:29,479 --> 01:13:31,559
Speaker 4: country a lot, and I don't know what I'm doing

1160
01:13:31,560 --> 01:13:36,840
Speaker 4: this year, but I'm a big fan of patterning based

1161
01:13:36,880 --> 01:13:40,519
Speaker 4: on everything from finding sheds to trail camera images in

1162
01:13:40,560 --> 01:13:43,120
Speaker 4: the same part of the property year and you're out

1163
01:13:43,760 --> 01:13:47,320
Speaker 4: and learning a deer and actually going deeper than that,

1164
01:13:47,920 --> 01:13:54,120
Speaker 4: finding a buck early in his life that's patternable that

1165
01:13:54,760 --> 01:13:58,920
Speaker 4: has daylight behaviors, that seems to be up and out

1166
01:13:59,000 --> 01:14:02,519
Speaker 4: at daytime, a lot, that's got above average anler growth,

1167
01:14:02,880 --> 01:14:05,400
Speaker 4: and trying to protect that buck and see him through

1168
01:14:05,400 --> 01:14:09,160
Speaker 4: an older age. That's kind of the niche that I

1169
01:14:09,280 --> 01:14:11,639
Speaker 4: like is just finding a deer that's one or two

1170
01:14:11,720 --> 01:14:15,400
Speaker 4: or even three that's just showing extreme potential, that is

1171
01:14:15,600 --> 01:14:17,320
Speaker 4: up and at him a lot at daytime, and just

1172
01:14:17,760 --> 01:14:20,120
Speaker 4: trying to keep him safe to the point where you

1173
01:14:20,200 --> 01:14:20,960
Speaker 4: might get a shot.

1174
01:14:21,920 --> 01:14:24,080
Speaker 2: And that's pretty fascinating when you can learn a single

1175
01:14:24,120 --> 01:14:26,920
Speaker 2: deer like that over course of several years, and then

1176
01:14:27,240 --> 01:14:29,040
Speaker 2: you know, if you're fortunate enough to put all the

1177
01:14:29,040 --> 01:14:31,280
Speaker 2: pieces together by the time he is fully mature and

1178
01:14:31,320 --> 01:14:34,000
Speaker 2: then actually you know, harvests that deer that's abouzz cools

1179
01:14:34,000 --> 01:14:38,200
Speaker 2: it gets. Yeah, so here here's kind of related to

1180
01:14:38,240 --> 01:14:40,439
Speaker 2: this point. All the ideas here about when you're trying

1181
01:14:40,479 --> 01:14:43,120
Speaker 2: to pattern a deer and understand a deer. And I

1182
01:14:43,160 --> 01:14:45,439
Speaker 2: have two takeaways from the study that you just mentioned

1183
01:14:45,439 --> 01:14:48,680
Speaker 2: that tracked mature buck movement during the rut, and the

1184
01:14:48,720 --> 01:14:51,360
Speaker 2: two big takeaways obviously mentions that yes, deer are taking

1185
01:14:51,360 --> 01:14:54,240
Speaker 2: these excursions, which I think is something that popular common

1186
01:14:54,280 --> 01:14:57,400
Speaker 2: knowledge when it comes to deer behavior during the rut

1187
01:14:58,080 --> 01:15:01,120
Speaker 2: has always been, you know, during the rut, bucks are

1188
01:15:01,120 --> 01:15:04,320
Speaker 2: going everywhere, They're going different places that they're changing the

1189
01:15:04,840 --> 01:15:07,400
Speaker 2: changing the usual routine, and you can't pattern a buck.

1190
01:15:07,479 --> 01:15:09,639
Speaker 2: So part of this I'm seeing in the data here

1191
01:15:09,680 --> 01:15:11,960
Speaker 2: shows that yes, there is some of that excursion behavior.

1192
01:15:12,280 --> 01:15:14,160
Speaker 2: But you know, from what you said and from the

1193
01:15:14,160 --> 01:15:15,960
Speaker 2: stuff I've read, it sounds like that's a little bit

1194
01:15:16,040 --> 01:15:18,160
Speaker 2: less than maybe some have made it out to be.

1195
01:15:18,240 --> 01:15:19,960
Speaker 2: I think a lot of people think it's happening every

1196
01:15:19,960 --> 01:15:22,160
Speaker 2: single day all the time. These bucks are NonStop moving

1197
01:15:22,200 --> 01:15:26,160
Speaker 2: all over to new places, But it sounds like they're

1198
01:15:26,240 --> 01:15:28,479
Speaker 2: actually the majority of time, Yes, they might take a

1199
01:15:28,520 --> 01:15:30,519
Speaker 2: couple of these excursions, but the majority of the time

1200
01:15:30,560 --> 01:15:32,960
Speaker 2: they're focusing still in their home range on a couple

1201
01:15:33,200 --> 01:15:36,320
Speaker 2: consistent places. So my big takeaway from this, and you

1202
01:15:36,360 --> 01:15:38,200
Speaker 2: tell me, Matt, if this is correct for me to

1203
01:15:38,479 --> 01:15:41,519
Speaker 2: take this, but my big takeaway is that during the rut,

1204
01:15:41,560 --> 01:15:44,920
Speaker 2: while there is going to be some randomness, there actually

1205
01:15:45,040 --> 01:15:48,200
Speaker 2: is still some type of consistency that we can dial

1206
01:15:48,280 --> 01:15:51,760
Speaker 2: in on and potentially pattern to a degree to take

1207
01:15:51,760 --> 01:15:54,640
Speaker 2: advantage of during the rut and while you're hunting. Is

1208
01:15:54,640 --> 01:15:55,160
Speaker 2: that accurate?

1209
01:15:55,880 --> 01:15:59,840
Speaker 4: Absolutely, because you're talking about the law of averages there.

1210
01:16:00,360 --> 01:16:03,240
Speaker 4: And although I'm telling you about every other buck will

1211
01:16:03,280 --> 01:16:07,519
Speaker 4: go on an excursion and when they leave, they're gone

1212
01:16:07,800 --> 01:16:10,559
Speaker 4: for a short period of time. I mean it's a

1213
01:16:10,600 --> 01:16:13,320
Speaker 4: day or two, you're talking about multiple weeks. That the

1214
01:16:13,439 --> 01:16:16,800
Speaker 4: rot can last two weeks. Even in terms of all

1215
01:16:16,840 --> 01:16:22,840
Speaker 4: the craziness of all that randomness, it is small percentages

1216
01:16:22,880 --> 01:16:25,200
Speaker 4: of when those things are occurring. It helps explain some

1217
01:16:25,320 --> 01:16:30,519
Speaker 4: of the head scratchers. But for the most part, if

1218
01:16:30,560 --> 01:16:34,640
Speaker 4: you can be in tune with your property and you

1219
01:16:34,720 --> 01:16:38,799
Speaker 4: can locate where deer are, they like to be during

1220
01:16:38,840 --> 01:16:42,639
Speaker 4: that frenzy, because certainly, and you can build your property

1221
01:16:42,680 --> 01:16:44,479
Speaker 4: that way too. You can manage it so that your

1222
01:16:44,520 --> 01:16:48,360
Speaker 4: property has specific locations where you know deer will hold

1223
01:16:48,439 --> 01:16:51,040
Speaker 4: up where they like to be, you know, it's got

1224
01:16:51,080 --> 01:16:53,439
Speaker 4: better cover in it, or things like that. That adds

1225
01:16:53,439 --> 01:16:56,320
Speaker 4: a lot of predictability to it. I mean, being within

1226
01:16:56,640 --> 01:17:00,120
Speaker 4: bull range or gun range and actually making the shot count. Well,

1227
01:17:00,479 --> 01:17:04,560
Speaker 4: that comes down to skill and practice and being proficient

1228
01:17:04,680 --> 01:17:08,559
Speaker 4: and being able to perform under pressure. But you can

1229
01:17:08,600 --> 01:17:14,439
Speaker 4: absolutely change the trajectory of your success by practicing QTM and

1230
01:17:14,840 --> 01:17:19,000
Speaker 4: managing the property and letting dear go and watching all

1231
01:17:19,040 --> 01:17:22,320
Speaker 4: those things unfold, and practicing the low patients. I mean,

1232
01:17:22,360 --> 01:17:26,120
Speaker 4: there's tens of thousands of QTM A members and other

1233
01:17:26,200 --> 01:17:30,240
Speaker 4: QTUM practitioners across the country that have extremely high success

1234
01:17:30,320 --> 01:17:34,240
Speaker 4: rates above the average hunter. And all and all due respects,

1235
01:17:34,240 --> 01:17:35,960
Speaker 4: we're all for the millions of hunters out there, but

1236
01:17:36,320 --> 01:17:39,800
Speaker 4: guys that are like you two and the listeners that

1237
01:17:39,840 --> 01:17:42,280
Speaker 4: are on this, that are listening to this, you can

1238
01:17:42,360 --> 01:17:46,120
Speaker 4: change your fate by that predictability. So I absolutely agree

1239
01:17:46,120 --> 01:17:46,439
Speaker 4: with that.

1240
01:17:46,960 --> 01:17:49,200
Speaker 2: So then here's the next question. Then, because if we're

1241
01:17:49,600 --> 01:17:52,000
Speaker 2: if we're learning trying to learn, these bucks and if

1242
01:17:52,040 --> 01:17:55,200
Speaker 2: we know that, hey, there is some ability to still

1243
01:17:55,280 --> 01:17:58,120
Speaker 2: learn and to some degree pattern and hunt these bucks

1244
01:17:58,160 --> 01:18:02,240
Speaker 2: during the rut, even as we understand the does do

1245
01:18:02,400 --> 01:18:04,800
Speaker 2: control the rut though, right, because everything a buck is

1246
01:18:04,840 --> 01:18:07,000
Speaker 2: doing during the rut during these next couple of weeks

1247
01:18:07,040 --> 01:18:09,400
Speaker 2: is revolved around trying to find that dough that's ready

1248
01:18:09,400 --> 01:18:12,240
Speaker 2: to breed. So when it comes to hunting the rut,

1249
01:18:12,240 --> 01:18:14,559
Speaker 2: then I think a big portion of, you know, what

1250
01:18:14,560 --> 01:18:17,320
Speaker 2: we're trying to do here is understanding where those dos

1251
01:18:17,360 --> 01:18:19,320
Speaker 2: are what they're doing, because that's where the buck wants

1252
01:18:19,320 --> 01:18:21,880
Speaker 2: to be. So is there anything out there that you've

1253
01:18:21,960 --> 01:18:24,720
Speaker 2: learned or that you know? I guess what is a

1254
01:18:24,760 --> 01:18:27,080
Speaker 2: dough doing during the rut? Because we talk a lot

1255
01:18:27,120 --> 01:18:29,160
Speaker 2: about what bucks are doing, but I guess the first

1256
01:18:29,160 --> 01:18:30,680
Speaker 2: thing we need to understand is what are the does doing?

1257
01:18:30,720 --> 01:18:33,520
Speaker 2: So how does dough behavior change during the rut?

1258
01:18:33,800 --> 01:18:37,080
Speaker 4: That's a great question. So a lot of it's similar,

1259
01:18:37,120 --> 01:18:39,599
Speaker 4: but there's some key differences. Obviously, It's just like you know,

1260
01:18:39,640 --> 01:18:43,080
Speaker 4: men are from Mars and all that dose do not

1261
01:18:43,200 --> 01:18:48,439
Speaker 4: have obviously influence of the immensity of what bucks do

1262
01:18:48,640 --> 01:18:52,160
Speaker 4: during the rut. There actually is some evidence like that

1263
01:18:52,200 --> 01:18:54,240
Speaker 4: Booty call example I gave you a few minutes ago,

1264
01:18:54,920 --> 01:18:59,040
Speaker 4: and some other research that actually show dose going out

1265
01:18:59,040 --> 01:19:01,760
Speaker 4: and speaking bucks. I mean that has been documented. It's

1266
01:19:01,800 --> 01:19:04,360
Speaker 4: again it's a proportion and it's a small proportion of

1267
01:19:04,680 --> 01:19:07,360
Speaker 4: the research that I've seen, but it's not like one

1268
01:19:07,400 --> 01:19:10,840
Speaker 4: hundred percent of time the buck is the pursuer. I mean,

1269
01:19:10,840 --> 01:19:13,519
Speaker 4: there is some of that happening, but for the most part,

1270
01:19:13,560 --> 01:19:16,400
Speaker 4: what those are doing is going through that same diet change,

1271
01:19:16,439 --> 01:19:19,120
Speaker 4: the physiological change. The bucks are that we talked to

1272
01:19:18,840 --> 01:19:21,679
Speaker 4: the beginning of the show. They're getting ready for winter,

1273
01:19:22,960 --> 01:19:25,160
Speaker 4: that they're they're bulking up. They need to be ready

1274
01:19:25,200 --> 01:19:27,640
Speaker 4: to survive. They need to make sure that their offspring

1275
01:19:27,960 --> 01:19:30,680
Speaker 4: are in the best condition because they're good mothers and

1276
01:19:30,720 --> 01:19:34,720
Speaker 4: they're trying to get to that point. They're also at

1277
01:19:34,720 --> 01:19:37,639
Speaker 4: the beginning, like right now, really, I mean there's still

1278
01:19:38,040 --> 01:19:41,679
Speaker 4: the majority of those are not quite ready to breed,

1279
01:19:41,760 --> 01:19:45,920
Speaker 4: so that's not going on in their you know, what

1280
01:19:45,960 --> 01:19:48,840
Speaker 4: they're focusing on. They're still trying to bulk up and

1281
01:19:48,920 --> 01:19:52,800
Speaker 4: eat and stay safe. And probably the number one thing

1282
01:19:52,880 --> 01:19:57,720
Speaker 4: that you can do in terms of tracking does is

1283
01:19:57,840 --> 01:20:01,040
Speaker 4: managing your hunting pressure, because they will key in on

1284
01:20:01,560 --> 01:20:04,240
Speaker 4: hunting pressure at a much finer level than bucks will

1285
01:20:04,280 --> 01:20:08,120
Speaker 4: because bucks are rut crazy, testosterone filled and they're not

1286
01:20:08,160 --> 01:20:10,559
Speaker 4: paying attention to what they're doing. Every hunter knows that,

1287
01:20:10,600 --> 01:20:12,880
Speaker 4: and that's why guys like to hunt, and guys like

1288
01:20:12,880 --> 01:20:15,800
Speaker 4: to hunt the rut because it's a time when you

1289
01:20:15,880 --> 01:20:19,320
Speaker 4: had the best chance at a buck, because he's going

1290
01:20:19,400 --> 01:20:22,479
Speaker 4: to make a mistake, he'll be out in daylight, he's

1291
01:20:22,520 --> 01:20:24,120
Speaker 4: going to come by you and not be looking up.

1292
01:20:24,200 --> 01:20:26,720
Speaker 4: All of those things are happening, those aren't under the

1293
01:20:26,760 --> 01:20:29,759
Speaker 4: same influence of testosterone. I mean, clearly, it's a pretty

1294
01:20:29,760 --> 01:20:32,360
Speaker 4: obvious thing. So the thing you need to really be

1295
01:20:32,439 --> 01:20:38,519
Speaker 4: cautious of is hunting pressure where you don't too heavily

1296
01:20:38,600 --> 01:20:41,920
Speaker 4: hunt the property. The really fine balance, but where you

1297
01:20:41,920 --> 01:20:46,000
Speaker 4: don't too heavily hunt the property, where you're alerting those

1298
01:20:46,040 --> 01:20:49,519
Speaker 4: to hunting pressure, elevating, you know, being on the property,

1299
01:20:49,560 --> 01:20:53,120
Speaker 4: all of those things that might make a dough change

1300
01:20:53,120 --> 01:20:55,640
Speaker 4: her behavior. But at the same time be able to

1301
01:20:55,680 --> 01:20:58,719
Speaker 4: manage all the things that we talk about in QDM,

1302
01:20:59,680 --> 01:21:02,240
Speaker 4: taking the right number dos and balancing the deer herd

1303
01:21:02,240 --> 01:21:04,840
Speaker 4: and the sex ratio and all those things. And there's

1304
01:21:04,880 --> 01:21:07,240
Speaker 4: some research out there behind it. I mean, for the

1305
01:21:07,280 --> 01:21:09,880
Speaker 4: most part, I can give you some basic numbers, but

1306
01:21:10,400 --> 01:21:13,519
Speaker 4: a lot of the concurrent research out there that and

1307
01:21:13,560 --> 01:21:17,120
Speaker 4: they agree with each other. Things out of Oklahoma and

1308
01:21:17,280 --> 01:21:20,599
Speaker 4: South Carolina and some other places show that it really

1309
01:21:20,640 --> 01:21:23,639
Speaker 4: only takes a few days of heavy pressure to alert

1310
01:21:23,680 --> 01:21:25,840
Speaker 4: a deer herd and they start changing the way they behave.

1311
01:21:25,920 --> 01:21:30,000
Speaker 4: And this includes dozen bucks, but they'll change when they're

1312
01:21:30,040 --> 01:21:33,920
Speaker 4: out during day versus night. How they get across the property.

1313
01:21:34,080 --> 01:21:36,600
Speaker 4: They still might do that bed to feed movement, but

1314
01:21:36,760 --> 01:21:39,599
Speaker 4: instead of going in a direct line, their path becomes

1315
01:21:39,680 --> 01:21:45,120
Speaker 4: much more complex. The observations of those animals go down.

1316
01:21:45,160 --> 01:21:48,320
Speaker 4: All of this stuff happened after about really three or

1317
01:21:48,479 --> 01:21:53,400
Speaker 4: plus days of pressure. So that's where kind of strategy

1318
01:21:53,479 --> 01:21:56,320
Speaker 4: changes how you do that. So I guess what I

1319
01:21:56,320 --> 01:21:58,720
Speaker 4: would recommend to somebody that wants to focus on that

1320
01:21:58,840 --> 01:22:03,479
Speaker 4: side of it is, first figure out how many dos

1321
01:22:03,520 --> 01:22:06,120
Speaker 4: you need to take, because that's the lowest hole in

1322
01:22:06,160 --> 01:22:08,320
Speaker 4: the bucket. If you have too many deer on the

1323
01:22:08,360 --> 01:22:12,160
Speaker 4: property and not enough food, or some combination of those

1324
01:22:12,160 --> 01:22:14,880
Speaker 4: two things depending on Again, if you're Dan and you

1325
01:22:14,960 --> 01:22:19,040
Speaker 4: live in Iowa, you know, if you have abundant food,

1326
01:22:19,080 --> 01:22:20,960
Speaker 4: you can hold a lot more deer. But if you're

1327
01:22:21,000 --> 01:22:24,479
Speaker 4: if you're limited by food, bacon does is the thing

1328
01:22:24,479 --> 01:22:26,760
Speaker 4: you need to worry about beyond tracking a big deer,

1329
01:22:26,800 --> 01:22:29,080
Speaker 4: because that buck's only going to be as big as

1330
01:22:29,120 --> 01:22:31,160
Speaker 4: he possibly can be if he's fed as well as

1331
01:22:31,160 --> 01:22:34,519
Speaker 4: he possibly can be. It's all antlers take a big

1332
01:22:34,600 --> 01:22:35,599
Speaker 4: gang from nutrition.

1333
01:22:36,320 --> 01:22:40,000
Speaker 2: Can you dive into the science of signposting? So rubs

1334
01:22:40,000 --> 01:22:42,840
Speaker 2: and scrapes are something that you know, we hunters associate

1335
01:22:42,920 --> 01:22:45,640
Speaker 2: with the rut, but can you go into both of

1336
01:22:45,640 --> 01:22:48,600
Speaker 2: those and you know, why are bucks making those? What

1337
01:22:48,680 --> 01:22:50,760
Speaker 2: are they doing with them? And when are they doing them?

1338
01:22:50,920 --> 01:22:52,760
Speaker 2: I guess this is what I'm first curious about from you.

1339
01:22:53,600 --> 01:22:58,439
Speaker 4: Okay, Yeah, there's actually very predictable times when both of

1340
01:22:58,479 --> 01:23:03,920
Speaker 4: those behaviors peak in the woods. Scraping, let's talk about

1341
01:23:03,960 --> 01:23:07,599
Speaker 4: that one. First, bucks we'll start making scrapes really after

1342
01:23:07,720 --> 01:23:10,519
Speaker 4: velvet appeal. They'll start doing a little bit, but by

1343
01:23:10,560 --> 01:23:15,400
Speaker 4: far it ramps up going through early fall. You know,

1344
01:23:15,479 --> 01:23:18,360
Speaker 4: let's just called the rot. The second week in November,

1345
01:23:19,080 --> 01:23:22,320
Speaker 4: you'll see scraping activity peak about a week and a

1346
01:23:22,400 --> 01:23:27,160
Speaker 4: half to fourteen days prior to that. That's when it peaks,

1347
01:23:27,200 --> 01:23:31,519
Speaker 4: and it will maintain that peak up until when breeding

1348
01:23:31,560 --> 01:23:37,160
Speaker 4: starts occurring. So right now, late October is the time

1349
01:23:37,240 --> 01:23:40,320
Speaker 4: when you see scrape more scrapes than any other time

1350
01:23:40,360 --> 01:23:43,120
Speaker 4: of the year. It'll continue for a couple of weeks

1351
01:23:43,120 --> 01:23:46,400
Speaker 4: and actually in the probably second week in November, when

1352
01:23:46,720 --> 01:23:49,320
Speaker 4: most breeding is occurring, it starts to drop off. There's

1353
01:23:49,360 --> 01:23:51,600
Speaker 4: usually a little bit of peak after that when you

1354
01:23:51,640 --> 01:23:55,400
Speaker 4: see second rod occurring or things like that, but there

1355
01:23:55,400 --> 01:23:58,280
Speaker 4: should be the most scrapes on your properties right now.

1356
01:23:59,600 --> 01:24:03,719
Speaker 4: Rubbing actually increases and peaks with the peak of breeding,

1357
01:24:04,240 --> 01:24:08,000
Speaker 4: so that usually will peak a little bit later in

1358
01:24:08,040 --> 01:24:11,280
Speaker 4: the fall. About when bucks are and dose are actually

1359
01:24:11,320 --> 01:24:12,840
Speaker 4: starting to breathe.

1360
01:24:13,680 --> 01:24:16,360
Speaker 2: And why, what's the science say about why they're doing

1361
01:24:16,360 --> 01:24:17,240
Speaker 2: those two things.

1362
01:24:18,840 --> 01:24:20,960
Speaker 4: Well, the science behind it is what they're doing is

1363
01:24:20,960 --> 01:24:24,400
Speaker 4: they're leaving sign for other deer. I mean, deer are

1364
01:24:24,720 --> 01:24:30,680
Speaker 4: very social animals. They speak to each other through vocalizations.

1365
01:24:30,680 --> 01:24:34,040
Speaker 4: That's while we buy grunt calls and dough bleats and

1366
01:24:34,160 --> 01:24:36,600
Speaker 4: cans and all those things because we know that, you know,

1367
01:24:36,680 --> 01:24:41,360
Speaker 4: deer are very vocal with each other. They have a

1368
01:24:41,400 --> 01:24:45,679
Speaker 4: lot of scent production. They will leave sent through seven

1369
01:24:45,800 --> 01:24:50,160
Speaker 4: different glands on their body with bucks seven those six

1370
01:24:51,400 --> 01:24:56,160
Speaker 4: they leave scent on scrapes they leave by urinating and

1371
01:24:56,360 --> 01:24:59,240
Speaker 4: or and or rub urinating in that spot. They will

1372
01:24:59,280 --> 01:25:02,479
Speaker 4: leave scent on a rub by once they make the

1373
01:25:02,560 --> 01:25:07,360
Speaker 4: rub their forehead gland, that's that dark spot between the antlers.

1374
01:25:07,400 --> 01:25:10,040
Speaker 4: All that hair gets really dark because there's a very

1375
01:25:10,080 --> 01:25:14,719
Speaker 4: oily substance. Basically like you know, if you don't shower

1376
01:25:14,760 --> 01:25:17,240
Speaker 4: for a couple of days, your hair gets really greasy.

1377
01:25:17,600 --> 01:25:20,840
Speaker 4: Every single hair follicle on our body has a spacious plant.

1378
01:25:20,840 --> 01:25:24,800
Speaker 4: It produces a little bit of oil to it. Deer

1379
01:25:24,880 --> 01:25:27,320
Speaker 4: mammals they have the same thing, but these areas of

1380
01:25:27,920 --> 01:25:32,520
Speaker 4: high glandular activity, like the forehead or the tarsal gland.

1381
01:25:33,240 --> 01:25:35,880
Speaker 4: You know, these areas will actually produce an abundance of

1382
01:25:35,920 --> 01:25:39,960
Speaker 4: those oils, and they're depositing scent through that. So what

1383
01:25:40,000 --> 01:25:42,599
Speaker 4: they're doing is they're basically leaving their calling card. So

1384
01:25:42,680 --> 01:25:46,719
Speaker 4: any deer bond dell back can go to those places,

1385
01:25:46,760 --> 01:25:52,720
Speaker 4: those signpost locations and smell it and pick up the

1386
01:25:52,800 --> 01:25:56,280
Speaker 4: pheromones of other individuals, and it's basically like going to

1387
01:25:56,360 --> 01:25:58,840
Speaker 4: the deli and leaven your business card to you know,

1388
01:25:58,920 --> 01:26:01,599
Speaker 4: win a free sandwich or free hogi. They're leaving their

1389
01:26:01,640 --> 01:26:04,920
Speaker 4: their business card. They're saying I was here again. It

1390
01:26:05,000 --> 01:26:08,920
Speaker 4: kind of blends into that territoriality of your territorial They're

1391
01:26:08,920 --> 01:26:12,280
Speaker 4: not they're just they're just leaving their sign out there.

1392
01:26:12,320 --> 01:26:15,680
Speaker 4: And it also alerts and also cues a little bit

1393
01:26:15,760 --> 01:26:18,680
Speaker 4: to the rut, you know, when those are priming and

1394
01:26:18,720 --> 01:26:22,160
Speaker 4: getting ready to come into estress. There's some suggestions out

1395
01:26:22,160 --> 01:26:29,040
Speaker 4: there through well known researchers. I haven't seen research that

1396
01:26:29,240 --> 01:26:32,599
Speaker 4: says it it's unfounded, but there's a lot of good

1397
01:26:32,640 --> 01:26:35,679
Speaker 4: research out there that talks about the number of chemical

1398
01:26:35,720 --> 01:26:40,599
Speaker 4: receptors and pheromones in box and immature box versus mature

1399
01:26:40,600 --> 01:26:44,960
Speaker 4: box that that that's irresputable that mature bucks do leave

1400
01:26:45,040 --> 01:26:48,120
Speaker 4: different sense, different types of sense, different types of compounds

1401
01:26:48,160 --> 01:26:50,840
Speaker 4: and young bucks do you know. So the theory there,

1402
01:26:50,880 --> 01:26:53,639
Speaker 4: and that's the theory, is that they're leaving their sign

1403
01:26:53,840 --> 01:26:57,040
Speaker 4: or they're sent for others to smell as a marking

1404
01:26:57,200 --> 01:27:01,479
Speaker 4: of we're here or I'm ready and uh. There's also

1405
01:27:01,560 --> 01:27:06,000
Speaker 4: some suggested evidence that those can be queued quicker into

1406
01:27:06,920 --> 01:27:09,640
Speaker 4: reading or being ready to breed with some of that

1407
01:27:09,840 --> 01:27:13,000
Speaker 4: out there, with more mature bucks and those those right compounds.

1408
01:27:13,040 --> 01:27:16,040
Speaker 4: So it's very interconnected. There's a lot we don't know

1409
01:27:16,080 --> 01:27:20,240
Speaker 4: about deer, but there's a lot of very interesting things

1410
01:27:20,280 --> 01:27:24,280
Speaker 4: going on, and they certainly are are tied together to

1411
01:27:24,360 --> 01:27:25,960
Speaker 4: each other through communication like that.

1412
01:27:37,280 --> 01:27:43,880
Speaker 2: So is there any takeaway for hunters in regards to signposts,

1413
01:27:43,960 --> 01:27:46,360
Speaker 2: because there's lots of different ideas and theories and it's

1414
01:27:46,400 --> 01:27:49,360
Speaker 2: it's changed over the years about hunting over or near

1415
01:27:49,479 --> 01:27:52,800
Speaker 2: rubs or scrapes, But is there any definitive takeaway that

1416
01:27:52,840 --> 01:27:55,720
Speaker 2: we that we have now regards to if it's worth

1417
01:27:55,760 --> 01:27:59,160
Speaker 2: hunting over those two different types of sign well.

1418
01:27:59,080 --> 01:28:02,360
Speaker 4: Scrapes three search shows and I didn't actually say this

1419
01:28:02,400 --> 01:28:05,559
Speaker 4: and then ago these scrapes have been shown the majority

1420
01:28:05,560 --> 01:28:09,800
Speaker 4: of them do happen at night, and rubs I don't

1421
01:28:09,840 --> 01:28:13,439
Speaker 4: actually remember what the research says windows occur, but I

1422
01:28:13,479 --> 01:28:16,280
Speaker 4: do know that that'll increase, you know, the more you

1423
01:28:16,400 --> 01:28:20,760
Speaker 4: have of well balanced bear heard. But just like any

1424
01:28:20,840 --> 01:28:24,160
Speaker 4: hunter that's listening, you know, I go into a woodlaw

1425
01:28:24,200 --> 01:28:26,240
Speaker 4: and I see a bunch of rubs scraps, I get excited.

1426
01:28:26,400 --> 01:28:30,599
Speaker 4: It looks like there's a buck using that property. That's

1427
01:28:30,880 --> 01:28:33,639
Speaker 4: part of the property. Now you can set up over

1428
01:28:33,720 --> 01:28:37,120
Speaker 4: that scrape and hope that you see that deer, but

1429
01:28:37,320 --> 01:28:40,880
Speaker 4: know that he's probably checking those at night. It's about

1430
01:28:40,920 --> 01:28:43,920
Speaker 4: eighty five percent of the research says that scrapes are

1431
01:28:43,920 --> 01:28:46,839
Speaker 4: made and checked at night. You could be a fifteen percenter.

1432
01:28:47,280 --> 01:28:50,320
Speaker 4: I have great daytime pictures of bucks using scrapes, you know,

1433
01:28:50,360 --> 01:28:52,599
Speaker 4: and I could have been sitting there in that stand

1434
01:28:52,680 --> 01:28:54,920
Speaker 4: during that time. Again, if you want to play the

1435
01:28:54,960 --> 01:28:58,960
Speaker 4: law of averages and listen to the research, you can say, well,

1436
01:28:59,040 --> 01:29:01,960
Speaker 4: I'll okay, there's a scrape line going through this part

1437
01:29:02,000 --> 01:29:04,760
Speaker 4: of the property, and there's really no true scrape line.

1438
01:29:04,760 --> 01:29:08,280
Speaker 4: A lot of the research has shown and disputed. You

1439
01:29:08,360 --> 01:29:11,320
Speaker 4: might find us several scrapes on a ridge, you could

1440
01:29:11,320 --> 01:29:14,720
Speaker 4: have a completely different set of bucks using. It's not

1441
01:29:14,800 --> 01:29:17,320
Speaker 4: made by one deer walking in a line. This is

1442
01:29:17,479 --> 01:29:21,360
Speaker 4: just a concentration of activity where bucks are leaving their

1443
01:29:21,400 --> 01:29:24,360
Speaker 4: sign and it's probably because there's doze near there, so

1444
01:29:24,400 --> 01:29:28,880
Speaker 4: they're leaving their sign for dose. You know. So I

1445
01:29:28,880 --> 01:29:30,840
Speaker 4: can go in there with my climber and say, wow,

1446
01:29:30,880 --> 01:29:32,519
Speaker 4: there's a bunch of scrapes right here. I'm going to

1447
01:29:32,560 --> 01:29:34,760
Speaker 4: set up right here and have the expectation of seeing

1448
01:29:34,800 --> 01:29:37,800
Speaker 4: a buck. I know, as a researcher, you know, and

1449
01:29:37,840 --> 01:29:40,320
Speaker 4: a deer biologist that you know what, these are probably

1450
01:29:40,400 --> 01:29:44,160
Speaker 4: being made at night. Eighty five percent of these are

1451
01:29:44,200 --> 01:29:45,920
Speaker 4: being made at night, and there's probably a chance I'm

1452
01:29:45,960 --> 01:29:47,679
Speaker 4: not going to see the buck, but I might still

1453
01:29:47,720 --> 01:29:49,400
Speaker 4: set up there saying, you know what, it could be

1454
01:29:49,400 --> 01:29:52,040
Speaker 4: one of those fifteen percent times at a buck coming through.

1455
01:29:52,320 --> 01:29:54,280
Speaker 4: I've gotten. I've seen a lot of different, you know,

1456
01:29:55,080 --> 01:29:58,320
Speaker 4: responses on our website and social media posts and other

1457
01:29:58,360 --> 01:30:01,879
Speaker 4: things when this research gets put out there in articles

1458
01:30:02,000 --> 01:30:04,240
Speaker 4: or posts or things like that. Is somebody that put

1459
01:30:04,280 --> 01:30:06,760
Speaker 4: a picture of a buck making a scrape, Well, of course, yes,

1460
01:30:06,840 --> 01:30:09,080
Speaker 4: that does. I mean, you can't say absolutely anything. And

1461
01:30:09,080 --> 01:30:12,920
Speaker 4: that's one of the things I mentioned about science is

1462
01:30:13,560 --> 01:30:17,280
Speaker 4: what peer reviewed research gives us is a moment in

1463
01:30:17,360 --> 01:30:21,800
Speaker 4: time the researchers collared bucks or they did this or

1464
01:30:21,880 --> 01:30:25,320
Speaker 4: did that on a property in Iowa to Maryland, to

1465
01:30:25,439 --> 01:30:28,360
Speaker 4: Texas to New York, and you can say, well, that's

1466
01:30:28,400 --> 01:30:31,320
Speaker 4: the case in New York. That the true test of

1467
01:30:31,360 --> 01:30:35,800
Speaker 4: science is repeatability, being able to try it again. And

1468
01:30:35,880 --> 01:30:38,320
Speaker 4: some of his research has been repeated in different parts

1469
01:30:38,320 --> 01:30:41,360
Speaker 4: of the country and they've done the same test and

1470
01:30:41,400 --> 01:30:45,200
Speaker 4: they've shown the same results. That's the real true, part

1471
01:30:45,240 --> 01:30:47,800
Speaker 4: of science is learning what the majority of the time

1472
01:30:47,880 --> 01:30:50,639
Speaker 4: things happen. But as a hunter you need to take

1473
01:30:50,680 --> 01:30:53,479
Speaker 4: that and synthesize and say, how do I apply that

1474
01:30:53,520 --> 01:30:56,559
Speaker 4: to my situation? Shoot, Mark and Dan, I'm still going

1475
01:30:56,640 --> 01:30:58,120
Speaker 4: to go out and set up a stand and be

1476
01:30:58,200 --> 01:31:00,960
Speaker 4: near scrapes because that means there's bucks area. Yeah, I'm

1477
01:31:00,960 --> 01:31:03,080
Speaker 4: not going to think, as you know, as a hunter,

1478
01:31:03,200 --> 01:31:06,040
Speaker 4: that's what I'm going to do. I know my expectations

1479
01:31:06,120 --> 01:31:10,439
Speaker 4: might be that Bock might not be here during daylight,

1480
01:31:11,479 --> 01:31:13,879
Speaker 4: but I also know that there's a lot of activity

1481
01:31:13,920 --> 01:31:15,720
Speaker 4: in that area, so it might be a shot. So

1482
01:31:15,760 --> 01:31:17,439
Speaker 4: you have to just kind of balance all that.

1483
01:31:18,360 --> 01:31:21,840
Speaker 2: Yeah. So true, it's the it's six to one way

1484
01:31:21,840 --> 01:31:23,960
Speaker 2: half does in the other, but finding, you know, taking

1485
01:31:23,960 --> 01:31:26,519
Speaker 2: what you can learn from that, and then you say, okay,

1486
01:31:26,520 --> 01:31:28,800
Speaker 2: well exactly like what you said. The way I think

1487
01:31:28,800 --> 01:31:31,320
Speaker 2: about it is, Okay, I understand that you know eighty

1488
01:31:31,360 --> 01:31:35,080
Speaker 2: percent of this might be happening during dark. But the

1489
01:31:35,080 --> 01:31:36,880
Speaker 2: same time, if you look at that point where there's

1490
01:31:36,920 --> 01:31:38,519
Speaker 2: a scrape, where I could say, okay, I know that

1491
01:31:38,560 --> 01:31:40,760
Speaker 2: there's a twenty percent chance he's touching that he's coming

1492
01:31:40,760 --> 01:31:43,040
Speaker 2: to this place potentially or I could go to some

1493
01:31:43,120 --> 01:31:45,200
Speaker 2: other random place one hundred yards away where there's no

1494
01:31:45,200 --> 01:31:47,200
Speaker 2: scrapes at all, and you know, okay, do I have

1495
01:31:47,240 --> 01:31:49,000
Speaker 2: a twenty percent chance of a buck coming here? Maybe

1496
01:31:49,000 --> 01:31:52,000
Speaker 2: it's even less there because there's you know, no particular reasons.

1497
01:31:52,040 --> 01:31:54,000
Speaker 2: So it's one more little piece of the puzzle you

1498
01:31:54,040 --> 01:31:56,800
Speaker 2: can put potentially in your favor if you apply it.

1499
01:31:57,439 --> 01:31:59,800
Speaker 2: But maybe maybe it's not something to rest your entire

1500
01:31:59,800 --> 01:32:04,840
Speaker 2: strategy on. So yeah, Dan, are you? Are you okay?

1501
01:32:04,880 --> 01:32:05,519
Speaker 2: Over there? You live?

1502
01:32:06,040 --> 01:32:08,720
Speaker 5: We're sucking it in. I'm sponging it up.

1503
01:32:08,760 --> 01:32:13,320
Speaker 7: I want to know one thing based off of yeah,

1504
01:32:13,479 --> 01:32:17,000
Speaker 7: just well, I mean there's literally another episode worth of

1505
01:32:17,080 --> 01:32:20,600
Speaker 7: questions that we could ask you, yeah, and go detail to,

1506
01:32:21,040 --> 01:32:22,000
Speaker 7: you know, to all get out.

1507
01:32:22,120 --> 01:32:24,440
Speaker 5: But you know a lot of people.

1508
01:32:25,680 --> 01:32:31,439
Speaker 7: Use I guess, hunting information that's not scientific to learn

1509
01:32:31,520 --> 01:32:36,559
Speaker 7: how to hunt, Like, oh man, when when the rooster crows,

1510
01:32:36,600 --> 01:32:38,800
Speaker 7: you better be in the timber, or you know, when

1511
01:32:38,840 --> 01:32:42,160
Speaker 7: the cows are standing with their whin their back to

1512
01:32:42,200 --> 01:32:44,040
Speaker 7: the east, you better be in the timber. You know,

1513
01:32:44,320 --> 01:32:48,840
Speaker 7: those kind of things or even myths that are even

1514
01:32:48,920 --> 01:32:52,240
Speaker 7: things that are on like the outdoor channel or on

1515
01:32:52,439 --> 01:32:55,439
Speaker 7: the you know, these celebrities are telling you how to

1516
01:32:56,200 --> 01:32:58,920
Speaker 7: do these things. Are there any myths that science has

1517
01:32:59,040 --> 01:33:04,080
Speaker 7: disproven that's basically just like, hey, that's that's you.

1518
01:33:04,240 --> 01:33:04,840
Speaker 5: You're wrong.

1519
01:33:07,040 --> 01:33:10,040
Speaker 4: That's a great question. There's probably a pile of them, uh,

1520
01:33:10,520 --> 01:33:13,720
Speaker 4: you know, one of the things. And again getting back

1521
01:33:13,760 --> 01:33:17,360
Speaker 4: to the you know what, peer reviewed research says the

1522
01:33:17,400 --> 01:33:20,240
Speaker 4: biggest is probably the moon phase. I mean that that

1523
01:33:20,360 --> 01:33:22,920
Speaker 4: is the one smack dab in the elephant in the

1524
01:33:22,960 --> 01:33:25,760
Speaker 4: room when it comes to the rot as far as

1525
01:33:25,800 --> 01:33:27,760
Speaker 4: I know, you know, I've looked at a lot of

1526
01:33:27,760 --> 01:33:34,280
Speaker 4: different projects where they've looked at moon phase in comparison

1527
01:33:34,360 --> 01:33:38,840
Speaker 4: to bock activity. Again, you don't necessarily know when deer

1528
01:33:38,960 --> 01:33:41,120
Speaker 4: are breeding when they have a collar on, but you

1529
01:33:41,160 --> 01:33:43,559
Speaker 4: can just see when they're on their feet, and that

1530
01:33:43,760 --> 01:33:48,280
Speaker 4: hasn't shown any evidence of, you know, being correlated. When

1531
01:33:48,400 --> 01:33:53,080
Speaker 4: moon phase changes that it's going to impact dear's behavior.

1532
01:33:53,760 --> 01:33:56,519
Speaker 4: It has to do with everything else. I do think

1533
01:33:56,920 --> 01:34:00,559
Speaker 4: the one thing that along those lines is the weather

1534
01:34:00,640 --> 01:34:02,920
Speaker 4: I mentioned earlier. You know, something tells me that weather

1535
01:34:03,040 --> 01:34:05,680
Speaker 4: must impact when deer are moving, but that also has

1536
01:34:05,720 --> 01:34:08,599
Speaker 4: been shown to not be correlated, which that's a head

1537
01:34:08,600 --> 01:34:11,200
Speaker 4: scratcher for me. So you know, I wouldn't be surprised

1538
01:34:11,800 --> 01:34:16,559
Speaker 4: if either of those cases, some some researcher finds evidence that,

1539
01:34:17,000 --> 01:34:20,360
Speaker 4: you know, moon phase in a certain situation. Now I'm

1540
01:34:20,360 --> 01:34:23,479
Speaker 4: talking about five six different projects that have looked at that,

1541
01:34:23,880 --> 01:34:26,320
Speaker 4: and in some cases, you know, half a million data

1542
01:34:26,320 --> 01:34:29,479
Speaker 4: points off of hundreds of bucks that are collared haven't

1543
01:34:29,520 --> 01:34:32,760
Speaker 4: found it. You got to feel like, okay, well, you

1544
01:34:32,800 --> 01:34:34,720
Speaker 4: know there's got to be some truth to that. It

1545
01:34:34,720 --> 01:34:38,599
Speaker 4: wouldn't be some surprise if somebody found one project that said, yeah,

1546
01:34:38,600 --> 01:34:42,680
Speaker 4: it does on the flip side. So somebody could do

1547
01:34:42,680 --> 01:34:44,760
Speaker 4: the same with weather and be as a hunter and

1548
01:34:44,760 --> 01:34:47,040
Speaker 4: wants to say I knew it, But I also know

1549
01:34:47,080 --> 01:34:49,559
Speaker 4: there's half a dozen projects out there that have tied

1550
01:34:49,600 --> 01:34:53,480
Speaker 4: all those data points the weather events, you know, barometric

1551
01:34:53,520 --> 01:34:57,400
Speaker 4: pressure and cold fronts and rain and all those things

1552
01:34:57,479 --> 01:34:59,880
Speaker 4: and haven't found or anything. So you know, I gotta

1553
01:35:00,560 --> 01:35:02,360
Speaker 4: be a hunter in some cases, and I got to

1554
01:35:02,400 --> 01:35:04,000
Speaker 4: be a researcher in others.

1555
01:35:04,400 --> 01:35:07,759
Speaker 7: So as someone like myself who likes to follow the science,

1556
01:35:07,760 --> 01:35:10,200
Speaker 7: I'm going to say something. I'm going to say this

1557
01:35:10,280 --> 01:35:15,280
Speaker 7: out loud just so people hear it. Based Yeah, based

1558
01:35:15,439 --> 01:35:20,240
Speaker 7: on the research that has been done, moon the moon

1559
01:35:20,320 --> 01:35:25,080
Speaker 7: phase does not influence deer movement, has been shown not

1560
01:35:25,160 --> 01:35:26,479
Speaker 7: to influence deer movement.

1561
01:35:27,240 --> 01:35:28,320
Speaker 3: Is that an accurate statement?

1562
01:35:28,840 --> 01:35:30,320
Speaker 4: Okay, very accurate.

1563
01:35:30,520 --> 01:35:31,800
Speaker 5: Based on research.

1564
01:35:33,960 --> 01:35:40,320
Speaker 7: Weather patterns do have been shown to not change deer

1565
01:35:40,800 --> 01:35:45,639
Speaker 7: movement or like influence deer movement. That's another accurate statement.

1566
01:35:45,800 --> 01:35:47,799
Speaker 4: Correct, That is also another accurate statement.

1567
01:35:48,160 --> 01:35:54,439
Speaker 7: So everything that we have as hunters have you know,

1568
01:35:54,960 --> 01:36:00,600
Speaker 7: thought over the years, science is showing that, ye guess what,

1569
01:36:01,240 --> 01:36:05,920
Speaker 7: it's really not so so then that just brings up

1570
01:36:05,960 --> 01:36:09,160
Speaker 7: these questions again, what is influencing deer movement?

1571
01:36:09,640 --> 01:36:09,760
Speaker 4: Here?

1572
01:36:09,880 --> 01:36:11,960
Speaker 2: Here's something I'd like to add on to that, Dan,

1573
01:36:12,920 --> 01:36:16,840
Speaker 2: because I like mash my brain together trying to figure

1574
01:36:16,840 --> 01:36:18,639
Speaker 2: this out too, because just like what you said, Matt,

1575
01:36:18,680 --> 01:36:23,320
Speaker 2: you said the research that these certain research scenarios have

1576
01:36:23,439 --> 01:36:26,920
Speaker 2: said this, But as a hunter, so many of us

1577
01:36:26,960 --> 01:36:30,720
Speaker 2: have anecdotally seen evidence that maybe there's something different. I

1578
01:36:31,320 --> 01:36:34,640
Speaker 2: wonder as I try to think through this, could we

1579
01:36:34,800 --> 01:36:38,120
Speaker 2: be comparing apples to oranges here, and that the study

1580
01:36:38,720 --> 01:36:40,920
Speaker 2: is looking at a certain criteria you're saying, you know,

1581
01:36:41,640 --> 01:36:44,320
Speaker 2: you know, deer movement or dear activity as they are

1582
01:36:44,360 --> 01:36:48,280
Speaker 2: measuring it might be very different from quote unquote dear

1583
01:36:48,320 --> 01:36:50,519
Speaker 2: movement or dear activity that we hunters are looking for.

1584
01:36:50,560 --> 01:36:54,960
Speaker 2: So hypothetically, could this be a scenario where the researcher

1585
01:36:55,080 --> 01:37:00,280
Speaker 2: is studying actual you know, number of feet traveled throughout

1586
01:37:00,320 --> 01:37:02,519
Speaker 2: a twenty four hour period something like that, you know,

1587
01:37:02,680 --> 01:37:05,519
Speaker 2: the actual movement of this year in twenty four hours,

1588
01:37:05,520 --> 01:37:09,679
Speaker 2: and they're saying, regardless of temperature or moon phase, the

1589
01:37:09,720 --> 01:37:13,120
Speaker 2: amount of actual distance traveled is not any different. While

1590
01:37:13,160 --> 01:37:15,920
Speaker 2: from a hunters standpoint, I might be curious in how

1591
01:37:16,000 --> 01:37:19,479
Speaker 2: much movement in the open is happening during daylight, that

1592
01:37:19,640 --> 01:37:21,880
Speaker 2: kind of thing, you know, that's the activity that I'm

1593
01:37:21,880 --> 01:37:24,719
Speaker 2: interested in. So could a cold front increase the amount

1594
01:37:24,720 --> 01:37:27,400
Speaker 2: of movement out of their bedding area during daylight?

1595
01:37:28,040 --> 01:37:28,240
Speaker 4: Is that?

1596
01:37:28,320 --> 01:37:30,560
Speaker 2: You know? Maybe that's what I'm interested in from a

1597
01:37:30,640 --> 01:37:33,240
Speaker 2: hunter standpoint, and maybe the cold front does trigger increased

1598
01:37:33,280 --> 01:37:36,920
Speaker 2: activity there, but it doesn't necessarily change the absolute distance

1599
01:37:36,960 --> 01:37:39,920
Speaker 2: of total travel in twenty four hours. That's my hypothesis.

1600
01:37:40,080 --> 01:37:44,040
Speaker 2: There might be some difference in the actual measurement criteria, Matt,

1601
01:37:44,160 --> 01:37:46,479
Speaker 2: Is there any possible Does that make any sense at all?

1602
01:37:47,040 --> 01:37:50,120
Speaker 4: It does? And not every one of those projects has

1603
01:37:50,120 --> 01:37:52,360
Speaker 4: looked in that, but some of them have. They've looked

1604
01:37:52,360 --> 01:37:57,880
Speaker 4: at vulnerability to harvest from daylight to night versus and

1605
01:37:58,000 --> 01:38:01,920
Speaker 4: also things like distance from tree stands, having like tree

1606
01:38:01,960 --> 01:38:05,400
Speaker 4: stands GPS on some of these properties and looking at

1607
01:38:05,400 --> 01:38:07,840
Speaker 4: how vulnerable they were within one hundred meters or one

1608
01:38:07,880 --> 01:38:11,120
Speaker 4: hundred yard distance if it was gun season or you know,

1609
01:38:11,280 --> 01:38:15,960
Speaker 4: thirty yard distance from those stands during bow. And again

1610
01:38:16,000 --> 01:38:19,080
Speaker 4: none of that stuff has shown any correlation. Is a

1611
01:38:19,200 --> 01:38:21,559
Speaker 4: lot of stuff, Yeah, a lot of stuff with the

1612
01:38:21,600 --> 01:38:26,120
Speaker 4: moon phase that was initially done looked at conceptions and

1613
01:38:26,160 --> 01:38:28,439
Speaker 4: that was a little based on fetal measurements. They looked

1614
01:38:28,439 --> 01:38:31,120
Speaker 4: at when the bulk of the deer those were being

1615
01:38:31,160 --> 01:38:35,640
Speaker 4: bred and correlated that to moon. No, no correlation. But

1616
01:38:35,680 --> 01:38:38,880
Speaker 4: a lot of this GPS research is also looking at

1617
01:38:39,160 --> 01:38:44,240
Speaker 4: breeding dates, but they're also looking at movements over a

1618
01:38:44,280 --> 01:38:47,559
Speaker 4: twenty four period and some of them would disagree that

1619
01:38:48,520 --> 01:38:52,000
Speaker 4: Mark is saying, you know, daytime versus night and other things.

1620
01:38:52,000 --> 01:38:56,760
Speaker 4: So and again, you know, I said this before it

1621
01:38:56,840 --> 01:38:59,479
Speaker 4: comes down to the property. I mean it really does.

1622
01:38:59,560 --> 01:39:01,920
Speaker 4: I mean, you can talk about this big umbrella of

1623
01:39:01,960 --> 01:39:05,080
Speaker 4: what research is saying if you want to believe, I

1624
01:39:05,120 --> 01:39:07,880
Speaker 4: mean I will. I'm glad Dan said that that is

1625
01:39:07,880 --> 01:39:10,400
Speaker 4: the take on message. There's no research to support any

1626
01:39:10,439 --> 01:39:14,120
Speaker 4: of those theories. It doesn't it doesn't come out. However,

1627
01:39:14,800 --> 01:39:18,200
Speaker 4: you know, when it comes down to it, don't don't

1628
01:39:18,240 --> 01:39:20,719
Speaker 4: throw that away. Don't throw it in the trash paper

1629
01:39:20,760 --> 01:39:23,360
Speaker 4: basket when you get off listen to this and say

1630
01:39:23,800 --> 01:39:26,120
Speaker 4: that guy doesn't know what he's talking about, or well,

1631
01:39:26,160 --> 01:39:29,679
Speaker 4: I know because I saw this. That's not good enough

1632
01:39:29,720 --> 01:39:33,520
Speaker 4: because you're not talking about hundreds of deers with collars,

1633
01:39:33,640 --> 01:39:36,799
Speaker 4: or you know, thousands or ten thousands of data points

1634
01:39:36,800 --> 01:39:40,719
Speaker 4: that are collared. That's very different. But I would still

1635
01:39:40,760 --> 01:39:44,280
Speaker 4: suggest to the hunter that wants to micro manage the

1636
01:39:44,320 --> 01:39:48,479
Speaker 4: property and understand this. Research might say all this. It

1637
01:39:48,560 --> 01:39:52,760
Speaker 4: might show that these things aren't correlated. But I can

1638
01:39:52,800 --> 01:39:56,400
Speaker 4: tell you might be the day that you go out

1639
01:39:56,400 --> 01:39:59,080
Speaker 4: there and say, you know what the moon phase is

1640
01:39:59,120 --> 01:40:01,920
Speaker 4: telling me this, or there's a cold front. And again

1641
01:40:02,000 --> 01:40:03,759
Speaker 4: I'm telling you, as a hunter, I feel like weather

1642
01:40:03,880 --> 01:40:06,800
Speaker 4: must do more than what the researchers said. But it

1643
01:40:06,840 --> 01:40:10,160
Speaker 4: hasn't shown up. It just has to be something related

1644
01:40:10,200 --> 01:40:15,080
Speaker 4: to it. But again, I'm a deer hunter too. You

1645
01:40:15,160 --> 01:40:17,800
Speaker 4: could be in a stand and have a buck make

1646
01:40:17,840 --> 01:40:20,400
Speaker 4: a decision that changes his fate and you kill him,

1647
01:40:20,840 --> 01:40:24,080
Speaker 4: and you might tie that to one of those multiple

1648
01:40:24,080 --> 01:40:28,320
Speaker 4: things we're talking about, but that's still anecdotal. But who

1649
01:40:28,360 --> 01:40:32,080
Speaker 4: cares You still kill that deer he changes behavior, or

1650
01:40:32,120 --> 01:40:34,800
Speaker 4: even at a property level, you might be on a

1651
01:40:34,840 --> 01:40:37,320
Speaker 4: property where things are in a frenzy. It has nothing

1652
01:40:37,360 --> 01:40:41,360
Speaker 4: to do with moon or weather. It's just because deer

1653
01:40:41,400 --> 01:40:45,200
Speaker 4: is so social. You know, something impacted them. You know

1654
01:40:45,280 --> 01:40:47,880
Speaker 4: a couple of those that went into estras early or

1655
01:40:47,920 --> 01:40:50,840
Speaker 4: a buck just that felt so you know, Randy, he

1656
01:40:50,920 --> 01:40:52,960
Speaker 4: was getting those up and moving them around, and that

1657
01:40:53,120 --> 01:40:55,439
Speaker 4: just triggered other deer to get up and move. I mean,

1658
01:40:55,479 --> 01:40:57,000
Speaker 4: I know you guys have seen this where you're in

1659
01:40:57,080 --> 01:41:00,559
Speaker 4: a in a stand and you see deer almost playing

1660
01:41:00,800 --> 01:41:04,000
Speaker 4: another deer react to it by playing, or deer running

1661
01:41:04,040 --> 01:41:06,920
Speaker 4: away from beer from a kyle or a hunter or

1662
01:41:06,960 --> 01:41:10,559
Speaker 4: a buck chasing, and other deer do that. I mean,

1663
01:41:10,680 --> 01:41:14,240
Speaker 4: there's no way to measure that randomness. All you can

1664
01:41:14,280 --> 01:41:18,439
Speaker 4: do as a hunter is know when your best chance

1665
01:41:18,760 --> 01:41:21,160
Speaker 4: of shooting a buck is or your best chance of

1666
01:41:21,160 --> 01:41:24,719
Speaker 4: getting that deer within range, and spend as much time

1667
01:41:24,760 --> 01:41:27,320
Speaker 4: as you possibly can in the stand because it's going

1668
01:41:27,400 --> 01:41:29,800
Speaker 4: to increase that percentage of Yes, you're going to get

1669
01:41:29,800 --> 01:41:32,600
Speaker 4: a shot. And I talked about earlier, your proficiency and

1670
01:41:32,680 --> 01:41:35,240
Speaker 4: your ability to pick a good stand location is going

1671
01:41:35,280 --> 01:41:37,080
Speaker 4: to be a big part of that success. So your

1672
01:41:37,120 --> 01:41:39,840
Speaker 4: skill level as a hunter certainly will play a part

1673
01:41:39,880 --> 01:41:42,880
Speaker 4: of it, and luck, of course obviously comes into it too.

1674
01:41:42,960 --> 01:41:46,240
Speaker 4: But if I was a betting man, I would you know,

1675
01:41:46,600 --> 01:41:48,640
Speaker 4: we know that deer are most active at dawn in

1676
01:41:48,720 --> 01:41:51,720
Speaker 4: dusk or around those hours. Deer killed all the time

1677
01:41:51,760 --> 01:41:55,040
Speaker 4: in the middle of the day, but you know, the

1678
01:41:55,040 --> 01:41:57,559
Speaker 4: bulk of the research says their most active at dawn

1679
01:41:57,640 --> 01:42:02,040
Speaker 4: and dusk. There going to be most vulnerable during the rot.

1680
01:42:02,080 --> 01:42:04,479
Speaker 4: You know, those first couple of weeks in November, they're

1681
01:42:04,560 --> 01:42:10,080
Speaker 4: going to be most vulnerable. Uh. What I feel like

1682
01:42:10,280 --> 01:42:12,839
Speaker 4: is when a weather event is happening. Although the research

1683
01:42:12,880 --> 01:42:15,200
Speaker 4: doesn't say that, I'm going to spend time in a

1684
01:42:15,240 --> 01:42:17,880
Speaker 4: stand when that stuff is happening, and just the fact

1685
01:42:17,920 --> 01:42:20,200
Speaker 4: that I'm spending time out there is going to increase

1686
01:42:20,200 --> 01:42:23,719
Speaker 4: my chances. Now, I might believe a magic rock will

1687
01:42:23,720 --> 01:42:25,600
Speaker 4: increase my chances, and I might keep it in my

1688
01:42:25,680 --> 01:42:27,880
Speaker 4: pocket and I might kill a buck and I'm gonna say,

1689
01:42:27,880 --> 01:42:30,479
Speaker 4: you know what, that rock made, that that happen. That

1690
01:42:30,520 --> 01:42:33,280
Speaker 4: doesn't necessarily it's not a cause and effect thing, but

1691
01:42:33,360 --> 01:42:37,360
Speaker 4: it doesn't matter because you know, through science, when it's

1692
01:42:37,400 --> 01:42:39,519
Speaker 4: telling you to spend the most time and just be

1693
01:42:39,640 --> 01:42:43,879
Speaker 4: out there, be present and be one, you know, mentally

1694
01:42:43,960 --> 01:42:46,840
Speaker 4: recording all of this stuff or just physically recording it

1695
01:42:46,880 --> 01:42:49,920
Speaker 4: like through that bear tracker app I mentioned earlier, to

1696
01:42:50,200 --> 01:42:55,000
Speaker 4: you know, allow for a better uh documentation of what's

1697
01:42:55,040 --> 01:42:58,800
Speaker 4: happening on the property across the country, as well as

1698
01:42:58,840 --> 01:43:00,519
Speaker 4: just being there to be able to make some of

1699
01:43:00,520 --> 01:43:03,559
Speaker 4: those choices and react to them. If you see there's

1700
01:43:03,560 --> 01:43:05,559
Speaker 4: a lot of activity in one corner of the property,

1701
01:43:06,080 --> 01:43:09,439
Speaker 4: be adapted to move your stand that day, get down,

1702
01:43:09,520 --> 01:43:13,360
Speaker 4: move it, and uh you might be more successful because

1703
01:43:13,360 --> 01:43:13,599
Speaker 4: of it.

1704
01:43:14,880 --> 01:43:18,439
Speaker 2: Yeah, I uh, this is one of those topics that

1705
01:43:18,920 --> 01:43:21,000
Speaker 2: is it's.

1706
01:43:21,000 --> 01:43:23,120
Speaker 5: Bullet Yeah, it's just for me.

1707
01:43:23,160 --> 01:43:26,040
Speaker 7: It's one of those things where it's like, man, you

1708
01:43:26,160 --> 01:43:29,120
Speaker 7: talk to so many people and they and they say, man,

1709
01:43:29,160 --> 01:43:31,240
Speaker 7: I'm telling you what you get in the stand when

1710
01:43:31,280 --> 01:43:33,720
Speaker 7: the moon is right here and there's high pressure, or

1711
01:43:33,720 --> 01:43:36,040
Speaker 7: when there's a coal front coming through and you're gonna

1712
01:43:36,120 --> 01:43:41,880
Speaker 7: kill a buck. And then here's research, actual factual research

1713
01:43:42,439 --> 01:43:46,960
Speaker 7: that shows that they're not one hundred percent correct.

1714
01:43:48,120 --> 01:43:51,799
Speaker 2: But then to Matt's point two, nothing is nothing is solid,

1715
01:43:51,920 --> 01:43:55,120
Speaker 2: nothing solid to his point about to your point Matt

1716
01:43:55,120 --> 01:43:57,960
Speaker 2: about weather, I mean, you talk to any serious deer

1717
01:43:58,000 --> 01:44:00,000
Speaker 2: hunter and they're gonna say, yes, we are seeing different

1718
01:44:00,080 --> 01:44:02,960
Speaker 2: and dear behavior when a weather front comes through, but

1719
01:44:03,000 --> 01:44:05,640
Speaker 2: the research doesn't support it. So that I mean that

1720
01:44:05,800 --> 01:44:08,080
Speaker 2: that raises similar questions about some other things too. There

1721
01:44:08,160 --> 01:44:11,599
Speaker 2: might be you know, I'm not I find this very interesting.

1722
01:44:11,880 --> 01:44:13,600
Speaker 2: I take it into account, I put it in the

1723
01:44:13,640 --> 01:44:16,200
Speaker 2: tool chest, but I'm not necessarily throwing out some of

1724
01:44:16,240 --> 01:44:18,920
Speaker 2: these other theories too that are intriguing because I you know,

1725
01:44:18,960 --> 01:44:21,479
Speaker 2: because I think here's one interesting thing that kind of

1726
01:44:21,479 --> 01:44:25,720
Speaker 2: my final thought on this is that you take a guy, hypothetically,

1727
01:44:25,800 --> 01:44:27,439
Speaker 2: let's say one of these one of these hunters that

1728
01:44:27,680 --> 01:44:32,080
Speaker 2: really strongly believes in the position of the moon and

1729
01:44:32,120 --> 01:44:34,479
Speaker 2: that influencing you know, a little bit of increased to

1730
01:44:34,479 --> 01:44:36,400
Speaker 2: your activity. And that's something that both you and me, Dan,

1731
01:44:36,400 --> 01:44:37,880
Speaker 2: We've been listening to a lot of guys talk about

1732
01:44:37,880 --> 01:44:40,080
Speaker 2: and it's really interesting and intriguing, and I've been trying

1733
01:44:40,080 --> 01:44:42,200
Speaker 2: to pay more attention to it too. But let's take

1734
01:44:42,200 --> 01:44:45,839
Speaker 2: a guy who's a diehard believer in it. When that person,

1735
01:44:46,000 --> 01:44:49,240
Speaker 2: let's hypothetically call him Ben, When Ben goes into the

1736
01:44:49,280 --> 01:44:52,640
Speaker 2: woods with this very strong belief in this theory that

1737
01:44:52,640 --> 01:44:54,960
Speaker 2: when the moon's overhead or whatever, that he's gonna have

1738
01:44:55,000 --> 01:44:57,360
Speaker 2: a great chance. When you go into a tree stand

1739
01:44:57,400 --> 01:44:59,280
Speaker 2: with a piece of data like that or a belief

1740
01:44:59,360 --> 01:45:02,559
Speaker 2: like that, you're believe in it so strongly. I believe

1741
01:45:02,600 --> 01:45:06,439
Speaker 2: your confidence level can be an influencer of the success

1742
01:45:06,479 --> 01:45:09,320
Speaker 2: you have merely because when you are very confident in

1743
01:45:09,320 --> 01:45:11,960
Speaker 2: your standsite and in the conditions on that day and

1744
01:45:12,040 --> 01:45:14,960
Speaker 2: on why you're hunting there. When your confidence is that high,

1745
01:45:15,040 --> 01:45:17,320
Speaker 2: I believe that you operate at a different level of

1746
01:45:17,360 --> 01:45:21,000
Speaker 2: efficiency and effectiveness as a hunter. So when you're super confident,

1747
01:45:21,160 --> 01:45:23,880
Speaker 2: you're paying attention to everything around you, you notice every

1748
01:45:23,880 --> 01:45:27,200
Speaker 2: flicker of movement, You're super quiet, you're super detailed, You're

1749
01:45:27,200 --> 01:45:29,040
Speaker 2: crossing all your t's and dotting all of your eyes.

1750
01:45:29,080 --> 01:45:32,160
Speaker 2: And I think maybe there's almost a self fulfilling prophecy

1751
01:45:32,200 --> 01:45:34,679
Speaker 2: happening here, or when you have such a strong belief

1752
01:45:34,680 --> 01:45:38,040
Speaker 2: in something happening, you just hunt better, and because of that,

1753
01:45:38,160 --> 01:45:41,519
Speaker 2: you have more success. I'm curious if maybe there's something

1754
01:45:41,560 --> 01:45:42,080
Speaker 2: to that.

1755
01:45:44,040 --> 01:45:46,120
Speaker 4: I'll comment, and I think there is and I think

1756
01:45:46,280 --> 01:45:50,920
Speaker 4: not only that mark is that not only they might

1757
01:45:50,960 --> 01:45:54,920
Speaker 4: be more confident, more efficient, but that type of hunter,

1758
01:45:55,360 --> 01:45:58,839
Speaker 4: that type of deer hunter that cares to look deeper

1759
01:45:59,479 --> 01:46:03,479
Speaker 4: and ask questions and try to improve their own ability,

1760
01:46:04,040 --> 01:46:06,960
Speaker 4: is the type of hunter that's going to improve anyway,

1761
01:46:07,439 --> 01:46:10,800
Speaker 4: you know what I mean, instead of just haphazardly going

1762
01:46:10,840 --> 01:46:16,759
Speaker 4: in the woods or hunting as a as a means

1763
01:46:16,840 --> 01:46:20,680
Speaker 4: of tradition, you know, going to the stand that Grandpapy

1764
01:46:20,760 --> 01:46:22,800
Speaker 4: talked me to, you know, opening day and that's the

1765
01:46:22,840 --> 01:46:24,880
Speaker 4: only day that he goes out or she goes out.

1766
01:46:25,360 --> 01:46:29,479
Speaker 4: The person that's saying how can I be better? Some

1767
01:46:29,680 --> 01:46:32,080
Speaker 4: of that is going to be self fulfilling. It has

1768
01:46:32,120 --> 01:46:36,160
Speaker 4: to be because they're trying to up their bed. I

1769
01:46:36,200 --> 01:46:38,679
Speaker 4: also think that there's probably as many, if not more

1770
01:46:38,720 --> 01:46:41,519
Speaker 4: people out there that are the hardcore you know, in

1771
01:46:41,600 --> 01:46:45,680
Speaker 4: his example, moon theory believers that are not killing a

1772
01:46:45,720 --> 01:46:49,559
Speaker 4: buck that day and the moon is the cause of it.

1773
01:46:50,560 --> 01:46:55,880
Speaker 4: You know, something happened that was wrong, And again, when

1774
01:46:55,920 --> 01:46:58,200
Speaker 4: it comes down to science is really hard to argue

1775
01:46:58,240 --> 01:47:01,320
Speaker 4: with and there's always going to be a you know,

1776
01:47:01,400 --> 01:47:03,479
Speaker 4: well that didn't work for me because our project was

1777
01:47:03,479 --> 01:47:06,360
Speaker 4: from Texas or you know, I live in New York

1778
01:47:06,520 --> 01:47:09,920
Speaker 4: and that research was from Florida. But yeah, of course,

1779
01:47:10,000 --> 01:47:11,760
Speaker 4: I mean, there's no way you can test at all,

1780
01:47:12,200 --> 01:47:15,200
Speaker 4: but if you can repeat results, you get a better

1781
01:47:15,240 --> 01:47:17,080
Speaker 4: sense of what's going on. But we're never going to

1782
01:47:17,160 --> 01:47:20,400
Speaker 4: have all the answers, and that's the case. So yeah,

1783
01:47:20,439 --> 01:47:23,360
Speaker 4: I do think, yeah, there's some truth to that, But

1784
01:47:23,400 --> 01:47:25,479
Speaker 4: I think it's also the type of hunter that you're

1785
01:47:25,479 --> 01:47:29,400
Speaker 4: talking about. You're categorizing. Out of the eleven to thirteen

1786
01:47:29,439 --> 01:47:32,160
Speaker 4: million deer hunters out there, I guarantee you not all

1787
01:47:32,160 --> 01:47:35,639
Speaker 4: of them are paying attention to moon phase. The ones

1788
01:47:35,680 --> 01:47:38,920
Speaker 4: that are are trying to make their situation better.

1789
01:47:41,400 --> 01:47:44,000
Speaker 2: All right, And that's a wrap. I hope you've enjoyed

1790
01:47:44,000 --> 01:47:48,799
Speaker 2: this two part conversation on all things research in science

1791
01:47:49,400 --> 01:47:52,120
Speaker 2: around the white tail rut. It's a beautiful time of year,

1792
01:47:52,640 --> 01:47:55,400
Speaker 2: the best stuff is ahead. It's going to be a

1793
01:47:55,439 --> 01:47:57,439
Speaker 2: great couple of weeks. I wish you all the luck

1794
01:47:57,479 --> 01:48:00,000
Speaker 2: in the world. I hope this helps, and until next time,

1795
01:48:00,560 --> 01:48:02,760
Speaker 2: stay Wired to Hunt.