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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, I'm joined by doctor Bronson Strickland, Professor of

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Speaker 2: Wildlife Management at Mississippi State University, to discuss the latest

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Speaker 2: in white tail research and its implications for deer hunters.

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Speaker 2: All right, welcome back to the Wired to Hunt pod cast,

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Speaker 2: brought to you by First Light and their brand new

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Speaker 2: line of white tail gear, which I've told you all about.

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Speaker 2: You can learn more about it over at first light

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Speaker 2: dot com and our CAMO for Conservation initiative, which means

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Speaker 2: that every purchase you make of any of first Light's

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Speaker 2: gear in the Specter Camel pattern, which is the white

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Speaker 2: tail pattern, you buy something in that pattern, and a

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Speaker 2: portion of that sale is donated to the National Deer

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Speaker 2: Association to help promote the conservation of our favorite big

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Speaker 2: game printer. So that's pretty cool. And in other news,

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Speaker 2: stay two and there is some exciting stuff coming up

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Speaker 2: next week related to white tails in the world of

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Speaker 2: First Light, in the world of meat Eater. Last year,

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Speaker 2: we had a big old celebration of white tail deer

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Speaker 2: for a week called white Tail Week. I don't know

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Speaker 2: if that's a creative name or not, but Whitetail Week

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Speaker 2: was pretty awesome last year, and we're bringing it back.

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Speaker 2: It's next week, so stay tuned for even more white

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Speaker 2: tailed business than usual, including and I'm just gonna say it.

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Speaker 2: I don't know if this is something we're not supposed

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Speaker 2: to tease it or not, but my first two films

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Speaker 2: from last year are coming out next week, including the

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Speaker 2: film that is going to be the Return to the

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Speaker 2: Back forty. So I got to go back to the

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Speaker 2: Back forty and spend some time with two of the

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Speaker 2: new hunters that I've been mentoring out there over previous seasons,

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Speaker 2: help them get their first year on the Back forty.

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Speaker 2: So we go back to the Back forty, we do

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Speaker 2: some hunting, we showcase everything that's happened on the property

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Speaker 2: in the year since that, barring some kind of unforeseen circumstance,

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Speaker 2: should be coming out next week. And the Wide nine film,

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Speaker 2: the film that recaps my four year journey kind of

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Speaker 2: following studying and then hunting this buck we call the

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Speaker 2: Wide nine here in Michigan. That's coming out next week,

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Speaker 2: so make sure you are subscribed to the Meat Either

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Speaker 2: YouTube channel as well. Very exciting things to come. But

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Speaker 2: that is all neither here nor there. Today we are

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Speaker 2: talking dear research, dear studies, the science of what deer

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Speaker 2: do and why they do it. And our guest today

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Speaker 2: is one of the absolute best people to discuss this

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Speaker 2: kind of thing with. It's doctor Bronson Strickland. He is

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Speaker 2: a professor of wildlife management at Mississippi State University. They've

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Speaker 2: got this very cool program there called Deer Lab in

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Speaker 2: which they are producing all sorts of different fascinating research,

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Speaker 2: publishing studies on deer behavior, dear habitat, all that kind

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Speaker 2: of stuff, and then they also produce a lot of

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Speaker 2: content to help educate folks on this kind of stuff.

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Speaker 2: So Bronson is the co host of their Dear University

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Speaker 2: podcast and also active with the Deer Lab TV YouTube

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Speaker 2: channel that they have tremendous resources to go into a

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Speaker 2: lot of the stuff we discussed today, but in more detail.

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Speaker 2: So Bronson joined me today to get us caught up

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Speaker 2: on the latest and greatest in deer research. What the

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Speaker 2: data is telling us about white tail deer and what

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Speaker 2: they're doing. There's a whole lot of folks that come

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Speaker 2: on this podcast and say, dear, do this, deer do that,

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Speaker 2: and that's anecdotal or that's because they've seen it a

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Speaker 2: lot over the years or heard it told to them

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Speaker 2: by someone, and it's a theory that they've picked up

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Speaker 2: and believe, which is great. I enjoy a good theory myself,

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Speaker 2: but it's great to see what the data actually says.

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Speaker 2: And Bronson and his team have access to a whole

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Speaker 2: hell of a lot of data which shines a light

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Speaker 2: on some of the many questions that we white tailed

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Speaker 2: deer hunters have about why deer would do what they do,

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Speaker 2: when do they do it, how do they do it,

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Speaker 2: all that kind of stuff. So that's what we cover

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Speaker 2: with Bronson. We get caught up on how things have

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Speaker 2: changed in the three to five years since we've had

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Speaker 2: this kind of conversation with him last and we talk

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Speaker 2: about some stuff around annual patterns, what the data, what

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Speaker 2: the study is show about how deer may or may

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Speaker 2: not do the same thing year after year. We dive

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Speaker 2: really deep into new research around deer betting, where deer bed,

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Speaker 2: why they bed, specifically buck betted, buck betting, how bucks

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Speaker 2: are betting, how often they bed, where they bed, how

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Speaker 2: many betting areas or sites do they have, all that

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Speaker 2: kind of stuff very interesting. We get into some things

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Speaker 2: around the rut, specifically patterning deer during the rut, and

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Speaker 2: then maybe most interesting for a lot of people, I

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Speaker 2: think Bronson shares a little bit of what will soon

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Speaker 2: be coming out around some very interesting new studies about

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Speaker 2: the impact of the moon on deer movement. This is

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Speaker 2: a theory. There's there's several theories around how the moon

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Speaker 2: might impact deer movement. Many well known hunters live and

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Speaker 2: die by these theories, and Bronson went and did a

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Speaker 2: deep dive specifically into these theories that hardcore deer hunters

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Speaker 2: have and then looking at the actual kinds of questions

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Speaker 2: that we have around that. So will the move get

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Speaker 2: a big buck on his feet ten minutes earlier? Maybe

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Speaker 2: maybe not. Bronson is gonna tell us here today. I'm

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Speaker 2: excited for you to hear about it. I'm excited for

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Speaker 2: the rest of his studies to come out later this fall,

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Speaker 2: and it's gonna be good stuff. So without any further, Ado,

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Speaker 2: I think we should just get to this very interesting

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Speaker 2: conversation and very applicable conversation. I want to make sure

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Speaker 2: that's very clear. This is this is applied science. This

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Speaker 2: is learning about the research and then discussing exactly how

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Speaker 2: this research, how this science can be applied on your

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Speaker 2: next hunt for this coming season, as you put together

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Speaker 2: your hunting plans and your strategies, how do you take

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Speaker 2: this new information and arm it or arm yourself in

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Speaker 2: a way that will make a more effective deer hunter.

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Speaker 2: That's my goal today. I hope it's helpful, hope you

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Speaker 2: enjoy it. Here we go, all right with me. Now

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Speaker 2: back on the show for the first time in too

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Speaker 2: long of a time is Bronson Strickland. Bronson, thank you

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Speaker 2: so much for being here.

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Speaker 3: Absolutely happy too.

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Speaker 2: Man, Like we were just talking about before we start recording. Uh,

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Speaker 2: somehow almost half a decade has passed since the last

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Speaker 2: time we did this, which is insane to me because

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Speaker 2: it feels it feels like it was almost just yesterday.

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Speaker 2: So it's been too long, too long coming. And I've been,

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Speaker 2: you know, following so much of your work over the years,

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Speaker 2: you know, since we have talked in the past, you

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Speaker 2: guys have launched the Deer University podcast and your YouTube

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Speaker 2: channel and really have done an excellent job of making

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Speaker 2: deer research in science accessible for the average hunter and

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Speaker 2: whitetail fishionados. So I just want to start this off

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Speaker 2: by saying thank you for doing that, and kudos to

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Speaker 2: you guys, because you're you're really making it excess and

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Speaker 2: understandable in a way that not a lot of other

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Speaker 2: people are. So great work on that well, I just

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Speaker 2: want to make sure.

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Speaker 3: Thank you very much. And it's a little bit self serving,

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Speaker 3: I will admit, because we're really having a lot of

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Speaker 3: fun doing that, and we we really have a conviction

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Speaker 3: of taking all of this really sophisticated research and how

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Speaker 3: can we apply it for management and for hunting. So

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Speaker 3: it's been a blast.

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Speaker 2: Yeah, Yeah, you guys are taking the applied science thing

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Speaker 2: to a new level when you're actually hunters yourselves and

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Speaker 2: truly taking this stuff into the field with you, I'm sure.

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Speaker 2: So all that said, you know, the plan today is

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Speaker 2: really to look at that very thing, like how do

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Speaker 2: we apply dear research and the science around deer behavior,

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Speaker 2: dear movement. How do we take all that from you know,

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Speaker 2: from the lab from a PDF, document from a podcast,

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Speaker 2: and apply it in the field for folks this hunting season.

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Speaker 2: Because when this when this one drops, it will be,

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Speaker 2: you know, right at the beginning for a lot of

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Speaker 2: different people's deer hunting season. So so everyone's chomping at

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Speaker 2: the bit to get in the woods, or maybe already

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Speaker 2: have been for a week or two. And now I

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Speaker 2: want to figure out how do we take our deer

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Speaker 2: hunting strategy and not let it just revolve around old

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Speaker 2: wives tales and you know, the trendy idea of the day,

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Speaker 2: but instead have our hunting plans grounded in true data,

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Speaker 2: research and insights. So that's a heavy burden that we're

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Speaker 2: going to place on your shoulders here today, Bronson, But

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Speaker 2: I will.

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Speaker 3: I'm gonna give you my best, that's all I can promise.

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Speaker 2: I trust that you will. So there's a lot of

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Speaker 2: different directions we go with this. There's a lot of

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Speaker 2: interesting stuff that your team's been working on. There's a

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Speaker 2: lot of things I know, folks, other folks around the

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Speaker 2: country have been working on that you guys are always

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Speaker 2: staying up to date on. I want to start at

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Speaker 2: the very top level. I want to I'm curious about

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Speaker 2: what is most fascinating to you when it comes to

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Speaker 2: the latest deer research, deer science, and kind of looking

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Speaker 2: at the window of the last three to five years

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Speaker 2: or so since we've done a big roundup like this

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Speaker 2: personally on this podcast, what in the most few recent

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Speaker 2: few years has been the most significant break from conventional

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Speaker 2: wisdom that has been illuminated by recent deer research or science.

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Speaker 2: Is there something that has really changed your perspective on

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Speaker 2: things more so than anything else.

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Speaker 3: Well, I guess there's a lot of things that have happened,

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Speaker 3: and you know, a lot of it in the past

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Speaker 3: five years has been centered around CWD and including movement

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Speaker 3: data and such as that. But I would say relative

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Speaker 3: to hunting, and not to pat ourselves on the back,

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Speaker 3: what's soever. I think the most eye opening thing for

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Speaker 3: me and the team here has probably been the betting

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Speaker 3: and the betting affinity and my story Mark, and you know,

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Speaker 3: for a lot of people as well, it's when you

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Speaker 3: go back to when we were cutting our teeth hunting

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Speaker 3: and getting our information from hunting magazines and so forth,

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Speaker 3: there was a lot of information about you know, a

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Speaker 3: buck's bedroom, per se, and that they had this one

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Speaker 3: spot on the landscape, and that is where that older buck,

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Speaker 3: that mature buck, that dominant buck, that was going to

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Speaker 3: be his territory, that only he was going to be

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Speaker 3: and what we saw and I have to put a

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Speaker 3: qualifier here that's going to apply for probably a lot

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Speaker 3: of the stuff we talk about. The results I talk

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Speaker 3: about are based on a landscape of the Southeast, and

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Speaker 3: so if you're in Michigan, if you're in Ohio or Iowa,

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Speaker 3: you may see something completely different. But what we generally

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Speaker 3: saw down here is that there are many, many more

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Speaker 3: betting areas and betting sites than we previously thought.

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Speaker 2: And so.

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Speaker 3: If we're trying to focus our hunting on there's that

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Speaker 3: one place, and hey, if you're in the Midwest and

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Speaker 3: you're in a cover restricted landscape, that may work very

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Speaker 3: very well. But if you're in more of the eastern

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Speaker 3: US or outside of an agricultural region, we're just seeing

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Speaker 3: that there's a lot more different betting areas and a

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Speaker 3: lot more movement and use of those throughout the course

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Speaker 3: of the season. So finding that one spot to intercept

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Speaker 3: that buck is probably probably going to take a lot

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Speaker 3: of stand sitting to catch him.

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Speaker 2: Yeah, and I will say at least in some of

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Speaker 2: the content you guys have put out where you've discussed

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Speaker 2: some of this betting research that that you guys have

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Speaker 2: been working on. You've shown some of the aerial views

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Speaker 2: of the of the sample sites where you're doing this.

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Speaker 2: A key thing is it is varied, right, There's there's

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Speaker 2: a strong diversity in fields versus timber, small food plots,

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Speaker 2: big egg fields, big chunks of timber, rivers, all that

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Speaker 2: kind of stuff. And and while that is different than

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Speaker 2: some stretches of like the very most open parts of

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Speaker 2: Kansas or Iowa, I also did not look at that

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Speaker 2: as being terribly divergent than what I'm used to in

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Speaker 2: the Midwest. So so I when I looked at what

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Speaker 2: you were showing me there, I still found it applicable

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Speaker 2: to a lot of places I hunt in Michigan or

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Speaker 2: Ohio or anything like that. So I think you bring

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Speaker 2: up a good point, but I would not discount it

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Speaker 2: so much to say that it's not applicable because it

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Speaker 2: didn't look at that different. That said, there there was

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Speaker 2: a lot of really interesting stuff in the at least

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Speaker 2: the things I've seen so far. We guys have broken

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Speaker 2: down the unique aspects of how deer are actually using

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Speaker 2: betting areas, and you mentioned something that I think is

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Speaker 2: the key thing, betting sites versus betting areas. Can you

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Speaker 2: elaborate a little bit on what that means, how you

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Speaker 2: guys have worked done to try to differentiate that or

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Speaker 2: measure that, and then maybe get into some of the

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Speaker 2: detail of specifically, you know, how many different sites on

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Speaker 2: average are we seeing a buck used during a day

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Speaker 2: or during a period of time, how many different areas

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Speaker 2: Because I think, again, from a hunting perspective, if I know, well,

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Speaker 2: he's he's got one or two big zones or he

246
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Speaker 2: has ten big zones, that's going to significantly change my

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Speaker 2: hunting strategy.

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Speaker 3: Yeah. Absolutely, so, I guess a little background here. We

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Speaker 3: were able to measure this. These are bucks that have

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Speaker 3: GPS callers on them, and we're getting a low cation

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Speaker 3: from them every fifteen minutes, so that really enables us

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Speaker 3: to fine tune, you know, their movements and how they're

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Speaker 3: navigating the landscape, how long they're bedded, where they're betted,

254
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Speaker 3: et cetera. We differentiated this by what we're calling is

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Speaker 3: a betting site is going to be the area where

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Speaker 3: we get greater than four consecutive points, and we called

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Speaker 3: that within a within a twenty yard area. Now you

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Speaker 3: might say, why would a betting site be twenty yards,

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Speaker 3: because there's error associated with the GPS callers, with the

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Speaker 3: triangulation and so forth. So we felt confident that if

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Speaker 3: we're getting four consecutive points within a twenty yard range,

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Speaker 3: then that buck is probably betted there. And it might

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Speaker 3: be six consecutive points for an hour and a half,

264
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Speaker 3: et cetera, but we had to have at least four,

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Speaker 3: and so we would call that a betting site, and

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Speaker 3: so we would draw a little dot on the map.

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Speaker 3: Whereas that betting site, now, a betting area is differentiating

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Speaker 3: that the area needs to be two sites that are

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Speaker 3: greater than a one hundred yards apart. So, in other words,

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Speaker 3: if a buck came in and had a betting site

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Speaker 3: and the very next day came back and had a

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Speaker 3: betting site within fifty yards of where it was the

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Speaker 3: day before, we would consider that the same betting area.

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Speaker 3: So that is how we calculated. We basically grouped sites

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Speaker 3: together and if they're close enough, we called it a

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Speaker 3: betting area. So that's how we broke that apart.

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Speaker 2: Okay, did you guys look into what the average length

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Speaker 2: of the use of a betting site is. So I'm

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Speaker 2: by that. I'm curious, you know, how long when a

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Speaker 2: buck does get into these zones, how long is he

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Speaker 2: usually there until it gets up and moves to another spot.

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Speaker 2: Because another key thing that I know that you guess

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Speaker 2: found is that they use multiple betting sites per day, right,

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Speaker 2: So I guess you could you dive into both the

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Speaker 2: average length if you have that handy, and then how

286
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Speaker 2: many different times they're shifting these locations throughout a given day.

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Speaker 3: Yeah. So I hate to say it, but this is

288
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Speaker 3: a this is a context as it depends on the

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Speaker 3: time of the year and mainly with the rut. But

290
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Speaker 3: if you smooth it out and just come out with

291
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Speaker 3: some averages, we're looking at four different betting sites and

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Speaker 3: four different betting bouts per day. Now, on some days

293
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Speaker 3: you might have three different betting sites during the day.

294
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Speaker 3: Some days you may have one. And so we see

295
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Speaker 3: this relationship, especially during the rut, where there's going to

296
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Speaker 3: be less time in the bed and more time on

297
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Speaker 3: their feet. So if we were going to look at

298
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Speaker 3: an average scenario, an average scenario is going to be

299
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Speaker 3: after the more arning movement about. So before sun up

300
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Speaker 3: and we have a movement about, and then roughly two

301
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Speaker 3: to three hours after sun up, they're going to have

302
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Speaker 3: their first bedding event for the day, and that's typically

303
00:18:15,320 --> 00:18:20,040
Speaker 3: going to be an hour or two. And surprisingly, we

304
00:18:20,119 --> 00:18:24,720
Speaker 3: see a lot of midday movement. Now that is not

305
00:18:24,880 --> 00:18:28,040
Speaker 3: going to be an extended movement about like you're going

306
00:18:28,119 --> 00:18:30,360
Speaker 3: to see in the morning or the afternoon. It's kind

307
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Speaker 3: of like getting up for a snack. So it's not

308
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Speaker 3: they're not going to the restaurant for a meal. They're

309
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Speaker 3: just getting up for a snack. So we'll see that

310
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Speaker 3: around midday, which of course is going to present some

311
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Speaker 3: hunting opportunity if they're on their fate during mid day.

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Speaker 2: And start to interrupt you Bronson, but is that specifically happening.

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Speaker 2: Are you speaking outside of the rut or is it

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Speaker 2: just inside the rut.

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Speaker 3: Yeah, I'm kind of smoothing it all over, so non

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Speaker 3: rut and rut, okay. And so when we get in

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Speaker 3: the rut, you're going to see less, uh less betting bouts,

318
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Speaker 3: and the duration of those betting bouts will be less,

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Speaker 3: if that makes sense.

320
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Speaker 2: Yeah, And then that that snack time in the rut

321
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Speaker 2: then expands to not just a snack time, but also

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Speaker 2: a seeking time too, right, so that at the same

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Speaker 2: time activity is higher. Conventional wisdom would say, right, right,

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Speaker 2: so I'll continue.

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Speaker 3: Yeah, yeah, So so we typically see that, So we're

326
00:19:31,800 --> 00:19:34,399
Speaker 3: going to see that first betting bout you know, after

327
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Speaker 3: their their morning movement, there's going to be a little

328
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Speaker 3: bit of movement during the day. We actually even pick

329
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Speaker 3: that up with food plots, and so we're able to detect.

330
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Speaker 3: We have all of our food plots on the landscape digitized,

331
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Speaker 3: so we know where they're at, and then we'll actually

332
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Speaker 3: see some visits to food plots at a time when

333
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Speaker 3: you normally don't think of it, so more than expected

334
00:19:56,760 --> 00:20:00,760
Speaker 3: they will be there at noon or at one pm.

335
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Speaker 3: And then we actually see the decrease or the most

336
00:20:06,800 --> 00:20:12,919
Speaker 3: infrequent use of food plots about three pm, which is

337
00:20:13,000 --> 00:20:15,919
Speaker 3: good if that's when you're typically you know, going to

338
00:20:15,960 --> 00:20:18,000
Speaker 3: the stand and going to be in the vicinity of

339
00:20:18,000 --> 00:20:20,320
Speaker 3: a food plot. That that part is good, but it

340
00:20:20,960 --> 00:20:24,840
Speaker 3: also was telling on you. You've probably seen this mark

341
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Speaker 3: on those days where I'm gonna get back right back

342
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Speaker 3: out there at noon, I'm gonna go, I'm gonna go

343
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Speaker 3: early and I'm gonna have a long and you get

344
00:20:33,800 --> 00:20:36,359
Speaker 3: to a food plot area and you and you bump

345
00:20:36,440 --> 00:20:39,720
Speaker 3: deer off the plot. Well, that's probably what you're seeing

346
00:20:39,800 --> 00:20:43,720
Speaker 3: right there, is that little snack movement there during midday,

347
00:20:43,760 --> 00:20:46,240
Speaker 3: and then they go bed down again and then have

348
00:20:46,400 --> 00:20:49,200
Speaker 3: the longer movement about in the afternoon.

349
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Speaker 2: So back to the betting side of things, and we

350
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Speaker 2: talked about how you guys differentiate between sites and areas,

351
00:21:11,960 --> 00:21:14,720
Speaker 2: and you mentioned it that at the front end, that

352
00:21:14,760 --> 00:21:18,359
Speaker 2: there's more betting or diversified betting maybe would be a

353
00:21:18,400 --> 00:21:21,359
Speaker 2: way to think about than some of us think. Do

354
00:21:21,480 --> 00:21:26,359
Speaker 2: we know on average how many different areas most bucks

355
00:21:26,480 --> 00:21:29,600
Speaker 2: will have or what at least you guys have found,

356
00:21:29,600 --> 00:21:32,399
Speaker 2: because I think you know, as you talked about, a

357
00:21:32,400 --> 00:21:34,320
Speaker 2: lot of people think, like, man, this is that Bucks

358
00:21:34,320 --> 00:21:37,399
Speaker 2: bedroom and we put all of our cards into that

359
00:21:37,520 --> 00:21:42,320
Speaker 2: one spot. What is the reality there? I'm not sure

360
00:21:42,359 --> 00:21:43,600
Speaker 2: if you touched on that actual number.

361
00:21:44,720 --> 00:21:49,639
Speaker 3: I think it depends on how large you define the bedroom.

362
00:21:50,280 --> 00:21:54,320
Speaker 3: And so we provided a great example and it's just

363
00:21:54,600 --> 00:21:58,440
Speaker 3: one buck during the rut, But we selected that buck

364
00:21:58,520 --> 00:22:02,520
Speaker 3: intentionally because his number of betting sites and his number

365
00:22:02,520 --> 00:22:07,200
Speaker 3: of betting areas was very indicative of the population average.

366
00:22:07,600 --> 00:22:12,000
Speaker 3: And so over a two week, over a fourteen day period,

367
00:22:12,560 --> 00:22:15,360
Speaker 3: a particular buck that was middle aged three and a half,

368
00:22:15,880 --> 00:22:21,040
Speaker 3: he had forty one different beds during that two week

369
00:22:21,119 --> 00:22:27,840
Speaker 3: period and seventeen different betting areas. Now, those seventeen betting

370
00:22:27,920 --> 00:22:32,919
Speaker 3: areas are spread out over about a square mile, So

371
00:22:33,520 --> 00:22:36,359
Speaker 3: you got to put all that into context. Is that

372
00:22:36,480 --> 00:22:39,360
Speaker 3: buck showing an affinity from when he's going to bed

373
00:22:39,440 --> 00:22:43,720
Speaker 3: down a particular area, Yes, yes he is, but it's

374
00:22:43,800 --> 00:22:46,359
Speaker 3: not a two acre spot. It's not even one hundred

375
00:22:46,400 --> 00:22:50,679
Speaker 3: acre spot. It's about a six hundred acre place. So

376
00:22:51,080 --> 00:22:54,480
Speaker 3: we know where he's spending time. But to be able

377
00:22:54,560 --> 00:22:58,080
Speaker 3: to go to the exact place where you think he's betted,

378
00:22:59,400 --> 00:23:04,560
Speaker 3: that's problem. Yeah, However, think of it this way, Mark too.

379
00:23:05,640 --> 00:23:10,480
Speaker 3: It also provides opportunity. And so the way I've been

380
00:23:10,680 --> 00:23:14,000
Speaker 3: trying to tell the story as well is we've got

381
00:23:14,760 --> 00:23:19,239
Speaker 3: two different ways to approach this. There's reactive meaning that

382
00:23:19,600 --> 00:23:23,080
Speaker 3: we're taking what the deer or the bucks are giving us.

383
00:23:23,760 --> 00:23:26,399
Speaker 3: We're trying to pattern them. We're trying to find the

384
00:23:26,440 --> 00:23:31,359
Speaker 3: ideal locations. If you have the luxury of being on

385
00:23:31,560 --> 00:23:35,920
Speaker 3: private land and if you own the land, especially now

386
00:23:35,960 --> 00:23:41,480
Speaker 3: you have the opportunity via habitat management to create areas

387
00:23:41,520 --> 00:23:45,040
Speaker 3: where there's going to be a higher probability of bedding.

388
00:23:45,400 --> 00:23:49,119
Speaker 3: And so now you go from reactive to proactive or

389
00:23:49,200 --> 00:23:53,240
Speaker 3: you're trying to relegate areas of your property where there's

390
00:23:53,280 --> 00:23:55,159
Speaker 3: going to be a greater likelihood that they're going to

391
00:23:55,240 --> 00:23:59,160
Speaker 3: bed and do those such that you can be able

392
00:23:59,240 --> 00:24:03,639
Speaker 3: to set up between where you know food is where

393
00:24:03,680 --> 00:24:06,280
Speaker 3: you have designated areas of cover and set up in

394
00:24:06,320 --> 00:24:07,000
Speaker 3: between those.

395
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Speaker 2: Yeah. So so speaking of that, one of the things

396
00:24:10,119 --> 00:24:12,600
Speaker 2: that I that I think I recall from a chat

397
00:24:12,640 --> 00:24:15,320
Speaker 2: that you and your team had about this data was

398
00:24:15,359 --> 00:24:23,359
Speaker 2: that it was harder than expected to define exactly the

399
00:24:23,520 --> 00:24:26,520
Speaker 2: key characteristics for a buck to want to bed there

400
00:24:26,520 --> 00:24:28,800
Speaker 2: because there seemed to be a lot of there' seem

401
00:24:28,800 --> 00:24:31,080
Speaker 2: to be a lot of diversity and what individual bucks prefer,

402
00:24:31,240 --> 00:24:34,160
Speaker 2: like one buck really wanted to bet around oxpos of rivers.

403
00:24:34,840 --> 00:24:37,840
Speaker 2: One buck though was all about you know, two different

404
00:24:37,840 --> 00:24:42,440
Speaker 2: cover types coming together or something like that. Is there

405
00:24:42,480 --> 00:24:46,520
Speaker 2: anything that you could say, like, yes, definitively, this was

406
00:24:46,560 --> 00:24:49,240
Speaker 2: a much more likely place to pull in buck bedding

407
00:24:50,000 --> 00:24:52,320
Speaker 2: or any kind of characteristics like that. I'm assuming there's

408
00:24:52,359 --> 00:24:54,520
Speaker 2: some conventional wisdom here, like the basic things we all

409
00:24:54,600 --> 00:24:59,120
Speaker 2: understand is still true. But could you, I guess, expand

410
00:24:59,160 --> 00:24:59,399
Speaker 2: on that.

411
00:24:59,760 --> 00:25:03,719
Speaker 3: Sure? Sure, So one of the things I know and

412
00:25:03,800 --> 00:25:07,159
Speaker 3: one of the things we're working on. And so we

413
00:25:07,240 --> 00:25:10,919
Speaker 3: got some wonderful feedback from people when we went online

414
00:25:10,960 --> 00:25:14,600
Speaker 3: with this and started talking about it. So here's what

415
00:25:14,640 --> 00:25:19,280
Speaker 3: we know, because here's what we measured is we went

416
00:25:19,359 --> 00:25:23,679
Speaker 3: to many of these areas where these bucks bedded, so

417
00:25:23,760 --> 00:25:27,600
Speaker 3: these betting sites, especially some places where numerous times a

418
00:25:27,640 --> 00:25:31,320
Speaker 3: buck was betting there, and we measured the vegetation. And

419
00:25:31,680 --> 00:25:35,639
Speaker 3: what was revealing was that it really did not matter

420
00:25:36,000 --> 00:25:42,760
Speaker 3: whatsoever the type of vegetation. So it could be hardwood stems,

421
00:25:43,200 --> 00:25:46,920
Speaker 3: it could be grass, it could be BlackBerry, it could

422
00:25:46,960 --> 00:25:51,120
Speaker 3: be any form of vegetation as long as it provided

423
00:25:51,320 --> 00:25:56,600
Speaker 3: really good screening cover, meaning visually, when the buck beds down,

424
00:25:57,000 --> 00:26:02,120
Speaker 3: it's essentially hidden and you can't see it. Now, the

425
00:26:02,160 --> 00:26:05,760
Speaker 3: next step that we're literally working on now is if

426
00:26:05,800 --> 00:26:09,360
Speaker 3: we zoom out a little bit more and rather than

427
00:26:09,440 --> 00:26:12,520
Speaker 3: just looking at the place it's bedded and the vegetation

428
00:26:12,800 --> 00:26:18,160
Speaker 3: characteristics around it, are there any landscape features that might

429
00:26:18,240 --> 00:26:23,240
Speaker 3: be more associated or facilitate a buck being bedded there.

430
00:26:23,560 --> 00:26:25,760
Speaker 3: And the one thing that we heard a lot based

431
00:26:25,760 --> 00:26:28,800
Speaker 3: on our study area is we have a big river

432
00:26:29,440 --> 00:26:33,920
Speaker 3: corridor that completely bisected this big sixty thousand acres study area.

433
00:26:34,440 --> 00:26:39,919
Speaker 3: And so for one thing obviously, is that adjacent to

434
00:26:40,040 --> 00:26:43,679
Speaker 3: that river is we have a lot of cover right there,

435
00:26:43,800 --> 00:26:46,600
Speaker 3: you know, And it could be switch cane, it could

436
00:26:46,600 --> 00:26:48,280
Speaker 3: be BlackBerry, it could be a lot of things, but

437
00:26:48,359 --> 00:26:51,760
Speaker 3: we have some cover adjacent to it. But is the

438
00:26:51,880 --> 00:26:57,159
Speaker 3: layout of that relative to the river. Is the layout

439
00:26:57,400 --> 00:27:01,159
Speaker 3: of that cover relative to the river relative to the

440
00:27:01,160 --> 00:27:05,280
Speaker 3: prevailing wind when that buck and go goes and selects

441
00:27:05,280 --> 00:27:07,840
Speaker 3: that area to bet down. That is what we're trying

442
00:27:07,880 --> 00:27:09,560
Speaker 3: to reconcile right now for any.

443
00:27:09,400 --> 00:27:13,119
Speaker 2: Patterns interesting Now correct me if I'm wrong, But I

444
00:27:13,119 --> 00:27:15,760
Speaker 2: feel like I read something in one of your reports

445
00:27:15,800 --> 00:27:20,840
Speaker 2: about there also being an increased usage of betting areas

446
00:27:20,840 --> 00:27:25,720
Speaker 2: that also had food or basis content present in them

447
00:27:25,320 --> 00:27:28,480
Speaker 2: as compared to a betting area that has screening cover

448
00:27:28,560 --> 00:27:30,840
Speaker 2: but does not have the food. Is that is that accurate?

449
00:27:31,760 --> 00:27:35,399
Speaker 3: That was a different study that was in a different

450
00:27:35,440 --> 00:27:38,560
Speaker 3: study area, and that was using camera trap data. But yes,

451
00:27:38,840 --> 00:27:43,560
Speaker 3: if we just if the metric were just use, meaning

452
00:27:43,560 --> 00:27:47,000
Speaker 3: how many times are we seeing deer photos or buck photos?

453
00:27:48,400 --> 00:27:52,280
Speaker 3: Then yes, we saw greater use in areas that combined

454
00:27:52,960 --> 00:27:57,320
Speaker 3: some cover with or adjacent to food. You're exactly right.

455
00:27:57,600 --> 00:28:01,399
Speaker 2: So that would that might I I guess, give someone

456
00:28:01,440 --> 00:28:04,679
Speaker 2: who's who has an ability to manage or and or

457
00:28:04,800 --> 00:28:08,880
Speaker 2: selecting places to hunt that a old field that has

458
00:28:08,920 --> 00:28:13,399
Speaker 2: a mix of grasses and forbs and any kind of

459
00:28:13,440 --> 00:28:16,719
Speaker 2: different kind of low lying, diverse habitat that might be

460
00:28:16,760 --> 00:28:20,720
Speaker 2: a more attractive bedding site than a purely switched grass

461
00:28:20,720 --> 00:28:23,080
Speaker 2: one hundred percent field that has that cover but doesn't

462
00:28:23,080 --> 00:28:23,879
Speaker 2: have the food content.

463
00:28:24,000 --> 00:28:30,440
Speaker 3: Right, Yes, but now we have to separate is that

464
00:28:30,480 --> 00:28:35,439
Speaker 3: what we want from a hunting perspective. So if we

465
00:28:35,520 --> 00:28:38,920
Speaker 3: break it down very simply, so think mark of a

466
00:28:39,800 --> 00:28:45,480
Speaker 3: think of a four year old clearcut regenerating, and it

467
00:28:45,560 --> 00:28:47,800
Speaker 3: is full of cover, and it is full of brows,

468
00:28:48,320 --> 00:28:51,920
Speaker 3: and there's forbes there, there's a diversity. It's going to

469
00:28:52,000 --> 00:28:55,040
Speaker 3: be very very difficult to get into that clear cut

470
00:28:55,520 --> 00:28:59,480
Speaker 3: and hunt that buck. On the other hand, if we

471
00:28:59,600 --> 00:29:02,680
Speaker 3: have let's simplify here, and let's say we had a

472
00:29:02,720 --> 00:29:07,240
Speaker 3: food plot or maybe a managed opening that we maintain

473
00:29:07,320 --> 00:29:10,680
Speaker 3: with fire but where the vegetation structure isn't as tall,

474
00:29:11,080 --> 00:29:13,520
Speaker 3: but it is providing a good open area where a

475
00:29:13,560 --> 00:29:17,200
Speaker 3: deer can feed, and one hundred yards away or adjacent

476
00:29:17,320 --> 00:29:21,160
Speaker 3: or several hundred yards away is cover. And whether that

477
00:29:21,240 --> 00:29:24,040
Speaker 3: cover be forests cover with little food, or whether it

478
00:29:24,080 --> 00:29:28,760
Speaker 3: be something like switchgrass. Now we have a scenario where

479
00:29:28,800 --> 00:29:32,280
Speaker 3: we've essentially are going to force the deer to leave

480
00:29:32,400 --> 00:29:35,800
Speaker 3: cover and move and go to an area for food,

481
00:29:36,080 --> 00:29:37,680
Speaker 3: and that presents hunting opportunity.

482
00:29:38,000 --> 00:29:40,320
Speaker 2: Yeah, okay, Yeah, you make a very good point there.

483
00:29:41,320 --> 00:29:43,640
Speaker 2: Speaking of that, it brings up another thing that I

484
00:29:43,680 --> 00:29:46,720
Speaker 2: think I saw you discussing with. I think it was

485
00:29:47,280 --> 00:29:50,920
Speaker 2: Natasha Ellison, who I believe has helped you with with

486
00:29:51,160 --> 00:29:54,080
Speaker 2: a lot of this analysis on your team there there

487
00:29:54,120 --> 00:29:58,560
Speaker 2: have been some discussion around distance or the most common

488
00:29:58,760 --> 00:30:01,200
Speaker 2: distance from a betting air to a food plot. So

489
00:30:01,200 --> 00:30:04,200
Speaker 2: you're looking at, you know what, where these bucks are

490
00:30:04,240 --> 00:30:07,120
Speaker 2: choosing to bet in relation to food, And that's something

491
00:30:07,200 --> 00:30:08,800
Speaker 2: that I think a lot of US hunters think about

492
00:30:08,880 --> 00:30:12,000
Speaker 2: a lot. And there's some commonly held beliefs like okay,

493
00:30:12,000 --> 00:30:14,520
Speaker 2: you're going to have a layer of does and fawns

494
00:30:14,680 --> 00:30:17,640
Speaker 2: first betted closest to food. And then a little bit

495
00:30:17,640 --> 00:30:20,160
Speaker 2: further back from that you might have some younger bucks,

496
00:30:20,160 --> 00:30:22,720
Speaker 2: and then maybe the furthest back in the prime betting

497
00:30:22,800 --> 00:30:25,360
Speaker 2: might be where those mature bucks are. That might be

498
00:30:25,480 --> 00:30:31,680
Speaker 2: like an over generalized theory that you commonly hear. You

499
00:30:31,720 --> 00:30:33,360
Speaker 2: have you guys been able to find if that, well,

500
00:30:33,400 --> 00:30:35,200
Speaker 2: it's to be true or what have you found?

501
00:30:37,960 --> 00:30:40,959
Speaker 3: What we have found is we can address that on

502
00:30:41,000 --> 00:30:45,280
Speaker 3: the buck side. But unfortunately in the study we did

503
00:30:45,320 --> 00:30:49,800
Speaker 3: not simultaneously have dose collared so that that would be

504
00:30:49,880 --> 00:30:53,240
Speaker 3: fantastic if we did, but maybe in the future we

505
00:30:53,240 --> 00:30:58,680
Speaker 3: can do that. But what we found is Natasha, who

506
00:30:58,760 --> 00:31:02,200
Speaker 3: is a mathematician so we can trust her math in

507
00:31:02,280 --> 00:31:07,000
Speaker 3: making these calculations, is she looked at all of the

508
00:31:07,040 --> 00:31:12,800
Speaker 3: betting sites and the location relative to food and mathematically

509
00:31:13,000 --> 00:31:17,160
Speaker 3: was able to determine all of the different distances that

510
00:31:17,200 --> 00:31:22,000
Speaker 3: would be possible from betting site to food and what

511
00:31:22,280 --> 00:31:25,960
Speaker 3: was the average or most frequent distance that a buck

512
00:31:26,080 --> 00:31:29,720
Speaker 3: was bedded to the food that he went to in

513
00:31:29,760 --> 00:31:33,400
Speaker 3: an afternoon feeding bout, and it came out to be

514
00:31:33,800 --> 00:31:37,360
Speaker 3: about two hundred yards. Of course, there are instances where

515
00:31:37,360 --> 00:31:39,800
Speaker 3: it's closer, and then that's going to depend on your

516
00:31:39,800 --> 00:31:42,600
Speaker 3: local area, where is the cover you know in that

517
00:31:42,640 --> 00:31:45,800
Speaker 3: particular area. And there were instances where the buck may

518
00:31:45,840 --> 00:31:49,080
Speaker 3: have traveled over one thousand yards. But if you look

519
00:31:49,080 --> 00:31:52,400
Speaker 3: at over both these years, over this large study area,

520
00:31:52,800 --> 00:31:56,640
Speaker 3: the sweet spot that we found was about two hundred yards.

521
00:31:57,240 --> 00:32:02,920
Speaker 2: Interesting. So another betting detail that I recall you guys

522
00:32:02,920 --> 00:32:05,200
Speaker 2: discussing at one point that I'd be curious to hear

523
00:32:05,200 --> 00:32:07,000
Speaker 2: a little bit more about is we're I think an

524
00:32:07,000 --> 00:32:11,160
Speaker 2: overarching thing here is that these deer, these bucks, likely

525
00:32:11,200 --> 00:32:13,480
Speaker 2: have more betting areas than we maybe have given them

526
00:32:13,480 --> 00:32:17,640
Speaker 2: credit for historically. Because he just described on average, maybe

527
00:32:17,640 --> 00:32:21,960
Speaker 2: there are several hundred yards away from food. Generalizing, but

528
00:32:23,160 --> 00:32:27,200
Speaker 2: one thing you guys measured was circuit time for betting areas, which,

529
00:32:27,200 --> 00:32:30,000
Speaker 2: as I understood it was how often a buck is

530
00:32:30,040 --> 00:32:32,760
Speaker 2: returning to a betting air, so he uses a betting site,

531
00:32:33,160 --> 00:32:35,120
Speaker 2: how long does it take him to return to that

532
00:32:35,200 --> 00:32:37,640
Speaker 2: site if he does, because I remember seeing some data

533
00:32:37,640 --> 00:32:40,640
Speaker 2: that showed that. And again, correct me if I'm wrong

534
00:32:40,680 --> 00:32:42,240
Speaker 2: on any of this, because I'm sure I am trying

535
00:32:42,280 --> 00:32:44,480
Speaker 2: to recall stuff that I looked at when I was

536
00:32:44,520 --> 00:32:46,600
Speaker 2: researching all this. But I think of all of the

537
00:32:46,600 --> 00:32:48,959
Speaker 2: betting sites, you guys looked at, fifty percent of them

538
00:32:48,960 --> 00:32:51,320
Speaker 2: were one and done. So about fifty percent of those

539
00:32:51,360 --> 00:32:53,640
Speaker 2: betting sites where they betted here and they never return.

540
00:32:54,280 --> 00:32:56,760
Speaker 2: But that means that there was another fifty percent of

541
00:32:56,840 --> 00:33:00,920
Speaker 2: betting sites that bucks did eventually return to, and three

542
00:33:01,000 --> 00:33:03,200
Speaker 2: percent of the sites they returned to more than two

543
00:33:03,280 --> 00:33:07,160
Speaker 2: hundred times. So that was wild to hear that that's

544
00:33:07,160 --> 00:33:10,000
Speaker 2: the case. But all that's to say that so fifty

545
00:33:10,040 --> 00:33:12,240
Speaker 2: percent of the time they were returning to one of

546
00:33:12,240 --> 00:33:16,240
Speaker 2: these betting airs. Again, could you walk me through I guess, Hey,

547
00:33:16,360 --> 00:33:18,560
Speaker 2: did I get any of that wrong? Could you expand

548
00:33:18,600 --> 00:33:20,520
Speaker 2: on any of that? As I try to remember these things?

549
00:33:20,520 --> 00:33:23,720
Speaker 2: And then finally, how often or how long is it

550
00:33:23,760 --> 00:33:25,720
Speaker 2: taking them to come back to these betting AARs for

551
00:33:25,760 --> 00:33:28,080
Speaker 2: those ones that they do return to, Because it basically,

552
00:33:28,880 --> 00:33:31,080
Speaker 2: as I ramble on and on here, what I'm getting

553
00:33:31,120 --> 00:33:33,840
Speaker 2: back to is, as a hunter, I'm frequently thinking to myself,

554
00:33:33,840 --> 00:33:36,400
Speaker 2: all right, if I think this buck is better than

555
00:33:36,480 --> 00:33:40,640
Speaker 2: this zone, and if I knew I saw him here yesterday,

556
00:33:41,160 --> 00:33:42,960
Speaker 2: is is he gonna be back every day this week?

557
00:33:43,040 --> 00:33:44,760
Speaker 2: Or is he coming back once a week? Or is

558
00:33:44,760 --> 00:33:46,840
Speaker 2: he coming back once every two weeks or should I

559
00:33:46,880 --> 00:33:49,840
Speaker 2: expect that man if I sit this zone for the

560
00:33:49,880 --> 00:33:52,960
Speaker 2: next three or four days, he's gonna come back eventually.

561
00:33:53,360 --> 00:33:55,240
Speaker 2: Those are the kinds of insights I'm trying to pick

562
00:33:55,320 --> 00:33:58,040
Speaker 2: up here. That's a lot of through at you.

563
00:33:58,120 --> 00:34:03,560
Speaker 3: Sorry, well, a remarkable job, so good retention there, knowledge

564
00:34:03,600 --> 00:34:07,320
Speaker 3: retention there. Yeah, you got it right. So about fifty

565
00:34:07,360 --> 00:34:12,000
Speaker 3: percent of those sites were one and done, never used again.

566
00:34:12,760 --> 00:34:15,120
Speaker 3: So then we had to look at, okay, for those

567
00:34:15,160 --> 00:34:19,440
Speaker 3: that are revisited, how often are they revisited? And then

568
00:34:19,480 --> 00:34:22,719
Speaker 3: that's where the circuit time comes into play. So we

569
00:34:22,800 --> 00:34:24,799
Speaker 3: have fifty percent or one and done, and then we

570
00:34:24,880 --> 00:34:30,719
Speaker 3: have about twenty five percent where they revisit a couple times, two, three,

571
00:34:30,880 --> 00:34:35,200
Speaker 3: four times. And then we had those supersites, which, to

572
00:34:35,280 --> 00:34:39,319
Speaker 3: be completely honest, now some of those super sites that

573
00:34:39,480 --> 00:34:42,560
Speaker 3: is more like what we where we began with the

574
00:34:42,600 --> 00:34:46,120
Speaker 3: Bucks bedroom. Maybe there are a few places on the

575
00:34:46,200 --> 00:34:48,279
Speaker 3: landscape where they do come back, you know, more and

576
00:34:48,280 --> 00:34:52,480
Speaker 3: more and more. But that circuit time was typically for

577
00:34:52,560 --> 00:34:57,600
Speaker 3: the ones they came back to was one to two days, okay,

578
00:34:57,080 --> 00:35:00,879
Speaker 3: So wasn't going to be the morning site and then

579
00:35:00,960 --> 00:35:05,000
Speaker 3: coming back you know the very next day. It might

580
00:35:05,040 --> 00:35:09,960
Speaker 3: be there one morning, afternoon somewhere else and then you know,

581
00:35:10,760 --> 00:35:13,040
Speaker 3: twelve to twenty four hours later coming back to it.

582
00:35:13,239 --> 00:35:21,719
Speaker 2: Interesting. Okay, So I suppose with this betting analysis that

583
00:35:22,120 --> 00:35:25,840
Speaker 2: you guys did, what has been the major application for you? Like,

584
00:35:26,000 --> 00:35:29,080
Speaker 2: now that you've as you as a hunter personally, have

585
00:35:29,080 --> 00:35:31,239
Speaker 2: have looked at this data, how have you applied that

586
00:35:31,280 --> 00:35:35,120
Speaker 2: to your hunting strategy.

587
00:35:36,520 --> 00:35:42,320
Speaker 3: I have become I've tried to become more aware of

588
00:35:43,800 --> 00:35:47,640
Speaker 3: what is going to be quality betting cover for Bucks

589
00:35:48,760 --> 00:35:51,120
Speaker 3: as best we can. And you know, you always have

590
00:35:51,200 --> 00:35:53,560
Speaker 3: to think about this too. Sometimes Bucks do things we

591
00:35:54,000 --> 00:35:58,480
Speaker 3: don't understand why was it betted there? But I try

592
00:35:58,560 --> 00:36:02,560
Speaker 3: to look at areas where I can at least assume

593
00:36:02,760 --> 00:36:06,200
Speaker 3: have good reason to think this is probably an area

594
00:36:06,280 --> 00:36:10,400
Speaker 3: that's going to be very undisturbed. You know, it's going

595
00:36:10,440 --> 00:36:13,200
Speaker 3: to be a little mini sanctuary place and that's probably

596
00:36:13,239 --> 00:36:16,680
Speaker 3: going to be a good cover area. But the hard part,

597
00:36:17,040 --> 00:36:20,239
Speaker 3: mark is that it's not just one area. There's going

598
00:36:20,280 --> 00:36:24,239
Speaker 3: to be a lot of these different places on the landscape.

599
00:36:24,280 --> 00:36:28,600
Speaker 3: So that again is what makes it difficult. I've kind

600
00:36:28,600 --> 00:36:33,640
Speaker 3: of moved more into picking out some of these betting

601
00:36:33,680 --> 00:36:37,560
Speaker 3: areas may be more difficult in the landscape, I am

602
00:36:37,640 --> 00:36:40,760
Speaker 3: in to be able to differentiate and make the call

603
00:36:41,040 --> 00:36:43,600
Speaker 3: unless we can go in there, like we said, proactively

604
00:36:43,760 --> 00:36:47,840
Speaker 3: and make these areas really good. But I've started looking

605
00:36:48,120 --> 00:36:52,120
Speaker 3: more at how food plots and this could also be

606
00:36:52,160 --> 00:36:55,000
Speaker 3: a managed opening. It's just it's easy to use the

607
00:36:55,000 --> 00:36:59,319
Speaker 3: food plot as an example and how these areas can

608
00:36:59,360 --> 00:37:04,520
Speaker 3: serve as hubs of activity and so hub a meaning

609
00:37:04,560 --> 00:37:07,640
Speaker 3: thinking of a train station. There's a lot of different

610
00:37:07,719 --> 00:37:11,839
Speaker 3: routes coming to it. But what we're seeing is that

611
00:37:11,880 --> 00:37:15,800
Speaker 3: those food plots, if they're good ones, is that there's

612
00:37:15,880 --> 00:37:20,239
Speaker 3: going to be a network of movement over the landscape

613
00:37:20,880 --> 00:37:25,799
Speaker 3: utilizing those food plots. And here's something else that Natasha

614
00:37:25,920 --> 00:37:31,360
Speaker 3: calculated that I thought was just fascinating. We started looking

615
00:37:31,440 --> 00:37:37,080
Speaker 3: at how often bucks are going to food plots when

616
00:37:37,120 --> 00:37:41,000
Speaker 3: we go from the nine rut into the pre rut,

617
00:37:41,040 --> 00:37:44,120
Speaker 3: into the peak rut and the post rut, and we

618
00:37:44,280 --> 00:37:48,920
Speaker 3: basically see this ascension of they're using it a lot,

619
00:37:49,040 --> 00:37:51,359
Speaker 3: and they use it more during the rut, and then

620
00:37:51,400 --> 00:37:54,200
Speaker 3: of course they're using it during the post rut, so

621
00:37:55,040 --> 00:37:57,200
Speaker 3: that when you look at it that way, it's just

622
00:37:57,239 --> 00:38:00,320
Speaker 3: a tally. Did a buck go into a food plot.

623
00:38:00,360 --> 00:38:02,759
Speaker 3: We caught him there. Yes, it's a tally of frequency,

624
00:38:04,160 --> 00:38:08,279
Speaker 3: but when we look at the duration of time that

625
00:38:08,320 --> 00:38:12,680
Speaker 3: they're spending on the food plots, it is completely different

626
00:38:13,239 --> 00:38:18,439
Speaker 3: during the rut as compared to the pre pre pre rut,

627
00:38:18,520 --> 00:38:21,560
Speaker 3: So before the pre rut and then the post rut,

628
00:38:22,040 --> 00:38:25,920
Speaker 3: and so at that point is there is a greater

629
00:38:26,200 --> 00:38:30,000
Speaker 3: duration of time. So before the rut they're going to

630
00:38:30,040 --> 00:38:35,040
Speaker 3: the food plot to eat. During the rut, that is

631
00:38:35,080 --> 00:38:38,760
Speaker 3: more of a social aspect, and we've if you've hunted,

632
00:38:38,800 --> 00:38:42,000
Speaker 3: you've seen that. The buck comes in, he looks around,

633
00:38:42,120 --> 00:38:45,040
Speaker 3: he win checks heat checks, et cetera, and he goes

634
00:38:45,080 --> 00:38:48,080
Speaker 3: on and he's probably going to go to another food

635
00:38:48,080 --> 00:38:50,480
Speaker 3: plot and do the same thing. So we see a

636
00:38:50,520 --> 00:38:55,200
Speaker 3: great amount of visitation but very low duration. And then

637
00:38:55,239 --> 00:38:58,040
Speaker 3: as the rut wines down and the post rut comes along,

638
00:38:58,560 --> 00:39:02,840
Speaker 3: that's where we see a boat feeding about is taking

639
00:39:02,880 --> 00:39:06,480
Speaker 3: place on those food plots. So that is changing the

640
00:39:06,480 --> 00:39:10,239
Speaker 3: way I'm hunting. And I guess to summarize, I was

641
00:39:10,360 --> 00:39:16,160
Speaker 3: very long winded. There is I'm thinking of during the rut.

642
00:39:16,480 --> 00:39:20,759
Speaker 3: Maybe I'm not hunting on the food plot, but I'm

643
00:39:20,800 --> 00:39:24,879
Speaker 3: trying as best I can discern to think of the

644
00:39:24,880 --> 00:39:30,120
Speaker 3: corridors and the connections linking a network of food plots,

645
00:39:30,880 --> 00:39:34,520
Speaker 3: whereas in the post rut, I may just hunt, you know,

646
00:39:34,719 --> 00:39:37,680
Speaker 3: decide I'm gonna hunt directly on food. He's going to

647
00:39:37,760 --> 00:39:38,080
Speaker 3: be there.

648
00:39:39,320 --> 00:39:45,719
Speaker 2: So I don't know how this changes my thinking, but

649
00:39:45,760 --> 00:39:48,879
Speaker 2: I think I frequently thought when I am looking at

650
00:39:48,880 --> 00:39:51,359
Speaker 2: a map and trying to think through how deer might

651
00:39:51,440 --> 00:39:54,440
Speaker 2: use an area, I often think of the of a

652
00:39:54,480 --> 00:39:56,920
Speaker 2: betting area as the hub of the wheel, and then

653
00:39:56,960 --> 00:39:59,320
Speaker 2: the spokes are all leading out from a main betting

654
00:39:59,320 --> 00:40:02,440
Speaker 2: era to the three or four different main food sources

655
00:40:02,440 --> 00:40:05,040
Speaker 2: that I might think that they're feeding on. So I've

656
00:40:05,040 --> 00:40:07,680
Speaker 2: always thought of the betting area being the hub and

657
00:40:07,719 --> 00:40:11,799
Speaker 2: that being the more consistent factor. But am I right

658
00:40:11,880 --> 00:40:15,440
Speaker 2: to take the data that you are sharing here and

659
00:40:16,360 --> 00:40:18,560
Speaker 2: should that make me rethink that? And is it more

660
00:40:19,360 --> 00:40:23,080
Speaker 2: accurate to think of the food sources as the hubs

661
00:40:23,760 --> 00:40:26,160
Speaker 2: and that the spokes should be leading to the many,

662
00:40:26,239 --> 00:40:29,200
Speaker 2: many different betting areas And might that be the better

663
00:40:29,239 --> 00:40:30,920
Speaker 2: way to think of a strategy?

664
00:40:31,800 --> 00:40:35,839
Speaker 3: Well, I think we're both right. I think you and

665
00:40:35,880 --> 00:40:39,719
Speaker 3: I could sit here with a crayon or PowerPoint and

666
00:40:39,760 --> 00:40:43,000
Speaker 3: we could come up with a scenario where cover is

667
00:40:43,120 --> 00:40:46,680
Speaker 3: limited in that landscape, and what you described would be

668
00:40:46,719 --> 00:40:50,120
Speaker 3: precisely correct. I think we could also come up with

669
00:40:50,200 --> 00:40:52,640
Speaker 3: scenario that would be common in the South to where

670
00:40:53,239 --> 00:40:58,000
Speaker 3: cover is abundant and food is limited. So you know,

671
00:40:58,040 --> 00:41:01,360
Speaker 3: when we think about Midwest versus the South or the East,

672
00:41:01,480 --> 00:41:05,840
Speaker 3: we go from a cover rich, food limited environment in

673
00:41:05,920 --> 00:41:11,239
Speaker 3: the South to a cover limited abundance of food in

674
00:41:11,280 --> 00:41:13,600
Speaker 3: the Midwest. And so I think it depends on the

675
00:41:13,680 --> 00:41:14,839
Speaker 3: landscape that you're at.

676
00:41:15,600 --> 00:41:20,640
Speaker 2: Yeah, that's a great point. Interesting. So we talked about

677
00:41:20,800 --> 00:41:24,600
Speaker 2: affinity for betting areas and how often they're gonna come

678
00:41:24,600 --> 00:41:26,880
Speaker 2: back to a betting area. But before we start recording,

679
00:41:27,320 --> 00:41:29,840
Speaker 2: you mentioned that there's been some stuff you've been looking into,

680
00:41:30,320 --> 00:41:34,640
Speaker 2: maybe a slightly larger scale, about how you know bucks

681
00:41:34,719 --> 00:41:37,879
Speaker 2: might be returning to certain areas, possibly year after year.

682
00:41:38,760 --> 00:41:40,120
Speaker 2: Do you want to expand on that a little bit,

683
00:41:40,120 --> 00:41:43,040
Speaker 2: because this is something that I think ties into something

684
00:41:43,040 --> 00:41:44,839
Speaker 2: that has been picking up more and more steam within

685
00:41:44,880 --> 00:41:48,080
Speaker 2: the hunting world, which is this idea of like annual patterns,

686
00:41:48,760 --> 00:41:50,719
Speaker 2: And I'd love to hear what you maybe have learned

687
00:41:50,760 --> 00:41:53,640
Speaker 2: about that from you know, backed up by data. Possibly.

688
00:41:54,000 --> 00:41:59,880
Speaker 3: Yeah, absolutely, and so this is interesting to me, and

689
00:42:00,160 --> 00:42:05,800
Speaker 3: this is a classic example of owning up to being wrong.

690
00:42:06,440 --> 00:42:10,959
Speaker 3: I was absolutely wrong about this five plus years ago.

691
00:42:11,360 --> 00:42:14,520
Speaker 2: And so we man say that, well, we have all the.

692
00:42:14,440 --> 00:42:15,719
Speaker 3: Subundance of camera data.

693
00:42:15,719 --> 00:42:15,919
Speaker 2: Now.

694
00:42:16,719 --> 00:42:20,200
Speaker 3: I would get this question constantly before we had this

695
00:42:20,320 --> 00:42:25,440
Speaker 3: GPS data of you know, I'd get an email, why

696
00:42:25,600 --> 00:42:27,759
Speaker 3: is it that I have this buck? I have them

697
00:42:27,760 --> 00:42:31,520
Speaker 3: on camera every day or every other day, and then

698
00:42:31,560 --> 00:42:34,279
Speaker 3: they would give me a specific week or even a

699
00:42:34,280 --> 00:42:37,040
Speaker 3: three day period, and they would say, it seems like

700
00:42:37,280 --> 00:42:41,120
Speaker 3: every time around the seventh of November or whatever day

701
00:42:41,640 --> 00:42:45,440
Speaker 3: he's gone, or just the opposite, seems like the third

702
00:42:45,480 --> 00:42:49,040
Speaker 3: week of November, this buck is always back. And what's

703
00:42:49,120 --> 00:42:53,839
Speaker 3: going on there? And before we learned about what we're

704
00:42:53,880 --> 00:42:57,239
Speaker 3: called these mobile personalities and these home range shifts and

705
00:42:57,280 --> 00:42:59,799
Speaker 3: the stuff, that we didn't really realize what was going on,

706
00:43:00,239 --> 00:43:03,439
Speaker 3: really had no way to measure that. I honestly thought

707
00:43:03,560 --> 00:43:07,640
Speaker 3: somebody's batteries in their camera had run out. I thought

708
00:43:07,640 --> 00:43:10,040
Speaker 3: they weren't checking it, or the buck was still there

709
00:43:10,080 --> 00:43:12,560
Speaker 3: on the property. He was just maybe not on that camera.

710
00:43:13,600 --> 00:43:17,400
Speaker 3: But what we started seeing is, first of all, we

711
00:43:17,480 --> 00:43:22,200
Speaker 3: have these mobile personality bucks that do these extreme shifts,

712
00:43:22,760 --> 00:43:26,319
Speaker 3: and they're typically doing this shifting at about the same

713
00:43:26,400 --> 00:43:31,040
Speaker 3: time a year. One of our great examples was the

714
00:43:31,080 --> 00:43:34,960
Speaker 3: buck that would fall and winter in Mississippi and spring

715
00:43:35,000 --> 00:43:37,880
Speaker 3: and summer in Louisiana and swim the Mississippi River to

716
00:43:37,920 --> 00:43:40,480
Speaker 3: do that, and the timing of when he did it

717
00:43:40,480 --> 00:43:43,759
Speaker 3: two years in a row was remarkably similar. So that

718
00:43:43,880 --> 00:43:46,600
Speaker 3: just got me thinking of the bucks that we have

719
00:43:47,520 --> 00:43:50,680
Speaker 3: two consecutive years of data. Because remember some of these

720
00:43:50,719 --> 00:43:52,920
Speaker 3: bucks we put a collar on them. In the first fall,

721
00:43:53,160 --> 00:43:56,560
Speaker 3: they die, they get shot, whatever. But of our subsample

722
00:43:56,640 --> 00:43:59,520
Speaker 3: of bucks that we have the collar on them for

723
00:43:59,520 --> 00:44:03,399
Speaker 3: two years in a row, Natasha was able to look

724
00:44:03,440 --> 00:44:08,960
Speaker 3: at from year to year. So from the first week

725
00:44:09,000 --> 00:44:12,279
Speaker 3: of November in twenty seventeen to the first week of

726
00:44:12,280 --> 00:44:17,120
Speaker 3: November in twenty eighteen, how much overlap? And she looked

727
00:44:17,120 --> 00:44:20,920
Speaker 3: at it two ways. If you do their little home

728
00:44:21,040 --> 00:44:24,600
Speaker 3: range on a weekly or monthly scale, is what is

729
00:44:24,640 --> 00:44:28,960
Speaker 3: the proportion of overlap of their range use or area

730
00:44:29,120 --> 00:44:32,200
Speaker 3: use from one year to the next. And then if

731
00:44:32,239 --> 00:44:35,840
Speaker 3: you go to the very center the hub of activity

732
00:44:35,840 --> 00:44:39,799
Speaker 3: of those two areas, what's the distance between those two areas?

733
00:44:40,920 --> 00:44:46,600
Speaker 3: And I have to look at my notes here, because

734
00:44:46,760 --> 00:44:51,239
Speaker 3: I don't have it committed to memory, the amount of

735
00:44:51,640 --> 00:44:55,600
Speaker 3: overlap from year one to year two. And this is

736
00:44:55,680 --> 00:44:58,719
Speaker 3: great because this is occurring during the hunting season. We

737
00:44:58,760 --> 00:45:03,200
Speaker 3: see the greatest amount of overlap during the hunting season

738
00:45:04,280 --> 00:45:09,680
Speaker 3: in October November to Bid November. We see greater than

739
00:45:10,239 --> 00:45:16,160
Speaker 3: eighty percent range overlap from year one to year two.

740
00:45:16,200 --> 00:45:19,560
Speaker 3: And we only see a difference from the center or

741
00:45:19,640 --> 00:45:24,040
Speaker 3: the hub of those activities of only about two to

742
00:45:24,080 --> 00:45:25,279
Speaker 3: three hundred yards.

743
00:45:25,600 --> 00:45:31,800
Speaker 2: Wow. And so in your study area, that time period

744
00:45:31,800 --> 00:45:35,680
Speaker 2: would be like early season to pre rut, right, because

745
00:45:36,400 --> 00:45:38,480
Speaker 2: in Mississippi, your peak of the rut down there would

746
00:45:38,480 --> 00:45:39,800
Speaker 2: be more like December January.

747
00:45:39,800 --> 00:45:42,080
Speaker 3: Correct, that's right, that would be pre rut.

748
00:45:42,400 --> 00:45:47,799
Speaker 2: Yes, okay, so let me stay that back to you

749
00:45:47,840 --> 00:45:49,400
Speaker 2: and make sure I've got this right. So what that

750
00:45:49,520 --> 00:45:53,080
Speaker 2: data says is that for me, on a year to

751
00:45:53,160 --> 00:45:57,560
Speaker 2: year basis, these bucks were essentially matching up this They

752
00:45:57,560 --> 00:46:00,000
Speaker 2: were hanging out in the same areas at the same

753
00:45:59,840 --> 00:46:02,200
Speaker 2: times about eighty percent of the time.

754
00:46:06,840 --> 00:46:10,200
Speaker 3: I think I would say it this way, if you

755
00:46:10,680 --> 00:46:13,120
Speaker 3: let's try to maybe frame it the way what a

756
00:46:13,200 --> 00:46:16,920
Speaker 3: hunter would do. Let's say a hunter on a property,

757
00:46:16,960 --> 00:46:21,320
Speaker 3: had a grid of cameras and plodded on a map

758
00:46:21,520 --> 00:46:25,520
Speaker 3: all the areas that they were getting photos of this buck,

759
00:46:26,360 --> 00:46:29,239
Speaker 3: and they drew a polygon around that area. It's like,

760
00:46:29,360 --> 00:46:31,880
Speaker 3: we know this buck is on this property and this

761
00:46:32,000 --> 00:46:36,759
Speaker 3: part of the property. The following year, on average, that

762
00:46:36,800 --> 00:46:41,239
Speaker 3: buck will be using eighty percent of the area it

763
00:46:41,320 --> 00:46:42,880
Speaker 3: was using the year before.

764
00:46:43,440 --> 00:46:48,400
Speaker 2: Okay, okay, So these home ranges, these core ranges, have

765
00:46:49,360 --> 00:46:52,440
Speaker 2: you know, year after year fidelity they're going to stick

766
00:46:52,520 --> 00:46:55,879
Speaker 2: to that. Now here's another fill up question. I don't

767
00:46:55,880 --> 00:46:58,080
Speaker 2: know if you guys have had the time to look

768
00:46:58,120 --> 00:47:01,919
Speaker 2: into this, but has that change or has that level

769
00:47:01,920 --> 00:47:06,360
Speaker 2: of certainty changed the older buck gets So would we

770
00:47:07,040 --> 00:47:09,839
Speaker 2: say that that overlap is even higher on a buck

771
00:47:09,920 --> 00:47:13,480
Speaker 2: that's four or five six versus two three four? Have

772
00:47:13,560 --> 00:47:15,239
Speaker 2: you been able to dive into that level yet?

773
00:47:16,880 --> 00:47:21,279
Speaker 3: We have not, and the primary reason for that. So

774
00:47:21,560 --> 00:47:26,080
Speaker 3: our sample is primarily three four and five year old bucks.

775
00:47:26,480 --> 00:47:28,680
Speaker 3: When we capture them they're three, four or five, that's

776
00:47:28,719 --> 00:47:31,279
Speaker 3: going to be like eighty percent of our sample right there.

777
00:47:32,440 --> 00:47:35,280
Speaker 3: The issue is we just have two years with them.

778
00:47:35,719 --> 00:47:39,319
Speaker 3: In a perfect world, we would get a yearling buck

779
00:47:39,520 --> 00:47:43,480
Speaker 3: post dispersal and be able to follow it if it lived,

780
00:47:43,719 --> 00:47:46,120
Speaker 3: follow it to six or seven years of age, that'd

781
00:47:46,120 --> 00:47:48,360
Speaker 3: be the best way to Thanks for that question, but

782
00:47:48,480 --> 00:47:49,719
Speaker 3: unfortunately we don't have that.

783
00:47:50,280 --> 00:47:56,680
Speaker 2: Yeah, well it's really interesting to see to see that.

784
00:47:56,800 --> 00:48:00,160
Speaker 2: Now here's another one that I know. I believe your

785
00:48:00,239 --> 00:48:02,560
Speaker 2: data has supported this, and I know other studies have

786
00:48:02,680 --> 00:48:07,640
Speaker 2: supported that. Many bucks have random excursions that they take

787
00:48:07,640 --> 00:48:10,840
Speaker 2: throughout the year, where they will, you know, for whatever reason,

788
00:48:11,120 --> 00:48:13,319
Speaker 2: randomly take off and go way outside of their home

789
00:48:13,400 --> 00:48:16,040
Speaker 2: range for a handful of days or twenty four hours again,

790
00:48:16,120 --> 00:48:19,680
Speaker 2: whatever it is, and then they come back. Have you

791
00:48:19,880 --> 00:48:23,799
Speaker 2: been able to see if those map up year after year?

792
00:48:23,960 --> 00:48:26,480
Speaker 2: Like will do you have any examples of a buck

793
00:48:26,600 --> 00:48:29,560
Speaker 2: going on an excursion at relatively the same time year

794
00:48:29,600 --> 00:48:32,839
Speaker 2: after year that might support you know, the people who say, like, hey,

795
00:48:32,880 --> 00:48:36,080
Speaker 2: this this random buck shows up the first week November

796
00:48:36,120 --> 00:48:37,960
Speaker 2: every year and then I don't see them the rest

797
00:48:38,000 --> 00:48:40,160
Speaker 2: of the year. Have we been able to document anything

798
00:48:40,239 --> 00:48:41,959
Speaker 2: like that in the study.

799
00:48:43,040 --> 00:48:47,080
Speaker 3: We have not. Marked that's a good question. We have not,

800
00:48:47,600 --> 00:48:51,560
Speaker 3: but we haven't looked at it that way. We were

801
00:48:51,600 --> 00:48:54,400
Speaker 3: able to go through and document the excursions and the

802
00:48:54,400 --> 00:48:57,600
Speaker 3: frequency of it and so forth, but we did not

803
00:48:57,760 --> 00:49:01,000
Speaker 3: look at the subset of the data of of the

804
00:49:01,040 --> 00:49:05,799
Speaker 3: bucks yet where where when they took their excursion and

805
00:49:05,840 --> 00:49:09,080
Speaker 3: where they excursed too, and see if it's related to

806
00:49:09,120 --> 00:49:12,040
Speaker 3: the following year. But we can put that on the list.

807
00:49:12,120 --> 00:49:13,280
Speaker 3: Look at that's a good question.

808
00:49:13,840 --> 00:49:15,960
Speaker 2: I imagine you've got a very long list because of

809
00:49:16,000 --> 00:49:18,760
Speaker 2: this kind of data. I imagine there's so many different

810
00:49:18,880 --> 00:49:22,319
Speaker 2: questions you'd want to apply to it right right, and

811
00:49:22,320 --> 00:49:25,280
Speaker 2: and dig into and see see what's there. There's probably

812
00:49:25,400 --> 00:49:28,000
Speaker 2: years and years worth of analysis you could do with

813
00:49:28,080 --> 00:49:35,879
Speaker 2: this stuff. Absolutely, you mentioned these mobile deer personalities. Could

814
00:49:35,920 --> 00:49:38,120
Speaker 2: you expand a little bit on that. I think you

815
00:49:38,120 --> 00:49:43,840
Speaker 2: guys have differentiated basically bucks into two categories, either mobile

816
00:49:44,200 --> 00:49:47,120
Speaker 2: personalities or sedentary. Could you could you discuss that a

817
00:49:47,160 --> 00:49:49,879
Speaker 2: little bit, how you differentiate them and and what's the

818
00:49:49,960 --> 00:49:51,399
Speaker 2: proportion of one to the other?

819
00:49:51,960 --> 00:49:57,879
Speaker 3: Sure thing? Yeah, so sedentary? Uh, that is essentially what

820
00:49:58,040 --> 00:50:02,239
Speaker 3: we think of a deer's home range is throughout the year.

821
00:50:02,760 --> 00:50:06,560
Speaker 3: Meaning if you were to take a bird's eye view

822
00:50:07,800 --> 00:50:11,160
Speaker 3: or nowadays we'd say a Google Earth view, is that

823
00:50:11,320 --> 00:50:14,520
Speaker 3: you would look at the points of all his locations

824
00:50:14,840 --> 00:50:18,160
Speaker 3: and they would just be in one big area. If

825
00:50:18,200 --> 00:50:22,680
Speaker 3: you took a particular time period apart. You know, for

826
00:50:22,800 --> 00:50:24,759
Speaker 3: this month, it might be in this part of its

827
00:50:24,800 --> 00:50:28,040
Speaker 3: annual home range and then this part in its annual

828
00:50:28,040 --> 00:50:33,400
Speaker 3: home range. But essentially it's all overlapping, and it's about

829
00:50:33,520 --> 00:50:36,480
Speaker 3: depending on the buck, it may be five hundred acres,

830
00:50:36,520 --> 00:50:39,480
Speaker 3: it may be fifteen hundred acres, but essentially a great

831
00:50:39,520 --> 00:50:42,560
Speaker 3: deal of overlap for twelve months out of the year.

832
00:50:43,040 --> 00:50:47,120
Speaker 3: But we found out about thirty percent of the bucks

833
00:50:47,560 --> 00:50:52,360
Speaker 3: have just what we termed as mobile personality, meaning that

834
00:50:52,680 --> 00:50:56,600
Speaker 3: for part of the year they have a completely distinct

835
00:50:56,880 --> 00:51:02,560
Speaker 3: and non overlapping home range. And so very simply well,

836
00:51:02,800 --> 00:51:05,960
Speaker 3: the buck I referred to earlier that swam the Mississippi

837
00:51:06,040 --> 00:51:10,359
Speaker 3: River is our best example. Those are non overlapping home

838
00:51:10,440 --> 00:51:12,560
Speaker 3: ranges that it had in Mississippi and it had in

839
00:51:12,640 --> 00:51:16,200
Speaker 3: Louisiana and the river in between, and there's like fifteen

840
00:51:16,320 --> 00:51:22,040
Speaker 3: to eighteen miles, you know, in between those, and so yeah,

841
00:51:22,120 --> 00:51:24,600
Speaker 3: what we learned is that there's about thirty percent of

842
00:51:24,640 --> 00:51:27,760
Speaker 3: the bucks are doing that is they're going to spend

843
00:51:28,040 --> 00:51:30,640
Speaker 3: part of their year in one location and then they

844
00:51:30,719 --> 00:51:35,160
Speaker 3: might go a mile or two or ten and move

845
00:51:35,320 --> 00:51:38,520
Speaker 3: completely somewhere else and spend a portion of the year.

846
00:51:39,800 --> 00:51:44,399
Speaker 2: So I feel like at least what I have kind

847
00:51:44,440 --> 00:51:47,200
Speaker 2: of zeroed in on and without data, have kind of

848
00:51:47,239 --> 00:51:49,960
Speaker 2: thought that about give or take that amount of the

849
00:51:50,000 --> 00:51:52,319
Speaker 2: bucks in the places I hunt, about a third of them,

850
00:51:52,320 --> 00:51:56,480
Speaker 2: give or take, seemed to disappear during what a lotus

851
00:51:56,520 --> 00:51:59,880
Speaker 2: called the September shift. So like that first week of September,

852
00:52:00,080 --> 00:52:02,920
Speaker 2: give or take, you know, most the bucks lose their velvet,

853
00:52:03,160 --> 00:52:05,360
Speaker 2: And that tends to be when I have seen and

854
00:52:05,560 --> 00:52:07,879
Speaker 2: most folks I talk to tend to see if there's

855
00:52:07,920 --> 00:52:10,960
Speaker 2: going to be some dispersal, some shift in range, that's

856
00:52:11,040 --> 00:52:14,000
Speaker 2: usually when it happens. Is that what you guys are

857
00:52:14,000 --> 00:52:16,040
Speaker 2: seeing as well with the data, is that when these

858
00:52:16,080 --> 00:52:18,680
Speaker 2: mobile personality bucks make that range shift, or is there

859
00:52:18,680 --> 00:52:20,280
Speaker 2: a different time of year that's happening.

860
00:52:22,160 --> 00:52:25,160
Speaker 3: Yeah, what what you're referring to there is I would

861
00:52:25,200 --> 00:52:29,719
Speaker 3: call the the bachelor group breakup, so that that is

862
00:52:30,000 --> 00:52:35,719
Speaker 3: uniformly happening right there. What we're seeing, I guess, is

863
00:52:36,600 --> 00:52:40,400
Speaker 3: a special case of that in two ways. Number One,

864
00:52:40,920 --> 00:52:45,640
Speaker 3: the distance that they shift to is further, and the

865
00:52:45,719 --> 00:52:50,040
Speaker 3: time of year can be completely different than during that

866
00:52:50,120 --> 00:52:55,920
Speaker 3: September bachelor group breakup and what we found on average,

867
00:52:55,960 --> 00:52:59,880
Speaker 3: And I keep saying on average, because bucks are individual,

868
00:53:00,600 --> 00:53:02,880
Speaker 3: and we have some that do extraordinary things and we

869
00:53:02,920 --> 00:53:07,040
Speaker 3: have some that are very predictable. But on average, if

870
00:53:07,080 --> 00:53:09,840
Speaker 3: you look at the center of their activity during the

871
00:53:09,880 --> 00:53:13,759
Speaker 3: summer and then the center of activity when they do

872
00:53:13,840 --> 00:53:18,200
Speaker 3: the shift, the shuffle that you're talking about, it can

873
00:53:18,239 --> 00:53:22,200
Speaker 3: be one thousand to fifteen hundred yards apart. That's kind

874
00:53:22,239 --> 00:53:25,440
Speaker 3: of a good range. And so when you break that

875
00:53:25,560 --> 00:53:30,000
Speaker 3: down into normal property sizes, not on a ten thousand

876
00:53:30,080 --> 00:53:32,879
Speaker 3: acre property, but on a five hundred acre property, when

877
00:53:32,920 --> 00:53:34,880
Speaker 3: you have that shuffle or that shift, you have some

878
00:53:35,080 --> 00:53:38,360
Speaker 3: bucks went off your property and you traded them for

879
00:53:38,440 --> 00:53:56,920
Speaker 3: some bucks that came on to your property.

880
00:53:57,640 --> 00:53:59,399
Speaker 2: I guess the only other thing I'd be curious would

881
00:53:59,400 --> 00:54:02,680
Speaker 2: be when it comes to this, this idea of site

882
00:54:02,680 --> 00:54:05,840
Speaker 2: affinity for bucks, going back to you know this, this

883
00:54:05,960 --> 00:54:10,759
Speaker 2: idea of of annual trends or patterns lining up. Has

884
00:54:10,800 --> 00:54:13,520
Speaker 2: that effected your hunting at all? Personally? Have have you?

885
00:54:13,880 --> 00:54:17,200
Speaker 2: Does this make you lean more into learning individual bucks

886
00:54:17,280 --> 00:54:19,719
Speaker 2: tendencies in some kind of way or is there any

887
00:54:19,719 --> 00:54:21,160
Speaker 2: other application to that for you?

888
00:54:21,960 --> 00:54:26,120
Speaker 3: Yeah? I think the uh for me? Anyway, I think

889
00:54:26,239 --> 00:54:30,920
Speaker 3: the take home is whether it is from observation or

890
00:54:30,960 --> 00:54:34,840
Speaker 3: more likely it would be from camera data. Is whenever

891
00:54:35,040 --> 00:54:39,080
Speaker 3: you see a buck using a portion of the property

892
00:54:39,680 --> 00:54:43,440
Speaker 3: in a particular time of the year and you're unsuccessful

893
00:54:43,520 --> 00:54:46,560
Speaker 3: connecting with that buck, or maybe it's a buck that

894
00:54:46,680 --> 00:54:50,759
Speaker 3: is of interest and he's three years of age and

895
00:54:50,840 --> 00:54:53,279
Speaker 3: I would really like to see him at four or

896
00:54:53,320 --> 00:54:59,799
Speaker 3: at five. Is we've demonstrated with with reasonable confidence that

897
00:55:00,239 --> 00:55:04,359
Speaker 3: you could predict that buck the following year is going

898
00:55:04,440 --> 00:55:08,480
Speaker 3: to be in that same area. So do your homework

899
00:55:08,520 --> 00:55:12,480
Speaker 3: ahead of time and be set up so when he

900
00:55:12,560 --> 00:55:15,200
Speaker 3: starts using that area you can capitalize on it.

901
00:55:15,520 --> 00:55:20,560
Speaker 2: Yeah. So another version of this would be this idea

902
00:55:21,360 --> 00:55:25,120
Speaker 2: around focal areas specifically during the rut. You know, for

903
00:55:25,160 --> 00:55:27,120
Speaker 2: a lot of years folks have talked about during the rut,

904
00:55:27,120 --> 00:55:29,800
Speaker 2: you can't pattern bucks. They're all over the place. It's crazy,

905
00:55:30,560 --> 00:55:33,799
Speaker 2: it's chaos. And then I can't remember how when this

906
00:55:33,920 --> 00:55:35,839
Speaker 2: verse came out, but some number of years ago, five

907
00:55:35,880 --> 00:55:38,280
Speaker 2: to ten years ago, I feel like you started hearing

908
00:55:38,320 --> 00:55:41,040
Speaker 2: about some data that came out about how I actually

909
00:55:41,040 --> 00:55:44,400
Speaker 2: there's been identified focal areas for many bucks during the

910
00:55:44,480 --> 00:55:46,960
Speaker 2: rut that they do return to over and over again,

911
00:55:47,239 --> 00:55:50,400
Speaker 2: and I'm just curious, did you guys see something similar

912
00:55:50,440 --> 00:55:53,360
Speaker 2: in your data more recently? Do you have any updates

913
00:55:53,440 --> 00:55:56,400
Speaker 2: on that? Is that still something that we can be

914
00:55:56,520 --> 00:55:59,040
Speaker 2: thinking about and looking for when we develop our rut

915
00:55:59,120 --> 00:55:59,960
Speaker 2: hunting strategies.

916
00:56:00,800 --> 00:56:06,960
Speaker 3: Yeah, And I think the group that first termed that

917
00:56:07,239 --> 00:56:11,840
Speaker 3: were my colleagues in South Texas and Aaron Foley and

918
00:56:11,920 --> 00:56:15,960
Speaker 3: Randy DeYoung and those guys. Yeah, they started finding. Now again,

919
00:56:16,360 --> 00:56:21,200
Speaker 3: different landscape, different landscape, the distribution of cover can be different,

920
00:56:21,840 --> 00:56:24,520
Speaker 3: but they started finding these areas and I think the

921
00:56:24,560 --> 00:56:28,640
Speaker 3: way they reconciled it was they essentially started seeing focal

922
00:56:28,680 --> 00:56:33,239
Speaker 3: areas and a circuit that these bucks were going to

923
00:56:33,360 --> 00:56:38,080
Speaker 3: these focal areas. But what they surmised was that in

924
00:56:38,120 --> 00:56:41,200
Speaker 3: every one of these focal areas where there was cover

925
00:56:41,760 --> 00:56:45,080
Speaker 3: is a group of doves, and so that buck is

926
00:56:45,120 --> 00:56:48,080
Speaker 3: on a circuit to go in heat check. Are there

927
00:56:48,080 --> 00:56:51,200
Speaker 3: any does in Estris? If they are great, If not,

928
00:56:51,320 --> 00:56:54,719
Speaker 3: they're going to go to the next focal area. I

929
00:56:54,719 --> 00:56:58,960
Speaker 3: would say with us, it's similar to what we just

930
00:56:59,040 --> 00:57:02,840
Speaker 3: talked about with this affinity and them coming back to

931
00:57:02,880 --> 00:57:06,440
Speaker 3: the same areas every year, and how that's even shifting

932
00:57:06,920 --> 00:57:10,280
Speaker 3: from year to year, but they're still using the same area.

933
00:57:10,719 --> 00:57:14,680
Speaker 3: Another thing mark that I thought was really interesting was

934
00:57:14,719 --> 00:57:20,160
Speaker 3: that we see throughout the year, as we've known for

935
00:57:20,200 --> 00:57:25,280
Speaker 3: a long long time, the home range, the area used

936
00:57:25,400 --> 00:57:28,640
Speaker 3: by a buck is going to increase during the rut,

937
00:57:29,040 --> 00:57:31,880
Speaker 3: probably by fifty percent, maybe one hundred percent. It could

938
00:57:31,880 --> 00:57:35,320
Speaker 3: possibly double. But what was interesting to me is that

939
00:57:35,400 --> 00:57:42,000
Speaker 3: on a daily scale it is absolutely uniform from no rut,

940
00:57:42,080 --> 00:57:46,040
Speaker 3: pre rut, peak rut, post rut, et cetera. They use

941
00:57:46,400 --> 00:57:49,240
Speaker 3: two hundred acres per day. If you look at a

942
00:57:49,440 --> 00:57:52,600
Speaker 3: daily home range, if you encapsulate the area that they

943
00:57:52,720 --> 00:57:56,800
Speaker 3: used in a day, it's two hundred acres. So how

944
00:57:56,840 --> 00:58:00,600
Speaker 3: that works out to the home range expanding is that

945
00:58:00,640 --> 00:58:03,280
Speaker 3: in the pre rut, from day to day, with those

946
00:58:03,320 --> 00:58:07,320
Speaker 3: two hundred acre home ranges, there's overlap. Then when we

947
00:58:07,360 --> 00:58:10,560
Speaker 3: get into the rut, there is a greater distance between

948
00:58:10,840 --> 00:58:13,600
Speaker 3: two hundred acres here two hundred acres there. That's what

949
00:58:13,720 --> 00:58:16,600
Speaker 3: makes the home range grow during the rut.

950
00:58:17,080 --> 00:58:20,920
Speaker 2: And so that's just the data supporting the generally believed

951
00:58:20,960 --> 00:58:24,320
Speaker 2: concept that you're gonna have bucks checking out different zones

952
00:58:24,680 --> 00:58:30,800
Speaker 2: to find a doing heat right, right, But so I

953
00:58:30,840 --> 00:58:33,280
Speaker 2: guess with that being said, and then what also what

954
00:58:33,520 --> 00:58:36,720
Speaker 2: Folly in their team saw though, does that still support

955
00:58:36,880 --> 00:58:41,040
Speaker 2: the idea that if you know of a core a

956
00:58:41,040 --> 00:58:45,600
Speaker 2: couple core dough hotspots that you effectively to it some

957
00:58:45,800 --> 00:58:50,360
Speaker 2: degree could hunt for a specific buck by dialing in

958
00:58:50,400 --> 00:58:53,240
Speaker 2: on that spot or two just knowing that, man, this

959
00:58:53,280 --> 00:58:55,600
Speaker 2: is where historically this buck has always wanted to check

960
00:58:55,640 --> 00:58:59,240
Speaker 2: dose here. And rather than chasing your tail going here,

961
00:58:59,360 --> 00:59:04,400
Speaker 2: going there, going everywhere, you could hypothetically volume set one

962
00:59:04,400 --> 00:59:06,720
Speaker 2: of these hot spots that you know historically he's been on,

963
00:59:06,840 --> 00:59:10,520
Speaker 2: because there should he should continue to return and check

964
00:59:10,560 --> 00:59:12,800
Speaker 2: those to some bearing degree.

965
00:59:12,920 --> 00:59:17,160
Speaker 3: Right, yep, yeah, I think it's right. So let's think

966
00:59:17,200 --> 00:59:21,600
Speaker 3: of it now, maybe not a focal area relative to cover.

967
00:59:21,880 --> 00:59:24,480
Speaker 3: Let's think about it as that hub or that focal

968
00:59:24,520 --> 00:59:28,880
Speaker 3: area relative to a productive food plot or a network

969
00:59:28,960 --> 00:59:32,840
Speaker 3: of food plots. And like we said earlier, bucks are

970
00:59:32,960 --> 00:59:36,160
Speaker 3: visiting food plots during the rut, but their duration of

971
00:59:36,240 --> 00:59:39,480
Speaker 3: stay is amenimal, but more subplots.

972
00:59:38,920 --> 00:59:41,520
Speaker 2: A right, right, more food plot visits, but just shorter duration.

973
00:59:41,600 --> 00:59:46,280
Speaker 3: Correct, it's actually about the same. Yeah, there's a little

974
00:59:46,280 --> 00:59:49,360
Speaker 3: bit less visit in the post rut. You're exactly right.

975
00:59:49,640 --> 00:59:51,440
Speaker 3: But they stay for a long time because there's a

976
00:59:51,440 --> 00:59:55,080
Speaker 3: feeding bout involved. So they're visiting those food plots. I

977
00:59:55,120 --> 00:59:59,520
Speaker 3: think that's very akin to what Folly found out, but

978
00:59:59,760 --> 01:00:02,920
Speaker 3: just the habitat of the vegetation is different. That was

979
01:00:02,960 --> 01:00:07,000
Speaker 3: more relative to cover patches. The buck is visiting. I'm

980
01:00:07,040 --> 01:00:10,680
Speaker 3: talking about them visiting food opportunities because at that food

981
01:00:10,720 --> 01:00:13,080
Speaker 3: opportunity is there is going to be a group of

982
01:00:13,160 --> 01:00:15,680
Speaker 3: does somewhere around that using it.

983
01:00:15,920 --> 01:00:18,880
Speaker 2: And so in both cases it's actually just where's the

984
01:00:18,920 --> 01:00:21,560
Speaker 2: dough hotspot? Like where's the dough concentration? And in some

985
01:00:21,760 --> 01:00:23,880
Speaker 2: areas that's going to be well, let's talk about with

986
01:00:23,880 --> 01:00:26,040
Speaker 2: the limiting factor is right, and the limiting factor in

987
01:00:26,080 --> 01:00:28,080
Speaker 2: certain places like for you, it was that we've got

988
01:00:28,080 --> 01:00:31,480
Speaker 2: these food those are the limited resources. So that's where

989
01:00:31,520 --> 01:00:35,360
Speaker 2: your best doll concentration will be versus middle of nowhere, Kansas.

990
01:00:35,400 --> 01:00:38,360
Speaker 2: It might be cover equals your dough hot spot because

991
01:00:38,400 --> 01:00:43,000
Speaker 2: that's the one concentration is something rare. That's a really

992
01:00:43,080 --> 01:00:45,120
Speaker 2: key insight I think for people is to think about

993
01:00:45,120 --> 01:00:47,520
Speaker 2: that way. It's so easy to get stuck on Well,

994
01:00:47,640 --> 01:00:51,560
Speaker 2: this person said cover, and so I've got a zero

995
01:00:51,600 --> 01:00:53,480
Speaker 2: in uncover at this time of year. But again you

996
01:00:53,560 --> 01:00:56,760
Speaker 2: have to always look at what's the context in your

997
01:00:56,800 --> 01:01:04,280
Speaker 2: situation in your place. Very interesting, So I want to

998
01:01:04,480 --> 01:01:06,560
Speaker 2: give us a little time here at the end to

999
01:01:06,760 --> 01:01:09,560
Speaker 2: cover off on a few things that every time I

1000
01:01:09,560 --> 01:01:12,960
Speaker 2: get to talk to someone who's so drastically smarter than

1001
01:01:13,000 --> 01:01:16,040
Speaker 2: I am, and so much more grounded and data than

1002
01:01:16,040 --> 01:01:21,800
Speaker 2: I am, that you can correct my preconceived notions myths

1003
01:01:21,840 --> 01:01:28,640
Speaker 2: that I believe in, you know, app of the day, algorithm,

1004
01:01:28,800 --> 01:01:33,280
Speaker 2: predicting trends of the day, etc. There As a diehard

1005
01:01:33,360 --> 01:01:36,480
Speaker 2: whitetail hunter, we all have these different ideas around what

1006
01:01:36,520 --> 01:01:41,120
Speaker 2: triggers deer movement, whether it be wind and weather conditions,

1007
01:01:41,400 --> 01:01:46,000
Speaker 2: whether it be the moon. Of course that's a big one.

1008
01:01:46,320 --> 01:01:49,520
Speaker 2: So I'd love to get your take on some of

1009
01:01:49,560 --> 01:01:51,640
Speaker 2: those and where things stand today. I know we talked

1010
01:01:51,680 --> 01:01:54,200
Speaker 2: about this the very first podcast we did together, like

1011
01:01:54,280 --> 01:01:58,320
Speaker 2: ten years ago maybe and you basically, if I remember right,

1012
01:01:58,360 --> 01:02:00,640
Speaker 2: I think most of it you said was hogwak. But

1013
01:02:01,080 --> 01:02:04,520
Speaker 2: I'd love to hear where things stand today. Let's start

1014
01:02:04,520 --> 01:02:07,680
Speaker 2: with the moon. Maybe do we have or is there

1015
01:02:07,720 --> 01:02:11,520
Speaker 2: anything new when it comes to any possible correlation between

1016
01:02:11,880 --> 01:02:14,040
Speaker 2: the moon and deer movement.

1017
01:02:17,040 --> 01:02:21,360
Speaker 3: So we did a survey mark online. Because we are

1018
01:02:21,440 --> 01:02:25,480
Speaker 3: we're doing an exhaust we are wrapping up a very

1019
01:02:25,680 --> 01:02:30,520
Speaker 3: exhaustive analysis. So a couple of years ago we put

1020
01:02:30,560 --> 01:02:34,960
Speaker 3: some data, a very crude analysis I did of just

1021
01:02:35,040 --> 01:02:39,400
Speaker 3: looking at moon phase meaning new moon, full moon, and

1022
01:02:39,440 --> 01:02:42,320
Speaker 3: then just looking at daytime movements, nighttime movements, et cetera.

1023
01:02:43,920 --> 01:02:47,840
Speaker 3: In my eyes, both as a researcher and as a hunter,

1024
01:02:48,840 --> 01:02:52,880
Speaker 3: I was not motivated by those data. There may on

1025
01:02:52,920 --> 01:02:56,320
Speaker 3: a particular day been a subtle increase, but it was

1026
01:02:56,360 --> 01:03:00,000
Speaker 3: not enough to motivate me. Well, then we got an,

1027
01:03:00,680 --> 01:03:04,520
Speaker 3: I guess, an outcry. We got a lot of feedback saying,

1028
01:03:05,600 --> 01:03:09,120
Speaker 3: but you didn't look at moon position, you didn't look

1029
01:03:09,120 --> 01:03:14,320
Speaker 3: at moon rising overhead, underfoot all that time. So we

1030
01:03:14,320 --> 01:03:18,720
Speaker 3: we have looked at that to a great detail, and

1031
01:03:19,440 --> 01:03:24,200
Speaker 3: we are hopefully we're just a few weeks away from

1032
01:03:24,240 --> 01:03:27,200
Speaker 3: doing uh, Natasha and I and others, we will do

1033
01:03:27,400 --> 01:03:34,080
Speaker 3: a one to two hour exhaustive, tedious review of those data.

1034
01:03:34,280 --> 01:03:39,600
Speaker 3: But but before that, we wanted to basically mark ask people.

1035
01:03:39,720 --> 01:03:43,439
Speaker 3: We did a survey and when you when you pin

1036
01:03:43,640 --> 01:03:47,200
Speaker 3: someone down, so mark, I might I might approach it

1037
01:03:47,280 --> 01:03:50,520
Speaker 3: like this with you, I might say number one. Here's

1038
01:03:50,520 --> 01:03:53,400
Speaker 3: the first question of the hierarchy of questions. Number one,

1039
01:03:53,720 --> 01:03:56,920
Speaker 3: Do you believe the moon is affecting deer movement? Yes

1040
01:03:57,040 --> 01:04:02,080
Speaker 3: or no? Do you can you can or cannot answer us?

1041
01:04:02,120 --> 01:04:02,560
Speaker 3: Up to you?

1042
01:04:02,800 --> 01:04:05,240
Speaker 2: Sure, Sure, I guess I will answer because I am

1043
01:04:05,400 --> 01:04:08,680
Speaker 2: so torn on this because I would say if I

1044
01:04:08,720 --> 01:04:12,720
Speaker 2: had to say yes or no, I gosh, I'm so

1045
01:04:12,920 --> 01:04:14,520
Speaker 2: right in the middle of Bronson. But I guess I'll

1046
01:04:14,560 --> 01:04:16,560
Speaker 2: say yes. But maybe it's a little.

1047
01:04:16,800 --> 01:04:21,240
Speaker 3: Okay, So eighty three percent of people are like you.

1048
01:04:22,160 --> 01:04:25,640
Speaker 3: They believe the moon has some type of influence. So

1049
01:04:25,840 --> 01:04:29,080
Speaker 3: then once you go into your dichotomous ski there now,

1050
01:04:29,160 --> 01:04:32,200
Speaker 3: So now we go into the yes, I think it influences.

1051
01:04:32,520 --> 01:04:34,920
Speaker 2: Now, just got to say, I love the fact that

1052
01:04:34,960 --> 01:04:37,680
Speaker 2: you just said dichotomus key on this podcast. That's one

1053
01:04:37,720 --> 01:04:39,280
Speaker 2: of you don't hear that one too often.

1054
01:04:41,240 --> 01:04:45,880
Speaker 3: So if you said yes, now we've got to ask you, Okay,

1055
01:04:46,200 --> 01:04:49,720
Speaker 3: how how do we measure it? Is it that they're

1056
01:04:50,640 --> 01:04:54,400
Speaker 3: moving a greater distance, Is it that they're up on

1057
01:04:54,440 --> 01:04:57,200
Speaker 3: their feet earlier in the day, is it that they

1058
01:04:57,240 --> 01:04:58,880
Speaker 3: are bedded less, et cetera.

1059
01:04:59,080 --> 01:05:01,400
Speaker 2: This is the key question. These are the questions that

1060
01:05:01,440 --> 01:05:04,160
Speaker 2: I've wanted to know for so long because every time

1061
01:05:04,200 --> 01:05:06,840
Speaker 2: this moon thing comes up, I've always felt like the

1062
01:05:06,920 --> 01:05:10,080
Speaker 2: studies have not looked at the right things. They haven't

1063
01:05:10,120 --> 01:05:13,360
Speaker 2: looked at the little stuff like that that doesn't maybe

1064
01:05:13,400 --> 01:05:18,000
Speaker 2: matter statistic it doesn't register its statistically significant for most studies,

1065
01:05:18,040 --> 01:05:22,560
Speaker 2: but for a hunter who cares if a mature buck

1066
01:05:22,760 --> 01:05:26,680
Speaker 2: travels fifty yards further ten minutes earlier in the day,

1067
01:05:26,720 --> 01:05:31,680
Speaker 2: that is a huge significance for the hunter. So, yeah,

1068
01:05:31,880 --> 01:05:34,440
Speaker 2: you're getting to the the key stuff.

1069
01:05:34,760 --> 01:05:39,640
Speaker 3: And that is precisely the feedback. And so before we

1070
01:05:39,720 --> 01:05:44,600
Speaker 3: did this, I wanted not as agotcha or to catch anybody,

1071
01:05:45,400 --> 01:05:48,160
Speaker 3: I really wanted to understand or you're familiar mark with

1072
01:05:48,200 --> 01:05:49,520
Speaker 3: the term effect size.

1073
01:05:50,160 --> 01:05:52,640
Speaker 2: That's the yeah, make sure I've got it right.

1074
01:05:53,040 --> 01:05:57,760
Speaker 3: The difference between a treatment, so treatment or control, the

1075
01:05:57,840 --> 01:06:01,920
Speaker 3: effect size is the difference those two. So you mentioned

1076
01:06:01,920 --> 01:06:05,400
Speaker 3: something a moment ago statistically different. So I wanted to

1077
01:06:05,480 --> 01:06:08,400
Speaker 3: move away from us saying such and such was or

1078
01:06:08,520 --> 01:06:12,000
Speaker 3: was not statistically different. I wanted to wanted it to

1079
01:06:12,040 --> 01:06:16,440
Speaker 3: be a useful effect size of treatment. And what we

1080
01:06:16,640 --> 01:06:19,240
Speaker 3: found is that there were again two groups of people.

1081
01:06:20,320 --> 01:06:23,800
Speaker 3: There was a group that, yeah, I think the moon

1082
01:06:23,840 --> 01:06:26,120
Speaker 3: affects them based on what I've seen and what my

1083
01:06:26,200 --> 01:06:29,760
Speaker 3: dad and my grandpa said. But when you pin them on, okay,

1084
01:06:29,760 --> 01:06:32,840
Speaker 3: what does that mean? Well, I don't know. I just

1085
01:06:32,880 --> 01:06:35,440
Speaker 3: know the moon affects them. And then you had what

1086
01:06:35,480 --> 01:06:41,439
Speaker 3: I would call the cover slash bed bow hunting out

1087
01:06:41,440 --> 01:06:46,640
Speaker 3: of a saddle sounds like me. Now that's the dialed

1088
01:06:46,840 --> 01:06:51,360
Speaker 3: in person. And what they said what was really really important.

1089
01:06:51,520 --> 01:06:55,360
Speaker 3: It was if that buck is on his feet five

1090
01:06:55,520 --> 01:06:59,640
Speaker 3: minutes earlier, and that buck moves one hundred more yards

1091
01:07:00,080 --> 01:07:05,520
Speaker 3: before sunset, then that gives me an advantage. Heck, yeah,

1092
01:07:06,160 --> 01:07:10,200
Speaker 3: so what we generally found. And again I don't want

1093
01:07:10,200 --> 01:07:13,160
Speaker 3: to steal Natasha's thunder. I know she wants to talk

1094
01:07:13,160 --> 01:07:16,000
Speaker 3: about all this in great detail on a podcast, but

1095
01:07:19,000 --> 01:07:23,600
Speaker 3: the result will be meaningful to the type of hunter

1096
01:07:23,800 --> 01:07:29,880
Speaker 3: that you are. So we did see some differences, meaning

1097
01:07:30,120 --> 01:07:34,560
Speaker 3: the value of the non moon day. However, we were

1098
01:07:34,560 --> 01:07:38,640
Speaker 3: calculating the effect of the moon the control day versus

1099
01:07:38,640 --> 01:07:44,080
Speaker 3: the treatment day. There were some subtle differences. To me

1100
01:07:44,440 --> 01:07:48,000
Speaker 3: and the way I hunt. It would not be meaningful

1101
01:07:48,200 --> 01:07:52,760
Speaker 3: enough for Mekay for someone that wanted to get I've

1102
01:07:52,800 --> 01:07:58,120
Speaker 3: got a two minute opportunity before sunset that that buck

1103
01:07:58,200 --> 01:08:01,560
Speaker 3: may be moving through two minutes before then. They may

1104
01:08:01,600 --> 01:08:04,920
Speaker 3: look at our results and say, all right, there's something

1105
01:08:04,960 --> 01:08:06,640
Speaker 3: there interesting.

1106
01:08:06,880 --> 01:08:08,760
Speaker 2: Okay, now, I know you don't want to spill too

1107
01:08:08,800 --> 01:08:11,080
Speaker 2: many of the beans, and I'll make sure people go

1108
01:08:11,200 --> 01:08:14,120
Speaker 2: listen to the rest or listen to the full scoop

1109
01:08:14,400 --> 01:08:17,920
Speaker 2: on your podcast. Dear University, everybody should go there subscribe

1110
01:08:18,720 --> 01:08:22,519
Speaker 2: like share, But tell me this, Can you tell me this?

1111
01:08:23,360 --> 01:08:26,680
Speaker 2: Can you tell me what variable or related to the

1112
01:08:26,720 --> 01:08:31,160
Speaker 2: moon seemed to have that strongest connection. Was it overhead

1113
01:08:31,240 --> 01:08:35,799
Speaker 2: underfoot times matching up with the first hour of daylight,

1114
01:08:35,880 --> 01:08:38,000
Speaker 2: last hour of daylight, that kind of thing, or was

1115
01:08:38,040 --> 01:08:42,120
Speaker 2: it moon rising setting times matching with that and anything

1116
01:08:42,160 --> 01:08:42,519
Speaker 2: like that?

1117
01:08:44,760 --> 01:08:50,559
Speaker 3: If I remember correctly, because my goodness, Natasha sliced this

1118
01:08:50,800 --> 01:08:57,559
Speaker 3: up every way possible, there were just about every one

1119
01:08:57,640 --> 01:09:03,240
Speaker 3: of those categories mark had the smallest difference. But remember

1120
01:09:03,320 --> 01:09:08,560
Speaker 3: sometimes the direction was not what you thought, meaning sometimes

1121
01:09:08,600 --> 01:09:12,160
Speaker 3: the effect was not positive that it moved more, but

1122
01:09:12,280 --> 01:09:16,760
Speaker 3: it was that it moved less. And so everything that

1123
01:09:16,800 --> 01:09:19,080
Speaker 3: we looked at had a little bit of an increase

1124
01:09:19,200 --> 01:09:21,519
Speaker 3: or a little bit of a decrease. And what we

1125
01:09:21,680 --> 01:09:27,080
Speaker 3: essentially did was we paired together the amount of time

1126
01:09:27,240 --> 01:09:30,080
Speaker 3: that it was in the bed that day, the time

1127
01:09:30,160 --> 01:09:32,439
Speaker 3: of day that it was up on its feet and

1128
01:09:32,479 --> 01:09:36,240
Speaker 3: started moving, and then from when it started moving to

1129
01:09:36,439 --> 01:09:40,320
Speaker 3: sunset the distance of ground that it covered. So we

1130
01:09:40,400 --> 01:09:42,160
Speaker 3: kind of have three different measurements there.

1131
01:09:42,280 --> 01:09:44,720
Speaker 2: That's perfect. Do you have any of those specifics you

1132
01:09:44,760 --> 01:09:46,880
Speaker 2: could share now or do we need to wait for them?

1133
01:09:48,040 --> 01:09:49,200
Speaker 3: We got to wait on that boat.

1134
01:09:50,560 --> 01:09:55,479
Speaker 2: I had to ask, Yeah, well, that's fascinating. That's very

1135
01:09:55,479 --> 01:09:58,479
Speaker 2: interesting that you guys were able to look to that degree.

1136
01:10:00,120 --> 01:10:02,080
Speaker 3: And what we're going to do with that mark is

1137
01:10:03,640 --> 01:10:06,160
Speaker 3: we went a different route. We didn't write like a

1138
01:10:06,200 --> 01:10:10,080
Speaker 3: twenty page document with a bunch of narrative. We basically

1139
01:10:10,120 --> 01:10:13,000
Speaker 3: went into every one of these theories and gave a

1140
01:10:13,080 --> 01:10:17,320
Speaker 3: heading of here's the hypothesis, here's the theory, here's how

1141
01:10:17,320 --> 01:10:20,400
Speaker 3: we did it, here's the result. Turn the page, here's

1142
01:10:20,439 --> 01:10:23,519
Speaker 3: the other one. So we've got about ten pages of

1143
01:10:23,680 --> 01:10:26,840
Speaker 3: every type of moon theory with the data and the

1144
01:10:26,880 --> 01:10:30,240
Speaker 3: graphs and the interpretation and all that is going to

1145
01:10:30,280 --> 01:10:31,920
Speaker 3: be free for download in a few weeks.

1146
01:10:32,040 --> 01:10:34,519
Speaker 2: Amazing. Yeah, So do we have a target date? When

1147
01:10:34,560 --> 01:10:35,320
Speaker 2: do we should look for that?

1148
01:10:36,600 --> 01:10:42,680
Speaker 3: My target is like October fifteenth, okay, but sometimes at

1149
01:10:42,680 --> 01:10:47,080
Speaker 3: the university the wheels turned slow. Sure, but it's going

1150
01:10:47,200 --> 01:10:49,680
Speaker 3: to be out of our hands and literally in a

1151
01:10:49,680 --> 01:10:51,639
Speaker 3: few days and then it has to go into production

1152
01:10:52,320 --> 01:10:54,680
Speaker 3: and so whenever that gets produced and put online.

1153
01:10:54,760 --> 01:10:56,599
Speaker 2: Okay. So there's been a lot of people that want

1154
01:10:56,640 --> 01:11:00,040
Speaker 2: to see that. Of course, where should they go to

1155
01:11:00,080 --> 01:11:02,640
Speaker 2: see that and or sign up to be notified of

1156
01:11:02,640 --> 01:11:04,120
Speaker 2: it or anything like that.

1157
01:11:05,680 --> 01:11:08,600
Speaker 3: We will have a copy of it for download on

1158
01:11:08,640 --> 01:11:12,759
Speaker 3: our website MSU deer lab dot com. We will also

1159
01:11:13,000 --> 01:11:16,040
Speaker 3: as soon as it is available with the download link,

1160
01:11:16,080 --> 01:11:19,799
Speaker 3: it'll be on social media, so MSU dear Lab, Facebook

1161
01:11:19,880 --> 01:11:22,160
Speaker 3: or Instagram, it'll be out there.

1162
01:11:22,479 --> 01:11:30,080
Speaker 2: Okay. Last couple quick questions. So you're doing this exhaustive

1163
01:11:30,120 --> 01:11:33,160
Speaker 2: moon study, which I'm so glad it's happening. Has there

1164
01:11:33,200 --> 01:11:36,479
Speaker 2: been anything done like this that I haven't heard of

1165
01:11:36,520 --> 01:11:41,400
Speaker 2: somehow when it comes to weather impacts, So exact same

1166
01:11:41,479 --> 01:11:45,400
Speaker 2: questions you're asking with the moon, but you know, barometric

1167
01:11:45,439 --> 01:11:48,320
Speaker 2: pressure being the variable we're controlling for and looking at,

1168
01:11:48,560 --> 01:11:54,120
Speaker 2: or wind speed or delta of temperature change over the

1169
01:11:54,120 --> 01:11:56,360
Speaker 2: course of a twenty four hour period or something like that.

1170
01:11:56,600 --> 01:11:58,639
Speaker 2: Because there's all these theories around that too. We all

1171
01:11:58,680 --> 01:12:00,880
Speaker 2: love cold fronts. We all think, not all of us,

1172
01:12:00,880 --> 01:12:03,680
Speaker 2: but many people think that a rising barometer gives us

1173
01:12:03,720 --> 01:12:07,600
Speaker 2: an increase in deer moent YadA, YadA, YadA. But again, historically,

1174
01:12:07,680 --> 01:12:11,759
Speaker 2: most studies have not backed that up. Is there anything

1175
01:12:11,800 --> 01:12:13,800
Speaker 2: new on that front that you know of or that

1176
01:12:13,840 --> 01:12:14,960
Speaker 2: you guys may be working on.

1177
01:12:16,240 --> 01:12:19,880
Speaker 3: Yeah, we are working on that, and so we're going

1178
01:12:19,960 --> 01:12:23,920
Speaker 3: to get the moon stuff out. After that, we're going

1179
01:12:24,000 --> 01:12:27,559
Speaker 3: to get on the home range shifting stuff that the

1180
01:12:27,640 --> 01:12:31,640
Speaker 3: year to year affinity stuff that is out. And Natasha

1181
01:12:31,760 --> 01:12:37,040
Speaker 3: is also working on that exact question. So she is

1182
01:12:37,080 --> 01:12:41,000
Speaker 3: going to be looking at that. And it's more complicated

1183
01:12:41,320 --> 01:12:45,120
Speaker 3: than it seems because with weather there are so many

1184
01:12:45,360 --> 01:12:49,360
Speaker 3: intercorrelated aspects of that. Is it the temperature, is it

1185
01:12:49,400 --> 01:12:51,719
Speaker 3: the wind speed? Is it the barometer? Is it the direction?

1186
01:12:52,439 --> 01:12:55,080
Speaker 3: And so we really need someone with her capability to

1187
01:12:55,120 --> 01:12:58,000
Speaker 3: really tease all that apart for cause and effect. But

1188
01:12:58,479 --> 01:13:01,720
Speaker 3: that is on the list and our goal it may

1189
01:13:01,760 --> 01:13:04,960
Speaker 3: be January, but our goal is to also get that

1190
01:13:05,080 --> 01:13:06,360
Speaker 3: out this steer season.

1191
01:13:06,920 --> 01:13:10,639
Speaker 2: Amazing, you guys are doing good work. I'm very thankful

1192
01:13:10,840 --> 01:13:16,840
Speaker 2: that these very nuanced looks at these questions are now

1193
01:13:17,160 --> 01:13:21,599
Speaker 2: being sliced and diced and examined because there's a handful

1194
01:13:21,720 --> 01:13:23,680
Speaker 2: of folks like got like me out there who are

1195
01:13:23,760 --> 01:13:27,040
Speaker 2: dying to know and very glad this is all going

1196
01:13:27,080 --> 01:13:30,840
Speaker 2: to be coming to light so ronson as I knew

1197
01:13:30,880 --> 01:13:34,400
Speaker 2: it would be. This has been fascinating. I appreciate your

1198
01:13:34,439 --> 01:13:37,640
Speaker 2: insight and everything you guys are doing over there at

1199
01:13:37,640 --> 01:13:42,320
Speaker 2: Mississippi State. You just gave folks a couple good action

1200
01:13:42,400 --> 01:13:43,840
Speaker 2: items there as far as where they can go to

1201
01:13:43,840 --> 01:13:46,360
Speaker 2: get the moon study. But is there anywhere else you

1202
01:13:46,360 --> 01:13:48,240
Speaker 2: want to direct folks if they want to learn more,

1203
01:13:48,560 --> 01:13:50,800
Speaker 2: if they want to connect with your team and the

1204
01:13:50,840 --> 01:13:52,120
Speaker 2: content you guys are putting out there.

1205
01:13:53,080 --> 01:13:56,960
Speaker 3: Yeah, thank you for mentioning this earlier. But yeah, all

1206
01:13:57,000 --> 01:14:00,559
Speaker 3: of the stuff we've talked about today and in the

1207
01:14:00,600 --> 01:14:03,799
Speaker 3: future is going to be on the Deer University podcast.

1208
01:14:04,479 --> 01:14:08,120
Speaker 3: And just be forewarned, it's a bunch of dorky scientists

1209
01:14:08,280 --> 01:14:10,839
Speaker 3: type people and so we can get into the weeds.

1210
01:14:11,080 --> 01:14:11,519
Speaker 2: We love it.

1211
01:14:11,560 --> 01:14:13,920
Speaker 3: But the good thing about a podcast is that you

1212
01:14:13,920 --> 01:14:18,040
Speaker 3: can hit fast forward. You know, if we get stuck there,

1213
01:14:18,080 --> 01:14:18,880
Speaker 3: just fast forward.

1214
01:14:19,800 --> 01:14:20,720
Speaker 2: But we do that.

1215
01:14:20,880 --> 01:14:23,880
Speaker 3: We also take that same information, especially when there's a

1216
01:14:23,960 --> 01:14:26,720
Speaker 3: visual context to it, and we put that on our

1217
01:14:26,760 --> 01:14:30,680
Speaker 3: YouTube channel, so go there, and then of course on

1218
01:14:30,800 --> 01:14:34,360
Speaker 3: social where we do little snapshots, little figures and so

1219
01:14:34,400 --> 01:14:38,479
Speaker 3: forth on Instagram and Facebook. So if you go to

1220
01:14:38,520 --> 01:14:41,640
Speaker 3: the podcast or social media, that pretty much captures it.

1221
01:14:42,040 --> 01:14:45,760
Speaker 2: All right, wonderful. Well, I will continue to be following

1222
01:14:45,800 --> 01:14:48,960
Speaker 2: from Afar and anxiously awaiting the Moon study and then

1223
01:14:49,560 --> 01:14:53,160
Speaker 2: V two of the weather. Yeah, I'm very excited about that.

1224
01:14:53,280 --> 01:14:56,080
Speaker 2: So thank you for doing that work. And man, just

1225
01:14:56,080 --> 01:14:57,960
Speaker 2: thank you again for this. It was great. Catching up

1226
01:14:58,040 --> 01:15:01,000
Speaker 2: is great, getting to get update on where all these

1227
01:15:01,040 --> 01:15:04,800
Speaker 2: things stand and getting all of our understanding on the

1228
01:15:04,880 --> 01:15:06,759
Speaker 2: science of deer up to speed.

1229
01:15:06,920 --> 01:15:10,000
Speaker 3: So thank you for that absolutely anytime. Happy to.

1230
01:15:14,439 --> 01:15:17,000
Speaker 2: All right, and that's it for today. Thank you for

1231
01:15:17,040 --> 01:15:19,280
Speaker 2: being here, Thanks for being a part of this community.

1232
01:15:19,720 --> 01:15:21,880
Speaker 2: As we just discussed, make sure you go follow all

1233
01:15:21,920 --> 01:15:26,320
Speaker 2: of Bronson's and the Deer Labs content and resources. Lots

1234
01:15:26,320 --> 01:15:30,200
Speaker 2: of good stuff out there. And I think with that said,

1235
01:15:30,880 --> 01:15:32,719
Speaker 2: I'll lets you all get out there to the woods.

1236
01:15:32,760 --> 01:15:38,360
Speaker 2: Best of luck and until next time, stay wired to hunt.