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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 your guest host

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Speaker 1: Tony Peterson, and today I'm speaking with Dr Justin Brown,

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Speaker 1: a researcher who specializes in wildlife, rereterenarian and biomedical sciences.

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Speaker 1: All right, folks, welcome to the Wire to Hunt podcast,

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Speaker 1: which is brought to you by First Light. You've probably

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Speaker 1: figured out that this is not the voice of Mark Kenyon.

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Speaker 1: Mark sent me a text last week saying that he

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Speaker 1: was attending a claymation course so he can finally make

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Speaker 1: a stop motion film about his friendship with Spencer. I

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Speaker 1: don't know, man, I gotta hand it to Kenyan anyway,

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Speaker 1: He's got a lot of interesting hobbies. Moving on, today,

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Speaker 1: I'm speaking with Dr Justin Brown, who currently works at

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Speaker 1: Penn State, where a good percentage of the most interesting

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Speaker 1: deer research comes from. Justin has studied a crazy amount

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Speaker 1: of viruses and pathogens and different game animals and is

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Speaker 1: a huge wealth of knowledge on the anatomy of our

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Speaker 1: favorite game animal. Throughout this episode, we get into a

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Speaker 1: wide variety of topics, but we mostly stick to the

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Speaker 1: ways in which white tails have adapted and evolved to

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Speaker 1: be able to thrive in so many different environments while

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Speaker 1: avoiding predation. Listen, I promise you, if you see this

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Speaker 1: episode all the way through to the end, you're gonna

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Speaker 1: learn a thing or two about white tails. Justin Brown,

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Speaker 1: I'm stoked to have you on, man, thank you for

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Speaker 1: having me. So you Uh, I guess, I guess before

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Speaker 1: we get into this. Uh, you and I have had

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Speaker 1: a couple of conversations, and I've had the chance to

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Speaker 1: look through some of your research, and I don't think

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Speaker 1: there's anyone out there who studied, uh, you know, random

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Speaker 1: viruses in birds and in big game animals and pathogens.

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Speaker 1: You've kind of you've kind of covered a wide array

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Speaker 1: of that stuff. I want to talk to you about that,

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Speaker 1: but first, can you just give the listeners a little

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Speaker 1: bit of your background and how you got into this

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Speaker 1: field that you're in. Absolutely absolutely well. So, I'm a

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Speaker 1: veterinarian by training. Uh and uh, you know, as I

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Speaker 1: got into vet school and and started to figure out

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Speaker 1: what I wanted to do with my veterinary degree, I

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Speaker 1: always had an interest in wildlife and in population health,

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Speaker 1: and so when it came time for my senior year

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Speaker 1: and you're going out on various externships trying to figure

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Speaker 1: out what you're gonna do with your veterinary degree. I

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Speaker 1: went to a few externships out west UM and worked

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Speaker 1: wildife veterinarians and some of the western states, and UH

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Speaker 1: fell in love with the career and and so after

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Speaker 1: VET school, I went on and completed a PhD um

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Speaker 1: in pathology, which is sort of the study of diseases

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Speaker 1: and and and I specifically looked at diseases of wildlife. UH.

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Speaker 1: And that include diseases that may be causing having an

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Speaker 1: impact on wildlife, or some that may be harbored by

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Speaker 1: wildlife but having impacts on humans or domestic animals. So

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Speaker 1: UM sort of looking at at how the health of

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Speaker 1: wildlife is linked to domestic animals and humans and UM.

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Speaker 1: So after that, I I've had a couple of different positions.

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Speaker 1: I've I've worked down at University of George's Vet School.

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Speaker 1: They have a large wildlife health and disease unit down there. UM.

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Speaker 1: I was the state wildlife veterinarian for Pennsylvania for several

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Speaker 1: years and currently I'm at penn State in their department

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Speaker 1: of veteranan biomedical sciences, and you didn't want to go

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Speaker 1: even though you went in uh to you know, study

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Speaker 1: veterinarian and sciences. You did you didn't want to go

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Speaker 1: the typical route people would think when you when you've

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Speaker 1: had that way, you didn't you didn't want to go

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Speaker 1: treat cats and dogs. Huh. No, it's probably better off

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Speaker 1: for the cats and dogs that I didn't. My interests

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Speaker 1: were elsewhere. My wife's actually a companion animal veterinarian, and

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Speaker 1: I think I went into VET school and knowing that

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Speaker 1: maybe I wasn't going to go the traditional traditional route.

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Speaker 1: I always enjoyed population health a little bit more than

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Speaker 1: an individual animal health. Um. And I've always sort of

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Speaker 1: had been a little bit more like a biologist that

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Speaker 1: went to VET school rather than in a pure veterinarian. So, um,

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Speaker 1: you know, I've always uh sort of stayed in the

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Speaker 1: realm of wildlife health and population health. Um. This this

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Speaker 1: is a total side tangent here, But is your wife

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Speaker 1: dealing dealing with companion animals the way she is? Is

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Speaker 1: she totally burned out on telling people how to keep

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Speaker 1: their dogs and cats healthy And I'm not listening. She's

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Speaker 1: probably not happy with how often I volunteer her you know. Um, yeah, yeah,

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Speaker 1: I think it's a it's a real thing that every

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Speaker 1: veterinarian faces, and and fatigue UM is a real issue

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Speaker 1: in our field, and and certainly we uh. I think

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Speaker 1: most veterinarians tend to be quiet when people ask what

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Speaker 1: we do because it's going to be quickly followed up

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Speaker 1: with either cell phone pictures of animals or or questions

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Speaker 1: about issues with their animals at home. Yeah. Yeah, It's

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Speaker 1: kind of like when you're outdoor writer who specializes in

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Speaker 1: deer hunting and you go to a deer show you

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Speaker 1: see an awful lot of pictures of people posing with

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Speaker 1: their bocks. Yeah, and unfortunately, I think I get hit

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Speaker 1: from both angles. So yeah, I'll bet you do work

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Speaker 1: in working at Penn State the way that you do.

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Speaker 1: So what was the interest though, in you know, pathology

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Speaker 1: with where did that come from in your life? Just

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Speaker 1: just always curious about it? Yeah, I mean, I think

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Speaker 1: with wildlife health, you know, there there's a variety of

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Speaker 1: ways you can approach that. We certainly have UM instances

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Speaker 1: where we are treating individual animals UM, whether that's for

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Speaker 1: you know, a larger purpose like a research study or

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Speaker 1: because um for that of an individual animal need um.

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Speaker 1: But but I've always had a fascination with diseases and

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Speaker 1: how they maintain and how they can impact populations and

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Speaker 1: individual animals. UM. And so I've always had an interest

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Speaker 1: in disease ecology, and so I just linked that together

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Speaker 1: with my interest in in population health and while left conservation.

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Speaker 1: So that's that's sort of how I I got into

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Speaker 1: the field. UM. But I think I'm sort of an

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Speaker 1: eternal student and I uh, I think I'm always sort

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Speaker 1: of adapting what I do within the field of wildlife

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Speaker 1: health and disease. Yeah, you seem to, uh, you know,

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Speaker 1: we're going to get into you know, white tail anatomy

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Speaker 1: and their evolution and in a little bit here, but

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Speaker 1: your research has covered a crazy amount of different animals,

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Speaker 1: mostly big game animals, a lot of birds, and you

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Speaker 1: you really seem to have kind of covered the gamut

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Speaker 1: as far as just looking into different species and not

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Speaker 1: really drilling down on one particular species. Yeah. And I think,

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Speaker 1: you know, I think once you have an interest in

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Speaker 1: in health and disease, UM, I think it's easy to

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Speaker 1: switch systems you're working in. UM. So I started out

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Speaker 1: when I was in bed school working on on a

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Speaker 1: project on box turtles, and then my PhD was looking

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Speaker 1: more at at waterfowl, goals and other alien species, and um,

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Speaker 1: you know, we've all I've always had an interest in

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Speaker 1: mammals too, So I think it's fairly easy to switch

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Speaker 1: systems and and oftentimes there may be an underlying health

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Speaker 1: issue or disease issue that affects birds, mammals, humans, amphibians, etcetera. So, um,

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Speaker 1: there there are a lot more links between those different

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Speaker 1: host systems, and probably people realize, yeah, it's it's an

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Speaker 1: interesting field, and it's what what was an eye opener

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Speaker 1: for me is just just looking at your published research.

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Speaker 1: I'm like, man, I had no idea some of these

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Speaker 1: animals I love, like wild turkeys and grouse, and obviously

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Speaker 1: what tales some of the some of the stuff that's

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Speaker 1: out there that can get them or you know, at

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Speaker 1: least infect them and alter their quality of life. Like

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Speaker 1: I it was kind of a kind of nightmare fuel Man.

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Speaker 1: It was a little bit of an eye opener. Yeah, yeah,

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Speaker 1: and I think you know, and we'll talk a lot

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Speaker 1: a lot about this, particularly um in regards to anatomy,

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Speaker 1: but you know, I think whenever we're in the field

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Speaker 1: of health and disease, the approach I always take is

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Speaker 1: define what normal is so that you can recognize and

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Speaker 1: hopefully respond appropriately to ab normal and and when we

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Speaker 1: talk about you know, viruses, bacteria, parasites, sometimes normal for

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Speaker 1: wildlife is is even having those things you know, you know,

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Speaker 1: so we can have a wild turkey that has you know, nema,

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Speaker 1: todes and sintestinal track and and that's not a that's

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Speaker 1: not a disease issue. They normally harbor those and so um.

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Speaker 1: When you when you start to get into this field,

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Speaker 1: there's a lot of work that can be done simply

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Speaker 1: on a identifying and characterizing what what what's normally out there,

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Speaker 1: even if it's in the absence of disease. So you

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Speaker 1: find yourself sometimes studying something just just to recognize what

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Speaker 1: the baseline is for a species before you can move

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Speaker 1: on to some of these outlier infections or something like that. Absolutely,

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Speaker 1: I think I think that is the approach I always

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Speaker 1: take because far too often we identify an abnormal disease

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Speaker 1: or a major disease issue and we don't have that

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Speaker 1: baseline information on what's normal, and so we often jump

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Speaker 1: to conclusions that aren't accurate or we don't know how

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Speaker 1: to interpret data. And you know, we see that all

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Speaker 1: the time, whether it's you know, something like stars Cove

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Speaker 1: two or you know, the influenza viruses that we're seeing

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Speaker 1: spreading around. You know, a lot of times we don't

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Speaker 1: have that normal data to put those abnormal events in

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Speaker 1: the context. Yeah, I mean, maybe maybe I'm way off here,

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Speaker 1: and maybe I'm biased because I'm sitting here with COVID

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Speaker 1: right now, uh, with a low level of misery coursing

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Speaker 1: through my body. But it's sort of it sort of

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Speaker 1: reminds me of this issue with the pandemic of of

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Speaker 1: how vastly different, you know, the consequences for contracting COVID

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Speaker 1: have been where you know, some people's immune system and whatever,

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Speaker 1: you know, contributing factors seem to just handle it really

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Speaker 1: really well, and other people seem to get really really hard.

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Speaker 1: So you kind of got to have a baseline there.

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Speaker 1: But it's that's not a simple thing to come across,

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Speaker 1: absolutely no. And when we talk about diseases of wildlife,

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Speaker 1: oftentimes the term that uses is multi factorial, and that

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Speaker 1: just means it's not a causal event. So it's not always.

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Speaker 1: If you get this pathogen or this bacteria, this virus,

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Speaker 1: you get a disease and it looks like this. You

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Speaker 1: can see a spectrum of signs from no, no clinical

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Speaker 1: signs too, fatal infection and and a lot of those

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Speaker 1: depend on various factors. And when you're dealing with something

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Speaker 1: like wildlife, a whole host of different things coming to

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Speaker 1: play on how severe disease will be, whether it's host genetics,

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Speaker 1: habitat they're using, what how they're exposed, and how much

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Speaker 1: are they exposed, do they have any underlying diseases, and so,

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Speaker 1: you know, as far as studying disease, there's there's almost

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Speaker 1: a limitless number of questions that we have to address

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Speaker 1: before we can really understand what's going on in wildlife.

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Speaker 1: Is there when you're you know, when you're sitting there

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Speaker 1: and you're like, I need somebody to hand me a

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Speaker 1: sick bird or something so you have something to study.

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Speaker 1: Is there? Uh? Is there? Sort of I don't know

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Speaker 1: how to put this kind of a push, you know,

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Speaker 1: I know at Penn State there's a big deer research

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Speaker 1: search program and a lot of the really cool research

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Speaker 1: studies come out of there. Is there somewhat of a

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Speaker 1: push to study animals like white tails over some of

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Speaker 1: the less popular animals that aren't you know, maybe so

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Speaker 1: tied into U to us socially. Absolutely, Yeah, there's always

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Speaker 1: going to be uh, you know, an emphasis or funding

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Speaker 1: that's available for certain species over others. You know, there's

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Speaker 1: a ton of work that needs to be done on

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Speaker 1: non game species. Uh. And some of that may be

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Speaker 1: because it's hard to get your hands on a forested songbird,

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Speaker 1: you know, and and so that's that that could be

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Speaker 1: an issue. But other other reasons might just be lack

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Speaker 1: of funding to support some of this research. When you

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Speaker 1: get into some of the disease research, it can be

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Speaker 1: really expensive and the costs can build up quite quickly.

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Speaker 1: And so you know, if it's an ego dying of something,

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Speaker 1: it's it's fairly easy to get funding for it. If

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Speaker 1: it's a turkey vulture, you're you're probably looking at a

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Speaker 1: smaller pool of funds that you're looking for. And so, um,

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Speaker 1: that is something that that we face. Um is is

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Speaker 1: trying to conduct research on some of these species where

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Speaker 1: the interest and funding maybe lacking. I could see that.

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Speaker 1: I suppose there's probably a benefit to an animal like

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Speaker 1: a white tail too. Just from the captive servant industry,

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Speaker 1: you can you can get your hands on test subjects. Yeah,

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Speaker 1: and there's it's just very easy to get samples. Now,

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Speaker 1: maybe it gets a little harder if you have a

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Speaker 1: specific sample you're targeting. But if you're just saying I

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Speaker 1: just need to go out and get samples, but then

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Speaker 1: you've got you know, a whole lot of harvested deer

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Speaker 1: during certain times a year, you can go out and

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Speaker 1: get road kills and sample them and that's pretty easy. Um.

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Speaker 1: So detection is a major issue. You know, we've done

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Speaker 1: work over the last few years with grouse looking at

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Speaker 1: at West Nyland. Some of the issues grouse are are

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Speaker 1: are facing. Um And one of the real challenges early

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Speaker 1: on was to find sick and dead birds because if

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Speaker 1: a grouse dies of a viral infection in the woods,

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Speaker 1: I mean, the chances of you getting your hands on

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Speaker 1: it are are pretty load and non existent. Um And so, yeah,

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Speaker 1: detection and getting samples in hand for some these species

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Speaker 1: is a real challenge. How do you get your hands

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Speaker 1: on a grouse that died from western ale Ben I

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Speaker 1: was hoping you could tell me, because pu you're here

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Speaker 1: looks like you got a grouse in the dog's mouth.

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Speaker 1: But yeah, I mean that's something we actually worked at.

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Speaker 1: And um, we utilized hunters quite a lot. And so

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Speaker 1: people that were out um you know, with their dogs,

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Speaker 1: you know, working their dogs, training their dogs, and came

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Speaker 1: upon you know, carcasses or birds that were down and

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Speaker 1: not doing well. Um, you know, those were all options

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Speaker 1: we looked at. So that's that's a unique advantage of

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Speaker 1: you know, studying something in a game animal. Then you

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Speaker 1: have you have people out there who are gonna be

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Speaker 1: gathering samples for you for totally unrelated reasons that you

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Speaker 1: might be able to tap into. Absolutely. Yeah, And I

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Speaker 1: think you know, one of the beauties of working with

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Speaker 1: hunters or trappers or or anyone that is is an

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Speaker 1: enthusiast of the outdoors is, um, if they're interested, it's

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Speaker 1: really easy to get them involved and excited about research project.

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Speaker 1: And so um, you know we used to go to

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Speaker 1: a wild turkey uh, you know check stations or or

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Speaker 1: bear check stations, and and and hunters are always interested

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Speaker 1: in hearing about what you're looking for and things like that. So, um,

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Speaker 1: it's a nice social component to the disease research. Yeah,

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Speaker 1: there's there's a benefit there. Um. Can we can we

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Speaker 1: talk about one of the research studies that I read

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Speaker 1: that you were a part of, was was dedicated to

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Speaker 1: looking for uh better ways to gather genetic samples from

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Speaker 1: white deal deer. Can we talk about that? Sure? What

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Speaker 1: was the idea behind that? Just just because of all

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Speaker 1: the disease study and everything, having a larger I don't know,

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Speaker 1: I guess pool to draw from in an easier way

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Speaker 1: to get those samples from across the country just makes

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Speaker 1: your job that much easier. We were looking UM in

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Speaker 1: that study at landscape genetics and genetics UM in white

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Speaker 1: tails across parts of Pennsylvania, and so UM you can

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Speaker 1: gain genetic material during certain times of the year and

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Speaker 1: from certain animals like harvested deer. You could you can

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Speaker 1: use hair, you can use tissue, anything like that. UM.

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Speaker 1: But what we're starting to look for, we're ways that

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Speaker 1: we could gather at without having that that carcass in

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Speaker 1: hand or without having a dead animal UM. And so

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Speaker 1: we were looking at things like oral swabs UM and

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Speaker 1: and things like that where we could sample UM in

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Speaker 1: in non traditional ways. But what what's the what's the

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Speaker 1: end goal there or what's the bigger picture with those samples?

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Speaker 1: I think whenever you're dealing with something like that and

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Speaker 1: you have limitations in wildlife, one of the things at

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Speaker 1: least that I always look for is you have all

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Speaker 1: these hurdles and limit taeans to answer some of these questions,

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Speaker 1: like you can only get deer during this time of

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Speaker 1: year or from from this type of animal, whether it's

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Speaker 1: a road kill or something like that, and and some

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Speaker 1: of those may not be of the highest quality. UM.

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Speaker 1: So some of that research was just trying to expand

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Speaker 1: the tools that we had in our our arsenal. Right,

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Speaker 1: so UM, you're not just reliant on finding a dead

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Speaker 1: animal or having someone harvested. And by having more tools,

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Speaker 1: that opens up opportunities to say, we can now sample here,

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Speaker 1: if you can get this, we can use that um.

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Speaker 1: And so it just gives us more options in regards

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Speaker 1: to sampling different parts in different different areas and categories

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Speaker 1: of animals UM. And by doing that, what the ultimate

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Speaker 1: goal would be is to expand that sampling to a

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Speaker 1: wider a wider area and and and different category of deer.

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Speaker 1: Because if you start to get into how can you

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Speaker 1: sample live animals, then that can address different questions in

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Speaker 1: regards to genetics. How so, um, if you had an

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Speaker 1: animal in hand, um, particularly if it was a captive

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Speaker 1: or something like that, you could collect a sample without

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Speaker 1: having to euthanize the animal or without having to do

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Speaker 1: an invasive procedure. And that's that's obviously something you'd want

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Speaker 1: over over the alternative. But but where I'm going with

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Speaker 1: this is what what's the larger picture usage that. Can

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Speaker 1: you give me an example of like why it would

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Speaker 1: be why it's beneficial for you to have that wider

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Speaker 1: range of genetic samples And in then that case, it

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Speaker 1: would really depend on on the question itself. Um. So

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Speaker 1: for dear, it's not as complicated because we have our

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Speaker 1: hands on so many of them. But if we're talking

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Speaker 1: about translocating or moving, or if it's a captive and

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Speaker 1: it's moving several different areas, then we can have different

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Speaker 1: samples that are available without having to collect tissue. Um.

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Speaker 1: If that makes sense, Yeah, that makes sense. Let's let's

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Speaker 1: switch up here and talk about some uh, some of

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Speaker 1: the history of white tails. And you're you're you're a

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Speaker 1: student of their anatomy. Is that correct? Correct? Yeah? So

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Speaker 1: I teach anatomy uh here at Penn State, and it's

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Speaker 1: always been one of my favorite subjects to teach. Again,

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Speaker 1: I think it relates back to as a pathologist. One

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Speaker 1: of the tools we use to study disease is knee cropsy,

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Speaker 1: which is an animal autopsy where we're actually dissecting them

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Speaker 1: and looking for any abnormalities or or things signs of

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Speaker 1: disease or lesions UM. And in order to do that, again,

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Speaker 1: we have to know what normal is UM and so

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Speaker 1: a lot of what we teach in anatomy is what

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Speaker 1: are the different tissues, what do they look like, what

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Speaker 1: do they do? Um and and part of that comes

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Speaker 1: down to again building that library of what normal is.

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Speaker 1: So then you can understand if you if you see

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Speaker 1: abnormal or disease, give me an example of something you've

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Speaker 1: seem really abnormal in a white tail you know I

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Speaker 1: can get. I'll give you even a better one. Some

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Speaker 1: some of the normals that people think are abnormal UM

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Speaker 1: so deer as you you you may know, have um

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Speaker 1: hemal nodes, which are sort of these little um They're

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Speaker 1: like a lymph node, but they're more related to blood.

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Speaker 1: And so there are these sort of little circular nodular

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Speaker 1: um structures that are that can be found throughout the body,

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Speaker 1: but oftentimes they're sort of in the abdomen and thorax

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Speaker 1: along sort of as you're looking up at the spine

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Speaker 1: in that fat area. A lot of people think that

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Speaker 1: there are areas of bleeding or hemorrhage, but it's actually

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Speaker 1: just a little normal anatomy of of deer that we

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Speaker 1: we frequently will have people send us pictures of. So

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Speaker 1: when people are field dressing or butchering their deer, they

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Speaker 1: see those and they think it's there's something severely wrong

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Speaker 1: with their deer, and they want they want to know

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Speaker 1: what it's got. But it's actually a pretty normal thing, exactly.

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Speaker 1: They always a lot of times what we'll hear is

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Speaker 1: it looks like there's areas of hemorrhage along sort of

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Speaker 1: the inner inner fat and the body cavity, and those

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Speaker 1: are just normal human nodes. What what what is their function?

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Speaker 1: They they like a lymph node, There are filtering organ

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Speaker 1: for the blood rather than for limb. Give me an

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Speaker 1: example of something in a white tail's anatomy besides that

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Speaker 1: that that fascinates you. I think you know, obviously deer

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Speaker 1: now now we can see white tails anywhere, but they've

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Speaker 1: historically obviously evolved to be enforceded environments and particularly on

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Speaker 1: those forest edges um And So I think what fascinates

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Speaker 1: me about deer is a lot of their muscular skeletal

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Speaker 1: anatomy UM. So you know, if we talk about deer

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Speaker 1: as a specifically white tails, as being really adapted to

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Speaker 1: be for for running UM, the term we often use

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Speaker 1: for that is curs orel um. And so what that

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Speaker 1: just means is a lot of the anatomy and and

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Speaker 1: adaptations we see are built so that they can be

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Speaker 1: really strong runners um. And And obviously any of us

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Speaker 1: that have have hunted for them or even just watch

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Speaker 1: them can can appreciate how how fast and how good

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Speaker 1: they are running UM. But some of the things that

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00:22:23,800 --> 00:22:27,280
Speaker 1: they've adapted to allow them to do that are are

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Speaker 1: pretty unique. UM. If you look at deer, and this

385
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Speaker 1: is true almost of any sort of cursorial or running species,

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Speaker 1: a lot of times what they'll do is put the

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Speaker 1: heavy part of the lower limbs up towards the body

388
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Speaker 1: and then it'll be skinnier the farther you go out

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Speaker 1: on the limb. And that's really true of deer. You know,

390
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Speaker 1: if you look, a lot of their muscles are on

391
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Speaker 1: the upper four limbs or in the rump area, and

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Speaker 1: then they come down and they have really skinny lower

393
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Speaker 1: lower distal limbs, And the reason for that is it

394
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Speaker 1: lessens the weight down that lower part of the limb

395
00:23:00,000 --> 00:23:03,600
Speaker 1: and allows them to just move move that lower limb faster.

396
00:23:04,320 --> 00:23:08,919
Speaker 1: The other thing they've done is adapted their length of

397
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Speaker 1: their limb. So if you look at a deer's leg um,

398
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Speaker 1: we can talk about different ways animals can stand right,

399
00:23:16,000 --> 00:23:19,879
Speaker 1: and so the slowest way an animal can stand is

400
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Speaker 1: what we call planet grade, which is what we are

401
00:23:22,680 --> 00:23:25,040
Speaker 1: right where our heel is completely flat and our whole

402
00:23:25,080 --> 00:23:27,880
Speaker 1: foot is flat on the ground. When you look at deer,

403
00:23:27,920 --> 00:23:32,480
Speaker 1: what they've actually done is adapted so they're basically walking

404
00:23:32,680 --> 00:23:37,040
Speaker 1: on on their tiptoes right. And so if you actually

405
00:23:37,040 --> 00:23:41,040
Speaker 1: look at the leg of a deer, they are almost

406
00:23:41,480 --> 00:23:44,280
Speaker 1: that whole almost the distal third of their leg is

407
00:23:44,320 --> 00:23:47,760
Speaker 1: actually their foot that's now adapted so that they're running

408
00:23:47,760 --> 00:23:53,440
Speaker 1: on their toes right. So it's significantly lengthened the length

409
00:23:53,440 --> 00:23:56,080
Speaker 1: of their limb and just allows them to run faster

410
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Speaker 1: and hold on in one second, So is that when

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Speaker 1: when you describe that. Is that just purely for uh,

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Speaker 1: for them to be really quick or is it is

413
00:24:07,119 --> 00:24:10,320
Speaker 1: it for them to go from you know, stationary to

414
00:24:10,440 --> 00:24:15,040
Speaker 1: move in fast quickly A little bit of both, so

415
00:24:15,359 --> 00:24:19,080
Speaker 1: you know what it basically the two ways the big

416
00:24:19,119 --> 00:24:22,320
Speaker 1: functions you can are ways you can run faster is

417
00:24:22,480 --> 00:24:28,680
Speaker 1: if your legs move faster and if each step is longer, right,

418
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Speaker 1: And so doer have adapted to have both of those

419
00:24:31,640 --> 00:24:33,600
Speaker 1: one again, they have they don't have a lot of

420
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Speaker 1: weight on that lower limb, so it allows them to

421
00:24:35,400 --> 00:24:37,840
Speaker 1: move it faster and then as a length in that

422
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Speaker 1: leg for each step they get to go further. Interesting.

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Speaker 1: I mean, I think that's one of the things that

424
00:24:45,840 --> 00:24:49,000
Speaker 1: always kind of surprises people who aren't that familiar with

425
00:24:49,040 --> 00:24:51,640
Speaker 1: white tails when you when they get their hands on

426
00:24:51,640 --> 00:24:54,040
Speaker 1: one for the first time, is how tiny their lower

427
00:24:54,119 --> 00:24:56,640
Speaker 1: legs are. And you know, when you watch a deer

428
00:24:56,720 --> 00:24:59,680
Speaker 1: jump like a six seven foot fence, it just doesn't

429
00:24:59,720 --> 00:25:01,800
Speaker 1: seem to add up in our minds. It's a different

430
00:25:01,920 --> 00:25:06,959
Speaker 1: kind of anatomy. Absolutely yeah, and it's it's it can

431
00:25:07,040 --> 00:25:10,560
Speaker 1: look ridiculous when they're walking on like a road. When

432
00:25:10,600 --> 00:25:13,760
Speaker 1: you actually see them move and run, it all sort

433
00:25:13,760 --> 00:25:18,400
Speaker 1: of ties together and makes sense. So is there in

434
00:25:18,440 --> 00:25:21,320
Speaker 1: the history of the white tail? You know, can you

435
00:25:21,440 --> 00:25:23,560
Speaker 1: can you go back in time and find an ancestor

436
00:25:23,600 --> 00:25:26,440
Speaker 1: to them who didn't have uh, you know, who maybe

437
00:25:26,440 --> 00:25:30,119
Speaker 1: had stock ear shorter legs. Can can we see this

438
00:25:30,119 --> 00:25:33,760
Speaker 1: this process playing out in any way? Um, you'd have

439
00:25:33,840 --> 00:25:37,200
Speaker 1: to go back pretty pretty far to get at those.

440
00:25:37,280 --> 00:25:40,080
Speaker 1: But there are some subtle differences even when you look

441
00:25:40,080 --> 00:25:44,119
Speaker 1: about look at like the movement patterns of even something

442
00:25:44,160 --> 00:25:47,000
Speaker 1: like mule deer relative to white tails, just in the

443
00:25:47,040 --> 00:25:49,720
Speaker 1: habitat they use. You don't see it as much in

444
00:25:49,800 --> 00:25:53,520
Speaker 1: the anatomy, but you do in sort of the the

445
00:25:53,560 --> 00:25:57,359
Speaker 1: way that they move whereas white tails are are you know,

446
00:25:57,400 --> 00:25:59,680
Speaker 1: built for speed Newley's maybe a little bit more of

447
00:25:59,760 --> 00:26:02,800
Speaker 1: that to to bouncing around a little bit more in

448
00:26:02,840 --> 00:26:06,480
Speaker 1: their their habitat. But are their legs have their legs

449
00:26:06,480 --> 00:26:08,679
Speaker 1: evolved any differently? Because I mean when you when you

450
00:26:08,680 --> 00:26:11,479
Speaker 1: watch those meal dear stot up the hillside, you know

451
00:26:11,840 --> 00:26:13,920
Speaker 1: that's a different gait than a white tail, but it's

452
00:26:14,240 --> 00:26:18,000
Speaker 1: is there a difference in their anatomy? You you would

453
00:26:18,600 --> 00:26:21,080
Speaker 1: you would have to put on your goggles pretty closely

454
00:26:21,119 --> 00:26:24,480
Speaker 1: to see subtle differences in in in regards to their

455
00:26:25,280 --> 00:26:30,280
Speaker 1: their muscular skeletal anatomy. Yeah. Do do the subspecies of

456
00:26:30,280 --> 00:26:33,000
Speaker 1: of deer in North America? Do they all share a

457
00:26:33,000 --> 00:26:36,680
Speaker 1: common ancestor they? Well? I think there are some comp

458
00:26:36,840 --> 00:26:39,200
Speaker 1: you know, and certainly I may not be the best

459
00:26:39,200 --> 00:26:41,280
Speaker 1: person to speak of that, but I think there are

460
00:26:41,359 --> 00:26:47,320
Speaker 1: some competing theories about that. Some people believe they do

461
00:26:47,359 --> 00:26:49,800
Speaker 1: and some people believe they don't. Right. Yeah, Yeah, I

462
00:26:49,840 --> 00:26:54,520
Speaker 1: know there's some some differences on, you know, in ancestry

463
00:26:54,560 --> 00:26:57,479
Speaker 1: between like black tails, white tails, and new Lee's as

464
00:26:57,520 --> 00:27:01,680
Speaker 1: far as how each of those evolved. Yeah, I suspect

465
00:27:01,720 --> 00:27:03,480
Speaker 1: if I guess, if you have to go back far enough,

466
00:27:03,520 --> 00:27:05,600
Speaker 1: you'd you'd figure out where the elk and the moose

467
00:27:05,600 --> 00:27:08,440
Speaker 1: and a couple of other species came from. Too. Exactly

468
00:27:08,560 --> 00:27:13,800
Speaker 1: exactly what do you think? That gets outside of my expertise?

469
00:27:15,480 --> 00:27:17,879
Speaker 1: Nobody cares, man, Just just take a guess. What is

470
00:27:17,920 --> 00:27:22,360
Speaker 1: your What does your gut say? Um? I I think

471
00:27:22,359 --> 00:27:26,920
Speaker 1: they share a comedy ancestor all the subspecies I do too.

472
00:27:26,960 --> 00:27:31,240
Speaker 1: Are you Are you willing to go on record for that? Sure?

473
00:27:31,680 --> 00:27:33,640
Speaker 1: I'm just I'm just a wildlife that I got nothing

474
00:27:33,640 --> 00:27:35,880
Speaker 1: to lose. I guess, I guess it really doesn't matter

475
00:27:36,640 --> 00:27:39,880
Speaker 1: what else, what else about their anatomy is is fascinating

476
00:27:39,880 --> 00:27:43,399
Speaker 1: to you as far as there you know adaptations to

477
00:27:43,440 --> 00:27:47,320
Speaker 1: their environments. Well, I think whenever you deal with a

478
00:27:47,480 --> 00:27:54,160
Speaker 1: rumin in um, that always fascinates me just how much

479
00:27:54,240 --> 00:27:58,800
Speaker 1: they can get in regards to energy and nutrition out

480
00:27:58,840 --> 00:28:04,080
Speaker 1: of out of food resources that we can't use as monogastrics.

481
00:28:04,119 --> 00:28:07,280
Speaker 1: And when you say monogastrics, we mean um species with

482
00:28:07,359 --> 00:28:09,399
Speaker 1: one stomach UM, so it's gonna be a lot of

483
00:28:09,400 --> 00:28:13,400
Speaker 1: your mammals um. But ruminants have four chambers to their

484
00:28:13,400 --> 00:28:17,840
Speaker 1: stomach uh, and it allows them to eat plant matter

485
00:28:18,640 --> 00:28:22,600
Speaker 1: uh and things that have cellulose in them um, which

486
00:28:22,920 --> 00:28:26,520
Speaker 1: is something obviously that monogastrics can't use. If you've seen

487
00:28:26,520 --> 00:28:29,280
Speaker 1: your dog eat grass and it comes out the other

488
00:28:29,359 --> 00:28:32,240
Speaker 1: end looking fairly similar, you realize that they're not getting

489
00:28:32,320 --> 00:28:34,760
Speaker 1: much from that. But when you look at deer or

490
00:28:34,880 --> 00:28:40,440
Speaker 1: ruminants um, they have rumen which allows them to have

491
00:28:40,600 --> 00:28:44,560
Speaker 1: microbes and bacteria and parasites that allow them to break

492
00:28:44,600 --> 00:28:48,120
Speaker 1: down that plant matter and get resources and nutrients from it,

493
00:28:48,400 --> 00:28:51,920
Speaker 1: whereas we cannot. And so that's always sort of fascinated

494
00:28:51,960 --> 00:28:56,200
Speaker 1: me just how how they've adapted to to to take

495
00:28:56,240 --> 00:29:01,000
Speaker 1: advantage something that we can. Yeah, they're they're pretty efficient

496
00:29:01,080 --> 00:29:05,520
Speaker 1: at at you know, ringing the most bioavailability out of

497
00:29:05,760 --> 00:29:09,280
Speaker 1: out of a wide variety of food sources. It's it's

498
00:29:09,280 --> 00:29:14,600
Speaker 1: pretty incredible. Yeah, And and there are subtle differences between um,

499
00:29:14,760 --> 00:29:17,360
Speaker 1: you know, and maybe even not subtle, but between like

500
00:29:17,440 --> 00:29:20,520
Speaker 1: some of our domestic ruminants and and and wild ruminants.

501
00:29:20,520 --> 00:29:24,200
Speaker 1: So if you think of cattle, they're they're considered more

502
00:29:24,240 --> 00:29:27,280
Speaker 1: of a general grazer. So it just means that they

503
00:29:27,320 --> 00:29:31,960
Speaker 1: are obviously obviously most often eating grass and other other

504
00:29:32,040 --> 00:29:36,040
Speaker 1: things in the field, whereas deer um are more selective.

505
00:29:36,360 --> 00:29:38,840
Speaker 1: They're more browsers where they're actually going through the woods

506
00:29:38,880 --> 00:29:42,960
Speaker 1: and and picking off the most nutrient rich parts of

507
00:29:43,000 --> 00:29:45,640
Speaker 1: the plant um. So they're a little bit more selective

508
00:29:45,680 --> 00:29:47,360
Speaker 1: in in what they eat, and you can see that

509
00:29:47,960 --> 00:29:51,080
Speaker 1: in the anatomy a little bit um. You know, grazers

510
00:29:51,160 --> 00:29:54,840
Speaker 1: have sort of that wide muzzle if you think of

511
00:29:54,880 --> 00:29:58,000
Speaker 1: like a cow, whereas deer have a more narrow muzzle,

512
00:29:58,000 --> 00:29:59,320
Speaker 1: and it allows them to be a little bit more

513
00:29:59,360 --> 00:30:01,920
Speaker 1: selective and pick off what they want as they're going

514
00:30:02,000 --> 00:30:08,160
Speaker 1: through the woods. How how do they study? So this

515
00:30:08,200 --> 00:30:11,080
Speaker 1: is gonna be a weird thing. But I got to

516
00:30:11,160 --> 00:30:13,440
Speaker 1: see I guess I'm not gonna say the company, but

517
00:30:13,480 --> 00:30:17,960
Speaker 1: I got to see how a company was studying how

518
00:30:18,040 --> 00:30:21,720
Speaker 1: dairy cows digest different foods one time, and there was

519
00:30:21,760 --> 00:30:23,440
Speaker 1: like a port right in the side of this cow

520
00:30:23,520 --> 00:30:25,640
Speaker 1: that they could reach into the various chambers of the

521
00:30:25,680 --> 00:30:30,000
Speaker 1: stomach and literally take stuff out. Uh. Is there anything

522
00:30:30,040 --> 00:30:32,680
Speaker 1: like that people are doing to white tails? I've not

523
00:30:32,840 --> 00:30:36,640
Speaker 1: seen it there. You're called fistulated cattle, and it's basically

524
00:30:36,640 --> 00:30:41,000
Speaker 1: where they um make, like you said, a port into

525
00:30:41,040 --> 00:30:43,800
Speaker 1: their room in um and then they have sort of

526
00:30:44,240 --> 00:30:47,520
Speaker 1: what looks almost like a cork and they can close

527
00:30:47,560 --> 00:30:51,040
Speaker 1: off that opening to the room and UM. I have

528
00:30:51,120 --> 00:30:54,440
Speaker 1: not seen one for deer, although you know, it wouldn't

529
00:30:54,440 --> 00:30:59,280
Speaker 1: surprise me if they did. UM, But now I've not

530
00:30:59,400 --> 00:31:01,840
Speaker 1: seen one. And some of it may be that the

531
00:31:01,920 --> 00:31:05,080
Speaker 1: rouman in DearS is quite a bit smaller than it

532
00:31:05,160 --> 00:31:09,520
Speaker 1: is in in cattle. Yeah, a little harder to get

533
00:31:09,520 --> 00:31:12,240
Speaker 1: in there and root around. I would guess. Do you

534
00:31:12,280 --> 00:31:16,560
Speaker 1: think that the average hunter really underestimates the diversity of

535
00:31:16,560 --> 00:31:21,520
Speaker 1: a white tails diet? I think so and I think

536
00:31:21,640 --> 00:31:25,520
Speaker 1: they underestimate or or not even underestimate. But um, I

537
00:31:25,520 --> 00:31:28,120
Speaker 1: think it would be surprising for them to realize how

538
00:31:29,240 --> 00:31:34,360
Speaker 1: adaptable it is. Um. You know, we the rouman and

539
00:31:34,400 --> 00:31:39,640
Speaker 1: the flora in it, in dear is dynamic, and so

540
00:31:40,440 --> 00:31:43,480
Speaker 1: it's not like they are getting the same concentrate and

541
00:31:43,600 --> 00:31:46,479
Speaker 1: same diet as they go through the year with white tails.

542
00:31:47,240 --> 00:31:50,960
Speaker 1: Their room in and the microbes inside of it are

543
00:31:51,000 --> 00:31:55,200
Speaker 1: adapting as the plants are changing on the landscape, right,

544
00:31:55,280 --> 00:31:58,400
Speaker 1: and so the ruman can change every It takes about

545
00:31:58,880 --> 00:32:01,160
Speaker 1: you know, two to three weeks maybe a little longer

546
00:32:01,480 --> 00:32:04,680
Speaker 1: for those microbes to adapt. And so as the seasons

547
00:32:04,680 --> 00:32:09,120
Speaker 1: are changing, the room and flora is adapting so that

548
00:32:09,120 --> 00:32:12,520
Speaker 1: they can take advantage of the changing plant matter on

549
00:32:12,560 --> 00:32:14,840
Speaker 1: the on the landscape, so as it goes from something

550
00:32:14,880 --> 00:32:18,120
Speaker 1: really lush in the summer to something that may be

551
00:32:18,240 --> 00:32:21,720
Speaker 1: more fibrous than the winter. They're their room and content

552
00:32:21,880 --> 00:32:25,640
Speaker 1: is changing to to allow that adaptation to it to occur.

553
00:32:26,280 --> 00:32:30,200
Speaker 1: So are you saying that they're they're gut microbiome essentially

554
00:32:30,280 --> 00:32:32,320
Speaker 1: is kind of changing throughout the year to adapt to

555
00:32:32,360 --> 00:32:35,080
Speaker 1: the food sources that are available. Correct, I mean, and

556
00:32:35,120 --> 00:32:38,160
Speaker 1: it happens slowly, and that's where we run into problems

557
00:32:38,760 --> 00:32:41,760
Speaker 1: at times with with feeding or something like that in

558
00:32:41,760 --> 00:32:45,600
Speaker 1: the winter because they're adapting to that high fibrous diet

559
00:32:45,880 --> 00:32:50,000
Speaker 1: and then they're getting a sudden bolus of a real lush,

560
00:32:50,120 --> 00:32:54,120
Speaker 1: concentrated material and and their their g i flora doesn't

561
00:32:54,120 --> 00:32:57,480
Speaker 1: have time to adapt, right. So is that like when

562
00:32:58,440 --> 00:33:00,960
Speaker 1: you know, you take a winner ring heard here, let's

563
00:33:00,960 --> 00:33:03,760
Speaker 1: say in Minnesota, and it's been you know, maybe living

564
00:33:03,760 --> 00:33:05,720
Speaker 1: off a kind of woody browse or whatever it can find,

565
00:33:05,760 --> 00:33:07,160
Speaker 1: and then all of a sudden, somebody goes and dumps

566
00:33:07,160 --> 00:33:09,160
Speaker 1: a bunch of corn out there and they've got this

567
00:33:09,160 --> 00:33:11,480
Speaker 1: this food source that they haven't had for months, and

568
00:33:11,520 --> 00:33:13,720
Speaker 1: it's full of carbs and not you know, not what

569
00:33:13,800 --> 00:33:16,320
Speaker 1: they're not what they're built for at the moment, and

570
00:33:16,360 --> 00:33:19,920
Speaker 1: they get really really sick and some of them die. Yeah, exactly.

571
00:33:20,040 --> 00:33:23,600
Speaker 1: The disease is called acidosis um and there's some other

572
00:33:23,600 --> 00:33:26,600
Speaker 1: diseases you can see with it um. But but but

573
00:33:26,640 --> 00:33:29,400
Speaker 1: it's that same idea, And so sometimes we'll get questions

574
00:33:29,440 --> 00:33:32,560
Speaker 1: of y see deer you know in my corn fields

575
00:33:32,560 --> 00:33:35,040
Speaker 1: in the summer and they're doing fine, and and again

576
00:33:35,040 --> 00:33:40,080
Speaker 1: it's because it's it's the speed at which you make

577
00:33:40,120 --> 00:33:42,720
Speaker 1: that change. Right, So if in the winter, if they're

578
00:33:43,160 --> 00:33:46,560
Speaker 1: slowly adapting over months to that fiberus diet and then

579
00:33:46,600 --> 00:33:49,840
Speaker 1: they get into a starchy high concentrate, is that abrupt

580
00:33:49,960 --> 00:33:54,920
Speaker 1: change more than it is just solely that they're getting

581
00:33:54,920 --> 00:33:57,760
Speaker 1: into carbs? Can I can I ask you something that

582
00:33:57,760 --> 00:34:00,360
Speaker 1: I've always thought was bullshit about what hunters say. So

583
00:34:02,120 --> 00:34:04,040
Speaker 1: you and you've heard this a million times, right, like

584
00:34:04,080 --> 00:34:06,080
Speaker 1: people will say, oh, you know, you shoot a deer

585
00:34:06,120 --> 00:34:08,719
Speaker 1: down in Iowa, it's that corn fed deer and it's delicious.

586
00:34:09,280 --> 00:34:11,960
Speaker 1: And when I you know, I hunt all over, hunt

587
00:34:12,040 --> 00:34:14,000
Speaker 1: big woods and hunt out west. I hunt a lot

588
00:34:14,000 --> 00:34:17,600
Speaker 1: of places where there aren't deer anywhere near cornfields, right,

589
00:34:18,120 --> 00:34:20,359
Speaker 1: And even when you do shoot a deer that's eaten

590
00:34:20,400 --> 00:34:23,120
Speaker 1: writing in a corn field, but you watch them come

591
00:34:23,160 --> 00:34:26,160
Speaker 1: out there, they're browsing their whole way out there? Is

592
00:34:26,200 --> 00:34:29,640
Speaker 1: that bs like the corn fed idea? But when you

593
00:34:29,680 --> 00:34:32,799
Speaker 1: talk about a browser like that, I think they can

594
00:34:32,840 --> 00:34:36,400
Speaker 1: get nutrition in a variety of different landscapes and not

595
00:34:36,560 --> 00:34:39,080
Speaker 1: just the corn um. You know, I think whenever you

596
00:34:39,120 --> 00:34:43,200
Speaker 1: get that high, that that big deer, the you know,

597
00:34:43,320 --> 00:34:47,240
Speaker 1: really well well nutrition and well muscle, and it's always

598
00:34:47,280 --> 00:34:50,160
Speaker 1: going to be a combination of genetics as well as

599
00:34:50,360 --> 00:34:53,600
Speaker 1: as nutrition. Um. And so No, I don't think it's

600
00:34:53,600 --> 00:34:57,320
Speaker 1: exclusive to the to the to the corn fields, although

601
00:34:57,360 --> 00:35:01,400
Speaker 1: that's probably a pretty uh pretty good die form there. Well,

602
00:35:01,440 --> 00:35:04,319
Speaker 1: I mean, they definitely use it. I just you know,

603
00:35:04,320 --> 00:35:06,160
Speaker 1: I hear people refer to it all the time like

604
00:35:06,200 --> 00:35:08,520
Speaker 1: it's a cow that was, you know, And it's just

605
00:35:09,000 --> 00:35:11,600
Speaker 1: you watch them in their natural world, and the white

606
00:35:11,600 --> 00:35:13,560
Speaker 1: tails are just not living like that. They're not going

607
00:35:13,640 --> 00:35:17,200
Speaker 1: up to a feed trough, and it's just seems like

608
00:35:17,200 --> 00:35:19,160
Speaker 1: they're so much more diverse. And that's that's one of

609
00:35:19,200 --> 00:35:22,359
Speaker 1: the things that always amazes me about deer and you know,

610
00:35:22,680 --> 00:35:26,360
Speaker 1: and turkeys and kind of everything, you know, trout everything

611
00:35:26,400 --> 00:35:28,400
Speaker 1: that you kind of start to look into their stomach

612
00:35:28,440 --> 00:35:31,919
Speaker 1: after you you kill them. It's like, man, they've they're

613
00:35:32,000 --> 00:35:34,080
Speaker 1: they're taking advantage of their environment in a way that

614
00:35:34,120 --> 00:35:37,399
Speaker 1: I think we just totally don't understand for the most part.

615
00:35:37,840 --> 00:35:40,480
Speaker 1: Oh yeah, no, And and again, I mean grouse are

616
00:35:40,480 --> 00:35:43,680
Speaker 1: probably the perfect for that, right you know, it's it's

617
00:35:43,719 --> 00:35:48,520
Speaker 1: so fun to look through grouse crops. Yeah, yeah, they've

618
00:35:48,640 --> 00:35:53,080
Speaker 1: they you know, we we hunt them quite often, and

619
00:35:53,239 --> 00:35:55,360
Speaker 1: so we but it's a lot of late season stuff.

620
00:35:55,400 --> 00:35:57,239
Speaker 1: So you you'd think, you know, like they're on some

621
00:35:57,280 --> 00:36:00,879
Speaker 1: kind of catkins or something, and you know, oftentimes they are.

622
00:36:01,239 --> 00:36:02,920
Speaker 1: But even then, if you, you know, kill one in

623
00:36:03,000 --> 00:36:06,160
Speaker 1: late December, it's still pretty incredible what they might have

624
00:36:06,200 --> 00:36:11,439
Speaker 1: in their crop. Yeah. Yeah, I love that. My little

625
00:36:11,480 --> 00:36:13,319
Speaker 1: girls do too. When you you know, like when we

626
00:36:13,360 --> 00:36:15,160
Speaker 1: go fishing, if I if I keep some fish and

627
00:36:15,200 --> 00:36:17,080
Speaker 1: open it up, they're always amazed at the bugs and

628
00:36:17,680 --> 00:36:33,879
Speaker 1: and in different things that that the fish eat. Um,

629
00:36:33,920 --> 00:36:37,280
Speaker 1: what what else about a white tails anatomy? What besides

630
00:36:37,360 --> 00:36:41,080
Speaker 1: besides their ability to run really fast and that adaptation, uh,

631
00:36:41,239 --> 00:36:43,359
Speaker 1: and their ability to eat just about everything that they

632
00:36:43,360 --> 00:36:45,640
Speaker 1: can come across that they could digest. What what else

633
00:36:45,719 --> 00:36:48,160
Speaker 1: is there that you just as a as a you know,

634
00:36:48,239 --> 00:36:51,480
Speaker 1: a science person who's just really into them, Like, what

635
00:36:51,600 --> 00:36:54,080
Speaker 1: is the thing that you're like? Man, that's so cool? Yeah,

636
00:36:54,120 --> 00:37:00,480
Speaker 1: I think they're Their senses are always are pretty amazing.

637
00:37:00,600 --> 00:37:05,720
Speaker 1: Again because they've now adapted, let's you know, say, evolutionarily

638
00:37:05,800 --> 00:37:08,440
Speaker 1: to be in in in the forest, and when they

639
00:37:08,480 --> 00:37:14,120
Speaker 1: do that, there are certain senses that may not function

640
00:37:14,160 --> 00:37:18,400
Speaker 1: as well. You know, vision may be poor, and and

641
00:37:18,600 --> 00:37:23,640
Speaker 1: site maybe poor. You know, sound maybe maybe not good

642
00:37:23,840 --> 00:37:27,480
Speaker 1: in the deep dense wooded forests. UM. And so they've

643
00:37:27,520 --> 00:37:32,120
Speaker 1: adapted a variety of different senses to allow communication to occur.

644
00:37:33,040 --> 00:37:35,680
Speaker 1: For the vision, I think what's really neat is how

645
00:37:36,320 --> 00:37:39,880
Speaker 1: a depth they are at seeing at night. And so

646
00:37:39,960 --> 00:37:41,840
Speaker 1: if you look at sort of the eyes of deer,

647
00:37:42,320 --> 00:37:46,759
Speaker 1: they have a higher proportion of of rods in their

648
00:37:46,760 --> 00:37:49,200
Speaker 1: retinas sort of that that back part of the eye.

649
00:37:49,760 --> 00:37:53,320
Speaker 1: And what that allows them to do is the rods

650
00:37:53,320 --> 00:37:55,640
Speaker 1: are the part of the photoreceptor in the eye that

651
00:37:55,680 --> 00:37:59,520
Speaker 1: allows you to see better at low light. UM. And

652
00:37:59,560 --> 00:38:02,360
Speaker 1: they're all so really good. Rods are also really good

653
00:38:02,440 --> 00:38:05,040
Speaker 1: at seeing motion. Right. And so when you think of

654
00:38:05,080 --> 00:38:08,160
Speaker 1: like what a deer needs to see at night, to

655
00:38:08,239 --> 00:38:10,719
Speaker 1: see in dark, to see in in areas where there's

656
00:38:10,719 --> 00:38:12,880
Speaker 1: not a lot of light, but also to see motion

657
00:38:13,200 --> 00:38:15,879
Speaker 1: as a as a prey species. UM. The other thing

658
00:38:15,880 --> 00:38:19,279
Speaker 1: that they've done is they have this this structure in

659
00:38:19,320 --> 00:38:21,800
Speaker 1: their eye. So if you've ever seen a deer in

660
00:38:21,880 --> 00:38:24,440
Speaker 1: headlights and they had sort of that reflective part of

661
00:38:24,480 --> 00:38:27,800
Speaker 1: their eye, UM, that's actually a structure that is within

662
00:38:27,880 --> 00:38:31,120
Speaker 1: it's a membrane in their eye that sort of reflects

663
00:38:31,520 --> 00:38:34,960
Speaker 1: the light back across the retina and it allows them

664
00:38:35,040 --> 00:38:37,880
Speaker 1: to see better at lower levels of light or species

665
00:38:37,920 --> 00:38:41,879
Speaker 1: like humans cannot. So um, they've got some pretty neat

666
00:38:41,920 --> 00:38:44,399
Speaker 1: things in their eyes to allow them to to see

667
00:38:44,400 --> 00:38:49,080
Speaker 1: well even at low light. Why maybe this is a

668
00:38:49,160 --> 00:38:53,720
Speaker 1: really really dumb question, but why why did they adapt

669
00:38:53,760 --> 00:38:57,240
Speaker 1: to move at night? Like? Why are they so adapted

670
00:38:57,280 --> 00:38:59,440
Speaker 1: to be out there feeding around at night? Because I

671
00:38:59,480 --> 00:39:02,439
Speaker 1: know I can't remember what it was, but I read

672
00:39:02,480 --> 00:39:06,680
Speaker 1: a book on tigers that you know, Mark the main

673
00:39:06,719 --> 00:39:08,480
Speaker 1: host of this recommended to me, and they were talking

674
00:39:08,560 --> 00:39:12,240
Speaker 1: about humans evolution and some of the primates and stuff,

675
00:39:12,280 --> 00:39:15,440
Speaker 1: and how how we were like, you know what, we're

676
00:39:15,440 --> 00:39:17,040
Speaker 1: getting up into a tree or we're getting into a

677
00:39:17,040 --> 00:39:19,640
Speaker 1: cave when nightfalls, because that's when the bad stuff comes out.

678
00:39:19,960 --> 00:39:22,279
Speaker 1: But there's other prey animals that are just that's their

679
00:39:22,280 --> 00:39:24,520
Speaker 1: time to shine. Why why is that the case for

680
00:39:24,560 --> 00:39:28,640
Speaker 1: the white tail? You know, and and and A biologists

681
00:39:28,680 --> 00:39:31,000
Speaker 1: may may even be better at answering that than me,

682
00:39:31,080 --> 00:39:34,320
Speaker 1: But I would think that anything anytime they can move

683
00:39:36,080 --> 00:39:42,919
Speaker 1: without um with with reduced likelihood of being predated upon,

684
00:39:43,080 --> 00:39:45,879
Speaker 1: they may take advantage of And so if you think

685
00:39:45,920 --> 00:39:50,120
Speaker 1: about them bedding down during the day and and taking

686
00:39:50,120 --> 00:39:53,000
Speaker 1: a meal and bedding down and sort of ruminating and

687
00:39:53,040 --> 00:39:57,359
Speaker 1: getting their digestion, do they're moving more at night where

688
00:39:57,440 --> 00:40:00,120
Speaker 1: visibility is going to be low. And again, they have

689
00:40:00,800 --> 00:40:03,200
Speaker 1: those that high level of rods in their eyes, So

690
00:40:03,239 --> 00:40:07,400
Speaker 1: if you do have movement in very dark conditions, you

691
00:40:07,520 --> 00:40:09,680
Speaker 1: are much more likely to pick that up than than

692
00:40:09,760 --> 00:40:12,759
Speaker 1: something that doesn't have those structures in their eyes. So

693
00:40:12,800 --> 00:40:18,040
Speaker 1: I think they they have an advantages. And it's always

694
00:40:18,040 --> 00:40:19,840
Speaker 1: gonna be the question of what came first, the chicken

695
00:40:19,920 --> 00:40:23,200
Speaker 1: or the egg. Did they have those evolutionary advantages to

696
00:40:23,280 --> 00:40:27,319
Speaker 1: move at night? Um or did they evolve because they

697
00:40:27,320 --> 00:40:31,640
Speaker 1: were moving at night? Do you think maybe you know

698
00:40:31,760 --> 00:40:35,960
Speaker 1: this is are their eyes better at night than you know,

699
00:40:36,040 --> 00:40:38,200
Speaker 1: some of the predators that they tend to deal with?

700
00:40:38,239 --> 00:40:41,200
Speaker 1: I mean, you know, can a wolf see as good

701
00:40:41,239 --> 00:40:44,120
Speaker 1: at night as a deer? You know, I don't know

702
00:40:44,160 --> 00:40:47,799
Speaker 1: if it can see if one might be better than

703
00:40:47,840 --> 00:40:52,000
Speaker 1: the other, but I would imagine dear with the structures

704
00:40:52,000 --> 00:40:54,000
Speaker 1: that we already listed, as well as sort of their

705
00:40:54,000 --> 00:40:57,480
Speaker 1: eyes being um on either side of their head align

706
00:40:57,520 --> 00:41:00,560
Speaker 1: them to see almost three degrees around them that they'd

707
00:41:00,600 --> 00:41:07,439
Speaker 1: be pretty good um at at out competing them at night. Yeah,

708
00:41:07,440 --> 00:41:10,279
Speaker 1: I guess even if they were you know, had had

709
00:41:10,360 --> 00:41:13,359
Speaker 1: very similar eyesight capabilities at night, if you can see

710
00:41:13,400 --> 00:41:17,480
Speaker 1: that range and your your eyesight is evolved to really

711
00:41:17,480 --> 00:41:19,920
Speaker 1: pick up movement and react to it, you know, in

712
00:41:20,000 --> 00:41:23,239
Speaker 1: like micro seconds, then that's all you need. I mean,

713
00:41:23,320 --> 00:41:25,120
Speaker 1: obviously you're not gonna win every time, but it's going

714
00:41:25,160 --> 00:41:28,360
Speaker 1: to give you an advantage in that, you know, in

715
00:41:28,440 --> 00:41:31,640
Speaker 1: that space anyway exactly. And then you combine that with

716
00:41:32,080 --> 00:41:35,080
Speaker 1: you know, they're keen sense of smell as well as

717
00:41:35,360 --> 00:41:39,160
Speaker 1: as good hearing. I think they've got a lot to

718
00:41:39,320 --> 00:41:41,719
Speaker 1: give them the upper hand at night. Do you think

719
00:41:41,800 --> 00:41:46,160
Speaker 1: the vision is the most important sense for predator detection

720
00:41:46,800 --> 00:41:51,040
Speaker 1: for predator detection detection, um, you know, I I'm not sure.

721
00:41:51,480 --> 00:41:54,280
Speaker 1: I think I think it's the compilation of all of them,

722
00:41:54,360 --> 00:41:56,840
Speaker 1: and it really sort of depends on on the environment

723
00:41:56,880 --> 00:42:02,279
Speaker 1: because you you know, certainly when dear look up, you

724
00:42:02,320 --> 00:42:05,359
Speaker 1: know they've they've they've got that good vision, particularly at night.

725
00:42:05,640 --> 00:42:07,680
Speaker 1: But I mean I think hearing, you know, when you

726
00:42:07,719 --> 00:42:10,440
Speaker 1: see him move those those sort of pinna of their

727
00:42:10,480 --> 00:42:14,280
Speaker 1: ear to sort of triangulate those noises that are coming,

728
00:42:14,320 --> 00:42:17,400
Speaker 1: and scent, which they have a really good sense of

729
00:42:17,480 --> 00:42:19,680
Speaker 1: smell of smell, I mean, I think all of those

730
00:42:19,719 --> 00:42:22,960
Speaker 1: work together. Yeah, they've they've got a few tools in

731
00:42:22,960 --> 00:42:26,440
Speaker 1: the kid anyway. Huh yeah. Yeah. Do you do you

732
00:42:26,440 --> 00:42:28,520
Speaker 1: think there's any validity to the argument that they have

733
00:42:28,560 --> 00:42:34,400
Speaker 1: a sixth sense about detecting predators? Um? I think it

734
00:42:34,480 --> 00:42:37,360
Speaker 1: might depend on what you're calling that. I mean, you know,

735
00:42:37,400 --> 00:42:41,959
Speaker 1: they do have um, something called vom or faction, which

736
00:42:42,000 --> 00:42:46,120
Speaker 1: is sort of like a mixture of smell and taste. Um.

737
00:42:47,040 --> 00:42:49,000
Speaker 1: But but I don't know if I don't think of

738
00:42:49,040 --> 00:42:52,480
Speaker 1: it as much with predators, more of like UM, dear

739
00:42:52,560 --> 00:42:56,120
Speaker 1: to deer communication. Hold on, can you explain that? So

740
00:42:56,360 --> 00:42:59,520
Speaker 1: have you ever looked at sort of there on the

741
00:42:59,600 --> 00:43:02,440
Speaker 1: upper part of their oral cavity, they have sort of

742
00:43:02,440 --> 00:43:05,959
Speaker 1: that little circular structure right where they're like dental pad

743
00:43:06,000 --> 00:43:12,520
Speaker 1: would be, so in it there that's um, they have, UM,

744
00:43:12,640 --> 00:43:15,160
Speaker 1: they have a bom or nasal organ So right above

745
00:43:15,200 --> 00:43:18,360
Speaker 1: there they have a structure that can take in some

746
00:43:18,480 --> 00:43:22,879
Speaker 1: of these scent chemicals and they can process it. It's

747
00:43:22,880 --> 00:43:25,960
Speaker 1: sort of in their their nasal cavity area. So if

748
00:43:26,000 --> 00:43:29,360
Speaker 1: you ever see um if you're like a flaming response,

749
00:43:29,760 --> 00:43:32,680
Speaker 1: where like boxes sort of curl their lips a little

750
00:43:32,680 --> 00:43:35,919
Speaker 1: bit when they get a scent of a dough um

751
00:43:35,920 --> 00:43:39,200
Speaker 1: in the rut. What they're doing is actually a response

752
00:43:39,360 --> 00:43:41,440
Speaker 1: to those chemicals, which is sort of a mixture of

753
00:43:41,520 --> 00:43:45,759
Speaker 1: taste and smell. So that that's an adaptation that goes

754
00:43:45,800 --> 00:43:49,160
Speaker 1: beyond just their amazing sense of smell. Yeah, it's it's

755
00:43:49,160 --> 00:43:51,560
Speaker 1: almost again like a combination of those two where it's

756
00:43:51,560 --> 00:43:56,440
Speaker 1: sort of sampling that um smell and taste chemical in

757
00:43:56,480 --> 00:44:01,359
Speaker 1: the in the air. Yeah, So that like in the

758
00:44:01,360 --> 00:44:03,600
Speaker 1: case of the flaming response, that or you take a

759
00:44:03,640 --> 00:44:05,840
Speaker 1: dough she she walks by, she PE's on the ground,

760
00:44:06,000 --> 00:44:09,319
Speaker 1: He heads over there, he smells it. There's there are

761
00:44:09,440 --> 00:44:12,879
Speaker 1: things that he can't get out of that just through

762
00:44:12,960 --> 00:44:15,040
Speaker 1: taking a whiff. He's got to actually kind of hork

763
00:44:15,080 --> 00:44:17,759
Speaker 1: it through his mouth and work it through that. Yeah,

764
00:44:17,800 --> 00:44:19,600
Speaker 1: he sort of takes it. And what happens is they

765
00:44:19,640 --> 00:44:22,200
Speaker 1: take it and they get it in their mouth and

766
00:44:22,239 --> 00:44:24,880
Speaker 1: they with their tongue sort of pushed the chemical up

767
00:44:24,920 --> 00:44:29,080
Speaker 1: into the romonasal organ uh. And then that's that's their

768
00:44:29,080 --> 00:44:33,440
Speaker 1: response off in that curling of the lips that response.

769
00:44:33,960 --> 00:44:36,319
Speaker 1: So let's let's take this back a second, and how

770
00:44:36,400 --> 00:44:39,080
Speaker 1: how does that tie into their potentially being a sixth sense.

771
00:44:40,560 --> 00:44:43,319
Speaker 1: Possibly that could be the sixth one they're talking about.

772
00:44:43,360 --> 00:44:46,960
Speaker 1: Oh so what I meant by that is hunters will

773
00:44:47,040 --> 00:44:49,680
Speaker 1: often say, you know, I never moved and never I

774
00:44:49,719 --> 00:44:51,799
Speaker 1: never did anything, and that buck looked up at me.

775
00:44:51,840 --> 00:44:53,959
Speaker 1: And people say this all the time. And we've even

776
00:44:54,000 --> 00:44:57,719
Speaker 1: had you know, we've we've had people designed suits that

777
00:44:57,760 --> 00:45:01,680
Speaker 1: were designed to block your electro magnet to signal thinking

778
00:45:01,760 --> 00:45:03,640
Speaker 1: that you know, deer might or you know, other big

779
00:45:03,640 --> 00:45:05,920
Speaker 1: game animals might be picking up on that. And it's

780
00:45:05,960 --> 00:45:09,759
Speaker 1: always mixed in with this idea that they've got a

781
00:45:09,840 --> 00:45:14,920
Speaker 1: sixth sense for detecting us somehow that we don't. That's

782
00:45:14,960 --> 00:45:19,360
Speaker 1: not like that's simply explainable biologically. Does that make sense?

783
00:45:19,760 --> 00:45:24,000
Speaker 1: It does? I'm not sure I I go that far.

784
00:45:24,880 --> 00:45:29,520
Speaker 1: You can say it's bullshit your scientist, Yeah, but I

785
00:45:30,480 --> 00:45:33,680
Speaker 1: you know, I like ideas like that. Um, I'm just

786
00:45:33,719 --> 00:45:35,960
Speaker 1: not sure I have the data support it yet, but

787
00:45:36,200 --> 00:45:39,120
Speaker 1: I'll keep looking. Yeah, so you don't believe it. I don't.

788
00:45:42,320 --> 00:45:44,680
Speaker 1: I don't believe it. Yeah, I think that we look

789
00:45:44,719 --> 00:45:48,080
Speaker 1: for excuses sometimes for the fact that we're you know,

790
00:45:48,120 --> 00:45:50,200
Speaker 1: two pounds and hanging off the side of a tree,

791
00:45:50,239 --> 00:45:53,480
Speaker 1: and that deer knows the entire area he lives in

792
00:45:53,640 --> 00:45:56,759
Speaker 1: so well that when there's suddenly something there like that,

793
00:45:57,000 --> 00:45:59,800
Speaker 1: he suddenly, you know, out of his peripheral vision goes, no,

794
00:46:00,440 --> 00:46:04,160
Speaker 1: that can't be right. Yeah. Yeah. It could be just

795
00:46:04,200 --> 00:46:07,520
Speaker 1: I smell like coffee, well maybe, or or it could

796
00:46:07,560 --> 00:46:10,239
Speaker 1: be just the fact that you're, you know, a full

797
00:46:10,280 --> 00:46:12,040
Speaker 1: size human hanging off the side of a tree and

798
00:46:12,040 --> 00:46:14,719
Speaker 1: they're like, yeah, I don't. I mean, I see deer

799
00:46:14,760 --> 00:46:17,000
Speaker 1: that that spooks sometimes when they see trail cameras in

800
00:46:17,080 --> 00:46:19,000
Speaker 1: new spots. And I mean, there might be a sentence

801
00:46:19,000 --> 00:46:21,319
Speaker 1: component to that, But when you when you see a

802
00:46:21,320 --> 00:46:23,080
Speaker 1: deer react to a little tiny box on the side

803
00:46:23,080 --> 00:46:25,399
Speaker 1: of a tree, it's not that it's not that hard

804
00:46:25,440 --> 00:46:27,359
Speaker 1: for me to believe that. Sometimes they just pick us

805
00:46:27,360 --> 00:46:30,239
Speaker 1: out even when we don't move. It is amazing what

806
00:46:30,280 --> 00:46:33,360
Speaker 1: they can pick up on, right, Yeah, let's react to

807
00:46:34,239 --> 00:46:36,640
Speaker 1: Yeah it is. And and I'm always curious and this

808
00:46:36,800 --> 00:46:40,680
Speaker 1: this is probably probably not entirely true, but I believe

809
00:46:40,680 --> 00:46:43,440
Speaker 1: it sometimes. So if I get if you know, this

810
00:46:43,440 --> 00:46:45,480
Speaker 1: happens to me a lot in northern Wisconsin for some reason.

811
00:46:45,480 --> 00:46:47,719
Speaker 1: But if I hang a camera, even if I put

812
00:46:47,760 --> 00:46:49,520
Speaker 1: it up six seven ft and I angle it down

813
00:46:49,560 --> 00:46:51,160
Speaker 1: to kind of get it out of their sight line

814
00:46:51,360 --> 00:46:56,200
Speaker 1: and just just be less intrusive, I still get dear

815
00:46:56,680 --> 00:47:00,160
Speaker 1: that that spot it and you can tell, and you know,

816
00:47:00,160 --> 00:47:02,160
Speaker 1: you might get a series of pictures of them spooking,

817
00:47:02,760 --> 00:47:06,279
Speaker 1: and if it's a specific buck, I often don't get

818
00:47:06,280 --> 00:47:09,120
Speaker 1: them back there, And I'm always like, is that possible?

819
00:47:09,239 --> 00:47:11,399
Speaker 1: Like are they are they that good at being like

820
00:47:11,719 --> 00:47:13,239
Speaker 1: that doesn't belong here? I'm not going to walk in

821
00:47:13,239 --> 00:47:17,040
Speaker 1: front of it again? Or is that all in my head? Yeah? Yeah,

822
00:47:17,280 --> 00:47:20,799
Speaker 1: something threw me off. Yeah, you know, I just don't know,

823
00:47:20,920 --> 00:47:23,080
Speaker 1: I mean, and it makes me wonder about their memory,

824
00:47:23,600 --> 00:47:25,879
Speaker 1: Like I don't. I don't know if if anybody's ever

825
00:47:25,920 --> 00:47:28,040
Speaker 1: studied that or not, like how how they can remember

826
00:47:28,080 --> 00:47:30,160
Speaker 1: things like that or how good their memory is. Do

827
00:47:30,200 --> 00:47:34,640
Speaker 1: you know? I actually don't. I don't. I'd like to

828
00:47:34,680 --> 00:47:37,360
Speaker 1: hear it. I mean, I don't. I don't know. I

829
00:47:37,760 --> 00:47:42,600
Speaker 1: think of if I've read any studies about memory like that?

830
00:47:43,920 --> 00:47:47,120
Speaker 1: Can I can I ask you something different? Then? Um?

831
00:47:47,160 --> 00:47:50,000
Speaker 1: I was I was reading this is really dumb. I

832
00:47:50,040 --> 00:47:55,160
Speaker 1: was reading about uh space and and the gravity of

833
00:47:55,200 --> 00:47:59,840
Speaker 1: black holes and how gravity can actually influence time, and

834
00:48:00,040 --> 00:48:02,120
Speaker 1: so it's kind of ties back to like Einstein's theory

835
00:48:02,120 --> 00:48:05,239
Speaker 1: of relativity, like how we perceive time is different than

836
00:48:05,239 --> 00:48:07,719
Speaker 1: how time is perceived in other places, or I mean,

837
00:48:08,000 --> 00:48:09,880
Speaker 1: you know, like other parts of the Solar system, in

838
00:48:09,920 --> 00:48:12,480
Speaker 1: other parts of the universe. As far as you know

839
00:48:12,760 --> 00:48:15,480
Speaker 1: how much gravity is bending time, I guess it is.

840
00:48:16,360 --> 00:48:20,280
Speaker 1: And anyway, when I was reading about this, the author

841
00:48:20,360 --> 00:48:24,320
Speaker 1: referenced to to kind of frame it up, said dogs

842
00:48:24,320 --> 00:48:28,000
Speaker 1: and cats experienced time differently than we do and and

843
00:48:28,000 --> 00:48:30,480
Speaker 1: and he put it in terms of like frames per second,

844
00:48:31,000 --> 00:48:35,560
Speaker 1: and dogs actually experienced time slower than we do, uh

845
00:48:35,640 --> 00:48:38,200
Speaker 1: in in this argument, and that you know they that

846
00:48:38,400 --> 00:48:41,080
Speaker 1: that's kind of like his explanation was, like, it's kind

847
00:48:41,080 --> 00:48:43,080
Speaker 1: of like, you know, if you watch a snake strike

848
00:48:43,080 --> 00:48:44,759
Speaker 1: at a dog, a lot of times, it's like their

849
00:48:44,800 --> 00:48:46,560
Speaker 1: slow motion moving out of the way. They can kind

850
00:48:46,560 --> 00:48:49,279
Speaker 1: of they can react really really well sometimes if they

851
00:48:49,280 --> 00:48:52,200
Speaker 1: know what's coming. And I always wonder about that with deer,

852
00:48:52,440 --> 00:48:54,520
Speaker 1: and it like breaks my brain. I'm not smart enough

853
00:48:54,520 --> 00:48:57,360
Speaker 1: to actually understand this, but I think about it with

854
00:48:57,400 --> 00:48:59,120
Speaker 1: like deer jump in the string when you when you

855
00:48:59,120 --> 00:49:01,760
Speaker 1: shoot at him, or some of the ways they react,

856
00:49:01,800 --> 00:49:04,400
Speaker 1: and like are they just experiencing time differently than us

857
00:49:04,400 --> 00:49:05,960
Speaker 1: and just getting out ahead of us and we don't

858
00:49:06,000 --> 00:49:10,600
Speaker 1: understand it why? I mean, I think there is there are,

859
00:49:10,600 --> 00:49:12,480
Speaker 1: there are some studies and I haven't seen him in dear,

860
00:49:12,520 --> 00:49:16,240
Speaker 1: but about like fast twitch muscles, you know, like speed

861
00:49:16,320 --> 00:49:20,719
Speaker 1: and quick reaction times and things like that. Um, I

862
00:49:20,719 --> 00:49:23,520
Speaker 1: don't know if they're actually perceiving it slower. Are they

863
00:49:23,560 --> 00:49:27,720
Speaker 1: just moving that much faster? You know? I wonder? Um,

864
00:49:27,800 --> 00:49:29,560
Speaker 1: But yeah, I mean I think I think definitely even

865
00:49:29,600 --> 00:49:34,560
Speaker 1: from like a muscle physiology standpoint, Um, deer, deer can

866
00:49:34,760 --> 00:49:38,319
Speaker 1: can react faster than things like humans. Do you know

867
00:49:38,360 --> 00:49:44,920
Speaker 1: how much faster? No clue are you going to study that?

868
00:49:44,920 --> 00:49:46,359
Speaker 1: That might be here? Let me just make a note

869
00:49:46,400 --> 00:49:47,640
Speaker 1: here that might be on my list. I've got a

870
00:49:47,640 --> 00:49:54,440
Speaker 1: few things here that sixth cents and and how fast? What?

871
00:49:54,440 --> 00:49:57,640
Speaker 1: What is something? What is something anatomy wise or in

872
00:49:57,680 --> 00:50:00,960
Speaker 1: the white tail's history? That is is the mystery to

873
00:50:01,040 --> 00:50:04,759
Speaker 1: you that you would like to figure out? Mm hmm.

874
00:50:08,080 --> 00:50:13,920
Speaker 1: You know, I don't know if one of the things

875
00:50:13,920 --> 00:50:18,600
Speaker 1: that from a health standpoint is we deal a lot

876
00:50:18,680 --> 00:50:23,880
Speaker 1: with capture myopathy and dear um and that is a

877
00:50:24,440 --> 00:50:31,120
Speaker 1: basically a disease we see that's associated with muscle exertion

878
00:50:31,320 --> 00:50:35,359
Speaker 1: and stress. And so whenever you're trapping deer catching deer um,

879
00:50:35,400 --> 00:50:38,560
Speaker 1: it can be a pretty significant issue that we deal

880
00:50:38,600 --> 00:50:43,719
Speaker 1: with UM. And you know, dear are just wound so

881
00:50:43,840 --> 00:50:46,680
Speaker 1: tight that I would love at some point to figure

882
00:50:46,719 --> 00:50:52,000
Speaker 1: out would there ever be anything we can do from

883
00:50:51,640 --> 00:50:55,799
Speaker 1: a from a handling standpoint to limit some of that

884
00:50:55,880 --> 00:50:58,880
Speaker 1: capture myopathy because it is probably one of the biggest

885
00:50:58,960 --> 00:51:02,520
Speaker 1: things we face whenever again, whenever we're trapping deer, coloring

886
00:51:02,680 --> 00:51:06,760
Speaker 1: anything like that, UM. They just have this high stress

887
00:51:07,680 --> 00:51:12,319
Speaker 1: physiologic reaction that can actually lead to to mortality in

888
00:51:12,360 --> 00:51:16,960
Speaker 1: them UM. And so I think from a functional standpoint,

889
00:51:17,000 --> 00:51:19,120
Speaker 1: trying to figure out, like what is it in dear

890
00:51:19,200 --> 00:51:21,680
Speaker 1: that makes them so stressed and that they're wound so

891
00:51:21,760 --> 00:51:25,480
Speaker 1: tight that that that we have this disease that we

892
00:51:25,480 --> 00:51:28,480
Speaker 1: we deal with UM when we handle them. Do you

893
00:51:28,480 --> 00:51:31,560
Speaker 1: see that in other animals? We do, and you can

894
00:51:31,560 --> 00:51:33,719
Speaker 1: see it in almost anything. We even have like a

895
00:51:33,760 --> 00:51:37,280
Speaker 1: woodpecker one time that uh that they put a backpack

896
00:51:37,320 --> 00:51:39,560
Speaker 1: harness on and that got it. So you can see

897
00:51:39,560 --> 00:51:43,239
Speaker 1: it in almost any species, um, but it tends to

898
00:51:43,280 --> 00:51:46,480
Speaker 1: be in those that are wound really tight, you know,

899
00:51:46,560 --> 00:51:49,239
Speaker 1: high stressed animals, and there's I just don't think there's

900
00:51:49,280 --> 00:51:52,239
Speaker 1: anything that's wound as tight as white tail. If you've

901
00:51:52,239 --> 00:51:53,920
Speaker 1: ever have you I don't know if you've ever a

902
00:51:53,960 --> 00:51:56,279
Speaker 1: clover trapped or done anything like that. I mean, they're

903
00:51:56,960 --> 00:52:02,400
Speaker 1: they're just wound tight. Does it happen with predators, Yeah,

904
00:52:03,320 --> 00:52:06,839
Speaker 1: predator species, like when we're handling them. Yeah, Like if

905
00:52:06,880 --> 00:52:09,640
Speaker 1: you're trapping predators that they get the same thing. Yeah,

906
00:52:10,320 --> 00:52:13,000
Speaker 1: not nearly as much though. I Mean again maybe if

907
00:52:13,040 --> 00:52:16,800
Speaker 1: you had like a long translocation event um, and you

908
00:52:16,920 --> 00:52:19,720
Speaker 1: had something that was a little bit more high stress,

909
00:52:20,120 --> 00:52:22,239
Speaker 1: it could happen. I'd have to go back. I mean

910
00:52:22,360 --> 00:52:26,160
Speaker 1: it's been reported in almost any species. I probably the

911
00:52:26,239 --> 00:52:28,319
Speaker 1: least likely you'd see it would be something like a

912
00:52:28,360 --> 00:52:33,080
Speaker 1: black bear that's just just more laid back. Yeah. Well,

913
00:52:33,080 --> 00:52:35,719
Speaker 1: it's something that isn't under you know, at least when

914
00:52:35,719 --> 00:52:37,880
Speaker 1: they get to a certain age, isn't under a constant

915
00:52:37,880 --> 00:52:42,200
Speaker 1: threat of you know, getting eating or attacked because they're

916
00:52:42,200 --> 00:52:44,640
Speaker 1: pretty confident in a lot of their environments unless they

917
00:52:44,640 --> 00:52:47,880
Speaker 1: mix it up with a grizzly or a bigger black bear. Um.

918
00:52:48,400 --> 00:52:52,360
Speaker 1: That's interesting because so I hunted in in South Africa

919
00:52:52,400 --> 00:52:54,680
Speaker 1: and like two thousand and seven for when I was

920
00:52:54,719 --> 00:52:57,960
Speaker 1: at Peterson's Bow Hunting magazine, and I didn't really understand

921
00:52:57,960 --> 00:52:59,720
Speaker 1: when they sent me over there. I didn't really understand

922
00:52:59,719 --> 00:53:02,839
Speaker 1: how the whole hunting thing worked over there. And you know,

923
00:53:03,200 --> 00:53:07,120
Speaker 1: everything's high fence and there's a big animal trade over there,

924
00:53:07,160 --> 00:53:09,759
Speaker 1: and it's it's a weird deal. But I was talking

925
00:53:09,840 --> 00:53:12,920
Speaker 1: to the outfitter about it because I was just fascinated

926
00:53:12,960 --> 00:53:14,759
Speaker 1: by m. Paula's I thought, I thought they were so

927
00:53:14,800 --> 00:53:18,680
Speaker 1: beautiful and they were so cagy, like if you you know,

928
00:53:18,680 --> 00:53:20,080
Speaker 1: you want to talk about something that would give a

929
00:53:20,080 --> 00:53:21,960
Speaker 1: white tail a run for its money. At least where

930
00:53:22,000 --> 00:53:24,759
Speaker 1: I was, Those in Paula were no joke as far

931
00:53:24,840 --> 00:53:27,440
Speaker 1: as what they would tolerate and how they'd react to

932
00:53:27,480 --> 00:53:30,520
Speaker 1: a shot. And I was talking to him about that

933
00:53:30,600 --> 00:53:34,480
Speaker 1: and he said they are a nightmare to trap and transplant,

934
00:53:34,880 --> 00:53:38,239
Speaker 1: and he he referenced, he said, you know, like a

935
00:53:38,280 --> 00:53:41,560
Speaker 1: lot of them don't make it, and yeah, I have

936
00:53:41,640 --> 00:53:44,080
Speaker 1: never heard anybody say that. I mean, you hear about

937
00:53:44,080 --> 00:53:47,360
Speaker 1: some mortality, you know, post trapping and mortality and white tails,

938
00:53:47,400 --> 00:53:49,560
Speaker 1: but not not framed up quite the way that you

939
00:53:49,600 --> 00:53:52,239
Speaker 1: put it. It's interesting, Yeah, it is, and and and

940
00:53:52,280 --> 00:53:55,080
Speaker 1: so the same exact thing. I mean, you know, whenever

941
00:53:55,160 --> 00:53:59,200
Speaker 1: you're doing uh, translocation or even just handling them and

942
00:53:59,239 --> 00:54:01,600
Speaker 1: putting in collar on them, you know, you you take

943
00:54:01,640 --> 00:54:04,480
Speaker 1: all these different measures to keep them cool because they're

944
00:54:04,480 --> 00:54:08,239
Speaker 1: going to get a spiking temperature. Um, you know, their

945
00:54:08,280 --> 00:54:13,080
Speaker 1: body just physiologically it just gets going, so gets gets

946
00:54:13,200 --> 00:54:17,279
Speaker 1: ramped up, and so um. It's an It's interesting to me.

947
00:54:17,480 --> 00:54:19,480
Speaker 1: It makes sense. I mean, it's a prey species, and

948
00:54:19,560 --> 00:54:24,480
Speaker 1: so that reaction makes sense to me. But it doesn't always,

949
00:54:24,480 --> 00:54:26,919
Speaker 1: at least as a scientist, makes sense that it would

950
00:54:26,960 --> 00:54:30,320
Speaker 1: be so bad that it could kill them. You know. Yeah,

951
00:54:30,360 --> 00:54:35,440
Speaker 1: well that it seems like a weird a weird reaction. Yeah. Yeah,

952
00:54:35,800 --> 00:54:40,080
Speaker 1: And it's your interest in trying to figure that out. Obviously,

953
00:54:40,120 --> 00:54:42,120
Speaker 1: there's the aspect of you just don't want your test

954
00:54:42,120 --> 00:54:44,160
Speaker 1: subjects to die, are like, you don't want a certain

955
00:54:44,160 --> 00:54:46,799
Speaker 1: percentage of them to die for you to be able

956
00:54:46,800 --> 00:54:48,879
Speaker 1: to do your science. But is it also is there

957
00:54:48,920 --> 00:54:51,880
Speaker 1: also something along the lines of like you know, tainted

958
00:54:51,920 --> 00:54:54,200
Speaker 1: samples or something like that, or you don't know, you know,

959
00:54:54,360 --> 00:54:57,600
Speaker 1: maybe you're not getting like a true representative sample of

960
00:54:57,640 --> 00:54:59,480
Speaker 1: that animal as soon as you capture it, because it's

961
00:54:59,560 --> 00:55:03,600
Speaker 1: it's body, it literally changes when it's trapped. I'm a

962
00:55:03,719 --> 00:55:05,640
Speaker 1: little bit of that, but I think probably more just

963
00:55:05,719 --> 00:55:08,200
Speaker 1: the survivability, you know, like we put callers. I mean,

964
00:55:08,239 --> 00:55:12,800
Speaker 1: there's so much interesting wildlife research going on, and and

965
00:55:13,600 --> 00:55:16,600
Speaker 1: we get so much data when we do these coloring

966
00:55:16,680 --> 00:55:21,440
Speaker 1: studies or movement studies, and and and I think the

967
00:55:21,480 --> 00:55:24,480
Speaker 1: biggest limitation we have for those right now is is

968
00:55:24,520 --> 00:55:28,080
Speaker 1: captured myopathy and so from a from a health and

969
00:55:28,640 --> 00:55:32,600
Speaker 1: disease standpoint of our our research. Um, I think that

970
00:55:32,600 --> 00:55:34,880
Speaker 1: that would open up a lot more um for a

971
00:55:34,960 --> 00:55:38,839
Speaker 1: lot of these wound up unlits. Yeah, that that that

972
00:55:38,880 --> 00:55:43,080
Speaker 1: makes sense. What one thing I'm always curious about with

973
00:55:43,120 --> 00:55:47,480
Speaker 1: white tails or just you know, game animals specifically, is

974
00:55:48,880 --> 00:55:52,399
Speaker 1: some of them are so adaptable, and some of them

975
00:55:52,440 --> 00:55:54,319
Speaker 1: don't seem to be like some of them play well

976
00:55:54,360 --> 00:55:57,200
Speaker 1: with man, some of them don't. And when you take

977
00:55:57,320 --> 00:55:59,920
Speaker 1: like just as an example, like meal deer, you know

978
00:56:00,239 --> 00:56:02,799
Speaker 1: you can you can see videos of meal deer in

979
00:56:02,800 --> 00:56:05,239
Speaker 1: the suburbs of Denver and in places like that, living

980
00:56:05,239 --> 00:56:07,319
Speaker 1: in people's backyards, and you can see some of that

981
00:56:07,400 --> 00:56:11,640
Speaker 1: interaction with man made stuff, but it's not they're They're

982
00:56:11,640 --> 00:56:13,680
Speaker 1: generally thought of as a species that's kind of like

983
00:56:13,680 --> 00:56:15,960
Speaker 1: likes to be out on its own, doesn't like a

984
00:56:16,000 --> 00:56:18,239
Speaker 1: lot of interference. And then you take something like a

985
00:56:18,280 --> 00:56:21,560
Speaker 1: white tail, and they can they seem to be able

986
00:56:21,600 --> 00:56:25,919
Speaker 1: to live just about anywhere. What why like why why

987
00:56:26,000 --> 00:56:28,919
Speaker 1: is the species like that so adaptable to so many

988
00:56:28,920 --> 00:56:34,360
Speaker 1: different environments and conditions. Yeah, I mean, I I I

989
00:56:34,760 --> 00:56:38,600
Speaker 1: wish I knew. I mean, they really are have just

990
00:56:39,080 --> 00:56:41,600
Speaker 1: filled every niche, right, I mean I remember I was

991
00:56:42,200 --> 00:56:44,560
Speaker 1: out on the outer banks of North Carolina and they

992
00:56:44,560 --> 00:56:46,680
Speaker 1: were they were there there on the beach. You know.

993
00:56:46,760 --> 00:56:52,359
Speaker 1: It's just they've filled every sort of habitat um And

994
00:56:52,440 --> 00:56:57,120
Speaker 1: so I I don't exactly know. I mean, I think

995
00:56:57,160 --> 00:57:01,880
Speaker 1: they can take advantage of a lot from uh nutrition

996
00:57:01,960 --> 00:57:07,640
Speaker 1: standpoint um, And I think all the adaptations that allowed

997
00:57:07,719 --> 00:57:10,680
Speaker 1: them to live in those sort of edge habitats can

998
00:57:10,719 --> 00:57:14,080
Speaker 1: work just as well when they're in sort of suburban neighborhoods.

999
00:57:15,400 --> 00:57:18,760
Speaker 1: Is there a is there like a fecundity or component

1000
00:57:18,840 --> 00:57:21,560
Speaker 1: to it, like is there is there just uh, you know,

1001
00:57:21,760 --> 00:57:23,920
Speaker 1: they're they're good at making new generations of deer, so

1002
00:57:24,360 --> 00:57:28,840
Speaker 1: these adaptations have a chance to take hold, I think,

1003
00:57:29,080 --> 00:57:31,360
Speaker 1: I mean, I think that's part of it. Um. I

1004
00:57:31,360 --> 00:57:36,400
Speaker 1: think whenever you have a species that that is doing

1005
00:57:36,520 --> 00:57:40,800
Speaker 1: well reproductively, um, then they're just going to have the

1006
00:57:40,880 --> 00:57:43,800
Speaker 1: baseline tools they need to do well. And the same

1007
00:57:43,840 --> 00:57:46,520
Speaker 1: thing with with bears or anything like that, black bears

1008
00:57:46,520 --> 00:57:50,040
Speaker 1: at least, you know that if if they can take

1009
00:57:50,080 --> 00:57:53,960
Speaker 1: advantage of the nutrition in an area, especially if they

1010
00:57:53,960 --> 00:57:57,560
Speaker 1: can can use a wide range of nutritional resources, and

1011
00:57:57,640 --> 00:58:01,600
Speaker 1: they can they're good at making more of themselves, then

1012
00:58:01,840 --> 00:58:06,320
Speaker 1: I think the default is that they're going to do well.

1013
00:58:07,040 --> 00:58:09,200
Speaker 1: I guess that's kind of like what you said before

1014
00:58:09,240 --> 00:58:11,400
Speaker 1: with the chicken or the egg thing, where you know,

1015
00:58:11,600 --> 00:58:15,080
Speaker 1: it's like if they're if they're really capable of adapting,

1016
00:58:15,120 --> 00:58:17,280
Speaker 1: they're going to make more babies. You know, it's not

1017
00:58:17,320 --> 00:58:19,880
Speaker 1: it's not necessarily that they just they're just blessed to

1018
00:58:19,960 --> 00:58:21,760
Speaker 1: be to be able to produce a bunch of babies

1019
00:58:21,760 --> 00:58:23,880
Speaker 1: and then adapt all over the place. It actually probably

1020
00:58:23,880 --> 00:58:27,280
Speaker 1: goes the other way more. Yeah, yeah, exactly, And I think,

1021
00:58:27,560 --> 00:58:31,840
Speaker 1: you know, when we look at disease, there are certainly

1022
00:58:31,920 --> 00:58:37,560
Speaker 1: some examples where disease by itself can impact a species.

1023
00:58:37,600 --> 00:58:39,720
Speaker 1: Certainly if we look at something like white nose syndrome,

1024
00:58:39,800 --> 00:58:42,680
Speaker 1: some of the amphibian fungal diseases, we've got a lot

1025
00:58:42,720 --> 00:58:46,120
Speaker 1: of those examples. But probably more often than not, when

1026
00:58:46,120 --> 00:58:50,880
Speaker 1: we're talking about population level impacts the disease, it's often

1027
00:58:52,000 --> 00:58:55,400
Speaker 1: again multi factorial, where there's a species that is already

1028
00:58:55,480 --> 00:59:00,360
Speaker 1: having issues, whether it's not not enough habitat or you know,

1029
00:59:00,440 --> 00:59:04,160
Speaker 1: something else. Then when disease comes on top of that,

1030
00:59:04,160 --> 00:59:07,680
Speaker 1: that's when we have a real problem. Yeah, what is that?

1031
00:59:07,800 --> 00:59:10,840
Speaker 1: Is that one of the biggest challenges for you know,

1032
00:59:10,880 --> 00:59:14,160
Speaker 1: somebody in your line of work, because I know, you know,

1033
00:59:14,200 --> 00:59:16,440
Speaker 1: I know, dealing with the general hunting population, it's like

1034
00:59:16,640 --> 00:59:18,960
Speaker 1: very easy to try to just boil something down to

1035
00:59:19,040 --> 00:59:21,520
Speaker 1: be like, oh, there's there's not enough deer. It's because

1036
00:59:21,560 --> 00:59:24,000
Speaker 1: the DNR wanted them killed, or there's not enough deer

1037
00:59:24,040 --> 00:59:27,880
Speaker 1: because of this or that, and and it really kind

1038
00:59:27,880 --> 00:59:31,840
Speaker 1: of dismisses the amount of environmental variables and the amount

1039
00:59:31,880 --> 00:59:36,560
Speaker 1: of individual animal variables and and the really nuanced like

1040
00:59:36,960 --> 00:59:40,600
Speaker 1: picture of wild game populations. It's it's pretty dismissive of that.

1041
00:59:40,680 --> 00:59:42,480
Speaker 1: Is that pretty? Is that? Is that a tough one

1042
00:59:42,480 --> 00:59:46,840
Speaker 1: to swallow for you? Sometimes it's it's a challenge, But

1043
00:59:46,960 --> 00:59:52,120
Speaker 1: I think oftentimes, you know, hunters can pick up on

1044
00:59:52,200 --> 00:59:56,400
Speaker 1: some of the issues more than just the general public.

1045
00:59:56,920 --> 00:59:59,040
Speaker 1: You know, and and and grouse might be the perfect

1046
00:59:59,040 --> 01:00:07,160
Speaker 1: example where you know, they were It took some discussions

1047
01:00:07,160 --> 01:00:10,040
Speaker 1: and showing them, showing data and things like that, but

1048
01:00:10,680 --> 01:00:14,840
Speaker 1: I think they understood that habitat and and not enough

1049
01:00:14,880 --> 01:00:17,920
Speaker 1: at least in the east, not enough young forest habitat

1050
01:00:18,000 --> 01:00:22,280
Speaker 1: was an issue. But the hunters recognized that they were

1051
01:00:22,320 --> 01:00:25,400
Speaker 1: seeing declines at even in areas where there was good

1052
01:00:25,400 --> 01:00:27,800
Speaker 1: habitat right. And that's sort of what sparked that whole

1053
01:00:27,840 --> 01:00:31,640
Speaker 1: research was hunters being like, I am hunting in like

1054
01:00:31,840 --> 01:00:34,320
Speaker 1: prime grouse habitat, and I'm not seeing the birds like

1055
01:00:34,360 --> 01:00:40,120
Speaker 1: I used to. And so, you know, once the strongest

1056
01:00:40,160 --> 01:00:43,200
Speaker 1: disease research you can do is when you can really

1057
01:00:43,240 --> 01:00:47,240
Speaker 1: bring sort of some of these outdoors men and women

1058
01:00:47,400 --> 01:00:52,520
Speaker 1: into into the research so that they can recognize that, yes,

1059
01:00:52,640 --> 01:00:56,440
Speaker 1: disease is having an impact in addition to some of

1060
01:00:56,440 --> 01:01:00,520
Speaker 1: these other challenges that that species might be facing. Yeah, well,

1061
01:01:00,560 --> 01:01:02,240
Speaker 1: I want to give you an example that I want.

1062
01:01:02,240 --> 01:01:03,680
Speaker 1: I want your take on it, because I know you've

1063
01:01:03,680 --> 01:01:07,200
Speaker 1: studied birds a lot. So this this spring, I was

1064
01:01:07,560 --> 01:01:11,560
Speaker 1: turkey hunting down in southwestern Wisconsin on a really good property,

1065
01:01:11,960 --> 01:01:14,360
Speaker 1: like it's this is normally not like that difficult of

1066
01:01:14,360 --> 01:01:16,919
Speaker 1: a place to kill a bird. You just isn't. And

1067
01:01:17,120 --> 01:01:22,040
Speaker 1: I went down there, uh, early May should have been prime,

1068
01:01:22,840 --> 01:01:24,920
Speaker 1: and I know in the winter there were there were

1069
01:01:25,000 --> 01:01:27,240
Speaker 1: quite a few birds wintering around there. There was a

1070
01:01:27,240 --> 01:01:30,120
Speaker 1: pretty good wintering flock, and it should have just been

1071
01:01:30,200 --> 01:01:34,280
Speaker 1: on and the weather was perfect. Everything. I couldn't hardly

1072
01:01:34,320 --> 01:01:36,800
Speaker 1: get a gobble, couldn't, you know. The TROI cameras had

1073
01:01:36,840 --> 01:01:40,120
Speaker 1: been just empty for days. And I was sitting there

1074
01:01:40,800 --> 01:01:43,760
Speaker 1: the first night I got there, the first evening, and

1075
01:01:43,840 --> 01:01:45,760
Speaker 1: you know, I mean that that happens with hunting. Sometimes

1076
01:01:45,760 --> 01:01:48,360
Speaker 1: it just sucks and it's just, you know, it's beyond

1077
01:01:48,480 --> 01:01:50,200
Speaker 1: me to be able to explain it why. But I

1078
01:01:50,240 --> 01:01:52,920
Speaker 1: was sitting there and I was texting my buddy and

1079
01:01:52,960 --> 01:01:54,720
Speaker 1: I was like, it feels like I'm hunting in a

1080
01:01:54,840 --> 01:01:57,920
Speaker 1: vacuum like I normally, when you're turkey hunting in the spring,

1081
01:01:58,400 --> 01:02:02,640
Speaker 1: you just hear this just coca puny of like songbird sounds,

1082
01:02:02,760 --> 01:02:04,320
Speaker 1: and you know, if you're close to water, there's all

1083
01:02:04,400 --> 01:02:06,760
Speaker 1: kinds of frogs and toads chirping, and it's just usually

1084
01:02:06,800 --> 01:02:09,919
Speaker 1: like a very audio wise, like a very vibrant time

1085
01:02:09,960 --> 01:02:12,840
Speaker 1: to be out there. And it was just dead. And

1086
01:02:12,920 --> 01:02:14,680
Speaker 1: my buddy texted me, He's like, I don't know, maybe

1087
01:02:14,680 --> 01:02:17,440
Speaker 1: avian flu went through there, And I was like, no way,

1088
01:02:17,840 --> 01:02:20,480
Speaker 1: but but I've never experienced that before. And I know

1089
01:02:20,560 --> 01:02:22,400
Speaker 1: you've studied that. Is that is it crazy to think

1090
01:02:22,480 --> 01:02:26,720
Speaker 1: that's possible? You know it, We've we've done this a

1091
01:02:26,760 --> 01:02:30,520
Speaker 1: few It's a real challenge with with turkeys and flu

1092
01:02:30,680 --> 01:02:36,480
Speaker 1: because I think anyone that studies it probably assumes that

1093
01:02:36,560 --> 01:02:39,520
Speaker 1: wild turkeys are highly susceptible to these viruses that are

1094
01:02:39,560 --> 01:02:44,880
Speaker 1: coming through UM. The difference is, I don't think they're

1095
01:02:44,920 --> 01:02:48,040
Speaker 1: exposed as much as what we think. UM. And we've

1096
01:02:48,040 --> 01:02:52,080
Speaker 1: done it a few times where we've looked for antibodies,

1097
01:02:52,120 --> 01:02:54,520
Speaker 1: and so basically, when you're looking for a virus, you

1098
01:02:54,560 --> 01:02:57,000
Speaker 1: can either look for the virus itself or you can

1099
01:02:57,000 --> 01:02:59,080
Speaker 1: look for antibodies. And all the antibodies tell you is

1100
01:02:59,120 --> 01:03:02,600
Speaker 1: that sometime in that animal's life, they were exposed to it,

1101
01:03:03,400 --> 01:03:06,680
Speaker 1: to that virus, even if they're not infected now. And

1102
01:03:06,760 --> 01:03:09,400
Speaker 1: so we've actually looked for antibodies just to say, has

1103
01:03:09,440 --> 01:03:12,160
Speaker 1: this turkey ever been exposed to a flu virus, and

1104
01:03:12,200 --> 01:03:15,360
Speaker 1: we haven't found any um and and I think Minnesota

1105
01:03:15,400 --> 01:03:18,600
Speaker 1: actually repeated that after the last high path outbreak um

1106
01:03:18,680 --> 01:03:23,560
Speaker 1: and So I think it's not out of question to

1107
01:03:23,680 --> 01:03:27,960
Speaker 1: say wild turkeys could be impacted. I think it's just

1108
01:03:28,160 --> 01:03:32,760
Speaker 1: uncommon that they would, at least under the current situation,

1109
01:03:33,640 --> 01:03:36,680
Speaker 1: be exposed, at least based on existing data. Now, if

1110
01:03:36,680 --> 01:03:39,840
Speaker 1: they were exposed and infected, I think this virus probably,

1111
01:03:39,920 --> 01:03:42,680
Speaker 1: if I had to guess, would um produce a fatal

1112
01:03:42,720 --> 01:03:46,400
Speaker 1: infection and would kill them pretty fast. What what what

1113
01:03:46,440 --> 01:03:49,520
Speaker 1: about the songbirds? Same thing the songbirds is a is

1114
01:03:49,520 --> 01:03:52,440
Speaker 1: a real question. And again just on that turkey. The

1115
01:03:52,480 --> 01:03:55,040
Speaker 1: one thing I would preface it by is just we've

1116
01:03:55,080 --> 01:03:58,280
Speaker 1: had high path virus has come through and haven't had

1117
01:03:59,000 --> 01:04:01,520
Speaker 1: a lot of wild turkey metality. Sporadic cases but not

1118
01:04:01,600 --> 01:04:04,840
Speaker 1: a lot. So I think it might be again just

1119
01:04:04,920 --> 01:04:08,080
Speaker 1: an issue that they're not being exposed at the level

1120
01:04:08,440 --> 01:04:12,680
Speaker 1: that we think with the songbirds. Again, there's been some

1121
01:04:12,800 --> 01:04:16,200
Speaker 1: questions that should we take feeders down? Should we not? Um,

1122
01:04:16,240 --> 01:04:19,560
Speaker 1: this virus is circulating now can cause mortality in a

1123
01:04:19,600 --> 01:04:23,000
Speaker 1: wide diversity of species, and and it wouldn't surprise me

1124
01:04:23,080 --> 01:04:27,320
Speaker 1: if there are songbirds that it can cause fatal infections in.

1125
01:04:28,760 --> 01:04:30,560
Speaker 1: I think when you're dealing with the virus, you have

1126
01:04:30,640 --> 01:04:34,440
Speaker 1: to say what's the highest risk and right now songbirds

1127
01:04:34,440 --> 01:04:37,520
Speaker 1: And this could change because these virus has changed very quickly,

1128
01:04:37,800 --> 01:04:39,720
Speaker 1: so this could change any time. But right now, I

1129
01:04:39,720 --> 01:04:44,080
Speaker 1: don't think songbirds are the highest risk for this virus.

1130
01:04:44,440 --> 01:04:49,400
Speaker 1: Right It's mainly ducks, waterfowl, some scavenging species, raptors, but

1131
01:04:49,520 --> 01:04:52,560
Speaker 1: songbirds are at least the ones that historically visit feeders

1132
01:04:52,720 --> 01:04:56,919
Speaker 1: are probably lower on that list. Now if it if

1133
01:04:56,960 --> 01:04:58,960
Speaker 1: you were in a situation where you had a poultry

1134
01:04:59,400 --> 01:05:01,920
Speaker 1: flocked near to you and you're trying to go for

1135
01:05:02,120 --> 01:05:05,040
Speaker 1: zero risk or really low risk, I might take the

1136
01:05:05,080 --> 01:05:10,240
Speaker 1: feeders down. Interesting, So it's just an anomaly that it was,

1137
01:05:10,400 --> 01:05:12,360
Speaker 1: like I was hunting under a bell jar there for

1138
01:05:12,400 --> 01:05:17,400
Speaker 1: a couple of hours. You never know. UM. I do

1139
01:05:17,480 --> 01:05:20,560
Speaker 1: think one of the challenges we face with disease is

1140
01:05:20,680 --> 01:05:26,600
Speaker 1: just what is sometimes the hardest thing to drive home

1141
01:05:26,640 --> 01:05:31,280
Speaker 1: to the public is is the emotional parts of disease.

1142
01:05:31,320 --> 01:05:33,520
Speaker 1: So when we deal with something like humor magic disease

1143
01:05:33,600 --> 01:05:37,200
Speaker 1: and you see dead animals everywhere, that is something that

1144
01:05:37,360 --> 01:05:40,400
Speaker 1: gets the public worked up, and that would be hunters

1145
01:05:40,480 --> 01:05:42,760
Speaker 1: or the general public because they can see dead things.

1146
01:05:43,760 --> 01:05:45,920
Speaker 1: If we deal with something like c w D, where

1147
01:05:46,200 --> 01:05:48,360
Speaker 1: you might not see sick animals, you may not see

1148
01:05:48,360 --> 01:05:51,200
Speaker 1: dead animals, um but it's more of an impact on

1149
01:05:51,200 --> 01:05:54,280
Speaker 1: the population level and more slow and smoldering. It's a

1150
01:05:54,280 --> 01:05:57,120
Speaker 1: little harder to drive home that this is an important disease.

1151
01:05:57,880 --> 01:06:01,840
Speaker 1: And so oftentimes when we deal with that an issue

1152
01:06:01,880 --> 01:06:05,880
Speaker 1: in wildlife, the knee jerk reaction is to grab at

1153
01:06:05,920 --> 01:06:08,360
Speaker 1: the short of the short of the the shiny thing

1154
01:06:08,440 --> 01:06:10,800
Speaker 1: that's coming through the population now. But but it may

1155
01:06:10,840 --> 01:06:14,600
Speaker 1: be multiple things going on. Yeah, well, let's let's end

1156
01:06:14,640 --> 01:06:16,680
Speaker 1: on that note. I've got I've got one question for

1157
01:06:16,760 --> 01:06:19,720
Speaker 1: you about deer and disease. We've we've talked a lot

1158
01:06:19,760 --> 01:06:22,760
Speaker 1: about c w D on this podcast Go I've got

1159
01:06:22,760 --> 01:06:26,400
Speaker 1: whole episodes coming out dedicated to it. So other than

1160
01:06:26,520 --> 01:06:29,280
Speaker 1: c w D, is there a disease out there in

1161
01:06:29,400 --> 01:06:33,240
Speaker 1: dear that you've run across that makes you a little

1162
01:06:33,240 --> 01:06:36,280
Speaker 1: bit nervous about the future of White Tales. About the

1163
01:06:36,320 --> 01:06:39,440
Speaker 1: future White Tales, is there anything you've seen where you're like,

1164
01:06:39,480 --> 01:06:41,120
Speaker 1: I don't know, Man, if that one gets out and

1165
01:06:41,280 --> 01:06:44,160
Speaker 1: it really gets going, it could be trouble, you know,

1166
01:06:44,280 --> 01:06:48,760
Speaker 1: I don't I don't know if one's gonna they're certainly

1167
01:06:48,800 --> 01:06:52,160
Speaker 1: humor magic disease can cause a lot of mortality, but

1168
01:06:52,360 --> 01:06:55,160
Speaker 1: we don't tend to see population level impacts. But that's

1169
01:06:55,200 --> 01:06:59,040
Speaker 1: something I think that's worth continuing the monitor because as

1170
01:06:59,480 --> 01:07:02,480
Speaker 1: as we see changes in where these midge vectors can go,

1171
01:07:02,560 --> 01:07:05,520
Speaker 1: we see outbreaks more frequently um. And so that's just

1172
01:07:05,600 --> 01:07:07,360
Speaker 1: something that even though I don't think it's going to

1173
01:07:07,440 --> 01:07:10,840
Speaker 1: cause declines in white tail, I think it's something worth

1174
01:07:10,880 --> 01:07:15,000
Speaker 1: monitoring because we can see these outbreaks more often um.

1175
01:07:15,040 --> 01:07:18,120
Speaker 1: And even though it doesn't cause that, we can tell

1176
01:07:18,200 --> 01:07:22,160
Speaker 1: disease and dear um. I think one challenge that we're

1177
01:07:22,160 --> 01:07:25,400
Speaker 1: gonna have to figure out how to manage is going

1178
01:07:25,440 --> 01:07:29,520
Speaker 1: to be uh stars coronavirus in in in COVID nineteen

1179
01:07:29,560 --> 01:07:35,320
Speaker 1: and deer. Why do you say that because existing data

1180
01:07:35,400 --> 01:07:38,920
Speaker 1: is just showing that high prevalence of deer are infected,

1181
01:07:40,600 --> 01:07:45,439
Speaker 1: and so what is the best way to continue to hunt,

1182
01:07:45,560 --> 01:07:50,840
Speaker 1: process your animals, and monitor this safely and just be

1183
01:07:50,960 --> 01:07:54,360
Speaker 1: something that, just like everything else with the pandemic, we're

1184
01:07:54,360 --> 01:07:57,360
Speaker 1: gonna have to learn to to adapt our practices a bit.

1185
01:07:58,680 --> 01:08:03,040
Speaker 1: Are you saying that because there's the potential for dear

1186
01:08:03,080 --> 01:08:05,080
Speaker 1: to be a carrier and then you go to butcher

1187
01:08:05,120 --> 01:08:07,240
Speaker 1: your buck and you might pick up a fresh case

1188
01:08:07,280 --> 01:08:10,640
Speaker 1: of COVID. You know, I don't know if we're at

1189
01:08:10,640 --> 01:08:14,720
Speaker 1: the level now to say that that's that is, um

1190
01:08:15,440 --> 01:08:18,800
Speaker 1: how feasible that is, but said certainly that in the

1191
01:08:18,840 --> 01:08:20,760
Speaker 1: grand scheme of things, would be the thing we're most

1192
01:08:20,800 --> 01:08:24,559
Speaker 1: concerned of. So it's it's it's too early. I mean,

1193
01:08:25,000 --> 01:08:28,320
Speaker 1: you can't say that yet, but there's there's just a

1194
01:08:28,360 --> 01:08:30,719
Speaker 1: general concern that deer seemed to be carrying this around

1195
01:08:30,720 --> 01:08:33,720
Speaker 1: and we don't know what it means correct the man, Well,

1196
01:08:33,760 --> 01:08:36,400
Speaker 1: I think the concern is just you've got this virus

1197
01:08:36,479 --> 01:08:39,559
Speaker 1: that we're all have been dealing with and and trying

1198
01:08:39,600 --> 01:08:44,320
Speaker 1: to manage for now two years. They are multiple concerns,

1199
01:08:44,400 --> 01:08:47,960
Speaker 1: but one being how are we going to manage it

1200
01:08:48,000 --> 01:08:51,519
Speaker 1: now that we may have this wildlife involvement, Because whenever

1201
01:08:51,600 --> 01:08:55,639
Speaker 1: wildlife is involved in a zoonotic disease or disease of humans.

1202
01:08:55,840 --> 01:08:59,400
Speaker 1: It's a control and management's a little harder. Uh. And

1203
01:08:59,479 --> 01:09:03,200
Speaker 1: then to you, what, how are we going to manage

1204
01:09:03,240 --> 01:09:05,719
Speaker 1: this in a game in probably one of the most

1205
01:09:05,920 --> 01:09:09,599
Speaker 1: important and popular game species, m as we move into

1206
01:09:09,640 --> 01:09:12,960
Speaker 1: this this hunting season. So this is this is a

1207
01:09:13,000 --> 01:09:17,679
Speaker 1: really good example of just pure scientific interest. We don't

1208
01:09:17,840 --> 01:09:19,880
Speaker 1: we we know this is happening, we don't know what

1209
01:09:19,920 --> 01:09:22,360
Speaker 1: it means. We know it could be bad, could be

1210
01:09:22,400 --> 01:09:25,479
Speaker 1: a maybe a nothing burger, but we we really want

1211
01:09:25,520 --> 01:09:28,840
Speaker 1: to figure out. I think what the pandemics taught us

1212
01:09:29,360 --> 01:09:31,479
Speaker 1: is that we have to be adaptable, and we have

1213
01:09:31,560 --> 01:09:34,360
Speaker 1: to be ready for things to change, and we have

1214
01:09:34,479 --> 01:09:38,240
Speaker 1: to take the best precautions we have at the time

1215
01:09:38,920 --> 01:09:41,200
Speaker 1: that the data suggests. And and I think that's what

1216
01:09:41,200 --> 01:09:44,160
Speaker 1: we're gonna have to do with this as well. Yeah,

1217
01:09:44,160 --> 01:09:46,000
Speaker 1: well I can tell you I agree with that as

1218
01:09:46,040 --> 01:09:48,960
Speaker 1: a guy who's sitting here with COVID. Uh. Justin thank

1219
01:09:49,000 --> 01:09:51,920
Speaker 1: you so much for coming on. Man, this was really

1220
01:09:51,920 --> 01:09:55,320
Speaker 1: really interesting. It was great talking to you. So we'll

1221
01:09:55,320 --> 01:09:58,519
Speaker 1: have to have to keep this up. Well, we'll do

1222
01:09:58,560 --> 01:10:01,320
Speaker 1: it again, but really, thank you, man. Yeah, thank you.

1223
01:10:02,960 --> 01:10:05,240
Speaker 1: That's it. For this week, folks, be sure to tune

1224
01:10:05,280 --> 01:10:07,400
Speaker 1: in next week for more white tailed goodness. This has

1225
01:10:07,439 --> 01:10:09,639
Speaker 1: been wirre to hunt and I'm your guest host, Tony Peterson.

1226
01:10:10,000 --> 01:10:11,800
Speaker 1: As I always thank you, thank you, thank you so

1227
01:10:11,880 --> 01:10:14,120
Speaker 1: much for listening. And if you're looking for more white

1228
01:10:14,120 --> 01:10:16,400
Speaker 1: tail content, be sure to head on over to the

1229
01:10:16,479 --> 01:10:19,840
Speaker 1: meat eater dot com slash wired. Again, that's the meat

1230
01:10:19,880 --> 01:10:23,040
Speaker 1: eater dot com slash wired, and you'll see a pile

1231
01:10:23,080 --> 01:10:25,160
Speaker 1: of new articles each week by Mark myself and a

1232
01:10:25,200 --> 01:10:27,840
Speaker 1: whole slew of white tail addicts. Or you can head

1233
01:10:27,840 --> 01:10:30,120
Speaker 1: on over to our wire to Hunt YouTube channel to

1234
01:10:30,160 --> 01:10:32,400
Speaker 1: see our weekly how to content that we put up