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Speaker 1: Welcome to the Wired to Hunt podcast, home of the

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Speaker 1: modern whitetail hunter and now your host, Mark Kenyon. Welcome

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Speaker 1: to the Wire to Hunt podcast. I'm a guest host

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Speaker 1: Tony Peterson, and today I'm speaking with dear biologist brad Cohen. Alright, folks,

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Speaker 1: welcome to the Wire to Hunt podcast, which is brought

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Speaker 1: to you by First Light. You might notice that this

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Speaker 1: is not the voice of Mark Kenyon. He sent me

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Speaker 1: an email last week and said that he picked up

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Speaker 1: a new hobby, which is learning fictional languages, and he's

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Speaker 1: off to a convention in Omaha to really become and

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Speaker 1: this is his words, not mine, fluent in Wookie. That's

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Speaker 1: no joke, and I don't know what it means, and

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Speaker 1: at this point I'm too scared to ask. So anyway,

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Speaker 1: today I'm speaking with Bradley Cohen, who is a dear

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Speaker 1: biologist and a researcher. Cohen is also a passionate bow hunter,

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Speaker 1: and he happens to be an expert on dear vision.

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Speaker 1: He's responsible for developing and seeing through one of the

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Speaker 1: most comprehensive studies on deer vision that exists his findings,

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Speaker 1: which we discuss in detail throughout this episode are fascinating

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Speaker 1: and some of them will directly help you become a

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Speaker 1: better deer hunter. Plus it's just really interesting stuff to

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Speaker 1: learn about our favorite game animal. Brad, Welcome to the

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Speaker 1: Wire to Hunt podcast. Ma'am, hey, good to be here, Tony.

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Speaker 1: You You come highly recommended from our very own Patrick Durkin.

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Speaker 1: And I told Pat that I was looking for somebody

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Speaker 1: who was an expert in deer vision and he said,

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Speaker 1: there's only one place you gotta go for this, and

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Speaker 1: so I really appreciate you taking some time to talk

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Speaker 1: with me today on on white tails of what they

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Speaker 1: really see and what you've learned about that. Yeah. Man,

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Speaker 1: anything I can do to help share the information we

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Speaker 1: have and the science we do, I'm happy to I'm

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Speaker 1: happy to be part of it. So can you just

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Speaker 1: before we dive into this, can you get the listeners

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Speaker 1: just a breakdown of you know, where you're doing your research,

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Speaker 1: what you're doing, Uh, how you got into it A

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Speaker 1: little bit right? So? UM, I originally am from New

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Speaker 1: York and then I got a master's degree at the

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Speaker 1: University of Georgia working under Carl Miller and Bob Warren,

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Speaker 1: and as part of that research, we started to examine uh,

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Speaker 1: deer vision a little bit more into what colors can

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Speaker 1: they see? And a lot of it was focused on

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Speaker 1: getting a behavioral measure or having them basically tell us

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Speaker 1: in their own unique deer way what colors they could see.

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Speaker 1: And then I stayed at University of Georgia working on

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Speaker 1: deer during my PhD doing something completely different. But during

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Speaker 1: that time we had several other students coming through working

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Speaker 1: on deer vision and I oversaw that research. Um So

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Speaker 1: basically we've done in total, I've been part of about

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Speaker 1: four studies focused on deer vision, anything from their color

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Speaker 1: to their visual or color sensitivity, of their visual acuity,

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Speaker 1: to their movement sensitivity. So let's let's back up a

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Speaker 1: second here. Out of all the stuff that you could

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Speaker 1: study out there, why how did you settle on? How

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Speaker 1: did this become a relevant topic of research? So let

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Speaker 1: me back up a little bit more and just tell

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Speaker 1: you that when I went I got my undergrad degree

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Speaker 1: from a general biology program in up state in New York,

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Speaker 1: and I went to this college it was called Singing

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Speaker 1: Genes CEO, and I went there because it was right

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Speaker 1: next to my hunting spot that I grew up. Deer

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Speaker 1: hunting my whole life, so like hunting has always kind

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Speaker 1: of been a big central role in my life. And

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Speaker 1: I actually just put into Google scholar the word dear

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Speaker 1: and Carl Miller came up, and that's how I kind

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Speaker 1: of met him. But when I went down there, my

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Speaker 1: degrees were actually got biology or bachelor degrees in biology

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Speaker 1: and psychology, all right, so that's gonna be important when

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Speaker 1: we talked about how I understood how to kind of

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Speaker 1: get these measures. But we went down there and had

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Speaker 1: a unique opportunity. Most students that come into the wildlife

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Speaker 1: profession they get money from a grant or from a grant,

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Speaker 1: and that granting agency typically has specific objectives. I want this,

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Speaker 1: this and this done well. I was on a on

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Speaker 1: money that was basically left over from the project like

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Speaker 1: they had. There was a lot of money and there

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Speaker 1: was no direct deliverables to be associated and I was

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Speaker 1: so lucky in that. Uh. Dr Warren and Dr Miller

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Speaker 1: were just like, let's talk about some of the things

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Speaker 1: we don't understand about dear biology. Now, in this case,

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Speaker 1: the money was coming from the Georgia Department of Transportation,

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Speaker 1: which it's kind of weird to think about that, but

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Speaker 1: they were interested in dear vision from the sake of

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Speaker 1: how can we build mitigation devices to get her off

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Speaker 1: the road. And some of that comes back to just

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Speaker 1: basic biology of deer. And so the world was my oyster.

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Speaker 1: I kind of had the experience to understand like this

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Speaker 1: was a cool study, and the funding agency had to

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Speaker 1: want to like know, and we just took off from there.

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Speaker 1: So you you kind of you kind of looked into

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Speaker 1: sort of an open lane there with that money already

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Speaker 1: being there. And this was driven by I'm assuming that

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Speaker 1: the d OT was looking at this going how do

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Speaker 1: we prevent vehicle collisions with deer? And you thought, well,

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Speaker 1: we've we've tried the little whistlers on the bumper, and

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Speaker 1: we've tried some you know, oral A U, R, O

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Speaker 1: A L methods. But you're you're like, well, how do

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Speaker 1: we how do we make something that's a visual deterrent

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Speaker 1: to them? Yeah, So, like the the Georgia Department Transportation

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Speaker 1: been funding research for a little bit of time trying

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Speaker 1: to be like, you know, what do fences due to

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Speaker 1: dear behavior, Well, it just creates a little hot spot

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Speaker 1: where they can cross the fence on the front and

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Speaker 1: back end of it. Uh, hey, do deer whistles work?

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Speaker 1: Do roadside reflectors work? And the answer was always no,

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Speaker 1: they don't work. They don't work because it's not based

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Speaker 1: on dear biology. So Georgia Department Transportation said, hold on,

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Speaker 1: let's take a step back, let's tell us what dear

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Speaker 1: ken see what they can here, and then let us

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Speaker 1: kind of work with you to design these devices that

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Speaker 1: would mitigate it. In this case, it was and we

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Speaker 1: wanted to understand things like how does dear vision affect

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Speaker 1: a deer in the headlight look. So one of the

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Speaker 1: applications of our research was working towards building a better

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Speaker 1: headlight that would be less bright to a deer, but

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Speaker 1: similar brightness to you and I. So they don't get like,

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Speaker 1: oh my god, they don't know cars there, They just

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Speaker 1: know that something very bright is coming at them. And

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Speaker 1: so that's kind of the application that Georgia Department Transportation

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Speaker 1: was in. But then, being the wildlife biologists and hunters

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Speaker 1: that we are, we're always thinking about, Okay, what are

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Speaker 1: the other applications. So you looked at this and you said,

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Speaker 1: this is a perfect opportunity. And I'm assuming as soon

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Speaker 1: as this this opportunity came up. You looked at to

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Speaker 1: see what other studies had been done, and you saw

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Speaker 1: there was kind of a pretty big gap there in

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Speaker 1: our understanding of dear vision. It was kind of it's

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Speaker 1: kind of stunning when you think about one of the

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Speaker 1: most well studied animals in the world and yet how

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Speaker 1: little we understood about their just basic biology. What do

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Speaker 1: they hear, what do they see? All of that. And

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Speaker 1: typically it's because the people that study dear tend to

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Speaker 1: be more application minded. We're always trying to say, Okay,

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Speaker 1: let's go out and ready them in their natural environment

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Speaker 1: and then we'll make hypoths and based on that. And

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Speaker 1: coming from that general biology background, I was more interested

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Speaker 1: in the Okay, well let's go back to the physiology

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Speaker 1: point of view, and then we can project out to

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Speaker 1: why they act like they do. Yeah, so you had

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Speaker 1: you had a unique opportunity to use penn raise deer

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Speaker 1: and design a I mean it looked like almost like

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Speaker 1: a Pavlavian path towards uh, you know, training deer and

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Speaker 1: then using that that food reward training to start to

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Speaker 1: figure out their their vision. So before before we get

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Speaker 1: into the design of that study, when you when you

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Speaker 1: were like, all right, I'm gonna do this, or you're

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Speaker 1: you're really kicking this around. You're talking to Carl Miller

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Speaker 1: and a couple of other people. Did you kind of

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Speaker 1: have it in your mind where you like, I think

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Speaker 1: I know how dear c or did Was there like

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Speaker 1: a basic biological understanding of the rods and cones and

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Speaker 1: the design of a deer's eye were so you went

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Speaker 1: into it and you're like, I think I got this

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Speaker 1: already or not really, so back about don't quote me

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Speaker 1: on it. Maybe twenty years before that, J Knights and

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Speaker 1: a bunch of other research. Now. Ja Knights is is

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Speaker 1: an interesting character in and of himself. He's he's now

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Speaker 1: at the University of Washington, and he's like the expert

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Speaker 1: on animal vision. He's single handedly is like working to

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Speaker 1: cure color blindness and humans working with monkeys. I mean,

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Speaker 1: the guys like way above my pay grade. But he

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Speaker 1: had actually come to the University of Georgie about twenty

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Speaker 1: years before that and basically done a study where you

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Speaker 1: simply hook up electrodes to an animal's head and you

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Speaker 1: flicker lights in their eye and you're looking for an

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Speaker 1: electrical signal pulsing from the retina. Every time you pulse

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Speaker 1: a light, you get that that signal, that cellular signal,

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Speaker 1: and from that we can at least tell um how

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Speaker 1: many cones an animal has, and that's cones are what

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Speaker 1: seeks color? Uh do we have rods which are basically

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Speaker 1: the more light sensitive black and white type um photo receptors.

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Speaker 1: And then also like what are the peak sensitivities of

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Speaker 1: those cones and rods? So he already had laid that out,

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Speaker 1: but you have to understand oftentimes that cellular response isn't

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Speaker 1: always the same way that an animal perceives it because

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Speaker 1: and the truth, perception is both mediated by the cellular

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Speaker 1: responses of of your retina or something like that, but

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Speaker 1: also your brain right and you know, for example, you

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Speaker 1: and I, if we just looked at our cellular responses

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Speaker 1: would be very sensitive to blue, like we're But the

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Speaker 1: truth is we have so many different ways we inhibit

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Speaker 1: blue in from being uh perceived by us, and that

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Speaker 1: we are very poor at perceiving any type of blue color.

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Speaker 1: But that same type of study, just looking at the

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Speaker 1: cellular would say, hey, we can see it pretty well.

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Speaker 1: So there's often this disconnect, which that's where we were going, was, Okay,

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Speaker 1: we have some guests in it on what we think,

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Speaker 1: dear my colors they might be able to see, but

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Speaker 1: we need a behavioral confirmation. We need them to tell

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Speaker 1: us that they're perceiving this color in real life so

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Speaker 1: that we know, hey, they see it. Does that make

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Speaker 1: sense yea. So essentially the four year study we we

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Speaker 1: you could kind of say, Okay, this is the spectrum

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Speaker 1: they can see. These are the colors they're they're picking up.

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Speaker 1: We think, we hope, well, okay it appears that way,

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Speaker 1: but that's as far as it goes, right, Like, you

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Speaker 1: don't know to what degree, and you don't know what

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Speaker 1: that means to them, right bingo? And so what did

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Speaker 1: you do with that? He said, that's not enough, dude,

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Speaker 1: Let's let's let's dive deeper. Yeah, so we had we

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Speaker 1: had tried to train. I was like, listen, I think

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Speaker 1: we can train dear to respond to light. In other words,

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Speaker 1: it's just a classic have lobby and dog type experiment

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Speaker 1: where we're gonna say, hey, um, there's two troughs with

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Speaker 1: a treat. One of them is gonna have allow you

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Speaker 1: to have access to treat, uh, and it's gonna have

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Speaker 1: a light on over it. The other trough will still

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Speaker 1: have the treat, but if you try and approach it,

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Speaker 1: it's gonna shut the doors and you're not gonna get

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Speaker 1: that treat at all. I said, that's just a it's

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Speaker 1: actually in psychology called the skinner box. It's it's a

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Speaker 1: super basic way we train animals. And to be honest,

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Speaker 1: I remember very distinctly Carl Miller kind of laughing it

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Speaker 1: off because we had tried to train dear before and

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Speaker 1: when your hand raising dear, and because he's have to

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Speaker 1: be tamed deer, and you're trying to do this in

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Speaker 1: a non automated way, it's it's impossible. You can't get

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Speaker 1: the control that you need, the number of repetitions that

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Speaker 1: you need. You know, I think the student would before me,

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Speaker 1: you know, took fifteen dear home and raised them by themselves,

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Speaker 1: only to have one that even like kind of could

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Speaker 1: do an experiment with vision. And so the first thing

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Speaker 1: was like, could we even get this to work with

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Speaker 1: the deer? Understand that if we presented them to to

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Speaker 1: troughs to eat out of both with the treat, but

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Speaker 1: only to approach the trough with the light on, just

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Speaker 1: a bright light, would they go to it? And boom.

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Speaker 1: We did it automated. We basically had to learn computer language.

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Speaker 1: It was just it was fun, but I never want

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Speaker 1: to do it again. But by the end of it,

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Speaker 1: we had shown very clearly, at least with one dear, hey,

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Speaker 1: we can at least train a dear to respond to

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Speaker 1: a light being on. Does that make sense? So the

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Speaker 1: first hurdle that you bumped into was just the people

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Speaker 1: thought you wouldn't be able to train dear for a

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Speaker 1: measured response. Yeah, and it was very difficult prior to

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Speaker 1: getting the type of technology that you needed to automate

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Speaker 1: the process. And basically all I did, along with David Osborne,

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Speaker 1: who's a research coordinator at the University of Georgia, is

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Speaker 1: we worked actually with what we call the Instrument Shop

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Speaker 1: at the University of Georgi. These are people that make

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Speaker 1: fancy instruments for specific purposes and they made an entire

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Speaker 1: set of devices that allowed us to automate the process entirely.

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Speaker 1: I would hit a button on my compurece ays start

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Speaker 1: and then I'd show up two days later to collect

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Speaker 1: the data. And during those two days I didn't have

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Speaker 1: to be there, but the deer was constantly being trained

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Speaker 1: on the series of trials where the troughs open up

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Speaker 1: the treats there, the light turns on. If the deer

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Speaker 1: picks the lit trough, you get to eat the treat.

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Speaker 1: If you don't, it closes and and very quickly, within

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Speaker 1: a couple of days though that first year learned okay,

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Speaker 1: I'm going to approach the lit trough and then it

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Speaker 1: was off to the races. So to say, how long

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Speaker 1: did it take those deer to start figuring that out?

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Speaker 1: Generally we were able to train them within a week

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Speaker 1: to two weeks, so and that means that they were

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Speaker 1: getting they were going to the trough with the light

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Speaker 1: on over it over eight percent of time, very quickly.

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Speaker 1: Now for animals, that's pretty good, you know, you and

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Speaker 1: I would have figured out in minutes. For an animal

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Speaker 1: to do it in a about two weeks span was

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Speaker 1: pretty impressive. Yeah, I mean it's I I work in

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Speaker 1: the dog space a lot and trained dogs a lot,

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Speaker 1: and it's so it's just you know, people will argue

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Speaker 1: whether treat training is valuable for puppies or not. I'm like,

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Speaker 1: so valuable and so easy, you know, and that's an

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Speaker 1: animal that's been bred to work with us. So you

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Speaker 1: get the eye contact, and you have some benefits over

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Speaker 1: deer in a multiple different ways. But so you you

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Speaker 1: look at this and did you did you do this

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Speaker 1: first stage just as a proof of concepts It was

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Speaker 1: a proof of concept. Uh. We came together as a

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Speaker 1: group and kind of said, Okay, we've got a little

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Speaker 1: bit of money that we're willing to put towards this

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Speaker 1: divide us and uh, they gave us the green light

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Speaker 1: to Dr Miller in particular, was skeptical about the idea

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Speaker 1: of it, and he's like, okay, not in a bad way,

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Speaker 1: just like, prove it to me. And when we proved it,

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Speaker 1: it was like all thumbs up, let's go this is

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Speaker 1: this is great. So it's what did you do from there?

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Speaker 1: We then so we wound up training a total of

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Speaker 1: seven deer so to the same process, and they and

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Speaker 1: they were all adult does, right, all adult does. And

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Speaker 1: And the reason is, let me just tell you why

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Speaker 1: we did choose Does versus Bucks. We had team Bucks. Um.

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Speaker 1: But the thing is that Bucks, during the rut or

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Speaker 1: just most of the time, they're angry, and when they're tame,

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Speaker 1: they're dangerous because they see you as just another deer.

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Speaker 1: So they'll happily attack every single thing that's around. So

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Speaker 1: females a lot easier to work with and and there

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Speaker 1: was no reason to believe that would buy us the

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Speaker 1: results at all, Right, No, not at all. No, there's

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Speaker 1: no there's really no difference across sexes in animals as

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Speaker 1: far as what they can see or can't see. UM.

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Speaker 1: So we we train them to that and then we

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Speaker 1: get very We worked with Energizer to get very very

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Speaker 1: specific wavelength l E. D s. Now, this is this

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Speaker 1: is where it kind of gets interesting. So you get

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Speaker 1: these LEDs and their specific wavelength, Like it's not just

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Speaker 1: like this is a blue color, it's like, this is

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Speaker 1: a wavelength in the blue color. And we started off

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Speaker 1: the light starts off as intense as possible, very bright,

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Speaker 1: and we trained and the deer instantly adapt. They're like, oh,

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Speaker 1: it's not a bright white light anymore. It's a bright

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Speaker 1: blue light, and so they start to um actually trained

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Speaker 1: to that, and then once you get them completely good

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Speaker 1: to go, then you start to actually have the intensity

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Speaker 1: because your ability to see something of a color, right,

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Speaker 1: isn't just the wavelength of the color itself, it's the

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Speaker 1: intensity at which it's being presented to you if that

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Speaker 1: makes sense, right, And so we have the intensities until

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Speaker 1: basically they were performing at chance. In other words, they

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Speaker 1: stopped being able to see the light and they were

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Speaker 1: just randomly approaching the tropicals. They couldn't see it um.

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Speaker 1: And so we did that for a bunch of different

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Speaker 1: wavelengths of light, both in the blue wavelength which is

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Speaker 1: like um in the four hundred nanometers which doesn't matter

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Speaker 1: for here, but in short wavelength and the long wavelength,

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Speaker 1: which is the reds. What did you start to find?

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Speaker 1: I was? I was fascinated. So with deer, deer we're

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Speaker 1: able to see blue much better than we can UH

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Speaker 1: typically something like twenty times UH enhanced sensitivity to blue

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Speaker 1: colors than we have. So there were times when I

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Speaker 1: was testing blue wavelengths where I couldn't see that the

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Speaker 1: light was on, but the deer could. And on the

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Speaker 1: other side of it is they could not see reds

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Speaker 1: hardly at all, very low sensitivity to reds. And so

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Speaker 1: what it what it did is enables to confirm those

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Speaker 1: kind of previous anatomical or physiological measurements. And now we

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Speaker 1: can go from there to figure out, okay, this is

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Speaker 1: how a geer precedes its world. So there before you

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Speaker 1: did this, there was the suspicion that this is what

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Speaker 1: it was gonna show, that that they were very sensitive

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Speaker 1: to blues and not reds m m m m. Yeah,

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Speaker 1: so that's what we thought would probably you know that

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Speaker 1: that was what the anatomical measures had suggested. So what

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Speaker 1: surprised you about it then was it was it like

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Speaker 1: the degree at which they could see the blues. So

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Speaker 1: the degree it wasn't just like, okay, actually seeing it

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Speaker 1: play out by your own experience, just how difference the

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Speaker 1: sensitivity is. But also we expected subtle or quite large

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Speaker 1: differences actually in the anatomical structure or the anatomical measurements

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Speaker 1: versus the behavioral measurements because time and time again that's

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Speaker 1: what we see in the animal world. But in this

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Speaker 1: case they almost overlapped perfectly. So um, whatever is going

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Speaker 1: on in the cells is being transferred perfectly to the brain.

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Speaker 1: It's like a beautiful synchrony, and there's no um like

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Speaker 1: for us, our lens is clean up a lot of light,

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Speaker 1: like they get rid of a bunch of different light

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Speaker 1: that you and I would otherwise process. For deer, that's

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Speaker 1: clearly not the case. You see where I'm going with

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Speaker 1: this was there was almost no difference. And so now

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Speaker 1: we have a really clear sense of what they can see.

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Speaker 1: Can you explain that further? Like how do you well,

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Speaker 1: first off, how do you know that? Like? How how

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Speaker 1: did this show that that was that clear to them? Oh? Okay,

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Speaker 1: all right, so we take let me back up a

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Speaker 1: little bit here, and I don't want to go in

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Speaker 1: the weeds, so if I go too much into it,

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Speaker 1: just play with me here and say, okay, that's enough.

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Speaker 1: But you get these measures, all right, So you actually

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Speaker 1: have the intensity of light that they no longer can

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Speaker 1: see right at each wavelength. Then there isn't a crazy

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Speaker 1: equation that has twenty three different variables. Don't ask me

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Speaker 1: to understand them or explain them, but it has twenty

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Speaker 1: three different variables. And you input the data we collected,

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Speaker 1: which is literally this wavelength at this intensing. You just

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Speaker 1: basically tracked it over time, and it spits out a

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Speaker 1: beautiful curve. Eve okay. And these curves are basically curves

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Speaker 1: for your photos, your actual true sensitivity to different wavelengths

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Speaker 1: of light. And so we're taking our real data and

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Speaker 1: taking what we understand about how photo receptors actually work,

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Speaker 1: and we're projecting that onto Uh this kind of sensitivity graph?

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Speaker 1: Does that make sense? Sort of? You gotta remember I

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Speaker 1: write squirrel hunting articles for a living, so this is

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Speaker 1: this is this is heavy stuff, but I'm not Yeah,

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Speaker 1: I don't mean to make it that heavy, and it's

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Speaker 1: still heavy stuff for me. I mean there's a lot

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Speaker 1: of stuff that a lot of heavy lifting that occurred

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Speaker 1: behind the scenes with Dr Knights and all those people.

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Speaker 1: You know what I'm saying, Well, I love it. So

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Speaker 1: you you start to figure this out and you and

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Speaker 1: by having the wavelengths, you start to go, Okay, we

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Speaker 1: know where the limits are on there. We know with

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Speaker 1: the with the deer's range. And you didn't see any

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Speaker 1: variants in those seven deer that were the test subjects.

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Speaker 1: They all were consistent across each deer. Yeah, so a deer,

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Speaker 1: we're remarkably similar. You know, there's always variation, right, There's

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Speaker 1: some there are more trainable and others. You know, you

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Speaker 1: always get the one dear that's the exceptional performing, you

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Speaker 1: get the one that's like kind of dumb. But they

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Speaker 1: were pretty similar. And um, then we go, okay, well,

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Speaker 1: this is the actual women's we understand the outer limits

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Speaker 1: on both sides, from the reds to the blues, and

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Speaker 1: we understand, now, okay, based on how we measured it,

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Speaker 1: they definitely only have two cones. They definitely have their

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Speaker 1: cones peaked at these wavelengths, and we understand now what

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Speaker 1: the different colors they can see, how the different intensities

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Speaker 1: of those colors in their play. Well, we can lay

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Speaker 1: out almost with perfect or with precision, how at least

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Speaker 1: a color world looks to dear. So let's let's talk

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Speaker 1: about that. Where where like evolutionary wise, where's the benefit

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Speaker 1: to seeing those those blues so vividly and not seeing

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Speaker 1: the reds? All right, let's back up a second here

406
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Speaker 1: and just understand that Dear do not perceive their world

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Speaker 1: anything similar to like how we do, especially with colors.

408
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Speaker 1: But what you have to understand is that you're very

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Speaker 1: sensitive to blue colors, which we are not sensitive to.

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Speaker 1: Dere are not sensitive to red colors, which you and

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Speaker 1: I are more sensitive to. So the blue coloration being

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Speaker 1: sensitive to that makes a lot of sense from kind

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Speaker 1: of uh an evolutionary perspective, if if only because if

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Speaker 1: you look at the light that's available during sunset and sunrise.

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Speaker 1: A lot of that is actually the blue spectrum. So

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Speaker 1: here's the crazy thing. I want you to sit back

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Speaker 1: and think when you're sitting there, thirty minutes prior to sunset,

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Speaker 1: and that just twins of highlight, twilights coming over or

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Speaker 1: on sunrise, right, just a little bit. That world is

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Speaker 1: lit up for a deer. But because you and I

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Speaker 1: don't see blues very well, it's dark to us. I

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Speaker 1: don't know. That's kind of crazy to think, right, but

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Speaker 1: our own our lack of sensitivity to the light that's

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Speaker 1: most available during sunrise and sunset is why it's dark

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Speaker 1: to us, and theoretically wide dear most sensitive to it

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Speaker 1: because it's the light that's available when they moved the most.

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Speaker 1: This is going to be a crazy So I just Mark,

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Speaker 1: the host of this podcast, recommended and uh a book

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Speaker 1: about by what was it called? It was about evolutionary

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Speaker 1: biology on islands. I can't I can't remember the technical

431
00:22:31,560 --> 00:22:35,080
Speaker 1: term for it, but in that book there was there

432
00:22:35,080 --> 00:22:38,720
Speaker 1: are some examples of studying baboons and some other primates

433
00:22:38,760 --> 00:22:44,439
Speaker 1: and how it tied loosely to us being so you know,

434
00:22:44,680 --> 00:22:49,240
Speaker 1: like so reliant on daylight and so scared of nighttime predators.

435
00:22:49,280 --> 00:22:52,239
Speaker 1: That our eyes evolved for lots of daylight coming in

436
00:22:52,680 --> 00:22:54,440
Speaker 1: because that's when we're most active and we have to

437
00:22:54,480 --> 00:22:56,119
Speaker 1: get our stuff done before the things that eat us

438
00:22:56,119 --> 00:22:59,000
Speaker 1: come out at night. And what you're saying is dear,

439
00:22:59,520 --> 00:23:02,600
Speaker 1: because they their primary movement periods, their first and last light,

440
00:23:03,160 --> 00:23:06,879
Speaker 1: their eyes have evolved or their vision has evolved to

441
00:23:07,119 --> 00:23:10,600
Speaker 1: take advantage of the wavelengths they're most popular then or

442
00:23:10,640 --> 00:23:14,320
Speaker 1: the most think most available then. So in times of

443
00:23:14,480 --> 00:23:18,560
Speaker 1: perceived darkness for us and other predators, right before nighttime

444
00:23:18,640 --> 00:23:22,160
Speaker 1: and before like you know, not peaked daytime, deer see

445
00:23:22,200 --> 00:23:25,680
Speaker 1: a well lit environment where they can easily distinguish predators.

446
00:23:25,960 --> 00:23:30,040
Speaker 1: No problem that, Okay, So then let me ask you

447
00:23:30,080 --> 00:23:33,399
Speaker 1: this if if that's the case, or that is the case,

448
00:23:34,080 --> 00:23:36,199
Speaker 1: how does that tie into their ability to see it

449
00:23:36,320 --> 00:23:39,240
Speaker 1: at night in the dark. Uh So, that's a little

450
00:23:39,240 --> 00:23:42,520
Speaker 1: bit different. That's an anatomical feature of their eye. They

451
00:23:42,560 --> 00:23:44,840
Speaker 1: have what's called they're taping them loosen them, which is

452
00:23:44,880 --> 00:23:47,719
Speaker 1: basically nothing more than a reflective lens on the back end.

453
00:23:47,960 --> 00:23:50,040
Speaker 1: You know that like when you spotlight deer and they

454
00:23:50,160 --> 00:23:54,480
Speaker 1: their eyes glow, that's actually the light being reflected back

455
00:23:55,160 --> 00:23:57,040
Speaker 1: into their retina, so it hits their retina and goes

456
00:23:57,080 --> 00:24:00,320
Speaker 1: out and then gets reflected back. So what they're doing

457
00:24:00,480 --> 00:24:02,920
Speaker 1: is like they're supersizing the amount of life that's there,

458
00:24:03,160 --> 00:24:08,520
Speaker 1: so they're super efficient about gathering light. So while things

459
00:24:08,640 --> 00:24:11,800
Speaker 1: might be an absolute darkness to us, it's actually a

460
00:24:11,840 --> 00:24:14,400
Speaker 1: little more lit to a deer. So that's how they're

461
00:24:14,440 --> 00:24:16,439
Speaker 1: able to see it at nighttime. So well is that

462
00:24:16,440 --> 00:24:19,679
Speaker 1: they're able to more efficiently harnessed, for lack of a

463
00:24:19,720 --> 00:24:25,679
Speaker 1: better word, available light. So that that's I mean that

464
00:24:25,840 --> 00:24:28,040
Speaker 1: sort of just plays off of like, you know, we

465
00:24:28,040 --> 00:24:30,960
Speaker 1: we just launched that James Wood Web telescope into space

466
00:24:31,280 --> 00:24:33,240
Speaker 1: and it's got a you know, it's got mirrors on

467
00:24:33,280 --> 00:24:35,199
Speaker 1: it that are the size of a tennis court to

468
00:24:35,280 --> 00:24:37,399
Speaker 1: be able to look, you know, at light that was

469
00:24:37,440 --> 00:24:40,160
Speaker 1: created thirteen billion years ago. This is a mini version

470
00:24:40,240 --> 00:24:43,240
Speaker 1: of just bouncing light back and forth and taking advantage

471
00:24:43,240 --> 00:24:46,480
Speaker 1: of it. Man, I'll tell you what. It's the coolest

472
00:24:46,480 --> 00:24:49,560
Speaker 1: part about being in this field is the crazy stuff

473
00:24:49,600 --> 00:24:52,000
Speaker 1: that you learn about how creators have adapted to live

474
00:24:52,040 --> 00:24:54,880
Speaker 1: in their environment. But that's exactly what they're doing. It's

475
00:24:54,920 --> 00:24:57,959
Speaker 1: nothing more than reflective merit and get more light at nighttime.

476
00:24:58,359 --> 00:25:01,359
Speaker 1: Is that considers we don't move during during night? Well? Sure,

477
00:25:01,520 --> 00:25:03,480
Speaker 1: I mean is that consistent across all you know? I

478
00:25:03,480 --> 00:25:06,040
Speaker 1: mean you see raccoons the same thing, walleye the same thing.

479
00:25:06,160 --> 00:25:08,840
Speaker 1: It's all. That's what that is. Will nocturnal out any

480
00:25:08,840 --> 00:25:11,560
Speaker 1: animal that has some kind of peak activity at night.

481
00:25:12,440 --> 00:25:15,080
Speaker 1: Most animals tend to have that kind of feature. It

482
00:25:15,160 --> 00:25:17,320
Speaker 1: goes by a bunch of different names. But yeah, a

483
00:25:17,359 --> 00:25:21,719
Speaker 1: reflective layer in their eye is there? Is there a

484
00:25:23,000 --> 00:25:25,800
Speaker 1: Is there some kind of loss in their ability in

485
00:25:25,800 --> 00:25:28,840
Speaker 1: the daylight because of that then, like in in bright daylight,

486
00:25:28,840 --> 00:25:31,640
Speaker 1: because I know, I know there is with the blue

487
00:25:31,720 --> 00:25:34,439
Speaker 1: light to some extent, right, but also you know that

488
00:25:34,520 --> 00:25:37,480
Speaker 1: ability to harness that much light when it's dark. To us,

489
00:25:38,000 --> 00:25:41,320
Speaker 1: is there a sacrifice on the daylight side. Not necessarily.

490
00:25:41,320 --> 00:25:44,280
Speaker 1: All they do is similar to us. They just contract

491
00:25:44,840 --> 00:25:47,520
Speaker 1: their their lens and everything real, real tight. So if

492
00:25:47,520 --> 00:25:51,040
Speaker 1: you actually look at the pupil of a dear's eye

493
00:25:51,119 --> 00:25:54,040
Speaker 1: during the daytime, it'll be really, really really small, but

494
00:25:54,160 --> 00:25:57,240
Speaker 1: during night that suckers huge. So basically all they're doing

495
00:25:57,280 --> 00:25:59,280
Speaker 1: is controlling the amount of light that's coming in during

496
00:25:59,280 --> 00:26:01,960
Speaker 1: the daytime by just letting them very little amount of

497
00:26:02,040 --> 00:26:04,600
Speaker 1: light come in, if that makes sense. Do they have

498
00:26:04,640 --> 00:26:07,080
Speaker 1: a greater degree of dilation that they can do with

499
00:26:07,119 --> 00:26:10,200
Speaker 1: their pupils then than us? It's it's quite a bit bigger, right, yeah. Yeah.

500
00:26:10,600 --> 00:26:12,320
Speaker 1: And they also have a slit eye, so they have

501
00:26:12,440 --> 00:26:15,440
Speaker 1: more surface area that they're exposing to their eye as

502
00:26:15,480 --> 00:26:20,200
Speaker 1: they dilate, So as they constrict there basically lift their

503
00:26:20,520 --> 00:26:23,280
Speaker 1: blessing and lessening to a much larger extent your than

504
00:26:23,320 --> 00:26:26,600
Speaker 1: yours and eyes I would be all right, So what

505
00:26:26,760 --> 00:26:30,879
Speaker 1: is what does this knowledge do for us as hunters?

506
00:26:30,920 --> 00:26:33,080
Speaker 1: I know everybody's listening. There's all right, getting past this side?

507
00:26:33,119 --> 00:26:35,320
Speaker 1: Like what kind of what? What color should I be

508
00:26:35,359 --> 00:26:37,760
Speaker 1: wearing out there? What should I be aware of? Just

509
00:26:37,840 --> 00:26:39,840
Speaker 1: color wise? Not movement yet, because I want to talk

510
00:26:39,840 --> 00:26:42,240
Speaker 1: about movement. Want me jump in the movement? Well, don't

511
00:26:42,240 --> 00:26:45,440
Speaker 1: wear blue jeans or any of that stuff. And you

512
00:26:45,520 --> 00:26:48,439
Speaker 1: gotta all right, so you've already made me go like

513
00:26:48,560 --> 00:26:51,840
Speaker 1: way down the rabbit hole here, I've got a mighty

514
00:26:51,840 --> 00:26:53,600
Speaker 1: there was a tect terms through out there. But let

515
00:26:53,640 --> 00:26:56,119
Speaker 1: me just tell you this. You gotta understand that, like

516
00:26:56,600 --> 00:27:00,199
Speaker 1: they only have two cones and a surface by I

517
00:27:00,200 --> 00:27:02,560
Speaker 1: don't think it's easy to appreciate what that means. But

518
00:27:03,080 --> 00:27:06,040
Speaker 1: we have three cones, and uh, it means that we

519
00:27:06,080 --> 00:27:11,680
Speaker 1: can see about a million variations in color, subtle variations, right, dear,

520
00:27:11,840 --> 00:27:13,960
Speaker 1: Just by having one less cone, it means that they

521
00:27:13,960 --> 00:27:19,160
Speaker 1: see much less variations in color. They basically only see

522
00:27:19,600 --> 00:27:25,040
Speaker 1: yellow into a yellowish green color and blue. That's it.

523
00:27:25,400 --> 00:27:29,320
Speaker 1: So your red is yellow, your orange is yellow, You're

524
00:27:29,359 --> 00:27:33,000
Speaker 1: blue is blue. When you start to think about how

525
00:27:33,040 --> 00:27:35,320
Speaker 1: everything we see out there, for even just something like

526
00:27:35,400 --> 00:27:39,000
Speaker 1: camo is you know, a variation of green and brown

527
00:27:39,119 --> 00:27:41,119
Speaker 1: and all this, and then you kind of throw that

528
00:27:41,160 --> 00:27:43,800
Speaker 1: into Howard deer season, it's all just the same yellow.

529
00:27:44,240 --> 00:27:47,040
Speaker 1: Like you're basically in a lot of different camo patterns,

530
00:27:47,119 --> 00:27:51,520
Speaker 1: one glorious big blob of yellow. Then we'll explain why

531
00:27:51,520 --> 00:27:53,399
Speaker 1: that really probably doesn't matter all that much. And when

532
00:27:53,400 --> 00:27:57,679
Speaker 1: you get to movement, So is this when you you know,

533
00:27:57,680 --> 00:28:01,199
Speaker 1: when I started hunting, and you know, you can kind

534
00:28:01,240 --> 00:28:04,480
Speaker 1: of follow the evolution of camo patterns to some extent.

535
00:28:04,600 --> 00:28:08,119
Speaker 1: For a while, there was a huge push for photo

536
00:28:08,160 --> 00:28:10,920
Speaker 1: realistic elements, right like, how do you take a high

537
00:28:10,920 --> 00:28:13,359
Speaker 1: resolution image of the woods or the leaves of the oak?

538
00:28:13,680 --> 00:28:15,959
Speaker 1: You know, oak leaves or whatever, get it right on

539
00:28:16,000 --> 00:28:18,679
Speaker 1: a on a shirt or a jacket, whatever, and it

540
00:28:18,720 --> 00:28:22,320
Speaker 1: looks just like that. And we've sort of moved beyond

541
00:28:22,400 --> 00:28:27,520
Speaker 1: that to these abstract jumbold kind of shading. That is

542
00:28:27,560 --> 00:28:31,720
Speaker 1: that a direct response to what we've learned about dear vision. No,

543
00:28:31,960 --> 00:28:35,720
Speaker 1: it's the camouflage world is simply just what people think

544
00:28:35,840 --> 00:28:38,440
Speaker 1: is cool in the moment. It's just sales in here, yeah,

545
00:28:38,480 --> 00:28:40,560
Speaker 1: I mean, it's all sales. I mean, but you know,

546
00:28:41,280 --> 00:28:44,000
Speaker 1: it's not to say that like SITCO type years aren't

547
00:28:44,120 --> 00:28:46,800
Speaker 1: based on some biology. They certainly are. Like they worked

548
00:28:46,800 --> 00:28:49,640
Speaker 1: with j Knights to come up with that pattern. Um.

549
00:28:49,720 --> 00:28:51,920
Speaker 1: But there's a couple of things to understand that. Let

550
00:28:51,920 --> 00:28:53,880
Speaker 1: me just continue with this one thing, all right, So

551
00:28:53,920 --> 00:28:56,719
Speaker 1: everything's kind of green. And then also when deer are

552
00:28:56,760 --> 00:28:58,920
Speaker 1: most active sunrise and sunset, there are a lot of

553
00:28:58,920 --> 00:29:02,480
Speaker 1: blues in the environment. Well, we I've done spectral reflectance

554
00:29:02,480 --> 00:29:04,800
Speaker 1: patterning on a lot of different camera patterns. Any type

555
00:29:04,840 --> 00:29:08,560
Speaker 1: of white color. Um, just through the dyeing process that

556
00:29:08,600 --> 00:29:12,880
Speaker 1: camouflage companies use, a deer would perceived that as blue. Um.

557
00:29:13,000 --> 00:29:16,200
Speaker 1: The other thing is like, so you have a bunch

558
00:29:16,240 --> 00:29:19,240
Speaker 1: of blue stuff. And then also like definitely don't use

559
00:29:19,280 --> 00:29:22,880
Speaker 1: any type of detergents with the typical detergency you guys

560
00:29:22,880 --> 00:29:25,800
Speaker 1: would use, because there are like you v your blue

561
00:29:25,840 --> 00:29:27,680
Speaker 1: enhancers in there that you and I don't see is

562
00:29:27,720 --> 00:29:29,840
Speaker 1: blue because we can't see it just brightens our shirts.

563
00:29:30,320 --> 00:29:32,320
Speaker 1: Deer would see that as is certainly some kind of

564
00:29:32,840 --> 00:29:36,320
Speaker 1: bluish hue or coloration. So all that comes together, Like

565
00:29:36,760 --> 00:29:38,440
Speaker 1: if you look at my count sol like I made,

566
00:29:38,480 --> 00:29:40,640
Speaker 1: I made a camera way back when after I did this,

567
00:29:40,680 --> 00:29:43,080
Speaker 1: and I kind of just use subtle variations of that,

568
00:29:43,120 --> 00:29:45,240
Speaker 1: like I just make my own camera nowadays, because it

569
00:29:45,240 --> 00:29:47,080
Speaker 1: doesn't look like anything you've ever seen. It's got a

570
00:29:47,120 --> 00:29:50,320
Speaker 1: ton of different colors on it, it's got different brightness

571
00:29:50,400 --> 00:29:53,200
Speaker 1: is and and it's all related to also like I

572
00:29:53,880 --> 00:29:56,960
Speaker 1: just bow hunt, so like it's also related to increasing

573
00:29:57,000 --> 00:29:59,680
Speaker 1: my movements. But it doesn't look like anything that's out there,

574
00:29:59,680 --> 00:30:02,560
Speaker 1: because most of what's out there is literally just based

575
00:30:02,600 --> 00:30:06,720
Speaker 1: on making it look cool, so hunters by it. Uh

576
00:30:07,640 --> 00:30:11,200
Speaker 1: I have have you ever? I'm sure you have uh

577
00:30:11,400 --> 00:30:15,200
Speaker 1: paid attention to you know, the fishing industry, and there's

578
00:30:15,360 --> 00:30:18,040
Speaker 1: very similar arguments for phishing lures as well, and the

579
00:30:18,360 --> 00:30:20,680
Speaker 1: colors that they think that you know, bass can see

580
00:30:20,720 --> 00:30:22,480
Speaker 1: for example, you know, like they see orange really well,

581
00:30:22,480 --> 00:30:25,920
Speaker 1: they think and they you know, red disappears pretty quickly underwater,

582
00:30:26,080 --> 00:30:29,760
Speaker 1: and you know, and it's it's made to catch. It

583
00:30:29,800 --> 00:30:33,080
Speaker 1: made to catch you know, your dollars and not fish necessarily.

584
00:30:33,480 --> 00:30:37,920
Speaker 1: But so is there is there any benefit then? Uh too?

585
00:30:38,040 --> 00:30:40,760
Speaker 1: You know the new wave of patterns that you see

586
00:30:40,760 --> 00:30:43,680
Speaker 1: out there, Yeah, I think they're a little bit more

587
00:30:43,720 --> 00:30:46,440
Speaker 1: efficient and kind of you know, they have so they'll

588
00:30:46,440 --> 00:30:50,320
Speaker 1: talk about this macro pattern versus micro pattern. That's that's

589
00:30:50,440 --> 00:30:53,800
Speaker 1: fancy words were saying, big blobs versus small blobs, and

590
00:30:53,840 --> 00:30:56,960
Speaker 1: the bigger blobs do tend to be like, um, more differentiated.

591
00:30:57,000 --> 00:31:00,200
Speaker 1: But yeah, right, it's all about and this isn't a

592
00:31:00,240 --> 00:31:02,160
Speaker 1: bad thing, right people. A lot of people are trying

593
00:31:02,200 --> 00:31:03,880
Speaker 1: to do their best. But you know it's like, you know,

594
00:31:03,880 --> 00:31:05,880
Speaker 1: shark true stor is my favorite law in the world

595
00:31:05,880 --> 00:31:08,000
Speaker 1: to use a shark trees And I found out like

596
00:31:08,040 --> 00:31:10,920
Speaker 1: two years ago that best can't even see that color.

597
00:31:11,360 --> 00:31:13,000
Speaker 1: I'm like, you know, so this is just some kind

598
00:31:13,000 --> 00:31:16,040
Speaker 1: of variation of silver to them. It's all a matter

599
00:31:16,160 --> 00:31:19,000
Speaker 1: of like you're selling the people on what they want

600
00:31:20,320 --> 00:31:24,120
Speaker 1: just to um to wear well, isn't there sort of

601
00:31:25,600 --> 00:31:29,000
Speaker 1: kind of like a who cares you know, component to

602
00:31:29,080 --> 00:31:31,239
Speaker 1: this because we know I mean, so I was just

603
00:31:31,280 --> 00:31:34,840
Speaker 1: writing a piece about getting turkeys to shack Gobble, and

604
00:31:35,800 --> 00:31:38,760
Speaker 1: you know, lately, especially Michael Chamberlain has been out talking

605
00:31:38,800 --> 00:31:40,680
Speaker 1: about turkeys a lot. He's I'm actually gonna have him

606
00:31:40,720 --> 00:31:43,280
Speaker 1: on one of these episodes too, and talking about how,

607
00:31:43,320 --> 00:31:45,920
Speaker 1: you know, we're reversing the order of turkeys right when

608
00:31:45,920 --> 00:31:47,520
Speaker 1: we when we go out and call a bird in,

609
00:31:48,200 --> 00:31:50,600
Speaker 1: you know, a long beard, inner town whatever, we're kind

610
00:31:50,600 --> 00:31:54,280
Speaker 1: of like reversing what the nature has you know, designed

611
00:31:54,280 --> 00:31:56,440
Speaker 1: this to do. But at the same time, thousands and

612
00:31:56,480 --> 00:31:59,160
Speaker 1: thousands of turkeys are killed that way. So part part

613
00:31:59,160 --> 00:32:02,320
Speaker 1: of it is like an is just an academic exercise

614
00:32:02,360 --> 00:32:03,800
Speaker 1: when we can go out and I can call in

615
00:32:03,840 --> 00:32:05,520
Speaker 1: turkeys and go, well, this one didn't follow the rules

616
00:32:05,520 --> 00:32:08,120
Speaker 1: and that one didn't. And we know that the camel

617
00:32:08,120 --> 00:32:10,160
Speaker 1: patterns that we use, I mean, regardless of what dear

618
00:32:10,200 --> 00:32:13,120
Speaker 1: c we still kill a lot of them wearing them.

619
00:32:13,160 --> 00:32:16,400
Speaker 1: It's it's all right, all right, I'm a big turkey hunter,

620
00:32:16,480 --> 00:32:19,680
Speaker 1: big duck hunter, big deer hunter. When you hunt, it's

621
00:32:19,720 --> 00:32:22,360
Speaker 1: not just enough to be like Okay, I've killed a deer.

622
00:32:22,400 --> 00:32:24,240
Speaker 1: You know what haunts you is the time that one

623
00:32:24,280 --> 00:32:27,120
Speaker 1: time that big deer came in and busted you. And

624
00:32:27,200 --> 00:32:29,520
Speaker 1: that's the stuff as hunters that we obsess over. It's

625
00:32:29,520 --> 00:32:32,360
Speaker 1: those little variations of like I shouldn't have moved, why

626
00:32:32,400 --> 00:32:34,720
Speaker 1: did I stand so early? Why you know, why didn't

627
00:32:34,720 --> 00:32:37,320
Speaker 1: I find a better background? All of these things. So yeah,

628
00:32:37,400 --> 00:32:40,160
Speaker 1: in the end, I think hunting has evolved into like

629
00:32:40,360 --> 00:32:44,520
Speaker 1: we know of what works right, it's all these little

630
00:32:44,600 --> 00:32:46,120
Speaker 1: things that we want to find out to make it

631
00:32:46,160 --> 00:32:48,480
Speaker 1: better because we're obsessive and where it's our you know,

632
00:32:48,600 --> 00:32:51,080
Speaker 1: our hobby, and when we think of it that way,

633
00:32:51,080 --> 00:32:53,880
Speaker 1: then maybe it does matter when you're talking about just

634
00:32:54,000 --> 00:32:59,080
Speaker 1: trying to tinker with things. But overall, no, you know what,

635
00:32:59,240 --> 00:33:01,000
Speaker 1: people could walk on the blue jeans and a white

636
00:33:01,040 --> 00:33:04,440
Speaker 1: shirt and shoot the crap out of deer anyway. You know, Well,

637
00:33:05,120 --> 00:33:10,360
Speaker 1: isn't there isn't there some level of a benefit to

638
00:33:10,520 --> 00:33:14,040
Speaker 1: from the materials they're used? Is it like a softer

639
00:33:14,160 --> 00:33:17,400
Speaker 1: material better as far as light reflection or not. So

640
00:33:17,480 --> 00:33:20,880
Speaker 1: there's different types of materials that go so the materials

641
00:33:20,920 --> 00:33:23,880
Speaker 1: themselves and don't quote me on everything here, but Basically

642
00:33:24,200 --> 00:33:27,200
Speaker 1: there's different materials go through different processing on the front

643
00:33:27,320 --> 00:33:30,600
Speaker 1: end to basically how they're going to be colored white

644
00:33:30,760 --> 00:33:34,080
Speaker 1: before they're actually camouflaged. And you can in that process

645
00:33:34,320 --> 00:33:37,880
Speaker 1: ingrain these ub brighteners into the fabric. Okay, and those

646
00:33:37,920 --> 00:33:40,240
Speaker 1: gonna be a little stiffer to the touch, They're gonna

647
00:33:40,280 --> 00:33:42,400
Speaker 1: be a little bit harder, but they're gonna last longer.

648
00:33:42,400 --> 00:33:45,040
Speaker 1: They're gonna be brighter longer. And that's why the kind

649
00:33:45,080 --> 00:33:48,400
Speaker 1: of everyone kind of went that way originally was like, Okay,

650
00:33:48,400 --> 00:33:51,960
Speaker 1: we're making a better quality product, but certainly to a

651
00:33:52,720 --> 00:33:55,520
Speaker 1: uh hunter, they're less comfortable and to a deer they're

652
00:33:55,520 --> 00:33:58,360
Speaker 1: more visibly blue. And so now they kind of switched

653
00:33:58,400 --> 00:34:01,280
Speaker 1: the other way around, which is a different eyeing process

654
00:34:01,320 --> 00:34:04,400
Speaker 1: that cuts down on that makes it more more subtle.

655
00:34:04,880 --> 00:34:07,600
Speaker 1: Got it, all right, let's move on from dear colors?

656
00:34:07,600 --> 00:34:10,440
Speaker 1: What what do we I think this might be more

657
00:34:10,440 --> 00:34:12,239
Speaker 1: important and to correct me if I'm wrong here, but

658
00:34:12,280 --> 00:34:15,560
Speaker 1: what did we learn about dear vision as far as

659
00:34:15,719 --> 00:34:18,680
Speaker 1: catching movement? All right, let me let me start off

660
00:34:18,680 --> 00:34:21,719
Speaker 1: with something first. Let me cover that movements related to

661
00:34:21,920 --> 00:34:24,440
Speaker 1: also their visual acuti or basically like you know how

662
00:34:24,480 --> 00:34:28,279
Speaker 1: you and I have vision all right, you've gotta understand that,

663
00:34:28,360 --> 00:34:32,759
Speaker 1: like Dear have about visions, so everything is blurry to them,

664
00:34:33,000 --> 00:34:36,200
Speaker 1: all right, So when you think about everything being blurry

665
00:34:36,320 --> 00:34:38,840
Speaker 1: to them, what they're trying to pick up on is

666
00:34:38,880 --> 00:34:43,759
Speaker 1: a blob that moves around. Okay, this is important to

667
00:34:43,800 --> 00:34:47,120
Speaker 1: understand because it also comes back to camouflage and being

668
00:34:47,160 --> 00:34:51,240
Speaker 1: one big blob. If you think about what their whole

669
00:34:51,320 --> 00:34:55,160
Speaker 1: job in life is to basically see that predator on

670
00:34:55,200 --> 00:34:57,279
Speaker 1: the horizon or see them in the woods, and it's

671
00:34:57,320 --> 00:35:03,040
Speaker 1: just a blob moving around, detect them and haul. Uh,

672
00:35:03,320 --> 00:35:05,680
Speaker 1: there's a couple of things you've got to understand. Dear's

673
00:35:05,760 --> 00:35:07,919
Speaker 1: eyes don't overlap that well, so they don't have good

674
00:35:07,920 --> 00:35:10,239
Speaker 1: depth perception, but they have a really good field of view.

675
00:35:10,600 --> 00:35:12,920
Speaker 1: And the way their rods are kind of spaced out

676
00:35:12,920 --> 00:35:15,719
Speaker 1: across their eyes, they can pick up detection like that.

677
00:35:16,239 --> 00:35:18,480
Speaker 1: Now it's not just a pick up movement, just like that.

678
00:35:18,719 --> 00:35:22,640
Speaker 1: And here's the thing. They also process this. This is

679
00:35:22,640 --> 00:35:25,279
Speaker 1: like philosophical, but it's actually important. Are just time with me.

680
00:35:26,120 --> 00:35:29,719
Speaker 1: We've done this experiment. It fascinates me. But like they

681
00:35:30,320 --> 00:35:31,719
Speaker 1: and if you want the details, if you want to

682
00:35:31,719 --> 00:35:33,920
Speaker 1: go down the rabbitable, I think it's awesome. But what

683
00:35:33,960 --> 00:35:37,000
Speaker 1: you have to understand is that they pick they perceive time,

684
00:35:37,160 --> 00:35:40,520
Speaker 1: to perceive time twice as slow. In other words, they

685
00:35:40,560 --> 00:35:44,600
Speaker 1: pick up movement twice as easily. They pick up movement

686
00:35:44,600 --> 00:35:48,480
Speaker 1: twice as easily. Okay, hold on how slow emotions them?

687
00:35:48,480 --> 00:35:50,680
Speaker 1: I know you won't believe me, but things happen in

688
00:35:50,800 --> 00:35:53,799
Speaker 1: slow motion. To them, they react twice as fast as

689
00:35:54,360 --> 00:35:57,640
Speaker 1: we do. They see things twice as quick. They detect

690
00:35:57,719 --> 00:36:02,680
Speaker 1: things twice as easy. How do we know how dear

691
00:36:02,760 --> 00:36:05,319
Speaker 1: perceived time? I gotta know that? All right, this is

692
00:36:05,360 --> 00:36:09,680
Speaker 1: this is crazy. All right? Look, okay, look up flicker

693
00:36:09,719 --> 00:36:11,600
Speaker 1: fusion rate. If you want to go down that rabbit hole.

694
00:36:11,600 --> 00:36:13,680
Speaker 1: Look look this up. But basically, you know, back in

695
00:36:13,680 --> 00:36:16,760
Speaker 1: the day when you when you had a TV before

696
00:36:16,920 --> 00:36:19,880
Speaker 1: LEDs and all these other TVs came out, you know,

697
00:36:20,280 --> 00:36:23,799
Speaker 1: basically you're stringing together these still pictures on TV, and

698
00:36:23,840 --> 00:36:25,920
Speaker 1: all TVs have to have what's called the flicker rate,

699
00:36:26,120 --> 00:36:28,279
Speaker 1: and they had to put it above sixty. And the

700
00:36:28,280 --> 00:36:30,000
Speaker 1: reason they had to put it above sixties because you

701
00:36:30,040 --> 00:36:34,440
Speaker 1: and I also see things instill frames and our brain

702
00:36:35,160 --> 00:36:38,839
Speaker 1: refreshes that still image at sixty times per second and

703
00:36:39,000 --> 00:36:42,560
Speaker 1: creates a holistic movement pattern, but that is not the

704
00:36:42,600 --> 00:36:46,640
Speaker 1: same across animals. And so um, this is why, like

705
00:36:46,680 --> 00:36:48,840
Speaker 1: a dog actually has a higher flicker fusion rate. And

706
00:36:48,840 --> 00:36:50,239
Speaker 1: back in the day when you had an old TV,

707
00:36:50,360 --> 00:36:53,400
Speaker 1: a dog wouldn't watch TV with you because it looked

708
00:36:53,440 --> 00:36:56,879
Speaker 1: like a bunch of still pictures to them. Okay, so

709
00:36:57,840 --> 00:37:01,120
Speaker 1: here all right, But that also flicker fusion rate is

710
00:37:01,160 --> 00:37:04,839
Speaker 1: also how fast you're refreshing time or movement or all

711
00:37:04,880 --> 00:37:07,799
Speaker 1: of these things. So like have you ever wondered how

712
00:37:07,840 --> 00:37:10,040
Speaker 1: the hell of bat can catch a bug? Or like

713
00:37:10,320 --> 00:37:13,200
Speaker 1: how a bird is able to in you know, hit

714
00:37:13,280 --> 00:37:16,719
Speaker 1: another bird in in air. It's because they're they're like

715
00:37:16,920 --> 00:37:21,080
Speaker 1: rapidly processing these images like super fast. Now, that relates

716
00:37:21,120 --> 00:37:23,440
Speaker 1: to their lifespan. Like if you look in the literature,

717
00:37:23,520 --> 00:37:26,160
Speaker 1: there's only so many flickers and so many times you

718
00:37:26,200 --> 00:37:28,920
Speaker 1: get that that refresh and then you're dead. So like

719
00:37:28,960 --> 00:37:33,640
Speaker 1: short lived animals refresh a lot, and long lived animals don't.

720
00:37:34,120 --> 00:37:36,160
Speaker 1: And if you want to go into the crazy world,

721
00:37:36,200 --> 00:37:39,280
Speaker 1: it's like, yes, a proof flight that lives twenty four hours,

722
00:37:39,400 --> 00:37:42,040
Speaker 1: even they perceived like an entire lifespan that in their

723
00:37:42,040 --> 00:37:44,879
Speaker 1: own theoretical world is as long as ours. So yes,

724
00:37:45,120 --> 00:37:49,160
Speaker 1: you're picking that stuff up twice as fast. So I

725
00:37:49,320 --> 00:37:51,919
Speaker 1: read something recently about that it might have had something

726
00:37:51,960 --> 00:37:53,840
Speaker 1: to do with space travelers, and but it was about

727
00:37:54,040 --> 00:37:56,480
Speaker 1: it was like from a fruit flies perspective on what

728
00:37:56,600 --> 00:37:58,160
Speaker 1: it would see if you were going to smash it.

729
00:37:58,680 --> 00:38:00,720
Speaker 1: And it was like a description of this hand coming

730
00:38:00,760 --> 00:38:05,160
Speaker 1: down forever, because that's their entire existence, even though to

731
00:38:05,320 --> 00:38:08,759
Speaker 1: us it's twenty four hours. Yeah, it's it's crazy, but yeah,

732
00:38:08,800 --> 00:38:11,279
Speaker 1: that's that's happening in absolute slow motion. So like they've

733
00:38:11,320 --> 00:38:13,440
Speaker 1: done studies where they've looked at like how does a

734
00:38:13,520 --> 00:38:16,120
Speaker 1: bird catch a fly? Like that should amaze you to

735
00:38:16,160 --> 00:38:18,719
Speaker 1: begin with, right, flies move so fast to us, No

736
00:38:19,160 --> 00:38:22,719
Speaker 1: to a bird that flies going in slow motion? All right,

737
00:38:22,719 --> 00:38:25,040
Speaker 1: And that's the same thing that's happening with dear. When

738
00:38:25,040 --> 00:38:27,600
Speaker 1: you're moving and you think you're moving fast, you're moving subtly.

739
00:38:27,680 --> 00:38:31,359
Speaker 1: You're doing that's slow for them. There's they're able they

740
00:38:31,400 --> 00:38:34,200
Speaker 1: have twice as long to pick it up, if if

741
00:38:34,239 --> 00:38:38,879
Speaker 1: that makes sense sort of, well, it all comes back

742
00:38:38,880 --> 00:38:41,200
Speaker 1: to this movie, this idea of like all they care about,

743
00:38:41,440 --> 00:38:45,319
Speaker 1: their whole eye, their whole all, their whole brain is

744
00:38:45,360 --> 00:38:48,760
Speaker 1: wired simply to pick up motion. That's how they detect predators.

745
00:38:48,800 --> 00:38:52,040
Speaker 1: You and I we don't. We're not made to pick

746
00:38:52,120 --> 00:38:55,319
Speaker 1: up motion. What we're made to do is discertain objects.

747
00:38:55,360 --> 00:38:57,720
Speaker 1: Like I see a person, I look at that person,

748
00:38:57,760 --> 00:39:00,279
Speaker 1: I discern who they are. Dear, don't care out that

749
00:39:00,280 --> 00:39:02,120
Speaker 1: they just wanted to have a basically scan of their

750
00:39:02,239 --> 00:39:04,560
Speaker 1: environment and just be able to pick up something that's moving.

751
00:39:04,760 --> 00:39:08,160
Speaker 1: That's what worries them. And given that, it also means

752
00:39:08,200 --> 00:39:11,719
Speaker 1: that quick motions are jagg emotions. Are you just trying

753
00:39:11,719 --> 00:39:14,640
Speaker 1: to be more subtle with your movements? That's not subtle

754
00:39:14,640 --> 00:39:16,759
Speaker 1: to a deer, that's not best to a deer. So

755
00:39:16,840 --> 00:39:20,600
Speaker 1: if you move, don't be surprised when they paid you. Okay,

756
00:39:20,640 --> 00:39:23,840
Speaker 1: there's gonna maybe be way off here is this is

757
00:39:23,880 --> 00:39:25,640
Speaker 1: this part of the reason why we figured out how

758
00:39:25,680 --> 00:39:28,440
Speaker 1: to throw stuff to kill things, because if we just

759
00:39:28,480 --> 00:39:31,400
Speaker 1: took off after a deer, we're not built for it.

760
00:39:31,400 --> 00:39:35,560
Speaker 1: It's one hunt. It's it's well, if you look all right,

761
00:39:35,560 --> 00:39:37,200
Speaker 1: you want me to go into this, But if you

762
00:39:37,239 --> 00:39:40,160
Speaker 1: look at like basically our perception, it's all about social

763
00:39:40,200 --> 00:39:44,120
Speaker 1: interactions with each other and a lot of our stuff,

764
00:39:44,160 --> 00:39:46,719
Speaker 1: Like you know, our eyes aren't necessarily just made to

765
00:39:46,719 --> 00:39:50,759
Speaker 1: be good predators, but also like the social interactions between us,

766
00:39:51,120 --> 00:39:54,359
Speaker 1: and so we had to at some point become good

767
00:39:54,360 --> 00:39:57,600
Speaker 1: at killing them without actually chasing them down and ambushing them,

768
00:39:57,600 --> 00:40:01,640
Speaker 1: because we're nothing compared to prey species that actually is

769
00:40:01,719 --> 00:40:06,080
Speaker 1: hunted all the time. Has have have has anyone done

770
00:40:06,080 --> 00:40:09,640
Speaker 1: any studies on so we're talking white tales, and you know,

771
00:40:09,680 --> 00:40:11,640
Speaker 1: we can we can go back through you know, the

772
00:40:11,719 --> 00:40:14,400
Speaker 1: last a couple hundred thousand years and figure out probably

773
00:40:14,440 --> 00:40:17,600
Speaker 1: what was the major predators for white tales and why

774
00:40:17,680 --> 00:40:20,799
Speaker 1: this would evolve. Has anybody done anything like this with

775
00:40:20,840 --> 00:40:23,839
Speaker 1: like access deer from India and the you know, tigers

776
00:40:24,000 --> 00:40:27,440
Speaker 1: or any of the other deer subspecies. So they've done

777
00:40:27,440 --> 00:40:32,240
Speaker 1: it with fallow deer and they're they're they're similar. Um,

778
00:40:32,280 --> 00:40:34,040
Speaker 1: you know, you gotta keep in mind that white Teldia

779
00:40:34,040 --> 00:40:36,880
Speaker 1: are pretty late evolutionarily, like they're they're one of the

780
00:40:36,920 --> 00:40:39,279
Speaker 1: more recent kind of additions to deer family. If you

781
00:40:39,280 --> 00:40:42,440
Speaker 1: look at the more historic ones or even something like fallows,

782
00:40:42,600 --> 00:40:47,920
Speaker 1: you know, it's still ancestrally held that it's all about detecting, detecting, detecting, yep.

783
00:40:48,400 --> 00:40:53,440
Speaker 1: So this this refresh rate and them sort of living

784
00:40:53,520 --> 00:40:55,919
Speaker 1: in this I don't know it. Maybe it isn't wrong,

785
00:40:55,960 --> 00:40:58,480
Speaker 1: but like compared to how we perceive stuff like a

786
00:40:58,560 --> 00:41:01,279
Speaker 1: little bit of a SlowMo world where they're, yeah, where

787
00:41:01,280 --> 00:41:03,960
Speaker 1: they're picking up stuff instantly, you know, we think of

788
00:41:04,000 --> 00:41:06,160
Speaker 1: it like if you look at it from the perspective

789
00:41:06,160 --> 00:41:07,919
Speaker 1: of what we're talking about, it's like, Okay, if there's

790
00:41:07,960 --> 00:41:10,040
Speaker 1: a tiger or a mountain lion or something crouched in

791
00:41:10,040 --> 00:41:13,920
Speaker 1: the grass and that sucker takes off the earlier detection

792
00:41:13,960 --> 00:41:16,440
Speaker 1: system you have, the better your chance you have to

793
00:41:16,440 --> 00:41:20,280
Speaker 1: get away. And now we can take that as hunters

794
00:41:20,320 --> 00:41:23,359
Speaker 1: and go, okay, well, just if I dumb this down.

795
00:41:23,360 --> 00:41:25,480
Speaker 1: When I'm sitting in my tree stand and that deer's

796
00:41:25,719 --> 00:41:28,000
Speaker 1: you know, approaching fifty yards away, and I think that

797
00:41:28,000 --> 00:41:29,880
Speaker 1: I'm out of its field of view, and I stand

798
00:41:29,960 --> 00:41:32,480
Speaker 1: up and that sucker catches me, it's over. I mean,

799
00:41:32,520 --> 00:41:36,480
Speaker 1: it's it's that. It's it's why they jump an arrow,

800
00:41:36,680 --> 00:41:38,640
Speaker 1: you know, like you and I are like, literally, I

801
00:41:38,719 --> 00:41:41,960
Speaker 1: can't even see that arrow go, and yet they can

802
00:41:42,120 --> 00:41:45,440
Speaker 1: hear and process that they're in danger and start the

803
00:41:45,520 --> 00:41:49,880
Speaker 1: duck within the time frame of an arrow getting to

804
00:41:49,960 --> 00:41:51,960
Speaker 1: him in twenty yards. I mean, just think about that,

805
00:41:52,200 --> 00:41:56,240
Speaker 1: and it's because that is actually happening slower to them.

806
00:41:56,280 --> 00:42:00,320
Speaker 1: And there. You know, that's an interesting example because every buddy,

807
00:42:00,600 --> 00:42:02,000
Speaker 1: we we say, oh, you're duck in the arrow or

808
00:42:02,080 --> 00:42:04,440
Speaker 1: jump in the string, and all they're doing is just

809
00:42:04,480 --> 00:42:07,320
Speaker 1: getting away from a perceived threat that they can perceive

810
00:42:07,560 --> 00:42:09,640
Speaker 1: real fat It's not like they're sitting there thinking an

811
00:42:09,719 --> 00:42:11,440
Speaker 1: arrow just got shot at me. They heard a noise

812
00:42:11,480 --> 00:42:13,840
Speaker 1: that alarmed them, and they loaded up their legs to

813
00:42:13,920 --> 00:42:17,840
Speaker 1: take off, bingo, bingo. It's just instinctual. It's like, Okay,

814
00:42:18,000 --> 00:42:20,400
Speaker 1: I didn't see it, but I heard it. Yeah, And

815
00:42:20,440 --> 00:42:22,680
Speaker 1: they're hearing isn't much different than ours, so you know,

816
00:42:22,760 --> 00:42:25,799
Speaker 1: it's about identical. So you know, just it's some kind

817
00:42:25,800 --> 00:42:28,320
Speaker 1: of spark, you know, some sound that doesn't sound natural

818
00:42:28,360 --> 00:42:31,360
Speaker 1: time to time to haul off. So they're hearing is

819
00:42:31,400 --> 00:42:34,040
Speaker 1: the same, but they're are you know, similar, but their

820
00:42:34,080 --> 00:42:40,000
Speaker 1: reaction time is way faster. Yep, that's exactly right. Uh,

821
00:42:41,120 --> 00:42:46,839
Speaker 1: how do hunters use that? Just don't move? Well, it's

822
00:42:46,880 --> 00:42:50,200
Speaker 1: nothing that hunters don't already know, you know, if you're gonna,

823
00:42:50,400 --> 00:42:52,880
Speaker 1: you know, get above them, right, if you look at

824
00:42:52,880 --> 00:42:55,399
Speaker 1: how deer basically see their world. They're meant to look

825
00:42:55,440 --> 00:42:59,120
Speaker 1: kind of down and scan the horizon, uh, straight ahead

826
00:42:59,120 --> 00:43:02,640
Speaker 1: of them. They're not used to being hunted up. You know,

827
00:43:02,640 --> 00:43:06,120
Speaker 1: you get about about up, you're out of their line

828
00:43:06,120 --> 00:43:09,640
Speaker 1: of side unless they're lifting their head. If you're gonna move,

829
00:43:09,840 --> 00:43:12,719
Speaker 1: always have something behind you. They have poor depth perception.

830
00:43:13,239 --> 00:43:16,480
Speaker 1: So this is just basic hunter knowledge. One oh one.

831
00:43:17,000 --> 00:43:19,920
Speaker 1: Get you know, get your silhouette covered if you're you know,

832
00:43:19,960 --> 00:43:22,480
Speaker 1: that will help them not see the movement. If you

833
00:43:22,480 --> 00:43:24,920
Speaker 1: don't have a silhouette, if you're silhouetted at all, be

834
00:43:25,239 --> 00:43:28,920
Speaker 1: really really tight with your movement. Don't be surprised when

835
00:43:28,920 --> 00:43:31,360
Speaker 1: they pay you there. It's basically like you hunt for

836
00:43:31,400 --> 00:43:34,320
Speaker 1: a couple of years and you know everything that I'm saying.

837
00:43:34,320 --> 00:43:35,680
Speaker 1: You know what I'm you know what I'm getting that

838
00:43:35,840 --> 00:43:50,799
Speaker 1: I know exactly what you're getting a man. Can we

839
00:43:50,840 --> 00:43:54,120
Speaker 1: talk a little bit about the difference between them perceiving movement,

840
00:43:54,560 --> 00:43:57,399
Speaker 1: you know, on the ground around them versus above them,

841
00:43:57,560 --> 00:44:03,359
Speaker 1: because that's that's an important distinct it right, So they

842
00:44:03,360 --> 00:44:07,640
Speaker 1: are anatomically made to perceive threats on the ground. Okay,

843
00:44:07,760 --> 00:44:10,680
Speaker 1: their biggest predators back in the day were some type

844
00:44:10,680 --> 00:44:16,760
Speaker 1: of you know, wolf and cougar type, you know, combination, right, uh,

845
00:44:16,800 --> 00:44:22,919
Speaker 1: And so when we think about that, it really comes

846
00:44:23,040 --> 00:44:26,360
Speaker 1: is no surprise that they have a hard time seeing

847
00:44:26,800 --> 00:44:29,680
Speaker 1: up above them. And it's not just like they have

848
00:44:29,719 --> 00:44:32,120
Speaker 1: a hard time seeing it, but actually their vision gets

849
00:44:32,160 --> 00:44:34,640
Speaker 1: more blurry. If you and I, like look straight ahead

850
00:44:34,680 --> 00:44:37,360
Speaker 1: of each other, actually on the periphery of our visions

851
00:44:37,400 --> 00:44:39,400
Speaker 1: quite blurry. And it's the same for deer, right, So

852
00:44:39,400 --> 00:44:41,279
Speaker 1: they're looking straight ahead, and like as you get further

853
00:44:41,320 --> 00:44:43,719
Speaker 1: and further higher up in the trees, if you will,

854
00:44:43,960 --> 00:44:46,239
Speaker 1: it's blurry and blurry or so you're able to camouflage

855
00:44:46,239 --> 00:44:49,000
Speaker 1: in more and more. But again remember that all these

856
00:44:49,080 --> 00:44:52,520
Speaker 1: nice fine detail camouflage patterns, that nice fine detail that

857
00:44:52,560 --> 00:44:54,560
Speaker 1: you're talking about doesn't mean crap to a deer that's

858
00:44:55,160 --> 00:44:57,680
Speaker 1: yards away from you. And already you're blurry. It's all

859
00:44:57,800 --> 00:44:59,680
Speaker 1: blending of those colors. So you want to you know,

860
00:45:00,239 --> 00:45:02,560
Speaker 1: if you're you're either got some good blobs going on

861
00:45:02,640 --> 00:45:05,840
Speaker 1: or you don't. At that point, that's where it comes into.

862
00:45:05,880 --> 00:45:07,799
Speaker 1: If you're one solid blob or you don't have a

863
00:45:07,800 --> 00:45:10,360
Speaker 1: really strong counter player, and that has big differences in

864
00:45:10,400 --> 00:45:14,399
Speaker 1: that those shapes. Then don't be surprised that that they'll

865
00:45:14,440 --> 00:45:16,960
Speaker 1: still trigger them to see you because you're even though

866
00:45:17,040 --> 00:45:18,960
Speaker 1: they they're not made to see you, you're still in

867
00:45:19,000 --> 00:45:21,960
Speaker 1: their field of view. Um, if you have a couple

868
00:45:21,960 --> 00:45:25,520
Speaker 1: of decent camel patterns or a bunch of bigger macro blobs,

869
00:45:25,560 --> 00:45:28,520
Speaker 1: if you will, you can get away with a lot. Yeah. So,

870
00:45:28,640 --> 00:45:30,640
Speaker 1: I mean what you're saying and what you alluded to

871
00:45:30,680 --> 00:45:35,160
Speaker 1: before with people hunters with some experience that definitely understand this.

872
00:45:35,760 --> 00:45:38,200
Speaker 1: When you know, when you get up there and you're

873
00:45:38,200 --> 00:45:40,759
Speaker 1: in a little tiny tree and you're silhouetted against a

874
00:45:40,800 --> 00:45:44,080
Speaker 1: bright sky or you know, the sunset or whatever, if

875
00:45:44,160 --> 00:45:47,680
Speaker 1: even something that had terrible vision would be able to

876
00:45:47,719 --> 00:45:51,719
Speaker 1: see the movement, they're better one million percent. You know.

877
00:45:52,040 --> 00:45:54,279
Speaker 1: That's that's what they're gonna key in on. It's all

878
00:45:54,320 --> 00:45:57,279
Speaker 1: about taking the blob I keep saying blob, but you know,

879
00:45:57,320 --> 00:46:00,720
Speaker 1: the figure that is yourself, and either hiding it behind

880
00:46:00,760 --> 00:46:04,480
Speaker 1: something or moving ever so subtly that you don't cue them.

881
00:46:04,520 --> 00:46:08,040
Speaker 1: But in the end, I can't stress enough the importance

882
00:46:08,080 --> 00:46:11,760
Speaker 1: of covering your silhouette that will break up the movement

883
00:46:11,800 --> 00:46:15,040
Speaker 1: pattern because it will basically, can they have poor depth perception,

884
00:46:15,080 --> 00:46:18,960
Speaker 1: it will converge. That tree basically converges with you into

885
00:46:19,080 --> 00:46:23,560
Speaker 1: one thing. Does that make sense absolutely? I mean I

886
00:46:23,600 --> 00:46:28,360
Speaker 1: set my stands to be behind the tree and shoot

887
00:46:28,360 --> 00:46:32,480
Speaker 1: around it. Um yeah. Those those those saddles, you know,

888
00:46:32,520 --> 00:46:35,279
Speaker 1: the saddle harness and all that, they make a really

889
00:46:35,320 --> 00:46:38,080
Speaker 1: big difference in that. Um. I remember, you know, the

890
00:46:38,160 --> 00:46:40,720
Speaker 1: first time I ever sat in a in a tower

891
00:46:41,840 --> 00:46:45,160
Speaker 1: deer hunting, I kept getting pegged by deer. I was like,

892
00:46:45,200 --> 00:46:48,080
Speaker 1: what I'm covered? Why am I getting And I realized

893
00:46:48,120 --> 00:46:51,440
Speaker 1: real quick I when instinctively, when I got into the

894
00:46:51,480 --> 00:46:54,440
Speaker 1: tower blind, I had put a bunch of twigs up

895
00:46:54,440 --> 00:46:58,560
Speaker 1: behind me to like stop my movement. And then well

896
00:46:58,600 --> 00:47:01,319
Speaker 1: the deer we're used to like seeing nothing there, since

897
00:47:01,320 --> 00:47:03,560
Speaker 1: all of a sudden, now there's a freak them out.

898
00:47:04,000 --> 00:47:07,680
Speaker 1: It's all about keeping with your environment. So I want

899
00:47:07,680 --> 00:47:11,319
Speaker 1: to ask you about that then, So when you know,

900
00:47:11,440 --> 00:47:13,840
Speaker 1: we we we've talked about how they can see certain

901
00:47:13,840 --> 00:47:16,560
Speaker 1: colors and what and what what light positions or what

902
00:47:16,680 --> 00:47:20,000
Speaker 1: light is beneficial to them. We've talked about movement, But

903
00:47:20,040 --> 00:47:23,040
Speaker 1: what about that, dear, when you know you're you're up

904
00:47:23,040 --> 00:47:24,680
Speaker 1: in your stand, you don't move. It's not a deer,

905
00:47:24,719 --> 00:47:27,080
Speaker 1: you want to shoot and it's just walking by you

906
00:47:27,120 --> 00:47:29,160
Speaker 1: feel like it's it's over, and that your stops and

907
00:47:29,200 --> 00:47:32,560
Speaker 1: looks right up at you and you haven't moved, right.

908
00:47:33,120 --> 00:47:35,839
Speaker 1: That has everything to do with your that you're not

909
00:47:35,880 --> 00:47:38,720
Speaker 1: blending in with your background because your camera pattern doesn't

910
00:47:39,200 --> 00:47:42,120
Speaker 1: remember it's almost like a bright yellow color. You've got

911
00:47:42,120 --> 00:47:44,600
Speaker 1: to remember that all your camera patterns are basically looking

912
00:47:44,600 --> 00:47:47,440
Speaker 1: at some subtle variation of yellow. When you start to

913
00:47:47,480 --> 00:47:51,080
Speaker 1: realize that, well, then you're not blending in with your

914
00:47:51,160 --> 00:47:54,520
Speaker 1: brown tree. In fact, most camera patterns aren't blending in

915
00:47:54,560 --> 00:47:57,000
Speaker 1: with their surroundings all that. Well, just do you have

916
00:47:57,080 --> 00:48:01,680
Speaker 1: really shitty vision? Right? So like yeah, at that point,

917
00:48:01,760 --> 00:48:05,319
Speaker 1: it's just they paged you because you're sitting still silhouette

918
00:48:05,800 --> 00:48:08,880
Speaker 1: against the tree. Well, isn't Is there an element to

919
00:48:08,960 --> 00:48:11,680
Speaker 1: that of just you know, their their life is condensed

920
00:48:11,719 --> 00:48:14,280
Speaker 1: to you know, say a square mile as their home range,

921
00:48:14,600 --> 00:48:20,000
Speaker 1: and they know it real, real, well and something new? No, yeah, no,

922
00:48:20,160 --> 00:48:22,560
Speaker 1: I mean anyone that's ever hunted, you know, out about

923
00:48:22,560 --> 00:48:24,640
Speaker 1: ground blind or something. They'll tell you like it's a yeah,

924
00:48:24,800 --> 00:48:26,239
Speaker 1: like all of a sudden, I know they can't see

925
00:48:26,239 --> 00:48:28,640
Speaker 1: me yet their snort and old day at that ground blind, like,

926
00:48:29,680 --> 00:48:32,839
Speaker 1: no doubt that there's something unique about it, but they

927
00:48:32,840 --> 00:48:34,360
Speaker 1: have to be able to pick up that it's something

928
00:48:34,440 --> 00:48:37,359
Speaker 1: unique about it. Uh. You know, ideally, what a good

929
00:48:37,640 --> 00:48:40,360
Speaker 1: pattern would do, or a good set would do is

930
00:48:40,800 --> 00:48:42,480
Speaker 1: you could sit up there and they wouldn't be able

931
00:48:42,520 --> 00:48:44,799
Speaker 1: to tell if there's something new going on there. So

932
00:48:44,880 --> 00:48:47,520
Speaker 1: it all comes back to again, like them being able

933
00:48:47,560 --> 00:48:50,600
Speaker 1: to detect you is the problem, not that you're something new. Yeah,

934
00:48:50,800 --> 00:48:54,280
Speaker 1: that makes sense absolutely, Um, what what about the deer?

935
00:48:54,560 --> 00:48:56,560
Speaker 1: What what are deer doing? When you get that old

936
00:48:56,560 --> 00:48:58,680
Speaker 1: doll that comes in and she's she looks up at

937
00:48:58,719 --> 00:49:01,600
Speaker 1: you and she's ups and does the head bob, what's

938
00:49:01,600 --> 00:49:05,959
Speaker 1: she doing? So it's like, um, so you got she's

939
00:49:05,960 --> 00:49:10,480
Speaker 1: actually trying to unsilhouette. You are getting depth perception from

940
00:49:10,520 --> 00:49:12,520
Speaker 1: her environment. So like, I don't know if you ever

941
00:49:12,520 --> 00:49:15,719
Speaker 1: see it, but when like squirrels jump trees, they'll move

942
00:49:15,800 --> 00:49:17,960
Speaker 1: their head a bunch before they jump a tree. And

943
00:49:18,000 --> 00:49:20,160
Speaker 1: that's squirrels have the same thing as dear. They have

944
00:49:20,200 --> 00:49:21,719
Speaker 1: a really good field of view because they don't want

945
00:49:21,719 --> 00:49:24,360
Speaker 1: to get by hawks and everything else. But that means

946
00:49:24,600 --> 00:49:27,000
Speaker 1: you only get good depth perception when your eyes actually

947
00:49:27,000 --> 00:49:28,560
Speaker 1: cross over each other. That's why you and I have

948
00:49:28,760 --> 00:49:31,439
Speaker 1: excellent depth perception. Our field of view on both eyes

949
00:49:31,440 --> 00:49:35,080
Speaker 1: crosses over for a majority of the view. Dear squirrels,

950
00:49:35,400 --> 00:49:37,640
Speaker 1: most pre animals don't have that. They they'd rather have

951
00:49:37,680 --> 00:49:39,319
Speaker 1: an expanded field of view, but not a lot of

952
00:49:39,719 --> 00:49:42,920
Speaker 1: overlap between their two eyes, say a terrible death perception.

953
00:49:43,200 --> 00:49:45,280
Speaker 1: So what they do to get that depth perception instead

954
00:49:45,360 --> 00:49:49,400
Speaker 1: is roll that image over their eye by moving their head.

955
00:49:50,040 --> 00:49:52,360
Speaker 1: And what they're doing is you're trying to get different

956
00:49:52,400 --> 00:49:55,160
Speaker 1: images of the same kind of picture, if you will,

957
00:49:55,160 --> 00:49:58,120
Speaker 1: and it enhances some kind of depth perception. So what

958
00:49:58,160 --> 00:50:00,680
Speaker 1: they're trying to do is see you versus their background.

959
00:50:01,600 --> 00:50:04,840
Speaker 1: They almost they almost have to like build up a

960
00:50:04,920 --> 00:50:08,279
Speaker 1: map of what they're looking at because of the way

961
00:50:08,280 --> 00:50:13,000
Speaker 1: they're that's interesting, so in some way, you're in some

962
00:50:13,040 --> 00:50:14,640
Speaker 1: ways if you want to think of it so simple

963
00:50:14,680 --> 00:50:17,000
Speaker 1: as like they're trying to make you a three dimensional

964
00:50:17,040 --> 00:50:19,960
Speaker 1: object from their two dimensional world. But it's not. It's

965
00:50:20,000 --> 00:50:24,279
Speaker 1: not that simple. But that's really kind of it. I

966
00:50:24,840 --> 00:50:28,759
Speaker 1: think it was in the study in the in the uh,

967
00:50:28,880 --> 00:50:30,839
Speaker 1: the original study we were talking about that you did

968
00:50:31,719 --> 00:50:36,880
Speaker 1: where the blue light gosh, I think this was what

969
00:50:36,880 --> 00:50:40,080
Speaker 1: what you had written about. When deer spooked and they

970
00:50:40,120 --> 00:50:42,480
Speaker 1: take off, they flip their tail up and that's a

971
00:50:42,760 --> 00:50:44,839
Speaker 1: very uh you know, we look at that and that's

972
00:50:44,840 --> 00:50:47,239
Speaker 1: a white flag going through the woods. But to them,

973
00:50:47,320 --> 00:50:51,719
Speaker 1: that's tied to their blue light sensitivity, right, yeah, So

974
00:50:51,760 --> 00:50:55,560
Speaker 1: if you look at what they're sensitive to, uh that

975
00:50:55,560 --> 00:50:58,400
Speaker 1: that tail reflects a ton of blue and so that

976
00:50:58,520 --> 00:51:01,719
Speaker 1: tail when it goes up is visible even the in

977
00:51:01,760 --> 00:51:06,560
Speaker 1: the darkest of dark two deer. So yeah, it's a

978
00:51:06,600 --> 00:51:10,040
Speaker 1: bright white tail to us, but to them, it's probably

979
00:51:10,080 --> 00:51:13,880
Speaker 1: a bright blue tail or a very like you know

980
00:51:13,880 --> 00:51:16,879
Speaker 1: what I'm saying, and it's gotta end. And so when

981
00:51:16,880 --> 00:51:20,280
Speaker 1: they throw it up there, it's an alert at any

982
00:51:20,680 --> 00:51:24,400
Speaker 1: light condition. So it's reflecting exactly what they're most sensitive to.

983
00:51:25,080 --> 00:51:29,040
Speaker 1: So this what makes me curious about that is a

984
00:51:29,040 --> 00:51:31,760
Speaker 1: lot of people will take a deer decoy and they'll

985
00:51:31,800 --> 00:51:34,200
Speaker 1: tie a little you know, paper towel or some kind

986
00:51:34,200 --> 00:51:37,279
Speaker 1: of white flag to the ears and the tail, but

987
00:51:37,360 --> 00:51:39,879
Speaker 1: they're probably that's probably not reflecting the same light as

988
00:51:39,880 --> 00:51:43,239
Speaker 1: a deer's actual tail. Huh, No, no, it's not. I

989
00:51:43,280 --> 00:51:46,040
Speaker 1: mean you're probably getting away more with you know, dear

990
00:51:46,120 --> 00:51:48,560
Speaker 1: coming into it because they're curious and it's another dear

991
00:51:48,600 --> 00:51:51,160
Speaker 1: and there's movement. There's a bunch going on there. But

992
00:51:51,280 --> 00:51:55,279
Speaker 1: it's not that doesn't necessarily have the same color as

993
00:51:55,280 --> 00:52:00,399
Speaker 1: a dear tail would to them. What's an X man?

994
00:52:00,520 --> 00:52:02,600
Speaker 1: So you do this that you've done and you said

995
00:52:02,600 --> 00:52:04,879
Speaker 1: you've done what four studies on deer vision? Now, yeah,

996
00:52:04,880 --> 00:52:09,600
Speaker 1: we've done four different studies on deer vision looking at

997
00:52:09,640 --> 00:52:12,719
Speaker 1: their color discrimination, so like things like it's not just

998
00:52:12,800 --> 00:52:14,719
Speaker 1: like we want to know just how well they can

999
00:52:14,760 --> 00:52:18,680
Speaker 1: pick out this color of green versus this color of yellow.

1000
00:52:18,719 --> 00:52:24,920
Speaker 1: Things like that, the movement, the refresh rate, all of that. Um,

1001
00:52:25,160 --> 00:52:29,960
Speaker 1: you know, we're working with a bunch. Are some companies

1002
00:52:30,680 --> 00:52:33,479
Speaker 1: actually to deter deer vehicle collisions, like you know, trying

1003
00:52:33,480 --> 00:52:37,080
Speaker 1: to make a headlight that actually you and I see

1004
00:52:37,080 --> 00:52:39,239
Speaker 1: as a normal headlight, but deer can't see. That's that's

1005
00:52:39,280 --> 00:52:41,880
Speaker 1: the main application for a lot of this stuff. And

1006
00:52:41,880 --> 00:52:47,480
Speaker 1: then you know, spreading the word about thinking about how

1007
00:52:47,560 --> 00:52:50,200
Speaker 1: you're actually proceeds its world versus how you perceive its

1008
00:52:50,239 --> 00:52:52,400
Speaker 1: world and why those are so different, and how you

1009
00:52:52,440 --> 00:52:56,759
Speaker 1: should kind of incorporate that into your hunting strategies. The

1010
00:52:56,800 --> 00:53:00,879
Speaker 1: other thing. But the research is basically more or less

1011
00:53:00,920 --> 00:53:04,160
Speaker 1: wrapped up. We know, we've we've gone and been there

1012
00:53:04,200 --> 00:53:07,760
Speaker 1: and done that for almost all of it. So what's next?

1013
00:53:08,239 --> 00:53:10,399
Speaker 1: Maybe just professionally, what are you like? Man? I want

1014
00:53:10,440 --> 00:53:14,080
Speaker 1: to know, I want to work on this. So I'm

1015
00:53:14,560 --> 00:53:16,800
Speaker 1: so I've pivoted completely, and I do a lot of

1016
00:53:16,840 --> 00:53:18,680
Speaker 1: work now with I work with Mike Chamberlain and a

1017
00:53:18,760 --> 00:53:21,319
Speaker 1: lot of turkey stuff. I do a lot of duck work,

1018
00:53:21,600 --> 00:53:24,000
Speaker 1: and I do a lot of deer management stuff at

1019
00:53:24,080 --> 00:53:27,160
Speaker 1: like broader scales and stuff with like working with state

1020
00:53:27,200 --> 00:53:31,040
Speaker 1: agencies to set regulations and stuff like that. So I've

1021
00:53:31,239 --> 00:53:33,680
Speaker 1: I've had my fill of all the dear physiology stuff,

1022
00:53:33,680 --> 00:53:36,359
Speaker 1: and I love to tinker with it, But professionally I'm

1023
00:53:36,400 --> 00:53:39,719
Speaker 1: onto more of the like applied management behaviors and animals

1024
00:53:39,719 --> 00:53:44,320
Speaker 1: type stuff. All right, what what drives you the most nuts?

1025
00:53:44,680 --> 00:53:48,719
Speaker 1: Knowing what you know about dear vision and dear biology,

1026
00:53:48,760 --> 00:53:50,960
Speaker 1: what drives you the most nuts that you hear just

1027
00:53:51,040 --> 00:53:57,080
Speaker 1: the average hunters say about dear There's there's there's a

1028
00:53:57,080 --> 00:53:59,239
Speaker 1: couple of things. So, Jill, I'll tell you what the

1029
00:53:59,239 --> 00:54:01,880
Speaker 1: worst thing with the toughest thing as a biologist is

1030
00:54:02,320 --> 00:54:08,480
Speaker 1: the general skepticism that hunters have towards scientists and regulators.

1031
00:54:08,520 --> 00:54:11,360
Speaker 1: Sometimes that's warranted in in the political world that we

1032
00:54:11,400 --> 00:54:14,759
Speaker 1: live in with decisions that are made, but oftentimes the

1033
00:54:14,800 --> 00:54:17,879
Speaker 1: people that are helping make decisions and inform science got

1034
00:54:17,880 --> 00:54:20,360
Speaker 1: into it for a reason. It's because we're obsessive hunters

1035
00:54:20,360 --> 00:54:24,040
Speaker 1: ourselves now from a dear physiology and all this other stuff,

1036
00:54:24,080 --> 00:54:26,040
Speaker 1: all the science I've done, And what bothers me the

1037
00:54:26,080 --> 00:54:29,239
Speaker 1: most is, uh, two things. When I go out there

1038
00:54:29,239 --> 00:54:33,120
Speaker 1: and I see a state that's the Department Transportation that's

1039
00:54:33,160 --> 00:54:36,239
Speaker 1: spending millions of dollars to put together here to turn

1040
00:54:36,320 --> 00:54:39,200
Speaker 1: stuff that simply won't work. That's frustrating and a waste

1041
00:54:39,200 --> 00:54:43,640
Speaker 1: of money. And then hunters that that basically latch onto

1042
00:54:43,680 --> 00:54:48,680
Speaker 1: a specific brand of clothing that they think the camouflage

1043
00:54:48,719 --> 00:54:51,000
Speaker 1: is somehow superior than another one when it's really all

1044
00:54:51,000 --> 00:54:52,600
Speaker 1: the same. And what you really just want to buy

1045
00:54:52,640 --> 00:54:56,719
Speaker 1: is a quality garment, not necessarily the camel pattern that's

1046
00:54:56,719 --> 00:55:00,359
Speaker 1: behind it. Yeah, you want something that's a you're gonna

1047
00:55:00,400 --> 00:55:01,919
Speaker 1: keep you warm and have a nice way to warmth

1048
00:55:02,080 --> 00:55:04,399
Speaker 1: ratio and allow you to draw your bow and move

1049
00:55:04,520 --> 00:55:06,000
Speaker 1: and stay out there in the cold, and all that

1050
00:55:06,040 --> 00:55:08,200
Speaker 1: good stuff. And we've moved. So you know, there in

1051
00:55:08,239 --> 00:55:12,000
Speaker 1: the last ten years, there's there's several really excellent camouflage

1052
00:55:12,040 --> 00:55:13,920
Speaker 1: brands that I don't care what color they are, what

1053
00:55:14,360 --> 00:55:17,360
Speaker 1: squares and circles they put together, but the quality of

1054
00:55:17,400 --> 00:55:19,160
Speaker 1: their clothing and the comfort that you have in the

1055
00:55:19,200 --> 00:55:21,080
Speaker 1: field is a game changer, and that's what you should

1056
00:55:21,120 --> 00:55:24,759
Speaker 1: be focusing on. So you you I wanted to ask

1057
00:55:24,760 --> 00:55:26,600
Speaker 1: you that you kind of just brought it up there.

1058
00:55:27,280 --> 00:55:28,480
Speaker 1: I kind of I kind of wanted to bring this

1059
00:55:28,520 --> 00:55:31,000
Speaker 1: full circle, you know, to ask you if you were

1060
00:55:31,040 --> 00:55:33,480
Speaker 1: keeping deer from getting hit down there in Georgia by

1061
00:55:33,560 --> 00:55:36,440
Speaker 1: cars now or is this this is still I'm guessing

1062
00:55:36,440 --> 00:55:39,600
Speaker 1: a work in progress. Huh. So there's one study that

1063
00:55:39,640 --> 00:55:41,920
Speaker 1: followed up on our study and all right, so you

1064
00:55:41,960 --> 00:55:44,839
Speaker 1: put together a headlight, right, and you say, okay, let's

1065
00:55:44,840 --> 00:55:47,319
Speaker 1: see if this this will work. Let's put together a

1066
00:55:47,360 --> 00:55:50,480
Speaker 1: headlight and uh see if dear, if the lower deer

1067
00:55:50,520 --> 00:55:53,080
Speaker 1: vehicle collisions. Well, the way you do that is you've

1068
00:55:53,080 --> 00:55:55,920
Speaker 1: got to drive a car at deer going fifty five

1069
00:55:56,520 --> 00:56:00,719
Speaker 1: and like, yeah, that's good luck with doing that through

1070
00:56:00,840 --> 00:56:03,479
Speaker 1: uh you know, your approvals. But hey, there's one group

1071
00:56:03,520 --> 00:56:06,680
Speaker 1: that did it and they found they didn't actually change

1072
00:56:06,719 --> 00:56:09,960
Speaker 1: the headlights. They instead did something which was interesting. They

1073
00:56:10,000 --> 00:56:14,280
Speaker 1: took a light and actually showed, reversed it and actually

1074
00:56:14,360 --> 00:56:16,200
Speaker 1: flashed it onto the car so that you and I

1075
00:56:16,280 --> 00:56:18,400
Speaker 1: it wouldn't you wouldn't see the light, but it basically

1076
00:56:18,440 --> 00:56:21,880
Speaker 1: puts a glow of a vehicle around while they were

1077
00:56:21,880 --> 00:56:24,680
Speaker 1: approaching Dear, and they found that a decreased the aralical collision.

1078
00:56:24,760 --> 00:56:27,640
Speaker 1: So there's ongoing work there, but it's a slow process

1079
00:56:27,640 --> 00:56:29,799
Speaker 1: anything that you ever want tinkerd not just with the

1080
00:56:29,800 --> 00:56:32,320
Speaker 1: science but the actual you know, it's a federal level

1081
00:56:32,360 --> 00:56:36,640
Speaker 1: regulation of like, let's change headlights. You know that's that's

1082
00:56:36,640 --> 00:56:39,240
Speaker 1: gonna be like a thirty year care year goal. Well sure,

1083
00:56:39,360 --> 00:56:41,600
Speaker 1: I mean there's a ton of money at stake there,

1084
00:56:41,800 --> 00:56:44,960
Speaker 1: which you know, I guess could go either way with it.

1085
00:56:45,120 --> 00:56:49,440
Speaker 1: But so when you talk about that and you say, okay,

1086
00:56:49,440 --> 00:56:51,799
Speaker 1: we're working on like a headlight with certain colors that

1087
00:56:51,840 --> 00:56:53,600
Speaker 1: you know, humans perceived as bright as we need to

1088
00:56:53,600 --> 00:56:56,560
Speaker 1: see them. But Dear don't see that. Why why do

1089
00:56:56,640 --> 00:56:59,600
Speaker 1: you want Dear to not see that light? Oh? Yeah,

1090
00:56:59,640 --> 00:57:02,160
Speaker 1: so it's it's because the deer in the headlight looking

1091
00:57:02,280 --> 00:57:04,480
Speaker 1: is a real thing. So they're so sensitive to light.

1092
00:57:04,520 --> 00:57:07,440
Speaker 1: We talked about that, that mirror reflectance and stuff like that.

1093
00:57:07,480 --> 00:57:10,960
Speaker 1: They're so sensitive to light at night, right, So, like

1094
00:57:11,000 --> 00:57:14,520
Speaker 1: I said, they're reflecting like like crazy, all of a sudden,

1095
00:57:16,000 --> 00:57:20,000
Speaker 1: too bright or a bright light that's combined literally blinds them.

1096
00:57:20,040 --> 00:57:23,280
Speaker 1: It shuts down their photo receptors. It's not like they just, oh,

1097
00:57:23,360 --> 00:57:26,800
Speaker 1: they don't know what's behind the cart. It's that like

1098
00:57:26,840 --> 00:57:29,640
Speaker 1: they don't know what what's behind those lights, that's no doubt.

1099
00:57:29,720 --> 00:57:34,400
Speaker 1: But they're literally standing still because they're temporarily blinded. They

1100
00:57:34,440 --> 00:57:36,440
Speaker 1: have no clue what's going on. So if you can

1101
00:57:36,480 --> 00:57:39,120
Speaker 1: get something that they're less sensitive to, you're less likely

1102
00:57:39,160 --> 00:57:41,320
Speaker 1: to blind them. They can then perceive that a car

1103
00:57:41,400 --> 00:57:44,400
Speaker 1: is coming at them sixty mile and get out of

1104
00:57:44,400 --> 00:57:47,959
Speaker 1: the way. It's that simple. Yeah, And and this would

1105
00:57:47,960 --> 00:57:52,960
Speaker 1: be something that would you know, if it worked, would

1106
00:57:53,000 --> 00:57:56,400
Speaker 1: reduce you know, dear vehicle collisions by you know, a

1107
00:57:56,400 --> 00:57:58,120
Speaker 1: certain degree. Right, Like, it's not gonna fix all of them,

1108
00:57:58,160 --> 00:57:59,960
Speaker 1: because they're gonna run out under roads, you know more.

1109
00:58:00,280 --> 00:58:02,800
Speaker 1: Most of the deer that get hit or I won't

1110
00:58:02,800 --> 00:58:05,680
Speaker 1: say mark, most a proportion of the deer that could

1111
00:58:05,720 --> 00:58:08,480
Speaker 1: hit are simply running bucks that are running around and

1112
00:58:08,680 --> 00:58:10,320
Speaker 1: are just got one thing on their mind. And it's

1113
00:58:10,360 --> 00:58:12,160
Speaker 1: not even a matter of them being blinded. They're gonna

1114
00:58:12,240 --> 00:58:15,000
Speaker 1: run straight into your car anyway. Like, Yeah, it's not

1115
00:58:15,200 --> 00:58:18,720
Speaker 1: the solve all, but it's all about small mitigation efforts,

1116
00:58:19,000 --> 00:58:21,240
Speaker 1: you know. They found that basically the best thing to

1117
00:58:21,560 --> 00:58:23,400
Speaker 1: gear vehicle collisions and simply put up a sign that

1118
00:58:23,440 --> 00:58:27,640
Speaker 1: says drive slow deer crossing, you know, and like flashing

1119
00:58:27,680 --> 00:58:30,560
Speaker 1: in front of people. It's a human it's a human

1120
00:58:30,560 --> 00:58:32,880
Speaker 1: problem more than it is a deer problem oftentimes. But

1121
00:58:33,000 --> 00:58:35,960
Speaker 1: our hope is that this can make a small incremental

1122
00:58:36,040 --> 00:58:39,640
Speaker 1: change to a pretty big problem. Yeah, I mean you

1123
00:58:39,640 --> 00:58:43,560
Speaker 1: you look at the driver awareness is a huge component.

1124
00:58:43,840 --> 00:58:46,760
Speaker 1: You know, how close the woods are the cover is

1125
00:58:46,800 --> 00:58:48,240
Speaker 1: to the road, or how much of a ditch you

1126
00:58:48,280 --> 00:58:49,840
Speaker 1: have to see them. There's there's a lot of things

1127
00:58:49,920 --> 00:58:54,120
Speaker 1: going on there. But what an interesting I just think

1128
00:58:54,120 --> 00:58:56,880
Speaker 1: that's a cool opportunity for somebody like you who's who's

1129
00:58:56,880 --> 00:59:00,360
Speaker 1: a die hard bow hunter to suddenly get that professional

1130
00:59:00,440 --> 00:59:02,920
Speaker 1: opportunity to dig into a problem like that that has

1131
00:59:03,000 --> 00:59:08,360
Speaker 1: all these ancillary potential discoveries beyond a pretty admirable goal

1132
00:59:08,400 --> 00:59:12,320
Speaker 1: of keeping people from hitting deer. You know, in general,

1133
00:59:12,360 --> 00:59:14,320
Speaker 1: I've lived the dream ever since I got in this field.

1134
00:59:14,360 --> 00:59:17,320
Speaker 1: You know, like I'm an obsessive hunter, and everything I

1135
00:59:17,360 --> 00:59:21,040
Speaker 1: do I won't lie. I related to how I'm hunting

1136
00:59:21,040 --> 00:59:23,720
Speaker 1: and how others are hunting and all that. What to

1137
00:59:23,800 --> 00:59:25,960
Speaker 1: be able to turn some of the information that we're

1138
00:59:25,960 --> 00:59:28,400
Speaker 1: getting and apply it to real world issues that are

1139
00:59:28,480 --> 00:59:31,200
Speaker 1: much bigger than just can I kill a deer more efficiently?

1140
00:59:31,680 --> 00:59:34,480
Speaker 1: It's very very satisfying. It's a it's a good feeling.

1141
00:59:34,880 --> 00:59:40,000
Speaker 1: So if you had, if if there was unlimited funding

1142
00:59:40,840 --> 00:59:43,920
Speaker 1: and no real oversight, you're just free. Man, this is

1143
00:59:44,000 --> 00:59:46,280
Speaker 1: this is you just do your thing. What would you

1144
00:59:46,320 --> 00:59:48,919
Speaker 1: study with dear? What what's one thing that you're like, Man,

1145
00:59:48,960 --> 00:59:50,960
Speaker 1: I want to learn this or or you do you

1146
00:59:50,960 --> 00:59:55,960
Speaker 1: feel like we're kind of tapped out on dear? Absolutely?

1147
00:59:56,480 --> 01:00:01,640
Speaker 1: I think I think we're tapped out onto your uh

1148
01:00:01,920 --> 01:00:04,680
Speaker 1: c w D, and questions regarding c w D are

1149
01:00:04,720 --> 01:00:07,840
Speaker 1: going to be ever present and ever important, and how

1150
01:00:07,880 --> 01:00:11,600
Speaker 1: c w D interacts with predation, hunting, all these other

1151
01:00:11,600 --> 01:00:15,440
Speaker 1: things that are killing dear super important to me. The

1152
01:00:15,520 --> 01:00:18,280
Speaker 1: more pressing issue, or what I'm more and more increasingly

1153
01:00:18,440 --> 01:00:20,960
Speaker 1: interested in, is how are we gonna aggregate and use

1154
01:00:21,000 --> 01:00:26,200
Speaker 1: that data to actually make a more robust UH management

1155
01:00:26,200 --> 01:00:29,520
Speaker 1: approach besides just kind of being like, Okay, coyotes killed, dear,

1156
01:00:29,720 --> 01:00:32,439
Speaker 1: what does that mean? Should we kill what? Two less?

1157
01:00:32,480 --> 01:00:34,560
Speaker 1: Those in the bank limit? That sounds right and just

1158
01:00:34,600 --> 01:00:36,280
Speaker 1: go with it. Instead, it's like, how are we going

1159
01:00:36,280 --> 01:00:38,400
Speaker 1: to integrate all of this science into some kind of

1160
01:00:38,440 --> 01:00:43,000
Speaker 1: holistic management practice that responds to conditions on the ground quickly.

1161
01:00:43,680 --> 01:00:46,080
Speaker 1: That's where I think science is gonna head, so that

1162
01:00:46,160 --> 01:00:51,160
Speaker 1: it's transparent, clear and quick to the actual population needs

1163
01:00:51,160 --> 01:00:53,240
Speaker 1: at a local level. That's probably where we're at with

1164
01:00:53,280 --> 01:00:57,520
Speaker 1: dear management. That's by the way, that that was sarcastic

1165
01:00:57,560 --> 01:01:00,400
Speaker 1: about people saying like, oh, yeah, just to let those

1166
01:01:00,760 --> 01:01:03,040
Speaker 1: but more or less you know, no, no, no, I

1167
01:01:03,120 --> 01:01:05,320
Speaker 1: get it. Uh. I mean that is one of the

1168
01:01:05,360 --> 01:01:06,959
Speaker 1: things I was I was just talking to a buddy

1169
01:01:07,000 --> 01:01:10,600
Speaker 1: of mine who's a conservation officer here in Minnesota, and

1170
01:01:10,640 --> 01:01:13,760
Speaker 1: so he deals with a wide range of you know, questions,

1171
01:01:13,840 --> 01:01:16,320
Speaker 1: and you know, he deals with the d n R

1172
01:01:16,360 --> 01:01:19,120
Speaker 1: as far as their biologists and their science research, and

1173
01:01:19,160 --> 01:01:21,360
Speaker 1: then he deals with the end user, you know, the

1174
01:01:21,400 --> 01:01:25,040
Speaker 1: wide variants of hunters and fishermen out there. And one

1175
01:01:25,040 --> 01:01:28,240
Speaker 1: of the things that we just kind of got to

1176
01:01:28,280 --> 01:01:30,400
Speaker 1: talking about is how easy it is as a hunter

1177
01:01:30,480 --> 01:01:32,880
Speaker 1: to be like, Okay, I look at deer management through

1178
01:01:32,880 --> 01:01:35,160
Speaker 1: my forty acres that I've hunted because my grandpa owns it,

1179
01:01:35,200 --> 01:01:37,280
Speaker 1: and that's what I care about. And then you look

1180
01:01:37,320 --> 01:01:40,200
Speaker 1: at you know, just take Minnesota as an example, a

1181
01:01:40,240 --> 01:01:44,640
Speaker 1: state that's pretty big, really diverse habitat wise and human

1182
01:01:44,640 --> 01:01:48,040
Speaker 1: population wise and deer population wise and predator wise, and

1183
01:01:48,080 --> 01:01:50,520
Speaker 1: then you start looking at an animal like the white

1184
01:01:50,520 --> 01:01:53,360
Speaker 1: taille and how there you know, there's a billion dollar

1185
01:01:53,440 --> 01:01:56,440
Speaker 1: industry built around this animal, and it's one of the

1186
01:01:56,440 --> 01:02:00,480
Speaker 1: most interactive species that we have out there. And so

1187
01:02:00,600 --> 01:02:02,640
Speaker 1: as far as like the science around it and what

1188
01:02:02,680 --> 01:02:06,760
Speaker 1: we have to learn, this is a complicated situation because

1189
01:02:06,920 --> 01:02:09,959
Speaker 1: we're so involved with them and they play so well

1190
01:02:10,000 --> 01:02:12,680
Speaker 1: with man in so many different ways. It's not you know,

1191
01:02:12,720 --> 01:02:15,000
Speaker 1: it's not quite as simple as an isolated population of

1192
01:02:15,040 --> 01:02:18,680
Speaker 1: some animal, you know, largely unaffected by man. I guess

1193
01:02:18,720 --> 01:02:23,280
Speaker 1: I'd say sure. And and in the dear ecology world,

1194
01:02:23,520 --> 01:02:26,000
Speaker 1: a lot of it is we're building off things that

1195
01:02:26,040 --> 01:02:28,640
Speaker 1: have already been done for many years. You know, each

1196
01:02:28,680 --> 01:02:32,280
Speaker 1: study is a is a logical step wise progress towards

1197
01:02:32,280 --> 01:02:36,160
Speaker 1: a better knowledge of how deer being affected by multiple processes,

1198
01:02:36,160 --> 01:02:37,920
Speaker 1: you know, And back in the day it was like, okay,

1199
01:02:38,360 --> 01:02:41,640
Speaker 1: how does this affect here? How does this affect here?

1200
01:02:41,640 --> 01:02:43,280
Speaker 1: And then we started to do how do this end

1201
01:02:43,280 --> 01:02:46,360
Speaker 1: this combined affect here? And now we're like three, four

1202
01:02:46,440 --> 01:02:49,000
Speaker 1: or five layers deep in it. And the whole goal

1203
01:02:49,080 --> 01:02:52,760
Speaker 1: here is, you know, nowadays, in a world of social

1204
01:02:52,800 --> 01:02:56,000
Speaker 1: media and constant information at your fingertips, how do we

1205
01:02:56,040 --> 01:02:58,960
Speaker 1: be as transparent with the science as as possible and

1206
01:02:59,000 --> 01:03:03,120
Speaker 1: how do we be as happidly responding to issues as possible?

1207
01:03:03,600 --> 01:03:07,000
Speaker 1: And that is where I think, dear management, it's already

1208
01:03:07,000 --> 01:03:08,920
Speaker 1: going there. I mean, like, I'm not saying anything new,

1209
01:03:08,960 --> 01:03:12,080
Speaker 1: I'm not saying anything surprising, but that's where we're heading.

1210
01:03:12,280 --> 01:03:15,920
Speaker 1: Is the process getting there might be painful because it

1211
01:03:15,960 --> 01:03:18,040
Speaker 1: takes a lot to get there, but that's where it's

1212
01:03:18,080 --> 01:03:20,960
Speaker 1: got to go. And is that are you? Are you

1213
01:03:21,000 --> 01:03:25,280
Speaker 1: so emphatic about that? Because without you know, support from

1214
01:03:25,280 --> 01:03:29,760
Speaker 1: the hunting population, this is a real this is a bit. Yeah,

1215
01:03:29,800 --> 01:03:32,280
Speaker 1: it's all about It's all about I think I told

1216
01:03:32,280 --> 01:03:35,040
Speaker 1: you from the beginning, is or when you said like

1217
01:03:35,040 --> 01:03:38,160
Speaker 1: what bothers me. It's the idea that the hunters don't

1218
01:03:38,200 --> 01:03:40,080
Speaker 1: buy into what we're talking unless. And I grew up

1219
01:03:40,080 --> 01:03:44,480
Speaker 1: in New York, uh On, Pennsylvania. I know what it's

1220
01:03:44,560 --> 01:03:47,120
Speaker 1: like to have you know some I've heard a lot

1221
01:03:47,200 --> 01:03:50,400
Speaker 1: of different people being skeptical about decisions that are made.

1222
01:03:51,280 --> 01:03:54,640
Speaker 1: And so to me, I'm only emphatic about it because

1223
01:03:54,680 --> 01:03:57,280
Speaker 1: I know these people. I know the good they're trying

1224
01:03:57,320 --> 01:04:00,560
Speaker 1: to do, and I know where the message get or

1225
01:04:00,560 --> 01:04:03,160
Speaker 1: you can see where the message gets lost. And so

1226
01:04:03,360 --> 01:04:05,919
Speaker 1: to me, the the idea is in a day where

1227
01:04:07,160 --> 01:04:09,760
Speaker 1: data is that can be at your fingertips, I want

1228
01:04:09,840 --> 01:04:11,960
Speaker 1: you to see that data. I want you to understand

1229
01:04:12,000 --> 01:04:14,640
Speaker 1: why we're doing what we're doing. It just makes sense

1230
01:04:14,680 --> 01:04:16,600
Speaker 1: to me. So that's where you give me unlimited money.

1231
01:04:16,640 --> 01:04:21,120
Speaker 1: It's it's promoting transparency, it's promoting data collection, and it's

1232
01:04:21,120 --> 01:04:24,320
Speaker 1: promoting sound management. It's not necessarily going to another spot

1233
01:04:24,360 --> 01:04:26,919
Speaker 1: to do a different type of iteration of the dear

1234
01:04:27,000 --> 01:04:29,960
Speaker 1: Vision study. If that makes sense. Yeah, there is. There

1235
01:04:30,000 --> 01:04:35,200
Speaker 1: has historically been a pretty significant disconnect between a lot

1236
01:04:35,200 --> 01:04:39,280
Speaker 1: of state game agencies and the end user and what's

1237
01:04:39,360 --> 01:04:42,040
Speaker 1: really going on there, and that has led to a

1238
01:04:42,040 --> 01:04:44,200
Speaker 1: lot of problems. Yeah, well it comes back to a

1239
01:04:44,240 --> 01:04:46,160
Speaker 1: bunch of different things. Right, one is messaging, but it

1240
01:04:46,160 --> 01:04:48,640
Speaker 1: also comes back to scale of management. You've talked about

1241
01:04:48,640 --> 01:04:51,440
Speaker 1: the guys forty acres, what that gout is forty years

1242
01:04:51,440 --> 01:04:53,960
Speaker 1: and me on my forty acres, what we see is

1243
01:04:53,960 --> 01:04:57,920
Speaker 1: a microcosm of of something that dear biologist isn't imagined for.

1244
01:04:58,280 --> 01:05:01,160
Speaker 1: They're imaging at best. You know, a region that's where

1245
01:05:01,160 --> 01:05:02,280
Speaker 1: we all want a head. You know, a lot of

1246
01:05:02,320 --> 01:05:04,760
Speaker 1: states have that already, but theoretically we make that region

1247
01:05:04,760 --> 01:05:06,720
Speaker 1: as small and small as possible so that the two

1248
01:05:06,840 --> 01:05:09,960
Speaker 1: match up. But oftentimes it's so misaligned. You've got somebody

1249
01:05:09,960 --> 01:05:12,880
Speaker 1: that's managing and doing their stuff well and an otherwise

1250
01:05:13,240 --> 01:05:16,440
Speaker 1: pretty desolate landscape for deer, and they have to suffer

1251
01:05:16,480 --> 01:05:19,200
Speaker 1: those consequences, and see what I'm saying, or vice versa,

1252
01:05:19,520 --> 01:05:21,680
Speaker 1: somebody's not doing the right thing and a place that's

1253
01:05:22,040 --> 01:05:26,040
Speaker 1: got a pretty liberal season. So in the end, decreasing

1254
01:05:26,080 --> 01:05:28,800
Speaker 1: the scale of our management and responsiveness. It all comes

1255
01:05:28,800 --> 01:05:32,080
Speaker 1: back to sound data collection, And honestly, that is not

1256
01:05:32,240 --> 01:05:37,800
Speaker 1: something that that's every that's something everybody wants, but time, money, effort,

1257
01:05:38,360 --> 01:05:41,280
Speaker 1: there's only so much you can possibly do. That's why

1258
01:05:41,320 --> 01:05:45,160
Speaker 1: I say it with an unlimited budget. Part all right, man,

1259
01:05:45,240 --> 01:05:48,080
Speaker 1: last question for you here, So as a as a

1260
01:05:48,080 --> 01:05:50,880
Speaker 1: passionate hunter and a and a dear lover and a

1261
01:05:50,880 --> 01:05:55,640
Speaker 1: guy who's involved in the science, what is one thing, Like,

1262
01:05:55,680 --> 01:05:57,480
Speaker 1: what's one thing that pops out into your mind that

1263
01:05:57,520 --> 01:06:00,560
Speaker 1: you've learned through your research or somebody else's research that

1264
01:06:00,600 --> 01:06:03,040
Speaker 1: you've been studying up on where you're like, you know what,

1265
01:06:03,080 --> 01:06:04,960
Speaker 1: I'm taking that into the field with me, and I'm

1266
01:06:05,000 --> 01:06:08,680
Speaker 1: never going to forget that. How sensitive deer are all

1267
01:06:08,720 --> 01:06:12,160
Speaker 1: animals are to hunting pressure. It's not I used to

1268
01:06:12,240 --> 01:06:16,240
Speaker 1: I used to know like, Okay, well, um, I'll just

1269
01:06:16,320 --> 01:06:20,360
Speaker 1: hunt this spot, my favorite spot with the right wind

1270
01:06:20,400 --> 01:06:22,440
Speaker 1: conditions with this, With this, I can hunt it when

1271
01:06:22,600 --> 01:06:24,960
Speaker 1: when I want. No, there's like a legacy effect of

1272
01:06:25,040 --> 01:06:28,680
Speaker 1: you going in and like and just being in that area. Like,

1273
01:06:28,720 --> 01:06:31,600
Speaker 1: I completely changed how I access my properties. I only

1274
01:06:31,600 --> 01:06:34,680
Speaker 1: go in from the edges. I minimize the servants, not

1275
01:06:34,720 --> 01:06:36,120
Speaker 1: just on a good wind, but like I take as

1276
01:06:36,120 --> 01:06:39,560
Speaker 1: few freaking steps as possible, like and because are the

1277
01:06:39,600 --> 01:06:42,720
Speaker 1: science has clearly shown that just like your presence in

1278
01:06:42,760 --> 01:06:46,880
Speaker 1: that area is a tangible thing that deer can pick

1279
01:06:46,960 --> 01:06:49,560
Speaker 1: up on and they'll they'll start to avoid it really quickly.

1280
01:06:50,080 --> 01:06:52,720
Speaker 1: And that's probably why so many many bucks are shot

1281
01:06:52,800 --> 01:06:56,080
Speaker 1: during the ruts. They give up that that awareness to

1282
01:06:56,200 --> 01:06:59,760
Speaker 1: chase those around, you know, plus being more active. Well yeah,

1283
01:06:59,840 --> 01:07:02,600
Speaker 1: and this is I love that you said that, because

1284
01:07:03,680 --> 01:07:05,680
Speaker 1: we are so I kind of look at this like

1285
01:07:05,720 --> 01:07:08,160
Speaker 1: it's really easy to spend somebody else's money, right, Like

1286
01:07:08,200 --> 01:07:10,160
Speaker 1: it's really easy to be like Jeff Bezos should pay

1287
01:07:10,160 --> 01:07:12,080
Speaker 1: this much in taxes, or he should spend this much

1288
01:07:12,160 --> 01:07:14,000
Speaker 1: or give this much a charity. But we don't look

1289
01:07:14,000 --> 01:07:17,320
Speaker 1: at ourselves the same way. Typically. We do the same

1290
01:07:17,400 --> 01:07:19,400
Speaker 1: thing with hunting pressure, right, like we're always like, oh,

1291
01:07:19,440 --> 01:07:22,760
Speaker 1: this asshole came in and he walks around at first

1292
01:07:22,760 --> 01:07:24,160
Speaker 1: and last light and scares him all the way. But

1293
01:07:24,200 --> 01:07:26,400
Speaker 1: we don't think about the thing we can control, which

1294
01:07:26,400 --> 01:07:30,200
Speaker 1: is our pressure in a situation, because we're so biased

1295
01:07:30,200 --> 01:07:32,440
Speaker 1: towards what we want to do. And like you said, well,

1296
01:07:32,480 --> 01:07:34,400
Speaker 1: if I go in here in the winds, right, it's

1297
01:07:34,440 --> 01:07:38,240
Speaker 1: all good. It's it's crazy, like you know, just even

1298
01:07:38,280 --> 01:07:40,800
Speaker 1: with the stuff we do with ducks and turkeys and

1299
01:07:40,840 --> 01:07:43,640
Speaker 1: what we see with deer and just about everything I mean,

1300
01:07:44,800 --> 01:07:49,040
Speaker 1: animals respond to hunters, and it's because I now realized

1301
01:07:49,040 --> 01:07:51,320
Speaker 1: you're coming sense like they have to. There's no other

1302
01:07:51,360 --> 01:07:54,439
Speaker 1: animal that they can interact with from eighty yards away

1303
01:07:54,480 --> 01:07:56,920
Speaker 1: and be killed. Yeah, like you know what I'm saying,

1304
01:07:56,960 --> 01:08:00,280
Speaker 1: Like they've got to be on it immediately. And so

1305
01:08:01,640 --> 01:08:06,280
Speaker 1: you know, them turning nocturnal, them shifting their patterns, it's immediate,

1306
01:08:06,520 --> 01:08:11,080
Speaker 1: it's quick, it's lasting, and so it completely changes how

1307
01:08:11,120 --> 01:08:16,040
Speaker 1: I approach almost every situation. I I'm very strategic and

1308
01:08:16,120 --> 01:08:18,519
Speaker 1: where I go, I'm very strategic, and how I approach

1309
01:08:18,600 --> 01:08:21,679
Speaker 1: it um strategic. And if I have to leave when

1310
01:08:21,920 --> 01:08:25,599
Speaker 1: things go differently, and I've seen you know, I've harvest

1311
01:08:25,680 --> 01:08:30,080
Speaker 1: more dear, bigger dear because but I think for sure, man, uh,

1312
01:08:30,160 --> 01:08:33,599
Speaker 1: such good stuff. I really appreciate you coming on the podcast. This.

1313
01:08:33,600 --> 01:08:36,320
Speaker 1: This was a blast man. Yeah, I appreciate you having me.

1314
01:08:36,360 --> 01:08:39,240
Speaker 1: Thanks again. That's it for this week, folks. Be sure

1315
01:08:39,280 --> 01:08:42,000
Speaker 1: to tune in next week for some more white tailed goodness.

1316
01:08:42,040 --> 01:08:44,120
Speaker 1: This has been Where to Hunt and I'm your guest host,

1317
01:08:44,120 --> 01:08:47,120
Speaker 1: Tony Peterson. As I always, thank you so much for listening,

1318
01:08:47,320 --> 01:08:49,680
Speaker 1: and if you're looking for more white tail content, be

1319
01:08:49,760 --> 01:08:51,920
Speaker 1: sure to check out the meat eator dot com slash

1320
01:08:52,000 --> 01:08:54,679
Speaker 1: wired to see a pile of new articles each week

1321
01:08:54,720 --> 01:08:57,439
Speaker 1: by Mark myself and a whole slew of white tail addicts,

1322
01:08:57,960 --> 01:08:59,880
Speaker 1: or head on over to our Wired to Hunt YouTube

1323
01:09:00,040 --> 01:09:08,640
Speaker 1: channel to view the weekly content we put up m