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

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

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

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Speaker 1: your host, Mark Kenyan, and this episode number two ordered

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Speaker 1: in nine one. Joining me today is accomplished public land

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Speaker 1: deer hunter Joe Elsinger to break down how he patterns

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Speaker 1: locations instead of bucks and the tools he uses to

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Speaker 1: analyze deer movements and correlation with weather related factors. All right,

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Speaker 1: welcome to the Wire to Hunt podcast, brought to you

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Speaker 1: by on X and today, as I just mentioned, we've

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Speaker 1: got Joe Elsinger, also commonly known as the Professor, and

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Speaker 1: we call him that because he is a thinker. He

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Speaker 1: is uh, he's an analyzer. He's he's a detail oriented

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Speaker 1: problem solver I think would be a good way to

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Speaker 1: put it when it comes to chasing mature bucks. And

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Speaker 1: he's doing this on public land in the Big Woods

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Speaker 1: of Wisconsin and up in Iowa. He's been the show

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Speaker 1: a couple of times before. He's proven to be a

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Speaker 1: fan favorite guest, just a really good educator. I think

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Speaker 1: something about the way he communicates makes it easily take in,

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Speaker 1: but a powerful stuff. So episode number one fifty in particular,

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Speaker 1: is a must listen if you have an artist. Checked

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Speaker 1: that one out, as it covers Joe's kind of total

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Speaker 1: all around hunting strategy. But today we're focusing on one

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Speaker 1: particular topic, which is patterning deer, or or I guess

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Speaker 1: at least at least that's what I thought we were

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Speaker 1: going to talk about when I started this conversation with it,

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Speaker 1: because I thought, Okay, let's dive deep into patterning deer.

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Speaker 1: But what I quickly discovered was that's that's not really

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Speaker 1: what Joe is focusing on. Rather, he's all about patterning

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Speaker 1: specific locations, and then he's using a really detailed kind

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Speaker 1: of process of collecting and analyzing data to help him

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Speaker 1: do that help him understand when to hunt certain spots

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Speaker 1: throughout the year and in different prime places to be

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Speaker 1: all throughout the year. So it's not just about when

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Speaker 1: to kill this bucket, when's the best day to go

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Speaker 1: in after him, it's what's the best place to be

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Speaker 1: no matter what time of year it is. And man,

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Speaker 1: it's just a really interesting way of thinking about this

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Speaker 1: um and it's different than what most other people talk about,

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Speaker 1: and really the way I talk about this stuff too,

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Speaker 1: So this is kind of a new idea for me

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Speaker 1: that I'm definitely gonna be taken to heart this year.

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Speaker 1: So that's what we'renna be talking about. We're also going

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Speaker 1: to be discussing the tools he's created to analyze all

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Speaker 1: sorts of data like this. He's got a really interesting

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Speaker 1: set of analyzes he runs on trail camera data. So

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Speaker 1: we're gonna talk about that. We're gonna talk about the

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Speaker 1: lessons he's learned and the trends he's uncovered when he's

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Speaker 1: studying all these different types of deer settings and photos

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Speaker 1: and then comparing them to changes and things like temperature

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Speaker 1: and pressure and the moon and much more. So, Man,

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Speaker 1: I'll tell you what. If you enjoy geeking out over deer,

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Speaker 1: today's episode is for you. That's said. Before we get

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Speaker 1: to it, we are going to take a really quick

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Speaker 1: break and then we will get to our chat with Joe. Alright,

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Speaker 1: So with me now back on the show for I

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Speaker 1: think the third time, maybe, is Joe Elsinger really to

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Speaker 1: call you the professor? Joe, thanks for thanks for coming back.

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Speaker 1: Thank you. It's happy to be here. Since since the

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Speaker 1: professor name was applied to you on the podcast A

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Speaker 1: couple of years ago. Do you have anyone yet that

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Speaker 1: makes jokes about like tweet vests and pipes and glasses everything.

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Speaker 1: I think I've gotten a couple of those comments. Yeah,

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Speaker 1: and that's definitely not me. Anyone who's hung out with me,

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Speaker 1: that's definitely not me. So yeah, maybe another twenty years.

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Speaker 1: That's good, Glenn, it's given you a hard time. Thank

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Speaker 1: you Andy May for that one. I think. Yeah. Yeah, Well,

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Speaker 1: I'm glad you are back with us because because each

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Speaker 1: time I've gotten a chat with you, it's just been

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Speaker 1: super interesting for me, and we've we've always gotten great feedback.

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Speaker 1: People have found your insights really helpful. And today I'm

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Speaker 1: sure we're gonna we're gonna get the same kind of thing.

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Speaker 1: But I'm excited and I kind of want to just

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Speaker 1: jump right into it. Joe Hope, you don't mind me

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Speaker 1: avoiding four play and just going right to the main event,

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Speaker 1: because we got a cool topic today of patterning deer

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Speaker 1: and correlating deer activity to other outside factors. And this

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Speaker 1: idea came about because I don't know, a month or

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Speaker 1: two ago, me and Dan we're doing a podcast just

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Speaker 1: the two of us, and we were talking about um,

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Speaker 1: how I how both of us really are looking at

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Speaker 1: annual patterns and looking through trial camera data and stuff.

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Speaker 1: And I happened to bring up the fact that I,

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Speaker 1: excuse me, I had made kind of uh, pretty rudimentary

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Speaker 1: spreadsheet in which I was tracking all the daylight pictures

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Speaker 1: and daylight observations I had of a certain buck and

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Speaker 1: then a certain number of other variables weather related things. UM.

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Speaker 1: So I was talking about that to damn, and then

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Speaker 1: a bunch of people asked me to share that spreadsheet.

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Speaker 1: So I shared a picture of it on Instagram, and

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Speaker 1: I think maybe that's what you saw, because then you

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Speaker 1: reached out to me and said, hey, um, you know

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Speaker 1: I do the same thing. You might remember we talked

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Speaker 1: about it a little bit a couple of years ago,

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Speaker 1: but you you, you shared with me this document and

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Speaker 1: you just take things to a whole new level as

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Speaker 1: far as tracking deer movement and all the different, um

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Speaker 1: correlating variables, and just the document itself kind of blew

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Speaker 1: my mind. The level of analysis you put in here

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Speaker 1: trying to understand what different things impact dear movement, how

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Speaker 1: you're tracking these things. Um, the level of detail on

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Speaker 1: analysis is just it's it's fascinating and I think it

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Speaker 1: would really help people. There's a lot of folks know, uh,

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Speaker 1: to pay attention like, oh yeah, I think you know,

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Speaker 1: cold friends will make dear move more, or I think

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Speaker 1: such and such thing, and there's lots of different theories. Um.

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Speaker 1: Not a lot of people I know actually go about

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Speaker 1: tracking it in a way that can be quantified. Um.

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Speaker 1: But I guess if you go by the by the

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Speaker 1: what's the word um not nickname is a fancier word

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Speaker 1: for nickname blanket on right now. But if people call

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Speaker 1: you the professor, you should probably quantify your work and

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Speaker 1: you do that. Yeah, I'm an engineer by trade, so

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Speaker 1: that's my disclaimers. I'm a numbers guy. I'm a nerd.

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Speaker 1: I'm happy to admit that I'm a nerd. So it's

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Speaker 1: definitely not for everybody, but yeah, thanks, um, yeah you

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Speaker 1: are a spot on and how that? How that? Um?

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Speaker 1: Why I passed it on to you. So, as I

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Speaker 1: mentioned to you before we started recording, I was a

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Speaker 1: little I'm a little behind on your podcast, which I love. Um,

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Speaker 1: I've you know, listen to every single one, but I'm

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Speaker 1: a couple of months behind here. A few weeks ago

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Speaker 1: I finally got to that podcast where you were talking

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Speaker 1: about track that spreadsheet with or You're you know how

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Speaker 1: you've been tracking observations with Dan, and yeah, that's exactly

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Speaker 1: what I was like, you know what, I think I

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Speaker 1: need to send that to Mark. So I'm glad. Yeah,

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Speaker 1: it's it's spurred this bigger conversation. So so what I

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Speaker 1: want to do here, Joe is is talk in detail

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Speaker 1: about kind of you know, we we spoke briefly about

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Speaker 1: patterning deer a couple of years ago, but now I

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Speaker 1: want to go really deep into everything you're thinking about

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Speaker 1: when it comes to understanding dear movement, maybe understanding specific

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Speaker 1: deer and all of these things you're tracking and monitoring

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Speaker 1: and trying to make decisions based off of. So to

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Speaker 1: kick it off, I think I first want to lay

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Speaker 1: some groundwork, lay some gets, some definitions, some some foundations

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Speaker 1: out here. So when I say something like pattern a deer,

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Speaker 1: when you think about patterning a deer, what does that

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Speaker 1: mean to you? Yeah, so it's interesting, Um it used

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Speaker 1: to mean, um, probably what the first thing that pops

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Speaker 1: into the head of everybody else You used to look

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Speaker 1: at individual deer And I still do don't get me wrong.

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Speaker 1: But now I have kind of UM that's actually pretty

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Speaker 1: very hard. Everybody knows it's very very hard to pattern

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Speaker 1: individual deer um bucks have individual personalities. Some are easier

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Speaker 1: to pattern. They may have a very small core area,

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Speaker 1: UM it's not necessarily easy to kill. And other ones

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Speaker 1: have huge core areas and they seem to drift around

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Speaker 1: and many many different betting areas across thousands of acres

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Speaker 1: and everything in between. So patterning an individual deer, you know, yeah,

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Speaker 1: I've done it, Um, I've killed a compauty or that

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Speaker 1: I can say I went to a spot to kill them,

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Speaker 1: and I've killed them. But you're going to fail more

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Speaker 1: often than you succeed. UM. So now I really look

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Speaker 1: um at locations and I try to identify, um, the

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Speaker 1: optimal times for locations UM. And so I'm really patterning

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Speaker 1: locations UM. And I found that's actually more outful to

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Speaker 1: me then when I was just going around and trying

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Speaker 1: to figure out, Okay, this deer's here and he's going

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Speaker 1: to be over there tomorrow and then and then you

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Speaker 1: know over there, uh, you know, two days from now, UM,

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Speaker 1: trying to figure that out. Now I look at individual

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Speaker 1: locations and I keep track of the bucks in the area.

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Speaker 1: But I can particularly betting areas because I hunt UM.

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Speaker 1: You know, the refresher from some of the things that

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Speaker 1: I've set a previous podcast. I hunt majority, vast majority

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Speaker 1: of public land UM and uh, you know, even the

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Speaker 1: private land hunts generally shared with UM, and I don't.

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Speaker 1: I used toime more private land. I'm very little, but

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Speaker 1: I you know, no exclusive access property. So all the

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Speaker 1: deer are getting pressured UM. So it's really you have

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Speaker 1: to dial into the betting UM to have success, especially

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Speaker 1: with the chair bus UM. And that's what I do.

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Speaker 1: So I'm trying to figure out when certain betting areas

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Speaker 1: are used UM. And I've learned a lot about that

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Speaker 1: over the last few years UM primary by using observations

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Speaker 1: while unning, but primarily UM setting trail cameras in bedding

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Speaker 1: or in travel routes right next to betting UM off fall.

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Speaker 1: I used to think having a trail camera for four

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Speaker 1: weeks straight and not touching it UM was a long time.

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Speaker 1: Now I think that's actually short. That kind of shows

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Speaker 1: how I've evolved in my thought process. And now I'll

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Speaker 1: put them out in July, August, September. I've got a

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Speaker 1: few cameras out already to get need to get a

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Speaker 1: few more out in the next month or two, and

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Speaker 1: they're going to stay there until the winter. Some of

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Speaker 1: them I won't pick up till next spring. And I

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Speaker 1: get these long, big chunks of data UM, and so

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Speaker 1: I can sift through it and I look at wind, temperature,

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Speaker 1: barometric pressure, UM and all these all these factors, UM.

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Speaker 1: And I've learned that betting areas, there's really strong correlations

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Speaker 1: between some of these things and when certain betting areas

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Speaker 1: are being used and and there'll being is by certain books.

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Speaker 1: Certainly because I'm targeting buck betting. I'm sure buck betting

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Speaker 1: UM that that really has helped my efficiency UM in

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Speaker 1: targeting these these spots. So you know, I may have

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Speaker 1: three or four months worth at the dead off of

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Speaker 1: one camera, and uh, you know there's strong correlations. And hey,

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Speaker 1: this betting area is used with you know, strong west winds,

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Speaker 1: not light winds, you know, UM, you know, colder than

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Speaker 1: average temperatures UM, things like that at certain times of

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Speaker 1: the year. You know, maybe it's mostly during the rut,

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Speaker 1: or maybe it's mostly in the early season. UM. I

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Speaker 1: found betting that's primarily used in hot weather primarily used

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Speaker 1: in you know, cool weather, UM, windy weather, calm weather,

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Speaker 1: stable weather, um, bad unpleasant rainy weather. UM. It's just

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Speaker 1: really kind of eye opening to me. And then you're

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Speaker 1: able to just be that much more efficient with your

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Speaker 1: hunts then at any given time of the year, you know,

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Speaker 1: the highest optimal, the optimal I'm to be in any

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Speaker 1: given area. Yeah, that's the goal. And it you know,

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Speaker 1: it's it's never don't get me wrong. I'm still far

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Speaker 1: from perfection, like, but I've come a long ways. Um.

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Speaker 1: Now I think you know, give me six eight clean

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Speaker 1: hunts at times that I can pick and you know

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Speaker 1: I can get an opportunity here too in just that

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Speaker 1: many hunts on public land. Granted this is Iowa. We

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Speaker 1: have a very good population with share box, but still

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Speaker 1: you know it's public land. UM. And I would point out, Um,

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Speaker 1: I've done us. I'm doing more and more of this

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Speaker 1: research and up in uh northern Wisconsin too, um, in

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Speaker 1: public land up there, so it's a completely different environment.

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Speaker 1: But UM, it helps me kind of um confirm a

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Speaker 1: lot of these things that it's not just an Iowa thing.

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Speaker 1: You know that I'm able to do this. I'm binding

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Speaker 1: very strong trends in Wisconsin as well in big woods

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Speaker 1: habitat swamps and Tamarack swamps and you know, uh around

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Speaker 1: clear cuts and oak blatts and that kind of thing.

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Speaker 1: So um, yeah, it's it's very interesting to me anyway.

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Speaker 1: I'm you know, I love um that side of things.

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Speaker 1: It's not for everybody. I know. I've talked to people

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Speaker 1: about it, and some you know, it sucks all fun

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Speaker 1: out of it. This is intended to take the fun

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Speaker 1: out of it. If it's not fun to think about this,

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Speaker 1: then okay, just go hunting. But if you do like

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Speaker 1: to really try to stack the deck in your favor

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Speaker 1: and play the odds, you know, that's what I That's

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Speaker 1: what I love. It's it's funny. Speaking of Iowa, I

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Speaker 1: gotta call out. I gotta call out Dan, who's not

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Speaker 1: with us here today, but he's not here to defend himself,

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Speaker 1: so so sorry. In advanced Dan, I'm going to call

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Speaker 1: you out. But last last time or two times ago

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Speaker 1: that you were on the show, Joe, Um, you were

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Speaker 1: talking about this idea of how you run your cameras,

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Speaker 1: and then at the end of the episode you gave

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Speaker 1: both Dan and I a piece of advice and your

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Speaker 1: piece of advice for Dan was don't check your cameras

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Speaker 1: so often you go and so often check them, check them,

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Speaker 1: check them. And he said, oh, he's okay, I'll take

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Speaker 1: your advice. And then I can confirm that he has

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Speaker 1: not been taking your advice. He keeps talking about going

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Speaker 1: there checking him. I've been listed. So yeah, if I

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Speaker 1: remember right, last year he left one camera out like

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Speaker 1: all fall. He forgot to check it or maybe you

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Speaker 1: forgot where it was or something. Yeah, I don't know.

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Speaker 1: And then he was super excited about that, you know,

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Speaker 1: like what he learned off that, And I was like,

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Speaker 1: oh my god, yeah, he realizes it now, I don't know. Yeah,

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Speaker 1: it's so. And that's the thing, like, yes, there are

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Speaker 1: some most of most of you know, I'm not targeting

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Speaker 1: food sources. I'm targeting betting. You can hang cameras on

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Speaker 1: the edge of the food sources and it's a lot

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Speaker 1: lower impact to check them in In many circumstances, I'm

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Speaker 1: still lary of regular checking anything near where I'm hunting.

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Speaker 1: But um, and now, of course you have wireless cameras,

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Speaker 1: and that's a whole another um. You know, it gives

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Speaker 1: a whole another um. People that want to use them.

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Speaker 1: You know, that really pens up a lot of options

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Speaker 1: for monitoring. But still, if you're intruding on betting, mature

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Speaker 1: bucks will notice it, they will move accordingly. They may

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Speaker 1: still be in the area, they might only move across

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Speaker 1: the ridge, but they will know you're there, and they will,

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Speaker 1: you know, make sure that they're not you know, they

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Speaker 1: don't have much risk from you. So um, it's uh.

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Speaker 1: You know, you can hang cameras and check them frequently

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Speaker 1: in near food sources. Sometimes that's certainly true. But where

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Speaker 1: I'm putting them, I couldn't go in and check. I

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Speaker 1: would blow every you know, I'd blow deer out of

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Speaker 1: Sometimes they're where I want to hunt, and I go

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Speaker 1: in there and do a hunt and that's it. But

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Speaker 1: I've seen when in circumstances like that, Um, I've seen

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Speaker 1: the impact where like you know, I'll get say three

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Speaker 1: or four mature buck sightings in daylight over the course

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Speaker 1: of a week, and then I'll have been in there,

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Speaker 1: or more often, because it's a public land, I'll see

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Speaker 1: a hunter on camera, you know, whether it's a squirrel

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Speaker 1: hunter or another bow hunter or whatever coming through, and um,

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Speaker 1: it'll be several days they'll be just very minimal activity

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Speaker 1: and then I'll pick up again. So, um, I see

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Speaker 1: that all the time, even during the rut. I mean

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Speaker 1: a lot of people who bucks are everywhere in the rut. Yeah,

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Speaker 1: they definitely move a lot more, but they're still very

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Speaker 1: intentional about where they move. Um and um that you

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Speaker 1: can disturb an area and there'll be a decrease in

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Speaker 1: what you'll see in front of there in these these

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Speaker 1: high impact areas. You know, it's one thing if you're

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Speaker 1: just walking along the field edge, but it's another thing

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Speaker 1: if you're walking in a bedding area wanted yards from

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Speaker 1: a field, or like a northern Wisconsin it's a it's

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Speaker 1: it's huge up there. I'll I'll hunting swamp island where

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Speaker 1: I'll have a camera and then like for a week,

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Speaker 1: I won't get a deer, you know. So um, it's

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Speaker 1: it's interesting. So so in this kind of situation, if

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Speaker 1: you're trying to pattern a location, before we get into

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Speaker 1: all the variables you're looking at and all the data

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Speaker 1: and stuff, let's first just cover let's check the box

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Speaker 1: on how you get the data, which is pictures. Right.

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Speaker 1: Can you just walk me through a little bit more

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Speaker 1: detail of when you're trying to patter on a location, UM,

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Speaker 1: you know what you're thinking about as far as where

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Speaker 1: you're gonna put a camera. How many cameras do you

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Speaker 1: put out? Do you have like any kind of system

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Speaker 1: in place, like if you wanted to learn this betting area,

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Speaker 1: do you put several cameras around it? Or what's your

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Speaker 1: thought process once you're you're starting. So, I mean, now

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Speaker 1: I have a lot of cameras. I by my standards,

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Speaker 1: I used to think ten cameras is a lot. Now

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Speaker 1: I probably have like fifteen. And you know, I just

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Speaker 1: bought a couple of years and for a number of

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Speaker 1: years now and now I've built up a larger quantity,

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Speaker 1: but still it's not many. And um, I know some

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Speaker 1: people just carpet an area with cameras. Um I've never

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Speaker 1: done that. Um, partly because I don't have that many cameras.

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Speaker 1: I don't have. Occasional I'll drop in a couple of

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Speaker 1: post pretty close together if I really can't figure out

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Speaker 1: an area. But you do have to have a baseline

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Speaker 1: knowledge of how mature bucks move across um the land.

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Speaker 1: And I know I've talked about this before, Like you've

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Speaker 1: got to achieve that cameras don't even necessarily tell you

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Speaker 1: all that. You've got to kind of learned that the

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Speaker 1: hard way by a lot of hunting and a lot

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Speaker 1: of time invested in observing them. UM. And then you

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Speaker 1: you know, I can go in and I can pick

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Speaker 1: out you know, if I think bucks betting in an area,

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Speaker 1: I can pick out two or three most likely travel

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Speaker 1: routes in and out of that um, you know, and

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Speaker 1: then sometimes it's one or two that seemed to be

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Speaker 1: clearly favored. UM. And it's really interesting. I've gotten to

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Speaker 1: the point of UM and anybody else can to UM.

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Speaker 1: You can predict like, oh, they'll come in this way,

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Speaker 1: you know, to bed in the morning, and they'll leave

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Speaker 1: that way in the evening. Um. You know, in the

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Speaker 1: morning jay hook they usually do jay hook um, and

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Speaker 1: in the evening they usually get up and just leave

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Speaker 1: straight going to oot or does whoever their destination is.

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Speaker 1: So UM, it's often two different directions. They don't come

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Speaker 1: and go. And I see that on camera too. UM

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Speaker 1: when I'm setting a camera, like on an access route

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Speaker 1: into bedding, I know. Okay, well, I don't always know

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Speaker 1: ahead of time. Sometimes they surprise me, but usually I

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Speaker 1: get mostly morning activity or even activity, even though the

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Speaker 1: dearer there all day, they don't come and go on

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Speaker 1: that camera. You know. There it's the heavily favor either

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Speaker 1: morning or evening. And if my cameras in the bedding,

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Speaker 1: I'll get a lot of midday movement. Um. Bucks are

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Speaker 1: up and moving mid day more than you'd believe. They

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Speaker 1: don't move very far usually, especially on public land and stuff,

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Speaker 1: but they do get up. They have to get up biologically, Um,

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Speaker 1: they have to stand up. They cannot lay on the

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Speaker 1: ground for twelve hours straight, from what I understand, or

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Speaker 1: they'll die. So they have to get up, brows, relieve

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Speaker 1: themselves and be back down. UM. So I'll get them

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Speaker 1: doing that all the time. If my camera is literally right,

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Speaker 1: you know, say watch a cluster of beds or right

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Speaker 1: between a couple of beds. So another reason deer get

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Speaker 1: up and move. And this is another big indicator of

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Speaker 1: peed in on its temperature. UM. As a temperature changes

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Speaker 1: over the course of the day, deer will move. Say

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Speaker 1: it's hot weather, they will move to the coolest spot.

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Speaker 1: It's kind of temperature based bedding. And that's um. There's

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Speaker 1: actually some really strong trends there. Um. You know they

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Speaker 1: will seek cool when it's hot, and they will seek

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Speaker 1: warmer areas when it's cold, and you know, getting out

376
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Speaker 1: of the elements, they'll seek the thermal cover. Um, and

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Speaker 1: that will change over the day. You know, they may

378
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Speaker 1: be I've I've pot cameras over over individual beds, and

379
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Speaker 1: I'll get deer consistently, Like they'll bed there up until

380
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Speaker 1: ten am, and then they'll get up and leave because

381
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Speaker 1: it gets too sunlit and warm in the say the

382
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Speaker 1: summer months, and they'll they'll be moving to a more shaded,

383
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Speaker 1: cooler area. So UM, yeah, that's that's Uh. In all

384
00:20:58,280 --> 00:21:02,480
Speaker 1: those little you know, there's no one thing that oh

385
00:21:02,640 --> 00:21:04,159
Speaker 1: you know you can use this and all of a

386
00:21:04,160 --> 00:21:06,560
Speaker 1: sudden you get killed big bucks. There's all these little

387
00:21:06,640 --> 00:21:09,520
Speaker 1: layers you can put together, and um, it's fascinating. I

388
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Speaker 1: don't I'm not even I'm definitely not an expert um

389
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Speaker 1: on it, but I've started to kind of grasp the

390
00:21:17,320 --> 00:21:22,199
Speaker 1: overall um picture. They still surprise me. Um surprised me

391
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Speaker 1: a lot. But yeah, that's so I'm taking what I'm

392
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Speaker 1: doing here. Um. You know, I'll leave a camera. Say

393
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Speaker 1: last year, I had roughly a dozen cameras sprinkled across

394
00:21:33,440 --> 00:21:37,000
Speaker 1: three or four counties in Iowa. And you know ten

395
00:21:37,000 --> 00:21:41,200
Speaker 1: thousand acres of public land up in Wisconsin. UM. Yeah,

396
00:21:41,200 --> 00:21:44,879
Speaker 1: it's not each less cameras up in Wisconsin. But um

397
00:21:45,160 --> 00:21:50,399
Speaker 1: and pull them, pull the cards and literally UM. So

398
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Speaker 1: I this me, being a nerd over the years, I

399
00:21:53,760 --> 00:21:58,040
Speaker 1: used to in a spreadsheet format, enter the date, enter

400
00:21:58,080 --> 00:22:02,439
Speaker 1: the time, Enter my estimate at each class of the book. UM.

401
00:22:02,480 --> 00:22:06,000
Speaker 1: To me, that's very important because different age class of

402
00:22:06,080 --> 00:22:11,159
Speaker 1: bucks act differently. Um. In Iowa once they hit four

403
00:22:11,240 --> 00:22:13,560
Speaker 1: years old, I think they really start to act differently.

404
00:22:14,000 --> 00:22:16,439
Speaker 1: Up in Wisconsin. I see that change more along the

405
00:22:16,520 --> 00:22:18,600
Speaker 1: three year old. And I think I think, you know,

406
00:22:18,760 --> 00:22:21,320
Speaker 1: like Michigan it might be similar. It's where there's more

407
00:22:21,359 --> 00:22:23,880
Speaker 1: hunting pressure and there you know, it's just less older deer.

408
00:22:24,040 --> 00:22:26,720
Speaker 1: So there's UM, they get just to a certain age,

409
00:22:26,720 --> 00:22:29,200
Speaker 1: a certain level of experience, and you know now they're

410
00:22:29,200 --> 00:22:32,200
Speaker 1: not in junior high anymore, nor a high school. You

411
00:22:32,240 --> 00:22:38,560
Speaker 1: know now they're collegiate level um in terms of staying alive. Um.

412
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Speaker 1: And so I just make a list of the deer

413
00:22:42,480 --> 00:22:44,320
Speaker 1: and it was a two year old, three year old,

414
00:22:44,359 --> 00:22:46,840
Speaker 1: four year old my estimate? Do I get it right?

415
00:22:46,880 --> 00:22:49,199
Speaker 1: Every time, definitely not. But most of the time I

416
00:22:49,200 --> 00:22:53,480
Speaker 1: think I know about what how old a deer is? UM,

417
00:22:53,520 --> 00:22:57,119
Speaker 1: and then UM I used to manually enter like it

418
00:22:57,240 --> 00:23:00,520
Speaker 1: was in the morning, it was you know, the temperature

419
00:23:00,560 --> 00:23:02,000
Speaker 1: at the time they movement. I would look it up

420
00:23:02,000 --> 00:23:06,159
Speaker 1: and I would enter, you know, fifty five degrees UM.

421
00:23:06,200 --> 00:23:09,359
Speaker 1: And that got old as I started to get more data.

422
00:23:09,480 --> 00:23:13,160
Speaker 1: So actually before you before you say that's the dumbass

423
00:23:13,160 --> 00:23:22,240
Speaker 1: way that I'm still doing it, and I, yeah, have

424
00:23:22,359 --> 00:23:26,439
Speaker 1: some help, Yeah, yeah, absolutely, UM. And that's UM up

425
00:23:26,520 --> 00:23:30,399
Speaker 1: until so I started doing this UM hardcore in about

426
00:23:30,440 --> 00:23:34,640
Speaker 1: two thousand and thirteen, like really like looking at observations

427
00:23:34,720 --> 00:23:38,640
Speaker 1: and trying to find trends related to weather, UM, time

428
00:23:38,680 --> 00:23:42,639
Speaker 1: of year, that kind of thing. UM. Here in about

429
00:23:42,680 --> 00:23:47,280
Speaker 1: twenty fifteen sixteen, I figured out a way to extract

430
00:23:47,400 --> 00:23:51,720
Speaker 1: weather data from the Noah website. It's a government website, UM,

431
00:23:51,840 --> 00:23:56,000
Speaker 1: and they have locations all across the US UM, and

432
00:23:56,040 --> 00:24:03,320
Speaker 1: I just about guarantee hunters are within you know, miles

433
00:24:03,320 --> 00:24:07,800
Speaker 1: of a Noah weather station that logs this data UM,

434
00:24:07,840 --> 00:24:10,840
Speaker 1: and you can you can download it in a spreadsheet format.

435
00:24:11,240 --> 00:24:14,760
Speaker 1: And now it's without getting too complex, just using a

436
00:24:14,800 --> 00:24:18,560
Speaker 1: simple v look up feature in xl UM. I type

437
00:24:18,600 --> 00:24:21,879
Speaker 1: in the date and time for the presiding and it

438
00:24:21,960 --> 00:24:27,560
Speaker 1: tells me temperature, precipitation, current, past and future precipitation, wind speed,

439
00:24:27,600 --> 00:24:32,560
Speaker 1: wind direction, sea level pressure. Um what else I'm looking

440
00:24:32,600 --> 00:24:36,480
Speaker 1: at it right now? Um yeah, sunrise, sunset, moon clock position,

441
00:24:36,560 --> 00:24:41,480
Speaker 1: moon percent illuminated all those fundal details to me anyway,

442
00:24:42,000 --> 00:24:45,720
Speaker 1: So UM yeah, it took in half an hour here,

443
00:24:45,760 --> 00:24:47,960
Speaker 1: an hour there over the course a number e years

444
00:24:48,000 --> 00:24:51,440
Speaker 1: to build that. But now literally all of the time

445
00:24:51,440 --> 00:24:54,000
Speaker 1: it takes with data entry for entering the time of

446
00:24:54,000 --> 00:24:57,960
Speaker 1: the date, and um, you know there is some set

447
00:24:58,040 --> 00:25:02,320
Speaker 1: up you know it's based on um, you know, location specifics.

448
00:25:02,440 --> 00:25:04,520
Speaker 1: You have to bring in the data for your you know,

449
00:25:04,560 --> 00:25:08,240
Speaker 1: whatever towns closest to you, usually their airports or or

450
00:25:08,400 --> 00:25:12,919
Speaker 1: just weather stations in in small towns. And uh now,

451
00:25:12,960 --> 00:25:15,919
Speaker 1: I um, you know, I have this enormous amount of

452
00:25:16,000 --> 00:25:19,640
Speaker 1: data Like last year, for example, I have almost five

453
00:25:19,840 --> 00:25:24,600
Speaker 1: data points from two year old and older bucks in Iowa,

454
00:25:24,680 --> 00:25:27,400
Speaker 1: and I have almost that number of data points from

455
00:25:27,600 --> 00:25:30,399
Speaker 1: two year old and older bucks up in Wisconsin. You know,

456
00:25:31,000 --> 00:25:36,280
Speaker 1: between say August and December of of last falls, so

457
00:25:36,400 --> 00:25:38,320
Speaker 1: for the whole fall, and I can look at the trends.

458
00:25:38,400 --> 00:25:41,840
Speaker 1: You know, I look at UM not just locations specific,

459
00:25:41,920 --> 00:25:45,679
Speaker 1: but I want to look at UM just trends overall

460
00:25:45,720 --> 00:25:48,800
Speaker 1: over the over the course of the rut. UM like

461
00:25:48,840 --> 00:25:51,840
Speaker 1: when did a peak? UM, what influences the peaks? And

462
00:25:52,080 --> 00:25:54,680
Speaker 1: we can really dive into that. UM. I've learned some

463
00:25:55,800 --> 00:26:03,080
Speaker 1: interesting things. But so so first off, to develop something

464
00:26:03,119 --> 00:26:09,359
Speaker 1: like this, UM, you go and you download the weather

465
00:26:10,040 --> 00:26:14,000
Speaker 1: data from the Noah website for a specific location, and

466
00:26:14,040 --> 00:26:16,560
Speaker 1: it gives you the whole and I'm I'm assuming you

467
00:26:16,600 --> 00:26:20,400
Speaker 1: pick a date range, so maybe October one through January

468
00:26:20,440 --> 00:26:22,720
Speaker 1: one or whatever it is. It gives you a spreadsheet

469
00:26:22,840 --> 00:26:25,639
Speaker 1: with all of the data for every single day. You

470
00:26:25,720 --> 00:26:28,760
Speaker 1: create a tab in your spreadsheet, paste that into the

471
00:26:28,800 --> 00:26:32,280
Speaker 1: tab on your spreadsheet, and then you have another tab

472
00:26:32,320 --> 00:26:34,600
Speaker 1: of that spreadsheet, which is where you're putting all of

473
00:26:34,760 --> 00:26:39,000
Speaker 1: your observations, so the dates and the times of the observation.

474
00:26:39,560 --> 00:26:43,359
Speaker 1: And then through some fancy spreadsheet magic, you connect to

475
00:26:43,400 --> 00:26:47,240
Speaker 1: those two so that spits out the data. It connects

476
00:26:47,240 --> 00:26:49,080
Speaker 1: between your dately you put in there, and then what

477
00:26:49,119 --> 00:26:52,560
Speaker 1: the actual weather is. UM. I do not remember how

478
00:26:52,560 --> 00:26:57,080
Speaker 1: to use Excel that well, so I couldn't do that myself. UM,

479
00:26:57,119 --> 00:27:00,800
Speaker 1: but I'm sure there's there's tutorials online um or maybe

480
00:27:01,080 --> 00:27:03,159
Speaker 1: maybe there's even a way to get a template out

481
00:27:03,200 --> 00:27:06,639
Speaker 1: there for people. I don't know, but yeah, there's all

482
00:27:06,720 --> 00:27:10,440
Speaker 1: kinds of tutorials you can. UM. I'm kind of hesitant

483
00:27:10,440 --> 00:27:14,240
Speaker 1: to just blast out my my my spreadsheet to everybody.

484
00:27:14,280 --> 00:27:17,199
Speaker 1: I've given it to a few people, but UM, you

485
00:27:17,240 --> 00:27:20,120
Speaker 1: know there's like you so and I. You know, say

486
00:27:20,200 --> 00:27:22,760
Speaker 1: November one at noon and you had a buck run

487
00:27:22,760 --> 00:27:26,200
Speaker 1: past your caturil camera, you enter that date and now

488
00:27:26,320 --> 00:27:28,840
Speaker 1: without instead of having to manually look up, oh, the

489
00:27:28,880 --> 00:27:32,760
Speaker 1: temperature was fifty eight degrees and all automatically poop it

490
00:27:32,800 --> 00:27:35,280
Speaker 1: was fifty eight degrees and this was a barometric pressure

491
00:27:35,320 --> 00:27:38,960
Speaker 1: and was it raining or not? Um, you know, um

492
00:27:38,960 --> 00:27:42,720
Speaker 1: those kind of things. So um again, the weather Web

493
00:27:43,200 --> 00:27:48,800
Speaker 1: weather website is the UM it's www dot n C,

494
00:27:50,080 --> 00:27:53,919
Speaker 1: d C, dot n O A A dot go. And

495
00:27:53,960 --> 00:27:58,280
Speaker 1: then you go to the local climate climate toological data

496
00:27:59,080 --> 00:28:01,560
Speaker 1: and it's free. Anybody can do it and you can

497
00:28:01,880 --> 00:28:05,040
Speaker 1: search by your location and download the weather and it's

498
00:28:05,400 --> 00:28:09,840
Speaker 1: usually recorded in fifteen minute or our intervals UM for

499
00:28:09,960 --> 00:28:12,800
Speaker 1: your location, so you know it documents what the weather

500
00:28:13,000 --> 00:28:16,480
Speaker 1: was and for everyone else's reference. I'm pretty sure UM

501
00:28:16,920 --> 00:28:21,080
Speaker 1: Underground and all the other websites. I guess WonderGround doesn't

502
00:28:21,080 --> 00:28:23,600
Speaker 1: even have historical data anymore. I'm not sure. I don't

503
00:28:23,600 --> 00:28:26,000
Speaker 1: think they do UM, but they all get it from

504
00:28:26,040 --> 00:28:29,080
Speaker 1: this Noah website. I think everybody does UM. I don't

505
00:28:29,080 --> 00:28:31,040
Speaker 1: think I like that's to go to place to get

506
00:28:31,280 --> 00:28:35,240
Speaker 1: UM weather data. So so in your spreadsheets you have

507
00:28:35,320 --> 00:28:39,160
Speaker 1: a whole bunch of different metrics or variables tracked. Are

508
00:28:39,240 --> 00:28:41,840
Speaker 1: these hand selected because you think that they have some

509
00:28:41,920 --> 00:28:44,440
Speaker 1: kind of impact or is this just everything Noah has

510
00:28:44,480 --> 00:28:47,520
Speaker 1: and you just allow it all to populate. Yeah, it's

511
00:28:47,560 --> 00:28:51,360
Speaker 1: not everything Noah has. Noah has tracks so much it's

512
00:28:51,360 --> 00:28:55,200
Speaker 1: it's ridiculous. Um. They have like heating degree days and

513
00:28:55,240 --> 00:28:58,400
Speaker 1: cooling degree days, which might be you know, might be

514
00:28:58,480 --> 00:29:00,440
Speaker 1: interested you if you're a hunter and doing a lot

515
00:29:00,440 --> 00:29:03,800
Speaker 1: of food plots. That's a measure of a lot of

516
00:29:04,000 --> 00:29:07,520
Speaker 1: UM farmer's day attention to you know, how warm or

517
00:29:07,600 --> 00:29:10,760
Speaker 1: cool versus average it is. And then I just started

518
00:29:10,800 --> 00:29:14,720
Speaker 1: hand picking things. UM. So it's things that I think

519
00:29:14,720 --> 00:29:17,200
Speaker 1: are indicators. Now I've looked into some of them, and

520
00:29:17,240 --> 00:29:20,600
Speaker 1: some of them don't end up being much of an indicator. Um,

521
00:29:20,640 --> 00:29:24,680
Speaker 1: but it's uh, it's really interesting. I mean I can

522
00:29:24,760 --> 00:29:26,760
Speaker 1: run it through. You know, I look at day of

523
00:29:26,800 --> 00:29:30,840
Speaker 1: the week. I look at a M versus PM versus

524
00:29:30,880 --> 00:29:33,760
Speaker 1: mid day, and I just defined midday a little arbitrarily

525
00:29:33,880 --> 00:29:38,000
Speaker 1: is between ten am and two pm. Um. Um, A

526
00:29:38,040 --> 00:29:41,080
Speaker 1: couple of things. I look at that our location specific

527
00:29:41,480 --> 00:29:45,760
Speaker 1: I try to I've just started, uh looking at what

528
00:29:45,840 --> 00:29:51,120
Speaker 1: direction bucks are headed on camera. So sometimes, um, when

529
00:29:51,160 --> 00:29:53,560
Speaker 1: I'm hanging a camera right in a bedding area and

530
00:29:53,560 --> 00:29:56,200
Speaker 1: there's a bunch of beds around, it can be confusing

531
00:29:56,240 --> 00:29:58,640
Speaker 1: to me, like where they're coming from, where they're going to.

532
00:29:59,240 --> 00:30:01,760
Speaker 1: And I've started to find trends. So I I enter,

533
00:30:01,880 --> 00:30:04,960
Speaker 1: say a buck's heading from left to right on camera,

534
00:30:05,480 --> 00:30:08,040
Speaker 1: you know, and I can remember what direction that camera

535
00:30:08,160 --> 00:30:11,520
Speaker 1: was pointed. So I'll enter left, right, left, right, and

536
00:30:11,560 --> 00:30:14,080
Speaker 1: it will say, oh, in the morning, bucks are coming

537
00:30:14,120 --> 00:30:16,520
Speaker 1: from the left, and the evening they're in the afternoon

538
00:30:16,560 --> 00:30:19,920
Speaker 1: they're coming from the right. And then I can that uh,

539
00:30:19,960 --> 00:30:23,640
Speaker 1: you know, that tells me a lot more than um,

540
00:30:23,680 --> 00:30:26,320
Speaker 1: you know, just so oh they're you know, they're betting

541
00:30:26,320 --> 00:30:28,480
Speaker 1: over there, you know, and then they're switching to betting

542
00:30:28,520 --> 00:30:33,280
Speaker 1: over here, so UM sky conditions. I look at sky conditions.

543
00:30:33,280 --> 00:30:38,400
Speaker 1: It's another stat from Noah overcast versus clear or versus

544
00:30:38,440 --> 00:30:42,400
Speaker 1: parley cloudy UM. And then I really dive into temperature.

545
00:30:42,640 --> 00:30:45,880
Speaker 1: In my opinion, temperature is the number one external factor

546
00:30:46,080 --> 00:30:49,280
Speaker 1: you know, outside like the rut um, time of year,

547
00:30:49,880 --> 00:30:54,200
Speaker 1: like weather factor, it's temperature UM, without a doubt, there's

548
00:30:54,240 --> 00:30:58,560
Speaker 1: nothing else that comes close actually um so in terms

549
00:30:58,600 --> 00:31:01,760
Speaker 1: of where a deer is and why he's moving UM

550
00:31:01,800 --> 00:31:05,320
Speaker 1: A certain to certain places. UM. So I look at

551
00:31:05,360 --> 00:31:09,320
Speaker 1: temperature like departure from average, meaning I look at the

552
00:31:09,320 --> 00:31:12,600
Speaker 1: average temperature that time of year, and is it you know, average,

553
00:31:12,680 --> 00:31:15,920
Speaker 1: or is a ten degrees hotter ten degrees cooler UM.

554
00:31:15,960 --> 00:31:18,200
Speaker 1: I look at two hour temperature change, which is kind

555
00:31:18,200 --> 00:31:21,479
Speaker 1: of an arbitrary number, but UM is that you know,

556
00:31:21,640 --> 00:31:26,280
Speaker 1: the temperature has a big impact on UM. When a

557
00:31:26,840 --> 00:31:29,000
Speaker 1: buckets up and moves to another bed during the middle

558
00:31:29,000 --> 00:31:30,800
Speaker 1: of the day, if it's warming up or cooling off.

559
00:31:31,560 --> 00:31:35,560
Speaker 1: So I've learned that UM. Then I look at precipitation UM.

560
00:31:35,560 --> 00:31:38,680
Speaker 1: Both when it's like at the time of the photo,

561
00:31:38,760 --> 00:31:40,560
Speaker 1: but I also want to know like what it's going

562
00:31:40,640 --> 00:31:42,440
Speaker 1: to do and then what it was doing before for

563
00:31:42,520 --> 00:31:47,200
Speaker 1: a few hours before and after because um, so so

564
00:31:47,200 --> 00:31:50,120
Speaker 1: what I found both in Wisconsin and down here, and

565
00:31:50,120 --> 00:31:52,560
Speaker 1: I've I've heard you talk about it, Mark if for To,

566
00:31:52,560 --> 00:31:55,880
Speaker 1: others talk about it UM, and it sounds like you know,

567
00:31:55,880 --> 00:31:59,120
Speaker 1: in Michigan, there's a theory that you know, older box

568
00:31:59,160 --> 00:32:01,040
Speaker 1: are prefer to move in the rain. What I've found

569
00:32:01,080 --> 00:32:04,720
Speaker 1: so far is, at least no Wisconsin, they dear don't

570
00:32:04,800 --> 00:32:07,680
Speaker 1: move so much in the rain. It's distinct drop. However,

571
00:32:07,720 --> 00:32:10,160
Speaker 1: they do really like to move right before and right

572
00:32:10,240 --> 00:32:13,400
Speaker 1: after the rain. UM. And I think there are just

573
00:32:13,600 --> 00:32:16,120
Speaker 1: a lot of time it's adjusting bedding to new locations,

574
00:32:16,560 --> 00:32:21,760
Speaker 1: UM to UH to seek shelter or leaving and maybe

575
00:32:21,800 --> 00:32:24,840
Speaker 1: going to feed UM in preparation for the rain or

576
00:32:24,840 --> 00:32:29,400
Speaker 1: after the rain. So that's what I found. UM. Wind direction,

577
00:32:29,800 --> 00:32:35,120
Speaker 1: I look at that's very locational thing. So UM it's is.

578
00:32:35,440 --> 00:32:39,680
Speaker 1: But I've also I've looked at all my observations altogether

579
00:32:39,720 --> 00:32:43,080
Speaker 1: for an entire fall. It's actually very eye opening just

580
00:32:43,240 --> 00:32:46,320
Speaker 1: what average wind digressive wind directions are. And this is

581
00:32:46,400 --> 00:32:49,920
Speaker 1: data goes back over several years, but basically a third

582
00:32:49,920 --> 00:32:53,120
Speaker 1: of the time the wind is south or southwest, a

583
00:32:53,200 --> 00:32:56,920
Speaker 1: third of the time the wind is where northwest or

584
00:32:56,960 --> 00:33:00,400
Speaker 1: north and then the remaining third of the time you're

585
00:33:00,480 --> 00:33:03,240
Speaker 1: left with west, which actually, isn't that a straight west

586
00:33:03,280 --> 00:33:06,200
Speaker 1: wind isn't that common for this part of the country

587
00:33:06,640 --> 00:33:10,239
Speaker 1: or some easterly um So, so it's very split up.

588
00:33:10,360 --> 00:33:13,080
Speaker 1: You usually get a southerly wind or a northerly wind,

589
00:33:13,640 --> 00:33:17,600
Speaker 1: you know, and occasionally something else. UM. I look at

590
00:33:17,600 --> 00:33:21,840
Speaker 1: barometric pressure, barometric pressure or something that I've really dove into.

591
00:33:22,200 --> 00:33:25,200
Speaker 1: I thought I was going to find actually more than

592
00:33:25,560 --> 00:33:28,880
Speaker 1: more than I end up finding. UM. So I look

593
00:33:28,880 --> 00:33:32,280
Speaker 1: at pressure change. UM. But I also look at just

594
00:33:32,360 --> 00:33:35,280
Speaker 1: like overall pressure. Um. And there's no denying. Do you

595
00:33:35,400 --> 00:33:38,520
Speaker 1: really like a high barometer? You get above for this

596
00:33:38,600 --> 00:33:41,600
Speaker 1: part of the country thirty point two five thirty point three,

597
00:33:42,160 --> 00:33:46,240
Speaker 1: and there's definitely um. Deer on their feet more in daylight.

598
00:33:46,760 --> 00:33:49,080
Speaker 1: Um And I should have lead with this, but you know,

599
00:33:49,120 --> 00:33:52,960
Speaker 1: I'm really looking at daylight photos only. I pretty much

600
00:33:53,000 --> 00:33:58,040
Speaker 1: ignored nighttime photos because well it's just you know, I'm

601
00:33:58,080 --> 00:34:01,719
Speaker 1: looking at times I can hide and deer, you know,

602
00:34:02,080 --> 00:34:04,240
Speaker 1: um do move a lot at night. But I really

603
00:34:04,240 --> 00:34:06,480
Speaker 1: want to know what's making them get on their feet

604
00:34:06,640 --> 00:34:09,879
Speaker 1: during the day. So UM, if that helps anybody along.

605
00:34:09,960 --> 00:34:12,960
Speaker 1: I'm really only I'm really only looking at daylight photos

606
00:34:13,040 --> 00:34:15,840
Speaker 1: from you know, during hunting hours, half an hour before daylight,

607
00:34:16,000 --> 00:34:18,799
Speaker 1: half an hour after dark. UM. Do you track your

608
00:34:18,800 --> 00:34:23,399
Speaker 1: observations to like what you see with your own eyes? Yes, yep, yep,

609
00:34:23,480 --> 00:34:29,600
Speaker 1: I do, UM, And that's actually so that's it's good, UM,

610
00:34:29,640 --> 00:34:32,120
Speaker 1: But it's kind of tricky and you and I hesitate

611
00:34:32,200 --> 00:34:34,439
Speaker 1: to let tell anyone to compare it to your trail

612
00:34:34,520 --> 00:34:38,239
Speaker 1: camera observations because of your UM, the size of your

613
00:34:38,440 --> 00:34:41,919
Speaker 1: what you're observing, and because with a trail camera you're

614
00:34:42,040 --> 00:34:45,200
Speaker 1: watching you know, something fifty feet or maybe at most

615
00:34:45,200 --> 00:34:48,479
Speaker 1: a hundred feet in front of that camera, UM, and

616
00:34:48,760 --> 00:34:52,760
Speaker 1: you know, a narrow window, whereas if you're sitting on stand, UM,

617
00:34:52,800 --> 00:34:56,480
Speaker 1: you could maybe record things in bow range, you know,

618
00:34:56,760 --> 00:34:59,000
Speaker 1: like because that's a fairly small area. But if you're

619
00:34:59,000 --> 00:35:02,239
Speaker 1: sitting on stand and you know, in one stand you

620
00:35:02,360 --> 00:35:04,360
Speaker 1: might in dense cover you might only be able to

621
00:35:04,400 --> 00:35:07,880
Speaker 1: see forty yards or thirty yards, and another stand, if

622
00:35:07,880 --> 00:35:10,440
Speaker 1: you can see across fields, you might be watching deer

623
00:35:10,920 --> 00:35:13,759
Speaker 1: four yards away. So that doesn't really tell you know,

624
00:35:13,840 --> 00:35:16,919
Speaker 1: if they're they're moving over there. That doesn't really tell

625
00:35:16,960 --> 00:35:21,600
Speaker 1: you anything about your spot, you know. Um, so, um,

626
00:35:21,640 --> 00:35:24,359
Speaker 1: just what you're what you're looking at has a big

627
00:35:24,400 --> 00:35:29,160
Speaker 1: impact there. You need to append that observation to where

628
00:35:29,160 --> 00:35:34,040
Speaker 1: the deer's location was, not to understand location. Yeah, back

629
00:35:34,080 --> 00:35:36,840
Speaker 1: to press, so pressure they deer like a high barometer.

630
00:35:37,320 --> 00:35:40,359
Speaker 1: But it's not. It's it's uh, it's not as big

631
00:35:40,360 --> 00:35:42,120
Speaker 1: of a trend as I thought it might be. I

632
00:35:42,200 --> 00:35:45,160
Speaker 1: compare it to like average barometric pressure for the year,

633
00:35:45,640 --> 00:35:48,719
Speaker 1: which um, I know Mark Drury way back and a

634
00:35:48,719 --> 00:35:51,200
Speaker 1: couple of his podcasts that they had talked about that

635
00:35:51,440 --> 00:35:53,960
Speaker 1: given into that, and it's true. You know, average barometric

636
00:35:54,000 --> 00:35:59,800
Speaker 1: pressure might be around thirty inches of mercury in early season,

637
00:36:00,000 --> 00:36:02,719
Speaker 1: and that goes up to you know, say thirty point

638
00:36:02,760 --> 00:36:06,839
Speaker 1: one or or or maybe a little more by November.

639
00:36:07,360 --> 00:36:12,160
Speaker 1: And um, you know, so, so departure from average is important.

640
00:36:12,200 --> 00:36:15,319
Speaker 1: It's not just um, you know, thirty point three inches

641
00:36:15,320 --> 00:36:20,160
Speaker 1: of mercury is screaming high in September October, but it's not.

642
00:36:20,320 --> 00:36:24,640
Speaker 1: It's high, but it's not as high come mid November. Um,

643
00:36:24,719 --> 00:36:29,160
Speaker 1: when there's an atmosphere cools, pressure on average gets still higher. Um.

644
00:36:29,239 --> 00:36:31,279
Speaker 1: And then of course I dive into the moon, which

645
00:36:31,320 --> 00:36:36,439
Speaker 1: is everyone's favorite theory. Um. And that so I look

646
00:36:36,480 --> 00:36:39,640
Speaker 1: at two different things. I look at both, you know,

647
00:36:39,920 --> 00:36:43,279
Speaker 1: phase of the moon, so um, you know, new moon,

648
00:36:43,360 --> 00:36:47,640
Speaker 1: full moon, and then also what I call I probably

649
00:36:47,680 --> 00:36:50,400
Speaker 1: spent way too much time that this by itself, I

650
00:36:50,480 --> 00:36:52,719
Speaker 1: probably have spent more time on than any other thing

651
00:36:52,800 --> 00:36:57,400
Speaker 1: because everybody else has to write, um, moon clock position,

652
00:36:57,640 --> 00:37:00,279
Speaker 1: so I call it moon clock position, but you know,

653
00:37:00,920 --> 00:37:02,640
Speaker 1: red moon, whatever you want to call it. If a

654
00:37:02,680 --> 00:37:06,240
Speaker 1: moon's overhead or underfoot, to me, if it's directly overhead

655
00:37:06,360 --> 00:37:09,640
Speaker 1: in my mind, that's twelve o'clock. Directly underfoot, that's six o'clock.

656
00:37:10,040 --> 00:37:12,120
Speaker 1: If it's rising, it's nine o'clock. If it's setting at

657
00:37:12,160 --> 00:37:15,120
Speaker 1: three o'clock. So that's how I break it down. Yeah.

658
00:37:15,360 --> 00:37:18,359
Speaker 1: So um, it's just you know, yeah, it's a you know,

659
00:37:18,400 --> 00:37:20,279
Speaker 1: and it's not the right way. It's just the way

660
00:37:20,320 --> 00:37:24,160
Speaker 1: I do it, and I can visualize in my head. Um, anyway,

661
00:37:24,239 --> 00:37:28,000
Speaker 1: what have I found. I have found absolutely no correlation

662
00:37:28,120 --> 00:37:32,879
Speaker 1: with moonblock position and deer movement. So um, and that's

663
00:37:32,920 --> 00:37:35,560
Speaker 1: out of and and I will say I used to

664
00:37:35,600 --> 00:37:38,960
Speaker 1: be one that I did think there was a slight correlation.

665
00:37:39,120 --> 00:37:42,759
Speaker 1: I just that was my qualitative observations from sitting on

666
00:37:42,840 --> 00:37:44,720
Speaker 1: stand man, I thought I saw a lot of deer

667
00:37:44,760 --> 00:37:47,120
Speaker 1: when you know the moon was overhead and underfoot or

668
00:37:47,239 --> 00:37:51,400
Speaker 1: rising and setting. But if it's there, it's such a

669
00:37:51,440 --> 00:37:54,600
Speaker 1: slight trend, and it's so overshadowed by whether whether whether

670
00:37:54,680 --> 00:37:58,279
Speaker 1: whether weather information or whether you know whatever the weather

671
00:37:58,320 --> 00:38:02,799
Speaker 1: is at the moment um. Um, I've about written it off.

672
00:38:03,560 --> 00:38:07,120
Speaker 1: Um moon face. I looked at that, like do they

673
00:38:07,160 --> 00:38:10,040
Speaker 1: prefer a full moon or new moon? And once again

674
00:38:10,160 --> 00:38:13,479
Speaker 1: I have not found hardly anything. I've found only one

675
00:38:13,560 --> 00:38:18,360
Speaker 1: thing that seems to be in associated with like moon face,

676
00:38:18,960 --> 00:38:22,600
Speaker 1: and that is for whatever reasons um so so. I

677
00:38:22,640 --> 00:38:26,600
Speaker 1: also I don't just track each observation, but then I

678
00:38:26,880 --> 00:38:29,080
Speaker 1: graph him on a plot him on a big graph

679
00:38:29,520 --> 00:38:34,160
Speaker 1: um over the course of the fall, so October, November, December, um,

680
00:38:34,360 --> 00:38:38,719
Speaker 1: and to see when peak activity is. Uh so you know,

681
00:38:38,800 --> 00:38:41,560
Speaker 1: I'm I'm getting a whole bunch of deer early November.

682
00:38:41,560 --> 00:38:45,040
Speaker 1: Obviously that's peak movement. That's the rout um that shouldn't

683
00:38:45,040 --> 00:38:49,320
Speaker 1: surprise anybody. But um, cold fronts, there's usually a spike

684
00:38:49,360 --> 00:38:52,640
Speaker 1: in activity. There's no cold fronts always, as everybody knows,

685
00:38:52,680 --> 00:38:55,640
Speaker 1: has a big impact on deer, and the bigger the

686
00:38:55,680 --> 00:38:57,919
Speaker 1: cold front, the better. And I'll dive into that later.

687
00:38:57,920 --> 00:39:03,680
Speaker 1: I'll finish talking about the moon. Um, the moon. Um.

688
00:39:03,719 --> 00:39:07,560
Speaker 1: Around that full moon, the running moon, quote unquote, I

689
00:39:07,680 --> 00:39:11,319
Speaker 1: am seeing a spike in activity. It's not huge, but

690
00:39:11,480 --> 00:39:14,279
Speaker 1: it seems to every single time there's a full moon

691
00:39:14,320 --> 00:39:18,759
Speaker 1: anywhere from you know, mid to late October to mid November.

692
00:39:19,239 --> 00:39:22,719
Speaker 1: Whenever that full moon falls, I'm seeing over you know,

693
00:39:22,920 --> 00:39:25,680
Speaker 1: in a three to five day window around that full moon,

694
00:39:26,920 --> 00:39:29,600
Speaker 1: mature bucks seem to be a little more on their

695
00:39:29,600 --> 00:39:33,760
Speaker 1: feet more during daylight. Why. I have absolutely no idea.

696
00:39:34,680 --> 00:39:38,040
Speaker 1: I know there's been you know, telemetry studies that have

697
00:39:38,160 --> 00:39:42,440
Speaker 1: not found anything. Um. However, I will say, um, I

698
00:39:42,440 --> 00:39:46,719
Speaker 1: think those telemetry studies are pretty limited because of how

699
00:39:46,760 --> 00:39:49,360
Speaker 1: they measure data. A lot of them I've noticed measure

700
00:39:49,440 --> 00:39:52,200
Speaker 1: like miles per day. Well, that's not nearly precise enough.

701
00:39:52,280 --> 00:39:55,440
Speaker 1: You need to dial into either like during daylight and

702
00:39:55,480 --> 00:39:58,040
Speaker 1: for weather events you need to dial into one hour

703
00:39:58,160 --> 00:40:01,839
Speaker 1: or two hour increments. Did you have any uh, you know,

704
00:40:02,160 --> 00:40:05,080
Speaker 1: uh clarity on like what a weather event has on

705
00:40:05,120 --> 00:40:07,640
Speaker 1: an animal a twenty four hour you know, put it

706
00:40:07,640 --> 00:40:11,680
Speaker 1: this way, I'm not surprised they're not finding any correlation

707
00:40:11,760 --> 00:40:14,880
Speaker 1: because if you're measuring distance over twenty four hour period,

708
00:40:15,040 --> 00:40:17,239
Speaker 1: they're getting up, they're going to eat, they're coming back,

709
00:40:17,480 --> 00:40:20,480
Speaker 1: you know, and they're doing that every single day, whatever

710
00:40:20,520 --> 00:40:23,359
Speaker 1: the weather is. But exactly when they're doing it, well,

711
00:40:23,440 --> 00:40:27,960
Speaker 1: that hasn't that is impacted by you know, environmental factors.

712
00:40:27,600 --> 00:40:32,000
Speaker 1: That it's literally that little change is what really matters

713
00:40:32,000 --> 00:40:34,560
Speaker 1: to those hunters. So if if a buck moved half

714
00:40:34,560 --> 00:40:37,600
Speaker 1: an hour early, that might not show up on their

715
00:40:37,760 --> 00:40:39,799
Speaker 1: on their big studies, but that sure as heck makes

716
00:40:39,800 --> 00:40:42,240
Speaker 1: a huge difference for us as the hunter. That's exactly

717
00:40:43,840 --> 00:40:46,520
Speaker 1: I'm hoping these telemetry studies get better, and I know

718
00:40:46,560 --> 00:40:48,480
Speaker 1: there are a lot of them are limited I believe

719
00:40:48,560 --> 00:40:51,200
Speaker 1: by battery life and those collars. They can't be you know,

720
00:40:51,239 --> 00:40:54,520
Speaker 1: strapping nine bowl batteries onto those deer. So you know,

721
00:40:54,719 --> 00:40:57,320
Speaker 1: the more the more data points, more pings. You know,

722
00:40:57,360 --> 00:40:59,520
Speaker 1: if they're doing it every fifteen minutes or every hour

723
00:41:00,200 --> 00:41:02,320
Speaker 1: or three times a day, that has a big impact

724
00:41:02,360 --> 00:41:04,279
Speaker 1: on how often they do it. So I understand it's

725
00:41:04,320 --> 00:41:07,799
Speaker 1: not it's not everybody's fault, but I'm hoping people start

726
00:41:07,880 --> 00:41:11,279
Speaker 1: to measure these researchers start to measure UM in much

727
00:41:11,320 --> 00:41:14,440
Speaker 1: more frequent intervals, and the sky's a limit on what

728
00:41:14,480 --> 00:41:19,480
Speaker 1: we'll learn about here. So so with all this stuff,

729
00:41:20,160 --> 00:41:23,719
Speaker 1: all this stuff that you're looking at, I mean, you

730
00:41:23,880 --> 00:41:28,080
Speaker 1: listed fifteen different variables or more that you're looking at.

731
00:41:28,520 --> 00:41:30,319
Speaker 1: You kind of mentioned a few that seem to have

732
00:41:30,360 --> 00:41:35,640
Speaker 1: a big impact, some others don't. When you're actually heading

733
00:41:35,640 --> 00:41:39,000
Speaker 1: out to hunt, or you're sitting at home before your

734
00:41:39,520 --> 00:41:42,799
Speaker 1: weekend hunts or whatever, and you're thinking through, what's your

735
00:41:42,840 --> 00:41:46,000
Speaker 1: game plan is going to be? Which one of these

736
00:41:46,120 --> 00:41:49,520
Speaker 1: actually factor into your decisions? Like is there a top

737
00:41:49,560 --> 00:41:51,560
Speaker 1: five lists or anything like that that are the real

738
00:41:51,920 --> 00:41:56,440
Speaker 1: heavy heaters that you really focus your time and energy around. Yeah, so, uh,

739
00:41:56,560 --> 00:42:00,920
Speaker 1: number one's temperature. Um, I've really learned to that. You know,

740
00:42:01,080 --> 00:42:03,800
Speaker 1: deer favor cool areas when it's warm out and warm

741
00:42:03,840 --> 00:42:07,960
Speaker 1: areas when it's cold out. Um. They still security's top priorities.

742
00:42:07,960 --> 00:42:10,960
Speaker 1: So you know, they're only in certain areas that they

743
00:42:11,040 --> 00:42:16,719
Speaker 1: feel safe, but within those UM, Like I have a theory, UM,

744
00:42:16,760 --> 00:42:19,759
Speaker 1: I'm not sure that deer move less when it's hot out.

745
00:42:19,840 --> 00:42:22,160
Speaker 1: I think they just move in different spots, and most

746
00:42:22,200 --> 00:42:24,920
Speaker 1: tunners aren't watching those spots. So that's part of the

747
00:42:24,920 --> 00:42:27,080
Speaker 1: reason why everybody, oh, it's a cold front deer moving

748
00:42:27,120 --> 00:42:30,640
Speaker 1: like crazy. I actually think nobody bothers to watch the

749
00:42:30,640 --> 00:42:34,719
Speaker 1: warm weather spots um. And a couple of reasons. They're one,

750
00:42:35,200 --> 00:42:38,640
Speaker 1: they're they're not necessarily where deer spend a lot of

751
00:42:38,680 --> 00:42:40,480
Speaker 1: the time in the fall. You know, in the fall

752
00:42:40,520 --> 00:42:43,799
Speaker 1: it's usually cooler. The cold days out number the warm

753
00:42:43,880 --> 00:42:48,080
Speaker 1: days on average. UM. And also the cool spots are

754
00:42:48,120 --> 00:42:54,480
Speaker 1: usually heavier cover, lower down by water. UM. And people

755
00:42:54,640 --> 00:42:57,239
Speaker 1: you know, you can't see cold front deer out in

756
00:42:57,239 --> 00:42:58,960
Speaker 1: a field feeding and you can see him from half

757
00:42:59,000 --> 00:43:02,280
Speaker 1: a mile away. Um. When it's hot weather, they're seeking shade,

758
00:43:02,400 --> 00:43:06,120
Speaker 1: they're seeking uh, you know, cool north slopes in hill country,

759
00:43:07,040 --> 00:43:08,880
Speaker 1: which is a lot of what I hunt. And you

760
00:43:08,960 --> 00:43:10,920
Speaker 1: can't see very far in a lot of these areas.

761
00:43:11,320 --> 00:43:14,239
Speaker 1: So people think, oh, they're not moving. Well, I've I've

762
00:43:14,280 --> 00:43:16,839
Speaker 1: got deer up and walking around and it's eighty five

763
00:43:16,840 --> 00:43:20,720
Speaker 1: degrees you know, with humidity. I'm getting them on camera.

764
00:43:21,200 --> 00:43:24,440
Speaker 1: They're you know, they're probably not running around and running heavily,

765
00:43:24,560 --> 00:43:29,560
Speaker 1: but um, they do move UM, but it's usually like UM.

766
00:43:29,719 --> 00:43:34,120
Speaker 1: So that really has improved my less than if I'm

767
00:43:34,200 --> 00:43:36,640
Speaker 1: hunting and it's less than optimal weather, you know, warm

768
00:43:36,680 --> 00:43:39,640
Speaker 1: weather or whatever. UM. And I used to think. I

769
00:43:39,680 --> 00:43:41,920
Speaker 1: still don't like sitting on standard sweating. I'll still call

770
00:43:41,960 --> 00:43:43,880
Speaker 1: it less than optimal. I just don't like doing it,

771
00:43:44,000 --> 00:43:46,799
Speaker 1: but UM, I will do it. And I've killed a

772
00:43:46,800 --> 00:43:49,200
Speaker 1: couple of bucks now here with you know, warmer than

773
00:43:49,200 --> 00:43:53,960
Speaker 1: average temperatures in spots you're looking, you know, shade, cool,

774
00:43:54,000 --> 00:44:00,320
Speaker 1: north slopes, water, um, often combinations of all those UM

775
00:44:00,360 --> 00:44:03,960
Speaker 1: and Uh, I'm getting into the deer. That's where they are,

776
00:44:04,120 --> 00:44:06,319
Speaker 1: and they're getting out, and they're moving in daylight. They're

777
00:44:06,320 --> 00:44:08,399
Speaker 1: not maybe moving very far, and they're you know, they're

778
00:44:08,440 --> 00:44:12,360
Speaker 1: not walking out into fields if it's bright sunshine, especially

779
00:44:12,840 --> 00:44:15,800
Speaker 1: you know, if they've grown their winter coat. UM. Eighty

780
00:44:15,800 --> 00:44:18,160
Speaker 1: degrees in September is a lot different than eighty degrees

781
00:44:18,160 --> 00:44:20,319
Speaker 1: in November. After a deer it's grown it's winter coat.

782
00:44:20,400 --> 00:44:22,719
Speaker 1: So as you know, it has a huge impact by

783
00:44:22,800 --> 00:44:25,799
Speaker 1: time of year or two. So temperature, UM. You know,

784
00:44:25,840 --> 00:44:28,560
Speaker 1: if it's really cold out, I like the extreams because

785
00:44:28,560 --> 00:44:33,080
Speaker 1: that really concentrates the deer. UM. You know, I can't

786
00:44:33,120 --> 00:44:35,080
Speaker 1: say I like counting and super hot weather but if

787
00:44:35,120 --> 00:44:38,800
Speaker 1: it's super hot, the deer really stack up well, UM

788
00:44:38,960 --> 00:44:42,520
Speaker 1: in some of these spots, if it's super cold out

789
00:44:42,560 --> 00:44:45,680
Speaker 1: there flipping, you know, they're getting out of the wind,

790
00:44:45,920 --> 00:44:48,440
Speaker 1: so they're getting on the lee of the leeward slopes.

791
00:44:48,480 --> 00:44:52,319
Speaker 1: They're getting down further on the leewards the slopes, UM,

792
00:44:52,400 --> 00:44:54,640
Speaker 1: so they're completely out of the wind instead of up

793
00:44:54,680 --> 00:44:56,920
Speaker 1: close to the top. They might get a little down lower,

794
00:44:57,040 --> 00:45:03,240
Speaker 1: but they'll try to. They'll seek shuns, sunshine, they will um,

795
00:45:03,280 --> 00:45:05,719
Speaker 1: you know, they'll get if it gets really cool, then

796
00:45:05,760 --> 00:45:08,000
Speaker 1: they'll get up and move a lot more frequently because

797
00:45:08,000 --> 00:45:10,640
Speaker 1: they have to UM. And of course as it gets colder,

798
00:45:11,280 --> 00:45:13,560
Speaker 1: they need more calories, so they are hitting, they are

799
00:45:13,600 --> 00:45:18,040
Speaker 1: putting the feedback on more. There's no denying that. So um,

800
00:45:18,040 --> 00:45:24,400
Speaker 1: the temperature is definitely number one. UM. Sorry, But to clarify,

801
00:45:24,640 --> 00:45:28,319
Speaker 1: are you saying absolute temperature or temperature change or are

802
00:45:28,360 --> 00:45:32,360
Speaker 1: you loving that altogether? So well, yeah, so all the

803
00:45:32,880 --> 00:45:38,160
Speaker 1: all the above. So both temperature in relation to UM

804
00:45:38,800 --> 00:45:42,319
Speaker 1: average temperature for that time of year. So say it's

805
00:45:42,360 --> 00:45:45,880
Speaker 1: October one and it is sixty degrees out and your

806
00:45:45,920 --> 00:45:50,680
Speaker 1: average temperature is say seventy degrees dear, going to you know,

807
00:45:51,320 --> 00:45:55,000
Speaker 1: be moving uh somewhat more. They'll probably be hitting the

808
00:45:55,040 --> 00:45:58,920
Speaker 1: food a little more, but they'll be really seeking the

809
00:45:59,000 --> 00:46:01,520
Speaker 1: kind of the warmer area is they're they're not going

810
00:46:01,520 --> 00:46:05,400
Speaker 1: to be in the areas necessarily that um, they would

811
00:46:05,480 --> 00:46:09,720
Speaker 1: be um if it was uh, you know, say eighty

812
00:46:09,760 --> 00:46:14,480
Speaker 1: degrees um. And just like in November. Say, and this

813
00:46:14,600 --> 00:46:17,560
Speaker 1: is kind of one of my the lightbulb team on

814
00:46:17,760 --> 00:46:21,040
Speaker 1: a couple of years ago. UM. And this was northern

815
00:46:21,040 --> 00:46:24,759
Speaker 1: Wisconsin hunt. Um. So not not an Iowa hunt, the

816
00:46:24,800 --> 00:46:28,160
Speaker 1: Big Woods hunt. Um. It was I was up there

817
00:46:28,160 --> 00:46:32,600
Speaker 1: early early November for at hunt and we're hunting around

818
00:46:32,640 --> 00:46:36,000
Speaker 1: the edge of the swamp. Um. And I could hear

819
00:46:36,040 --> 00:46:38,000
Speaker 1: deer chasing out in the swamp. And I didn't see

820
00:46:38,000 --> 00:46:40,200
Speaker 1: a deer the whole day that first day. And the

821
00:46:40,239 --> 00:46:42,600
Speaker 1: second day, I was like, well, I'm gonna go um

822
00:46:42,640 --> 00:46:44,120
Speaker 1: you know. And I was right up to the edge

823
00:46:44,120 --> 00:46:46,000
Speaker 1: of the swamp. You know, I really thought I was

824
00:46:46,040 --> 00:46:49,000
Speaker 1: in it. Lots of time there nothing but I could

825
00:46:49,000 --> 00:46:52,200
Speaker 1: hear crashing out in the swamp. And the next day

826
00:46:52,239 --> 00:46:55,440
Speaker 1: I got up um and I started to just sneak

827
00:46:55,480 --> 00:46:58,839
Speaker 1: out into this island. And long story short, I didn't

828
00:46:58,880 --> 00:47:01,480
Speaker 1: kill a buck, but I had a ton of action.

829
00:47:02,200 --> 00:47:05,040
Speaker 1: And as soon as I started walking in the swamp,

830
00:47:05,120 --> 00:47:06,800
Speaker 1: you know, it was one of those days. The temperature

831
00:47:06,840 --> 00:47:09,480
Speaker 1: was nudge and eighty degrees in early November, and as

832
00:47:09,480 --> 00:47:11,200
Speaker 1: soon as I went onto the swamp, it just felt

833
00:47:11,239 --> 00:47:13,719
Speaker 1: a lot cooler. And the rout was going full bore

834
00:47:13,880 --> 00:47:16,759
Speaker 1: out in that swamp, and there was nothing out um

835
00:47:17,040 --> 00:47:21,400
Speaker 1: outside of that because it was hotter um. And it

836
00:47:21,440 --> 00:47:23,759
Speaker 1: wasn't that I was outside the curious. I was in

837
00:47:23,800 --> 00:47:26,680
Speaker 1: the thick stuff, but I wasn't down into into that water,

838
00:47:26,960 --> 00:47:29,120
Speaker 1: and those deer were running from island to island down

839
00:47:29,160 --> 00:47:32,000
Speaker 1: the swamp, you know, because it was warmer and and

840
00:47:32,280 --> 00:47:37,640
Speaker 1: uh and correspondingly, actually um, I founded that same area um.

841
00:47:37,840 --> 00:47:40,239
Speaker 1: The year after that, it was actually snow and so

842
00:47:40,239 --> 00:47:43,720
Speaker 1: it was colder than average. It was spitting snow, miserable weather.

843
00:47:44,160 --> 00:47:47,040
Speaker 1: I went out and sat um the same spot that

844
00:47:47,080 --> 00:47:50,320
Speaker 1: I sat a year ago, a year before that, except

845
00:47:50,360 --> 00:47:53,760
Speaker 1: it was instead of seventy eight degrees, it was thirty

846
00:47:53,800 --> 00:47:57,560
Speaker 1: eight degrees. And I didn't see a deer out in

847
00:47:57,560 --> 00:48:00,799
Speaker 1: that swamp all day long. I sat there all day long, um.

848
00:48:00,880 --> 00:48:02,760
Speaker 1: And then I'd had a camera there and I looked

849
00:48:02,800 --> 00:48:05,440
Speaker 1: and yeah, there's they were out there that was a

850
00:48:05,440 --> 00:48:08,960
Speaker 1: hot weather spot. They were out, you know, running around

851
00:48:09,239 --> 00:48:12,560
Speaker 1: in the islands and you know, and uh from island

852
00:48:12,560 --> 00:48:16,120
Speaker 1: to island Chason does. When it was um really hot

853
00:48:16,160 --> 00:48:20,000
Speaker 1: out but as as a temperature plunged, there was significantly

854
00:48:20,080 --> 00:48:22,440
Speaker 1: less movement out there. And then I think they were

855
00:48:22,440 --> 00:48:24,959
Speaker 1: out around the perimeter of the swamp more on drier

856
00:48:25,040 --> 00:48:26,719
Speaker 1: areas because it was cold and they didn't want to

857
00:48:26,719 --> 00:48:30,239
Speaker 1: walk around in the water. So Um, there's a little

858
00:48:30,280 --> 00:48:34,840
Speaker 1: stuff like that. So it's both departure from average temperature

859
00:48:35,239 --> 00:48:39,120
Speaker 1: and then also change of temperature. And that's really location

860
00:48:39,160 --> 00:48:44,040
Speaker 1: specific like is it heating up or is it cooling down? Um?

861
00:48:44,120 --> 00:48:47,000
Speaker 1: And uh are they seeking warmth or are they seeking

862
00:48:47,320 --> 00:48:53,800
Speaker 1: you know cool so um Yeah that m yeah for

863
00:48:53,800 --> 00:49:00,439
Speaker 1: for um location specific, it's definitely wind direction. Um. Deer

864
00:49:00,560 --> 00:49:03,759
Speaker 1: use the wind. Um, There's no no doubt about it.

865
00:49:04,080 --> 00:49:06,160
Speaker 1: Now they aren't always just walking into the wind or

866
00:49:06,200 --> 00:49:08,400
Speaker 1: walking with the wind. They're back um, although I have

867
00:49:08,520 --> 00:49:11,280
Speaker 1: seen like up north it seems like dear favor walking

868
00:49:11,320 --> 00:49:13,640
Speaker 1: with the wind. They're back more often, but it's not

869
00:49:13,719 --> 00:49:17,359
Speaker 1: a strong trend. But it's every location, um, and it's

870
00:49:17,400 --> 00:49:19,919
Speaker 1: I think it's because of wind based betting. I'll see

871
00:49:19,920 --> 00:49:24,279
Speaker 1: a lot more deer with certain wind directions. So um,

872
00:49:24,360 --> 00:49:27,000
Speaker 1: that's one of my favorite ways to filter spots. You know,

873
00:49:27,239 --> 00:49:30,320
Speaker 1: it's a it's an easy way. It's like, oh, this spot,

874
00:49:30,640 --> 00:49:33,359
Speaker 1: two thirds of movement was on southerly winds. So you're

875
00:49:33,440 --> 00:49:35,560
Speaker 1: hunting with a southerly wind, you have to even if

876
00:49:35,600 --> 00:49:38,360
Speaker 1: it doesn't seem to work right for you as a hunter,

877
00:49:38,560 --> 00:49:40,319
Speaker 1: you have to figure out a way to hunt it

878
00:49:40,360 --> 00:49:42,719
Speaker 1: effectively with that kind of wind because if it's a

879
00:49:42,760 --> 00:49:44,799
Speaker 1: sutherly wind spot and you're hunting with the north wind,

880
00:49:44,840 --> 00:49:51,200
Speaker 1: you are wasting your time. So um, that's a big one. Yeah, yeah, yeah.

881
00:49:51,280 --> 00:49:53,840
Speaker 1: So and I used to I think back of a

882
00:49:53,920 --> 00:49:56,640
Speaker 1: lot of hunts that I'd had, and I would hunt

883
00:49:57,280 --> 00:50:01,080
Speaker 1: before I even really understood like the windward leeward slope

884
00:50:01,120 --> 00:50:05,080
Speaker 1: deer definitely most of the time favor leward slopes. UM.

885
00:50:05,160 --> 00:50:14,480
Speaker 1: Never say always or never, but um wind slopes absolutely. Um.

886
00:50:14,600 --> 00:50:18,680
Speaker 1: So a windward slope UM a ridge. Imagine a ridge

887
00:50:18,880 --> 00:50:21,240
Speaker 1: and the wind is blowing across it, so it's blowing

888
00:50:21,280 --> 00:50:23,600
Speaker 1: from one side to the other. So the side of

889
00:50:23,600 --> 00:50:26,400
Speaker 1: that ridge that the wind is hitting is the windward

890
00:50:26,440 --> 00:50:29,560
Speaker 1: slope and the side away from the wind, so the

891
00:50:29,560 --> 00:50:31,479
Speaker 1: wind's kind of blowing over the top of your head

892
00:50:31,920 --> 00:50:33,960
Speaker 1: if you're out, you know, out of the wind on

893
00:50:34,040 --> 00:50:38,239
Speaker 1: a ridge that's the leeward slope. So, um, there's many

894
00:50:38,280 --> 00:50:40,680
Speaker 1: different theories. I really haven't made up my mind for

895
00:50:40,760 --> 00:50:46,000
Speaker 1: exactly why, um the wind, you know, the deer travel

896
00:50:46,040 --> 00:50:49,480
Speaker 1: on the leeward side. Um. Dan Infault has a good theory.

897
00:50:49,520 --> 00:50:52,319
Speaker 1: I think there's something too, you know about a thermal tunnel. Um.

898
00:50:52,360 --> 00:50:55,120
Speaker 1: I think there might be other things, um yeah going

899
00:50:55,200 --> 00:50:58,680
Speaker 1: on there too, not just that. Um. You know, deer

900
00:50:58,719 --> 00:51:02,120
Speaker 1: basically can smell the whole will ridge up wind of them,

901
00:51:02,400 --> 00:51:06,000
Speaker 1: and then you know they also can smell and also

902
00:51:06,160 --> 00:51:10,359
Speaker 1: see often the down the ridge below them, So they

903
00:51:10,400 --> 00:51:13,200
Speaker 1: have the eyesight and the and the scent than their

904
00:51:13,200 --> 00:51:18,600
Speaker 1: nose protecting them. Um. Anyway, so dear favorite leeward slopes,

905
00:51:18,760 --> 00:51:20,960
Speaker 1: and I would hunt windward slopes and I'd have a

906
00:51:21,080 --> 00:51:23,279
Speaker 1: rotten lock and there'd be deer sign everywhere, and why

907
00:51:23,320 --> 00:51:26,120
Speaker 1: I'm not seeing deer there? And then you know, after

908
00:51:26,160 --> 00:51:28,000
Speaker 1: a little while, and I've learned it from the hunting beast,

909
00:51:28,000 --> 00:51:31,839
Speaker 1: which is a great thing for pete. I don't hang

910
00:51:31,840 --> 00:51:34,360
Speaker 1: out there a whole lot anymore, only because I have

911
00:51:34,480 --> 00:51:36,920
Speaker 1: no time left in my day, in my life, it

912
00:51:36,920 --> 00:51:40,200
Speaker 1: seems like to spend on there. But um, it's a

913
00:51:40,200 --> 00:51:42,600
Speaker 1: great resource for hunters to learn more about you know,

914
00:51:42,880 --> 00:51:48,080
Speaker 1: um hunting and competitive environments, public land and stuff. Um.

915
00:51:48,120 --> 00:51:51,200
Speaker 1: But you know, the wind direction is huge, and that's

916
00:51:51,239 --> 00:51:54,680
Speaker 1: really you know, I have I have spots you know

917
00:51:54,760 --> 00:51:57,120
Speaker 1: for west wind I have spots for north winds and

918
00:51:57,280 --> 00:52:02,480
Speaker 1: southwest winds. Um. That's how I filter my setups, um,

919
00:52:02,520 --> 00:52:06,160
Speaker 1: based on where the deer are. Would you agree that

920
00:52:06,200 --> 00:52:10,040
Speaker 1: if we're looking at patterning deer, not by specific bucks,

921
00:52:10,080 --> 00:52:15,799
Speaker 1: but for patterning areas locations, is it my assumption would

922
00:52:15,800 --> 00:52:19,080
Speaker 1: be that wind direction and time of year are probably

923
00:52:19,120 --> 00:52:23,360
Speaker 1: the most impactful variables that would tell you when a

924
00:52:23,360 --> 00:52:25,799
Speaker 1: given spot would be good versus when it's not. Is

925
00:52:25,840 --> 00:52:28,480
Speaker 1: that right with you? For if I'm looking locations, so

926
00:52:28,520 --> 00:52:31,560
Speaker 1: those are the two biggest maybe yeah, and well and

927
00:52:31,719 --> 00:52:36,399
Speaker 1: temperature and temperature, like I I have really learned, like, um,

928
00:52:36,600 --> 00:52:40,160
Speaker 1: there's cold weather spots and there's hot weather spots. So

929
00:52:40,239 --> 00:52:42,600
Speaker 1: that's the that's the big three. And so you're saying

930
00:52:42,600 --> 00:52:45,840
Speaker 1: it's not just that deer will move more in general first,

931
00:52:46,080 --> 00:52:48,160
Speaker 1: just like they'd be moving. I guess my assumption is

932
00:52:48,160 --> 00:52:50,920
Speaker 1: going to be that you're gonna say temperature would just

933
00:52:51,040 --> 00:52:54,439
Speaker 1: change dear movement everywhere, but then wind direction in time

934
00:52:54,480 --> 00:52:57,000
Speaker 1: of year would say, okay, well this little betting air

935
00:52:57,040 --> 00:52:59,520
Speaker 1: picks up with those conditions. But you're saying, actually, there's

936
00:52:59,520 --> 00:53:03,200
Speaker 1: going to be specific locations that are when it's cold

937
00:53:03,239 --> 00:53:04,800
Speaker 1: deer for the spot, I guess, kind of like the

938
00:53:04,800 --> 00:53:08,800
Speaker 1: swamp example. Yep, yep, yep. And I've got other examples.

939
00:53:08,840 --> 00:53:12,440
Speaker 1: I've got, Um, I've got I've got a really killer

940
00:53:12,480 --> 00:53:15,640
Speaker 1: bed location picked out on public land here in Iowa,

941
00:53:15,680 --> 00:53:17,719
Speaker 1: and I have not killed a buck there yet. I'm

942
00:53:17,760 --> 00:53:20,279
Speaker 1: just waiting, um for the right buck to be in

943
00:53:20,320 --> 00:53:22,960
Speaker 1: the area. Um. I've hunted it a couple of times.

944
00:53:23,239 --> 00:53:27,719
Speaker 1: But it's a hot weather bed and buck consistently bed

945
00:53:27,719 --> 00:53:31,560
Speaker 1: there when it's over seventy degrees in October and November. Um,

946
00:53:31,760 --> 00:53:35,480
Speaker 1: and uh, it's it's you. You can't find it if

947
00:53:35,520 --> 00:53:38,879
Speaker 1: if it's colder than average. I've had a camera there

948
00:53:38,960 --> 00:53:42,160
Speaker 1: now watching this area for three years in a row

949
00:53:42,400 --> 00:53:45,840
Speaker 1: all fall. Um. I drop it out there late summer

950
00:53:45,840 --> 00:53:47,880
Speaker 1: and I picked it up in the winter and in

951
00:53:47,920 --> 00:53:51,280
Speaker 1: the cold weather, there's rarely a deer in that area.

952
00:53:51,400 --> 00:53:59,600
Speaker 1: Hot weather, it's I mean it's a strong percentage. Yeah. Um,

953
00:53:59,640 --> 00:54:02,319
Speaker 1: it's right above a big spring, so there's a big

954
00:54:02,320 --> 00:54:05,319
Speaker 1: spring coming out of the hillside and it's um about

955
00:54:05,360 --> 00:54:08,200
Speaker 1: halfway down a north slope, so it's in dense shade,

956
00:54:08,640 --> 00:54:10,880
Speaker 1: and it's right above water, so they can get a drink.

957
00:54:10,920 --> 00:54:13,520
Speaker 1: And it's also cooler like that cool water. You go

958
00:54:13,680 --> 00:54:17,000
Speaker 1: there and you just notice it's five to ten degrees

959
00:54:17,080 --> 00:54:20,600
Speaker 1: cooler than if you were at the top of the hill. Um.

960
00:54:20,719 --> 00:54:23,200
Speaker 1: It's harder than heck to hunt because it's you know,

961
00:54:23,320 --> 00:54:26,279
Speaker 1: kind of in a hole. But I think I think

962
00:54:26,320 --> 00:54:28,360
Speaker 1: I can hunt one side of it with a south wind,

963
00:54:28,600 --> 00:54:31,400
Speaker 1: which of course they favor. With a south wind not

964
00:54:31,560 --> 00:54:34,480
Speaker 1: so much, I think, because the wind kind of swirls.

965
00:54:34,600 --> 00:54:36,920
Speaker 1: I think it's mostly the south wind is hot, you know,

966
00:54:37,239 --> 00:54:41,880
Speaker 1: associated with hot weather. So um. You know. So it's

967
00:54:41,880 --> 00:54:45,760
Speaker 1: that's the big three. It's time of year, temperature, wind direction,

968
00:54:46,560 --> 00:54:50,200
Speaker 1: so um to me and everything else Like I used

969
00:54:50,239 --> 00:54:53,200
Speaker 1: to think, you know, it's pressure, but pressure pressure doesn't

970
00:54:53,200 --> 00:54:56,719
Speaker 1: dictate where a deer is. It might dictate how much

971
00:54:56,760 --> 00:54:59,880
Speaker 1: they're interested in food. UM, so they might get up

972
00:55:00,120 --> 00:55:03,240
Speaker 1: little earlier, UM if it's a good high pressure system.

973
00:55:03,320 --> 00:55:07,920
Speaker 1: But it's not a big UM. It's not quite the

974
00:55:08,000 --> 00:55:11,399
Speaker 1: driver that I thought it was. And people should keep

975
00:55:11,400 --> 00:55:15,000
Speaker 1: in mind, you know, pressure and temperature UM and wind

976
00:55:15,080 --> 00:55:17,759
Speaker 1: or and wind speed actually all they all go hand

977
00:55:17,800 --> 00:55:19,960
Speaker 1: in hand. That they get too complicated. But you know,

978
00:55:19,960 --> 00:55:22,720
Speaker 1: when you have screaming hard winds, your temper, your brometer

979
00:55:22,800 --> 00:55:25,759
Speaker 1: is probably moving because that's you know, wind is air

980
00:55:25,840 --> 00:55:28,200
Speaker 1: molecules going from one spot to another. And the reason

981
00:55:28,239 --> 00:55:30,840
Speaker 1: they're doing that is probably because between a low pressure

982
00:55:30,840 --> 00:55:33,600
Speaker 1: system and a high pressure system, you know, so so

983
00:55:33,960 --> 00:55:37,839
Speaker 1: that they're correlated. You know. UM. Wind speeds another one UM,

984
00:55:37,920 --> 00:55:42,120
Speaker 1: not just wind direction, the wind speed UM. The harder

985
00:55:42,200 --> 00:55:45,319
Speaker 1: the wind, the lower the deer getting the hills. In

986
00:55:45,360 --> 00:55:50,400
Speaker 1: my experience, UM, especially with cold winds, but in general

987
00:55:50,400 --> 00:55:53,680
Speaker 1: all winds, I see them drop down at elevation UM.

988
00:55:53,840 --> 00:55:56,480
Speaker 1: And if it's you know, it's a really screaming cold wind,

989
00:55:56,680 --> 00:56:00,320
Speaker 1: they may be down in those valleys. UM temper early

990
00:56:00,760 --> 00:56:03,360
Speaker 1: to seek shelter. So I've I've got a spot or

991
00:56:03,360 --> 00:56:06,439
Speaker 1: two that if I know, I'm a big cold front

992
00:56:06,520 --> 00:56:08,839
Speaker 1: during the rut with at least twenty mile in our

993
00:56:09,640 --> 00:56:12,520
Speaker 1: wind gus, I'm getting down there and the rutted on

994
00:56:12,680 --> 00:56:14,799
Speaker 1: down down in the valleys, and I could be up

995
00:56:14,800 --> 00:56:16,279
Speaker 1: on the ridge and freezing my butt off and I

996
00:56:16,280 --> 00:56:21,239
Speaker 1: wouldn't see a thing. So um, stuff like that. So,

997
00:56:21,640 --> 00:56:27,440
Speaker 1: if I'm trying to implement a tracking system kind of

998
00:56:27,480 --> 00:56:30,480
Speaker 1: like you have here, um, and I'm starting to think

999
00:56:30,520 --> 00:56:34,640
Speaker 1: about patterning locations in this kind of way, what are

1000
00:56:34,680 --> 00:56:37,880
Speaker 1: some key things to make sure I'm doing or I

1001
00:56:37,920 --> 00:56:40,279
Speaker 1: mean there's the basics, track a bunch of data, put

1002
00:56:40,320 --> 00:56:43,000
Speaker 1: it in here. Is there anything or any mistakes you

1003
00:56:43,040 --> 00:56:45,359
Speaker 1: hear people commonly make when they talk about how they're

1004
00:56:45,360 --> 00:56:48,360
Speaker 1: trying to track dear movement activity or any anything you

1005
00:56:48,400 --> 00:56:51,920
Speaker 1: haven't mentioned to just kind of tie up the tracking

1006
00:56:51,960 --> 00:56:57,080
Speaker 1: part of this. Yeah, I guess that, in my opinion,

1007
00:56:57,200 --> 00:56:59,680
Speaker 1: the most common mistake is trying to just follow around

1008
00:56:59,680 --> 00:57:02,560
Speaker 1: it into visual deer. Like if you it's okay to

1009
00:57:02,600 --> 00:57:05,520
Speaker 1: try to go after an individual deer, but you need

1010
00:57:05,560 --> 00:57:10,319
Speaker 1: to fill the approach of that instead of trying to, um,

1011
00:57:10,400 --> 00:57:13,320
Speaker 1: you know, uh, just draw a line on a map

1012
00:57:13,400 --> 00:57:15,920
Speaker 1: for where he's where he's going. And I know there's

1013
00:57:16,160 --> 00:57:19,120
Speaker 1: some tools that have been marketed for that it's really

1014
00:57:19,120 --> 00:57:21,280
Speaker 1: you need to look at like where is he frequenting?

1015
00:57:21,880 --> 00:57:25,080
Speaker 1: And then look at those spots and why are he frequenting? UM?

1016
00:57:25,440 --> 00:57:28,280
Speaker 1: You know, in what conditions is he frequenting them? UM?

1017
00:57:28,320 --> 00:57:31,120
Speaker 1: Instead of UM looking at it from the you know,

1018
00:57:31,400 --> 00:57:33,640
Speaker 1: I guess trying to just project out a line on

1019
00:57:33,680 --> 00:57:36,280
Speaker 1: a map or where the deer is going to go. UM,

1020
00:57:36,440 --> 00:57:39,000
Speaker 1: you look at like, well, a majority of the time

1021
00:57:39,320 --> 00:57:43,360
Speaker 1: he's over you know, on this ridge system. UM. When

1022
00:57:44,160 --> 00:57:46,800
Speaker 1: you know, when it's you know, late October and it's

1023
00:57:46,840 --> 00:57:49,640
Speaker 1: cold weather, and then when it was warm weather, I

1024
00:57:49,680 --> 00:57:52,000
Speaker 1: would get him over here, you know, more down in

1025
00:57:52,040 --> 00:57:54,880
Speaker 1: this bottom you know that's UM. I find a lot

1026
00:57:54,920 --> 00:58:00,480
Speaker 1: more UM value in so UM. If you try to

1027
00:58:00,560 --> 00:58:04,120
Speaker 1: target started to interrupt your strength thought there, If you

1028
00:58:04,160 --> 00:58:07,320
Speaker 1: were trying to target a specific buck, would you change

1029
00:58:07,360 --> 00:58:10,360
Speaker 1: anything that you're doing here from a tracking system? Would

1030
00:58:10,400 --> 00:58:14,080
Speaker 1: you add any variables? Would you ignore any of them? Uh?

1031
00:58:14,160 --> 00:58:17,720
Speaker 1: Anything you did? Just That's that's where UM and I

1032
00:58:17,800 --> 00:58:20,160
Speaker 1: and I do. You know. I I try to keep

1033
00:58:20,200 --> 00:58:24,160
Speaker 1: tabs on on individual boks. I rarely go after one

1034
00:58:24,800 --> 00:58:27,440
Speaker 1: all by itself, you know, but it's usually like, oh,

1035
00:58:27,480 --> 00:58:29,320
Speaker 1: there's a couple of bucks in this area, that I

1036
00:58:29,360 --> 00:58:35,720
Speaker 1: want to go after. UM, I think the you may

1037
00:58:35,760 --> 00:58:39,400
Speaker 1: want to bring in um more data sources, so that's

1038
00:58:39,440 --> 00:58:41,480
Speaker 1: where it comes into. Okay, well, maybe you want to

1039
00:58:41,520 --> 00:58:44,240
Speaker 1: move a few more cameras into the area. UM. The

1040
00:58:44,320 --> 00:58:46,880
Speaker 1: challenge with this, you know, I am playing the long game,

1041
00:58:47,400 --> 00:58:51,520
Speaker 1: and I know in the majority of the country, hunters

1042
00:58:51,560 --> 00:58:55,240
Speaker 1: don't especially in public land, hunters don't have the benefit

1043
00:58:55,400 --> 00:58:57,600
Speaker 1: of being able to follow a deer when he's three

1044
00:58:57,640 --> 00:59:00,720
Speaker 1: and four and five years old. And I mean most

1045
00:59:01,200 --> 00:59:03,160
Speaker 1: three year old bucks get killed in Iowa two I'm

1046
00:59:03,160 --> 00:59:06,200
Speaker 1: publicly and okay it's not you know, some get through,

1047
00:59:06,320 --> 00:59:10,720
Speaker 1: but most of them will die. Um and uh so

1048
00:59:11,560 --> 00:59:13,919
Speaker 1: uh if what I'm doing is playing a long game

1049
00:59:13,920 --> 00:59:16,920
Speaker 1: for next year, you know, I'm really not looking at

1050
00:59:16,960 --> 00:59:20,840
Speaker 1: this year's all my cameras. Um, It's I have a

1051
00:59:20,880 --> 00:59:24,040
Speaker 1: few cameras that I have in more easy to access

1052
00:59:24,440 --> 00:59:28,560
Speaker 1: and maybe a couple that I check more frequently, but

1053
00:59:28,760 --> 00:59:32,360
Speaker 1: the vast majority I hunt hang and I literally I

1054
00:59:32,400 --> 00:59:35,480
Speaker 1: will not check them until the season's over at least

1055
00:59:35,520 --> 00:59:40,000
Speaker 1: I'm done hunting and I'm I'm picking stuff up. So, UM,

1056
00:59:40,040 --> 00:59:42,800
Speaker 1: if you're looking after an individual buck, you know, if

1057
00:59:44,000 --> 00:59:47,760
Speaker 1: you you know you watch closely those good up and

1058
00:59:47,800 --> 00:59:50,320
Speaker 1: coming three year olds. A lot of times those patterns

1059
00:59:50,400 --> 00:59:53,240
Speaker 1: change a little bit, but there's still somewhat similar. Um.

1060
00:59:53,240 --> 00:59:55,760
Speaker 1: They definitely get more nocturnal and more secretive when they

1061
00:59:55,800 --> 01:00:00,800
Speaker 1: hit four UM, but their areas of and are the same.

1062
01:00:00,920 --> 01:00:04,959
Speaker 1: Sometimes they do even shift home ranges UM. But gather

1063
01:00:05,000 --> 01:00:07,360
Speaker 1: as much dead as you can. UM. You know, the

1064
01:00:07,400 --> 01:00:10,640
Speaker 1: more options you have after a bucket, the better. You

1065
01:00:10,680 --> 01:00:12,800
Speaker 1: don't want to just pin your hopes on just one

1066
01:00:12,880 --> 01:00:16,720
Speaker 1: location you're probably going to fail. UM. But especially like

1067
01:00:17,280 --> 01:00:20,760
Speaker 1: once again, I'm targeting betting, UM, you know I'm going

1068
01:00:20,800 --> 01:00:24,280
Speaker 1: to hunt that spot once. UM. Occasionally I might hunt

1069
01:00:24,320 --> 01:00:28,960
Speaker 1: a spot twice, but it's really rare. That's ninety percent

1070
01:00:29,320 --> 01:00:34,720
Speaker 1: of my public land skills have been first time since

1071
01:00:34,880 --> 01:00:37,760
Speaker 1: for that location for that year. UM. And that trend

1072
01:00:37,800 --> 01:00:40,760
Speaker 1: to expect to continue. So and I used to hunt

1073
01:00:41,120 --> 01:00:44,880
Speaker 1: spots many more times, and I just wouldn't have this success. UM.

1074
01:00:44,960 --> 01:00:48,600
Speaker 1: So now I've just basically almost quit hunting spots more

1075
01:00:48,640 --> 01:00:53,240
Speaker 1: than once UM, unless it's kind of special circumstances. And

1076
01:00:53,280 --> 01:00:55,600
Speaker 1: I really think I got in and out clean um.

1077
01:00:55,720 --> 01:00:58,360
Speaker 1: And the cleaner your entry and act the exit by

1078
01:00:58,400 --> 01:01:01,120
Speaker 1: all means, you know, there are some you can hunt repeatedly,

1079
01:01:01,160 --> 01:01:06,520
Speaker 1: but it's a very rare spot, I think, um, dear

1080
01:01:06,560 --> 01:01:11,640
Speaker 1: will Semellia, I was gonna ask going back a little bit, um,

1081
01:01:11,680 --> 01:01:16,200
Speaker 1: pivoting away from individual bucks back to locations. Um. You

1082
01:01:16,280 --> 01:01:19,640
Speaker 1: mentioned you're typically targeting betting areas, but are there any

1083
01:01:19,680 --> 01:01:21,960
Speaker 1: other types of places you try to pattern? Like? Do

1084
01:01:21,960 --> 01:01:25,440
Speaker 1: you also set you know, fall like five months long

1085
01:01:25,480 --> 01:01:30,320
Speaker 1: trail camera sets over typical funnels or anything like that

1086
01:01:30,320 --> 01:01:32,440
Speaker 1: that you want to see what's happening year after year

1087
01:01:32,480 --> 01:01:35,000
Speaker 1: and you can get an annual pattern off anything other

1088
01:01:35,040 --> 01:01:38,960
Speaker 1: than betting areas. Yeah, Um, yeah, that's it's usually a

1089
01:01:38,960 --> 01:01:43,920
Speaker 1: shorter window because bucks it's all about betting outside of

1090
01:01:44,000 --> 01:01:46,560
Speaker 1: the rut, and even during the rut it's still all

1091
01:01:46,600 --> 01:01:49,040
Speaker 1: about betting, but sometimes it's not just about their betting,

1092
01:01:49,040 --> 01:01:52,320
Speaker 1: it's about their dough betting. So um. Usually the travel

1093
01:01:52,440 --> 01:01:55,600
Speaker 1: routes that I'm targeting still connect to betting areas, they

1094
01:01:55,640 --> 01:01:59,040
Speaker 1: doe betting areas and such. But I do put cameras

1095
01:01:59,360 --> 01:02:04,960
Speaker 1: um like um on good really good funnels. Um. Not

1096
01:02:05,120 --> 01:02:08,840
Speaker 1: usually the super obvious funnels, mostly because in public land

1097
01:02:08,960 --> 01:02:12,280
Speaker 1: other people are always hunting them. Um. The classic inside

1098
01:02:12,280 --> 01:02:17,600
Speaker 1: corners and stuff. I just I just don't see fully

1099
01:02:17,640 --> 01:02:21,240
Speaker 1: mature bucks using them regularly on public land. Yeah, they

1100
01:02:21,320 --> 01:02:23,800
Speaker 1: may occasionally, and you can occasionally kill one, but I

1101
01:02:23,840 --> 01:02:27,120
Speaker 1: have a lot of higher odds getting into the more um,

1102
01:02:27,200 --> 01:02:29,480
Speaker 1: the harder to c funnels. Like you've got a really

1103
01:02:29,520 --> 01:02:32,240
Speaker 1: long ridge system, and on the leeward side there's this

1104
01:02:32,320 --> 01:02:35,880
Speaker 1: great big ditch and basically all the deer bottlenecked into

1105
01:02:35,880 --> 01:02:39,000
Speaker 1: a ditch crossing, you know, a third of the way

1106
01:02:39,200 --> 01:02:41,760
Speaker 1: down from the top, or maybe there's a fence line,

1107
01:02:41,920 --> 01:02:43,760
Speaker 1: you know, and a tree fell across the fence line,

1108
01:02:44,040 --> 01:02:48,000
Speaker 1: and so that's that crossing on the leeward side. UM.

1109
01:02:48,040 --> 01:02:49,920
Speaker 1: So I'm looking for things like that, and I'll put

1110
01:02:49,920 --> 01:02:52,040
Speaker 1: a camera and say, you know, maybe there's betting up

1111
01:02:52,040 --> 01:02:53,640
Speaker 1: the ridge and bending down the ridge, so I know,

1112
01:02:53,800 --> 01:02:56,960
Speaker 1: deer coming back back and forth through and I'll monitor that.

1113
01:02:57,320 --> 01:03:01,720
Speaker 1: So it's not always you know too, it's got to

1114
01:03:01,760 --> 01:03:05,040
Speaker 1: do with betting, but it's it's sometimes it's uh bucks

1115
01:03:05,040 --> 01:03:10,680
Speaker 1: behavior chasing dose um and seeking dose after you know,

1116
01:03:10,840 --> 01:03:14,480
Speaker 1: around the betting areas. So yeah, I do, UM, I

1117
01:03:14,560 --> 01:03:17,480
Speaker 1: do that as well, UM and I do. I don't

1118
01:03:17,840 --> 01:03:22,080
Speaker 1: have much luck trying to determine trends for deer coming

1119
01:03:22,080 --> 01:03:25,880
Speaker 1: out and food sources. Um. So you know, I have occasionally,

1120
01:03:25,920 --> 01:03:29,200
Speaker 1: occasionally do have cameras watching food sources, but I don't

1121
01:03:29,280 --> 01:03:33,560
Speaker 1: usually you I don't usually look at that data that close. Um,

1122
01:03:33,600 --> 01:03:37,200
Speaker 1: just because deer have to eat, so they come out

1123
01:03:37,200 --> 01:03:40,320
Speaker 1: and eat. Um. And they may they may be more

1124
01:03:40,360 --> 01:03:44,120
Speaker 1: likely to come out during the day if it's optimal conditions,

1125
01:03:44,160 --> 01:03:47,040
Speaker 1: then they're betting closer by. But um, I have a

1126
01:03:47,080 --> 01:03:51,440
Speaker 1: lot better luck looking at betting trends than if they're

1127
01:03:51,480 --> 01:03:53,640
Speaker 1: coming out and eating a food source. You know, usually

1128
01:03:53,720 --> 01:03:55,439
Speaker 1: they're they're going to come out and eat every night.

1129
01:03:55,680 --> 01:03:58,160
Speaker 1: Maybe maybe you can find a trend er. Oh you

1130
01:03:58,200 --> 01:03:59,919
Speaker 1: came out in daylight and it was a big cold,

1131
01:04:00,320 --> 01:04:04,440
Speaker 1: that kind of thing. But um. And also, um, you

1132
01:04:04,480 --> 01:04:07,560
Speaker 1: actually end up with kind of too much data when

1133
01:04:07,600 --> 01:04:09,760
Speaker 1: you're looking at food sources because if they're on camera

1134
01:04:09,840 --> 01:04:12,520
Speaker 1: every single day, Okay, they're on camera every single day,

1135
01:04:12,560 --> 01:04:14,959
Speaker 1: what are you gonna do about it? Kind of thing? Um.

1136
01:04:15,000 --> 01:04:18,440
Speaker 1: Whereas betting, Oh, you know, he was using this betting

1137
01:04:18,440 --> 01:04:21,160
Speaker 1: area for four days out of a seven day window,

1138
01:04:21,800 --> 01:04:23,800
Speaker 1: and then he wasn't there for two weeks and then

1139
01:04:23,840 --> 01:04:27,520
Speaker 1: he was back. Um. That tells me a lot more

1140
01:04:28,320 --> 01:04:30,400
Speaker 1: so I interrupted you when you were talking. There's some

1141
01:04:30,560 --> 01:04:32,840
Speaker 1: steaks people made. Did you finish up what you wanted

1142
01:04:32,880 --> 01:04:37,080
Speaker 1: to cover? As far as where do you if you

1143
01:04:37,160 --> 01:04:42,160
Speaker 1: did hut by the moon? Hunting by the moon, I'll

1144
01:04:42,200 --> 01:04:43,680
Speaker 1: come out and say it. A lot of people I

1145
01:04:43,760 --> 01:04:45,800
Speaker 1: know on the beast are probably mad about that. But

1146
01:04:45,840 --> 01:04:50,440
Speaker 1: I don't know. I just I I A lot of

1147
01:04:50,440 --> 01:04:53,280
Speaker 1: people have had success doing it, but man, I just um,

1148
01:04:53,480 --> 01:04:56,840
Speaker 1: after looking at a couple of thousand data points, I

1149
01:04:56,920 --> 01:05:00,600
Speaker 1: just have not found any correlation between where the moon

1150
01:05:00,760 --> 01:05:05,480
Speaker 1: is and when they're moving. Um. It's and I'm not

1151
01:05:05,560 --> 01:05:08,200
Speaker 1: to say maybe it does exist, but the weather is

1152
01:05:08,200 --> 01:05:11,360
Speaker 1: in order of magnitude more important. UM, if he's going

1153
01:05:11,400 --> 01:05:13,680
Speaker 1: to get up that and and things like hunting pressure,

1154
01:05:13,800 --> 01:05:16,640
Speaker 1: you know, and that important. I've I've talked about all

1155
01:05:16,680 --> 01:05:20,480
Speaker 1: this stuff. This like hunting pressure. If you're looking at

1156
01:05:20,480 --> 01:05:24,320
Speaker 1: big three, UM like that probably comes first. Honestly, with

1157
01:05:24,440 --> 01:05:27,040
Speaker 1: a mature box. Once he's reached that age, he will

1158
01:05:27,080 --> 01:05:32,160
Speaker 1: not move if he doesn't feel UM safe. So UM.

1159
01:05:33,400 --> 01:05:37,240
Speaker 1: For example, in Iowa, UM we have an early muzzleloader

1160
01:05:37,280 --> 01:05:40,720
Speaker 1: season which runs third week of October. Some just as

1161
01:05:40,760 --> 01:05:43,120
Speaker 1: the ruts starting the heat up or second week October.

1162
01:05:43,160 --> 01:05:46,280
Speaker 1: I guess usually something like that, Um, depends on how

1163
01:05:46,280 --> 01:05:49,760
Speaker 1: the calendar lays out. UM. And I've learned, you know,

1164
01:05:50,040 --> 01:05:54,360
Speaker 1: there's certain areas that muzzleloader hunter hunters will frequent and

1165
01:05:55,520 --> 01:05:58,560
Speaker 1: I won't get anything on my cameras in that time

1166
01:05:58,560 --> 01:06:00,920
Speaker 1: period and for a little while after towards and then dear,

1167
01:06:00,960 --> 01:06:05,280
Speaker 1: I'll start showing back up um and others that Actually

1168
01:06:05,320 --> 01:06:07,800
Speaker 1: I see pressure betting even here. Uh you know, I

1169
01:06:07,880 --> 01:06:10,160
Speaker 1: over right now we don't have quite the pressure that say,

1170
01:06:10,560 --> 01:06:13,840
Speaker 1: uh you deal with in Michigan, certainly not in public

1171
01:06:13,880 --> 01:06:18,120
Speaker 1: land there, UM, but I see pressure betting meeting um.

1172
01:06:18,120 --> 01:06:21,600
Speaker 1: When there's a bunch of people in the woods, certain areas,

1173
01:06:21,760 --> 01:06:25,040
Speaker 1: deer really frequent because humans just do not get in there.

1174
01:06:25,120 --> 01:06:27,280
Speaker 1: It's just too nasty or you have to cross water

1175
01:06:27,480 --> 01:06:31,240
Speaker 1: something like that. They really um you know, and when

1176
01:06:31,320 --> 01:06:33,600
Speaker 1: pete humans aren't in the woods, you may not see

1177
01:06:33,640 --> 01:06:37,760
Speaker 1: as many deer or mature buck sightings in those areas.

1178
01:06:38,080 --> 01:06:40,720
Speaker 1: So UM, it's been eye opening to me. I have

1179
01:06:40,720 --> 01:06:42,880
Speaker 1: a couple of spots that I like to hunt during

1180
01:06:42,880 --> 01:06:46,280
Speaker 1: that early muscle or season because human, no other human

1181
01:06:46,320 --> 01:06:49,880
Speaker 1: other than me gets anywhere near there, and uh I

1182
01:06:50,040 --> 01:06:54,400
Speaker 1: have um uh, you know, and or maybe they do

1183
01:06:54,480 --> 01:06:56,720
Speaker 1: get someone near there, but they still can't see it.

1184
01:06:56,760 --> 01:06:59,280
Speaker 1: They're not hunting it, and dear no, they can move

1185
01:06:59,360 --> 01:07:02,160
Speaker 1: just that extra two hundred yards or hundred yards and

1186
01:07:02,200 --> 01:07:05,440
Speaker 1: be completely secure after the humans stick up the woods

1187
01:07:05,440 --> 01:07:09,160
Speaker 1: for a few days. An interesting thing is I'm hearing

1188
01:07:09,200 --> 01:07:14,439
Speaker 1: you talk about all this is I follow a lot

1189
01:07:14,480 --> 01:07:17,760
Speaker 1: of this data. Similarly, I pay attention to a lot

1190
01:07:17,760 --> 01:07:20,080
Speaker 1: of these things, and I really think there's something to it.

1191
01:07:20,360 --> 01:07:22,800
Speaker 1: But we're kind of using it in slightly different ways.

1192
01:07:23,120 --> 01:07:25,040
Speaker 1: It seems like, correct me if I'm wrong here. I'm

1193
01:07:25,040 --> 01:07:26,520
Speaker 1: curious to hear if you think about it in the

1194
01:07:26,560 --> 01:07:29,680
Speaker 1: same way I do. Sometimes. Um, So, it sounds like

1195
01:07:29,760 --> 01:07:31,560
Speaker 1: most of the time you're looking at this data to

1196
01:07:31,560 --> 01:07:34,880
Speaker 1: help you know where to hunt, like when to hunt

1197
01:07:34,880 --> 01:07:39,120
Speaker 1: which spot, and you're finding that a certain wind direction

1198
01:07:39,240 --> 01:07:41,840
Speaker 1: or a certain temperature will lead to the deer movement

1199
01:07:42,000 --> 01:07:44,680
Speaker 1: or the deer being better than a certain location coming

1200
01:07:44,680 --> 01:07:47,880
Speaker 1: to a certain location. I've always thought more so of

1201
01:07:47,920 --> 01:07:50,919
Speaker 1: looking at this kind of data to tell me when

1202
01:07:51,000 --> 01:07:53,840
Speaker 1: the chances of the very best or when's the very

1203
01:07:53,880 --> 01:07:55,800
Speaker 1: best chance that I'm mature buck will get up moved

1204
01:07:55,880 --> 01:07:58,720
Speaker 1: during daylight, and then when I get those special days,

1205
01:07:59,200 --> 01:08:01,680
Speaker 1: then I target like my sweet spots, like the spots

1206
01:08:01,680 --> 01:08:04,240
Speaker 1: where I think are like the highest odds for other

1207
01:08:04,280 --> 01:08:08,600
Speaker 1: reasons though. Um yeah, so I'm probably missing the boat

1208
01:08:08,600 --> 01:08:10,880
Speaker 1: a little bit. Maybe, but maybe is what I do

1209
01:08:11,320 --> 01:08:14,240
Speaker 1: in your box of tricks too? Is that how you're

1210
01:08:14,240 --> 01:08:20,240
Speaker 1: thinking about as well? Yeah, um, there are, yeah, there

1211
01:08:20,560 --> 01:08:25,320
Speaker 1: are those optimal days, but I've I've kind of learned, um,

1212
01:08:25,520 --> 01:08:28,439
Speaker 1: there's optimal. Yeah. We definitely are looking at it a

1213
01:08:28,439 --> 01:08:31,160
Speaker 1: little differently, you know, because I I see optimal days

1214
01:08:31,160 --> 01:08:33,799
Speaker 1: for optimal spots, you know, and maybe you're just stopping

1215
01:08:33,800 --> 01:08:35,840
Speaker 1: at the optimal days period and then you're going to

1216
01:08:35,840 --> 01:08:38,840
Speaker 1: go try to find a spot. Um. So everything ties

1217
01:08:38,880 --> 01:08:44,400
Speaker 1: back to like location for me. Um and uh yeah,

1218
01:08:44,439 --> 01:08:47,000
Speaker 1: so that I guess I try to complete that loop

1219
01:08:47,720 --> 01:08:52,880
Speaker 1: um so um. And I maybe my thinking has uh

1220
01:08:53,200 --> 01:08:55,720
Speaker 1: you know, just kind of can evolved a little bit

1221
01:08:55,760 --> 01:08:58,800
Speaker 1: from that because I did used to be like, oh man,

1222
01:08:58,880 --> 01:09:01,599
Speaker 1: there's a cold front hit next week, you know, get

1223
01:09:01,600 --> 01:09:03,640
Speaker 1: in the woods. You know, this is the time to

1224
01:09:03,720 --> 01:09:08,000
Speaker 1: hunt my best stand. Um you know, quote unquote best. Well,

1225
01:09:08,160 --> 01:09:10,240
Speaker 1: now I'm like, what is my best stand? I have

1226
01:09:10,280 --> 01:09:12,400
Speaker 1: a best stand. Every every single day that I want

1227
01:09:12,400 --> 01:09:14,080
Speaker 1: to hunt, I have a best stand. I like that.

1228
01:09:14,120 --> 01:09:18,280
Speaker 1: So yeah, so that's uh and I think that really

1229
01:09:18,320 --> 01:09:22,400
Speaker 1: did help help me, um, start to think like that.

1230
01:09:23,360 --> 01:09:26,560
Speaker 1: And I guess it does depend on how many options

1231
01:09:26,600 --> 01:09:29,320
Speaker 1: you have, right, So someone who has ten dad acres

1232
01:09:29,320 --> 01:09:31,080
Speaker 1: a public land can probably have a whole bunch of

1233
01:09:31,080 --> 01:09:34,599
Speaker 1: different options. Instead for whatever reason, you just hunt fifteen

1234
01:09:34,600 --> 01:09:40,400
Speaker 1: acres you own or something, all of a sudden you're limited. Yeah,

1235
01:09:40,479 --> 01:09:43,439
Speaker 1: and that and that, um, you know, like just to

1236
01:09:43,439 --> 01:09:47,680
Speaker 1: be like clear, I um, it's there's a lot of

1237
01:09:47,720 --> 01:09:50,720
Speaker 1: blanket statements made and I have a certain strategy and

1238
01:09:50,760 --> 01:09:54,559
Speaker 1: it's not for everybody, and I I do like and

1239
01:09:54,640 --> 01:09:57,479
Speaker 1: Iowa doesn't have much public land period. That's part of

1240
01:09:57,479 --> 01:10:00,200
Speaker 1: the reason I love to go up into Wisconsin because

1241
01:10:00,200 --> 01:10:02,040
Speaker 1: then I can I can really walk all day and

1242
01:10:02,040 --> 01:10:04,559
Speaker 1: still be on public land. In Iowa, that ain't happening.

1243
01:10:04,920 --> 01:10:07,920
Speaker 1: You know, thousand acre parcels humongous. You know, most of

1244
01:10:07,960 --> 01:10:11,160
Speaker 1: time it's too undern acre parcel um, so you're really limited.

1245
01:10:11,400 --> 01:10:13,960
Speaker 1: And even I know, you know, um, Michigan is even

1246
01:10:14,040 --> 01:10:16,920
Speaker 1: more partialized from listening to you know, a sixty acre

1247
01:10:16,960 --> 01:10:20,720
Speaker 1: parcel is a big parcel there southern problems, and then

1248
01:10:20,760 --> 01:10:24,599
Speaker 1: you go out east and you have a ten acreup parcel.

1249
01:10:24,680 --> 01:10:28,639
Speaker 1: That's a big parcel, you know. So it's crazy how

1250
01:10:28,720 --> 01:10:32,559
Speaker 1: different that that requires your strategy to be different. You

1251
01:10:32,600 --> 01:10:35,840
Speaker 1: know you maybe you don't have a thousand acres or

1252
01:10:35,840 --> 01:10:39,040
Speaker 1: ten thousand acres um, maybe you have one, you know,

1253
01:10:39,120 --> 01:10:42,040
Speaker 1: twenty acre parcel. I encourage you to get more options.

1254
01:10:42,120 --> 01:10:46,360
Speaker 1: More is better, but um, you may be limited. You know,

1255
01:10:46,479 --> 01:10:48,080
Speaker 1: you may be limited to where you can hunt. You

1256
01:10:48,080 --> 01:10:49,559
Speaker 1: can just go and hunt, and you have to find

1257
01:10:49,560 --> 01:10:52,680
Speaker 1: a different strategy to fit that. This, Like I've I've

1258
01:10:52,760 --> 01:10:55,559
Speaker 1: kind of developed this to fit my strategy of I

1259
01:10:55,600 --> 01:10:58,719
Speaker 1: have ten times as many spots as I can hunt

1260
01:10:58,920 --> 01:11:02,280
Speaker 1: as I have time to hunt any fall. So, um,

1261
01:11:02,640 --> 01:11:04,840
Speaker 1: how do I figure out what are the best? You know,

1262
01:11:05,040 --> 01:11:08,479
Speaker 1: I've scouted my butt off for years to find a

1263
01:11:08,520 --> 01:11:10,800
Speaker 1: bunch of really good spots and I don't have time

1264
01:11:10,800 --> 01:11:13,120
Speaker 1: to hunt them. So okay, well I have to filter

1265
01:11:13,240 --> 01:11:15,920
Speaker 1: out and try to figure out what you know and

1266
01:11:16,000 --> 01:11:19,639
Speaker 1: I and I know I'm a little bit I'm wired,

1267
01:11:19,960 --> 01:11:23,040
Speaker 1: wired a little bit different because um, I'm I'm just

1268
01:11:23,160 --> 01:11:27,320
Speaker 1: fanatical about efficiency, you know, Like I'm not a good

1269
01:11:27,320 --> 01:11:29,200
Speaker 1: spot is not good enough for me. I want the

1270
01:11:29,200 --> 01:11:32,280
Speaker 1: best spot. I want to find the best spot because

1271
01:11:32,560 --> 01:11:34,080
Speaker 1: you know it's gonna be better than the good spot.

1272
01:11:34,400 --> 01:11:38,479
Speaker 1: So um, and I'm always thinking like that. Over the

1273
01:11:38,520 --> 01:11:42,160
Speaker 1: course of you know, twenty years upon public Land, I've

1274
01:11:42,360 --> 01:11:45,800
Speaker 1: I've you know, started to I don't think I've just

1275
01:11:45,880 --> 01:11:51,200
Speaker 1: changed my thinking. Now. What I'm curious about is if

1276
01:11:51,240 --> 01:11:54,519
Speaker 1: you could now tie a big, nice bowl on this

1277
01:11:54,600 --> 01:11:58,599
Speaker 1: for me by walking me through an example or two

1278
01:11:58,880 --> 01:12:03,080
Speaker 1: about how you something from your data set that you've

1279
01:12:03,120 --> 01:12:05,400
Speaker 1: tracked over a year or two or however many years,

1280
01:12:05,720 --> 01:12:08,760
Speaker 1: and then implemented that into a hunt, um, and how

1281
01:12:08,920 --> 01:12:13,000
Speaker 1: you picked the optimal spot for the optimal time, and

1282
01:12:13,000 --> 01:12:15,639
Speaker 1: and what that all looks like. I'd love to better

1283
01:12:15,720 --> 01:12:18,160
Speaker 1: understand your whole thought process as you go through that

1284
01:12:18,200 --> 01:12:23,960
Speaker 1: and then execute the hunt. Yeah. Um, so let's see.

1285
01:12:24,479 --> 01:12:30,320
Speaker 1: It's it's ingrained. It's been ingrained into most of my Uh. Well,

1286
01:12:30,439 --> 01:12:33,200
Speaker 1: first of all, um, i haven't hunted a whole lot

1287
01:12:33,240 --> 01:12:36,720
Speaker 1: the last two years. I've killed a couple of bucks. Um.

1288
01:12:36,760 --> 01:12:39,639
Speaker 1: But um, you know, I've been going I'm working full

1289
01:12:39,680 --> 01:12:41,960
Speaker 1: time and going back to school and two little kids,

1290
01:12:42,160 --> 01:12:45,200
Speaker 1: and you know, last year I hunted seven times total,

1291
01:12:45,720 --> 01:12:48,280
Speaker 1: and it wore me out. It was pretty pathetic. UM.

1292
01:12:48,320 --> 01:12:50,200
Speaker 1: But the good news is my school is getting done.

1293
01:12:50,360 --> 01:12:51,760
Speaker 1: I'll be done with that and I'll be able to

1294
01:12:51,800 --> 01:12:53,720
Speaker 1: hit the woods a little harder here this fall. So

1295
01:12:54,320 --> 01:12:59,479
Speaker 1: I'm happy that it's happy to announce that. But UM anyway, UM,

1296
01:12:59,720 --> 01:13:04,360
Speaker 1: like even last year, I killed a buck UM and

1297
01:13:04,760 --> 01:13:07,600
Speaker 1: it was it was October. It was right on that

1298
01:13:07,680 --> 01:13:13,519
Speaker 1: full moon. UM, so I was expecting UM, I was

1299
01:13:13,600 --> 01:13:17,800
Speaker 1: expecting more pre rod activity because of the full moon. UM.

1300
01:13:18,000 --> 01:13:24,000
Speaker 1: There there's a spot that is it's a trial corridor UM,

1301
01:13:24,040 --> 01:13:27,240
Speaker 1: and it's used to a high frequency for southerly south

1302
01:13:27,280 --> 01:13:29,720
Speaker 1: and southeast winds for betting. So it's kind of a

1303
01:13:29,800 --> 01:13:33,840
Speaker 1: warmer weather betting. And it happened to be UM at

1304
01:13:33,880 --> 01:13:38,120
Speaker 1: that day. I think it was south south or southeast

1305
01:13:38,200 --> 01:13:43,160
Speaker 1: wind UM. And uh I knew that from turro camera

1306
01:13:43,200 --> 01:13:47,560
Speaker 1: sitting there for the lab for from the fall previous UM.

1307
01:13:47,640 --> 01:13:51,400
Speaker 1: And so bucks preferred to bed there when it was

1308
01:13:51,400 --> 01:13:56,240
Speaker 1: a little warmer than average. UM. And uh they did.

1309
01:13:56,600 --> 01:13:59,360
Speaker 1: They usually betted there when uh, you know the pre

1310
01:13:59,720 --> 01:14:03,280
Speaker 1: pre I was hunting heating up. So in the October

1311
01:14:03,400 --> 01:14:06,519
Speaker 1: twentie thirty, the pre rod usually heating up. But it's

1312
01:14:06,520 --> 01:14:09,080
Speaker 1: not linear, you know, as everybody knows who's hunted that

1313
01:14:09,120 --> 01:14:12,479
Speaker 1: time period. It's really um on off hot cold. You're

1314
01:14:12,520 --> 01:14:15,280
Speaker 1: either seeing nothing or you're seeing some really good action.

1315
01:14:15,320 --> 01:14:18,320
Speaker 1: There doesn't seem to be much in the middle. UM.

1316
01:14:18,439 --> 01:14:20,800
Speaker 1: I love the pre rod. That's my favorite time to hunt.

1317
01:14:21,080 --> 01:14:27,080
Speaker 1: Um that the last half of October really um so UM.

1318
01:14:27,280 --> 01:14:29,719
Speaker 1: And I knew that buck was in the area. UM

1319
01:14:29,840 --> 01:14:34,720
Speaker 1: and you know, I still remember he you know, he

1320
01:14:34,840 --> 01:14:37,720
Speaker 1: came in and I wish i'd gotten a camera, and I,

1321
01:14:38,120 --> 01:14:40,640
Speaker 1: in general, I don't have any interest in recording and

1322
01:14:40,880 --> 01:14:44,040
Speaker 1: videoing my hunts. But I shot him and he ran

1323
01:14:44,120 --> 01:14:45,880
Speaker 1: and he tipped over right in front of the rising

1324
01:14:45,880 --> 01:14:47,840
Speaker 1: full moon, you know. So it was coming up over

1325
01:14:47,840 --> 01:14:53,240
Speaker 1: the horizon just a few minutes before um the uh

1326
01:14:53,720 --> 01:15:00,200
Speaker 1: the closing time, you know. UM. So that's one example. UM,

1327
01:15:00,400 --> 01:15:07,320
Speaker 1: I've a few years let's see, UM a few years

1328
01:15:07,360 --> 01:15:10,080
Speaker 1: before that. I like, that's that's a good example because

1329
01:15:10,080 --> 01:15:15,639
Speaker 1: it was a it was a good um a number

1330
01:15:15,640 --> 01:15:19,519
Speaker 1: of years ago. UM. I used to think that deer

1331
01:15:19,600 --> 01:15:22,960
Speaker 1: didn't like to move in the um when the moon

1332
01:15:23,080 --> 01:15:26,720
Speaker 1: was full. And I also didn't like to think all right,

1333
01:15:26,880 --> 01:15:32,200
Speaker 1: I didn't think that dear really um um moved as

1334
01:15:32,320 --> 01:15:34,680
Speaker 1: much when it was warm out until I figured out, hey,

1335
01:15:34,720 --> 01:15:38,320
Speaker 1: they're really moving differently. Maybe they're moving slightly less, but

1336
01:15:38,400 --> 01:15:40,439
Speaker 1: like they're moving differently, and you can get on deer

1337
01:15:40,479 --> 01:15:46,080
Speaker 1: when it's warm out. So um, it was definitely eye opening.

1338
01:15:46,160 --> 01:15:48,040
Speaker 1: You know, it's like, hey, I can have success. It

1339
01:15:48,120 --> 01:15:51,960
Speaker 1: was you know, seventy degrees as a daytime high in

1340
01:15:52,240 --> 01:15:54,519
Speaker 1: late October, which is a little warmer than average for

1341
01:15:54,800 --> 01:15:57,680
Speaker 1: this part of the country. And um, you know, there

1342
01:15:57,760 --> 01:16:01,080
Speaker 1: was a big four year old, you know, twenty two

1343
01:16:01,080 --> 01:16:04,960
Speaker 1: inch wide nine pointer you know, up um and he

1344
01:16:05,080 --> 01:16:08,160
Speaker 1: was up well before dark um and then came wandering in.

1345
01:16:08,479 --> 01:16:13,240
Speaker 1: So um. It's it's really ingrained. And just about every

1346
01:16:13,360 --> 01:16:17,960
Speaker 1: single you know, one of my hunts has has that background. Um.

1347
01:16:18,439 --> 01:16:22,439
Speaker 1: Let's see that. I can't remember what I killed the

1348
01:16:22,520 --> 01:16:26,439
Speaker 1: year before I shot something, but it was another story

1349
01:16:26,520 --> 01:16:31,800
Speaker 1: like that. They all kind of lending together. Yeah, yeah,

1350
01:16:32,000 --> 01:16:34,720
Speaker 1: that um. That example I shared a couple of years

1351
01:16:34,720 --> 01:16:37,040
Speaker 1: ago up in Wisconsin where you know, all of a sudden,

1352
01:16:37,080 --> 01:16:39,519
Speaker 1: the rut was on full boar. It was hot out

1353
01:16:39,640 --> 01:16:41,840
Speaker 1: and there was on full boar in the islands out

1354
01:16:41,840 --> 01:16:45,280
Speaker 1: in the swamp, whereas there was no activity out around

1355
01:16:45,280 --> 01:16:48,479
Speaker 1: the perimeter the swamp, you know. And it wasn't that

1356
01:16:48,520 --> 01:16:51,080
Speaker 1: there was hunting pressure right there. It was just they

1357
01:16:51,080 --> 01:16:54,880
Speaker 1: were out where it was cold cool. Um. And that's

1358
01:16:54,880 --> 01:16:58,280
Speaker 1: been really eye opening. So UM, two or three years ago,

1359
01:16:58,320 --> 01:17:02,599
Speaker 1: I shot a two years that was another really good example. UM.

1360
01:17:02,960 --> 01:17:04,800
Speaker 1: I showed this is a late season hunt, and it

1361
01:17:04,840 --> 01:17:07,320
Speaker 1: was a muzzloader hunt. It wasn't a bow hunt, but

1362
01:17:07,720 --> 01:17:11,400
Speaker 1: we had a real cold snap and the temperature was

1363
01:17:11,600 --> 01:17:16,880
Speaker 1: about um, how about negative five degrees or something like

1364
01:17:16,920 --> 01:17:23,000
Speaker 1: that daytime high. UM. And I knew deer Um, I knew.

1365
01:17:23,120 --> 01:17:26,240
Speaker 1: I knew there was a couple of betting areas that

1366
01:17:27,000 --> 01:17:30,479
Speaker 1: we're more open. They were facing the south, Um they

1367
01:17:30,520 --> 01:17:33,800
Speaker 1: were the bucks still fell secure, they betted in them. UM.

1368
01:17:33,840 --> 01:17:36,040
Speaker 1: And I actually, um, you know, saw him from a

1369
01:17:36,080 --> 01:17:40,120
Speaker 1: distance and uh shot him there. Uh you know, I

1370
01:17:40,240 --> 01:17:42,080
Speaker 1: was on the far side of the valley, watching the

1371
01:17:42,120 --> 01:17:45,000
Speaker 1: sunlit side of the valley and there was nothing, no

1372
01:17:45,120 --> 01:17:47,280
Speaker 1: activity on my side of the valley because I was

1373
01:17:47,320 --> 01:17:50,639
Speaker 1: and it was cold, I was I was freezing um

1374
01:17:51,080 --> 01:17:53,599
Speaker 1: in on the shaded side of the valley. The sun

1375
01:17:53,680 --> 01:17:56,880
Speaker 1: was kind of behind me, you know. Uh, shining on

1376
01:17:56,960 --> 01:18:00,960
Speaker 1: that southerly facing slope and you know there he was,

1377
01:18:01,360 --> 01:18:04,840
Speaker 1: um and uh, you know picked him off, and it

1378
01:18:04,880 --> 01:18:07,439
Speaker 1: was it was I wouldn't have been there if it

1379
01:18:07,520 --> 01:18:10,000
Speaker 1: wasn't for the brew of cold weather we were having.

1380
01:18:10,040 --> 01:18:11,880
Speaker 1: And there was a stiff wind, so there's a pretty

1381
01:18:11,880 --> 01:18:17,240
Speaker 1: screaming windshell too. So um yeah, it's it's really you know,

1382
01:18:17,560 --> 01:18:21,160
Speaker 1: it's all conditional like that. So I feel like this

1383
01:18:21,200 --> 01:18:25,720
Speaker 1: is a great I mean, like my big takeaway for

1384
01:18:25,800 --> 01:18:30,920
Speaker 1: me personally is you know, it's good to be smart

1385
01:18:30,960 --> 01:18:32,880
Speaker 1: with the timing of your hunts and you don't want

1386
01:18:32,880 --> 01:18:36,639
Speaker 1: to overhunt things and you want to be targeted. Of course. Um,

1387
01:18:36,680 --> 01:18:39,240
Speaker 1: that doesn't mean though that you shouldn't hunt a lot.

1388
01:18:39,640 --> 01:18:42,080
Speaker 1: If you have a lot of locations to hunt, you

1389
01:18:42,160 --> 01:18:45,320
Speaker 1: can find ways to pattern specific locations to know the

1390
01:18:45,439 --> 01:18:48,280
Speaker 1: right spot to hunt, regardless of whether you have the

1391
01:18:48,360 --> 01:18:52,200
Speaker 1: stereotypical dynamite weather big cold front and high pressure, or

1392
01:18:52,240 --> 01:18:54,800
Speaker 1: maybe you've got hot weather and it's windy. But if

1393
01:18:54,800 --> 01:18:59,160
Speaker 1: you happen to have been tracking all these different locations,

1394
01:18:59,479 --> 01:19:02,719
Speaker 1: you'll start to see these patterns laid up with certain

1395
01:19:02,880 --> 01:19:04,680
Speaker 1: maybe on the hot days are over here on the

1396
01:19:04,720 --> 01:19:07,680
Speaker 1: cool great coal friends, are over here and all of

1397
01:19:07,720 --> 01:19:09,760
Speaker 1: a sudden, you maybe all of a sudden double your

1398
01:19:09,760 --> 01:19:12,759
Speaker 1: opportunities because you're not just paying attention to those spots

1399
01:19:12,760 --> 01:19:14,680
Speaker 1: that are great for coal friends that we all key

1400
01:19:14,680 --> 01:19:19,000
Speaker 1: in on. Um, yeah, it's not you know, I'm not

1401
01:19:19,080 --> 01:19:22,360
Speaker 1: saying don't hunt, you know, like that's another like, oh,

1402
01:19:22,439 --> 01:19:24,000
Speaker 1: you know, if you're not in the woods, you don't

1403
01:19:24,040 --> 01:19:26,200
Speaker 1: kill it. That's all right, But that's right. But I

1404
01:19:26,200 --> 01:19:28,800
Speaker 1: always add to that. You know, if you're not in

1405
01:19:28,840 --> 01:19:31,280
Speaker 1: the woods, you can't kill one. But if you hunt

1406
01:19:31,320 --> 01:19:34,080
Speaker 1: the wrong spot at the right spot at the wrong time,

1407
01:19:34,280 --> 01:19:38,080
Speaker 1: you're not going to kill on either. So um, like

1408
01:19:38,160 --> 01:19:41,040
Speaker 1: go hunt, hunt observation sets. That just gives you more data.

1409
01:19:41,160 --> 01:19:43,720
Speaker 1: You learn so much more are you know? Now, I

1410
01:19:43,760 --> 01:19:47,280
Speaker 1: don't have that much time, so I don't hunt observation sets.

1411
01:19:47,280 --> 01:19:49,320
Speaker 1: But that's not because I don't think it's effective. It's

1412
01:19:49,360 --> 01:19:52,479
Speaker 1: just I don't have much time. I strongly encourage people

1413
01:19:52,479 --> 01:19:53,880
Speaker 1: who go sit in a tree where they can see

1414
01:19:53,880 --> 01:19:55,840
Speaker 1: a long ways and you know, just watch what deer

1415
01:19:55,840 --> 01:19:58,439
Speaker 1: are doing. That gives you more information on stuff we're

1416
01:19:58,439 --> 01:20:01,840
Speaker 1: talking about right now. Um. But the important thing is

1417
01:20:01,880 --> 01:20:05,760
Speaker 1: when you figure out preferred conditions for a spot, stay

1418
01:20:05,800 --> 01:20:07,800
Speaker 1: the heck out of that spot until you have those

1419
01:20:07,800 --> 01:20:13,160
Speaker 1: conditions you know, on somewhere else. So one other follow

1420
01:20:13,200 --> 01:20:15,600
Speaker 1: up on this idea of if if I'm going to

1421
01:20:15,720 --> 01:20:20,320
Speaker 1: start tracking locations, um, that means we're setting cameras for

1422
01:20:20,880 --> 01:20:23,320
Speaker 1: you know, much longer maybe than the average person is

1423
01:20:23,320 --> 01:20:25,000
Speaker 1: putting them out there. So if I'm running a camera

1424
01:20:25,040 --> 01:20:26,920
Speaker 1: and put out there in August, I'm not gonna go

1425
01:20:27,000 --> 01:20:29,479
Speaker 1: pick it up till late January. Maybe. You know, that's

1426
01:20:29,600 --> 01:20:31,080
Speaker 1: that's a lot of time to put the camera out

1427
01:20:31,080 --> 01:20:35,240
Speaker 1: there for. Is there anything you've done differently for those cameras,

1428
01:20:35,280 --> 01:20:37,479
Speaker 1: whether it be how you set them up, or the

1429
01:20:37,520 --> 01:20:40,280
Speaker 1: batteries you use, or anything, any little set up tips

1430
01:20:40,280 --> 01:20:42,400
Speaker 1: that we should be thinking about if we're gonna start

1431
01:20:42,640 --> 01:20:46,439
Speaker 1: putting a bunch out for this purpose. Yeah. Um, so

1432
01:20:46,560 --> 01:20:49,080
Speaker 1: with cold weather cold water operate, if you're going to

1433
01:20:49,200 --> 01:20:52,800
Speaker 1: leave it there until the cold weather, Um, you kind

1434
01:20:52,800 --> 01:20:55,280
Speaker 1: of have to use lithium batteries. I've learned that the

1435
01:20:55,320 --> 01:20:59,720
Speaker 1: hard way. Um, you know how the standard batteries just

1436
01:21:00,240 --> 01:21:03,080
Speaker 1: they crope pretty fast, and cold weather lithium batteries are

1437
01:21:03,160 --> 01:21:06,680
Speaker 1: temperature they aren't affected by temperature. Um, you also need

1438
01:21:06,720 --> 01:21:09,880
Speaker 1: a good camera. Um, and that you know there's pros

1439
01:21:09,960 --> 01:21:11,880
Speaker 1: and cons. You're leaving there a long time, so you're

1440
01:21:11,920 --> 01:21:15,479
Speaker 1: worried about somebody walking off with it. But um, you know,

1441
01:21:15,560 --> 01:21:19,439
Speaker 1: I I like, you know, my Bushnell Trophy cam is

1442
01:21:19,439 --> 01:21:22,639
Speaker 1: my workhorse. UM. I just have had really good results

1443
01:21:22,640 --> 01:21:24,720
Speaker 1: with that. Browning are fairly good. There's a few other

1444
01:21:24,760 --> 01:21:27,200
Speaker 1: brands that I think are pretty much garbage that I

1445
01:21:27,200 --> 01:21:32,800
Speaker 1: won't mention. UM. And uh, anyway you leave it. UM.

1446
01:21:32,960 --> 01:21:36,080
Speaker 1: I like to put them up high. I've lost and

1447
01:21:36,439 --> 01:21:38,120
Speaker 1: like two years ago, I think I had my very

1448
01:21:38,120 --> 01:21:41,000
Speaker 1: first camera stole on public land. So it's going to happen,

1449
01:21:41,400 --> 01:21:44,920
Speaker 1: but it's actually very rare. UM. I put them up high. UM.

1450
01:21:45,200 --> 01:21:47,240
Speaker 1: I put a climbing stick and get them up their

1451
01:21:47,280 --> 01:21:50,439
Speaker 1: ten foot off the ground. Angled down, you actually cover

1452
01:21:50,560 --> 01:21:52,960
Speaker 1: more area. I've learned if you angle it just the

1453
01:21:53,000 --> 01:21:55,479
Speaker 1: right amount and get it up that high instead of

1454
01:21:55,479 --> 01:21:57,360
Speaker 1: hanging it, you know, three and a half feet off

1455
01:21:57,360 --> 01:22:00,160
Speaker 1: the ground like some people do, and you can your

1456
01:22:00,200 --> 01:22:03,679
Speaker 1: detection area can get a little bigger. Um. And man,

1457
01:22:03,760 --> 01:22:06,679
Speaker 1: it's very you know, I've lost a couple of cameras

1458
01:22:06,680 --> 01:22:10,639
Speaker 1: now and that's it. Um to theft UM. I've had

1459
01:22:10,680 --> 01:22:15,120
Speaker 1: a few male function. UM. I've this is completely my fault.

1460
01:22:15,400 --> 01:22:18,599
Speaker 1: I forgot to turn on UM, and my buddy, who

1461
01:22:18,720 --> 01:22:20,720
Speaker 1: was probably gonna listen to this, I forgot to turn

1462
01:22:20,720 --> 01:22:23,639
Speaker 1: it on, and I, you know, get up and walk off.

1463
01:22:23,720 --> 01:22:25,720
Speaker 1: And then three months later I walk up, you know,

1464
01:22:25,840 --> 01:22:28,360
Speaker 1: and it's still on set up mode. And you want

1465
01:22:28,360 --> 01:22:33,840
Speaker 1: to hurt yourself when you do that, let me tell you. Yeah,

1466
01:22:34,160 --> 01:22:38,759
Speaker 1: so guilty as charge. Most of us probably have done that. Yeah.

1467
01:22:41,000 --> 01:22:45,200
Speaker 1: It's a brutal feeling. Yeah yeah, So UM, use a

1468
01:22:45,240 --> 01:22:49,200
Speaker 1: camera that you have faith in, UM and work your

1469
01:22:49,240 --> 01:22:53,719
Speaker 1: way up. Like I would like, just be gnawed with worry.

1470
01:22:53,760 --> 01:22:56,280
Speaker 1: If I had gone straight from checking a camera every

1471
01:22:56,280 --> 01:22:58,639
Speaker 1: two weeks, which I used to do, you know, eight

1472
01:22:58,640 --> 01:23:02,160
Speaker 1: ten years ago. UM too, you know, I would check

1473
01:23:02,200 --> 01:23:05,040
Speaker 1: it more frequently even than that before that to four months,

1474
01:23:05,120 --> 01:23:07,040
Speaker 1: you know, But then I started leaving him a month

1475
01:23:07,120 --> 01:23:08,639
Speaker 1: than I started leaving him a month and a half,

1476
01:23:08,640 --> 01:23:12,400
Speaker 1: two months and now UM, and I have very few failures,

1477
01:23:12,600 --> 01:23:14,200
Speaker 1: you know, Like last year, I think I had one

1478
01:23:14,280 --> 01:23:18,519
Speaker 1: camera that failed um part way through UM out of

1479
01:23:18,680 --> 01:23:22,679
Speaker 1: a dozen plus. So, UM, it doesn't happen, it doesn't

1480
01:23:22,680 --> 01:23:25,840
Speaker 1: happen very often. UM. You just kind of have to

1481
01:23:25,880 --> 01:23:29,960
Speaker 1: figure out what works for you. UM. I will say,

1482
01:23:30,040 --> 01:23:33,519
Speaker 1: in the summer, it's much tougher to figure out optimal

1483
01:23:33,720 --> 01:23:37,280
Speaker 1: camera placement. So even though I do get some cameras

1484
01:23:37,280 --> 01:23:39,479
Speaker 1: out you know this time of year, it's only in

1485
01:23:39,720 --> 01:23:42,360
Speaker 1: spots that I'm really really confident I know exactly where

1486
01:23:42,400 --> 01:23:46,000
Speaker 1: to put it. Um. I really prefer to wait until

1487
01:23:46,200 --> 01:23:50,680
Speaker 1: like late August into September, um, because the vegetation is

1488
01:23:50,760 --> 01:23:52,800
Speaker 1: dying down. It's so much easier to get in place

1489
01:23:52,840 --> 01:23:54,559
Speaker 1: because otherwise, yeah, you get it up this and then

1490
01:23:54,560 --> 01:23:57,559
Speaker 1: you're gonna have ten thousand pictures of a leaf blown

1491
01:23:57,600 --> 01:23:59,360
Speaker 1: back and forth in front of your camera, you know.

1492
01:23:59,680 --> 01:24:04,200
Speaker 1: I know. So I used to run more cameras in

1493
01:24:04,240 --> 01:24:06,240
Speaker 1: the summer than I do now. Actually I get a

1494
01:24:06,240 --> 01:24:09,400
Speaker 1: few out, but they're only in spots where like either

1495
01:24:09,439 --> 01:24:11,840
Speaker 1: there's just not much vegetation or you know, I just

1496
01:24:11,960 --> 01:24:16,200
Speaker 1: know this is exactly how I need to put it, um,

1497
01:24:16,240 --> 01:24:19,360
Speaker 1: because I've gotten burned a lot on the summertime cameras,

1498
01:24:19,400 --> 01:24:24,120
Speaker 1: and it's harder on your cameras. Ants and um, you know, uh,

1499
01:24:24,240 --> 01:24:28,800
Speaker 1: spiders building cobwebs over your you know, um, over your

1500
01:24:29,040 --> 01:24:31,479
Speaker 1: sensor and stuff like that. Your or your lens or

1501
01:24:31,479 --> 01:24:35,479
Speaker 1: something like that. I've seen it all so um yeah,

1502
01:24:35,560 --> 01:24:39,800
Speaker 1: so um, you know I've minimized how much I hang

1503
01:24:39,840 --> 01:24:44,439
Speaker 1: in the in the summer. Um. Yeah. One other thing,

1504
01:24:44,560 --> 01:24:48,720
Speaker 1: so um you know talk about like optimal weather, Um,

1505
01:24:48,760 --> 01:24:51,360
Speaker 1: I will I will say, you know, I've I've looked

1506
01:24:51,360 --> 01:24:55,559
Speaker 1: at weather so much here um as everybody knows, like

1507
01:24:55,600 --> 01:24:57,960
Speaker 1: cold fronts are good. Um, but it's not just that

1508
01:24:58,360 --> 01:25:01,640
Speaker 1: um optimal. Whether the worst weather you can have for

1509
01:25:01,680 --> 01:25:05,080
Speaker 1: a fall is like dead average the whole fall, um.

1510
01:25:05,400 --> 01:25:09,439
Speaker 1: You I've learned. I really want swings, like I want

1511
01:25:09,520 --> 01:25:12,760
Speaker 1: warmer than average, and then I want colder than average.

1512
01:25:13,240 --> 01:25:16,080
Speaker 1: I want and I want those to last a little while.

1513
01:25:16,120 --> 01:25:18,280
Speaker 1: If those only last a day or two each, it

1514
01:25:18,320 --> 01:25:20,800
Speaker 1: seems like the deer get not confused, but they don't

1515
01:25:20,800 --> 01:25:23,519
Speaker 1: have time to establish any kind of a pattern. And

1516
01:25:24,960 --> 01:25:29,280
Speaker 1: it's really it just seems tougher hunting. So ideally there

1517
01:25:29,280 --> 01:25:32,000
Speaker 1: would be like one great big weather system every week,

1518
01:25:32,360 --> 01:25:34,360
Speaker 1: you know. In my mind, like if I could plan

1519
01:25:34,439 --> 01:25:36,720
Speaker 1: out the perfect weather for a fall, you know, and

1520
01:25:36,760 --> 01:25:40,880
Speaker 1: it would get well warm, significantly warmer than average, and

1521
01:25:40,920 --> 01:25:43,280
Speaker 1: then you'd have a twenty five degree temperature drop and

1522
01:25:43,320 --> 01:25:45,000
Speaker 1: then it would get warmer than average, and then you

1523
01:25:45,080 --> 01:25:47,519
Speaker 1: have a twenty five degree temperature drop every you know,

1524
01:25:48,320 --> 01:25:50,240
Speaker 1: five to seven days. So I thought I'd throw that

1525
01:25:50,280 --> 01:25:53,519
Speaker 1: out there, like that's perfect. You can have too turbulent

1526
01:25:53,600 --> 01:25:55,439
Speaker 1: a weather if it's all back and forth and the

1527
01:25:55,479 --> 01:25:57,439
Speaker 1: winds you know, from the south one day in the

1528
01:25:57,479 --> 01:26:00,679
Speaker 1: north the next day, and south and north you see

1529
01:26:00,720 --> 01:26:05,800
Speaker 1: sawing that, Um, that isn't optimal and nor is like

1530
01:26:06,040 --> 01:26:09,760
Speaker 1: south winds for you know, two weeks straight. Um, it's

1531
01:26:09,880 --> 01:26:13,280
Speaker 1: it's definitely Um. It just seems dear kind of settle

1532
01:26:13,320 --> 01:26:15,840
Speaker 1: into a routine after you know, when you get at

1533
01:26:15,920 --> 01:26:19,720
Speaker 1: least two three days of consistent weather, whether whether it's

1534
01:26:19,760 --> 01:26:22,360
Speaker 1: warm or cold, and then you can get a break

1535
01:26:22,400 --> 01:26:25,240
Speaker 1: in a weather system and you can you can restart

1536
01:26:25,280 --> 01:26:28,640
Speaker 1: those trends. So I thought, I throw that's something I've observed.

1537
01:26:28,920 --> 01:26:33,439
Speaker 1: That's a great point. Those swings are pretty key. So, uh, Joe,

1538
01:26:33,479 --> 01:26:35,280
Speaker 1: I know you need to run. We've taken a bunch

1539
01:26:35,280 --> 01:26:37,760
Speaker 1: of your time here, and uh, I want to be

1540
01:26:37,800 --> 01:26:41,320
Speaker 1: respectful of that. So maybe we can get you on again,

1541
01:26:41,360 --> 01:26:43,120
Speaker 1: because there's always more and more wish you could talk

1542
01:26:43,160 --> 01:26:46,080
Speaker 1: about with you. Uh, but it's always a pleasure and

1543
01:26:46,280 --> 01:26:50,639
Speaker 1: uh problem thank you for doing this, Jo, no problem,

1544
01:26:50,800 --> 01:26:54,280
Speaker 1: So thanks Mark, best to let this season. Let's uh,

1545
01:26:54,400 --> 01:26:56,920
Speaker 1: let's stay in touch. And I'm very curious to see

1546
01:26:56,960 --> 01:26:58,519
Speaker 1: how I might be able to start doing a little

1547
01:26:58,520 --> 01:27:00,840
Speaker 1: bit more of this because I think you're you're on

1548
01:27:00,960 --> 01:27:03,920
Speaker 1: do You're really onto something here about kind of expanding

1549
01:27:04,040 --> 01:27:06,080
Speaker 1: the types of places that we're tracking and not just

1550
01:27:06,280 --> 01:27:10,000
Speaker 1: dear not just the best weather, but also how alternate

1551
01:27:10,040 --> 01:27:12,599
Speaker 1: weather patterns can lead dear to certain spots and maybe

1552
01:27:12,640 --> 01:27:16,320
Speaker 1: we're not paying attention to so intriguing and good stuff,

1553
01:27:17,640 --> 01:27:20,400
Speaker 1: and that is it. I hope all of you fellow

1554
01:27:20,479 --> 01:27:22,720
Speaker 1: dear geeks out there enjoyed this one as much as

1555
01:27:22,760 --> 01:27:26,160
Speaker 1: I did. Um after talking to Joe, I actually went

1556
01:27:26,240 --> 01:27:29,759
Speaker 1: and started compiling a new data set for the number

1557
01:27:29,760 --> 01:27:31,560
Speaker 1: one buck I'm after here in Michigan, this dear have

1558
01:27:31,560 --> 01:27:34,639
Speaker 1: been calling. Tran just logged all of his daytime activity.

1559
01:27:34,680 --> 01:27:38,000
Speaker 1: I've got fifteen different days worth of dailight activity that

1560
01:27:38,080 --> 01:27:41,000
Speaker 1: I'm now going to be cross referencing to all this

1561
01:27:41,080 --> 01:27:43,840
Speaker 1: different kind of data that we talked about. So needless

1562
01:27:43,840 --> 01:27:48,920
Speaker 1: to say, I'm I'm excited and uh just can't wait

1563
01:27:48,920 --> 01:27:51,280
Speaker 1: for the season to get here. So thanks for listening,

1564
01:27:51,640 --> 01:27:54,680
Speaker 1: and you can check out more by heading over to

1565
01:27:54,840 --> 01:27:57,960
Speaker 1: the meat Eater dot com, which is where all the

1566
01:27:57,960 --> 01:28:00,320
Speaker 1: new podcasts are going, where my new articles are going,

1567
01:28:00,320 --> 01:28:03,280
Speaker 1: where future new videos are going, as well as all

1568
01:28:03,320 --> 01:28:06,880
Speaker 1: the rest of the mediat networks great podcasts and contributors.

1569
01:28:06,880 --> 01:28:10,320
Speaker 1: You can see articles from the Baritone himself, spend some

1570
01:28:10,400 --> 01:28:12,519
Speaker 1: new hearth. You can check out our podcast with Steve

1571
01:28:12,600 --> 01:28:16,559
Speaker 1: ron Ellen, Ryan Callahan and Ben O'Brien and April Vokey. Um,

1572
01:28:16,600 --> 01:28:19,240
Speaker 1: all sorts of good stuff out there, and uh, I

1573
01:28:19,280 --> 01:28:20,960
Speaker 1: think you'll find a little bit of something no matter

1574
01:28:21,000 --> 01:28:24,120
Speaker 1: what kind of person you are. So with all that said,

1575
01:28:24,640 --> 01:28:28,719
Speaker 1: thank you for listening, and until next time, stay wired

1576
01:28:29,240 --> 01:28:29,679
Speaker 1: to hunt.