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Speaker 1: This is the Meat Eater Podcast coming at you shirtless, severely,

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Speaker 1: bug bitten, and in my case, underware listening podcast. You

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Speaker 1: can't predict anything. The Meat Eater Podcast is brought to

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Speaker 1: you by First Light. Whether you're checking trail cams, hanging

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Speaker 1: deer stands, or scouting for ELP, First Light has performance

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Speaker 1: apparel to support every hunter in every environment. Check it

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Speaker 1: out at first light dot com. F I R S

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Speaker 1: T L I t E dot com.

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Speaker 2: Hey everyone, fill the engineer here. We will get right

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Speaker 2: to our conversation with doctor Kristin Schuler, but I just

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Speaker 2: wanted to let you all know that there is actually

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Speaker 2: a video component to this podcast as well. If you

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Speaker 2: would like to head over to the meat Eater YouTube channel,

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Speaker 2: you can check out a little tour that we had

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Speaker 2: of the knee cropsy lab at the Cornell University Animal

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Speaker 2: Health Diagnostics Center. We got a tour from Kristin and

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Speaker 2: a pathologist, doctor Gavin Hitchner, and he went over some

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Speaker 2: of the basics of what their team does over there

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Speaker 2: when studying animal mortality and trauma and whatnot. And get

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Speaker 2: a look at an eagle, a bobcat, even the turtle.

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Speaker 2: So again, head over to the meat Eater YouTube channel

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Speaker 2: if you want to see about thirteen minutes of some

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Speaker 2: really cool video footage we got over at Cornell.

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Speaker 3: Thank you.

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Speaker 1: Okay, we're at Cornell University's Animal Health Diagnostics Center next

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Speaker 1: to a stuffed two headed calf with Kristin Schuler, a

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Speaker 1: wildlife disease ecologist. Explain that to me, like the ecologist part,

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Speaker 1: the wildlife disease part I get okay, wildlife disease expert,

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Speaker 1: I get sure.

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Speaker 3: A collegist Okay, you know what ecology is, right, So

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Speaker 3: studying the linkages between things, so with wildlife diseases, trying

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Speaker 3: to put all that together with like the biology, but

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Speaker 3: then also thinking about communication other social sciences, like human dimensions.

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Speaker 3: There's economic elements that I pull in my research. There's

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Speaker 3: political angles and things like that. So trying to put

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Speaker 3: the whole synthesis together, all the way from the animal

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Speaker 3: to people and rules, regulations and how we can make

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Speaker 3: that better for wildlife populations.

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Speaker 1: Got it and explain? Or I can run through it,

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Speaker 1: or you can run through the scope of things you're

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Speaker 1: working on.

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Speaker 3: We can run through it together.

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Speaker 1: Steve all Right, current projects very diverse in scope and species.

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Speaker 1: You head up the New York State Interagency Working Group

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Speaker 1: on Chronic Wasting Disease. Yes, and you're again we're at

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Speaker 1: Cornell in Ithaca. Yes, New York and New York is

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Speaker 1: not yet a CWD state.

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Speaker 3: Well, New York has the distinction of being the only

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Speaker 3: state that has eliminated CWD once it was found in

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Speaker 3: the wild. The New York Department of Environmental Conservation actually

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Speaker 3: found it first in captive deer and then in wild

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Speaker 3: deer in two thousand and five, and they responded pretty aggressively,

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Speaker 3: and they formed a containment area. They depopulated the two

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Speaker 3: herds where they found CWD. They subsequently found two deer

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Speaker 3: in the wild when they were doing some management activities,

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Speaker 3: and in the seventeen years since we haven't detected it.

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Speaker 1: Knockunwood Man, the only state that's done that. Yep, I

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Speaker 1: didn't even know until you just said that, I wouldn't

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Speaker 1: known that had ever happened.

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Speaker 3: Yeah, it's I mean, I.

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Speaker 1: Would have known that it's ever been eliminated from a state.

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Speaker 3: Yeah, I mean it was, you know, either good or lucky.

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Speaker 3: Either way, it's helped out New York a lot where

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Speaker 3: we haven't had the disease to deal with There have

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Speaker 3: been other what we call one and done's where it's

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Speaker 3: been found in a single location and you go in

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Speaker 3: and do management activity and don't find any subsequent cases.

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Speaker 3: So there are conditions where that can happen, but it

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Speaker 3: isn't the norm.

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Speaker 1: You feel it's uh, we'll come back around talk about

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Speaker 1: this a bunch more later. But do you feel that

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Speaker 1: it's inevitable it'll come.

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Speaker 3: Back inevitable to New York?

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Speaker 1: Yeah, yeah, potentially, but you're creasing all forty eight, like

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Speaker 1: eventually all forty eight states will have it.

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Speaker 3: Well, even even with all states having it, you know,

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Speaker 3: if we say that, you know there's thirty plus states

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Speaker 3: that have it. Now, it's not everywhere, and there's certain

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Speaker 3: conditions that seem to support it better than others. So

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Speaker 3: I think if we can do regulations that prohibit movement

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Speaker 3: prews so you don't introduce it to new areas, and

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Speaker 3: then you set up the environment where it's not the

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Speaker 3: disease is going to take off once it's there. It's

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Speaker 3: not necessarily inevitable that it's going to be everywhere, and

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Speaker 3: I think you can do a lot of stuff to

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Speaker 3: prevent it from getting there faster. I think right now.

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Speaker 3: We're helping it along quite a bit.

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Speaker 1: Got it? And I guess it's like you mentioned, it's

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Speaker 1: not everywhere, so the state's almost become I guess talking

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Speaker 1: about it in the state sense is almost a little

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Speaker 1: bit arbitrary, as opposed to maybe looking at it at

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Speaker 1: like a county.

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Speaker 4: Wide exactly right.

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Speaker 1: Like across the lower forty eight, that you'd look at

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Speaker 1: it by county or something rather than by state, because

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Speaker 1: the lines are kind of arbitrary, and a state could

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Speaker 1: be surrounded by it, but it just hasn't turned up

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Speaker 1: in the state, but often cases managed at the state level,

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Speaker 1: so it's in some way it is a reflection of

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Speaker 1: what a state might be doing or not doing exactly. Yeah, okay,

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Speaker 1: you do that. You a bunch of stuff here, Moose healthy,

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Speaker 1: moose health and the Adirondacks. I want to talk about

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Speaker 1: that a whole bunch.

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Speaker 3: Uh.

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Speaker 1: Geographical epidemiology of bear mange ye meaning black bears mange

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Speaker 1: and black bears white tailed, do your fawn survival, historical

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Speaker 1: accounts of lead and bald eagles which we looked at

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Speaker 1: a bald eagle this morning in your lab, and how

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Speaker 1: do you pronounce this blank fun fungus in Eastern hell benders. Okay, yeah,

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Speaker 1: I find I've been I kind of got into hell benders.

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Speaker 1: I didn't know that hell benders were a thing. I

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Speaker 1: mean I knew like big, you know, aquatic salamanders, but

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Speaker 1: I had no idea there's one as big as a

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Speaker 1: hell bender.

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Speaker 2: Yeah.

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Speaker 3: They seem made up, don't they. They can get up

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Speaker 3: to two feet long. Yeah, and yeah some of the

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Speaker 3: the names for them like snot.

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Speaker 1: Otter, OLSONI sides.

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Speaker 3: Yeah, it's really They're really interesting animals and they're super

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Speaker 3: slimy and hard to hold on to.

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Speaker 1: So is what is like real briefly, what is going

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Speaker 1: on with hellbenders?

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Speaker 3: So hellbenders throughout most of their range have been extirpated

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Speaker 3: or you know, aren't found anymore, just because of things

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Speaker 3: like water quality. They need really clear, clean fascillating stream,

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Speaker 3: so you know, damming rivers and things like that isn't

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Speaker 3: good for them sedimentation. And they are also being impacted

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Speaker 3: by this Caittrid fungus, which is a fungus that has

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Speaker 3: decimated some frog populations all globally. And there's a new

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Speaker 3: strain of Kittrid called B sal which affects salamanders as well.

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Speaker 3: So we've done some work here on captive hellbenders trying

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Speaker 3: to see if we could vaccinate them against this fungus.

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Speaker 3: So we had them here at the lab and you know,

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Speaker 3: gave them some topical killed fungus just like you had

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Speaker 3: get in sort of a vaccine, and it really didn't

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Speaker 3: do much to them. You know, they got sick, but

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Speaker 3: they didn't die. We were expecting them to die from it.

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Speaker 3: And so what we've seen in working with partnerships with

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Speaker 3: the New York State Department of Environmental Conservation and the

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Speaker 3: Seneca Nation of Indians, is that it looks like they

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Speaker 3: can survive with low levels because this Kittrid fungus is

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Speaker 3: found in the water and so it's kind of ubiquitous everywhere,

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Speaker 3: and so you know they're at low levels anyway just

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Speaker 3: because of habitat conditions. But this is just another challenge

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Speaker 3: that they sort of have to deal with. But it

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Speaker 3: seems like they're dealing with it okay.

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Speaker 1: And that came from somewhere New.

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Speaker 3: Kittred. It's been introduced all over the world. Basically, the

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Speaker 3: way that they used to test for pregnancy was through

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Speaker 3: xenopus frogs. These African Claude frogs and they had kittred,

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Speaker 3: and you.

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Speaker 1: Know, hospitals for human pregnancs.

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Speaker 3: Yeah, like people. So I don't I don't know the

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Speaker 3: intricacies of it, since I'm not you know, a human person,

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Speaker 3: a human doctor, human person. But they would use frogs

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Speaker 3: and inject them to see if they would start producing eggs.

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Speaker 3: And it was a way to tell if human females

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Speaker 3: were pregnant. And so a lot of the hospitals that

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Speaker 3: had these frog colonies to test for pregnancy release them

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Speaker 3: after they you know, developed new tests, and so that

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Speaker 3: sort of when was that?

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Speaker 1: When was that a.

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Speaker 3: Thing decades ago?

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Speaker 1: So a hospital to keep a little gang of frogs around,

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Speaker 1: That's that's the story.

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Speaker 3: I was not alive when this was all happening. But

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Speaker 3: there's other species now that can harbor kittred, like American

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Speaker 3: bull frogs can have kittred, and then it gets in

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Speaker 3: the water and other species are exposed to it, so

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Speaker 3: it can do a number on native frog species.

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Speaker 1: So at some point they cut their frogs loose, their

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Speaker 1: pregnancy tester frogs. This is perhaps an explanation. They cut

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Speaker 1: the frogs loose, and these frogs had developed this.

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Speaker 3: They had the fungus like they were used to it

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Speaker 3: and fine with it.

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Speaker 1: But then introduced it into the wild.

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Speaker 3: Yeah, we see. I mean there's introduced pathogens all the time.

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Speaker 3: So we can talk more about that with some of

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Speaker 3: these other things that come from different places and then

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Speaker 3: native species can't handle them.

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Speaker 1: Got it?

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Speaker 5: Uh?

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Speaker 1: I want to hit a crackh real quick from someone

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Speaker 1: we both know, Jim Hefflefinger. The other day we did

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Speaker 1: a we're standing also where did it go? It was

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Speaker 1: like a puppy display. Oh, there it is, all those

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Speaker 1: puppy feet on display. We recently did an episode what

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Speaker 1: episode number was it cream with Doctor Perry. It's the

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Speaker 1: whatever the hell it's called dire wolves and ancient honeydogs.

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Speaker 1: We got to talking on there about taxonomic lumpers and

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Speaker 1: taxonomic splitters.

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Speaker 3: Episode four sixty six.

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Speaker 1: Episode four sixty six, we got to talk about the

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Speaker 1: taxonomy of wolves, and I use an example of what

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Speaker 1: I use an example of the somewhat arbitrary divisions that

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Speaker 1: will occur in wildlife. Meaning, once upon a time you

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Speaker 1: had wolves that if you just look at the western hemisphere,

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Speaker 1: you look at North America, let's just say North America.

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Speaker 1: You add wolves that existed from the top of Alaska

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Speaker 1: way down into Mexico, and there was no distinct borders

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Speaker 1: between these different wolf groups. So at some point you

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Speaker 1: kind of divide them up, even though no matter where

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Speaker 1: you put a division line, there's like genetic crossflow. So

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Speaker 1: if you'd traveled from Alaska, northern Alaska down into the

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Speaker 1: Sierra Madre of Mexico, let's say you would have always

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Speaker 1: been by wolves, and you would gradually notice that those

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Speaker 1: wolves changed, but there wouldn't have been actual lines of

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Speaker 1: demarcation between these wolves. They would just get smaller in

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Speaker 1: different colors. But there's no You wouldn't have been able

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Speaker 1: to really say they were all canis lupis. You would

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Speaker 1: never been able to say, like, oh, these wolves are

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Speaker 1: fundamentally different than the wolves a mile that way, right

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Speaker 1: a line of continuity. I'm looking at Christ but this

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Speaker 1: isn't her, this isn't your area.

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Speaker 3: I don't know where you're going.

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Speaker 1: So I use an example of the Mexican wolf. Okay,

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Speaker 1: and I was using the example of the Mexican wolf,

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Speaker 1: and I forty meaning that they're saying that there was

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Speaker 1: this sort of arbitrary division where if a Mexican wolf

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Speaker 1: crosses IE forty, it becomes a normal wolf. That was wrong.

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Speaker 1: I was saying if a Mexican wolf crosses IE forty

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Speaker 1: going north, he becomes a different wolf. Heffelfinger was rolled

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Speaker 1: in to say. Frequent gas and commentator Jim Heffelfinger was saying,

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Speaker 1: they're always Mexican wolves, Okay, when they crow, well, he says,

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Speaker 1: what yours. Here's what he says, you're screwing up. When

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Speaker 1: they cross IE forty going north, they leave the experimental

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Speaker 1: non essential ten j area, okay, and become a fully

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Speaker 1: endangered Mexican wolf. So south of I forty they have

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Speaker 1: greater management flexibility because they're an experimental introduced population. There's

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Speaker 1: just a different management structure. So that thing remains a

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Speaker 1: Mexican wolf, it just enters a different management zone when

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Speaker 1: it crosses I forty. He goes on to further correct

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Speaker 1: Mexican wolves are physically, genetically and ecologically different from southern

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Speaker 1: planes wolves that were in northern Arizona and northern New Mexico.

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Speaker 1: He says, there's no way they could have retained those

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Speaker 1: differences that they freely interchanged with wolves in the north

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Speaker 1: like you said they did. Mexican wolves existed in almost

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Speaker 1: complete isolation in the Sierra Madre of Mexico in the

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Speaker 1: sky islands of southern Arizona and New Mexico, with large

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Speaker 1: expanses of dry desert and a lack of ungulates between

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Speaker 1: them and the wolves to the north. This is why

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Speaker 1: they are listed separately from other gray wolves on the ESA.

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Speaker 1: People who are not I'm good. This is all giant quote.

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Speaker 1: People who are not familiar with wolf distribution historically in

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Speaker 1: the Southwest often like to say that Mexican wolves just

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Speaker 1: naturally blend into other wolves without a definite break. I

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Speaker 1: think most of the other subspecies did blend into one

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Speaker 1: another because of their adjacent distributions. But he points out

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Speaker 1: that is not the case in his argument with Mexican wolves.

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Speaker 6: That's a correction, Christen, excellent, good job, Jim uh.

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Speaker 1: Okay, let me meet with another thing from the news.

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Speaker 1: Remember I told you this is a news item, but

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Speaker 1: I'm moving into it not a news item.

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Speaker 3: Okay.

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Speaker 1: We're gonna talk a bunch about c WD and some

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Speaker 1: other wildlife diseases. And you and I were recently at

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Speaker 1: a little conference about CWD and kind of like updates

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Speaker 1: about c WD, and for your listeners who are unaware

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Speaker 1: of c WD is as we say, chronic waste and disease.

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Speaker 1: It's a it's a preon disease. It's an infectious disease.

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Speaker 3: Right, Yeah, you've got ten out of ten in your

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Speaker 3: quiz at the meet inside that.

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Speaker 1: Uh, that's great. I don't think I told Krann about

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Speaker 1: that my high score?

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Speaker 3: Did you read prody?

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Speaker 1: It's an infectious disease that tax deer tacks all servids

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Speaker 1: not sorry, what am I saying? Not all services?

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Speaker 3: Not all? Yeah, it's mostly white tailed deer, mule deer,

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Speaker 3: elk moose.

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Speaker 1: Okay, what servant has never had it?

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Speaker 3: Like fallow deer and some of the other uh. Like,

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Speaker 3: I mean you can use like munch jack deer and

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Speaker 3: give it to them experimentally, but like naturally infected deer.

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Speaker 3: It's only those four species so far? Is that not

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Speaker 3: cariban caribou? Yes?

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Speaker 1: Because I thought in Europe they had a caribou.

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Speaker 3: They do. Yeah, sorry, I was just thinking in North America,

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Speaker 3: But yeah, reindeer in Scandinavia.

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Speaker 1: Have reindeer in Scandinavia. Okay, but it's so far, like,

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Speaker 1: so far, not in any North American cariboo.

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Speaker 3: No, uh they've done some experimental trials and so there's

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Speaker 3: concern about them, but they haven't been listed by the

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Speaker 3: USDA yet, even though they seem like they're susceptible.

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Speaker 1: Okay, so to date and maybe forever, I don't know,

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Speaker 1: to date. White tail deer, mule, deer.

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Speaker 4: Elk, moose, Yeah, North America.

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Speaker 1: Okay, in North America. Uh So, a news piece came

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Speaker 1: out reported from where is this damn thing? Got it? Okay?

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Speaker 1: You w Madison researcher have found that black legged tis

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Speaker 1: commonly referred to as deer ticks, can harbor transmissible amounts

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Speaker 1: of prions. Are you a prion or preon person? Preon

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Speaker 1: the protein particle that causes chronic waste and disease and

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Speaker 1: white tail deer? What do you make of that?

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Speaker 3: Well, there's a couple different parts of it. So I

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Speaker 3: guess I'm not surprised that they found preons and blood

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Speaker 3: because we knew that happens already, that preons are found

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Speaker 3: throughout the animal, the infected animal's body. It's just different concentrations.

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Speaker 3: So the most preons are going to be in the

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Speaker 3: central nervous system like the brain, the spine cord. In

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Speaker 3: other lymphatic tissues, which is why we sample the lymph nodes,

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Speaker 3: like the things in the throat there that are easiest

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Speaker 3: to get at. Spleen are pretty high, eyeballs are pretty

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Speaker 3: high in preons, but they're in the blood, they're in

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Speaker 3: muscle tissue, they're in urine, but in much much lower concentrations.

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Speaker 3: And now we have very sensitive assays where we can

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Speaker 3: detect those lower concentrations which weren't previously available. One that

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Speaker 3: I think was used in this study is called rt

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Speaker 3: QUICK or real time quaking induced conversion. If you're familiar

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Speaker 3: with PCR, which is a lot of you know testing

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Speaker 3: that we do, it's sort of like that, so if

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Speaker 3: you have one little piece of something, you can amplify

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Speaker 3: it multiple times. And so you know, they were able

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Speaker 3: to feed ticks on membranes with preon infected blood and

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Speaker 3: then look at the ticks and yeah, there was still

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Speaker 3: preons in there. And they also collected preons off of

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Speaker 3: deer in the wild that we're CWT infected, and they

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Speaker 3: were able to find preons in that blood too. So

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Speaker 3: that's that's another way that preons could get around, I guess, but.

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Speaker 1: Because it's not but it's not totally. I guess why

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Speaker 1: I bring up with something that, like I should clarify

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Speaker 1: my question about it is we can't always explain how

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Speaker 1: it moves right right, It'll just it'll emerge in some

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Speaker 1: that the disease will be found in some place. And granted,

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Speaker 1: you're not able to test every deer, right, you know,

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Speaker 1: we're not testing every deer on the planet.

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Speaker 3: Yeah, we can, we can get to that. Let me

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Speaker 3: just finish this one thing where you know, they say

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Speaker 3: transmissible amounts, but that's the issue that we don't really

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Speaker 3: know what transmissible amounts of preon are yet, and we

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Speaker 3: don't know that a tick feeding on something else would

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Speaker 3: have the mechanism to push the preons back into whatever

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Speaker 3: they're feeding on, because usually after it tick feeds, it

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Speaker 3: molts and goes through another growth phase or you know,

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Speaker 3: if it's an adult, it lays eggs and you know dies,

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Speaker 3: So the odds of it, you know, a nymph that

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Speaker 3: would feed on a deer that has preons like then

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Speaker 3: molts and goes through another stage and feeds on another

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Speaker 3: deer that it would have enough preons to push through

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Speaker 3: the skin into the blood to cause an infection, Like

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Speaker 3: I don't know that that's happening.

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Speaker 1: Yet I see what you're saying.

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Speaker 3: So, you know, I'm sure preons could be there, but

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Speaker 3: whether that's actually going to do anything or not, we

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Speaker 3: don't know.

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Speaker 1: Do you feel that when you see that kind of

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Speaker 1: story and it makes a suggestion of that, do you

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Speaker 1: feel that there's a risk of it being misunderstood?

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Speaker 3: Well? I think CWD is a really complicated disease in general,

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Speaker 3: and the one truth that I've kind of found with

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Speaker 3: it is it always gets worse. Call the news always

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Speaker 3: seems to get worse. So I think it's hard to

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Speaker 3: sometimes talk about these scientific discoveries because they're interesting, but

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Speaker 3: you know, as far as what that means in the

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Speaker 3: grand scheme of things and how how we should take

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Speaker 3: that is scaling it up to management at you know

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Speaker 3: for a state wildlife agency is sometimes a little bit challenging.

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Speaker 3: But you asked about movement of preons, and that's something

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Speaker 3: that I work a lot on and I'm really interested

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Speaker 3: in because in New York we were one of the

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Speaker 3: first states to write a risk minimization plan, and it

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Speaker 3: was pretty simple, you know, like one, don't bring preons

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Speaker 3: into the state. Two, don't let preons be out in

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Speaker 3: the environment where deer scavengers can access them. And three

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Speaker 3: educate the public about it. So by taking steps not

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Speaker 3: to bring preons into the state, and that could be

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Speaker 3: in a live deer or dead deer, you know. So

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Speaker 3: that involves the captive survey industry, and it involves hunters

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Speaker 3: and making sure that there's steps taken so they're not

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Speaker 3: bringing preons in. And then don't let preons get out

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Speaker 3: into the environment. By educating hunters about how to dispose

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Speaker 3: of carcasses. You know, if you're going out of state

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Speaker 3: to hunt, what should you bring back, what's legal, what's not,

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Speaker 3: How can you process your carcass, and you know, debone

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Speaker 3: it so that you're bringing back the meat, you know,

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Speaker 3: the antlers or whatever other things you want to bring

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Speaker 3: back in a safe manner. Those are the type of

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Speaker 3: things that I'm interested in. So like when people get

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Speaker 3: all wound up about birds moving preons or you know,

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Speaker 3: coyotes or ticks, that could be a problem, but it's

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Speaker 3: probably like infinitesimally small compared to what people are doing

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Speaker 3: moving stuff around.

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Speaker 1: Oh, I got you, Yeah, can you explain where we

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Speaker 1: spent time this morning?

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Speaker 3: Sure, we were in the n cropsy Lab of the

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Speaker 3: Animal Health Diagnostic Center at Cornell, and that is a

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Speaker 3: place where we bring in animal carcasses. So I deal

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Speaker 3: with wildlife. So we bring in uh specimens that may

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Speaker 3: be found by the public or the New York State

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Speaker 3: Department of Environmental Conservation. Biologists want to find out why

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Speaker 3: something died, they bring it in and then we go

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Speaker 3: through a whole process called ane cropsy, which is the

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Speaker 3: equivalent of an animal autopsy, and we take tissues for

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Speaker 3: testing and examine the animal and try to figure out

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Speaker 3: why it died and collect specimens for other research or archiving.

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Speaker 1: Yeah, this morning, we looked at a bald eagle. We

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Speaker 1: looked at a turtle. We looked at a bobcat, looked

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Speaker 1: at a horse.

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Speaker 3: Well, we didn't look we looked at a horse. We

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Speaker 3: didn't like look at us said, we.

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Speaker 1: Looked at a horse.

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Speaker 3: Well, yeah, we didn't examin exam there you go.

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Speaker 1: There was a there was a horse that was collaterally observed.

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Speaker 1: There was a collaterally observed horse, and we partially examined a.

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Speaker 3: I don't want people think I'm looking at horses on

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Speaker 3: the side.

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Speaker 1: No, No, it was there was a horse presence. Yes,

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Speaker 1: other people had interest in the horse, and we just

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Speaker 1: observed the horse being there. Uh so I've done this before.

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Speaker 1: A person, you know, a hunter, fishermen, a homeowner, whoever

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Speaker 1: comes out and they they shoot or discover or whatever

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Speaker 1: something that doesn't look right. Yep, it's acting weird. A

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Speaker 1: coyote shows up in their yard, it's missing its hair,

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Speaker 1: it's trying to bite their dog. They call their game warden.

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Speaker 3: Yep.

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Speaker 1: Whoever, like, something's not right with this thing. What's going on?

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Speaker 1: It by some avenue winds up with folks like you.

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Speaker 3: Yes, So there's a whole process, and we go out

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Speaker 3: and do training for the biologists in the state to

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Speaker 3: tell them, you know, what to look for and try

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Speaker 3: to educate them about different diseases. So if it is

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Speaker 3: a coyote with hair and missing, they are familiar with

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Speaker 3: mange and they know what to tell the homeowner. And

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Speaker 3: if it's something serious that they want us to take

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Speaker 3: a look at, they'll, you know, either tell the homeowner

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Speaker 3: to shoot it or they'll come out and euthanize the animal.

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Speaker 3: They know how to keep it cool enough so the

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Speaker 3: carcass is still in good shape to get it to us.

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Speaker 3: And so there's our lab here in Ithaca, but then

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Speaker 3: the New York Department of Environmental Conservation also has a

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Speaker 3: Wildlife health unit over by Albany, New York. So having

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Speaker 3: multiple locations makes it so they can get carcasses. The

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Speaker 3: biologists can get the carcasses to us relatively quickly, so

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Speaker 3: we can figure out what's going on. So they let

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Speaker 3: us know what species they want to prioritize looking at,

436
00:25:52,760 --> 00:25:57,320
Speaker 3: you know, which populations, which areas are important to them.

437
00:25:57,400 --> 00:25:59,560
Speaker 3: And we also let them know what diseases to look

438
00:25:59,600 --> 00:26:01,920
Speaker 3: out for or what might be coming down the pike

439
00:26:02,119 --> 00:26:04,879
Speaker 3: that we've heard about from you know, other surrounding states

440
00:26:05,000 --> 00:26:10,800
Speaker 3: is being problematic and ask them to be on the lookout.

441
00:26:10,880 --> 00:26:13,560
Speaker 3: So like for chronic wasting disease, we always want to

442
00:26:13,560 --> 00:26:18,359
Speaker 3: hear about sick or abnormal deer. So those are ones

443
00:26:18,359 --> 00:26:20,280
Speaker 3: that we say yes, like if you get a call

444
00:26:20,480 --> 00:26:23,800
Speaker 3: from anybody about that, go out get that deer. We

445
00:26:23,840 --> 00:26:26,480
Speaker 3: want the whole thing to come in here and take

446
00:26:26,520 --> 00:26:30,080
Speaker 3: a look at it. Whereas like bear marange, we did

447
00:26:30,080 --> 00:26:32,600
Speaker 3: a pretty intensive study for a few years and we said, yes,

448
00:26:33,000 --> 00:26:36,480
Speaker 3: we want to know about every report of bears with mange,

449
00:26:36,640 --> 00:26:38,320
Speaker 3: and if you can get your hands on any we

450
00:26:38,359 --> 00:26:41,960
Speaker 3: want you to bring them in. We published a paper

451
00:26:42,000 --> 00:26:43,639
Speaker 3: on that. We said, okay, you know, we kind of

452
00:26:43,680 --> 00:26:45,200
Speaker 3: know that we have bear range. Now we know where

453
00:26:45,200 --> 00:26:49,240
Speaker 3: it's happening. But right now there's sort of a larger

454
00:26:49,280 --> 00:26:52,560
Speaker 3: regional study where other researchers from the University of Georgia

455
00:26:52,600 --> 00:26:55,120
Speaker 3: came to us and said, hey, we want samples from

456
00:26:55,240 --> 00:26:56,879
Speaker 3: all these states. Can you help us out and we

457
00:26:56,920 --> 00:27:00,480
Speaker 3: said okay. And there's different protocols, so if a byllogist

458
00:27:00,520 --> 00:27:02,920
Speaker 3: is out there, he can he or she can do

459
00:27:03,200 --> 00:27:05,760
Speaker 3: different things anywhere from like taking a skin scraping to

460
00:27:05,800 --> 00:27:08,359
Speaker 3: bringing in the whole animal, and we'll take samples and

461
00:27:08,359 --> 00:27:10,960
Speaker 3: then send them to Georgia for participation in that.

462
00:27:11,240 --> 00:27:13,280
Speaker 1: Is black bear mange killing black bears.

463
00:27:14,200 --> 00:27:17,640
Speaker 3: It does kill black bears, and Pennsylvania has been doing

464
00:27:17,640 --> 00:27:21,919
Speaker 3: a lot of good work on this because when we

465
00:27:21,960 --> 00:27:24,000
Speaker 3: first started with mange, we kind of said, okay, if

466
00:27:24,440 --> 00:27:27,719
Speaker 3: fifty percent of the body is hairless and the animal

467
00:27:27,720 --> 00:27:29,960
Speaker 3: looks pretty bad, you should probably euthanize it. We don't

468
00:27:29,960 --> 00:27:33,560
Speaker 3: think it's going to recover, but some of the work

469
00:27:33,600 --> 00:27:38,879
Speaker 3: being done now indicates that you can treat bears and

470
00:27:38,960 --> 00:27:41,920
Speaker 3: they recover. But some bears even with severe mange seem

471
00:27:41,960 --> 00:27:44,560
Speaker 3: to recover on their own, so it's kind of a

472
00:27:44,960 --> 00:27:48,080
Speaker 3: crapshoot on whether they're going to recover or not.

473
00:27:48,400 --> 00:27:49,880
Speaker 1: But well, how is it new?

474
00:27:51,760 --> 00:27:57,680
Speaker 3: It's expanded in its range, so it didn't really occur

475
00:27:57,760 --> 00:28:02,320
Speaker 3: in New York until you know, I'll probably get the

476
00:28:02,400 --> 00:28:04,520
Speaker 3: year wrong, but in the last decade or so, it's

477
00:28:04,600 --> 00:28:07,240
Speaker 3: become more prevalent. And so one of the questions was

478
00:28:08,680 --> 00:28:11,520
Speaker 3: is it a specific it's our Coptis scaby but is

479
00:28:11,560 --> 00:28:13,800
Speaker 3: it specific to bears or is it the same it

480
00:28:13,840 --> 00:28:19,359
Speaker 3: sar copty scabye that occurs on coyotes and foxes, because

481
00:28:19,359 --> 00:28:22,040
Speaker 3: we know red foxes get mange and it usually kills them.

482
00:28:22,600 --> 00:28:25,080
Speaker 3: So did it jump from a species or did it

483
00:28:25,119 --> 00:28:28,919
Speaker 3: become more speciated to just being a bear one? But

484
00:28:28,960 --> 00:28:31,080
Speaker 3: it looks like it's spilled over from other species, and

485
00:28:31,240 --> 00:28:33,720
Speaker 3: just as going through bears now like.

486
00:28:33,880 --> 00:28:37,840
Speaker 1: At some point that that happened here or at some

487
00:28:37,920 --> 00:28:39,040
Speaker 1: point it happened somewhere else.

488
00:28:39,760 --> 00:28:43,640
Speaker 3: It's probably spilled over multiple places because in New York

489
00:28:43,680 --> 00:28:47,040
Speaker 3: there's two sort of different areas of bears, So you've

490
00:28:47,040 --> 00:28:49,200
Speaker 3: got the southern tier and the cat skills, which is

491
00:28:49,240 --> 00:28:52,560
Speaker 3: continuous with Pennsylvania and bears you know, move across the

492
00:28:53,040 --> 00:28:56,160
Speaker 3: PA line, and then there's bears up towards the Adirondack,

493
00:28:56,240 --> 00:28:58,360
Speaker 3: so there's sort of the northern population, and then there's

494
00:28:58,400 --> 00:29:01,200
Speaker 3: a strip kind of where were bears are just sort

495
00:29:01,240 --> 00:29:04,160
Speaker 3: of coming into to being here they weren't there.

496
00:29:04,480 --> 00:29:06,960
Speaker 1: More and more of them and that and that mange

497
00:29:06,960 --> 00:29:09,560
Speaker 1: will hit them and they'll lose their hair, yeah.

498
00:29:09,360 --> 00:29:14,200
Speaker 3: Yeah, and they get it's actually called lignification like a

499
00:29:14,240 --> 00:29:19,200
Speaker 3: lichen where they get really thick skin and they'll have

500
00:29:19,240 --> 00:29:22,880
Speaker 3: bacterial overgrowth on their skin, and you know, their eyes

501
00:29:23,000 --> 00:29:25,920
Speaker 3: might crust over and they're super itchy, so they've just

502
00:29:25,960 --> 00:29:30,160
Speaker 3: got a lot of ooze and crustyness. Yeah, they smell

503
00:29:30,240 --> 00:29:30,640
Speaker 3: bad too.

504
00:29:31,000 --> 00:29:32,960
Speaker 1: Really really, how many of those come through here.

505
00:29:33,960 --> 00:29:36,880
Speaker 3: We would see a few a year, and when we

506
00:29:37,560 --> 00:29:40,640
Speaker 3: look when we did that research study, we found it

507
00:29:40,720 --> 00:29:43,760
Speaker 3: was more likely to be adult females. We would get,

508
00:29:44,040 --> 00:29:46,600
Speaker 3: you know, not a ton of them, but we would

509
00:29:46,600 --> 00:29:49,120
Speaker 3: probably have you know, a dozen a year, and then

510
00:29:49,160 --> 00:29:51,880
Speaker 3: there would be more reports. One of the big things

511
00:29:52,120 --> 00:29:55,320
Speaker 3: is that these bears with mange usually end up in

512
00:29:55,400 --> 00:29:57,760
Speaker 3: bad places where they shouldn't be, so they'll be on

513
00:29:57,840 --> 00:30:00,720
Speaker 3: like people's porches, or they'll be more likely to have

514
00:30:00,840 --> 00:30:03,920
Speaker 3: an interaction with a human because they're starving to death.

515
00:30:04,600 --> 00:30:07,120
Speaker 3: They're they're in bad shape, and so then we get

516
00:30:07,120 --> 00:30:09,480
Speaker 3: calls about them and then they get euthanized.

517
00:30:18,960 --> 00:30:21,360
Speaker 1: How does something get how does something get mange? Like

518
00:30:21,360 --> 00:30:24,800
Speaker 1: I remember when I was a kid, even older than

519
00:30:24,800 --> 00:30:29,640
Speaker 1: a kid, but high school encountering just like an encountering

520
00:30:29,680 --> 00:30:33,080
Speaker 1: red foxes with mange, and a strong association with red

521
00:30:33,080 --> 00:30:36,440
Speaker 1: foxes and mange. Even in the trapping world. It would

522
00:30:36,480 --> 00:30:39,120
Speaker 1: be that I got a red fox but it had mange,

523
00:30:39,600 --> 00:30:41,320
Speaker 1: and it was just taken as like, you know, a

524
00:30:41,360 --> 00:30:44,080
Speaker 1: thing that happened. They were out there, but people it wasn't.

525
00:30:44,120 --> 00:30:46,600
Speaker 1: People weren't seeming like alarmed by it.

526
00:30:47,120 --> 00:30:49,800
Speaker 3: Yeah, I mean, red fox mange has been around for

527
00:30:49,840 --> 00:30:52,720
Speaker 3: a long time. And so these mites can live off

528
00:30:52,720 --> 00:30:55,240
Speaker 3: the animal for a little while, you know, depending on

529
00:30:55,320 --> 00:30:59,480
Speaker 3: the conditions and the moisture, they can last in burrows

530
00:30:59,560 --> 00:31:02,960
Speaker 3: and things like that for a few days. So then

531
00:31:03,080 --> 00:31:06,240
Speaker 3: another animal comes along and uses that burrow and might

532
00:31:06,240 --> 00:31:09,160
Speaker 3: pick up the mites, and then the cycle starts again.

533
00:31:09,240 --> 00:31:12,720
Speaker 1: So it's moved, like the disease is moved from animal

534
00:31:12,720 --> 00:31:16,040
Speaker 1: to animal by a mite. Nothing's born with mange.

535
00:31:16,280 --> 00:31:20,000
Speaker 3: No, No, it's a parasite. So they pick it up

536
00:31:20,000 --> 00:31:24,280
Speaker 3: and then the female is you know, burrow into the

537
00:31:24,320 --> 00:31:26,720
Speaker 3: skin and lay a bunch of eggs and those hatch

538
00:31:26,720 --> 00:31:29,440
Speaker 3: out and then they start burrowing and lay more eggs

539
00:31:29,480 --> 00:31:31,360
Speaker 3: and the infection just gets worse and worse.

540
00:31:32,480 --> 00:31:36,080
Speaker 1: And then talk to what's going on with moose right

541
00:31:36,120 --> 00:31:38,320
Speaker 1: now and what you're interested moose because when you when

542
00:31:38,360 --> 00:31:41,000
Speaker 1: I read that you were looking at moose, I assumed

543
00:31:41,000 --> 00:31:44,520
Speaker 1: it's what we keep hearing about with moose, which is

544
00:31:44,600 --> 00:31:46,280
Speaker 1: like some tickborn.

545
00:31:46,080 --> 00:31:49,920
Speaker 4: Pathogen winter tick.

546
00:31:49,280 --> 00:31:51,800
Speaker 1: Killing a lot of moose. But you mentioned another thing

547
00:31:51,880 --> 00:31:52,440
Speaker 1: killing moose.

548
00:31:52,680 --> 00:31:58,160
Speaker 3: Yeah, So we have a project on moose right now.

549
00:31:58,240 --> 00:32:00,680
Speaker 3: We've caught moose with a helicopter after the last two

550
00:32:00,760 --> 00:32:04,719
Speaker 3: years and put GPS collars on them. There's a student

551
00:32:04,800 --> 00:32:10,720
Speaker 3: out there with camera traps and within New York it's

552
00:32:10,720 --> 00:32:13,640
Speaker 3: a pretty small population. This is like the southernmost part

553
00:32:13,680 --> 00:32:16,479
Speaker 3: of moose range, so we know, you know, moose are

554
00:32:16,560 --> 00:32:21,440
Speaker 3: challenged by warm temperatures and then throughout most of the

555
00:32:21,600 --> 00:32:25,920
Speaker 3: range moose have problems with brainworm or parallelristrongelst tenuous which

556
00:32:25,960 --> 00:32:30,320
Speaker 3: is really long long word, and that is a parasite

557
00:32:30,360 --> 00:32:34,760
Speaker 3: that's carried by deer and there's an intermediate host, which

558
00:32:34,800 --> 00:32:37,840
Speaker 3: is a snail. So if moose accidentally eat that snail,

559
00:32:37,920 --> 00:32:41,480
Speaker 3: they can get infected. And the parasite migrates through the

560
00:32:41,480 --> 00:32:44,400
Speaker 3: spinal cord up into the brain and sometimes it ends

561
00:32:44,480 --> 00:32:47,080
Speaker 3: up in the eye and it causes damage in moose.

562
00:32:48,040 --> 00:32:49,240
Speaker 1: Tell me the life cycle again.

563
00:32:50,040 --> 00:32:56,080
Speaker 3: So deer are infected, they poop, the larvae come out

564
00:32:56,080 --> 00:32:58,880
Speaker 3: in the poop, The snail feeds on the poop, and

565
00:32:58,920 --> 00:33:00,920
Speaker 3: then a moose acts the larvae.

566
00:33:01,480 --> 00:33:03,080
Speaker 1: It's a snail larvae.

567
00:33:03,320 --> 00:33:07,880
Speaker 3: No, the worm, the parasite larvae is in the deer poop,

568
00:33:08,360 --> 00:33:10,920
Speaker 3: and then the snail comes along and likes to feed

569
00:33:11,040 --> 00:33:14,160
Speaker 3: on the poop. It takes the larvae actually burrow into

570
00:33:14,200 --> 00:33:18,560
Speaker 3: the snail and then the you know, snail gets infected.

571
00:33:19,720 --> 00:33:22,800
Speaker 3: Moose comes along accidentally eats a snail and then they

572
00:33:22,880 --> 00:33:24,600
Speaker 3: get infected because.

573
00:33:24,400 --> 00:33:26,080
Speaker 1: The snails on a piece of grass or whatever.

574
00:33:26,240 --> 00:33:29,680
Speaker 4: Right, they're just cruising on the convoluted path.

575
00:33:29,920 --> 00:33:31,960
Speaker 3: I know. Wait until you hear about the liver fluke.

576
00:33:32,000 --> 00:33:33,040
Speaker 3: It's even crazier.

577
00:33:33,200 --> 00:33:34,960
Speaker 1: Well, that's what I'll make sure I got it right.

578
00:33:35,600 --> 00:33:39,280
Speaker 1: So the deer that poops out the larvae. How did

579
00:33:39,320 --> 00:33:40,480
Speaker 1: it get infected anyway?

580
00:33:41,040 --> 00:33:46,080
Speaker 3: Uh, same process where it accidentally ate something that was infected.

581
00:33:46,120 --> 00:33:48,680
Speaker 1: So there's enough stuff out there. There's enough stuff out

582
00:33:48,720 --> 00:33:50,920
Speaker 1: there accidentally eating snails.

583
00:33:50,440 --> 00:33:51,760
Speaker 4: To keep this going apparently.

584
00:33:53,720 --> 00:33:53,960
Speaker 3: Yep.

585
00:33:54,880 --> 00:33:59,360
Speaker 1: But the parasite is not a snail, correct, It's just

586
00:33:59,400 --> 00:33:59,920
Speaker 1: a carrier.

587
00:34:00,080 --> 00:34:00,680
Speaker 3: It's the worm.

588
00:34:00,760 --> 00:34:01,760
Speaker 1: What is the worm?

589
00:34:01,920 --> 00:34:04,480
Speaker 3: The worm is parallepistron, just tenuous, that's the par.

590
00:34:04,760 --> 00:34:08,280
Speaker 1: What does it ever have a does it remain a worm?

591
00:34:08,440 --> 00:34:11,200
Speaker 1: Does it ever have a bug phase? Okay, so it's

592
00:34:11,239 --> 00:34:12,920
Speaker 1: just a worm, just a worm, I got it.

593
00:34:13,040 --> 00:34:13,239
Speaker 4: Yeah.

594
00:34:13,320 --> 00:34:14,919
Speaker 3: It goes into your brain, all right.

595
00:34:15,239 --> 00:34:17,320
Speaker 1: So a deer gets infected.

596
00:34:19,440 --> 00:34:21,520
Speaker 3: And they pass it through.

597
00:34:21,640 --> 00:34:25,400
Speaker 1: Yep, winds up in their digestive system, they poop.

598
00:34:25,760 --> 00:34:29,319
Speaker 3: Yeah, there's a whole complicated process because it goes to

599
00:34:29,360 --> 00:34:32,520
Speaker 3: their brain too, and then it lays eggs and that's

600
00:34:32,560 --> 00:34:33,200
Speaker 3: what comes out.

601
00:34:33,440 --> 00:34:34,479
Speaker 1: Lays eggs in their brain.

602
00:34:36,040 --> 00:34:38,400
Speaker 3: Yes, and that actually can cause some problems with deer,

603
00:34:38,480 --> 00:34:43,439
Speaker 3: but usually deer fine with it. But yeah, they they

604
00:34:43,920 --> 00:34:47,760
Speaker 3: lay eggs, it goes, the eggs end up in their lungs.

605
00:34:47,760 --> 00:34:49,399
Speaker 3: They cough them up and then they poop them out

606
00:34:49,800 --> 00:34:53,520
Speaker 3: and then through the poop through the snail and a

607
00:34:53,560 --> 00:34:54,719
Speaker 3: moose accidentally gets it.

608
00:34:54,960 --> 00:34:59,440
Speaker 1: But if the mood k let's say, a moose ate, we.

609
00:34:59,400 --> 00:35:03,160
Speaker 3: Need to get a die like some animals to like Okay,

610
00:35:03,200 --> 00:35:04,200
Speaker 3: here's the poop.

611
00:35:04,120 --> 00:35:05,919
Speaker 1: But if the moose ate the poop, that doesn't work.

612
00:35:06,719 --> 00:35:10,239
Speaker 3: No, it has to go through the snaw. Yeah, just

613
00:35:10,280 --> 00:35:12,440
Speaker 3: like what like yeah, I know, right.

614
00:35:12,880 --> 00:35:16,640
Speaker 1: I mean it just seems like something that would just

615
00:35:16,680 --> 00:35:19,960
Speaker 1: be so random, I mean, not random, but so isolated

616
00:35:20,280 --> 00:35:22,480
Speaker 1: the fact that this is happening enough to have population

617
00:35:22,640 --> 00:35:23,400
Speaker 1: level impacts.

618
00:35:23,440 --> 00:35:27,400
Speaker 3: Yep. Then well, I guess if you're a moose, you're

619
00:35:27,440 --> 00:35:30,160
Speaker 3: eating a lot of stuff, so you're gonna.

620
00:35:30,000 --> 00:35:32,680
Speaker 1: Big mouthfuls and stuff. And you know, I've gone out

621
00:35:32,719 --> 00:35:34,960
Speaker 1: to my garden right and we're eating. I remember we

622
00:35:35,040 --> 00:35:37,320
Speaker 1: have a story that floats around in our family where

623
00:35:38,360 --> 00:35:41,560
Speaker 1: my I used to have a little greenhouse and I

624
00:35:41,800 --> 00:35:43,600
Speaker 1: didn't wash the lettuce out of the greenhouse, so I

625
00:35:43,600 --> 00:35:46,120
Speaker 1: thought nothing could get on it because it's in the greenhouse.

626
00:35:46,360 --> 00:35:46,640
Speaker 3: Yep.

627
00:35:46,880 --> 00:35:50,399
Speaker 1: And there's a story where I look over and there's

628
00:35:50,440 --> 00:35:55,440
Speaker 1: a ranch dressing coated worm making a break forward across

629
00:35:55,480 --> 00:35:58,680
Speaker 1: my kid's plate. Oh yeah, so yeah, you're just eating stuff.

630
00:35:58,719 --> 00:36:01,400
Speaker 3: I stopped growing broccoli for that exact reason. Those things,

631
00:36:01,480 --> 00:36:03,200
Speaker 3: like the little worms that get in your broccoli are

632
00:36:03,200 --> 00:36:05,319
Speaker 3: the same color as the broccoli. And I'm pretty sure

633
00:36:05,320 --> 00:36:07,000
Speaker 3: I more worms than broccoli.

634
00:36:07,080 --> 00:36:07,400
Speaker 5: Maybe.

635
00:36:07,480 --> 00:36:10,240
Speaker 1: Yeah. And I've read that people would like to point

636
00:36:10,239 --> 00:36:12,640
Speaker 1: out that, like, oh, a vegan, you can't be a

637
00:36:12,680 --> 00:36:16,279
Speaker 1: vegan because of vitamin B twelve. But someone's saying that

638
00:36:16,640 --> 00:36:18,960
Speaker 1: there's you get you need such a small amount of

639
00:36:18,960 --> 00:36:20,880
Speaker 1: B twelve that you're going to get it through insect

640
00:36:20,960 --> 00:36:23,840
Speaker 1: contamination on produce. Yeah, it's just not a problem.

641
00:36:23,960 --> 00:36:24,200
Speaker 3: Right.

642
00:36:24,800 --> 00:36:26,640
Speaker 1: So back to these moose.

643
00:36:26,719 --> 00:36:27,640
Speaker 3: Okay, back to the moose.

644
00:36:27,680 --> 00:36:31,760
Speaker 1: So we're talking about we're talking about the winter okay.

645
00:36:31,960 --> 00:36:35,040
Speaker 3: So yeah, there's there's a whole bunch of parasites that

646
00:36:35,080 --> 00:36:37,239
Speaker 3: moose are dealing with right now. So we just went

647
00:36:37,280 --> 00:36:41,839
Speaker 3: over brainworm. M h, winter tick are one.

648
00:36:42,160 --> 00:36:43,840
Speaker 1: Well I want to go back to brainworm from it?

649
00:36:43,960 --> 00:36:45,200
Speaker 3: Okay, you'd like brain worm?

650
00:36:45,360 --> 00:36:47,200
Speaker 1: Well no, no, I just want to I want to just

651
00:36:47,200 --> 00:36:51,759
Speaker 1: make sure I'm understand it right. It doesn't bother white

652
00:36:51,760 --> 00:36:57,920
Speaker 1: tail deer mostly, okay, but it so most mostly doesn't

653
00:36:57,920 --> 00:37:01,040
Speaker 1: bother white tail deer, but it mostly bothers moose. Like

654
00:37:01,080 --> 00:37:06,600
Speaker 1: it's it's hard on moose. Yes, why why?

655
00:37:06,640 --> 00:37:13,719
Speaker 3: Because we're not entirely sure yet. But my theory on

656
00:37:13,760 --> 00:37:17,000
Speaker 3: this is that the worms get lost and they think,

657
00:37:17,400 --> 00:37:19,839
Speaker 3: you know, they're they're used to migrating a certain way

658
00:37:19,880 --> 00:37:21,840
Speaker 3: and a deer, and like a deer is much smaller

659
00:37:21,840 --> 00:37:24,880
Speaker 3: than a moose, and so they kind of get lost

660
00:37:25,000 --> 00:37:28,240
Speaker 3: along their way and will end up in weird places

661
00:37:28,280 --> 00:37:34,160
Speaker 3: like the eye. So when we have moose acting abnormally

662
00:37:34,800 --> 00:37:40,759
Speaker 3: or are emaciated, we usually suspect that is brainworm associated mortality.

663
00:37:41,120 --> 00:37:45,520
Speaker 3: But it moose are what's called an aberrant hoast, and

664
00:37:45,719 --> 00:37:49,359
Speaker 3: other things like lamas and alpacas also can get brainworm

665
00:37:49,719 --> 00:37:53,200
Speaker 3: and die from it. And so these species that haven't

666
00:37:53,239 --> 00:37:57,400
Speaker 3: adapted with this worm may have more of a reaction

667
00:37:57,600 --> 00:38:01,120
Speaker 3: to it migrating through their scent nervous system in their

668
00:38:01,160 --> 00:38:05,160
Speaker 3: brain than whitetail deer do, and so that reaction may

669
00:38:05,200 --> 00:38:07,240
Speaker 3: cause more problems for them.

670
00:38:07,320 --> 00:38:10,759
Speaker 1: Well, okay, if it comes to something like this, how

671
00:38:10,880 --> 00:38:14,200
Speaker 1: is the worm so picky? Anyways? Why would the worm

672
00:38:14,440 --> 00:38:18,360
Speaker 1: Let's say you ate the snail? What is it about

673
00:38:18,440 --> 00:38:21,239
Speaker 1: your What is it about a human's body chemistry that

674
00:38:21,280 --> 00:38:23,520
Speaker 1: would make it that the worm didn't take off that

675
00:38:24,080 --> 00:38:25,600
Speaker 1: the worm didn't go into your brain.

676
00:38:26,520 --> 00:38:28,480
Speaker 3: I don't know because I don't study people.

677
00:38:28,760 --> 00:38:31,359
Speaker 1: Okay, but like do we know that it doesn't. Yeah?

678
00:38:31,680 --> 00:38:33,920
Speaker 3: Well yeah, well, I mean there's a bunch of worms

679
00:38:33,960 --> 00:38:36,480
Speaker 3: that do. Like we don't hear about brainworm being a

680
00:38:36,520 --> 00:38:39,240
Speaker 3: problem in people, But there are other ones in raccoon

681
00:38:39,320 --> 00:38:44,040
Speaker 3: called Ballis ascaris. That's problematic. That's in raccoon poop. And

682
00:38:44,080 --> 00:38:47,280
Speaker 3: so they tell people like cleaning out addicts and stuff

683
00:38:47,280 --> 00:38:51,840
Speaker 3: to make sure that you wear you know, a respirator

684
00:38:51,880 --> 00:38:54,080
Speaker 3: or something and wear gloves because if you inhale these

685
00:38:55,000 --> 00:38:57,360
Speaker 3: eggs from raccoon poop, then you can get infected. And

686
00:38:57,360 --> 00:38:59,320
Speaker 3: that one ends up in your brain and causes problems.

687
00:38:59,360 --> 00:38:59,759
Speaker 1: Oh it does.

688
00:39:00,080 --> 00:39:00,799
Speaker 4: Yeah.

689
00:39:00,840 --> 00:39:03,280
Speaker 1: And then what's the one what's the cat scratch fever? One?

690
00:39:04,560 --> 00:39:05,720
Speaker 3: Is that bord oftella?

691
00:39:06,320 --> 00:39:09,200
Speaker 1: No, that's doc plasmosis. Oh, that's what I'm thinking of.

692
00:39:10,200 --> 00:39:14,960
Speaker 3: Toxic plasmosis. Yeah, that is another one that you know,

693
00:39:15,040 --> 00:39:19,120
Speaker 3: you get from feces, like inhaling the eggs and everything,

694
00:39:19,280 --> 00:39:21,200
Speaker 3: so and that can be a problem from Yeah, that

695
00:39:21,280 --> 00:39:24,560
Speaker 3: one also can end up in your brain. It's a

696
00:39:24,600 --> 00:39:28,520
Speaker 3: different type of parasite though, So it's a like an

697
00:39:28,560 --> 00:39:30,960
Speaker 3: intracellular one that causes a lot of problems. I did

698
00:39:30,960 --> 00:39:34,520
Speaker 3: some work on sea otters and they would get that

699
00:39:34,560 --> 00:39:38,240
Speaker 3: one a lot. And they think it's from cat feces

700
00:39:38,360 --> 00:39:41,719
Speaker 3: washing out into the ocean. What yeah, and other and

701
00:39:41,800 --> 00:39:46,279
Speaker 3: snails might be involved in that one too, So okay.

702
00:39:46,200 --> 00:39:48,040
Speaker 1: Back the brain weren't from men now, and then we're

703
00:39:48,080 --> 00:39:52,680
Speaker 1: jumping around a bunch. That's all right, Uh, I have heard,

704
00:39:55,320 --> 00:39:57,879
Speaker 1: and I might be getting part of this wrong. I've

705
00:39:57,920 --> 00:40:02,600
Speaker 1: heard that we're seeing that one of the problems for

706
00:40:02,719 --> 00:40:07,160
Speaker 1: moose that are enorming in the northern latitudes. Okay, one

707
00:40:07,160 --> 00:40:11,160
Speaker 1: of the problems for moose has been as white tailed

708
00:40:11,160 --> 00:40:20,200
Speaker 1: deer populations have expanded, habitat changes Okay, Warmer winters have

709
00:40:20,320 --> 00:40:24,000
Speaker 1: allowed white tail deer to move and croach on areas

710
00:40:24,000 --> 00:40:27,640
Speaker 1: that were historically moose. Yes, they've always you know, there's

711
00:40:27,640 --> 00:40:30,239
Speaker 1: probably always been some intermingling, but there's been a more

712
00:40:30,360 --> 00:40:34,319
Speaker 1: radical shift of deer moving into moose areas and that

713
00:40:34,400 --> 00:40:39,319
Speaker 1: being problematic for moose. Yep, this is what they're talking about.

714
00:40:39,360 --> 00:40:43,400
Speaker 1: I gather exactly the movement of these parasites into moose.

715
00:40:43,480 --> 00:40:45,160
Speaker 3: And so we'll talk about the other one that we

716
00:40:45,160 --> 00:40:49,600
Speaker 3: think is affecting New York moose. Is liver flukeka. So

717
00:40:49,680 --> 00:40:53,319
Speaker 3: this has anybody ever asked you about little livers, like

718
00:40:53,360 --> 00:40:56,759
Speaker 3: if you if you're eating the liver. I actually had

719
00:40:57,080 --> 00:40:58,839
Speaker 3: somebody ask me this and they're like, oh, what are

720
00:40:58,840 --> 00:41:01,360
Speaker 3: those other little livers? They're kind of like leaf shape,

721
00:41:01,360 --> 00:41:01,759
Speaker 3: they might.

722
00:41:01,640 --> 00:41:03,000
Speaker 1: Get no, no, exactly.

723
00:41:03,560 --> 00:41:07,400
Speaker 3: Yeah, so those are liver fluke, giant liver fluke, vaciloidis magna.

724
00:41:07,520 --> 00:41:09,879
Speaker 1: Oh no, I'm not talking about that, Okay, I'm talking

725
00:41:09,880 --> 00:41:11,680
Speaker 1: about like you know, there's like lobes of a liver.

726
00:41:12,360 --> 00:41:14,399
Speaker 1: There's like a little side car liver on there.

727
00:41:14,600 --> 00:41:17,440
Speaker 3: Sure, yeah, that's what I thought. No, but these are

728
00:41:17,520 --> 00:41:21,520
Speaker 3: parasites that are in there that some people have fry

729
00:41:21,600 --> 00:41:22,359
Speaker 3: up and eat.

730
00:41:24,880 --> 00:41:27,360
Speaker 1: I'm back up on that. Yeah, tell me what it

731
00:41:27,400 --> 00:41:27,719
Speaker 1: looks like.

732
00:41:28,239 --> 00:41:31,480
Speaker 3: It's it's like yay big and it kind of looks

733
00:41:31,480 --> 00:41:34,319
Speaker 3: like a leaf. We can I'm sure I can find a.

734
00:41:34,280 --> 00:41:35,560
Speaker 1: Picture the girls on a liver.

735
00:41:36,080 --> 00:41:38,200
Speaker 3: Yeah. It grows in the liver. So those migrate through

736
00:41:38,239 --> 00:41:40,920
Speaker 3: the liver and make these cysts that are full of

737
00:41:40,960 --> 00:41:45,120
Speaker 3: like brown gunk and they destroy the liver. And so

738
00:41:45,160 --> 00:41:46,120
Speaker 3: that's the big problem.

739
00:41:46,280 --> 00:41:48,719
Speaker 1: People like eating them or they're accidentally eating them.

740
00:41:49,000 --> 00:41:52,359
Speaker 3: They probably accidentally eat them. I was just curious because

741
00:41:52,400 --> 00:41:54,200
Speaker 3: you get a lot of weird questions.

742
00:41:55,080 --> 00:41:59,759
Speaker 1: And the little sidecar thing I'm talking about.

743
00:42:00,160 --> 00:42:02,520
Speaker 3: I know what you're talking about. Yeah, No, these are

744
00:42:02,640 --> 00:42:05,560
Speaker 3: are parasites within the liver. The kind of they kind

745
00:42:05,560 --> 00:42:09,120
Speaker 3: of look like a liver almost, I guess, like, but yeah,

746
00:42:09,480 --> 00:42:10,920
Speaker 3: they're they're different.

747
00:42:12,160 --> 00:42:16,000
Speaker 1: I want to ask you later about the the nasal bots.

748
00:42:16,120 --> 00:42:17,160
Speaker 3: Oh, those are fun.

749
00:42:19,200 --> 00:42:19,920
Speaker 1: But let's go on.

750
00:42:20,239 --> 00:42:24,160
Speaker 3: We'll keep going, all right. So liver flukes also are

751
00:42:24,840 --> 00:42:29,200
Speaker 3: carried by white tail deer and have a snail intermediate host,

752
00:42:29,640 --> 00:42:34,680
Speaker 3: but these have an aquatic snail as the intermediate host.

753
00:42:34,719 --> 00:42:37,759
Speaker 3: So it's even sort of more complicated. The moose have

754
00:42:37,840 --> 00:42:43,480
Speaker 3: to get this aquatic vegetation where the the fluke actually

755
00:42:43,520 --> 00:42:47,360
Speaker 3: insists on the vegetation and stays there, and then the

756
00:42:47,360 --> 00:42:49,280
Speaker 3: moose comes along and eats it and gets infected.

757
00:42:49,480 --> 00:42:51,080
Speaker 1: Okay, so he's got to be underwater.

758
00:42:51,400 --> 00:42:55,840
Speaker 3: Well he can you know, eating in a wetland or something,

759
00:42:55,920 --> 00:42:58,839
Speaker 3: you know, eating some plants out of there and gets

760
00:42:58,840 --> 00:43:02,160
Speaker 3: it that way, and so out of the moose that

761
00:43:02,200 --> 00:43:04,200
Speaker 3: we have on the air right now, I think we've

762
00:43:04,239 --> 00:43:07,880
Speaker 3: had explain what that means with GPS callers on. Okay,

763
00:43:09,160 --> 00:43:12,400
Speaker 3: that we've we've had about three already that have died

764
00:43:12,520 --> 00:43:15,760
Speaker 3: from liver fluke and these were captured as year lanes.

765
00:43:16,400 --> 00:43:20,359
Speaker 3: And so we're worried about the survival of juvenile moose

766
00:43:20,400 --> 00:43:22,480
Speaker 3: in the Adirondack. So there's been a lot of studies

767
00:43:22,520 --> 00:43:25,759
Speaker 3: by folks here at Cornell in the USGS co Op

768
00:43:26,000 --> 00:43:31,000
Speaker 3: unit at the Sunni School Environmental Science and Forestry doing

769
00:43:31,120 --> 00:43:33,719
Speaker 3: moose work, you know, to see if the habitats appropriate

770
00:43:33,920 --> 00:43:38,239
Speaker 3: to look at adult reproduction. A few years ago we

771
00:43:38,360 --> 00:43:43,560
Speaker 3: collared a bunch of females and all the signs pointed

772
00:43:43,680 --> 00:43:47,799
Speaker 3: to juveniles as their survivorship wasn't very good. So that's

773
00:43:47,800 --> 00:43:49,880
Speaker 3: why we're doing the study. And so the fact that

774
00:43:49,880 --> 00:43:53,120
Speaker 3: we've already seen three die over you know, the course

775
00:43:53,160 --> 00:43:55,280
Speaker 3: of our two years already.

776
00:43:55,000 --> 00:43:56,480
Speaker 1: How many how many moose do you have out there

777
00:43:56,520 --> 00:43:57,319
Speaker 1: with collars on them?

778
00:43:57,560 --> 00:44:00,480
Speaker 3: I forget. I want to say we caught like fifteen

779
00:44:00,560 --> 00:44:02,560
Speaker 3: the first year and nineteen the second year.

780
00:44:03,560 --> 00:44:05,799
Speaker 1: And three have died from that? How many have died

781
00:44:05,840 --> 00:44:06,480
Speaker 1: from other stuff?

782
00:44:08,800 --> 00:44:11,719
Speaker 3: Jen, my PhD student, would have the numbers, but I

783
00:44:11,760 --> 00:44:14,560
Speaker 3: don't think too many others. I mean we have, I

784
00:44:14,600 --> 00:44:16,799
Speaker 3: think most of them are are still in the air.

785
00:44:17,360 --> 00:44:19,439
Speaker 1: So you got a mortality signal, you go out there,

786
00:44:19,560 --> 00:44:22,680
Speaker 1: you get it. Yep, you dissect it, bring it here

787
00:44:22,760 --> 00:44:26,200
Speaker 1: or whatever. Dissect it, open it up. Liver flute. Yeah,

788
00:44:26,280 --> 00:44:27,160
Speaker 1: the liver's destroyed.

789
00:44:27,360 --> 00:44:29,320
Speaker 3: Liver's destroyed so much.

790
00:44:29,239 --> 00:44:30,759
Speaker 1: As someone would know when you're looking at it.

791
00:44:32,440 --> 00:44:34,320
Speaker 3: I mean, you have to kind of get a feel

792
00:44:34,480 --> 00:44:37,719
Speaker 3: for what normal is. And when you have a liver

793
00:44:37,840 --> 00:44:42,120
Speaker 3: that just looks like pulp. And the other thing was

794
00:44:42,520 --> 00:44:46,040
Speaker 3: I was at one of the Moostnee cropsies and it

795
00:44:46,080 --> 00:44:49,520
Speaker 3: was full of fluid, so like liver was failing and

796
00:44:49,560 --> 00:44:54,560
Speaker 3: it couldn't process any any thing anymore. So it was

797
00:44:54,600 --> 00:44:57,080
Speaker 3: just filling up with fluid. Like we punched a hole

798
00:44:57,080 --> 00:44:59,320
Speaker 3: in it and had a five gallon bucket under the table,

799
00:44:59,440 --> 00:45:01,960
Speaker 3: and it like it was overflowing. It had so much

800
00:45:02,000 --> 00:45:06,120
Speaker 3: fluid in it, five gallons over five gallons. Yeah, there

801
00:45:06,160 --> 00:45:08,560
Speaker 3: was so much pressure in its abdomen, Like the fluid

802
00:45:08,680 --> 00:45:11,960
Speaker 3: was like shooting out a couple feet when we punched

803
00:45:11,960 --> 00:45:12,520
Speaker 3: a hole in it.

804
00:45:13,200 --> 00:45:17,200
Speaker 1: Oh how many flukes does it take to do that?

805
00:45:17,480 --> 00:45:20,680
Speaker 3: I don't know, but it was a lot like it.

806
00:45:20,680 --> 00:45:23,680
Speaker 3: It was pretty pretty trashed when you got to the liver,

807
00:45:23,840 --> 00:45:26,719
Speaker 3: and for an animal that young to have that much

808
00:45:26,800 --> 00:45:28,200
Speaker 3: damage when.

809
00:45:28,080 --> 00:45:29,640
Speaker 1: You get to something like that, how do you ever,

810
00:45:31,400 --> 00:45:36,360
Speaker 1: how do you ever make the determination that this is new?

811
00:45:36,520 --> 00:45:38,759
Speaker 1: This has always been here but we hadn't noticed it,

812
00:45:39,160 --> 00:45:41,360
Speaker 1: do you? I mean, like, how do you ever go

813
00:45:41,480 --> 00:45:42,160
Speaker 1: down that path?

814
00:45:42,360 --> 00:45:45,279
Speaker 3: Yeah, it's hard because you're just comparing it to what

815
00:45:45,360 --> 00:45:49,160
Speaker 3: you know already. And that's why we have challenges sometimes

816
00:45:49,200 --> 00:45:51,840
Speaker 3: with saying things are a problem because we get so

817
00:45:51,920 --> 00:45:56,160
Speaker 3: many things opportunistically where we can't go out and just

818
00:45:56,200 --> 00:45:59,200
Speaker 3: sample the population like we'd like to randomly and say, okay,

819
00:45:59,280 --> 00:46:03,200
Speaker 3: is this normal or not normal? So yeah, we have

820
00:46:03,280 --> 00:46:07,680
Speaker 3: to do some some creative statistics and just doing this

821
00:46:07,760 --> 00:46:10,560
Speaker 3: work over time, you get a baseline so you kind

822
00:46:10,560 --> 00:46:13,200
Speaker 3: of know are things you know, we've never seen this

823
00:46:13,320 --> 00:46:15,919
Speaker 3: before in the you know, New York has a really

824
00:46:15,960 --> 00:46:19,960
Speaker 3: long history of doing wildlife health and knowing what's normal

825
00:46:20,000 --> 00:46:23,399
Speaker 3: and what's not. So those people are are really good

826
00:46:23,440 --> 00:46:26,799
Speaker 3: resource of institutional knowledge to compare against to say, hey,

827
00:46:27,280 --> 00:46:28,560
Speaker 3: what do you normally see.

828
00:46:28,719 --> 00:46:31,239
Speaker 1: And to return that real quick to the work you

829
00:46:31,280 --> 00:46:33,440
Speaker 1: guys are doing downstairs. But we're looking at this morning,

830
00:46:33,640 --> 00:46:38,160
Speaker 1: is that gives you that influx of animals coming in,

831
00:46:38,880 --> 00:46:44,200
Speaker 1: gives you a chance to look for this stuff. Meaning,

832
00:46:44,239 --> 00:46:47,200
Speaker 1: someone hits a moose with a car, the moose comes

833
00:46:47,200 --> 00:46:50,560
Speaker 1: in and you'd be like, oh, does not or does yeah, right,

834
00:46:50,600 --> 00:46:53,000
Speaker 1: and it allows you to start getting a snapshot of

835
00:46:53,000 --> 00:46:55,000
Speaker 1: what's going on out there, even though like we looked

836
00:46:55,040 --> 00:46:59,759
Speaker 1: at a bobcat this morning that obviously ques.

837
00:47:01,160 --> 00:47:02,439
Speaker 3: Pretty yeah, that one was.

838
00:47:02,440 --> 00:47:05,919
Speaker 1: Fairly obviously like probably from the report of whoever brought

839
00:47:05,960 --> 00:47:08,439
Speaker 1: it in, but also just like at a glance, it's

840
00:47:08,480 --> 00:47:10,320
Speaker 1: like this thing's been run over by a car bad

841
00:47:11,680 --> 00:47:15,080
Speaker 1: but you're able to check to see its other what

842
00:47:15,120 --> 00:47:17,200
Speaker 1: its health might have been like prior to it getting

843
00:47:17,239 --> 00:47:17,560
Speaker 1: hit by.

844
00:47:17,520 --> 00:47:19,960
Speaker 3: A car, exactly was there something that may have predisposed

845
00:47:20,000 --> 00:47:22,640
Speaker 3: it to getting hit by a car. So it's sort

846
00:47:22,680 --> 00:47:24,800
Speaker 3: of a judgment call, Like we don't run every test

847
00:47:24,840 --> 00:47:28,400
Speaker 3: on every animal because that would be prohibitively expensive, but

848
00:47:29,040 --> 00:47:33,400
Speaker 3: by saving the tissues, if there's sort of you know,

849
00:47:33,520 --> 00:47:36,880
Speaker 3: a potential smoking gun out there that might have caused

850
00:47:36,920 --> 00:47:41,319
Speaker 3: something like you know, for some of these animals, like

851
00:47:41,600 --> 00:47:45,080
Speaker 3: maybe they had something you know, on board, or maybe

852
00:47:45,080 --> 00:47:46,719
Speaker 3: they were sick and that's why they walked in front

853
00:47:46,760 --> 00:47:48,279
Speaker 3: of the car. Maybe they weren't quite right.

854
00:47:48,600 --> 00:47:52,080
Speaker 1: So yep, you know, yeah, Christ was saying to me earlier,

855
00:47:52,120 --> 00:47:56,600
Speaker 1: you were talking about found dead, and you were saying,

856
00:47:57,560 --> 00:47:59,600
Speaker 1: tell me more found dead.

857
00:48:00,040 --> 00:48:02,200
Speaker 3: Found dead isn't really helpful, Like we know the animal's

858
00:48:02,239 --> 00:48:03,279
Speaker 3: dead already, like.

859
00:48:03,400 --> 00:48:05,800
Speaker 1: Found dead below a power line. Yep, found dead and

860
00:48:05,840 --> 00:48:09,399
Speaker 1: the birds nests found dead below a big window, right,

861
00:48:09,560 --> 00:48:12,480
Speaker 1: found dead where yeah, found dead after it bit your

862
00:48:12,480 --> 00:48:13,640
Speaker 1: dog exactly.

863
00:48:14,080 --> 00:48:16,400
Speaker 3: Yeah. So one of the things when I go out

864
00:48:16,440 --> 00:48:20,319
Speaker 3: and talk to the biologists about sort of doing like

865
00:48:20,680 --> 00:48:24,560
Speaker 3: a crime scene investigation type work, because a lot of

866
00:48:24,600 --> 00:48:27,120
Speaker 3: times they get tunnel vision on the animal and they

867
00:48:27,160 --> 00:48:28,759
Speaker 3: go and pick up the animal and they're worried about

868
00:48:28,800 --> 00:48:31,359
Speaker 3: putting their gloves on and doing everything right. But you know,

869
00:48:32,160 --> 00:48:35,960
Speaker 3: letting us know what the situation is because that is

870
00:48:36,000 --> 00:48:39,120
Speaker 3: really a huge part of solving the puzzle. You know,

871
00:48:39,320 --> 00:48:42,759
Speaker 3: is it hot out? Is you know, is a species migrating?

872
00:48:42,880 --> 00:48:46,879
Speaker 3: Is it? You know we've been seeing sort of low

873
00:48:46,960 --> 00:48:49,520
Speaker 3: level of mortality in this area for a long time.

874
00:48:49,719 --> 00:48:53,080
Speaker 3: Or you know, we found a twenty turkeys dead and

875
00:48:53,160 --> 00:48:55,680
Speaker 3: there was a smoking tree because it got hit by lightning.

876
00:48:56,160 --> 00:48:59,880
Speaker 3: You know, those types of things are really very helpful

877
00:49:00,080 --> 00:49:03,600
Speaker 3: and getting a diagnosis because a lot of times carcasses

878
00:49:03,640 --> 00:49:05,640
Speaker 3: will come in and you know, if it's found dead,

879
00:49:05,680 --> 00:49:08,399
Speaker 3: you don't know how long this thing's been dead for.

880
00:49:09,080 --> 00:49:11,920
Speaker 3: And if it's hot out, they tend to rot really quickly,

881
00:49:11,960 --> 00:49:15,839
Speaker 3: and when that happens, the pathologist will call that autolized.

882
00:49:16,560 --> 00:49:20,440
Speaker 3: And so you know, it's different than having a pet

883
00:49:20,560 --> 00:49:23,160
Speaker 3: that might be euthanized at a clinic and then you

884
00:49:23,200 --> 00:49:26,320
Speaker 3: open it up and it's you know, fresh, these things

885
00:49:26,320 --> 00:49:27,920
Speaker 3: a lot of times we don't know how long they've

886
00:49:27,960 --> 00:49:31,920
Speaker 3: been dead for, and you know, they rot pretty quickly

887
00:49:31,960 --> 00:49:33,080
Speaker 3: depending on the conditions.

888
00:49:33,840 --> 00:49:37,120
Speaker 1: When earlier we explored the idea of being a disease ecologist,

889
00:49:37,120 --> 00:49:40,640
Speaker 1: and you talked about because you know, you're looking at

890
00:49:40,920 --> 00:49:47,759
Speaker 1: a complex situation with all these other inputs. If you're

891
00:49:47,800 --> 00:49:49,919
Speaker 1: looking at a bunch, if you're looking at a lot

892
00:49:49,960 --> 00:49:52,439
Speaker 1: of moose and you're seeing that in northern New York,

893
00:49:52,480 --> 00:49:55,880
Speaker 1: you're having a high prevalence of liver flukes in moose

894
00:49:56,400 --> 00:50:00,640
Speaker 1: you call their moose. A significant percentage of those moose

895
00:50:00,680 --> 00:50:06,319
Speaker 1: die from liver flukes. Who you're not the one that

896
00:50:07,239 --> 00:50:11,240
Speaker 1: declares that liver flukes are having a population level impact

897
00:50:11,320 --> 00:50:15,680
Speaker 1: on moose or are you or does that? Does that?

898
00:50:16,200 --> 00:50:19,480
Speaker 1: Does that information then go to down the road to

899
00:50:19,560 --> 00:50:22,520
Speaker 1: someone else who's doing population level work?

900
00:50:22,640 --> 00:50:25,480
Speaker 3: Oh no, We so we work really closely with the

901
00:50:25,520 --> 00:50:28,680
Speaker 3: New York Department of Environmental Conservation and so we have

902
00:50:28,840 --> 00:50:34,240
Speaker 3: a PhD student working on this because it's a research

903
00:50:34,520 --> 00:50:38,080
Speaker 3: project with them to try to define what's happening with

904
00:50:38,160 --> 00:50:43,000
Speaker 3: the moose population. So, yeah, the student will do population modeling,

905
00:50:43,080 --> 00:50:45,680
Speaker 3: will help out with that based on you know, the

906
00:50:45,719 --> 00:50:49,839
Speaker 3: mortality information that we have from you know, collected over

907
00:50:49,880 --> 00:50:52,239
Speaker 3: the years. She just sent me a manuscript about it.

908
00:50:54,200 --> 00:50:56,319
Speaker 3: You know, we'll we'll try to figure out what's going

909
00:50:56,360 --> 00:50:58,760
Speaker 3: on with that population and then we'll feed that directly

910
00:50:58,800 --> 00:51:01,839
Speaker 3: to the state agency so they can put it into

911
00:51:01,880 --> 00:51:04,480
Speaker 3: their moose management plan and decide how they want to proceed.

912
00:51:05,000 --> 00:51:10,040
Speaker 1: Had it been flagged prior that moose numbers are declining,

913
00:51:10,160 --> 00:51:13,040
Speaker 1: and that opened up the sort of investigation about what

914
00:51:13,120 --> 00:51:14,320
Speaker 1: is going on exactly.

915
00:51:14,360 --> 00:51:17,719
Speaker 3: So there was work done by a lot of other

916
00:51:17,800 --> 00:51:22,799
Speaker 3: researchers looking at archery, hunters, fighting logs to try to

917
00:51:22,920 --> 00:51:27,040
Speaker 3: estimate the population. There's been helicopter surveys for moose trying

918
00:51:27,040 --> 00:51:29,680
Speaker 3: to estimate the population over time and see if it's

919
00:51:29,760 --> 00:51:34,920
Speaker 3: you know, stable, declining increasing. So yeah, and then like

920
00:51:35,080 --> 00:51:37,719
Speaker 3: all the mortality tracking that we've done and trying to

921
00:51:37,760 --> 00:51:41,360
Speaker 3: determine cause of death, we did some preliminary modeling to say, okay,

922
00:51:41,400 --> 00:51:45,320
Speaker 3: we think it's juvenile moose that are being most impacted,

923
00:51:45,360 --> 00:51:48,000
Speaker 3: so those are the ones we should emphasize for putting

924
00:51:48,040 --> 00:51:50,960
Speaker 3: the callers on. You know, there was a previous helicopter

925
00:51:51,560 --> 00:51:55,000
Speaker 3: capture with adult females and with that we were able

926
00:51:55,080 --> 00:51:59,360
Speaker 3: to work with other researchers to develop a test to

927
00:51:59,400 --> 00:52:03,000
Speaker 3: look for brain worm. And when we tested those adult females,

928
00:52:03,040 --> 00:52:05,200
Speaker 3: it showed that they had exposure to brainworm, but they

929
00:52:05,239 --> 00:52:08,400
Speaker 3: obviously survived, So that kind of changed the dogma that

930
00:52:08,640 --> 00:52:11,799
Speaker 3: brainworm might be a death sentence for moose because we

931
00:52:11,880 --> 00:52:14,600
Speaker 3: think that these females got infected and they were able

932
00:52:14,640 --> 00:52:19,000
Speaker 3: to survive the infection. So yeah, trying to get a

933
00:52:19,040 --> 00:52:21,440
Speaker 3: sense of that, we knew the females were also pregnant.

934
00:52:22,440 --> 00:52:25,080
Speaker 3: The state biologists walked in on a lot of these

935
00:52:25,120 --> 00:52:28,279
Speaker 3: to actually get eyes on calves to see if they

936
00:52:28,280 --> 00:52:31,400
Speaker 3: had you know, singles or twins, to kind of get

937
00:52:31,400 --> 00:52:32,879
Speaker 3: those reproductive numbers as well.

938
00:52:32,960 --> 00:52:36,080
Speaker 1: Yep, let's talk about the ego for a minute.

939
00:52:36,120 --> 00:52:37,879
Speaker 3: Oh wait, we didn't finish winter tech though.

940
00:52:37,880 --> 00:52:39,279
Speaker 1: Oh no, okay, let's go back to winter tix.

941
00:52:39,360 --> 00:52:42,960
Speaker 3: Okay. So winter tick is a problem that they're seeing

942
00:52:43,120 --> 00:52:47,799
Speaker 3: in Maine, in New Hampshire and Vermont pretty significantly, also

943
00:52:47,920 --> 00:52:52,879
Speaker 3: killing juvenile moose, and they're trying some new management strategies

944
00:52:52,920 --> 00:52:56,319
Speaker 3: to decrease the population density of moose to see if

945
00:52:56,320 --> 00:52:59,560
Speaker 3: that slows down the transmission of winter tick. Because winter

946
00:52:59,600 --> 00:53:03,680
Speaker 3: tick aren't another thing that deer carry. That deer can manage.

947
00:53:03,800 --> 00:53:06,680
Speaker 3: They can groom themselves enough to get the winter ticks off,

948
00:53:06,719 --> 00:53:08,759
Speaker 3: but moose don't seem to be able to do that

949
00:53:09,320 --> 00:53:11,640
Speaker 3: quite as well. So when they.

950
00:53:11,239 --> 00:53:13,320
Speaker 1: Trying to lower moose numbers.

951
00:53:13,080 --> 00:53:14,520
Speaker 3: Yeah, just stop save moose.

952
00:53:14,680 --> 00:53:18,759
Speaker 1: That that's a logic that is going to be that's

953
00:53:18,760 --> 00:53:21,759
Speaker 1: a logic that's going to be challenged by a lot

954
00:53:21,800 --> 00:53:22,719
Speaker 1: of moose hunters.

955
00:53:22,920 --> 00:53:25,600
Speaker 3: Yeah. Well, I guess, I guess if you're a moose

956
00:53:25,640 --> 00:53:27,839
Speaker 3: hunter right now, it's good for you, right like, if

957
00:53:27,880 --> 00:53:29,320
Speaker 3: you're out there helping.

958
00:53:29,560 --> 00:53:33,840
Speaker 1: That's how they're doing the boring of density. But you know,

959
00:53:33,920 --> 00:53:38,400
Speaker 1: the old line, I don't know if it came from Vietnam,

960
00:53:38,520 --> 00:53:45,280
Speaker 1: but the concept of burning a village to save it, right. Uh,

961
00:53:45,719 --> 00:53:46,600
Speaker 1: that's hard for people.

962
00:53:46,760 --> 00:53:48,279
Speaker 3: Oh, I know, I deal with that all the time

963
00:53:48,280 --> 00:53:52,719
Speaker 3: with gronic wasting disease. Yeah, but in New York, we

964
00:53:52,840 --> 00:53:56,160
Speaker 3: hadn't seen winter tick the same way that other Northeast

965
00:53:56,160 --> 00:54:01,120
Speaker 3: states had until recently. So with some of the captured

966
00:54:01,200 --> 00:54:04,880
Speaker 3: moose when they do assessments for winter ticks, so they

967
00:54:04,920 --> 00:54:08,200
Speaker 3: look for them on live animals, and they found more

968
00:54:08,239 --> 00:54:11,560
Speaker 3: than we had seen previously. And we're starting to get

969
00:54:11,600 --> 00:54:17,280
Speaker 3: some game cam pictures in that looks like the typical

970
00:54:17,320 --> 00:54:19,759
Speaker 3: hair loss pattern that you see on moose where they're

971
00:54:19,760 --> 00:54:23,320
Speaker 3: missing hair on the shoulder. And some of the states

972
00:54:23,320 --> 00:54:27,160
Speaker 3: that have it pretty significantly, like Maine and Minnesota, they

973
00:54:27,160 --> 00:54:29,399
Speaker 3: actually call them ghost moose where they've lost so much

974
00:54:29,440 --> 00:54:32,120
Speaker 3: hair that they appear more white because they've rubbed off

975
00:54:32,320 --> 00:54:33,720
Speaker 3: all those guard hairs and everything.

976
00:54:33,800 --> 00:54:42,120
Speaker 1: Really, yeah, let's say you knew, not that you don't know.

977
00:54:42,760 --> 00:54:46,400
Speaker 1: Let's say we know that has a population level impact,

978
00:54:47,560 --> 00:54:49,919
Speaker 1: or the brain worm for instance, or either one of these.

979
00:54:51,280 --> 00:54:54,200
Speaker 1: There's nothing I mean, is there is there any control

980
00:54:54,360 --> 00:54:57,800
Speaker 1: mechanism that would wind up being helpful?

981
00:54:58,440 --> 00:55:00,839
Speaker 3: Well, if you didn't have as many white tail deer

982
00:55:01,120 --> 00:55:02,960
Speaker 3: in moose territory that would be helpful.

983
00:55:03,160 --> 00:55:03,759
Speaker 1: Yeah.

984
00:55:04,080 --> 00:55:10,719
Speaker 3: So, I mean Maine is trying really hard to talk

985
00:55:10,760 --> 00:55:14,840
Speaker 3: about chronic waste and disease and deer numbers and moose

986
00:55:15,719 --> 00:55:17,960
Speaker 3: because a lot of the towns there have started feeding

987
00:55:18,000 --> 00:55:21,000
Speaker 3: programs and those are really hard to stop once they

988
00:55:21,040 --> 00:55:23,480
Speaker 3: get started, because then you have too many deer and

989
00:55:23,520 --> 00:55:25,680
Speaker 3: then you know, people obviously don't want to see them

990
00:55:25,680 --> 00:55:28,240
Speaker 3: starving in winter, so they keep feeding and keep feeding,

991
00:55:28,280 --> 00:55:32,120
Speaker 3: and so then you just can't get people to stop

992
00:55:32,160 --> 00:55:33,800
Speaker 3: it because they don't want the animals to die.

993
00:55:34,160 --> 00:55:39,000
Speaker 1: You know. In Alaska, in eastern Alaska, they have like

994
00:55:39,239 --> 00:55:44,120
Speaker 1: basically a shoot on site policy for meal deer coming

995
00:55:44,160 --> 00:55:49,640
Speaker 1: into Alaska. Yeah, not welcome right around disease issues, right, yep,

996
00:55:50,000 --> 00:55:57,560
Speaker 1: being that those deer are getting there due to human

997
00:55:58,200 --> 00:56:02,439
Speaker 1: induced habitat changes yep. Following road systems, right.

998
00:56:02,640 --> 00:56:04,759
Speaker 3: I mean you see that west with white tails edge

999
00:56:04,800 --> 00:56:07,000
Speaker 3: habitats right, following rivers.

1000
00:56:06,800 --> 00:56:10,320
Speaker 1: Up, Yeah, and just and it's changed the policy on

1001
00:56:10,400 --> 00:56:13,360
Speaker 1: it's changed mildly, but basically it's like if you see it,

1002
00:56:13,440 --> 00:56:15,279
Speaker 1: kill it, yeah, like not wanting the man.

1003
00:56:15,560 --> 00:56:19,880
Speaker 3: I know, it's it's challenging because we've you know, I

1004
00:56:19,920 --> 00:56:24,080
Speaker 3: think people forget that we've altered habitats so significantly and

1005
00:56:24,400 --> 00:56:27,280
Speaker 3: have an impact, and that we're not We're not separate

1006
00:56:27,320 --> 00:56:31,920
Speaker 3: from nature. And you know, I hunt deer and you know,

1007
00:56:32,040 --> 00:56:34,239
Speaker 3: eat a lot of venison. But in a lot of

1008
00:56:34,239 --> 00:56:36,920
Speaker 3: places there's deer densities that are way too high, and

1009
00:56:37,200 --> 00:56:39,400
Speaker 3: they're problematic for a lot of different reasons, you know,

1010
00:56:39,520 --> 00:56:43,719
Speaker 3: chronic wasting disease being one of them. So, uh, if

1011
00:56:43,840 --> 00:56:47,560
Speaker 3: there are ways to lower deer densities or not encourage

1012
00:56:47,600 --> 00:56:51,440
Speaker 3: them to encroach in new areas, that's something that I

1013
00:56:51,480 --> 00:56:52,760
Speaker 3: think hunters should be behind.

1014
00:56:55,360 --> 00:56:58,000
Speaker 1: Can we talk about eagles? Yes, go ahead, you guys

1015
00:56:58,000 --> 00:56:58,479
Speaker 1: had an eagle.

1016
00:56:58,560 --> 00:56:59,280
Speaker 3: We had an eagle.

1017
00:57:00,160 --> 00:57:02,839
Speaker 1: Your interests we're looking. We're looking at an eagle. They

1018
00:57:02,840 --> 00:57:07,120
Speaker 1: hadn't begun the knee cropsy on it yet. I asked

1019
00:57:07,719 --> 00:57:09,600
Speaker 1: if eagle I could see if eagles were still in

1020
00:57:09,719 --> 00:57:15,640
Speaker 1: EESA species and Endangered Species Act Endangered Species Act, Protected Species,

1021
00:57:15,640 --> 00:57:17,640
Speaker 1: I could see that like every eagle that died, you'd

1022
00:57:17,680 --> 00:57:18,880
Speaker 1: want to take a gander at it.

1023
00:57:19,000 --> 00:57:20,240
Speaker 3: That's how it was for years, and.

1024
00:57:21,720 --> 00:57:26,440
Speaker 1: That's not the case anymore. Eagles are recovered, Eagle populations

1025
00:57:26,440 --> 00:57:31,480
Speaker 1: are increasing. What is why are you guys interest in eagles?

1026
00:57:32,320 --> 00:57:33,560
Speaker 1: And then I want to get to the turtle.

1027
00:57:33,840 --> 00:57:36,160
Speaker 4: Okay, we'll talk eagles.

1028
00:57:37,160 --> 00:57:38,640
Speaker 1: What's of interest about eagles?

1029
00:57:38,960 --> 00:57:43,400
Speaker 3: So, eagles are good indicator species for a lot of

1030
00:57:43,440 --> 00:57:45,800
Speaker 3: things because the public pays attention to them. You know,

1031
00:57:45,840 --> 00:57:48,600
Speaker 3: if people find a bald eagle, they feel compelled to

1032
00:57:48,680 --> 00:57:50,320
Speaker 3: report it, so they.

1033
00:57:50,160 --> 00:57:52,760
Speaker 1: Feel compelled to tell you that they're seeing one. When

1034
00:57:52,760 --> 00:57:54,760
Speaker 1: I have friends come up to my place in Alaska,

1035
00:57:55,280 --> 00:57:58,400
Speaker 1: I have to wait a few days for them to

1036
00:57:58,440 --> 00:58:00,840
Speaker 1: stop reporting to me that there's eagles.

1037
00:58:01,320 --> 00:58:03,200
Speaker 3: Oh, I've got to go to eagle in Alaska store.

1038
00:58:03,720 --> 00:58:05,479
Speaker 1: You know what, Wait a minute, and there'll be twenty

1039
00:58:05,560 --> 00:58:07,560
Speaker 1: in a tree. And at some point on this trip

1040
00:58:07,560 --> 00:58:10,320
Speaker 1: you're going to stop observing them. They're just there.

1041
00:58:11,320 --> 00:58:15,520
Speaker 3: I had in my previous job with the US Geological

1042
00:58:15,520 --> 00:58:17,520
Speaker 3: Survey at the National Wildlife Health Center. I would hear

1043
00:58:17,520 --> 00:58:20,800
Speaker 3: about these mass mortality events and do investigations on them,

1044
00:58:21,320 --> 00:58:25,320
Speaker 3: and one of them was about all these bald eagles

1045
00:58:25,360 --> 00:58:30,120
Speaker 3: in Alaska where if a truck leaves a fish processing facility,

1046
00:58:30,160 --> 00:58:32,960
Speaker 3: it needs to be covered because if it's not, the

1047
00:58:33,000 --> 00:58:35,600
Speaker 3: eagles will start dive bombing it to get all the

1048
00:58:35,640 --> 00:58:40,240
Speaker 3: fish parts and so one left and was not covered.

1049
00:58:40,280 --> 00:58:43,120
Speaker 3: Eagles started going into it and just kept like piling

1050
00:58:43,200 --> 00:58:45,360
Speaker 3: in on top of each other, and like twenty bald

1051
00:58:45,360 --> 00:58:49,200
Speaker 3: eagles died in this fish truck, just mothering each other,

1052
00:58:49,400 --> 00:58:50,040
Speaker 3: like trying to get.

1053
00:58:49,920 --> 00:58:53,240
Speaker 1: To thunds after a soccer tournament. Yeah, yeah, so like

1054
00:58:53,280 --> 00:58:56,480
Speaker 1: a stampede. It really.

1055
00:58:56,640 --> 00:59:00,200
Speaker 3: Yeah, so a bunch of eagles died just smashing each

1056
00:59:00,200 --> 00:59:02,160
Speaker 3: other trying to get to the fish parts. God, it's

1057
00:59:02,200 --> 00:59:07,200
Speaker 3: so like uneagle, Like everybody has these ideas of eagles

1058
00:59:07,240 --> 00:59:08,400
Speaker 3: being so majestic.

1059
00:59:10,400 --> 00:59:10,920
Speaker 1: Didn't like him.

1060
00:59:11,600 --> 00:59:13,560
Speaker 3: He wanted the turkey, right, Well.

1061
00:59:13,200 --> 00:59:17,840
Speaker 1: That's a myth, but he was being uh, he was

1062
00:59:17,920 --> 00:59:21,680
Speaker 1: very disappointed the eagle, and he when he threw the

1063
00:59:21,680 --> 00:59:24,680
Speaker 1: turkey thing, he was disappoint because the eagles are scavengers,

1064
00:59:24,760 --> 00:59:26,960
Speaker 1: and you know, I didn't think it was a great

1065
00:59:27,000 --> 00:59:31,680
Speaker 1: symbol for the country and the way most historians regard

1066
00:59:31,760 --> 00:59:35,080
Speaker 1: his comment about the wild turkeys. He was saying basically

1067
00:59:35,200 --> 00:59:39,000
Speaker 1: was like, puh, might as well do the wild turkey. Okay,

1068
00:59:39,080 --> 00:59:44,600
Speaker 1: at least he's vain, right, Yeah, it wasn't. It's not.

1069
00:59:45,120 --> 00:59:47,720
Speaker 1: It's but most people are.

1070
00:59:48,040 --> 00:59:49,920
Speaker 3: They don't get the attitude with it.

1071
00:59:50,440 --> 00:59:55,040
Speaker 1: Because he didn't write down. But you know that is implied.

1072
00:59:56,360 --> 00:59:59,000
Speaker 1: Just make it the turkey. At least he's vain. But

1073
00:59:59,040 --> 01:00:03,040
Speaker 1: it wasn't. They think it was in uh In the

1074
01:00:03,080 --> 01:00:06,000
Speaker 1: suggestion wasn't jestice, but he was legitimately not happy with

1075
01:00:06,040 --> 01:00:06,440
Speaker 1: the eagle.

1076
01:00:06,640 --> 01:00:06,960
Speaker 3: Okay.

1077
01:00:07,200 --> 01:00:09,400
Speaker 1: Well, yeah, a scavenger bird.

1078
01:00:09,320 --> 01:00:12,280
Speaker 3: Right, And we see like conspecific trauma all the time,

1079
01:00:12,360 --> 01:00:15,960
Speaker 3: like eagles puncturing each other from fighting and things like that.

1080
01:00:16,200 --> 01:00:17,760
Speaker 3: So yeah, I mean.

1081
01:00:17,720 --> 01:00:20,760
Speaker 1: But they're a good indicator species, right because people taking

1082
01:00:20,760 --> 01:00:22,480
Speaker 1: hovere of seb them exactly. And I did. Man if

1083
01:00:22,520 --> 01:00:24,200
Speaker 1: I was walking down the road and I saw a dead.

1084
01:00:24,040 --> 01:00:26,200
Speaker 4: Eagle, you do something I would.

1085
01:00:26,520 --> 01:00:28,040
Speaker 1: Let's say I was walking on the road and on

1086
01:00:28,040 --> 01:00:29,760
Speaker 1: the right hand side of the road is a dead eagle.

1087
01:00:29,800 --> 01:00:31,280
Speaker 1: In the left hand side of the road is a

1088
01:00:31,320 --> 01:00:33,360
Speaker 1: dead buzzard. I'm gonna pay way more attention to the

1089
01:00:33,360 --> 01:00:33,800
Speaker 1: dead eagle.

1090
01:00:33,920 --> 01:00:34,120
Speaker 4: I know.

1091
01:00:34,160 --> 01:00:35,760
Speaker 3: Buzzards don't get any love to.

1092
01:00:35,720 --> 01:00:37,480
Speaker 1: The point where I might even notify someone about it

1093
01:00:38,000 --> 01:00:39,040
Speaker 1: because I would think of.

1094
01:00:39,080 --> 01:00:40,840
Speaker 4: Someone shooting eagles exactly.

1095
01:00:41,040 --> 01:00:43,560
Speaker 1: They pissed at eagles, people having that eagle.

1096
01:00:44,000 --> 01:00:58,560
Speaker 3: Yeah, So we're looking at them for a couple different reasons.

1097
01:00:58,600 --> 01:01:01,520
Speaker 3: One of them is just to keep that historic trend

1098
01:01:01,840 --> 01:01:04,200
Speaker 3: going where we want to keep an eye on what's

1099
01:01:04,240 --> 01:01:09,800
Speaker 3: happening with them. I mentioned we just finished a study

1100
01:01:09,840 --> 01:01:12,640
Speaker 3: a few years ago looking at bald eagles and lead,

1101
01:01:13,280 --> 01:01:18,560
Speaker 3: and so we gathered data from seven different Northeast states

1102
01:01:19,120 --> 01:01:24,920
Speaker 3: and it was diagnostic labs, rehabers, you know, state agencies

1103
01:01:25,440 --> 01:01:28,080
Speaker 3: and put all that together to answer that exact question,

1104
01:01:28,360 --> 01:01:32,200
Speaker 3: you know, does lead have a population level impact on eagles?

1105
01:01:32,760 --> 01:01:36,800
Speaker 3: And so we put that together and then did some

1106
01:01:37,040 --> 01:01:41,880
Speaker 3: very fancy mathematics with my research associate, Brenda Hanley, and

1107
01:01:42,680 --> 01:01:46,400
Speaker 3: figured out that led you know, we all know eagles

1108
01:01:46,400 --> 01:01:49,560
Speaker 3: have recovered, like there's no debate about that, but the

1109
01:01:49,640 --> 01:01:54,080
Speaker 3: long term growth rate of eagles was depressed by three

1110
01:01:54,080 --> 01:01:58,600
Speaker 3: to five percent by lead poisoning. And so it wasn't

1111
01:01:58,680 --> 01:02:01,360
Speaker 3: that it stopped the the recovery. It was just like

1112
01:02:01,640 --> 01:02:04,600
Speaker 3: driving a car forward with your foot on the brake.

1113
01:02:04,680 --> 01:02:08,480
Speaker 3: So it just kind of you know, didn't didn't make

1114
01:02:08,520 --> 01:02:12,320
Speaker 3: it optimal, and so we used a bunch.

1115
01:02:12,080 --> 01:02:14,440
Speaker 1: Of slowing like slowing recovery.

1116
01:02:14,600 --> 01:02:19,760
Speaker 3: Yeah, so it it still happened, but it would have

1117
01:02:19,760 --> 01:02:23,640
Speaker 3: been more efficient if eagles weren't getting lead poisoning.

1118
01:02:24,000 --> 01:02:30,000
Speaker 1: So now would there be uh, we were talking about

1119
01:02:30,000 --> 01:02:32,920
Speaker 1: trigger warnings. I'm going to give it. I'm going to

1120
01:02:32,960 --> 01:02:34,440
Speaker 1: issue my first ever trigger warning.

1121
01:02:34,560 --> 01:02:37,200
Speaker 3: Really should I do I get like a plaque or something.

1122
01:02:37,320 --> 01:02:38,400
Speaker 1: No, you're not cann get triggered.

1123
01:02:38,600 --> 01:02:40,800
Speaker 4: Okay, but this is I was I was.

1124
01:02:40,720 --> 01:02:48,600
Speaker 1: Trying to see clarification about what. You know, I heard

1125
01:02:48,600 --> 01:02:50,000
Speaker 1: it all the time. I never knew what the element

1126
01:02:50,800 --> 01:02:52,680
Speaker 1: it would be. You notify someone if they were going

1127
01:02:52,760 --> 01:02:56,480
Speaker 1: to hear something upset. People get riled up about this subject.

1128
01:02:56,760 --> 01:02:57,040
Speaker 3: I know.

1129
01:02:57,200 --> 01:03:02,439
Speaker 1: You know, when we're talking about lead in birds, it's

1130
01:03:03,080 --> 01:03:04,160
Speaker 1: we're talking about bullets.

1131
01:03:04,280 --> 01:03:04,560
Speaker 3: I know.

1132
01:03:05,240 --> 01:03:07,560
Speaker 1: Okay, So for you people that are going to get

1133
01:03:07,600 --> 01:03:11,800
Speaker 1: all one way, like, no matter what, you're gonna if

1134
01:03:11,840 --> 01:03:15,040
Speaker 1: you hate lead ammo, if you love let ammo, here's

1135
01:03:15,040 --> 01:03:18,439
Speaker 1: a trigger warning, okay, because you're gonna hear yourself.

1136
01:03:18,080 --> 01:03:20,400
Speaker 3: To discuss something I know, right.

1137
01:03:20,880 --> 01:03:23,120
Speaker 1: And like, there's very few people who just want to

1138
01:03:23,160 --> 01:03:28,439
Speaker 1: discuss things. Not very few. There's a lot of people

1139
01:03:28,440 --> 01:03:30,080
Speaker 1: who just don't even want to talk about ship.

1140
01:03:30,200 --> 01:03:34,000
Speaker 3: Oh I know, yeah, I know, my dad not interested

1141
01:03:34,080 --> 01:03:35,480
Speaker 3: in discussing this whatsoever.

1142
01:03:36,360 --> 01:03:38,480
Speaker 1: They'd rather just be that I don't want to talk

1143
01:03:38,480 --> 01:03:41,640
Speaker 1: about it. But we're going to talk about it so,

1144
01:03:42,240 --> 01:03:44,400
Speaker 1: and I'll and I'll do my best to throw in

1145
01:03:44,720 --> 01:03:47,840
Speaker 1: I'll do my best to interject, like, well, what about isms?

1146
01:03:48,080 --> 01:03:54,680
Speaker 4: Okay, we're talking about bullet lead, Yes.

1147
01:03:55,080 --> 01:04:01,200
Speaker 1: And birds get bullet lead by eating shit that's been

1148
01:04:01,200 --> 01:04:05,800
Speaker 1: shot with a bullet. With a bullet. So you go,

1149
01:04:07,320 --> 01:04:12,840
Speaker 1: you get a deer, say you shoot a deer hid

1150
01:04:12,920 --> 01:04:15,960
Speaker 1: in the shoulder blade. You're using some kind of you know,

1151
01:04:16,360 --> 01:04:19,120
Speaker 1: jacketed bullet, some kind of bonded bullet. Whatever, it's a

1152
01:04:19,280 --> 01:04:28,400
Speaker 1: it's it's probably copper sheathed around of small what what

1153
01:04:28,760 --> 01:04:31,920
Speaker 1: what would strike you as a very small monol lead? Okay,

1154
01:04:32,000 --> 01:04:35,000
Speaker 1: so most bullets have there's a lot of different bullet construction.

1155
01:04:35,120 --> 01:04:38,000
Speaker 1: But if we're going to divide two big game, big

1156
01:04:38,000 --> 01:04:41,680
Speaker 1: game bullets into two classifications for our purposes here, we

1157
01:04:41,680 --> 01:04:45,560
Speaker 1: would talk about like monolithic, meaning that they're all copper

1158
01:04:48,040 --> 01:04:51,040
Speaker 1: or there are some combination of copper and lead. There

1159
01:04:51,080 --> 01:04:53,400
Speaker 1: was a time when it was just lead, but they've

1160
01:04:53,440 --> 01:04:56,560
Speaker 1: mostly most bullets are jacketed. So you shoot a deer

1161
01:04:56,720 --> 01:05:00,560
Speaker 1: hit in the shoulder blade, that bullet breaks up, sends

1162
01:05:00,560 --> 01:05:04,640
Speaker 1: little bits of stuff here and there, deer dies. Everybody's

1163
01:05:04,640 --> 01:05:08,760
Speaker 1: happy you except for the deer. Yeah, deer's dead. Everybody

1164
01:05:08,760 --> 01:05:13,280
Speaker 1: else is happy. You got the deer. Maybe while you're

1165
01:05:13,280 --> 01:05:18,760
Speaker 1: gutting the deer, you uh, you have some damage, some

1166
01:05:18,800 --> 01:05:22,840
Speaker 1: severe damage on the opposing shoulder. You cut that out

1167
01:05:22,840 --> 01:05:27,040
Speaker 1: and don't even bring that home. Later you butcher your

1168
01:05:27,040 --> 01:05:30,160
Speaker 1: deer at home. There's some parts that are shot up

1169
01:05:30,360 --> 01:05:33,120
Speaker 1: that you don't want. You take those and you go

1170
01:05:33,160 --> 01:05:35,720
Speaker 1: out and put them out back because you like to

1171
01:05:35,760 --> 01:05:40,360
Speaker 1: watch birds and whatnot. Whatever you dump in a ditch somewhere,

1172
01:05:40,480 --> 01:05:43,720
Speaker 1: whatever you do, and it winds up being introduced into

1173
01:05:43,760 --> 01:05:48,480
Speaker 1: the food chain in the wild. Things consume it and

1174
01:05:48,520 --> 01:05:50,880
Speaker 1: they can get let And if you're like we had

1175
01:05:50,960 --> 01:05:55,120
Speaker 1: mentioned earlier in a bald eagle whose specialty is scavenging, right,

1176
01:05:55,800 --> 01:05:59,800
Speaker 1: and your long lived bird, and you get tuned into

1177
01:05:59,800 --> 01:06:02,960
Speaker 1: deer gut piles, and you're like most things where you

1178
01:06:03,000 --> 01:06:06,680
Speaker 1: realize that a great place to there's certain great places

1179
01:06:06,680 --> 01:06:08,920
Speaker 1: to hang out in the fall because that's going on.

1180
01:06:09,440 --> 01:06:12,200
Speaker 1: I remember to give an example of how would animals

1181
01:06:12,200 --> 01:06:15,560
Speaker 1: ever tune into this. I remember hearing about this project

1182
01:06:15,600 --> 01:06:21,320
Speaker 1: where there's a particular highway in northern Montana where there's

1183
01:06:21,400 --> 01:06:24,280
Speaker 1: really high white tailed deer populations, and it would actually

1184
01:06:24,400 --> 01:06:29,520
Speaker 1: impact Golden Eagle distribution that there were eagles who would

1185
01:06:29,560 --> 01:06:32,400
Speaker 1: spend their winter they were specially they were roadkill specialists.

1186
01:06:33,320 --> 01:06:35,400
Speaker 1: It would spend their winter around ninety three.

1187
01:06:36,400 --> 01:06:37,240
Speaker 4: Yeah, psycha.

1188
01:06:37,360 --> 01:06:40,680
Speaker 1: You could make a living all winter flying up and

1189
01:06:40,720 --> 01:06:43,920
Speaker 1: down and picking off eat one carcass, go to the

1190
01:06:43,960 --> 01:06:47,160
Speaker 1: next carcass. So you're an eagle, you live a long time.

1191
01:06:47,560 --> 01:06:50,880
Speaker 1: Over the course of your lifetime, you'd enough little tidbits

1192
01:06:50,920 --> 01:06:56,240
Speaker 1: of lead that you get a lead poisoning, you get

1193
01:06:56,240 --> 01:06:58,080
Speaker 1: a fatal mono lettin. You're shaking your head.

1194
01:06:58,120 --> 01:07:01,760
Speaker 3: No, well potentially, yeah, But the way we looked at

1195
01:07:01,760 --> 01:07:06,600
Speaker 3: it was so there would be eagles that didn't have

1196
01:07:06,680 --> 01:07:10,600
Speaker 3: any lead on board, then there would be exposed eagles

1197
01:07:10,840 --> 01:07:12,480
Speaker 3: that had, you know, a little bit. And we have

1198
01:07:12,560 --> 01:07:15,880
Speaker 3: problems sometimes people talk about like chronic and acute, which

1199
01:07:15,920 --> 01:07:18,400
Speaker 3: you're kind of talking about like chronic over the course

1200
01:07:18,440 --> 01:07:24,400
Speaker 3: of your lifetime, and then there's sort of levels that

1201
01:07:24,840 --> 01:07:26,640
Speaker 3: are enough to kill.

1202
01:07:26,440 --> 01:07:28,120
Speaker 4: You over in a dose.

1203
01:07:28,360 --> 01:07:31,840
Speaker 3: Yeah, nice, and then that would be like lead poisoning.

1204
01:07:32,320 --> 01:07:35,720
Speaker 3: And so people would often term like if you get

1205
01:07:35,800 --> 01:07:38,520
Speaker 3: a big helping like if you ease lead sinker or

1206
01:07:38,560 --> 01:07:41,840
Speaker 3: something that would be acute lead poisoning and that would

1207
01:07:41,840 --> 01:07:44,040
Speaker 3: be a level you know, enough to kill you.

1208
01:07:44,320 --> 01:07:46,200
Speaker 1: So dying from a single meal.

1209
01:07:46,600 --> 01:07:49,800
Speaker 3: Yeah, we can't tell that the way that we get

1210
01:07:49,800 --> 01:07:52,360
Speaker 3: the data, So we don't know if they accumulate enough

1211
01:07:52,440 --> 01:07:55,400
Speaker 3: lead over time to kill you know, to get to

1212
01:07:55,440 --> 01:07:57,960
Speaker 3: that level, or if they just eat, you know, happen

1213
01:07:58,040 --> 01:08:01,400
Speaker 3: to get a fragment that's big enough. Because a lead

1214
01:08:01,400 --> 01:08:04,120
Speaker 3: fragment a small grain of rice is enough to kill

1215
01:08:04,160 --> 01:08:07,320
Speaker 3: an eagle because they have a very acidic digestive system

1216
01:08:07,880 --> 01:08:11,520
Speaker 3: and so they break that down a lot more than

1217
01:08:11,600 --> 01:08:14,200
Speaker 3: like people eating lead. You know, it tends to pass

1218
01:08:14,280 --> 01:08:17,479
Speaker 3: through and not get as digested as it might in

1219
01:08:17,560 --> 01:08:18,040
Speaker 3: an eagle.

1220
01:08:18,520 --> 01:08:20,599
Speaker 1: See, that's the thing I'll hear from people is they'll

1221
01:08:20,640 --> 01:08:23,080
Speaker 1: say that that lad and a lot of things that

1222
01:08:23,200 --> 01:08:26,320
Speaker 1: lead just passes through and you're not breaking it down.

1223
01:08:26,600 --> 01:08:29,639
Speaker 3: But we did. We're doing a study now looking at

1224
01:08:29,640 --> 01:08:32,960
Speaker 3: fissure so mammalian scavengers to try to figure out, you know,

1225
01:08:33,439 --> 01:08:37,759
Speaker 3: our other animals getting led from what we term attractive

1226
01:08:37,840 --> 01:08:41,400
Speaker 3: nuisances like a gut pile. And I want to say

1227
01:08:41,520 --> 01:08:45,880
Speaker 3: it's like seventy percent of the fissures we've texted from

1228
01:08:46,080 --> 01:08:50,360
Speaker 3: hunter from trapped animals had some levels of lead in them.

1229
01:08:50,600 --> 01:08:53,240
Speaker 3: So a lot of fishers are getting.

1230
01:08:53,160 --> 01:08:56,160
Speaker 1: Lead to some level led in them in what way

1231
01:08:57,080 --> 01:09:00,360
Speaker 1: from scavenging? No, no, but detected where in them?

1232
01:09:00,400 --> 01:09:03,760
Speaker 3: In their liver? Okay, So we asked trappers did to

1233
01:09:03,800 --> 01:09:06,840
Speaker 3: give us carcasses. We took out the livers and tested them.

1234
01:09:06,880 --> 01:09:10,599
Speaker 1: And you incinerate the liver and then it leaves behind

1235
01:09:10,760 --> 01:09:14,200
Speaker 1: these metal residues. Ye, you can find the lead. And

1236
01:09:14,280 --> 01:09:16,120
Speaker 1: how much lead might you get out of a fisher's liver?

1237
01:09:16,880 --> 01:09:20,160
Speaker 3: They were like just trace amount, so not not super high,

1238
01:09:20,240 --> 01:09:22,919
Speaker 3: not enough to kill them obviously because they were trapped animals.

1239
01:09:23,240 --> 01:09:26,760
Speaker 1: And I'm not trying to like create like a suspicion

1240
01:09:26,760 --> 01:09:31,320
Speaker 1: of doubt, but what else might they have done to

1241
01:09:31,320 --> 01:09:32,920
Speaker 1: get that lead? Is there any other.

1242
01:09:33,439 --> 01:09:38,280
Speaker 3: I mean, people throw out ideas like tire weights, you know,

1243
01:09:38,320 --> 01:09:40,559
Speaker 3: if there's tire weights that have been thrown off tires

1244
01:09:40,600 --> 01:09:46,080
Speaker 3: alongside the chewing on that potentially discarded batteries.

1245
01:09:46,479 --> 01:09:47,439
Speaker 1: And they're chewing on that.

1246
01:09:48,520 --> 01:09:50,920
Speaker 3: I don't but these are the things that people.

1247
01:09:50,680 --> 01:09:53,799
Speaker 1: To create a suspicion or to throw out some possible

1248
01:09:53,800 --> 01:09:55,000
Speaker 1: other explanations exactly.

1249
01:09:55,040 --> 01:09:58,479
Speaker 3: And so for some of the eagle stuff, we did

1250
01:09:58,520 --> 01:10:03,040
Speaker 3: try to look at the isotopes, the lead isotopes to

1251
01:10:03,120 --> 01:10:05,240
Speaker 3: figure out is it coming from bullets or is it

1252
01:10:05,280 --> 01:10:08,640
Speaker 3: coming from you know, lead paint chips or whatever. And

1253
01:10:08,680 --> 01:10:13,719
Speaker 3: so we did that analysis. But apparently because there's such

1254
01:10:14,320 --> 01:10:18,240
Speaker 3: a limited amount of lead smelting anymore, that a lot

1255
01:10:18,280 --> 01:10:21,759
Speaker 3: of the lead is recycled, so you can't tell where

1256
01:10:21,800 --> 01:10:25,160
Speaker 3: that lead actually came from. So we weren't able to

1257
01:10:25,479 --> 01:10:26,200
Speaker 3: identify it.

1258
01:10:26,240 --> 01:10:29,240
Speaker 1: But you know, logic bullet for bullet making, that's all

1259
01:10:29,320 --> 01:10:31,559
Speaker 1: recycled lead, right, so all from batteries.

1260
01:10:32,680 --> 01:10:35,280
Speaker 3: Yeah, we did try, but it wasn't helpful.

1261
01:10:35,400 --> 01:10:38,000
Speaker 1: Here's the thing people say, and this isn't your area,

1262
01:10:38,120 --> 01:10:40,920
Speaker 1: but this is a thing people would point to. Here

1263
01:10:41,000 --> 01:10:44,400
Speaker 1: you have that. Let's take the eagle example, and the

1264
01:10:44,439 --> 01:10:46,360
Speaker 1: eagle we look at today will have we'll be tested

1265
01:10:46,360 --> 01:10:52,640
Speaker 1: for lead. Yes, Okay, there's an argument that it's not

1266
01:10:52,680 --> 01:10:54,439
Speaker 1: an argument you can ignore really because it's a it's

1267
01:10:54,479 --> 01:11:00,599
Speaker 1: a sound argument. Be eagles were endangered, and they were

1268
01:11:00,640 --> 01:11:04,599
Speaker 1: endangered because of DDT Okay, they were endangered because of

1269
01:11:04,640 --> 01:11:08,439
Speaker 1: a pesticide that would cause their shells to be very thin,

1270
01:11:09,280 --> 01:11:12,160
Speaker 1: and they would kill their own shells, they'd break their

1271
01:11:12,200 --> 01:11:17,320
Speaker 1: own eggs when they incubated them. That's simplified, but that's

1272
01:11:17,320 --> 01:11:21,760
Speaker 1: what happened with eagles. The use of DDT was prohibited,

1273
01:11:22,760 --> 01:11:27,400
Speaker 1: eagle numbers started to increase. They were one of these few,

1274
01:11:27,479 --> 01:11:29,200
Speaker 1: I think one of I don't know what it is.

1275
01:11:29,320 --> 01:11:31,679
Speaker 1: Two percent of the species that go on the endangered

1276
01:11:31,720 --> 01:11:35,599
Speaker 1: Species list come off because of recovery. It's not very common.

1277
01:11:37,680 --> 01:11:41,800
Speaker 1: Some come off because they're gone, they go extinct. Some

1278
01:11:41,880 --> 01:11:44,240
Speaker 1: come off because we realized that they didn't warrant being

1279
01:11:44,280 --> 01:11:51,160
Speaker 1: on there anyway. But it's a rare thing to hit recovery,

1280
01:11:51,160 --> 01:11:53,439
Speaker 1: and it's always celebrated to hit recovery. The peregrine, faulk

1281
01:11:53,479 --> 01:11:56,719
Speaker 1: and bald ego actually hit recovery and now their numbers

1282
01:11:56,720 --> 01:11:59,840
Speaker 1: are growing. And someone would point to and say it, well,

1283
01:12:00,920 --> 01:12:06,640
Speaker 1: bullet lead, Yes, they're growing, but bullet lead is slowing

1284
01:12:06,760 --> 01:12:13,800
Speaker 1: the growth. And an argument, and it's true, would be

1285
01:12:16,000 --> 01:12:18,880
Speaker 1: there are many kinds of human cause mortality that are

1286
01:12:18,920 --> 01:12:25,920
Speaker 1: slowing recovery. Vehicles like they're increasing, Yes, would they be

1287
01:12:26,160 --> 01:12:32,000
Speaker 1: increasing faster without vehicles? Sure? Would they be increasing faster

1288
01:12:32,120 --> 01:12:36,240
Speaker 1: without power lines? Would they be increasing faster without X,

1289
01:12:36,360 --> 01:12:39,719
Speaker 1: Y and z? So there are many human cause things

1290
01:12:39,720 --> 01:12:44,599
Speaker 1: that are slowing the increase, but the increase is there,

1291
01:12:45,360 --> 01:12:49,040
Speaker 1: and they would argue this is an acceptable level. This

1292
01:12:49,080 --> 01:12:52,840
Speaker 1: is how the argument goes. It's a you know, like

1293
01:12:52,920 --> 01:12:57,120
Speaker 1: I said, can't be ignored. We as a species have

1294
01:12:57,200 --> 01:13:02,080
Speaker 1: an accepted level of human mortality. Meaning that's like they're growing.

1295
01:13:02,320 --> 01:13:03,920
Speaker 1: There are certain there's a bunch of things you could

1296
01:13:03,920 --> 01:13:06,680
Speaker 1: do to make them grow faster, but we're not going

1297
01:13:06,760 --> 01:13:09,840
Speaker 1: to do all those things because there's an inconvenience level.

1298
01:13:10,120 --> 01:13:13,360
Speaker 3: Well that I think that's the rub right there, is

1299
01:13:13,400 --> 01:13:17,960
Speaker 3: that if we're talking about lead from bullets and hunters

1300
01:13:18,560 --> 01:13:23,480
Speaker 3: are hunting because you know they're conservationists, they love the animals,

1301
01:13:24,320 --> 01:13:28,320
Speaker 3: to think about that you're causing you know, if you

1302
01:13:28,320 --> 01:13:31,519
Speaker 3: think about all the rules for shooting and know your target,

1303
01:13:31,520 --> 01:13:34,280
Speaker 3: what's beyond and one bullet one kill all those types

1304
01:13:34,320 --> 01:13:38,200
Speaker 3: of things. If you're leaving something out in the environment

1305
01:13:38,280 --> 01:13:41,680
Speaker 3: that's killing other animals that you didn't intend to know

1306
01:13:41,800 --> 01:13:45,280
Speaker 3: that that might be happening, I think is something that

1307
01:13:45,720 --> 01:13:48,800
Speaker 3: hunters should should know about. And you know, I'm not

1308
01:13:49,160 --> 01:13:52,200
Speaker 3: sitting here saying don't use lead bullets, but knowing that

1309
01:13:52,240 --> 01:13:57,000
Speaker 3: this is happening to other species, and if people want

1310
01:13:57,040 --> 01:14:00,760
Speaker 3: to make a change and how they hunt. Maybe they

1311
01:14:00,800 --> 01:14:03,360
Speaker 3: don't leave the gut pile out in the field. You know,

1312
01:14:03,400 --> 01:14:06,639
Speaker 3: if you want to shoot lead, okay, but don't leave

1313
01:14:06,680 --> 01:14:09,920
Speaker 3: that out there for scavengers. You know, if you want

1314
01:14:09,920 --> 01:14:14,280
Speaker 3: to change your AMMO, that's okay too. But just knowing

1315
01:14:14,320 --> 01:14:16,960
Speaker 3: that it's a problem and getting back to your argument,

1316
01:14:17,080 --> 01:14:19,840
Speaker 3: like there's a lot of things out there that they

1317
01:14:20,040 --> 01:14:24,040
Speaker 3: could slow the population growth, that's absolutely true. This is

1318
01:14:24,080 --> 01:14:29,000
Speaker 3: one where hunters are making this choice. And the thing

1319
01:14:29,040 --> 01:14:33,320
Speaker 3: that I thought was most interesting about the work we

1320
01:14:33,400 --> 01:14:36,360
Speaker 3: did with the population modeling. Yeah, people are like a

1321
01:14:36,400 --> 01:14:40,200
Speaker 3: three to five percent we can okay, it's not you know,

1322
01:14:40,240 --> 01:14:44,160
Speaker 3: stop the recovery. That's not a huge number. But one

1323
01:14:44,200 --> 01:14:46,200
Speaker 3: of the elements that we were able to look at

1324
01:14:46,280 --> 01:14:50,400
Speaker 3: was the population resilience. So how much flexibility does that

1325
01:14:50,479 --> 01:14:57,479
Speaker 3: population have to absorb other threats and keep operating. And

1326
01:14:58,920 --> 01:15:01,599
Speaker 3: bald eagles are kind of mac out, so they've stretched

1327
01:15:01,600 --> 01:15:05,840
Speaker 3: their population, and how much they can adjust their population

1328
01:15:06,040 --> 01:15:10,479
Speaker 3: parameters to make that recovery happen. And so in the

1329
01:15:10,520 --> 01:15:13,519
Speaker 3: paper we talked about how if other threats come along,

1330
01:15:13,880 --> 01:15:17,519
Speaker 3: you know, like you know, with climate change, we're talking

1331
01:15:17,520 --> 01:15:20,200
Speaker 3: about using more wind energy and if that, you know,

1332
01:15:20,280 --> 01:15:24,360
Speaker 3: the wind turbans are knocking down eagles and killing them

1333
01:15:24,880 --> 01:15:27,920
Speaker 3: or other infectious disease. And we actually put this paper

1334
01:15:27,960 --> 01:15:33,080
Speaker 3: out before highly pathogenic gaving influenza came out and then

1335
01:15:33,280 --> 01:15:35,400
Speaker 3: poof that happened and we saw a lot of ball

1336
01:15:35,439 --> 01:15:39,320
Speaker 3: eagles dying from that. So it's it's not that they

1337
01:15:39,400 --> 01:15:42,919
Speaker 3: can't absorb that stress. It's that you might be stressing

1338
01:15:42,960 --> 01:15:45,200
Speaker 3: them too much. So the same thing you were talking

1339
01:15:45,200 --> 01:15:49,439
Speaker 3: about with DDT, like maybe having lead and DDT was

1340
01:15:50,040 --> 01:15:51,920
Speaker 3: just too much for the eagles and that's why the

1341
01:15:51,920 --> 01:15:54,200
Speaker 3: population was crashing. So you can't just say it was

1342
01:15:54,360 --> 01:15:57,760
Speaker 3: just DDT that was doing that. And you also had

1343
01:15:58,760 --> 01:16:01,960
Speaker 3: the ban on ld A in waterfowl in nineteen ninety one,

1344
01:16:02,240 --> 01:16:04,599
Speaker 3: and so all the eagles eat a lot of ducks

1345
01:16:04,600 --> 01:16:08,559
Speaker 3: and you know, wounded animals, and so they we might

1346
01:16:08,560 --> 01:16:13,120
Speaker 3: have actually seen less lead poisoning recently, you know, since

1347
01:16:13,200 --> 01:16:17,120
Speaker 3: that ban, because they weren't eating you know, shot waterfowl.

1348
01:16:17,960 --> 01:16:23,320
Speaker 3: Now there's other places that are expanding use of bullets

1349
01:16:23,360 --> 01:16:25,599
Speaker 3: in different areas, like in New York, they're adding new

1350
01:16:25,640 --> 01:16:29,160
Speaker 3: counties that can use rifles instead of shotguns. So it's

1351
01:16:29,360 --> 01:16:32,840
Speaker 3: it's not necessarily things like shots. Uh, like slugs that

1352
01:16:32,880 --> 01:16:36,240
Speaker 3: are the problem even well, slugs a hell of a

1353
01:16:36,240 --> 01:16:38,760
Speaker 3: lot more lead, right, exactly a lot more lead. Yeah,

1354
01:16:38,800 --> 01:16:41,040
Speaker 3: So hopefully the eagle's smart enough that it doesn't eat that.

1355
01:16:41,200 --> 01:16:45,760
Speaker 3: But it's it's those small fragments when a bullet shatters,

1356
01:16:45,920 --> 01:16:48,440
Speaker 3: you know, that go everywhere, like up to thirty centimeters

1357
01:16:48,439 --> 01:16:51,160
Speaker 3: away from the womb channel that can be problematic.

1358
01:16:51,320 --> 01:16:55,360
Speaker 1: So I understand what, I understand the point you're making

1359
01:16:55,439 --> 01:17:01,360
Speaker 1: that three percent from this some percent for that years ago,

1360
01:17:02,640 --> 01:17:04,880
Speaker 1: I was looking at and had a conversation with someone

1361
01:17:04,920 --> 01:17:11,920
Speaker 1: about impacts of wolves on elk Okay. And you know,

1362
01:17:11,960 --> 01:17:14,000
Speaker 1: you start with this thing where let's say you look

1363
01:17:14,040 --> 01:17:19,240
Speaker 1: and you have one hundred calves, right, and you were

1364
01:17:19,600 --> 01:17:24,360
Speaker 1: maybe you were of those hundred calves you were always

1365
01:17:24,439 --> 01:17:28,439
Speaker 1: losing for many decades in the past, you were always

1366
01:17:28,520 --> 01:17:31,519
Speaker 1: losing thirty of those calves to mountain lions. And that

1367
01:17:31,600 --> 01:17:37,440
Speaker 1: stayed the same. And wolves come in, wolves are reintroduced

1368
01:17:37,479 --> 01:17:40,800
Speaker 1: into an area, and you see a real decline and elk.

1369
01:17:44,160 --> 01:17:46,680
Speaker 1: But the wolves aren't even eating as much, they're not

1370
01:17:46,800 --> 01:17:49,600
Speaker 1: killing as many calves as mountain lions are. But that

1371
01:17:49,680 --> 01:17:52,240
Speaker 1: mountain lion thing had always been there, right, you know,

1372
01:17:52,439 --> 01:17:57,240
Speaker 1: they were always carrying off thirty percent. Yeah, there was

1373
01:17:57,280 --> 01:18:00,760
Speaker 1: some sort of equilibrium there and you're used to that.

1374
01:18:00,840 --> 01:18:02,280
Speaker 1: But then you add another.

1375
01:18:02,400 --> 01:18:05,400
Speaker 3: Seven ye and it's enough to tip it to a

1376
01:18:05,400 --> 01:18:06,040
Speaker 3: different direction.

1377
01:18:06,479 --> 01:18:09,519
Speaker 1: You notice this thing, but you still look and everyone

1378
01:18:09,560 --> 01:18:14,360
Speaker 1: will be like, wolves are killing all the elk, and

1379
01:18:14,400 --> 01:18:17,080
Speaker 1: in some areas it's well, mountain lions are killing all

1380
01:18:17,080 --> 01:18:19,880
Speaker 1: the elk. The wolf thing is just a new additive

1381
01:18:19,960 --> 01:18:24,839
Speaker 1: thing that has disrupted what we've accepted to be normal

1382
01:18:24,960 --> 01:18:26,720
Speaker 1: exactly if you want to talk about what like when

1383
01:18:26,760 --> 01:18:29,719
Speaker 1: an elk calf hits the ground, its biggest risk remains

1384
01:18:29,720 --> 01:18:33,679
Speaker 1: a mountain line. But that's just always been so consistent

1385
01:18:34,560 --> 01:18:38,320
Speaker 1: and this is a new party. So I get the

1386
01:18:38,400 --> 01:18:41,120
Speaker 1: point that you know, with I think that you can

1387
01:18:41,960 --> 01:18:46,120
Speaker 1: that that someone could make the decision, you know, to say,

1388
01:18:46,240 --> 01:18:49,679
Speaker 1: like I use copper ammunition, I use ammunition with leading.

1389
01:18:49,720 --> 01:18:52,920
Speaker 1: It still all right, Like I'm pretty well versed on

1390
01:18:52,960 --> 01:18:56,000
Speaker 1: the pros and cons of it, the efficacy of it,

1391
01:18:58,720 --> 01:19:01,720
Speaker 1: the technology on copper. Animals come a long way, but

1392
01:19:01,760 --> 01:19:04,400
Speaker 1: there's some you know, when you get into efficacy, there's

1393
01:19:04,439 --> 01:19:09,280
Speaker 1: some definite arguments for there's some real arguments for lead ammunition.

1394
01:19:09,800 --> 01:19:14,559
Speaker 1: I have found generally that in cases where people are

1395
01:19:14,680 --> 01:19:21,320
Speaker 1: given in cases where people are offered an alternative, they'll

1396
01:19:21,320 --> 01:19:25,280
Speaker 1: often go for it. Voluntary compliance areas, here's free AMMO.

1397
01:19:25,439 --> 01:19:27,120
Speaker 1: You know, if you're gonna be hunting on the Kaibab

1398
01:19:27,160 --> 01:19:32,080
Speaker 1: plateau where there's a lot of condors, will give you

1399
01:19:32,160 --> 01:19:34,800
Speaker 1: AMMO to use. People are like eager to accept AMMO.

1400
01:19:34,880 --> 01:19:36,599
Speaker 1: We want you to remove your gut pile. People are

1401
01:19:36,600 --> 01:19:44,639
Speaker 1: eager to move the gut pile. That goodwill evaporates when

1402
01:19:44,680 --> 01:19:48,680
Speaker 1: it comes to mandatory Yes, the goodwill evaporates when it

1403
01:19:48,720 --> 01:19:52,439
Speaker 1: comes to mandatory restrictions. And a lot of it's driven

1404
01:19:52,479 --> 01:19:55,840
Speaker 1: by some amount of efficacy, for sure. A lot of

1405
01:19:55,880 --> 01:19:59,760
Speaker 1: that pushback, a lot of that goodwill evaporates also around

1406
01:19:59,800 --> 01:20:00,200
Speaker 1: call lost.

1407
01:20:00,640 --> 01:20:02,599
Speaker 3: Yeah, well, I mean it's hard.

1408
01:20:02,600 --> 01:20:04,680
Speaker 1: Very different cost card right now to get that to

1409
01:20:04,720 --> 01:20:07,200
Speaker 1: get to move away from lead amim what can be expensive?

1410
01:20:07,200 --> 01:20:10,120
Speaker 3: Oh yeah, for sure. And I mean just having ammunition

1411
01:20:10,200 --> 01:20:13,400
Speaker 3: shortages in general, trying to tell people to to use

1412
01:20:13,439 --> 01:20:16,240
Speaker 3: non lead when they can't find anything at all. So

1413
01:20:16,439 --> 01:20:19,320
Speaker 3: trying to make sure that that people sort of know

1414
01:20:19,360 --> 01:20:21,800
Speaker 3: what their options are and don't just say, oh, you know,

1415
01:20:21,920 --> 01:20:24,720
Speaker 3: using lead is bad. Like, Okay, if that's the only

1416
01:20:24,720 --> 01:20:26,400
Speaker 3: thing you can use, that's the only thing you can use.

1417
01:20:26,520 --> 01:20:29,559
Speaker 3: But you know, think about what you want to do

1418
01:20:29,840 --> 01:20:31,040
Speaker 3: after you pull the trigger.

1419
01:20:31,200 --> 01:20:35,800
Speaker 1: Yeah, I think that it's keeping all that in mind

1420
01:20:35,880 --> 01:20:38,120
Speaker 1: is important. But I also think it's important that people

1421
01:20:38,160 --> 01:20:43,240
Speaker 1: realize you make choices. I make choices, right, And it

1422
01:20:43,280 --> 01:20:48,400
Speaker 1: is like lead kills raptors. Now does that mean rather

1423
01:20:48,479 --> 01:20:50,840
Speaker 1: that we're gonna lose raptors, that they're you know, they're

1424
01:20:50,840 --> 01:20:55,799
Speaker 1: gonna vansh from the landscape, not necessarily, but it kills rafters.

1425
01:20:56,840 --> 01:20:58,719
Speaker 3: So it comes down what's your your choice?

1426
01:20:58,800 --> 01:21:01,760
Speaker 1: Yeah, one of being an uncalledfort reality that it's the

1427
01:21:01,760 --> 01:21:05,120
Speaker 1: thing that happens and how many how often do they

1428
01:21:05,120 --> 01:21:10,720
Speaker 1: come through here that you find, and what is the symptomology.

1429
01:21:11,760 --> 01:21:17,120
Speaker 3: So lead is really a tough thing that affects all

1430
01:21:17,160 --> 01:21:21,960
Speaker 3: sorts of different systems in the body. So it's a neurotoxin.

1431
01:21:22,200 --> 01:21:26,240
Speaker 3: It's bad for your kidneys, Like it gets circulated in

1432
01:21:26,280 --> 01:21:28,600
Speaker 3: the blood and taken up into your bones, so you

1433
01:21:28,760 --> 01:21:31,519
Speaker 3: kind of lose it constantly through your bones. I had

1434
01:21:31,520 --> 01:21:35,400
Speaker 3: one case a long time ago with a mountain lion

1435
01:21:35,680 --> 01:21:38,240
Speaker 3: that had eaten a whole bunch of lead shot from

1436
01:21:38,439 --> 01:21:41,960
Speaker 3: something or other, and the lion was so rotten that

1437
01:21:42,000 --> 01:21:45,479
Speaker 3: we actually tested the bones in its paw and it

1438
01:21:45,520 --> 01:21:47,679
Speaker 3: came out screamingly high for lead.

1439
01:21:48,200 --> 01:21:51,760
Speaker 1: So it had been like metabolized that fast.

1440
01:21:51,520 --> 01:21:53,880
Speaker 3: So that it killed it and we could find it

1441
01:21:53,920 --> 01:21:56,639
Speaker 3: in its bones already, So it goes throughout the body.

1442
01:21:56,760 --> 01:21:58,840
Speaker 3: And I mean the main thing that people focus on,

1443
01:21:59,000 --> 01:22:02,240
Speaker 3: especially like with chill and is the neurological development. So

1444
01:22:02,280 --> 01:22:05,720
Speaker 3: it's not necessarily you know, at this point, if you

1445
01:22:05,800 --> 01:22:08,720
Speaker 3: lose a few brain cells from eating lead, it's you know,

1446
01:22:09,240 --> 01:22:12,000
Speaker 3: hard to tell the difference maybe, but for kids that

1447
01:22:12,040 --> 01:22:16,280
Speaker 3: are developing, that's the real challenge, you know that.

1448
01:22:16,439 --> 01:22:19,839
Speaker 1: You know that work where they looked at rural people

1449
01:22:19,880 --> 01:22:23,200
Speaker 1: who had a high consumption of game meat versus urban

1450
01:22:23,280 --> 01:22:27,320
Speaker 1: people who had no consumption of game meat, but the

1451
01:22:27,479 --> 01:22:30,480
Speaker 1: urban people were having higher levels.

1452
01:22:30,280 --> 01:22:31,920
Speaker 3: Lead from paint chips.

1453
01:22:32,000 --> 01:22:33,480
Speaker 1: Yeah, yeah, just environmental.

1454
01:22:33,640 --> 01:22:38,559
Speaker 3: Yeah. We have had this working group on lead in

1455
01:22:38,640 --> 01:22:40,720
Speaker 3: New York for a few years. It's got Department of

1456
01:22:40,720 --> 01:22:43,920
Speaker 3: Health on it, it's got venison donation people on it,

1457
01:22:43,920 --> 01:22:46,679
Speaker 3: it's got autobond to talk about all these different issues.

1458
01:22:47,160 --> 01:22:51,280
Speaker 3: And I've done work here with people in the communications Department,

1459
01:22:51,600 --> 01:22:54,280
Speaker 3: And one of the things that we talk about is backlash,

1460
01:22:54,360 --> 01:22:56,360
Speaker 3: Like if you tell people like using lead is bad,

1461
01:22:56,400 --> 01:22:58,840
Speaker 3: then they're just going to dig in harder on that,

1462
01:22:59,600 --> 01:23:02,639
Speaker 3: so that, you know, really backfires. And what you want

1463
01:23:02,680 --> 01:23:06,479
Speaker 3: to do so just making people aware of what the

1464
01:23:06,520 --> 01:23:10,599
Speaker 3: issue is in a non judgmental way and letting them

1465
01:23:10,640 --> 01:23:12,160
Speaker 3: make the choice about what they want to do.

1466
01:23:12,960 --> 01:23:16,320
Speaker 1: I remember sending in some garden soil one time and

1467
01:23:17,960 --> 01:23:19,360
Speaker 1: the garden soil having.

1468
01:23:19,400 --> 01:23:21,719
Speaker 4: Oh yeah, it's everywhere, super high lead levels.

1469
01:23:21,800 --> 01:23:23,679
Speaker 1: The thing you can think, I was back from leaded

1470
01:23:23,720 --> 01:23:24,639
Speaker 1: gas exactly.

1471
01:23:24,720 --> 01:23:26,800
Speaker 3: I mean we used to have lead in everything. It's

1472
01:23:26,880 --> 01:23:27,960
Speaker 3: kind of interesting like.

1473
01:23:28,040 --> 01:23:30,760
Speaker 1: Leaded fuel would just like would drift down oh yeah, right,

1474
01:23:30,800 --> 01:23:34,120
Speaker 1: and getting your soil. And then then the recommendation was

1475
01:23:34,120 --> 01:23:38,040
Speaker 1: wash the produce that it's like the plants aren't taking

1476
01:23:38,120 --> 01:23:41,400
Speaker 1: it up. Yeah, it's just you're ingesting it because it's

1477
01:23:41,439 --> 01:23:44,479
Speaker 1: folded within the leaves of your spinach. Sure it's like

1478
01:23:44,600 --> 01:23:47,400
Speaker 1: you know, leaf like cabbage or whatever, things where it's

1479
01:23:47,400 --> 01:23:49,840
Speaker 1: hard to clean. Yep, we're more likely that you're going

1480
01:23:49,920 --> 01:23:54,439
Speaker 1: to be just consuming it. You know. I don't the

1481
01:23:55,000 --> 01:23:56,800
Speaker 1: lead thing, like one of the areas where I don't

1482
01:23:57,560 --> 01:24:00,439
Speaker 1: have personal concern I guess because it's me or whatever.

1483
01:24:00,479 --> 01:24:05,160
Speaker 1: I never have personal concern about personally ingesting it, but

1484
01:24:05,200 --> 01:24:08,719
Speaker 1: it weighs on me about the wildlife fissue, Yeah, especially

1485
01:24:08,720 --> 01:24:10,320
Speaker 1: when I learned from you today about fishers.

1486
01:24:10,840 --> 01:24:14,080
Speaker 3: Right. And it just depends. Some people are moved by

1487
01:24:14,120 --> 01:24:17,519
Speaker 3: the wildlife issues, depending on what your ethos is. Some

1488
01:24:17,560 --> 01:24:20,040
Speaker 3: people are more concerned about it, and you know, their

1489
01:24:20,200 --> 01:24:25,120
Speaker 3: their own human consumption. It just it's variable, you know,

1490
01:24:25,200 --> 01:24:27,000
Speaker 3: just the same way people punch for a lot of

1491
01:24:27,000 --> 01:24:30,880
Speaker 3: different reasons, whatever is meaningful to them.

1492
01:24:31,120 --> 01:24:36,120
Speaker 1: Uh, crystal ball, mean, give me some crystal ball stuff

1493
01:24:36,120 --> 01:24:36,840
Speaker 1: on CWD.

1494
01:24:37,280 --> 01:24:43,040
Speaker 3: On CWD. Oh man, that's that's sort of tough, I am.

1495
01:24:43,280 --> 01:24:45,760
Speaker 1: I know researchers get really uncomfortable with the crystal boy.

1496
01:24:45,920 --> 01:24:49,439
Speaker 3: I know we don't do that. We're looking backwards most

1497
01:24:49,439 --> 01:24:49,920
Speaker 3: of the time.

1498
01:24:52,280 --> 01:24:54,040
Speaker 1: So just give me some thoughts. It doesn't need to

1499
01:24:54,040 --> 01:24:58,240
Speaker 1: be predictions. Just give me some, you know, some thoughts.

1500
01:24:58,560 --> 01:25:02,040
Speaker 3: Well, U, I'll talk about one of the things that

1501
01:25:02,080 --> 01:25:05,880
Speaker 3: we're doing that I hope people can get behind. So

1502
01:25:06,640 --> 01:25:11,559
Speaker 3: right now, we have a large program called the Surveillance

1503
01:25:11,600 --> 01:25:16,120
Speaker 3: Optimization Project for Chronic Wasting Disease, and this started out

1504
01:25:16,520 --> 01:25:20,519
Speaker 3: as a way based on what we did here in

1505
01:25:20,560 --> 01:25:24,280
Speaker 3: New York with doing more risk based surveillance, because there's

1506
01:25:24,479 --> 01:25:26,479
Speaker 3: you know, still a lot of states that haven't found

1507
01:25:26,479 --> 01:25:30,559
Speaker 3: CWD and they don't necessarily know where to look. So

1508
01:25:30,760 --> 01:25:32,639
Speaker 3: like New York had it in two thousand and five,

1509
01:25:34,400 --> 01:25:37,680
Speaker 3: you know, sampled really intensively until twenty ten for their

1510
01:25:37,680 --> 01:25:40,240
Speaker 3: five years, and then they're sort of like, now, what,

1511
01:25:40,520 --> 01:25:43,120
Speaker 3: like what do we do? So we set up this

1512
01:25:43,200 --> 01:25:48,240
Speaker 3: risk based surveillance based on deer density, based on hazards,

1513
01:25:48,320 --> 01:25:52,040
Speaker 3: like you know, Pennsylvania's got a lot of CWD, so

1514
01:25:52,080 --> 01:25:54,040
Speaker 3: we want to make sure we sample intensively along the

1515
01:25:54,040 --> 01:25:56,760
Speaker 3: southern tier and then going back to the you know,

1516
01:25:56,760 --> 01:25:58,559
Speaker 3: how do preons get into the state and how could

1517
01:25:58,600 --> 01:26:01,839
Speaker 3: they get spread? We want to sample more around those areas.

1518
01:26:02,520 --> 01:26:04,439
Speaker 3: And so we did this for New York, and then

1519
01:26:04,800 --> 01:26:08,400
Speaker 3: Tennessee asked us to do the same surveillance program for them,

1520
01:26:09,320 --> 01:26:12,879
Speaker 3: So we did that and that was how they found CWD,

1521
01:26:13,360 --> 01:26:16,640
Speaker 3: and we've subsequently done it for seven other states and

1522
01:26:16,680 --> 01:26:19,240
Speaker 3: now Alabama found it and then Florida just found it.

1523
01:26:20,640 --> 01:26:23,240
Speaker 3: So we said, okay, we can't. We don't want to

1524
01:26:23,240 --> 01:26:26,840
Speaker 3: do this just for individual states anymore, like states are

1525
01:26:26,880 --> 01:26:29,960
Speaker 3: struggling spending a lot of money on CWD. How can

1526
01:26:30,000 --> 01:26:33,880
Speaker 3: they do their surveillance more efficiently and effectively? So right

1527
01:26:33,920 --> 01:26:34,400
Speaker 3: now we.

1528
01:26:34,479 --> 01:26:40,479
Speaker 1: Have uh kind of erupted from Yell, if you did

1529
01:26:40,479 --> 01:26:42,439
Speaker 1: it for a bunch more states, doesn't it make sense

1530
01:26:42,479 --> 01:26:44,200
Speaker 1: that you'd have a bunch more states, it would be

1531
01:26:44,760 --> 01:26:46,480
Speaker 1: the head CWD, meaning.

1532
01:26:46,439 --> 01:26:48,920
Speaker 3: Well some of them already had CWD, like Pennsylvania and

1533
01:26:48,960 --> 01:26:49,759
Speaker 3: Virginia Florida.

1534
01:26:49,840 --> 01:26:51,800
Speaker 1: This got added to the list. Yeah, and they got

1535
01:26:51,800 --> 01:26:53,240
Speaker 1: added the list because they went and looked for it.

1536
01:26:53,280 --> 01:26:55,920
Speaker 1: I mean, do you remember this conversation around COVID being

1537
01:26:55,960 --> 01:27:00,559
Speaker 1: like there'd be the arguments that, well, yeah, there's a

1538
01:27:00,560 --> 01:27:05,280
Speaker 1: lot you're seeing high levels of COVID seemed to go

1539
01:27:05,320 --> 01:27:07,559
Speaker 1: hand in hand with high levels of COVID testing.

1540
01:27:07,640 --> 01:27:09,519
Speaker 3: But the thing is, it's not that they're not looking

1541
01:27:09,560 --> 01:27:11,640
Speaker 3: for it, it's just they don't know where. Like they

1542
01:27:11,680 --> 01:27:17,240
Speaker 3: were doing surveillance, but it wasn't like innecessarily emphasized in

1543
01:27:17,320 --> 01:27:20,680
Speaker 3: the right places. So like Tennessee was doing about the

1544
01:27:20,720 --> 01:27:23,920
Speaker 3: same numbers as they were previously, but before they were

1545
01:27:23,920 --> 01:27:26,720
Speaker 3: getting samples where they were easy to get them, and

1546
01:27:26,760 --> 01:27:29,439
Speaker 3: so they kept coming from, you know, the same places

1547
01:27:29,479 --> 01:27:31,839
Speaker 3: where we're like, no, no, look in these two counties

1548
01:27:32,080 --> 01:27:34,080
Speaker 3: are your highest risk and that's where they found it.

1549
01:27:34,160 --> 01:27:37,320
Speaker 3: Yet you so.

1550
01:27:36,520 --> 01:27:39,960
Speaker 1: They might have been conducting some number one hundred samples

1551
01:27:40,040 --> 01:27:41,040
Speaker 1: kind of pulled from.

1552
01:27:41,280 --> 01:27:44,720
Speaker 3: Roadkilled deer, well thousands of samples, but you know, just

1553
01:27:45,080 --> 01:27:47,320
Speaker 3: not in the places where they needed to be getting

1554
01:27:47,320 --> 01:27:47,800
Speaker 3: them from.

1555
01:27:47,920 --> 01:27:52,200
Speaker 1: And so the so the idea or program or whatever

1556
01:27:52,200 --> 01:27:55,760
Speaker 1: that you were exporting was a was the more precise.

1557
01:27:56,160 --> 01:27:59,760
Speaker 3: Yeah, survey, collect data on where your risks are and

1558
01:27:59,800 --> 01:28:04,280
Speaker 3: then sample in those high risk areas. And we also

1559
01:28:04,400 --> 01:28:07,639
Speaker 3: know from other states that have CDBD like adult males

1560
01:28:07,760 --> 01:28:10,400
Speaker 3: are more likely to have you know, test positive than

1561
01:28:10,640 --> 01:28:14,040
Speaker 3: you know fawns usually don't test positive. So put the

1562
01:28:14,120 --> 01:28:16,679
Speaker 3: test costs the same whether you test an adult mail

1563
01:28:16,760 --> 01:28:19,680
Speaker 3: or a fund, so put your money into that. We

1564
01:28:19,720 --> 01:28:22,080
Speaker 3: don't get a lot of adult mails in just because

1565
01:28:22,600 --> 01:28:24,559
Speaker 3: they usually go to a taxidermist. So how can we

1566
01:28:24,600 --> 01:28:28,200
Speaker 3: engage with taxidermists to help us collect samples so that

1567
01:28:28,240 --> 01:28:30,959
Speaker 3: we can get those in and make it more efficient

1568
01:28:31,080 --> 01:28:33,360
Speaker 3: for the agency where the biologist isn't having to pool

1569
01:28:33,400 --> 01:28:36,240
Speaker 3: samples all the time. Kick some money over the taxidermists

1570
01:28:36,280 --> 01:28:38,000
Speaker 3: to have them do it for you. You know, they're

1571
01:28:38,000 --> 01:28:42,360
Speaker 3: just going to throw that stuff away anyway. So but

1572
01:28:42,479 --> 01:28:44,720
Speaker 3: every state sort of recreates the wheel and how they

1573
01:28:44,760 --> 01:28:49,439
Speaker 3: do this, and so they're collecting data separately. They're you know,

1574
01:28:49,479 --> 01:28:53,280
Speaker 3: spending a lot of time invested in surveillance and how

1575
01:28:53,280 --> 01:28:55,439
Speaker 3: they should do these things. When if we had a

1576
01:28:56,320 --> 01:28:59,519
Speaker 3: more regional or more collective approach, we could compare data

1577
01:29:00,400 --> 01:29:03,280
Speaker 3: across states. Other states would know, you know, like what

1578
01:29:03,479 --> 01:29:06,679
Speaker 3: their neighbors are doing, how much sampling they're doing, and

1579
01:29:07,120 --> 01:29:10,519
Speaker 3: they can have some decision making tools associated with that.

1580
01:29:10,640 --> 01:29:12,920
Speaker 3: So the management agency can say, hey, you know, we're

1581
01:29:12,920 --> 01:29:16,400
Speaker 3: mostly worried about you know, this particular area. What should

1582
01:29:16,439 --> 01:29:19,080
Speaker 3: we be sampling in there? How many should we sample?

1583
01:29:19,439 --> 01:29:22,760
Speaker 3: So we have software now that we're giving away to

1584
01:29:23,520 --> 01:29:27,680
Speaker 3: state agencies or tribes or provinces for free that they

1585
01:29:27,720 --> 01:29:31,080
Speaker 3: can use this system and use it to whatever. You know,

1586
01:29:31,080 --> 01:29:33,600
Speaker 3: if they want to upgrade from their Excel spreadsheet that

1587
01:29:33,640 --> 01:29:36,639
Speaker 3: they've been using, fine, If they want to run models

1588
01:29:36,640 --> 01:29:39,840
Speaker 3: in it to make decisions, they can do that. If

1589
01:29:39,880 --> 01:29:41,760
Speaker 3: they want to see what their neighbors are doing, it's

1590
01:29:41,920 --> 01:29:46,160
Speaker 3: useful for that. So that's that's sort of as far

1591
01:29:46,200 --> 01:29:49,400
Speaker 3: as you know, maybe aspirational crystal ball. I really want

1592
01:29:49,439 --> 01:29:54,479
Speaker 3: to see wildlife agencies sort of come together with more

1593
01:29:54,520 --> 01:29:59,920
Speaker 3: standardized work that can be compared across the country rather

1594
01:30:00,040 --> 01:30:02,400
Speaker 3: in each states were to doing their their own thing.

1595
01:30:03,320 --> 01:30:08,800
Speaker 1: And then if you look at a state, let's say

1596
01:30:08,800 --> 01:30:11,400
Speaker 1: take Florida, which was very recently added to the list

1597
01:30:11,439 --> 01:30:16,200
Speaker 1: of states that had a positive case in it they

1598
01:30:16,240 --> 01:30:18,479
Speaker 1: get a positive case, then there's kind of a you know,

1599
01:30:18,600 --> 01:30:19,320
Speaker 1: there's a so what.

1600
01:30:21,320 --> 01:30:25,000
Speaker 3: Or not? So what happens then? Or what does that

1601
01:30:25,080 --> 01:30:25,880
Speaker 3: mean for the public?

1602
01:30:26,040 --> 01:30:29,920
Speaker 1: Or yes, meaning meaning let's say you're doing a bunch

1603
01:30:29,920 --> 01:30:33,880
Speaker 1: of testing. Let's say all the states are doing the

1604
01:30:33,920 --> 01:30:36,200
Speaker 1: same testing. You can't expect that all the states are

1605
01:30:36,240 --> 01:30:39,040
Speaker 1: going to have the same response to the testing, because

1606
01:30:39,040 --> 01:30:42,920
Speaker 1: there's some there's some states, and where the agencies have

1607
01:30:43,000 --> 01:30:45,760
Speaker 1: a perspective, it's kind of like a little bit of

1608
01:30:46,160 --> 01:30:50,599
Speaker 1: inevitability doesn't matter. We're not going to do any kind

1609
01:30:50,640 --> 01:30:56,680
Speaker 1: of we're not going to do any real extreme containment measures.

1610
01:30:57,640 --> 01:30:59,519
Speaker 1: So that would lead you to be like, well, then

1611
01:31:00,320 --> 01:31:06,760
Speaker 1: perhaps testing isn't like valuable to them if you're you know, so,

1612
01:31:07,320 --> 01:31:12,920
Speaker 1: how are you from a national perspective? How are people

1613
01:31:12,960 --> 01:31:14,360
Speaker 1: coping with with that?

1614
01:31:16,400 --> 01:31:16,639
Speaker 3: Yeah?

1615
01:31:16,680 --> 01:31:19,840
Speaker 1: I don't know the ambivalent, like the ambivalence or great

1616
01:31:19,920 --> 01:31:22,880
Speaker 1: care or whatever that the state takes about the disease, right,

1617
01:31:22,920 --> 01:31:23,360
Speaker 1: And I.

1618
01:31:23,320 --> 01:31:25,880
Speaker 3: Know I sort of step out when it gets to

1619
01:31:25,960 --> 01:31:30,200
Speaker 3: management because it's so much people orientated and what they

1620
01:31:30,200 --> 01:31:35,320
Speaker 3: can do. So if you if a state finds CWD

1621
01:31:35,600 --> 01:31:38,040
Speaker 3: and they want to do something about it, their first

1622
01:31:38,080 --> 01:31:42,839
Speaker 3: detection is their best opportunity because from a human centric perspective,

1623
01:31:42,880 --> 01:31:44,880
Speaker 3: that's when people are fired up and care about it

1624
01:31:44,920 --> 01:31:46,960
Speaker 3: and like, yeah, let's get rid of it. I mean,

1625
01:31:47,000 --> 01:31:49,800
Speaker 3: I totally lean on New York having been the only

1626
01:31:49,840 --> 01:31:51,280
Speaker 3: state to get rid of it, to be like, yeah,

1627
01:31:51,360 --> 01:31:53,880
Speaker 3: we're we're going to try to kick its ass again

1628
01:31:53,880 --> 01:31:57,400
Speaker 3: if we find it, because that's the only real opportunity

1629
01:31:57,439 --> 01:32:00,360
Speaker 3: you have to do something. So it just depends on

1630
01:32:01,120 --> 01:32:04,639
Speaker 3: the management agency and what they choose. I mean maybe

1631
01:32:05,000 --> 01:32:08,280
Speaker 3: like with Tennessee, their first detection they had you know,

1632
01:32:08,560 --> 01:32:11,799
Speaker 3: over a dozen animals, so they're kind of like, crap,

1633
01:32:11,840 --> 01:32:14,880
Speaker 3: we're way further in it than you know, the very

1634
01:32:14,920 --> 01:32:17,360
Speaker 3: first detection. So like if you get a whole bunch

1635
01:32:17,400 --> 01:32:19,040
Speaker 3: all at once, you're like, Okay, we've had it for

1636
01:32:19,080 --> 01:32:21,599
Speaker 3: a while and just haven't found this pocket now we

1637
01:32:21,720 --> 01:32:24,559
Speaker 3: know what to do with it. But I think you'd

1638
01:32:24,560 --> 01:32:28,439
Speaker 3: be hard pressed to find any agency that wouldn't do something,

1639
01:32:28,640 --> 01:32:31,719
Speaker 3: you know, as far as like carcass or feeding or baiting,

1640
01:32:32,000 --> 01:32:34,040
Speaker 3: like bands that they would put in place on that

1641
01:32:34,360 --> 01:32:37,639
Speaker 3: to try not to spread it further. Because the thing

1642
01:32:37,640 --> 01:32:41,080
Speaker 3: that I talk about is, you know, CWD pools away

1643
01:32:41,479 --> 01:32:45,479
Speaker 3: resources from everything else in an agency. If you know,

1644
01:32:45,680 --> 01:32:48,439
Speaker 3: in some agencies they have their fish biologists out there,

1645
01:32:48,840 --> 01:32:51,559
Speaker 3: you know, pulling samples opening week in a gun season.

1646
01:32:51,800 --> 01:32:55,080
Speaker 3: So it's even if you don't care about deer deer hunting,

1647
01:32:55,280 --> 01:33:00,280
Speaker 3: like maybe your thing is butterflies, Like it's pulling resources away.

1648
01:33:00,320 --> 01:33:03,360
Speaker 3: So everybody should kind of care about it at that

1649
01:33:03,400 --> 01:33:06,240
Speaker 3: point because it's such a behemoth and it sucks so

1650
01:33:06,360 --> 01:33:09,760
Speaker 3: much money and time away from agencies that it's it's

1651
01:33:09,800 --> 01:33:25,639
Speaker 3: worth caring about.

1652
01:33:20,680 --> 01:33:24,479
Speaker 1: Doesn't Uh. There's gotta be some part of you. And

1653
01:33:24,479 --> 01:33:26,320
Speaker 1: I understand you know, if you if you got to

1654
01:33:26,320 --> 01:33:28,519
Speaker 1: punt this one, that's fine. There's got to be some

1655
01:33:28,720 --> 01:33:32,280
Speaker 1: part of you, because it definitely exists in my head

1656
01:33:33,920 --> 01:33:39,920
Speaker 1: that that it's just like an inevitability. Meaning I know

1657
01:33:40,040 --> 01:33:41,080
Speaker 1: New York did it, but.

1658
01:33:42,760 --> 01:33:44,400
Speaker 3: Like you said, you think it's just going to be

1659
01:33:44,439 --> 01:33:45,719
Speaker 3: everywhere at some point.

1660
01:33:45,920 --> 01:33:48,320
Speaker 1: Man, Like, let's say I had to make a like

1661
01:33:48,360 --> 01:33:51,960
Speaker 1: a huge bet okay, like every dollar I'll ever have

1662
01:33:52,600 --> 01:33:55,160
Speaker 1: or ever have had, and I had to bet it

1663
01:33:55,240 --> 01:34:01,479
Speaker 1: on that it'll uh me. This isn't a demonstration of

1664
01:34:01,479 --> 01:34:03,720
Speaker 1: what I'd like to see, Yes, but I've had it

1665
01:34:03,760 --> 01:34:05,320
Speaker 1: bet on what I thought would happen.

1666
01:34:08,760 --> 01:34:09,639
Speaker 3: Is there a time frame?

1667
01:34:10,960 --> 01:34:15,120
Speaker 1: I just like, I hate to say it by but

1668
01:34:15,280 --> 01:34:16,880
Speaker 1: I hate to say but I don't want to be dishonest,

1669
01:34:16,880 --> 01:34:18,400
Speaker 1: like it just feels unstoppable.

1670
01:34:19,840 --> 01:34:23,599
Speaker 3: I agree with you to some extent, but my point is,

1671
01:34:23,880 --> 01:34:26,800
Speaker 3: what's what's what's the time frame on that? You know,

1672
01:34:26,960 --> 01:34:31,280
Speaker 3: like are are we gonna you know, young people right

1673
01:34:31,320 --> 01:34:33,479
Speaker 3: now feel that way about the earth, like that the

1674
01:34:33,520 --> 01:34:36,599
Speaker 3: earth is just gonna die at some point? Like what's

1675
01:34:36,640 --> 01:34:37,760
Speaker 3: the time frame for that?

1676
01:34:37,960 --> 01:34:38,680
Speaker 4: So I feel like.

1677
01:34:38,640 --> 01:34:40,879
Speaker 1: It's a little bit of a way. That's that's bad parenting.

1678
01:34:41,520 --> 01:34:49,160
Speaker 3: Okay, Well we're not here to discuss guarantee. Yeah, I

1679
01:34:49,479 --> 01:34:51,200
Speaker 3: don't know. It's it's one of those like fight the

1680
01:34:51,200 --> 01:34:54,280
Speaker 3: good fight, like do what you can while you've got it.

1681
01:34:54,720 --> 01:34:57,439
Speaker 1: I understood that, but I wasn't talking. I know I

1682
01:34:57,520 --> 01:35:00,800
Speaker 1: was talking, and I know it's counterproductive. It's kind underproductive

1683
01:35:00,880 --> 01:35:04,439
Speaker 1: to point it out. But I just wish there were more.

1684
01:35:07,520 --> 01:35:08,759
Speaker 1: I wish there were more victories.

1685
01:35:09,800 --> 01:35:13,040
Speaker 3: I do too, and.

1686
01:35:15,880 --> 01:35:19,559
Speaker 1: To me, and I've had friends of mine and my

1687
01:35:19,600 --> 01:35:22,639
Speaker 1: friend Dog I'm sure is listening to every word I say.

1688
01:35:22,720 --> 01:35:23,639
Speaker 3: Right now, Hi Dog.

1689
01:35:25,960 --> 01:35:28,519
Speaker 1: If it pisses Doug off when I say this, but

1690
01:35:28,560 --> 01:35:32,160
Speaker 1: I've brought up to him, I've said I used to say,

1691
01:35:33,800 --> 01:35:36,679
Speaker 1: if I somehow knew that it would never jump to humans,

1692
01:35:37,640 --> 01:35:42,479
Speaker 1: I would care a lot less. Meaning like it's clear

1693
01:35:42,479 --> 01:35:45,519
Speaker 1: what I mean. Meaning when I think about chronic wasting disease,

1694
01:35:45,760 --> 01:35:49,880
Speaker 1: I think about it's never happened, But I think about

1695
01:35:49,880 --> 01:35:55,120
Speaker 1: how catastrophic it would be to deer hunters, to deer

1696
01:35:55,200 --> 01:36:00,880
Speaker 1: hunting if we suddenly had these cases. You know, some

1697
01:36:00,920 --> 01:36:09,040
Speaker 1: community or some family that they developed some novel, uh

1698
01:36:09,240 --> 01:36:12,160
Speaker 1: you know, they developed some novel version of mad cow

1699
01:36:12,240 --> 01:36:16,000
Speaker 1: disease and chacka kreutz felt whatever the hell they call it,

1700
01:36:16,640 --> 01:36:19,800
Speaker 1: and it, you know, killed some family and it turned

1701
01:36:19,840 --> 01:36:24,720
Speaker 1: out that that family had, you know, eating some quantity

1702
01:36:24,720 --> 01:36:27,719
Speaker 1: of infected deer meat, and it became you know, the

1703
01:36:27,720 --> 01:36:31,400
Speaker 1: Holy cow. Right, it would be catastrophic. It would change

1704
01:36:31,439 --> 01:36:34,400
Speaker 1: the way people think about deer. It would change.

1705
01:36:34,120 --> 01:36:35,959
Speaker 4: The way people peons.

1706
01:36:36,640 --> 01:36:40,599
Speaker 1: It'd be like you'd look at now you look at

1707
01:36:40,600 --> 01:36:44,360
Speaker 1: deer and you get excited. It's great. People are excited

1708
01:36:44,400 --> 01:36:46,760
Speaker 1: to see deer, they're excited to see deer tracks in

1709
01:36:46,840 --> 01:36:50,519
Speaker 1: new places, like and if it was, if it was

1710
01:36:50,600 --> 01:36:56,639
Speaker 1: a disease causing animal, it's just it's so devastating, right,

1711
01:36:57,120 --> 01:36:59,760
Speaker 1: and you can look and and and be that. And

1712
01:36:59,800 --> 01:37:01,519
Speaker 1: I'm remember one day you and I had an argument

1713
01:37:01,520 --> 01:37:04,120
Speaker 1: about this where I said, and I stand by it.

1714
01:37:04,560 --> 01:37:08,200
Speaker 1: I've said, hundreds of thousands of people have eaten CWD

1715
01:37:08,360 --> 01:37:10,120
Speaker 1: infacted deer. Maybe it was you, maybe as someone else

1716
01:37:10,160 --> 01:37:15,160
Speaker 1: that said hundreds, not tens. And then then that caused

1717
01:37:15,200 --> 01:37:16,760
Speaker 1: me to think about it a bunch, and I thought

1718
01:37:16,800 --> 01:37:17,719
Speaker 1: about it a bunch.

1719
01:37:17,520 --> 01:37:19,559
Speaker 3: And man, it's like sticking with hundreds hundreds.

1720
01:37:19,720 --> 01:37:22,759
Speaker 1: Okay, hundreds of thousands of people have eaten CWD infected

1721
01:37:22,760 --> 01:37:24,599
Speaker 1: deer meat, but it has to be true.

1722
01:37:24,680 --> 01:37:25,400
Speaker 3: Yeah, I don't think so.

1723
01:37:25,560 --> 01:37:28,200
Speaker 1: But okay, how about ninety nine.

1724
01:37:28,479 --> 01:37:29,240
Speaker 3: Ninety nine people?

1725
01:37:29,360 --> 01:37:30,920
Speaker 1: Ninety nine thousand, No.

1726
01:37:31,640 --> 01:37:32,600
Speaker 3: Come on, I don't think so.

1727
01:37:32,960 --> 01:37:37,320
Speaker 1: Give me your number lower prices, right, tens of thousands.

1728
01:37:37,360 --> 01:37:39,439
Speaker 1: There's no way you're going to argue there's a case

1729
01:37:39,479 --> 01:37:40,960
Speaker 1: for one hundred people in one meat.

1730
01:37:41,320 --> 01:37:44,839
Speaker 3: That was in New York. I'll give you ten thousand.

1731
01:37:46,200 --> 01:37:50,599
Speaker 1: It's okay, hundreds of thousands, tens, okay, whatever the hell

1732
01:37:50,600 --> 01:37:51,280
Speaker 1: it is a.

1733
01:37:51,200 --> 01:37:55,040
Speaker 3: Lot, Okay. People have eaten infected deer and have not

1734
01:37:55,240 --> 01:37:57,720
Speaker 3: gotten CWD that we know about, we.

1735
01:37:57,720 --> 01:37:59,880
Speaker 1: Know about have not yet. Okay. So I look at that,

1736
01:38:01,040 --> 01:38:05,000
Speaker 1: and that's heartening. I'd rather that than the opposite, for sure,

1737
01:38:05,240 --> 01:38:09,760
Speaker 1: anybody would. But people, there's no one that can come

1738
01:38:09,800 --> 01:38:12,439
Speaker 1: and tell you. There's no one that can come and

1739
01:38:12,439 --> 01:38:15,000
Speaker 1: tell you. And if someone does like you can't believe them.

1740
01:38:15,040 --> 01:38:16,960
Speaker 1: There's no one could come and tell you that it's impossible.

1741
01:38:17,360 --> 01:38:17,599
Speaker 3: Right.

1742
01:38:18,520 --> 01:38:22,400
Speaker 1: So in the past I have brought up that if

1743
01:38:22,680 --> 01:38:26,320
Speaker 1: if I knew somehow for some as some fact, which

1744
01:38:26,360 --> 01:38:28,960
Speaker 1: is impossible, but if I knew as a fact that

1745
01:38:29,520 --> 01:38:34,840
Speaker 1: it wasn't possible, my concern about it would go way

1746
01:38:34,920 --> 01:38:42,040
Speaker 1: way down. But now there's emerging evidence in places where

1747
01:38:44,640 --> 01:38:49,280
Speaker 1: they're seeing evidence of population level impacts. Because it used

1748
01:38:49,320 --> 01:38:51,559
Speaker 1: to be that you could look and you'd look at

1749
01:38:51,560 --> 01:38:55,840
Speaker 1: these areas of CWD and they oddly would be areas

1750
01:38:55,880 --> 01:38:57,160
Speaker 1: that had some of damned.

1751
01:38:56,880 --> 01:39:01,560
Speaker 3: Dear right, Yeah, because GWd.

1752
01:39:01,280 --> 01:39:04,360
Speaker 1: And Southwest Wisconsin's like, why South Wisconsin has some many

1753
01:39:04,400 --> 01:39:08,280
Speaker 1: deer and they got all these giant bucks. So how

1754
01:39:08,320 --> 01:39:10,160
Speaker 1: do I look at that and get worried If I'm

1755
01:39:10,160 --> 01:39:13,400
Speaker 1: not worried about catching it and dying And there are

1756
01:39:13,439 --> 01:39:15,840
Speaker 1: places that have have a lot of deer and they're

1757
01:39:15,840 --> 01:39:19,280
Speaker 1: still shooting big bucks, I'm not going to worry about that.

1758
01:39:20,280 --> 01:39:23,280
Speaker 1: But the but the emerg like this emerging stuff, and

1759
01:39:23,320 --> 01:39:25,519
Speaker 1: I haven't looked at it a lot, but I've heard it.

1760
01:39:25,640 --> 01:39:29,040
Speaker 1: I've seen it demonstrated by people that I have faith in.

1761
01:39:29,120 --> 01:39:32,839
Speaker 1: That it's beginning to see where the populations can't support

1762
01:39:32,880 --> 01:39:36,680
Speaker 1: it exactly, and that you might wind up down the

1763
01:39:36,760 --> 01:39:43,439
Speaker 1: road with really damaged deer populations exactly.

1764
01:39:43,840 --> 01:39:46,799
Speaker 3: I Mean we were talking earlier about kids and hunting,

1765
01:39:47,240 --> 01:39:50,960
Speaker 3: and so when I first started studying CWD twenty years ago,

1766
01:39:51,080 --> 01:39:53,320
Speaker 3: I'd say, you know, this probably isn't going to impact you.

1767
01:39:53,720 --> 01:39:55,400
Speaker 3: It's probably not going to impact your kids, but it

1768
01:39:55,479 --> 01:40:00,040
Speaker 3: might impact your grandkids. And now the speed with which this,

1769
01:40:00,720 --> 01:40:03,559
Speaker 3: like the prevalence curve, you know, the exponential curve of

1770
01:40:03,600 --> 01:40:08,400
Speaker 3: how the disease takes off once it gets told, like

1771
01:40:08,439 --> 01:40:12,320
Speaker 3: I'm worried about my son hunting and like the opportunities

1772
01:40:12,320 --> 01:40:14,720
Speaker 3: that he's going to have or if he even wants to,

1773
01:40:14,840 --> 01:40:17,080
Speaker 3: because you know, it's not much fun going out and

1774
01:40:17,520 --> 01:40:19,519
Speaker 3: you know, shooting a deer and you come up to

1775
01:40:19,560 --> 01:40:22,519
Speaker 3: it and it's all scrawny and sickly, and I mean,

1776
01:40:22,600 --> 01:40:25,320
Speaker 3: like on Doug's place, they can practically identify them by

1777
01:40:25,360 --> 01:40:28,559
Speaker 3: sight now because they know they have the disease and like, yeah,

1778
01:40:28,560 --> 01:40:29,599
Speaker 3: that one's probably infected.

1779
01:40:29,760 --> 01:40:32,080
Speaker 1: Yeah, and you're when you're shooting deer in an area

1780
01:40:32,120 --> 01:40:35,080
Speaker 1: and the majority of the deer you're shooting are positive. Yeah,

1781
01:40:35,120 --> 01:40:37,880
Speaker 1: if you've made the call that, if you've made the

1782
01:40:37,920 --> 01:40:41,800
Speaker 1: call that you don't want to eat it. I came

1783
01:40:41,880 --> 01:40:43,080
Speaker 1: up with a test.

1784
01:40:43,320 --> 01:40:46,560
Speaker 4: I haven't done it yet, Okay, I'm curious.

1785
01:40:46,280 --> 01:40:48,200
Speaker 1: When I'm talking to friends of mine, and I got

1786
01:40:48,200 --> 01:40:52,120
Speaker 1: a lot of friends who really don't they're not hostile,

1787
01:40:52,200 --> 01:40:54,919
Speaker 1: Like they'll listen to us have this conversation. They're not hostile.

1788
01:40:54,920 --> 01:40:56,840
Speaker 1: They discussing it, but in the end of the day,

1789
01:40:56,880 --> 01:41:00,439
Speaker 1: they don't care. Yeah, and it's like, not these are

1790
01:41:00,479 --> 01:41:05,000
Speaker 1: not dumb people. It's like they don't care because they

1791
01:41:05,000 --> 01:41:10,839
Speaker 1: have a hard time with the population level decline, especially

1792
01:41:10,880 --> 01:41:13,840
Speaker 1: when people say, hey, the cre to CWD is the

1793
01:41:13,840 --> 01:41:16,839
Speaker 1: lower deer numbers, or else they might get a disease

1794
01:41:16,920 --> 01:41:19,320
Speaker 1: that will lower deer numbers. And then it's like, okay,

1795
01:41:19,400 --> 01:41:21,839
Speaker 1: well is the is the medicine worse than the sickness?

1796
01:41:22,640 --> 01:41:24,920
Speaker 1: You're telling we should kill all of our deer so

1797
01:41:24,960 --> 01:41:28,280
Speaker 1: that all of our deer don't die. It's like a

1798
01:41:28,400 --> 01:41:31,000
Speaker 1: challenging logic. Yeah, I'm just trying.

1799
01:41:30,800 --> 01:41:33,200
Speaker 3: To get boys back to the burning down the village

1800
01:41:33,240 --> 01:41:33,680
Speaker 3: to save it.

1801
01:41:34,000 --> 01:41:37,200
Speaker 1: Burning the village to save it. And also they're like,

1802
01:41:37,560 --> 01:41:39,840
Speaker 1: no one's gotten it. What are the chances I'll get it?

1803
01:41:39,880 --> 01:41:43,360
Speaker 1: And so really smart people, really smart people who are

1804
01:41:43,640 --> 01:41:50,680
Speaker 1: not adverse to scientific information, have decided that they're not

1805
01:41:50,680 --> 01:41:53,120
Speaker 1: concerned or they're not as concerned as they are about

1806
01:41:53,120 --> 01:41:56,520
Speaker 1: other stuff. And I'll debate this with them, but I formulate,

1807
01:41:56,800 --> 01:41:59,560
Speaker 1: like there's a test I would like to do, and

1808
01:41:59,840 --> 01:42:01,880
Speaker 1: I've talked was before. I would like to go and

1809
01:42:01,920 --> 01:42:07,120
Speaker 1: get deer meat. I'd like to find twenty CWD positive

1810
01:42:07,160 --> 01:42:12,360
Speaker 1: deer sick looking ones and get all that meat and

1811
01:42:12,360 --> 01:42:16,639
Speaker 1: make a burger. Okay, and I make a big batch

1812
01:42:16,640 --> 01:42:21,160
Speaker 1: of burgers. And when I meet a friend that says,

1813
01:42:21,200 --> 01:42:25,320
Speaker 1: they don't care. I make them that burger. I fry

1814
01:42:25,360 --> 01:42:28,120
Speaker 1: them that burger. Yeah, okay, this is me making the patty.

1815
01:42:28,240 --> 01:42:29,720
Speaker 3: Okay, good visual.

1816
01:42:30,960 --> 01:42:32,080
Speaker 1: Fry just how they like it.

1817
01:42:32,720 --> 01:42:36,559
Speaker 3: Meet him rare because killed by cookie, him rare.

1818
01:42:36,720 --> 01:42:39,200
Speaker 4: Pickles who pickles?

1819
01:42:39,439 --> 01:42:41,639
Speaker 1: And I and I would say to them, I'm like, man,

1820
01:42:41,680 --> 01:42:46,120
Speaker 1: in the minute you eat that burger, I'll talk to

1821
01:42:46,160 --> 01:42:47,559
Speaker 1: you about how you don't give a shit about c

1822
01:42:47,720 --> 01:42:50,000
Speaker 1: w D exactly. And I want to know how many

1823
01:42:50,000 --> 01:42:52,080
Speaker 1: burger takers. And I've got people that said, you do it.

1824
01:42:52,120 --> 01:42:53,040
Speaker 1: I'll eat that burger.

1825
01:42:53,400 --> 01:42:53,679
Speaker 3: Man.

1826
01:42:53,880 --> 01:42:59,720
Speaker 1: Here's the thing. If if I made that burger and

1827
01:42:59,720 --> 01:43:01,679
Speaker 1: and someone said, okay, I want you to give those

1828
01:43:01,720 --> 01:43:06,000
Speaker 1: burgers to your kids now, dude, right sitting where I'm

1829
01:43:06,000 --> 01:43:09,960
Speaker 1: sitting right now, there's no even if I could hide

1830
01:43:10,000 --> 01:43:13,360
Speaker 1: it from my wife, which is probably not smart, there

1831
01:43:13,439 --> 01:43:16,080
Speaker 1: is no I just like, right now what I know

1832
01:43:16,240 --> 01:43:18,679
Speaker 1: right now, and i'd like pay a lot of attention,

1833
01:43:18,760 --> 01:43:21,920
Speaker 1: and I try not to be alarmist, right and I'm

1834
01:43:21,920 --> 01:43:26,160
Speaker 1: not alarmist. I could not give that burger to my kid. Yeah,

1835
01:43:26,960 --> 01:43:27,559
Speaker 1: I couldn't do it.

1836
01:43:27,960 --> 01:43:31,920
Speaker 3: I agree, I couldn't do it either, you know, knowing

1837
01:43:32,439 --> 01:43:37,280
Speaker 3: that similar disease in sheep scrapy. They've never had a

1838
01:43:37,320 --> 01:43:43,040
Speaker 3: documented human case that they've known about, even with mad cow.

1839
01:43:43,600 --> 01:43:45,720
Speaker 3: You know, there, I would agree with you about the

1840
01:43:45,840 --> 01:43:49,479
Speaker 3: hundreds of thousands of people. It was only you know,

1841
01:43:49,520 --> 01:43:53,040
Speaker 3: there's been two hundred plus people that have ever gotten

1842
01:43:53,439 --> 01:43:57,439
Speaker 3: mad cow, and they likely have some genetic makeup that

1843
01:43:58,120 --> 01:44:00,800
Speaker 3: you know, made them susceptible to get that, And I

1844
01:44:00,840 --> 01:44:03,879
Speaker 3: suspect it's the same way that there is some genetic

1845
01:44:04,080 --> 01:44:06,840
Speaker 3: makeup in humans that would allow that preon to cross

1846
01:44:06,840 --> 01:44:07,880
Speaker 3: that species barrier.

1847
01:44:08,040 --> 01:44:10,840
Speaker 1: If you know, you're making me want to eat the burger, But.

1848
01:44:10,880 --> 01:44:13,840
Speaker 3: How do you know? Right? Like, so I don't can they?

1849
01:44:14,200 --> 01:44:15,920
Speaker 1: Yeah, that's what we need to find out, so you

1850
01:44:15,960 --> 01:44:18,440
Speaker 1: can test and they give you the green light on burgers.

1851
01:44:18,600 --> 01:44:22,040
Speaker 3: But it's very human centric, isn't it, Like we only

1852
01:44:22,120 --> 01:44:25,760
Speaker 3: care if it's going to affect us, Like, why not

1853
01:44:26,080 --> 01:44:30,240
Speaker 3: care if it affects the wildlife population? Why not care

1854
01:44:30,400 --> 01:44:33,640
Speaker 3: that deer dying this horrible disease that puts holes in

1855
01:44:33,680 --> 01:44:36,480
Speaker 3: their brain? You know, we say we love these animals,

1856
01:44:36,960 --> 01:44:40,439
Speaker 3: but yet we're not willing to do the few steps

1857
01:44:40,760 --> 01:44:44,160
Speaker 3: or like let the management agency take some action in

1858
01:44:44,240 --> 01:44:47,400
Speaker 3: the short term to try to stop that disease because

1859
01:44:47,400 --> 01:44:49,200
Speaker 3: it might be inconveniencing us.

1860
01:44:49,680 --> 01:44:51,920
Speaker 1: I'll tell you a thing. Let's move on from burgers

1861
01:44:51,920 --> 01:44:53,360
Speaker 1: from it. Let's just talk about big giants.

1862
01:44:53,360 --> 01:44:55,679
Speaker 3: How you're making me hungry, I know, but let's.

1863
01:44:55,520 --> 01:44:56,639
Speaker 1: Talk about big giant bucks.

1864
01:44:56,680 --> 01:44:57,080
Speaker 3: Okay.

1865
01:44:59,320 --> 01:45:02,639
Speaker 1: This is This is to my friends who aren't concerned

1866
01:45:02,680 --> 01:45:08,240
Speaker 1: abouty eating burgers they haven't yet seen or are convinced

1867
01:45:08,280 --> 01:45:12,479
Speaker 1: about they haven't yet seen, and are not convinced about

1868
01:45:12,520 --> 01:45:17,000
Speaker 1: population level declines, but they love big giant bucks, and

1869
01:45:17,080 --> 01:45:20,799
Speaker 1: all of their recreational free time is put into the

1870
01:45:20,880 --> 01:45:27,920
Speaker 1: finding of the making suitable habitat for big giant bucks. Now,

1871
01:45:28,000 --> 01:45:29,760
Speaker 1: I was recently in a room with you, and we

1872
01:45:29,760 --> 01:45:33,200
Speaker 1: were looking at prevalence rates and the fact that it's

1873
01:45:33,280 --> 01:45:37,559
Speaker 1: always fatal, right, always kills you. Yep, there's no getting

1874
01:45:37,560 --> 01:45:39,519
Speaker 1: around it. You get it. A deer gets it. That

1875
01:45:39,560 --> 01:45:40,360
Speaker 1: deer is gonna die.

1876
01:45:40,439 --> 01:45:42,919
Speaker 3: Yes, probably sooner than it would have otherwise.

1877
01:45:43,160 --> 01:45:46,000
Speaker 1: Takes six or seven years to make a trophy buck,

1878
01:45:46,520 --> 01:45:49,280
Speaker 1: I mean sometimes less, but like right, yeah, I mean

1879
01:45:49,280 --> 01:45:51,040
Speaker 1: you have a trophy bucket four years. But it takes

1880
01:45:51,120 --> 01:45:54,280
Speaker 1: a long time to make a trophy buck. When the

1881
01:45:54,320 --> 01:45:58,800
Speaker 1: disease becomes so prevalent that a deer's not going to

1882
01:45:58,880 --> 01:46:01,519
Speaker 1: get to two years of AI without getting it right,

1883
01:46:01,560 --> 01:46:05,320
Speaker 1: because prevalency rates are like you know, prevalency rates among

1884
01:46:05,360 --> 01:46:08,679
Speaker 1: two year olds, get this, like sixty percent, seventy percent.

1885
01:46:08,800 --> 01:46:13,080
Speaker 1: It's always fatal. It's fatal within a couple of years.

1886
01:46:13,800 --> 01:46:17,320
Speaker 1: The idea that you might be that if that ideal

1887
01:46:17,439 --> 01:46:19,879
Speaker 1: big bucket is five years old, six years.

1888
01:46:19,680 --> 01:46:22,000
Speaker 3: Old, whatever, yeah, you're not going to get there.

1889
01:46:23,280 --> 01:46:26,639
Speaker 1: That you get to be where they're You're still seeing

1890
01:46:26,680 --> 01:46:28,639
Speaker 1: tons of deer on the ground because there's a carrying

1891
01:46:28,640 --> 01:46:32,360
Speaker 1: capacity to the landscape right right, you might still be

1892
01:46:32,400 --> 01:46:34,200
Speaker 1: seeing there's like, yeah, you go out and see deer

1893
01:46:34,200 --> 01:46:37,000
Speaker 1: all the time, but you just start to be that man,

1894
01:46:37,080 --> 01:46:39,000
Speaker 1: I don't know what's going on. But they are not

1895
01:46:39,080 --> 01:46:41,800
Speaker 1: getting to five years old. Nope, they're not getting to

1896
01:46:41,840 --> 01:46:45,760
Speaker 1: six years old because they're getting sick at one or two,

1897
01:46:46,800 --> 01:46:50,160
Speaker 1: and they're succumbing to the illness at three or four.

1898
01:46:50,400 --> 01:46:50,639
Speaker 3: Yep.

1899
01:46:51,960 --> 01:46:54,679
Speaker 1: And they just aren't making it through because so few

1900
01:46:54,720 --> 01:46:58,200
Speaker 1: make it through anyway, right, remember time early about additive mortality.

1901
01:46:58,280 --> 01:46:58,559
Speaker 3: Yep.

1902
01:46:58,840 --> 01:47:02,200
Speaker 1: So few make it through anyway that they don't get

1903
01:47:02,200 --> 01:47:05,120
Speaker 1: shot by someone, they don't get hit by a car.

1904
01:47:05,760 --> 01:47:07,680
Speaker 1: They don't get killed by a kyle, They don't get

1905
01:47:07,720 --> 01:47:10,920
Speaker 1: hung up on a fence, all the shit that kills deer. Yeah,

1906
01:47:11,160 --> 01:47:13,120
Speaker 1: that all of a sudden, there's this other little thing

1907
01:47:13,200 --> 01:47:16,880
Speaker 1: that just kind of helps guarantee they never get that old.

1908
01:47:18,000 --> 01:47:24,280
Speaker 1: That will make people. Yeah, And it's like that's perhaps

1909
01:47:24,720 --> 01:47:27,000
Speaker 1: we're already seeing that. And one of the things that

1910
01:47:27,200 --> 01:47:33,240
Speaker 1: I think about when I think about CWD is if

1911
01:47:34,920 --> 01:47:37,799
Speaker 1: I wish that all areas had a lot more tracking

1912
01:47:37,880 --> 01:47:40,200
Speaker 1: of not just Boone and Crocket, but like a lot

1913
01:47:40,240 --> 01:47:43,519
Speaker 1: more scoring trafficking. It'd be very interesting if you could

1914
01:47:43,520 --> 01:47:48,599
Speaker 1: have gone back and looked at counties, how many Boone

1915
01:47:48,640 --> 01:47:51,920
Speaker 1: and Crocket entries were counties getting And do you see

1916
01:47:52,000 --> 01:47:53,760
Speaker 1: boone and Crocket entry.

1917
01:47:54,280 --> 01:47:55,120
Speaker 3: As much anymore?

1918
01:47:55,200 --> 01:47:55,639
Speaker 4: Lowering?

1919
01:47:55,920 --> 01:47:59,040
Speaker 1: Yeah, in areas that have had long term infection. Because

1920
01:47:59,080 --> 01:48:04,000
Speaker 1: if what it seems to be true is true, I

1921
01:48:04,040 --> 01:48:06,599
Speaker 1: think that you're gonna have yet another thing.

1922
01:48:07,479 --> 01:48:10,480
Speaker 3: We'll steal that idea for box Master's.

1923
01:48:10,120 --> 01:48:11,960
Speaker 4: Projects back to college.

1924
01:48:12,080 --> 01:48:16,080
Speaker 1: Yeah, that prevents another thing, making bucks not be five

1925
01:48:16,160 --> 01:48:17,880
Speaker 1: years old or six years old or whatever. To help

1926
01:48:18,040 --> 01:48:23,519
Speaker 1: right trophy bucks. That man, I got friends that will

1927
01:48:23,520 --> 01:48:27,760
Speaker 1: go out and spend days hinge, cutting trees and plantness

1928
01:48:27,760 --> 01:48:31,160
Speaker 1: and that, right mm hm. And if you told them, like, man,

1929
01:48:31,200 --> 01:48:33,280
Speaker 1: you know what your biggest problem is about making big

1930
01:48:33,320 --> 01:48:35,080
Speaker 1: bucks and getting big bucks is they're not gonna live

1931
01:48:35,120 --> 01:48:37,480
Speaker 1: that long anyway, dude, because of a disease.

1932
01:48:39,000 --> 01:48:41,040
Speaker 3: That that would change their tune.

1933
01:48:41,680 --> 01:48:44,040
Speaker 1: Yeah, me, it's the it's the eat.

1934
01:48:44,400 --> 01:48:47,120
Speaker 3: I know well that. I mean people, You're called meat

1935
01:48:47,120 --> 01:48:49,799
Speaker 3: eater for a reason, right, like people hunt for different reasons.

1936
01:48:49,960 --> 01:48:51,880
Speaker 1: Yeah, that's not why I with that, kid, So why

1937
01:48:51,880 --> 01:48:53,080
Speaker 1: I came up with that name? You know, I came

1938
01:48:53,160 --> 01:48:55,720
Speaker 1: up with that name the show. No. I had a

1939
01:48:55,760 --> 01:48:57,680
Speaker 1: little little teeny kid, and we'd read a lot of

1940
01:48:57,680 --> 01:49:01,120
Speaker 1: books about animals, and we'd read about books about dinosaurs

1941
01:49:01,400 --> 01:49:04,440
Speaker 1: and you get to the t rex chapter, this frocious

1942
01:49:04,479 --> 01:49:08,120
Speaker 1: meat eater. That sounds a cool word, very good. I

1943
01:49:08,200 --> 01:49:11,200
Speaker 1: like the word sabertooth too. Oh yeah, it could have

1944
01:49:11,280 --> 01:49:12,080
Speaker 1: just been that all long.

1945
01:49:12,160 --> 01:49:12,360
Speaker 3: Yeah.

1946
01:49:13,240 --> 01:49:15,679
Speaker 1: I eat a lot of deer meat. Yeah, me too,

1947
01:49:15,800 --> 01:49:18,400
Speaker 1: And I get and that scares me. Man.

1948
01:49:18,760 --> 01:49:21,400
Speaker 3: Well, that's the thing. Everybody's going to have a different

1949
01:49:21,520 --> 01:49:23,600
Speaker 3: just like the lead, Like, people are going to have

1950
01:49:23,640 --> 01:49:27,360
Speaker 3: different reasons for caring about something. So you asked about

1951
01:49:27,360 --> 01:49:30,400
Speaker 3: ecology like I try to give people a suite of

1952
01:49:30,479 --> 01:49:33,519
Speaker 3: like all the different why they should care about these,

1953
01:49:33,880 --> 01:49:36,920
Speaker 3: Like maybe they don't care about deer populations, Maybe they

1954
01:49:36,960 --> 01:49:38,559
Speaker 3: just care about what it's going to do to them,

1955
01:49:38,600 --> 01:49:40,880
Speaker 3: Maybe they care about big bucks. Like try to give

1956
01:49:40,880 --> 01:49:44,400
Speaker 3: them enough information that they can make an informed choice

1957
01:49:44,400 --> 01:49:44,880
Speaker 3: about it.

1958
01:49:45,400 --> 01:49:50,840
Speaker 1: But you mentioned a thing about eagles and lead, and

1959
01:49:50,880 --> 01:49:53,240
Speaker 1: I think it's true of deer diseases. There's another thing

1960
01:49:53,240 --> 01:49:56,560
Speaker 1: I think about when I said around worrying about CWD

1961
01:49:56,960 --> 01:50:04,439
Speaker 1: is like, uh, there's no doubt that human activities are

1962
01:50:04,840 --> 01:50:09,400
Speaker 1: increasing the spread. Yes, it's not really a debatable point.

1963
01:50:09,439 --> 01:50:11,600
Speaker 1: I mean, there's other things, and there's other things spreading it,

1964
01:50:11,680 --> 01:50:19,000
Speaker 1: but humans are facilitating increasing spread. We are stewards of

1965
01:50:19,040 --> 01:50:26,559
Speaker 1: the landscape. Ideally, we're stewards of the landscape. Do you

1966
01:50:27,360 --> 01:50:30,800
Speaker 1: if you love deer and you love deer hunting, is

1967
01:50:30,880 --> 01:50:35,160
Speaker 1: it in you to be like really okay with the

1968
01:50:35,240 --> 01:50:39,800
Speaker 1: disease that kills deer.

1969
01:50:40,360 --> 01:50:45,600
Speaker 3: Yeah. That's why I don't understand how people can be like, eh.

1970
01:50:44,240 --> 01:50:48,800
Speaker 1: No, no big thing, because there's well, someone's gonna and

1971
01:50:49,240 --> 01:50:51,920
Speaker 1: I want to talk about this before we close. Someone

1972
01:50:52,320 --> 01:50:53,840
Speaker 1: and I do it too, is going to be what

1973
01:50:53,880 --> 01:50:54,800
Speaker 1: about EHD.

1974
01:50:55,120 --> 01:50:56,759
Speaker 3: Yeah, we can talk about.

1975
01:50:57,080 --> 01:50:59,880
Speaker 1: What about EHD. I mean, here's a disease that not

1976
01:51:00,240 --> 01:51:06,240
Speaker 1: some hard it's got to be painful, sure, but they

1977
01:51:06,240 --> 01:51:06,840
Speaker 1: give me the butt.

1978
01:51:06,960 --> 01:51:07,280
Speaker 3: Okay.

1979
01:51:07,760 --> 01:51:09,559
Speaker 1: So I say that we're talking about CWD, and I

1980
01:51:09,600 --> 01:51:11,760
Speaker 1: say to you, yeah, well, what about EHD that kills

1981
01:51:11,800 --> 01:51:12,519
Speaker 1: all kinds of deer?

1982
01:51:12,640 --> 01:51:17,440
Speaker 3: So EHD episodic chemorrhagic disease, and we'll throw in blue tongue,

1983
01:51:17,560 --> 01:51:20,599
Speaker 3: which is another haemorrhagic disease, and just call them HD

1984
01:51:20,720 --> 01:51:23,800
Speaker 3: for hemorrhagic diseases because they function pretty much the same.

1985
01:51:23,920 --> 01:51:27,080
Speaker 1: So I said, okay, what about HD that kills all

1986
01:51:27,120 --> 01:51:27,639
Speaker 1: kinds of deer?

1987
01:51:27,720 --> 01:51:32,200
Speaker 3: There you go, Yes, it does kill deer, but not

1988
01:51:32,320 --> 01:51:36,640
Speaker 3: all deer. It's not fatal like CWD. And in New

1989
01:51:36,720 --> 01:51:41,640
Speaker 3: York we've been doing another study on this because we

1990
01:51:41,760 --> 01:51:46,040
Speaker 3: had some pretty significant outbreaks in like two thousand, you know,

1991
01:51:46,080 --> 01:51:50,839
Speaker 3: which was super inconvenient timing, since you know, the pandemic

1992
01:51:50,920 --> 01:51:53,000
Speaker 3: was going on and people were in their houses and

1993
01:51:53,200 --> 01:51:57,439
Speaker 3: just kind of trying to deal with a big deer

1994
01:51:57,560 --> 01:52:00,880
Speaker 3: die off. So it started in the Hudson Valley and

1995
01:52:01,240 --> 01:52:04,200
Speaker 3: we started tracking reports from the public. The biologists were

1996
01:52:04,240 --> 01:52:07,160
Speaker 3: going out and collecting deer. It's challenging.

1997
01:52:07,680 --> 01:52:11,240
Speaker 6: We can we include the creepy part, sure, which one

1998
01:52:11,360 --> 01:52:15,519
Speaker 6: floating in ponds? Yeah so okay, laying next to the creek,

1999
01:52:15,560 --> 01:52:17,759
Speaker 6: floating in ponds in a water trough.

2000
01:52:18,400 --> 01:52:23,880
Speaker 3: So with hemorrhagic diseases, the deer get a bad viral

2001
01:52:24,240 --> 01:52:28,280
Speaker 3: infection and so they get very feverish. So a lot

2002
01:52:28,280 --> 01:52:30,040
Speaker 3: of times they'll go to water, which is why you

2003
01:52:30,120 --> 01:52:33,360
Speaker 3: find these deer around water. But we have to be

2004
01:52:33,400 --> 01:52:35,639
Speaker 3: careful because we mentioned that we had a Raby's positive

2005
01:52:35,640 --> 01:52:38,800
Speaker 3: deer a couple weeks ago. Rabies a lot of times

2006
01:52:38,800 --> 01:52:42,240
Speaker 3: will find the deer in water too, again another viral disease.

2007
01:52:42,520 --> 01:52:44,280
Speaker 3: They tend to go to water when they get feverish.

2008
01:52:44,360 --> 01:52:46,200
Speaker 1: We got to do a big digression. I'm sorry, okay,

2009
01:52:46,200 --> 01:52:50,040
Speaker 1: because this is the thing I've always wandered about. Why

2010
01:52:50,680 --> 01:52:53,280
Speaker 1: how can rabies at one time be that they're drawn

2011
01:52:53,360 --> 01:52:57,800
Speaker 1: to water, but then also in humans it induces hydrophobia.

2012
01:52:57,960 --> 01:53:00,240
Speaker 3: Yeah, well, I mean you can be thirsty and go

2013
01:53:00,280 --> 01:53:05,200
Speaker 3: to water, but then the paralysis of being able to drink.

2014
01:53:05,920 --> 01:53:07,800
Speaker 3: So it might not be that you're afraid of water.

2015
01:53:08,000 --> 01:53:11,599
Speaker 3: It might be that your body stops functioning to allow

2016
01:53:11,640 --> 01:53:12,240
Speaker 3: you to drink.

2017
01:53:13,600 --> 01:53:16,599
Speaker 1: But I thought, so, it's not that people get scared

2018
01:53:16,640 --> 01:53:17,160
Speaker 1: of water.

2019
01:53:17,400 --> 01:53:21,600
Speaker 3: Yeah, I'm not up on the human hydra, I know,

2020
01:53:22,439 --> 01:53:27,640
Speaker 3: but yeah, it's more that they're not physiologically able to

2021
01:53:27,720 --> 01:53:31,240
Speaker 3: drink anymore. So they're very thirsty, they're feverish, but they

2022
01:53:31,240 --> 01:53:32,120
Speaker 3: can't drink.

2023
01:53:32,360 --> 01:53:34,880
Speaker 1: John defended my family from a rabid squirrel one time.

2024
01:53:35,040 --> 01:53:36,520
Speaker 3: No, but please tell.

2025
01:53:37,560 --> 01:53:39,360
Speaker 1: It was in this state. We were at a friend's.

2026
01:53:39,479 --> 01:53:43,200
Speaker 1: We were visiting a friend. A squirrel came to that

2027
01:53:43,240 --> 01:53:45,519
Speaker 1: My kids were al swimming in the pool. The squirrel

2028
01:53:45,600 --> 01:53:48,480
Speaker 1: ran up to the edge of the pool, very agitated,

2029
01:53:49,280 --> 01:53:52,400
Speaker 1: started running up and down the pool like if this

2030
01:53:52,439 --> 01:53:55,320
Speaker 1: is the pool? My kids are like sprinkled throughout here,

2031
01:53:55,840 --> 01:53:57,599
Speaker 1: and the squirrel is going like this back and forth,

2032
01:53:57,640 --> 01:54:00,759
Speaker 1: back and forth, back and forth, jumps in the pool,

2033
01:54:01,840 --> 01:54:06,360
Speaker 1: starts chasing kids around. I grabbed the squirrel by the

2034
01:54:06,360 --> 01:54:08,080
Speaker 1: tail and gave.

2035
01:54:07,920 --> 01:54:09,040
Speaker 4: It a no.

2036
01:54:10,479 --> 01:54:11,280
Speaker 3: Squirrel flinger.

2037
01:54:12,560 --> 01:54:17,760
Speaker 1: Yeah, I had you been rabbit rodents.

2038
01:54:18,120 --> 01:54:20,439
Speaker 3: We don't see a lot of rabies and squirrels. Rodents

2039
01:54:20,439 --> 01:54:22,960
Speaker 3: don't get it as often. But we just did have

2040
01:54:23,040 --> 01:54:26,040
Speaker 3: the beaver that was positive for rabies.

2041
01:54:26,080 --> 01:54:27,840
Speaker 1: How else would you explain what that squirrel was doing?

2042
01:54:27,880 --> 01:54:29,720
Speaker 3: Squirrels have a lot of issues. I don't know if

2043
01:54:29,720 --> 01:54:30,280
Speaker 3: you've noticed this.

2044
01:54:33,320 --> 01:54:33,720
Speaker 4: I don't know.

2045
01:54:34,000 --> 01:54:36,240
Speaker 1: You guys have had a rabid beaver. Yeah, you've been

2046
01:54:36,360 --> 01:54:39,160
Speaker 1: five people. He did make a movie about that.

2047
01:54:39,840 --> 01:54:41,680
Speaker 3: There you go, you're the production company.

2048
01:54:41,720 --> 01:54:43,520
Speaker 1: Okay, So back to EHD.

2049
01:54:44,000 --> 01:54:44,680
Speaker 3: Back to HD.

2050
01:54:45,000 --> 01:54:48,240
Speaker 1: So deer could get rabies, yes, and they'll wind up

2051
01:54:48,240 --> 01:54:49,560
Speaker 1: in the water sometimes.

2052
01:54:49,680 --> 01:54:52,840
Speaker 3: Yeah, But if I hear about deer in water, I

2053
01:54:53,280 --> 01:54:58,200
Speaker 3: usually think rabies or hamorrhagic disease. So why should we

2054
01:54:58,280 --> 01:55:03,560
Speaker 3: worry more about CWD than and hamorrhagic diseases? Even though

2055
01:55:03,880 --> 01:55:05,920
Speaker 3: we had a big die off there were you know,

2056
01:55:06,200 --> 01:55:09,760
Speaker 3: over two thousand dead deer reported. We don't know how

2057
01:55:09,840 --> 01:55:13,280
Speaker 3: many actually died in the area that.

2058
01:55:13,280 --> 01:55:15,040
Speaker 1: Was affected in a short period of time.

2059
01:55:15,320 --> 01:55:18,560
Speaker 3: Yeah, one summer wow. And then the next year we

2060
01:55:18,600 --> 01:55:21,960
Speaker 3: also had pretty significant die off, but it was more

2061
01:55:22,080 --> 01:55:25,080
Speaker 3: sprinkled around the state, so Hudson Valley up towards Lake,

2062
01:55:25,120 --> 01:55:30,000
Speaker 3: Ontario and then on Long Island. But what we did

2063
01:55:30,320 --> 01:55:33,680
Speaker 3: in those hunting seasons was we collected spleen and we

2064
01:55:33,680 --> 01:55:39,320
Speaker 3: collected blood from hunters, and we looked for antibodies to

2065
01:55:39,600 --> 01:55:42,240
Speaker 3: the viruses to see if any of the deer survived,

2066
01:55:42,320 --> 01:55:45,320
Speaker 3: and we found a pretty good proportion of deer had

2067
01:55:45,360 --> 01:55:49,800
Speaker 3: been infected and then survived. So those are very sort

2068
01:55:49,840 --> 01:55:56,400
Speaker 3: of short term transitory mortality events. And then last year

2069
01:55:56,680 --> 01:55:59,880
Speaker 3: there was like nothing like no big, big mortality. So

2070
01:56:00,120 --> 01:56:02,720
Speaker 3: it might hit local areas pretty hard and knock the

2071
01:56:02,720 --> 01:56:08,240
Speaker 3: deer down because that virus is transmitted by a biting

2072
01:56:08,320 --> 01:56:13,640
Speaker 3: midge puliqoites, and those don't travel very far. So we're

2073
01:56:13,640 --> 01:56:16,600
Speaker 3: still trying to figure out the right environmental conditions, whether

2074
01:56:16,720 --> 01:56:20,680
Speaker 3: they get dry or they wet for breeding in mudflats,

2075
01:56:21,400 --> 01:56:25,400
Speaker 3: and the deer seem to rebound really quickly with those,

2076
01:56:25,520 --> 01:56:28,320
Speaker 3: so that is something that they can build up immunity

2077
01:56:28,360 --> 01:56:33,680
Speaker 3: to and you know, continue on without a long term

2078
01:56:33,720 --> 01:56:37,120
Speaker 3: population impact. It might be short term, but you know,

2079
01:56:37,280 --> 01:56:40,040
Speaker 3: over the long term it's not gonna you know, do

2080
01:56:40,120 --> 01:56:41,960
Speaker 3: as much damage as chronic wasting disease.

2081
01:56:42,160 --> 01:56:44,000
Speaker 1: And I'll point out too that you don't have to

2082
01:56:44,080 --> 01:56:50,600
Speaker 1: have that nagging fear right about catching a brain disease.

2083
01:56:50,880 --> 01:56:55,480
Speaker 3: That's true. Yeah, we generally recommend people don't eat stuff

2084
01:56:55,520 --> 01:56:59,760
Speaker 3: they find dead, so like, yeah, haemorrhagic disease, they kind

2085
01:56:59,760 --> 01:57:02,680
Speaker 3: of mushy real fast because it's usually in the heat

2086
01:57:02,680 --> 01:57:03,200
Speaker 3: of the summer.

2087
01:57:03,640 --> 01:57:07,200
Speaker 1: Got it. Well, I just mean I don't mean that

2088
01:57:07,240 --> 01:57:12,400
Speaker 1: about finding dead deer and eating it. Yeah, I just mean, like, yeah,

2089
01:57:12,480 --> 01:57:15,440
Speaker 1: why you know, as much as I was pointing, I

2090
01:57:15,480 --> 01:57:17,680
Speaker 1: was playing Devil's advocate to point out that, well, why

2091
01:57:17,760 --> 01:57:20,000
Speaker 1: not worry about EHD because that kills a bunch of deer,

2092
01:57:20,200 --> 01:57:21,960
Speaker 1: And I would just say, added to the fact that

2093
01:57:22,000 --> 01:57:25,960
Speaker 1: there's some resiliency that that they bounce back quickly, that

2094
01:57:26,040 --> 01:57:28,160
Speaker 1: you can get it passed through a deer herd, and

2095
01:57:28,200 --> 01:57:29,840
Speaker 1: then a couple of years later it's not in the

2096
01:57:29,880 --> 01:57:34,200
Speaker 1: deer herd, it's different. But then there's also that later

2097
01:57:34,400 --> 01:57:35,720
Speaker 1: when you harvest a deer.

2098
01:57:35,560 --> 01:57:40,360
Speaker 6: That yeah, you added, you're not at right, Well, we

2099
01:57:40,400 --> 01:57:42,720
Speaker 6: don't know if you're at risk, you're not at potential risk.

2100
01:57:43,480 --> 01:57:45,760
Speaker 4: There you go, do you.

2101
01:57:48,640 --> 01:57:50,480
Speaker 1: I don't know how well you can word this. This

2102
01:57:50,520 --> 01:57:57,080
Speaker 1: is my last question about CWD. Do you don't eat it?

2103
01:57:57,760 --> 01:57:59,160
Speaker 1: I'm just asking you personally what.

2104
01:57:59,320 --> 01:58:00,840
Speaker 3: I hate to see to be positive deer?

2105
01:58:00,920 --> 01:58:01,160
Speaker 1: Yeah?

2106
01:58:01,280 --> 01:58:02,160
Speaker 3: No, m.

2107
01:58:03,800 --> 01:58:05,360
Speaker 4: Why I don't have to.

2108
01:58:08,600 --> 01:58:11,320
Speaker 3: I have a choice there, so you know, I'm not starving,

2109
01:58:11,520 --> 01:58:16,640
Speaker 3: like I think the risk to humans is very small,

2110
01:58:16,800 --> 01:58:18,520
Speaker 3: but why chance it?

2111
01:58:19,120 --> 01:58:19,480
Speaker 1: Yeah?

2112
01:58:19,560 --> 01:58:23,200
Speaker 3: I mean Honestly, I've with the field work I've done

2113
01:58:23,200 --> 01:58:26,280
Speaker 3: and taking CBD samples from live deer, I've been in

2114
01:58:26,320 --> 01:58:29,400
Speaker 3: their mouth like I was taking Tounsler biopsies when I

2115
01:58:29,440 --> 01:58:32,760
Speaker 3: was doing my research. So like, I've had c TOBD

2116
01:58:32,880 --> 01:58:36,560
Speaker 3: positive deer breathing in my face that I know they

2117
01:58:36,600 --> 01:58:40,520
Speaker 3: were positive. So yeah, I'm probably going to donate my

2118
01:58:40,520 --> 01:58:43,120
Speaker 3: brain to science, like when I kick the bucket, just

2119
01:58:43,160 --> 01:58:44,640
Speaker 3: so people can check for preance.

2120
01:58:47,240 --> 01:58:49,000
Speaker 1: We've been talking a lot about we're trying to get

2121
01:58:49,040 --> 01:58:53,680
Speaker 1: someone from the body farm. Where's that place, Tennessee? I believe, yeah,

2122
01:58:53,880 --> 01:58:55,720
Speaker 1: they don't if you want to donate your science.

2123
01:58:55,760 --> 01:58:58,080
Speaker 3: We heard that they are, like are they not taking more?

2124
01:58:58,720 --> 01:58:59,960
Speaker 1: Everybody wants their body there?

2125
01:59:00,040 --> 01:59:01,720
Speaker 3: Oh really it's a popular thing now.

2126
01:59:02,120 --> 01:59:03,120
Speaker 1: They can't accept them all.

2127
01:59:03,720 --> 01:59:06,440
Speaker 3: Well, maybe you can get on a wait list.

2128
01:59:07,440 --> 01:59:10,960
Speaker 1: Did some work there. You know raccoons like, uh, when

2129
01:59:10,960 --> 01:59:14,080
Speaker 1: it comes to carcass preference on raccoons, they're like people

2130
01:59:14,080 --> 01:59:15,200
Speaker 1: who have diabetes.

2131
01:59:15,800 --> 01:59:19,800
Speaker 5: Interesting, I did not know that, I mean just from

2132
01:59:19,800 --> 01:59:23,440
Speaker 5: this person who wrote in, Yeah, I would verify that.

2133
01:59:23,520 --> 01:59:26,080
Speaker 5: I didn't know raccoons were that picky about there.

2134
01:59:26,680 --> 01:59:28,160
Speaker 1: They're like thighs and buttocks.

2135
01:59:28,400 --> 01:59:30,360
Speaker 3: Well, don't you.

2136
01:59:30,480 --> 01:59:35,520
Speaker 1: Yeah, for sure. Okay, diabetes.

2137
01:59:36,000 --> 01:59:39,000
Speaker 3: Interesting. I'm going to have to check this one out.

2138
01:59:39,360 --> 01:59:41,200
Speaker 1: We're gonna try to do a site visit down there.

2139
01:59:41,400 --> 01:59:44,360
Speaker 3: Okay, that'll be good. Now that you got some more

2140
01:59:44,400 --> 01:59:45,800
Speaker 3: dead animals, can I come with.

2141
01:59:45,720 --> 01:59:48,160
Speaker 1: You where you hang out and work? You probably have

2142
01:59:48,320 --> 01:59:49,440
Speaker 1: zero problem with that.

2143
01:59:50,360 --> 01:59:52,120
Speaker 3: I don't know. Humans screws me out a little bit.

2144
01:59:52,160 --> 01:59:55,640
Speaker 3: Like animals I'm okay with, but people are kind of gross.

2145
01:59:56,200 --> 01:59:59,800
Speaker 1: I was when did our tour today, I was thinking that.

2146
02:00:02,800 --> 02:00:08,560
Speaker 1: I was thinking about the resiliency of of your colleagues.

2147
02:00:09,160 --> 02:00:10,720
Speaker 3: The ones that have to cut up the dead stuff.

2148
02:00:10,760 --> 02:00:11,520
Speaker 1: It's a lot.

2149
02:00:12,760 --> 02:00:14,000
Speaker 3: Oh smells, it could be.

2150
02:00:14,160 --> 02:00:19,800
Speaker 1: It's a lot. It's it's like just the environment, right,

2151
02:00:20,280 --> 02:00:26,280
Speaker 1: that lab environment, and and just the the what's coming

2152
02:00:26,320 --> 02:00:28,480
Speaker 1: through the door, right, it's a lot.

2153
02:00:28,960 --> 02:00:32,160
Speaker 4: Yeah, I mean I'm sure you used pretty quick, right.

2154
02:00:32,280 --> 02:00:32,480
Speaker 1: Yeah.

2155
02:00:32,520 --> 02:00:34,560
Speaker 3: It's just what their job is. I mean, you think

2156
02:00:34,560 --> 02:00:37,800
Speaker 3: about people working like nurses having to deal with live

2157
02:00:37,880 --> 02:00:41,120
Speaker 3: people complaining all the time like at least these are dead.

2158
02:00:41,240 --> 02:00:43,960
Speaker 1: So well, it's different though, because I don't get that.

2159
02:00:44,120 --> 02:00:47,920
Speaker 1: It's a lot feeling hanging out in a butcher shop

2160
02:00:48,040 --> 02:00:50,160
Speaker 1: or in a slaughterhouse that doesn't because you're because it's

2161
02:00:50,160 --> 02:00:54,480
Speaker 1: like food production, but the disease element, okay, the sickness element.

2162
02:00:54,560 --> 02:00:58,440
Speaker 3: But see they find joy and people that really like

2163
02:00:58,520 --> 02:01:02,840
Speaker 3: kid exactly. You know, they get very excited. I mean

2164
02:01:02,880 --> 02:01:07,000
Speaker 3: you mentioned nasal bots previously, and when I was doing

2165
02:01:07,520 --> 02:01:10,600
Speaker 3: the helicopter capture for my PhD to catch deer, we

2166
02:01:10,600 --> 02:01:14,560
Speaker 3: had immortality and so we euthanized it. And one of

2167
02:01:14,600 --> 02:01:18,000
Speaker 3: my fellow grad students, who loves dead stuff, you know,

2168
02:01:18,200 --> 02:01:21,520
Speaker 3: started doing the knee cropsy and it was chock full

2169
02:01:21,560 --> 02:01:26,160
Speaker 3: of nasal bots. And this again, this was in South Dakota, Okay.

2170
02:01:26,960 --> 02:01:29,720
Speaker 3: And she was so excited, I mean she pulled. She

2171
02:01:29,760 --> 02:01:31,840
Speaker 3: had two handfuls with the nasal bots and came like

2172
02:01:31,880 --> 02:01:34,920
Speaker 3: skipping around to show us all and just thought it

2173
02:01:35,000 --> 02:01:36,080
Speaker 3: was like the best thing ever.

2174
02:01:36,520 --> 02:01:39,200
Speaker 1: We had some guys in Sonora one time tell us

2175
02:01:39,680 --> 02:01:45,640
Speaker 1: some cowboys and Sonora tell us that that thing does

2176
02:01:45,680 --> 02:01:49,240
Speaker 1: the thinking for the deer. O interesting, It'll take over

2177
02:01:49,840 --> 02:01:52,680
Speaker 1: and do it the deer's thinking, and the deer does

2178
02:01:52,720 --> 02:01:53,360
Speaker 1: it's bidding.

2179
02:01:54,240 --> 02:01:58,120
Speaker 3: All right. You were asking about ticks earlier at the

2180
02:01:58,160 --> 02:02:01,400
Speaker 3: c TOB conference in Denver. Recently they found preons and

2181
02:02:01,480 --> 02:02:02,320
Speaker 3: nasal bots too.

2182
02:02:02,720 --> 02:02:05,240
Speaker 4: Oh yep, sure so.

2183
02:02:06,920 --> 02:02:09,720
Speaker 1: And the fact that one day he just goes it's

2184
02:02:09,760 --> 02:02:10,160
Speaker 1: the hacks.

2185
02:02:10,640 --> 02:02:13,520
Speaker 3: I know, a big old nasal bug out.

2186
02:02:15,400 --> 02:02:16,800
Speaker 1: That a good video to capture.

2187
02:02:16,880 --> 02:02:18,920
Speaker 3: There, there you go aspirational.

2188
02:02:19,680 --> 02:02:22,680
Speaker 1: So how do people what if? What's the best when

2189
02:02:22,720 --> 02:02:24,040
Speaker 1: you want to wake up in the morning and find

2190
02:02:24,040 --> 02:02:26,120
Speaker 1: out about wildlife diseases? What do you like to read?

2191
02:02:27,000 --> 02:02:28,080
Speaker 3: What do I like to read?

2192
02:02:28,360 --> 02:02:31,200
Speaker 1: Yeah? Like where if someone wants to track wildlife disease issues.

2193
02:02:31,240 --> 02:02:33,200
Speaker 1: Let's say someone's so inspired.

2194
02:02:33,000 --> 02:02:37,240
Speaker 3: So inspired? So I a I am line of work

2195
02:02:38,560 --> 02:02:41,200
Speaker 3: with the Cornell Wildlife Health Lab, which is part of

2196
02:02:41,240 --> 02:02:45,400
Speaker 3: the New York State Wildlife Health Program. Uh, you can

2197
02:02:46,080 --> 02:02:48,320
Speaker 3: look up Cornell Wildlife Health Lab. We've got lots of

2198
02:02:48,360 --> 02:02:51,640
Speaker 3: fact sheets and information about all the research that we do.

2199
02:02:51,840 --> 02:02:54,640
Speaker 3: You can go you know, New York State has a

2200
02:02:54,680 --> 02:02:58,120
Speaker 3: great website. There's other states out there that have really

2201
02:02:58,120 --> 02:03:01,040
Speaker 3: good information. Depending on you know where they are, it's

2202
02:03:01,080 --> 02:03:07,080
Speaker 3: probably more applicable to them. So check out uh your

2203
02:03:07,120 --> 02:03:11,120
Speaker 3: state agency website and see what wildlife health information. But

2204
02:03:11,120 --> 02:03:12,840
Speaker 3: if you want general stuff, you can always come to

2205
02:03:13,320 --> 02:03:14,560
Speaker 3: Cornell Wildlife Health Lab.

2206
02:03:15,360 --> 02:03:17,440
Speaker 1: And don't try to bring a dead animal down here.

2207
02:03:17,880 --> 02:03:20,440
Speaker 3: Well, you can just call us first, call us first. Yeah, yeah,

2208
02:03:20,840 --> 02:03:26,360
Speaker 3: well you can, but it might take a little bit.

2209
02:03:26,400 --> 02:03:26,680
Speaker 1: I might.

2210
02:03:26,880 --> 02:03:29,480
Speaker 3: I might get a nasty phone call and they're like, who.

2211
02:03:29,360 --> 02:03:33,360
Speaker 1: Is this at the right door? The door is so tall,

2212
02:03:34,760 --> 02:03:37,480
Speaker 1: the intake door. Yes, it's so tall.

2213
02:03:38,120 --> 02:03:39,400
Speaker 3: You can back a semi in that.

2214
02:03:39,480 --> 02:03:45,080
Speaker 1: It is known to have accommodated elephants and drafts. Yep,

2215
02:03:45,520 --> 02:03:46,360
Speaker 1: look for that door.

2216
02:03:46,560 --> 02:03:47,960
Speaker 3: That's the door you want.

2217
02:03:48,200 --> 02:03:50,320
Speaker 1: If you say to yourself, that's not going to accommodate

2218
02:03:50,360 --> 02:03:51,560
Speaker 1: an elephant, you're at the wrong door.

2219
02:03:52,480 --> 02:03:53,920
Speaker 3: You can't put an elephant through there.

2220
02:03:55,560 --> 02:03:57,959
Speaker 1: Well, thank you very much for coming on the show. Absolutely,

2221
02:03:58,120 --> 02:04:02,880
Speaker 1: it's fascinating work. I love wildlife and I like to

2222
02:04:03,160 --> 02:04:04,920
Speaker 1: and it's good to know that there's people out there

2223
02:04:04,960 --> 02:04:09,360
Speaker 1: monitoring wildlife diseases and it becomes upsetting to me when

2224
02:04:09,400 --> 02:04:13,520
Speaker 1: there's things that humans. There's preventable things we're doing. Yeah,

2225
02:04:14,000 --> 02:04:16,680
Speaker 1: they could make it that less wildlife vanishes from the

2226
02:04:16,760 --> 02:04:19,080
Speaker 1: landscape because of diseases. So it's good to have a

2227
02:04:19,120 --> 02:04:19,600
Speaker 1: heads up.

2228
02:04:19,760 --> 02:04:23,840
Speaker 3: Yeah. I think remembering we're part of the ecosystem, and

2229
02:04:24,040 --> 02:04:25,720
Speaker 3: you know, if we can be part of the solution

2230
02:04:25,840 --> 02:04:28,520
Speaker 3: rather than part of the problem, that's always a good thing.

2231
02:04:28,760 --> 02:04:32,760
Speaker 1: We're part of the disease ecollogy. We are well, all right, Well,

2232
02:04:32,800 --> 02:04:36,360
Speaker 1: thank you for coming on. I appreciate it. Kristen Schuler, thank.

2233
02:04:36,160 --> 02:04:37,240
Speaker 3: You, thank you.

2234
02:04:43,280 --> 02:04:51,560
Speaker 1: Right all seal great.

2235
02:04:53,800 --> 02:05:07,160
Speaker 3: Like silving the Sun. Ride Ride, Ride on a line, sweetheart.

2236
02:05:08,040 --> 02:05:12,800
Speaker 1: We're done beat this damp horse to death, So taking

2237
02:05:12,880 --> 02:05:15,280
Speaker 1: a new one and ride away.

2238
02:05:18,240 --> 02:05:23,760
Speaker 3: We're done beat this damn horse today, mh. So take

2239
02:05:23,840 --> 02:05:26,080
Speaker 3: a new one and ride on.