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Speaker 1: This is me eater podcast coming at you shirtless, severely,

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Speaker 1: bug bitten, and in my case, underwear listening Hunt podcast,

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Speaker 1: you can't predict anything presented by on X. Hunt creators

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Speaker 1: are the most comprehensive digital mapping system for hunters. Download

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Speaker 1: the Hunt app from the iTunes or Google play store.

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Speaker 1: Nor where you stand with on X. You know we

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Speaker 1: had a guy. We had a guy right in that

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Speaker 1: He's a He's a nudist. You know, do I know

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Speaker 1: what a new this is? My kids they were wondering

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Speaker 1: what nudists where and I had to explain it to him. Um,

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Speaker 1: how much time do you have to spend in the

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Speaker 1: nude to be a nudist? That's what I don't understand,

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Speaker 1: the percentage of the day. That's why when I ran,

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Speaker 1: when I was talking to my kids, I left it

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Speaker 1: to be that a nudist, Um, I kind of let

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Speaker 1: it be that, like a nudist would be someone who

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Speaker 1: would sort of go out of their way to do

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Speaker 1: their chosen activities in a place that would allow them

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Speaker 1: to do them nude like that was sort of where

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Speaker 1: I drew it is nude when possible. I just looked

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Speaker 1: it up. I had watched the intro of a nudist documentary.

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Speaker 1: That's as far as I got. That makes me, and

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Speaker 1: that makes me largely a nudist. No, no, because you'd

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Speaker 1: be nude right now. No, I'd get in all kinds

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Speaker 1: of trouble, like from a like an HR type thing.

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Speaker 1: Phil doesn't need to Phil, Phil would complain. Probably the

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Speaker 1: Oxford Dictionary just defines it as a person who does

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Speaker 1: not wear any clothes because they believe this is more

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Speaker 1: natural and healthy. Yeah, you know, I'm not. I'm not

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Speaker 1: a nudist, but um, we got a talk about with

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Speaker 1: our kids. I was explained to them that there is

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Speaker 1: a there's a thing that happens like because like if

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Speaker 1: they were kind of one night, why do they wear pajamas?

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Speaker 1: I'm gonna get to this nudie that rode in, But

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Speaker 1: they're talking about, like why do we wear pajamas? And

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Speaker 1: I said that people tend to begin life in pajamas

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Speaker 1: and they end life in pajamas. But then there's a long,

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Speaker 1: long window in the middle when you don't wear pajamas.

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Speaker 1: I find I think that's accurate, Like you spend decades

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Speaker 1: not in pajamas. I don't know like when it ends

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Speaker 1: and begins, but you you start and end in pjs. Yeah,

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Speaker 1: if I wear him now, it's more like to have

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Speaker 1: Sunday brunch or something, you know, at the house. Yeah,

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Speaker 1: I can't. I don't even I haven't owned a pair

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Speaker 1: of pajamas, probably since I would got rid of my

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Speaker 1: Superman pajamas. The reason I'm right about this guy's he

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Speaker 1: l counts in the nude. He says, he's. He says,

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Speaker 1: I've une with trad bows, compound bows, rifles. Being nude

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Speaker 1: is the most difficult obstacle i'veter on it myself. He

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Speaker 1: checked into it. Says his nudity is completely legal where

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Speaker 1: he hunts um. He says, you have to be very

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Speaker 1: careful about where you pick. He leaves his shoes on.

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Speaker 1: He says he's been a nudist for many years, and

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Speaker 1: he says one of the main things that you wouldn't

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Speaker 1: think about is just your awareness of wind direction mm

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Speaker 1: hmm is enhanced. You got a lot of receptors. He says.

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Speaker 1: It's it's very free, and he says you pick your

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Speaker 1: routes very carefully. Has he had any kills in the nude?

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Speaker 1: I don't think so. He's got a nudist friend who

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Speaker 1: also hunts different state, also hunts naked. I wonder if

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Speaker 1: they have to wear hunter orange during a modern firearm season.

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Speaker 1: That's when you get that, you get that body paint. Man,

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Speaker 1: if that's all they wear, Uh, ladies and Gentlemanell's Bart,

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Speaker 1: George Bart, how's it going going. Well, I'm gonna you know,

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Speaker 1: we've got a couple of days to cover before we

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Speaker 1: start talking about your work, your recent work. But so

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Speaker 1: all the caribou. Last time we had you on, we

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Speaker 1: talked about Mountain cariboo that are in the Lower Fort

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Speaker 1: which had at the time had like shrank srank, shrank,

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Speaker 1: shrank until there was kind of a couple in Idaho. Yeah,

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Speaker 1: that's been They are gone, like gone gone. We're pretty

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Speaker 1: sure they're gone, gone, gone. Yeah. Um, we did get

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Speaker 1: a little surprised. Oh, I can't even remember when it was.

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Speaker 1: It's been eighteen months or so ago. A couple of

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Speaker 1: caribou turned up in northern Montana. Um, just a fleeting glimpse,

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Speaker 1: you know, they passed through, you know, photographs they were

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Speaker 1: able to document them and then never saw them again.

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Speaker 1: Where do you think they came from? Probably out of

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Speaker 1: the purse cells. But nobody really knows. Um. So that's

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Speaker 1: a lot ask cariboo documented in the lower forty eight

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Speaker 1: and they're actually in Montana. What year was that, I said,

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Speaker 1: it's about eighteen months ago. I think it is right

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Speaker 1: when all the self right when the selker occurred was

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Speaker 1: just yeah, I remember seeing that they had turned up there,

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Speaker 1: and I didn't I never really heard what the story was.

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Speaker 1: Nobody knows the story. They there's no conclusion to that story.

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Speaker 1: They just disappeared. What was the nail on the coffin

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Speaker 1: for the Selkirk. We're not exactly sure they were, you know,

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Speaker 1: there's I think when we talked, there was what a

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Speaker 1: dozen caribou left in that herd, and we were putting

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Speaker 1: together a big project to do a maternal pen and

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Speaker 1: put together a whole bunch of pretty heavy handed management actions.

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Speaker 1: And that march, when we were about to really get

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Speaker 1: going with captures and stuff like that, we started our

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Speaker 1: census and we only found three cows. Um obviously none

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Speaker 1: of them were pregnant, so that everything got put on hold.

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Speaker 1: We completed our senses, only found the three. We found

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Speaker 1: three over in the purcels. I think two of them

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Speaker 1: were bulls. So we decided we'd better get those animals together,

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Speaker 1: and then moved them all north up to revel Stoke

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Speaker 1: and put them in the maternal pin that they have

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Speaker 1: in place up there. Um, put collars on them obviously,

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Speaker 1: and then let Actually it was a kind of a

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Speaker 1: stroke of luck. One of the resident animals from the

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Speaker 1: revel Stoke area dropped down close to the penning site

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Speaker 1: about the time they were being placed, and they captured

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Speaker 1: her and put her in the pen to kind of

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Speaker 1: help them acclimate or whatever. Hopefully the mother up to her,

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Speaker 1: and that did work. They followed her back up to

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Speaker 1: the top and as far as we know right now,

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Speaker 1: they're alive and doing well. And that heard m hm.

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Speaker 1: So the last of the Selkirk Mountains don't live in

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Speaker 1: the last the Selkirk Caribou don't really live in the

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Speaker 1: Selkirks anymore. They're up north, man, I invite you. What

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Speaker 1: was the name of that episode, bart and we covered

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Speaker 1: this whole thing. I don't know it was. We always

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Speaker 1: give them qute, clever names that are hard to remember. Yeah,

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Speaker 1: I think it was number forty three, but I'm not sure. Um,

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Speaker 1: a guy at a guy that guy know that works

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Speaker 1: at Old Town Canoes sent me this article about a dude,

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Speaker 1: a hunter who became the first World Slam recipient across

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Speaker 1: four categories. So everyone knows, um, everyone knows that I

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Speaker 1: happened to be a turkey super slam holder now almost

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Speaker 1: two times super Slam holder. What what? What? What? I what?

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Speaker 1: What that is? Is I have gotten? There's there's there's

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Speaker 1: this notion that there are five subspecies of wild turkeys

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Speaker 1: and and they have in different places, you know, and

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Speaker 1: I've gotten them all and once I get another Florida

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Speaker 1: one I'll have I'll be a two time super slam holder.

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Speaker 1: A world Slam holder is a fellow who gets all

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Speaker 1: five of the you know, all five of our wild turkeys,

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Speaker 1: and then it goes down to get this all five

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Speaker 1: subspecies of our just you know, American wild turkey, and

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Speaker 1: then you go down to Central America or you know,

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Speaker 1: bleeze southern Yucatan area and you get yourself what's called

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Speaker 1: an oscillated turkey, which is a whole different species of turkey.

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Speaker 1: At the point you get an oscillated turkey, you become

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Speaker 1: a world Slam holder. And there's an article about this dude.

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Speaker 1: What's really funny as he's standing there doing a grip

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Speaker 1: and grin with a turkey, and it described the caption

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Speaker 1: says that it's a deceased so he's holding his turkey,

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Speaker 1: says that he poses with a deceased oscillated turkey. Deceased,

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Speaker 1: but so the dude, So this is what the dude did. Though,

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Speaker 1: this is kind of like I can't tell. I can't

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Speaker 1: tell if I think this is the greatest or what thing.

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Speaker 1: So he's done the world slam, So all five North Americans,

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Speaker 1: you know, all all five American wild turkey subspecies, and

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Speaker 1: then the oscillated turkey he's got four times. He's done

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Speaker 1: it with a bow, a crossbow, a modern firearm, and

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Speaker 1: a muzzle loader. So he's he's like a four time

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Speaker 1: for like, I don't know, at what point you're hunting

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Speaker 1: turkeys and at what point you're just sort of checking

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Speaker 1: things off a list. I don't know. I think he's

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Speaker 1: still hunting turkeys. I mean he's out there having fun,

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Speaker 1: still chasing gobblers every time, just choosing a different different fires.

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Speaker 1: I saw it wasn't a gripping. Granted was a picture

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Speaker 1: of a dead turkey today on Instagram, and uh, it

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Speaker 1: was with a pistol shotgun, and I was like, I

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Speaker 1: could get like that, you know, because it's obviously shorter range,

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Speaker 1: you know, easy to pack around. Check the dude a

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Speaker 1: decade to get it done. M So that's what I

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Speaker 1: wish I was better at math six times four four

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Speaker 1: twenty four. So he's getting two point four turkeys per

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Speaker 1: year to work towards the That makes it seem very achievable. Yeah,

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Speaker 1: not crazy heard on the pocket book either, if you

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Speaker 1: think of it that way. Mike Petrasca, congrats, Mike. That's good.

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Speaker 1: When I retire, man, I'm gonna be hot on that

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Speaker 1: dude's heels. Um. Oh, you know, this is the last

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Speaker 1: thing I'm gonna bring up and the this is the

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Speaker 1: last part of the last installment of our I shouldn't

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Speaker 1: say are the Yeah, the ongoing story of like our

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Speaker 1: flip flops appropriate footwear. This has been beaten to death,

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Speaker 1: but you got room from one last Sure, you never

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Speaker 1: know he might learn something. So dude, he's out like

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Speaker 1: supposedly scouting for turkeys, and which he says, comes down

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Speaker 1: to sit in the back of a drinking beer, listening

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Speaker 1: for gobbles at night and his dog. He doesn't put

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Speaker 1: the he doesn't put an e collar on his dog,

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Speaker 1: and his dog just sized to get up and take

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Speaker 1: off and heads out into a swamp. He he's got

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Speaker 1: his flip flops on. So the dog goes off in

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Speaker 1: the swamp and it's like knee deep muck. And because

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Speaker 1: he has his flip flops on, he can't go in

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Speaker 1: there after it, but his body he's got sneakers on,

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Speaker 1: goes in there after it. The go out the flip flops,

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Speaker 1: takes his flip flops off so he can run and

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Speaker 1: runs around on the road and in so doing encounters

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Speaker 1: the neighbor and scores a hunting permission on the neighbor's place.

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Speaker 1: So it's sort of like an inadvertent uh af fect.

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Speaker 1: You were in flip flops, Scalel's got him on right

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Speaker 1: now and inverted upside to to run around flip flops.

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Speaker 1: Uh oh, companies, I want to talk about two real

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Speaker 1: quick back when when people used to just freely fly

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Speaker 1: around the country on um airplanes back in the long

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Speaker 1: long ago. Yeah, the boy, you just get on an

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Speaker 1: airplane and just go where the hell you felt like,

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Speaker 1: no mask, nothing, I travel Yanni. I keep wanting to

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Speaker 1: bring this up with Yannie. I realized Yanni is a

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Speaker 1: Yanni's a he's like a chatter, like you like to

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Speaker 1: chit chat with your seatmates, m more so than than

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Speaker 1: I'm comfortable doing, more so than you, that's for sure.

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Speaker 1: And uh, I keep my I never like brought this

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Speaker 1: up with you life, like, yeah, that's talking with someone.

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Speaker 1: They're talking behind me. I can hear him talking, and yeah,

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Speaker 1: he's telling them all about just different stuff and what

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Speaker 1: he's coming from doing. And Yannie gets to talking about

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Speaker 1: game meat, wild game meating. I laughed about this because

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Speaker 1: the woman he's sitting to, sitting next to you, tries

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Speaker 1: to up him, and I even wrote it down in

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Speaker 1: my nose and she told him my favorite meat of

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Speaker 1: all time is kangaroo. Yeah. I remember that too, because

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Speaker 1: I didn't realize you're sitting in front of me until

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Speaker 1: much later in the flight. But I remember when she

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Speaker 1: said that. I said, is that right? And I opened

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Speaker 1: my book and she's like, don't what you be telling

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Speaker 1: me about this wild game? You know? I also heard this,

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Speaker 1: do it? Like this interesting story on an airplane not

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Speaker 1: long ago, one of my final flights, and the guy

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Speaker 1: was it was like we were on a plane there's

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Speaker 1: a bunch of dudes going down to We're going home

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Speaker 1: from the Midge, Minnesota, And there was a bunch of

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Speaker 1: Minnesota dudes going down to watch their team do spring

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Speaker 1: training in Florida, which that that is a trip I

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Speaker 1: cannot I cannot imagine. Did you know that people did

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Speaker 1: this prior to that? No, No, they're real excited. They're

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Speaker 1: traveling down to watch people practice playing baseball in Florida.

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Speaker 1: But he was observing to someone how the area where

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Speaker 1: their Minnesota team carries on this activity that he was

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Speaker 1: talking about how it got built up over the years,

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Speaker 1: and he was saying, how you used to be when

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Speaker 1: you went down there, it was all cows and now

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Speaker 1: it's all buildings. And I started thinking about that. Um,

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Speaker 1: remember the old cows, not condos, the bumber stick are. Yeah,

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Speaker 1: you still see a few around this town. I never

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Speaker 1: think of that like applied to Florida, But like that,

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Speaker 1: the cattle operations replaced by just buildings. I look at

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Speaker 1: like every day, I look friendly when I look at cows.

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Speaker 1: I think I feel like more affection toward than normal. Um.

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Speaker 1: Over time, So Bart, what's going on? Man? What else

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Speaker 1: has happening? We got a bunch of stuff to talk

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Speaker 1: about about your mountain lion, crazy mountain lion projects. Yeah,

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Speaker 1: that's been the highlight of my spring. The you know,

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Speaker 1: the COVID stuff is everything kind of weird right now

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Speaker 1: at work, But are you able to get out and

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Speaker 1: do your work still? Kind of Uh, not as much

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Speaker 1: as I'd like. I'm really working like two or three

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Speaker 1: days a week. We don't have childcare available, so my

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Speaker 1: wife and I are kind of fighting over who gets

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Speaker 1: to go to work and who stays home with kids.

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Speaker 1: Got both both of us are supposed to be doing

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Speaker 1: our jobs. But we do have callers out working, but

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Speaker 1: we're still generating quite a bit of data. Like you

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Speaker 1: have you have collars on how many mountain lions. We

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Speaker 1: have five callers out right now. But so these these

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Speaker 1: collars are on a like a five week rotation, So

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Speaker 1: I really don't want more than about four or five

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Speaker 1: because we have to see every one of these cats

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Speaker 1: every week, and that's quite a bit of work, you know,

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Speaker 1: capturing five cats a week. Oh man, you gotta go.

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Speaker 1: You gotta go catch the cat and mess with it

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Speaker 1: every week to put a new collar on it. We

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Speaker 1: don't have to put a new collar on it that

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Speaker 1: once it's collared, though, we revisit that cat once a

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Speaker 1: week tree it, basically approach it and tree it to

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Speaker 1: do what just to grab that data point. So we're

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Speaker 1: measuring the distance. I can get into the details now

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Speaker 1: if you if you want to get a little far

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Speaker 1: ahead of yourself, am I okay? But but here's no,

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Speaker 1: I'm not getting into the details of what you doing yet.

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Speaker 1: I'm just trying to understand, uh, in today's day, in

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Speaker 1: this day and age, what like I thought, you just

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Speaker 1: put them on it and then you just sit back

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Speaker 1: and reap the information the caller and faller research. No,

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Speaker 1: we're not doing that. This is pretty hands on as

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Speaker 1: far as big cat research goes. So we're looking at

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Speaker 1: these cats every week and we are then measuring their

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Speaker 1: response to that. Okay, so let's let's let's let's back up.

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Speaker 1: Let's yeah, we we can't. We can't start there. Okay,

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Speaker 1: you tell me where to start, Well, I can tell

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Speaker 1: you about the project. Project. So the reason the project

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Speaker 1: is occurring because UM, we're seeing lots of depredations. I'm

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Speaker 1: seeing lots of cats hanging around um X urban you know,

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Speaker 1: urban interface areas. UM depredations depredations on what typically small

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Speaker 1: livestock and pets. Typically the way those work, um, a

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Speaker 1: cat kills whatever X number of sheep or a couple

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Speaker 1: of llamas or whatever eats its phil cashes the carcass

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Speaker 1: you know, near the pasture usually and then just lays around,

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Speaker 1: comes back and feeds that night. By the time we

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Speaker 1: get the call, usually it's a day or two. Um.

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Speaker 1: And we started noticing that the cats are right there,

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Speaker 1: Like we would turn the dogs loose from the carcass

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Speaker 1: and they'd have the cat caught, and like thirty seconds

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Speaker 1: sometimes cataby laying two yards away, bedded someplace near the carcass, um,

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Speaker 1: totally not concerned about us pulling in with the pickups

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Speaker 1: and our voices and the dogs making a racket on

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Speaker 1: the box and barking and everything else that's going on. Um.

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Speaker 1: So it kind of got me wondering, like, what's it take.

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Speaker 1: Why are these cats not concerned about all of this

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Speaker 1: goings on that's happening so close to them? Why aren't

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Speaker 1: they running away from all that noise? And then you know,

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Speaker 1: the next question was out, how close can we it

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Speaker 1: to these cats if we don't know it, how close

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Speaker 1: are we walking up to these cats and and maybe

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Speaker 1: never knowing it. Um. They got a couple of questions,

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Speaker 1: all right when they killed someone's dog, Like, is a

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Speaker 1: dog small enough where they don't kind of like eat

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Speaker 1: some and stash it and then come back and feed

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Speaker 1: on it some more. Or do you see him doing

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Speaker 1: that with house dogs? Well, it depends on the size

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Speaker 1: of the dog. A small dog they'll just pick up

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Speaker 1: and walk off with. Oftentimes we don't find, you know,

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Speaker 1: a carcass cashed away if it's a docks in or something.

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Speaker 1: We have found big dogs. We had a like a

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Speaker 1: pit bull that was killed and mostly eaten, and that

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Speaker 1: cat hung around on that carcass for a couple of

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Speaker 1: days eating it before we caught him. And that was

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Speaker 1: some dude's dog. Yeah, it's actually a service dog dog. Yeah.

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Speaker 1: They let it out to pee and the middle of

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Speaker 1: the night and the cat was just laying there waiting

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Speaker 1: and grabbed it and packed it up the hill a

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Speaker 1: few hundred yards and eight him. Obviously, a cat like

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Speaker 1: that I wouldn't want in my study. I get a

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Speaker 1: little alley about running dog killers. Um, because we're running

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Speaker 1: them with dogs. I don't want one of my hounds

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Speaker 1: to get out in front and encounter a bad cat

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Speaker 1: like that. Uh, and then tell me where this is

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Speaker 1: kind of happening where you're doing your work, just so

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Speaker 1: people understand. Like where we are on the map, we're

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Speaker 1: up in northeast Washington, So Spokane, Washington is where I live,

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Speaker 1: north all the way to Canada and then out towards

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Speaker 1: like Lake Roosevelt, Lincoln County. Um, so we have a

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Speaker 1: five county area that we're kind of working in. Pretty rural, Yeah,

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Speaker 1: pretty rural. We have cats close to town. Um had

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Speaker 1: a cat callar like ten minutes away from my house

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Speaker 1: and like I said, I'm right on the north side

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Speaker 1: of Spokane. Um. You know you could see the city

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Speaker 1: from where that cat lived. We have another one right

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Speaker 1: now that's working over by like Mount Spokane, Ski area

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Speaker 1: between there in the city. So rural but not remote.

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Speaker 1: Definitely that kind of wildland urban interface, you know, ex

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Speaker 1: urban however you want to describe it. A lot of

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Speaker 1: white tails to write, lots of white tails, lots of

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Speaker 1: edge habitat, a lot of turkeys there. Um. But yeah,

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Speaker 1: just broken landscape, a lot of small landowners. Ten and

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Speaker 1: twenty acre parcels are very common. When you say Uh,

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Speaker 1: dog killers. Have you found like habitual dog eating cats,

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Speaker 1: like cats that have kind of started to specialize on

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Speaker 1: I don't think that they have started to specialize on

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Speaker 1: hunting and you know, preying on dogs. But I think

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Speaker 1: cats do start to figure things out once they work.

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Speaker 1: And it might have been with a coyote or something,

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Speaker 1: but some sometimes I think those cats do figure out

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Speaker 1: that they can turn and fight a single dog and

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Speaker 1: and pretty easily take care of that problem. Gotcha. Uh

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Speaker 1: do you have Do you see where they kill many

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Speaker 1: lions or lamas, because that's the thing off. I was

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Speaker 1: a mountain lionman. I wouldn't eat anything but lamas. When

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Speaker 1: they get them, do they give them? They just twist

359
00:21:07,560 --> 00:21:11,480
Speaker 1: that neck. Oh yeah, they grabbed that big long neck anywhere.

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Speaker 1: I think it's probably works with them. Uh. We see

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Speaker 1: a lot of lamas, Yeah, a lot of alpacas, to

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Speaker 1: which I guess are slightly smaller than a full sized lama.

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Speaker 1: We had one cat it was a big, young tom

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Speaker 1: hundred and sixty pound tom cat and he's only like

365
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Speaker 1: three years old, right outside of Spokane. That killed fourteen

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Speaker 1: alpacas over over the course of the sweaters. You could

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Speaker 1: make with that and they were. It was a strange

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Speaker 1: one because a cat must have spent quite a bit

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Speaker 1: of time there. He had moved between three different pastures

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Speaker 1: to kill all of these alpacas a period of time

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Speaker 1: to take him to kill all those alpacas, I think

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Speaker 1: just overnight. But it was like a battle scene. There's

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Speaker 1: just carcasses everywhere. It was awful. He knows how uh

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Speaker 1: surplus like there's people that, Um, there's depending on how

375
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Speaker 1: you feel about predators, right, depends on whether you're that

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Speaker 1: whether you embrace or try to deny the thing of

377
00:22:14,160 --> 00:22:20,520
Speaker 1: surplus killing. Yeah, there's it's well documented. Um with livestock,

378
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Speaker 1: we see, I would venture your guests. More than half

379
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Speaker 1: of our depredations right now are multiple animals. What does

380
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Speaker 1: it like? What do you think? I know, you don't

381
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Speaker 1: know what he's thinking, But what is he thinking when

382
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Speaker 1: he wants to when he goes in and kills fourteen

383
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Speaker 1: al pack Because it's just that he's wired, you know,

384
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Speaker 1: like it's opportunity and he can't really picture his future

385
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Speaker 1: food needs or is he like this is a hoot,

386
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Speaker 1: this is great fun. Yeah, I mean it doesn't make

387
00:22:46,960 --> 00:22:50,480
Speaker 1: sense for him, right, Like I knew that if I Yeah,

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Speaker 1: I would look at that as a savings account if

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Speaker 1: I was a lion and just come back when I

390
00:22:54,400 --> 00:22:58,240
Speaker 1: was hungry, but rather than kill them all at once. Um, yeah,

391
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Speaker 1: I don't know what he's thinking. I think it's just irresistible.

392
00:23:00,800 --> 00:23:02,919
Speaker 1: I think all those animals running around in a frenzy,

393
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Speaker 1: and they're sort of in a you know, frenzy themselves,

394
00:23:06,680 --> 00:23:09,200
Speaker 1: and I don't think they can help themselves at that point.

395
00:23:09,359 --> 00:23:11,280
Speaker 1: How does he how is he killing the alpaca? Is

396
00:23:12,200 --> 00:23:15,800
Speaker 1: typically bites to the neck and head, not typically almost

397
00:23:15,840 --> 00:23:18,639
Speaker 1: every single time bites to the neck and head. Was

398
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Speaker 1: the owner of the alpac is pretty distraught? Yeah? They

399
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Speaker 1: were distraught? My brother, he would my brother would. I

400
00:23:26,119 --> 00:23:28,520
Speaker 1: can't imagine he'd be catatonic man if he came out

401
00:23:28,520 --> 00:23:31,119
Speaker 1: and all of his lamas are dead. Yeah, well there.

402
00:23:31,160 --> 00:23:34,639
Speaker 1: I mean, there's a financial value, right, But the bigger

403
00:23:34,800 --> 00:23:37,760
Speaker 1: thing is a lot of these animals they have a

404
00:23:37,800 --> 00:23:39,920
Speaker 1: lot more value than just the finances. There are a

405
00:23:40,000 --> 00:23:42,160
Speaker 1: lot of these people count on their goats and their

406
00:23:42,640 --> 00:23:46,159
Speaker 1: sheep for meat or for four h show animals, for

407
00:23:46,200 --> 00:23:50,240
Speaker 1: their kids and other things. Most of our depredations aren't

408
00:23:50,240 --> 00:23:54,240
Speaker 1: on big like whatever corporate ranches, or something. These are

409
00:23:54,400 --> 00:23:57,560
Speaker 1: like ten acre places with a couple of animals, and

410
00:23:58,440 --> 00:24:00,159
Speaker 1: you know that they used to feed their family or

411
00:24:00,200 --> 00:24:04,440
Speaker 1: do whatever with so yeah, typically the landowner is pretty

412
00:24:04,560 --> 00:24:07,760
Speaker 1: upset about it. How do people the people that lose

413
00:24:07,800 --> 00:24:10,879
Speaker 1: their dog, do they tend to be that this is

414
00:24:10,920 --> 00:24:14,080
Speaker 1: like I'm just asking for a girl's generalization here. Do

415
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Speaker 1: people to lose their dog tend to be circumspect and

416
00:24:16,880 --> 00:24:19,600
Speaker 1: and sort of be like, well, he's just that lions

417
00:24:19,640 --> 00:24:22,280
Speaker 1: just trying to make a living, and you know, I

418
00:24:22,320 --> 00:24:25,920
Speaker 1: can't really blame them. Are they like do they want blood?

419
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Speaker 1: They want its head on a on a spike, like

420
00:24:28,520 --> 00:24:34,080
Speaker 1: what's there? General, most of the time, if it's a dog,

421
00:24:34,160 --> 00:24:36,960
Speaker 1: like you know, particularly a pet dog, not a working dog,

422
00:24:37,040 --> 00:24:39,080
Speaker 1: people are pretty fired up. They want that cat dead.

423
00:24:40,119 --> 00:24:43,919
Speaker 1: That's been our experience we have. You know, that's the

424
00:24:43,960 --> 00:24:46,680
Speaker 1: other issue. When they kill dogs. It's hard for us

425
00:24:46,720 --> 00:24:48,720
Speaker 1: because you know, if they kill a goat, most people,

426
00:24:49,800 --> 00:24:52,199
Speaker 1: you know, they understand what we're doing and you know

427
00:24:52,240 --> 00:24:54,280
Speaker 1: that we're going to catch this cat and probably kill it.

428
00:24:55,160 --> 00:24:58,040
Speaker 1: So they'll let us leave that goat tied out. You know,

429
00:24:58,080 --> 00:24:59,760
Speaker 1: we'll tie it to a tree, put it. You know,

430
00:25:00,680 --> 00:25:02,760
Speaker 1: can't trail camera on. It will come back the next morning,

431
00:25:02,760 --> 00:25:04,439
Speaker 1: and if that cat's hit it, we'll go catch it

432
00:25:04,480 --> 00:25:07,480
Speaker 1: and kill it. It's hard to talk somebody into letting

433
00:25:07,520 --> 00:25:10,800
Speaker 1: us do that with their dogs. Yeah, they don't want

434
00:25:10,800 --> 00:25:13,800
Speaker 1: their dog bees as bait and I I understand that. Um,

435
00:25:13,960 --> 00:25:16,919
Speaker 1: I would be that way too. So yeah, dogs are

436
00:25:16,960 --> 00:25:20,080
Speaker 1: a little different than small livestock. Typically, you gotta wonder

437
00:25:20,359 --> 00:25:23,439
Speaker 1: if a cat starts out with a llama or an

438
00:25:23,480 --> 00:25:28,440
Speaker 1: alpaca as pray species, if that cat tries to move

439
00:25:28,480 --> 00:25:32,119
Speaker 1: on to something else, it has a very steep learning

440
00:25:32,160 --> 00:25:34,600
Speaker 1: curve ahead of it due to the margin of error

441
00:25:35,440 --> 00:25:38,400
Speaker 1: for the length and size of the neck. Oh yeah,

442
00:25:38,400 --> 00:25:41,760
Speaker 1: like an alpaca and a little teeny enclosure. Yeah, I

443
00:25:41,760 --> 00:25:44,320
Speaker 1: got kill it with my teeth, you know, I just

444
00:25:44,359 --> 00:25:48,879
Speaker 1: gotta get it in the neck. Now, move over to

445
00:25:48,960 --> 00:25:54,600
Speaker 1: a badger that has I don't know, I don't think

446
00:25:54,640 --> 00:26:00,320
Speaker 1: that there are very many lines that get that opportunity enough. Um,

447
00:26:00,359 --> 00:26:03,480
Speaker 1: you know, if they kill an alpaca, typically we have

448
00:26:03,640 --> 00:26:05,879
Speaker 1: to respond pretty quickly. I say we, it's really the

449
00:26:06,000 --> 00:26:11,200
Speaker 1: w DFW enforcement that responds, um. And then you guys

450
00:26:11,280 --> 00:26:15,160
Speaker 1: met Bruce. Bruce does a lot of those I help

451
00:26:15,240 --> 00:26:17,200
Speaker 1: with as many as I can, and we go out

452
00:26:17,240 --> 00:26:20,680
Speaker 1: and help enforcement take care of that cat. And can

453
00:26:20,720 --> 00:26:24,560
Speaker 1: you guys do anything with with the carcass at that point?

454
00:26:25,200 --> 00:26:30,680
Speaker 1: Do you guys? Not? Really? Um? Unfortunately, most of those animals.

455
00:26:30,800 --> 00:26:34,639
Speaker 1: Sometimes if the landowner has a tag um or once

456
00:26:34,760 --> 00:26:37,440
Speaker 1: a cat, sometimes they can get it um if they

457
00:26:37,440 --> 00:26:42,480
Speaker 1: request that animal. But typically no, we've had a handful

458
00:26:42,520 --> 00:26:45,600
Speaker 1: of them donated to the tribes. Um. You know, the

459
00:26:45,680 --> 00:26:47,520
Speaker 1: tribes will make use of that cat and the meat

460
00:26:47,520 --> 00:26:50,879
Speaker 1: and the hide and the bones and different things. What

461
00:26:51,000 --> 00:26:53,440
Speaker 1: do you do with them? Just burn them up? Yeah?

462
00:26:53,480 --> 00:26:55,160
Speaker 1: They just go in the dump with the road killed

463
00:26:55,160 --> 00:26:57,360
Speaker 1: deer and everything else. And people don't fish them out

464
00:26:57,359 --> 00:26:59,520
Speaker 1: of the dump. Well maybe they do. I don't. I

465
00:26:59,520 --> 00:27:02,000
Speaker 1: wouldn't else they did. I don't know, man, I don't

466
00:27:02,000 --> 00:27:03,760
Speaker 1: want to see who this was. But a good friend

467
00:27:03,800 --> 00:27:07,720
Speaker 1: of mine, he um. When he was in college, he

468
00:27:07,800 --> 00:27:11,960
Speaker 1: was working on these nets surveys, these fish surveys where

469
00:27:11,960 --> 00:27:15,439
Speaker 1: they go out and set nets uh in lakes and

470
00:27:15,480 --> 00:27:17,360
Speaker 1: what they were supposed to do is they're not supposed

471
00:27:17,359 --> 00:27:20,080
Speaker 1: to use the fish. They had to just dump the

472
00:27:20,080 --> 00:27:21,720
Speaker 1: fish because they put it put you put out gil

473
00:27:21,800 --> 00:27:25,600
Speaker 1: nets for surveys and he would. He would. They'd go

474
00:27:25,600 --> 00:27:28,879
Speaker 1: out in the work truck and dump the fish. He'd

475
00:27:28,880 --> 00:27:31,879
Speaker 1: come home, get his regular vehicle, wait an hour and

476
00:27:31,880 --> 00:27:33,520
Speaker 1: go back and get the fish, and then take the

477
00:27:33,520 --> 00:27:36,879
Speaker 1: fish back home and flame him and freeze him. Couldn't

478
00:27:36,880 --> 00:27:39,400
Speaker 1: bring himself to dump fils fish. What kind of fish

479
00:27:39,400 --> 00:27:41,879
Speaker 1: were we talking about? He he liked it, he was

480
00:27:41,960 --> 00:27:43,760
Speaker 1: he was kind of a white fish specialists. He'd like

481
00:27:43,800 --> 00:27:45,000
Speaker 1: to go get the white fish out of there with

482
00:27:45,080 --> 00:27:51,680
Speaker 1: other stuff too. Yeah, his initials were m d and

483
00:27:51,800 --> 00:27:53,560
Speaker 1: he had a thirty thirty, so we called him m

484
00:27:53,640 --> 00:27:58,639
Speaker 1: d Ye. You had a point. Yeah, you were going

485
00:27:58,680 --> 00:28:02,600
Speaker 1: to say something Yanni's take. Just stroking his beard. Yeah,

486
00:28:02,640 --> 00:28:05,280
Speaker 1: because again it's it's not something I'm used to. Its

487
00:28:05,640 --> 00:28:09,080
Speaker 1: found a new thing on space. Um No, I don't

488
00:28:09,080 --> 00:28:12,000
Speaker 1: think I did. Just having a glance over even you'd

489
00:28:12,040 --> 00:28:15,240
Speaker 1: happened to be glancing this drect. No, you like I

490
00:28:15,280 --> 00:28:17,600
Speaker 1: thought you had, like think you started to say maybe that,

491
00:28:17,920 --> 00:28:21,560
Speaker 1: Maybe he didn't. I spoke to on the disposal side

492
00:28:21,560 --> 00:28:25,920
Speaker 1: of things. I spoke to Montana game warden, longtime game warden,

493
00:28:26,359 --> 00:28:33,480
Speaker 1: and somebody in his area hit a abnormally large black

494
00:28:33,520 --> 00:28:37,480
Speaker 1: bear and killed it, and he went to his normal

495
00:28:37,520 --> 00:28:41,920
Speaker 1: disposal site, which was a sharp turn on the highway,

496
00:28:42,560 --> 00:28:46,719
Speaker 1: um where he'd give critters that hev ho because he

497
00:28:46,800 --> 00:28:51,160
Speaker 1: didn't he couldn't stomach taking them to a dump. He

498
00:28:51,200 --> 00:28:54,440
Speaker 1: thought it was too disrespectful to take animals just throw

499
00:28:54,480 --> 00:28:58,680
Speaker 1: them in with all the plastics and ship like that. Yeah. Um,

500
00:28:58,720 --> 00:29:01,840
Speaker 1: but he did. And he said, he said, his physics

501
00:29:01,840 --> 00:29:05,160
Speaker 1: were a little off on just how big this bear was.

502
00:29:06,280 --> 00:29:09,440
Speaker 1: And uh so he slit it out of the back

503
00:29:09,480 --> 00:29:12,920
Speaker 1: of the truck. Down mountain it went, and he drove off.

504
00:29:14,240 --> 00:29:20,000
Speaker 1: And the next day he got from the town police department,

505
00:29:20,080 --> 00:29:23,760
Speaker 1: he got a phone call saying, hey, we have a

506
00:29:23,880 --> 00:29:27,400
Speaker 1: very large black bear that must have rolled off the

507
00:29:27,440 --> 00:29:31,000
Speaker 1: mountain above town that is now on the bike path.

508
00:29:33,480 --> 00:29:35,720
Speaker 1: It did the whole hill. It did the whole hill.

509
00:29:35,800 --> 00:29:38,680
Speaker 1: Remember that very that chainsaw commercials like that, Like some

510
00:29:38,760 --> 00:29:41,160
Speaker 1: dude coming down the switchbacks and his chainsaw rolls out

511
00:29:41,200 --> 00:29:43,760
Speaker 1: the back of his truck and eventually like finaggles his

512
00:29:43,760 --> 00:29:46,160
Speaker 1: way down all the switchbacks and there's a sauce landing

513
00:29:46,160 --> 00:29:49,600
Speaker 1: in a mud puddles, still running. Yes, is it running? No,

514
00:29:50,080 --> 00:29:51,920
Speaker 1: it was not running. He picks up and starts with

515
00:29:52,000 --> 00:30:05,080
Speaker 1: one crank. No, okay, so here you are. You got

516
00:30:05,120 --> 00:30:13,400
Speaker 1: a dead alpaca tied to a tree mark. Yeah about it.

517
00:30:13,680 --> 00:30:15,880
Speaker 1: So when we show up show up the next morning.

518
00:30:15,920 --> 00:30:19,160
Speaker 1: Typically the cat comes back and feeds overnight. Um, we

519
00:30:19,640 --> 00:30:21,760
Speaker 1: check trail camera if we have one out. If we don't,

520
00:30:21,800 --> 00:30:24,680
Speaker 1: we just walked dogs over to the to the alpaca

521
00:30:24,760 --> 00:30:28,080
Speaker 1: and h turn loose from that spot. And it's kind

522
00:30:28,080 --> 00:30:30,040
Speaker 1: of you know, without snow, it's typically just up to

523
00:30:30,080 --> 00:30:31,920
Speaker 1: the dogs to figure out the end track and the

524
00:30:31,960 --> 00:30:34,200
Speaker 1: out track, which way the cat came from and went.

525
00:30:34,840 --> 00:30:37,440
Speaker 1: But what we're seeing is these cats are just laying

526
00:30:37,480 --> 00:30:40,840
Speaker 1: around pretty close to us the whole just listening. Um.

527
00:30:40,880 --> 00:30:43,440
Speaker 1: And we're not sneaking around out there. We're at a house,

528
00:30:43,480 --> 00:30:46,040
Speaker 1: we're at a farm with the dog box in the

529
00:30:46,040 --> 00:30:48,720
Speaker 1: back of the truck and yapping dogs and people talking

530
00:30:48,760 --> 00:30:52,880
Speaker 1: at normal voices, and um, it was shocking how close

531
00:30:53,000 --> 00:30:55,200
Speaker 1: the cats would be to us. Sometimes we would hear

532
00:30:56,200 --> 00:30:58,360
Speaker 1: there were times where we heard the jump race where

533
00:30:58,400 --> 00:31:00,720
Speaker 1: the dogs ran into the cat while he was just

534
00:31:00,800 --> 00:31:04,200
Speaker 1: laying around in a bed listening to this whole racket

535
00:31:04,280 --> 00:31:07,760
Speaker 1: that we were making. But and is that would that

536
00:31:07,840 --> 00:31:09,920
Speaker 1: not be like when you could you'd like to run

537
00:31:10,000 --> 00:31:12,520
Speaker 1: lions as the hell of a start to a sentence, Listen,

538
00:31:12,880 --> 00:31:15,240
Speaker 1: you like to run lions out in the very remote

539
00:31:15,240 --> 00:31:18,239
Speaker 1: wilderness areas too down and then right? Is that not

540
00:31:18,320 --> 00:31:20,360
Speaker 1: the same though? They're like when you find if you

541
00:31:20,360 --> 00:31:23,560
Speaker 1: find where he just killed elk Um, is he more

542
00:31:23,640 --> 00:31:26,840
Speaker 1: inclined to as you approach scoot out? Like? Is this

543
00:31:26,920 --> 00:31:30,200
Speaker 1: something that's peculiar? Is this a behavior that you were

544
00:31:30,280 --> 00:31:34,840
Speaker 1: curious about because you felt that it was counter to

545
00:31:34,920 --> 00:31:37,760
Speaker 1: what you'd see in other areas or aren't you factoring

546
00:31:37,760 --> 00:31:40,880
Speaker 1: in the proximity to to to the proximity to a

547
00:31:40,880 --> 00:31:45,200
Speaker 1: suburb people. Does that make any sense? That was a

548
00:31:45,240 --> 00:31:47,240
Speaker 1: bad sense? If I think I can clarify. I think

549
00:31:47,360 --> 00:31:49,800
Speaker 1: what you're saying is that you were oblivious to it, right,

550
00:31:49,840 --> 00:31:52,120
Speaker 1: And you were only realizing this because some of those

551
00:31:52,120 --> 00:31:56,200
Speaker 1: cats were collared. We were on the well, no, we were.

552
00:31:56,280 --> 00:31:58,560
Speaker 1: We realized that when the dogs would jump that cat

553
00:31:58,880 --> 00:32:02,600
Speaker 1: so close to us. But you got me wondering, like,

554
00:32:03,000 --> 00:32:05,600
Speaker 1: what would it take for this cat to be afraid

555
00:32:05,640 --> 00:32:09,600
Speaker 1: of people like you don't obviously, we don't want cats

556
00:32:09,640 --> 00:32:13,080
Speaker 1: hanging out two feet from people's houses, laying around in

557
00:32:13,120 --> 00:32:17,440
Speaker 1: the brush listening to people listening to engines and dogs

558
00:32:17,480 --> 00:32:19,400
Speaker 1: bark and whatever else that goes on to the house

559
00:32:19,480 --> 00:32:22,640
Speaker 1: or a farm. So is that normal cat behavior or

560
00:32:22,720 --> 00:32:25,840
Speaker 1: is that some level of habituation? I got you, I

561
00:32:25,880 --> 00:32:28,400
Speaker 1: got you, all right. That kind of answers my stupid,

562
00:32:28,280 --> 00:32:33,960
Speaker 1: my not well articulated question of meaning. Uh, are you

563
00:32:34,520 --> 00:32:40,720
Speaker 1: curious about um? Is that just like ambivalence to human

564
00:32:40,760 --> 00:32:44,240
Speaker 1: presence a factor of just exposure to humans? Or do

565
00:32:44,240 --> 00:32:46,239
Speaker 1: you think it might be innate and cats? But you're

566
00:32:46,280 --> 00:32:48,160
Speaker 1: gonna find You're gonna probably explain how you're going to

567
00:32:48,240 --> 00:32:52,200
Speaker 1: find that out, Well, we hope. So whether or not

568
00:32:52,240 --> 00:32:55,680
Speaker 1: we find out if it's just normal cat behavior that's

569
00:32:55,680 --> 00:32:59,800
Speaker 1: innate two cats, or or if it's a learned behavior.

570
00:32:59,800 --> 00:33:03,320
Speaker 1: I don't know if we'll pin that down necessarily, but

571
00:33:03,400 --> 00:33:05,400
Speaker 1: we are going to know whether or not we can

572
00:33:05,480 --> 00:33:09,400
Speaker 1: change that behavior and and modify the way they respond

573
00:33:10,160 --> 00:33:13,320
Speaker 1: after being chased around by dogs and and yelled at

574
00:33:13,320 --> 00:33:17,400
Speaker 1: by people in different things. You guys are in Montana,

575
00:33:17,440 --> 00:33:20,560
Speaker 1: you know where they've chased cats around. I'm sure any

576
00:33:20,600 --> 00:33:24,040
Speaker 1: cat that gets within Bozeman city limits probably has some

577
00:33:24,120 --> 00:33:26,920
Speaker 1: eyes on it pretty fast. If there's snow, especially, there's

578
00:33:26,960 --> 00:33:29,959
Speaker 1: a lot of there's an active community of hound hunters

579
00:33:30,000 --> 00:33:32,000
Speaker 1: there that are going to go chase that cat and

580
00:33:32,000 --> 00:33:33,840
Speaker 1: get a look at it. They probably won't kill it,

581
00:33:34,160 --> 00:33:37,960
Speaker 1: particularly if it's a female. But um, those cats, they

582
00:33:37,960 --> 00:33:41,480
Speaker 1: get looked at a lot, they get chased around, harassed

583
00:33:42,000 --> 00:33:45,520
Speaker 1: by hound hunters. Washington doesn't have that. We're chasing cats

584
00:33:45,560 --> 00:33:49,280
Speaker 1: that have very very likely never been pursued by a

585
00:33:49,360 --> 00:33:52,680
Speaker 1: dog we're chasing. We're chasing cats that have probably never

586
00:33:52,680 --> 00:33:57,920
Speaker 1: had a negative interaction with a person at all. So, um,

587
00:33:57,960 --> 00:34:01,760
Speaker 1: that's another one of the questions, because it's how it's

588
00:34:01,760 --> 00:34:04,040
Speaker 1: been how many years since they banned Uh, it's been

589
00:34:04,040 --> 00:34:06,320
Speaker 1: how many years since they banned lion hunting with dogs

590
00:34:06,480 --> 00:34:11,480
Speaker 1: in Washington? Over twenty now. I think they've banned it

591
00:34:11,520 --> 00:34:14,279
Speaker 1: in ninety six, remember, so long enough ago. There's no

592
00:34:14,400 --> 00:34:18,200
Speaker 1: cat living today that remembers those times. Yeah, yeah, any

593
00:34:18,239 --> 00:34:21,719
Speaker 1: of those any cat that was legally pursued, And um,

594
00:34:21,880 --> 00:34:23,840
Speaker 1: you know, I say that because they're I don't know

595
00:34:23,880 --> 00:34:26,880
Speaker 1: if people are out bootlegging around or chasing cats. That

596
00:34:26,960 --> 00:34:29,440
Speaker 1: used to be a problem. Right after the right after

597
00:34:29,480 --> 00:34:30,960
Speaker 1: they made it illegal, there was still a lot of

598
00:34:30,960 --> 00:34:33,120
Speaker 1: hound hunters around and they were pretty upset about it,

599
00:34:33,120 --> 00:34:36,160
Speaker 1: and I think that probably happened more than um. The

600
00:34:36,200 --> 00:34:39,440
Speaker 1: hound hunting ranks in Washington now are pretty slim. There's

601
00:34:39,480 --> 00:34:41,840
Speaker 1: not a lot of people that still keep dogs because

602
00:34:41,880 --> 00:34:46,080
Speaker 1: the opportunities are really tough. Okay, so you get curious

603
00:34:46,080 --> 00:34:48,799
Speaker 1: about this, like why do these cats just not care?

604
00:34:50,239 --> 00:34:53,040
Speaker 1: What's your next step? Well, we started kind of think

605
00:34:53,080 --> 00:34:57,360
Speaker 1: about ways to protect you know, the farms and pets

606
00:34:57,360 --> 00:35:00,359
Speaker 1: and small livestock that we kind of suspect that are

607
00:35:00,360 --> 00:35:03,719
Speaker 1: going to get picked off by cats. And a lot

608
00:35:03,760 --> 00:35:06,880
Speaker 1: of times, like real, a real common narrative when we

609
00:35:06,920 --> 00:35:10,000
Speaker 1: showed up at the site of a depredation would be

610
00:35:10,080 --> 00:35:13,799
Speaker 1: this landowner that would say, you know, oh yeah, such

611
00:35:13,840 --> 00:35:15,680
Speaker 1: and such saw this cat a couple of days ago,

612
00:35:15,800 --> 00:35:17,239
Speaker 1: and my kids saw it when he was walking to

613
00:35:17,280 --> 00:35:21,279
Speaker 1: the bus yesterday, and you know, it's been around um,

614
00:35:21,360 --> 00:35:24,719
Speaker 1: but nobody. There's no method to track those cats down

615
00:35:24,719 --> 00:35:26,560
Speaker 1: and do anything about them. They're just sightings, right, And

616
00:35:26,560 --> 00:35:30,080
Speaker 1: it's kind of a meaningless metric. So the state wasn't

617
00:35:30,080 --> 00:35:32,600
Speaker 1: really responding to a sighting because that's just a cat

618
00:35:32,680 --> 00:35:36,799
Speaker 1: being a cat until it actually does something. There's no

619
00:35:36,920 --> 00:35:38,919
Speaker 1: real reason for them to send somebody out to chase

620
00:35:38,920 --> 00:35:41,480
Speaker 1: it and get a look at it. So I kind

621
00:35:41,480 --> 00:35:43,319
Speaker 1: of got to thinking like, all right, well, if that

622
00:35:43,360 --> 00:35:45,440
Speaker 1: cat's hanging around this farm for a couple of days

623
00:35:45,480 --> 00:35:48,640
Speaker 1: and you know, showing up on whatever porch or something

624
00:35:48,680 --> 00:35:51,239
Speaker 1: like that, that's probably going to cause a problem sooner

625
00:35:51,320 --> 00:35:54,400
Speaker 1: or later. It's obviously showing that it's not that concerned

626
00:35:54,440 --> 00:35:56,880
Speaker 1: about people and noise and all the things that we

627
00:35:56,920 --> 00:36:01,239
Speaker 1: have going on on a farm. So the project then

628
00:36:01,320 --> 00:36:03,120
Speaker 1: kind of got started, like, all right, so when these

629
00:36:03,160 --> 00:36:05,600
Speaker 1: cats show up and they're being cited, I want to

630
00:36:05,600 --> 00:36:06,680
Speaker 1: know about it, and I want to come out and

631
00:36:06,719 --> 00:36:09,200
Speaker 1: chase them and see if they come back to that spot. Um.

632
00:36:09,239 --> 00:36:12,160
Speaker 1: So initially it was a little bit of a geographic thing,

633
00:36:12,200 --> 00:36:15,480
Speaker 1: but really it's a stretch to think that that cat

634
00:36:15,560 --> 00:36:18,440
Speaker 1: is going to make a connection between hanging around a

635
00:36:18,480 --> 00:36:20,320
Speaker 1: farm and then all of a sudden, you know, twelve

636
00:36:20,320 --> 00:36:22,640
Speaker 1: hours later, here comes these dogs and pop it up

637
00:36:22,640 --> 00:36:24,520
Speaker 1: a tree, and then here comes these people yelling at

638
00:36:24,520 --> 00:36:26,480
Speaker 1: it and stressing it out. Um, it's a stretch for

639
00:36:26,480 --> 00:36:28,560
Speaker 1: the cat to make that connection back to that original

640
00:36:28,600 --> 00:36:32,080
Speaker 1: sighting that might have been the previous day. So it

641
00:36:32,120 --> 00:36:35,880
Speaker 1: really became more of a behavior study. How does this

642
00:36:35,920 --> 00:36:40,360
Speaker 1: cat respond over time if we do this, um, you know,

643
00:36:40,440 --> 00:36:42,719
Speaker 1: how does it respond to this stimuli, which in this

644
00:36:42,800 --> 00:36:46,760
Speaker 1: case is your voice, Steve over time if we pursue

645
00:36:46,800 --> 00:36:49,640
Speaker 1: it and give this negative interaction every time it hears

646
00:36:49,719 --> 00:36:53,520
Speaker 1: that human voice. Yeah, I love that you're using this podcast.

647
00:36:53,800 --> 00:36:57,080
Speaker 1: Who's gonna who cares enough about this research mark to

648
00:36:57,120 --> 00:37:00,960
Speaker 1: pay for it? That's a good question. It's a it's

649
00:37:01,000 --> 00:37:03,320
Speaker 1: a shoe string operation right now. We're looking for funding.

650
00:37:03,320 --> 00:37:06,760
Speaker 1: If you guys know any plays the ground money. Imagine

651
00:37:06,800 --> 00:37:11,040
Speaker 1: like the Los Angeles Pet Owners Association. Okay, so you're

652
00:37:11,040 --> 00:37:13,839
Speaker 1: saying this is on your own dimeond that the Washington

653
00:37:13,960 --> 00:37:16,120
Speaker 1: Game of Fish isn't helping you out. You have a

654
00:37:16,160 --> 00:37:18,960
Speaker 1: bartworks to the tribe, I know, but he also does

655
00:37:19,040 --> 00:37:21,960
Speaker 1: work for the game of fish, correct with these probably animals,

656
00:37:22,560 --> 00:37:25,800
Speaker 1: So a lot of the fishing game is the manager

657
00:37:25,840 --> 00:37:27,640
Speaker 1: of the wildlife in the state. Spell tribe has a

658
00:37:27,640 --> 00:37:29,880
Speaker 1: pretty keen interest in cougar stuff right now because we

659
00:37:29,960 --> 00:37:33,480
Speaker 1: have um a reservation that's smack dab in the middle

660
00:37:33,520 --> 00:37:37,080
Speaker 1: of prime winter habitat, and we have had years where

661
00:37:37,080 --> 00:37:40,640
Speaker 1: we've had terrible problems with cougars on the reservation. UM

662
00:37:40,640 --> 00:37:42,759
Speaker 1: In yards at bus stops and all the things that

663
00:37:42,760 --> 00:37:46,880
Speaker 1: you hear about. So my initial kind of seed money

664
00:37:46,960 --> 00:37:50,080
Speaker 1: came from the the U CUT Group, which is the

665
00:37:50,120 --> 00:37:53,480
Speaker 1: Upper Columbia United Tribes. It's a consortium of five tribes

666
00:37:53,920 --> 00:37:58,319
Speaker 1: UM in North Idaho and Northeast Washington. So that was

667
00:37:58,320 --> 00:38:00,799
Speaker 1: the initial seed money for the research. UM. I've got

668
00:38:00,800 --> 00:38:03,480
Speaker 1: a couple of grants outstanding right now. But it doesn't

669
00:38:03,520 --> 00:38:06,640
Speaker 1: cost that much what we're doing. Um I said, we

670
00:38:06,680 --> 00:38:09,759
Speaker 1: only had to. We bought six collars. I've got uh

671
00:38:10,520 --> 00:38:14,560
Speaker 1: that w Buddy would bury his company doing the garment

672
00:38:14,640 --> 00:38:17,399
Speaker 1: stuff for me. He's been real super helpful with Alva Tech.

673
00:38:18,360 --> 00:38:22,399
Speaker 1: Um questions that I've got with garment, and we're using

674
00:38:22,440 --> 00:38:25,960
Speaker 1: their equipment. I think it's on loan, but they're not

675
00:38:26,000 --> 00:38:27,279
Speaker 1: going to get a lot of it back because it

676
00:38:27,360 --> 00:38:29,360
Speaker 1: just gets trashed out there in the woods for a

677
00:38:29,440 --> 00:38:32,279
Speaker 1: month on a cat. Um. Yeah, we're just kind of

678
00:38:32,280 --> 00:38:35,160
Speaker 1: piecing it together as we go. Okay, so keep going on,

679
00:38:35,560 --> 00:38:38,759
Speaker 1: keep going on the story here, so you explain how

680
00:38:38,800 --> 00:38:41,000
Speaker 1: you work and how you use how you use this

681
00:38:41,000 --> 00:38:45,760
Speaker 1: this this podcast to help Okay. So as we're designing

682
00:38:45,760 --> 00:38:49,480
Speaker 1: this project, we're starting to you know, we're one question

683
00:38:49,520 --> 00:38:51,640
Speaker 1: tends to lead to another, and that's where we ended up.

684
00:38:51,880 --> 00:38:54,200
Speaker 1: So we have all these questions starting to pile up

685
00:38:54,239 --> 00:38:57,360
Speaker 1: and all the the protocol that we ended up settling

686
00:38:57,400 --> 00:38:59,760
Speaker 1: on to try to answer as many questions as possible.

687
00:39:00,000 --> 00:39:05,440
Speaker 1: I'll try to describe um succinctly, but we we capture

688
00:39:05,440 --> 00:39:08,760
Speaker 1: a cat that's been sited. So Washington Department of Fishing Wildlife,

689
00:39:08,760 --> 00:39:12,359
Speaker 1: the enforcement group is cooperating with us pretty well. So um,

690
00:39:12,440 --> 00:39:15,799
Speaker 1: they are the ones fielding the calls for sightings or

691
00:39:15,800 --> 00:39:18,960
Speaker 1: depredations or whatever. So when there's a cat hanging around

692
00:39:19,320 --> 00:39:22,600
Speaker 1: a farm or a home that's spotted under a porch

693
00:39:22,719 --> 00:39:25,640
Speaker 1: or wherever that it probably shouldn't be, they call me

694
00:39:26,360 --> 00:39:29,879
Speaker 1: and then we get our stuff together and go get

695
00:39:29,880 --> 00:39:32,520
Speaker 1: a look at this cat and we go capture the cat.

696
00:39:32,560 --> 00:39:35,680
Speaker 1: It's you know, Bruce is volunteering for the project and

697
00:39:35,680 --> 00:39:39,080
Speaker 1: put a ton of hours into it, and I, UM,

698
00:39:39,320 --> 00:39:40,960
Speaker 1: we'll go out and capture this cat and get it

699
00:39:41,000 --> 00:39:43,279
Speaker 1: in a tree, and then we make a decision if

700
00:39:43,320 --> 00:39:45,000
Speaker 1: it's a cat that we want to add to the project.

701
00:39:45,040 --> 00:39:48,560
Speaker 1: We're really looking for adult cats. We don't want sub

702
00:39:48,560 --> 00:39:51,080
Speaker 1: adults that are still with their mom, and we don't

703
00:39:51,080 --> 00:39:54,520
Speaker 1: want lactating females. I don't want to disrupt um a

704
00:39:54,600 --> 00:39:59,680
Speaker 1: cat that's actively nursing or feeding young. So we take

705
00:39:59,760 --> 00:40:02,040
Speaker 1: this at we end up. We darted, put a collar

706
00:40:02,120 --> 00:40:04,360
Speaker 1: on it, and we put it. Explain that darting process

707
00:40:04,400 --> 00:40:07,480
Speaker 1: real quick. Uh So we just use a you know,

708
00:40:07,520 --> 00:40:10,600
Speaker 1: it's an air gun, and we're we're working on a

709
00:40:10,680 --> 00:40:13,200
Speaker 1: drug formulation. I think I'm actually really excited about because

710
00:40:13,920 --> 00:40:18,080
Speaker 1: a lot of people use ketamine zylazine on cats. We're

711
00:40:18,080 --> 00:40:19,879
Speaker 1: trying to save some money. We're trying to do some

712
00:40:19,960 --> 00:40:22,680
Speaker 1: things different and test some different stuff, and um use

713
00:40:22,719 --> 00:40:25,600
Speaker 1: a safer drug. Keta means a little bit dangerous and

714
00:40:25,640 --> 00:40:28,360
Speaker 1: it's also evidently a street drug that we have to

715
00:40:28,520 --> 00:40:30,600
Speaker 1: be really careful about. So we're using a drug called

716
00:40:30,640 --> 00:40:35,080
Speaker 1: bam um that we've used on sheep successfully, so we're

717
00:40:35,120 --> 00:40:37,719
Speaker 1: testing that on the cats and um, it seems to

718
00:40:37,800 --> 00:40:40,400
Speaker 1: be working real well. So we're darting this cat. When

719
00:40:40,440 --> 00:40:42,520
Speaker 1: you when someone uses ketemine, what are the what like?

720
00:40:42,560 --> 00:40:44,799
Speaker 1: What are they? What's it like? What what's the what

721
00:40:44,840 --> 00:40:49,440
Speaker 1: are they after? I have no idea. UM, I know

722
00:40:49,560 --> 00:40:52,120
Speaker 1: for cats, it's a it's it looks like it looks

723
00:40:52,120 --> 00:40:54,080
Speaker 1: like It would suck when you see a cat under

724
00:40:54,120 --> 00:40:58,160
Speaker 1: ketemine because they're like rigid and uncomfortable, and then when

725
00:40:58,160 --> 00:41:00,880
Speaker 1: they recover their drooling in their heads, bobbing around and

726
00:41:00,920 --> 00:41:06,360
Speaker 1: they could they just staggering. It looks awful. Um I can't.

727
00:41:06,600 --> 00:41:08,759
Speaker 1: So you know, you never like, you never like put

728
00:41:08,760 --> 00:41:11,000
Speaker 1: a little give a little squirt in your mouth because

729
00:41:11,000 --> 00:41:12,919
Speaker 1: you don't want to replicate that experience of the cat.

730
00:41:13,320 --> 00:41:15,440
Speaker 1: It doesn't know. It does not look like they're having

731
00:41:15,440 --> 00:41:17,800
Speaker 1: a good time at all. I was really hoping you

732
00:41:17,800 --> 00:41:23,880
Speaker 1: guys were gonna go into like the marijuana side of things,

733
00:41:23,920 --> 00:41:27,799
Speaker 1: like super shot of CBD, just get them high. Yeah,

734
00:41:28,080 --> 00:41:32,680
Speaker 1: we can try. Um uh, people use ether and knock

735
00:41:32,760 --> 00:41:34,799
Speaker 1: cats out. I've heard of the old timers doing that.

736
00:41:35,040 --> 00:41:38,440
Speaker 1: Um starting fluid. Basically they'll knock a cat out to

737
00:41:38,480 --> 00:41:41,399
Speaker 1: get it out of a trap or something. And then

738
00:41:42,040 --> 00:41:43,959
Speaker 1: did you hear the story we had on this show

739
00:41:43,960 --> 00:41:46,440
Speaker 1: one time about a guy that got accidentally shot by

740
00:41:46,440 --> 00:41:50,359
Speaker 1: the tranquil as You're gone, No, Yeah, it was quite

741
00:41:50,360 --> 00:41:52,880
Speaker 1: an ordeal for him. Yeah, it could be very dangerous.

742
00:41:52,880 --> 00:41:55,640
Speaker 1: They had to pack him down on a mule. They

743
00:41:55,640 --> 00:41:57,200
Speaker 1: had to load the guy up on a mule and

744
00:41:57,200 --> 00:41:59,359
Speaker 1: pack him out of the mountains. I thought he's gonna

745
00:41:59,440 --> 00:42:04,160
Speaker 1: die shot by dart. That's another reason I would you

746
00:42:04,200 --> 00:42:08,719
Speaker 1: trying to experiment with his bam. Yeah, um, I would

747
00:42:08,760 --> 00:42:10,480
Speaker 1: get that wrong if I told you it's a three

748
00:42:10,560 --> 00:42:14,560
Speaker 1: drug formulation. It's beautourphin al I think at a pamazole

749
00:42:14,640 --> 00:42:19,319
Speaker 1: and metatomadine. Um. So it's a three drugs mixed one.

750
00:42:19,400 --> 00:42:23,160
Speaker 1: It's made by a wildlife vet in Colorado. It's a

751
00:42:23,160 --> 00:42:26,200
Speaker 1: great product, super safe. They're using it on moose and

752
00:42:26,239 --> 00:42:28,640
Speaker 1: sheep and deer and other things. The one thing it

753
00:42:28,680 --> 00:42:30,800
Speaker 1: does have it's reverse herble. So if I was to

754
00:42:30,920 --> 00:42:34,360
Speaker 1: dart myself, I could also reverse myself. You can carry

755
00:42:34,440 --> 00:42:38,560
Speaker 1: like you can carry like a vial of antidote. Right,

756
00:42:38,880 --> 00:42:42,080
Speaker 1: so I have a reversal agent. Um. I'll get back

757
00:42:42,080 --> 00:42:43,880
Speaker 1: to darting the cat and we'll talk about reversing it

758
00:42:43,920 --> 00:42:46,759
Speaker 1: and everything else. But um, so it's a dark the cat.

759
00:42:47,320 --> 00:42:49,600
Speaker 1: We get it in the right tree. So if it's

760
00:42:49,600 --> 00:42:51,800
Speaker 1: in a super high tree someplace where it's going to

761
00:42:51,880 --> 00:42:54,239
Speaker 1: get injured, if it comes out of the tree, we'll

762
00:42:54,360 --> 00:42:56,360
Speaker 1: jump it out of that tree and put dogs on

763
00:42:56,400 --> 00:42:58,160
Speaker 1: it right away. So they put a lot of pressure

764
00:42:58,160 --> 00:43:00,560
Speaker 1: on it and get it to grab another tree. When

765
00:43:00,560 --> 00:43:03,560
Speaker 1: you do that, typically that cats running from those dogs

766
00:43:03,560 --> 00:43:05,279
Speaker 1: and it's gonna grab the first tree it can get to.

767
00:43:05,400 --> 00:43:07,880
Speaker 1: It's so it's less likely that's going to pick a

768
00:43:07,880 --> 00:43:09,680
Speaker 1: great bit. But how do you climb at the top?

769
00:43:09,920 --> 00:43:11,760
Speaker 1: How do you prompt it when it's when it finds

770
00:43:11,760 --> 00:43:13,080
Speaker 1: a tree and it goes into a tree and it's

771
00:43:13,120 --> 00:43:15,600
Speaker 1: the wrong tree, it's gonna fall, presumably right, it's gonna

772
00:43:15,600 --> 00:43:17,880
Speaker 1: fall and get hurt if you tranquilize it. How do

773
00:43:17,920 --> 00:43:20,520
Speaker 1: you how do you willfully prompt it to jump to

774
00:43:20,560 --> 00:43:23,879
Speaker 1: another tree, to jump to the ground. So we've done

775
00:43:23,880 --> 00:43:26,480
Speaker 1: in a couple of ways. Um, Sometimes you can just

776
00:43:26,560 --> 00:43:28,680
Speaker 1: bang on that tree with a stick or a log,

777
00:43:28,719 --> 00:43:30,919
Speaker 1: and that I think that vibration drives them a little

778
00:43:30,960 --> 00:43:33,239
Speaker 1: bit crazy and they'll just climb out. If they haven't

779
00:43:33,239 --> 00:43:36,399
Speaker 1: been treed very much and they are pretty nervous, it's

780
00:43:36,440 --> 00:43:38,800
Speaker 1: easy to get them to jump. We also carry a

781
00:43:38,840 --> 00:43:42,520
Speaker 1: paintball gun and you can rattle the tree above them

782
00:43:42,600 --> 00:43:44,719
Speaker 1: or actually shoot the cat with the paintballs and they'll

783
00:43:44,760 --> 00:43:47,080
Speaker 1: come out of the tree and try to get them

784
00:43:46,800 --> 00:43:50,600
Speaker 1: in a better tree. Right, So I want them in,

785
00:43:50,760 --> 00:43:52,640
Speaker 1: you know, I don't want them over about you know,

786
00:43:52,760 --> 00:43:57,160
Speaker 1: thirty five ft high, and then we carry climbing equipment

787
00:43:57,160 --> 00:44:00,480
Speaker 1: because sometimes they go down in the tree. But once

788
00:44:00,680 --> 00:44:02,480
Speaker 1: so I deliver the dart and we get this tar

789
00:44:02,600 --> 00:44:04,960
Speaker 1: pung out and it's like you would expect. It's a

790
00:44:05,000 --> 00:44:07,600
Speaker 1: big you know, I don't know if it's canvas or not,

791
00:44:07,640 --> 00:44:09,840
Speaker 1: but it's that kind of heavy cotton material. Hey, do

792
00:44:09,880 --> 00:44:12,279
Speaker 1: you have to hit him in any special spot with

793
00:44:12,360 --> 00:44:15,200
Speaker 1: that dart? I always try to hit him in the hamstring.

794
00:44:15,960 --> 00:44:18,200
Speaker 1: You can, you can shoot him in the shoulder, but

795
00:44:18,239 --> 00:44:21,600
Speaker 1: there's just more connective tissue and more likelihood of an injury.

796
00:44:21,920 --> 00:44:23,279
Speaker 1: So I always try to shoot him right in the

797
00:44:23,320 --> 00:44:29,040
Speaker 1: hamstring or yeah, the butt, and that's a big muscle group.

798
00:44:29,040 --> 00:44:30,759
Speaker 1: I'm using a one and a half inch long needle,

799
00:44:30,800 --> 00:44:32,239
Speaker 1: so it's a big needle and you have to get

800
00:44:32,239 --> 00:44:34,960
Speaker 1: into a pretty deep muscle. Um. So these are just

801
00:44:35,040 --> 00:44:38,839
Speaker 1: pressurized darts that are reusable. Um. So, once the dart

802
00:44:38,920 --> 00:44:42,360
Speaker 1: hits you know, typically the dart comes out of the

803
00:44:42,400 --> 00:44:46,160
Speaker 1: cat um in the tree. It's not barbed or anything

804
00:44:46,200 --> 00:44:49,879
Speaker 1: like that, so the drugs delivered dart typically falls out

805
00:44:49,880 --> 00:44:52,880
Speaker 1: of the tree. We get our tarp up and just

806
00:44:52,960 --> 00:44:56,080
Speaker 1: watching that cat. About half the time they go down

807
00:44:56,120 --> 00:44:57,759
Speaker 1: and they're stuck in the tree and we have to

808
00:44:57,920 --> 00:45:00,920
Speaker 1: climb up, tie a rope around him and push him

809
00:45:00,920 --> 00:45:03,560
Speaker 1: out of the tree down into the tarp. How long

810
00:45:03,600 --> 00:45:08,320
Speaker 1: is it to put that cat asleep? That depends typically.

811
00:45:09,200 --> 00:45:11,799
Speaker 1: If so, the BAM itself is going to take like

812
00:45:11,880 --> 00:45:15,840
Speaker 1: ten minutes, which seems like an eternity when you're out there. Um,

813
00:45:15,880 --> 00:45:19,560
Speaker 1: we have decided we have started adding a really small

814
00:45:19,680 --> 00:45:22,640
Speaker 1: dose of ketamine to that BAM. Um, and it's a

815
00:45:22,640 --> 00:45:26,359
Speaker 1: low dose ketamine. It's a one a hundred milligrams per

816
00:45:26,400 --> 00:45:28,399
Speaker 1: mill leader ketamine. So it's like the stuff that used

817
00:45:28,400 --> 00:45:31,120
Speaker 1: on horses. Um. We put just a whisper of that

818
00:45:31,200 --> 00:45:34,120
Speaker 1: in there, and that knocks him out way faster. So

819
00:45:34,200 --> 00:45:36,319
Speaker 1: those two drugs really work together well. And they'll go

820
00:45:36,360 --> 00:45:37,880
Speaker 1: down on about two and a half minutes with that,

821
00:45:39,000 --> 00:45:41,280
Speaker 1: And then you guys are like fireman catching someone jumping

822
00:45:41,280 --> 00:45:44,160
Speaker 1: out of a window pretty much. Yeah, we'll tie the

823
00:45:44,200 --> 00:45:46,000
Speaker 1: tarp up and hold the tarp up in the corners,

824
00:45:46,040 --> 00:45:49,640
Speaker 1: do whatever and catch it when it lands. Um. So

825
00:45:49,719 --> 00:45:51,400
Speaker 1: you had a funny dark story. I had one of

826
00:45:51,400 --> 00:45:54,759
Speaker 1: those just a week ago. I was shot a cat

827
00:45:54,800 --> 00:45:57,560
Speaker 1: and we're we look at the cat the through the

828
00:45:57,640 --> 00:45:59,919
Speaker 1: scope of our air rifle, you know, and I could

829
00:46:00,040 --> 00:46:02,680
Speaker 1: see the plunger wasn't all the way down on the dart,

830
00:46:02,880 --> 00:46:05,680
Speaker 1: which is a concern because it means there's still half

831
00:46:05,680 --> 00:46:07,359
Speaker 1: the drugs and the dart. It also means the cat

832
00:46:07,400 --> 00:46:09,680
Speaker 1: only got half the delivery. So we're wondering if we're

833
00:46:09,719 --> 00:46:13,600
Speaker 1: gonna have to dart him again. And um, he started

834
00:46:13,840 --> 00:46:15,839
Speaker 1: looking like he was going to come out of the tree.

835
00:46:15,920 --> 00:46:17,839
Speaker 1: He was getting pretty heavy, his head was hanging. So

836
00:46:17,880 --> 00:46:19,880
Speaker 1: we've gotten position with the dart and the cat started

837
00:46:19,880 --> 00:46:22,839
Speaker 1: moving around, and that I wasn't paying attention to the cat.

838
00:46:22,880 --> 00:46:26,560
Speaker 1: I was looking at the tarp and I felt something.

839
00:46:26,920 --> 00:46:29,319
Speaker 1: I thought a stick landed on me. And I looked

840
00:46:29,360 --> 00:46:31,440
Speaker 1: down and that dart had come out of the tree

841
00:46:31,480 --> 00:46:36,960
Speaker 1: and stuck in my palm like a with half the

842
00:46:37,040 --> 00:46:39,080
Speaker 1: drug still in it, and the sleeve was off and

843
00:46:39,120 --> 00:46:41,120
Speaker 1: his pressure eyes. I was like, oh my god, this

844
00:46:41,160 --> 00:46:44,879
Speaker 1: is gonna be embarrassing. But it didn't. It didn't. Um,

845
00:46:46,200 --> 00:46:47,960
Speaker 1: I just cleaned I cleaned it up, of course, and

846
00:46:48,000 --> 00:46:51,440
Speaker 1: nowadays everybody has hand sanitizer in every pocket, so I

847
00:46:51,520 --> 00:46:54,120
Speaker 1: pulled it out and smeared some hand sanitizer in there,

848
00:46:54,160 --> 00:46:56,960
Speaker 1: and UM kept a pretty close eye on it. And

849
00:46:57,040 --> 00:46:58,680
Speaker 1: I don't think I got any of the drug at all.

850
00:46:58,719 --> 00:47:03,160
Speaker 1: I didn't feel like I did any So you you've

851
00:47:03,200 --> 00:47:07,000
Speaker 1: got a lot of practice shooting cats with darts, though, right, Like,

852
00:47:07,040 --> 00:47:10,400
Speaker 1: how many cats? This is a two part question? Teeny

853
00:47:10,520 --> 00:47:13,799
Speaker 1: up for what's the first part of the question, how

854
00:47:13,800 --> 00:47:20,439
Speaker 1: many cats have you darted? UM, I'm not sure where

855
00:47:20,440 --> 00:47:26,080
Speaker 1: I'm at right now. So we have, oh, probably twenty

856
00:47:26,160 --> 00:47:29,080
Speaker 1: cats so far. We're permitted for thirty five this year.

857
00:47:29,800 --> 00:47:31,680
Speaker 1: You in your whole career, I figured you would have

858
00:47:31,760 --> 00:47:35,279
Speaker 1: shot more than that. Yeah, beyond this study. Uh, Now,

859
00:47:35,320 --> 00:47:37,080
Speaker 1: I didn't really shoot very many when I was helping

860
00:47:37,080 --> 00:47:40,160
Speaker 1: the state. UM, that was typically their their biologists delivering

861
00:47:40,160 --> 00:47:42,040
Speaker 1: the drugs on that I was just I was just

862
00:47:42,080 --> 00:47:46,440
Speaker 1: doing the capture part with the dogs. So this project

863
00:47:46,520 --> 00:47:49,560
Speaker 1: is really my most of my cat darting. I've darted

864
00:47:49,960 --> 00:47:53,319
Speaker 1: sheep and deer and some other things, but UM, for

865
00:47:53,320 --> 00:47:57,319
Speaker 1: this one, probably twenty cats and probably oh, I think

866
00:47:57,480 --> 00:47:59,560
Speaker 1: three or four of those cats we've had to handle

867
00:47:59,600 --> 00:48:02,919
Speaker 1: three different times, most of them twice. So I don't

868
00:48:02,920 --> 00:48:05,040
Speaker 1: know how many darts I've delivered fifty or sixty, I guess.

869
00:48:05,400 --> 00:48:10,080
Speaker 1: And since they're reusable, have you identified the lucky dart? Like,

870
00:48:10,239 --> 00:48:13,200
Speaker 1: what is the dart that comes out of like that

871
00:48:13,239 --> 00:48:17,640
Speaker 1: one is gonna that puts Tabby down? Um? We have

872
00:48:18,120 --> 00:48:22,960
Speaker 1: identified a couple of unlucky darts. I don't know. Um,

873
00:48:23,239 --> 00:48:25,360
Speaker 1: we don't have one that I like better than the others.

874
00:48:25,360 --> 00:48:27,600
Speaker 1: But we always test them before we load the drugs,

875
00:48:27,640 --> 00:48:29,880
Speaker 1: and sometimes those you know, it's just a syringe, right,

876
00:48:29,920 --> 00:48:32,399
Speaker 1: and sometimes those plungers and get sticky and things like that.

877
00:48:32,880 --> 00:48:34,880
Speaker 1: So we're always testing them and that has helped. But

878
00:48:35,880 --> 00:48:38,200
Speaker 1: I had a terrible time the first cat we treat.

879
00:48:38,239 --> 00:48:40,759
Speaker 1: I missed twice before I got a dart in it,

880
00:48:40,800 --> 00:48:43,400
Speaker 1: and the gun was shooting like a foot high, and

881
00:48:43,480 --> 00:48:46,440
Speaker 1: I I sighted the gun in on the flat, like

882
00:48:46,480 --> 00:48:48,759
Speaker 1: a total rookie move, you know, I just took it

883
00:48:48,800 --> 00:48:50,640
Speaker 1: out in the lawn and sighted it in. And that's

884
00:48:50,680 --> 00:48:54,080
Speaker 1: a very different trajectory than shooting almost straight up into

885
00:48:54,080 --> 00:48:56,319
Speaker 1: a tree. So I was shooting over the cat and

886
00:48:56,360 --> 00:49:00,360
Speaker 1: it was launching these eighteen dollar darts full, you know,

887
00:49:00,440 --> 00:49:05,520
Speaker 1: forty worth the drugs out into the stratosphere. UM. So

888
00:49:05,560 --> 00:49:07,560
Speaker 1: that was embarrassing, But we've got it dialed in now,

889
00:49:07,560 --> 00:49:09,719
Speaker 1: I haven't missed a cat in a long time. Good

890
00:49:09,760 --> 00:49:12,279
Speaker 1: for you. Okay. So the cat falls out of the tree,

891
00:49:12,360 --> 00:49:16,919
Speaker 1: lands on the trampoline the cop. Yeah, if the lot

892
00:49:16,960 --> 00:49:18,759
Speaker 1: of times the cat comes out still has some life

893
00:49:18,800 --> 00:49:20,319
Speaker 1: in it, we tied up. We just put a loop

894
00:49:20,320 --> 00:49:22,399
Speaker 1: around its hind leg and tied to the tree and

895
00:49:22,520 --> 00:49:26,800
Speaker 1: let the drugs take the full effect. Um. The dogs

896
00:49:26,800 --> 00:49:29,120
Speaker 1: are tied back a hundred or so yards to kind

897
00:49:29,120 --> 00:49:31,600
Speaker 1: of lower stimulation. We tell people to be quiet and

898
00:49:31,600 --> 00:49:35,960
Speaker 1: not talk, not stimulate the cat. Um they are. They're

899
00:49:36,239 --> 00:49:41,680
Speaker 1: really paying it like visual stimulation will really bother them

900
00:49:41,719 --> 00:49:44,719
Speaker 1: when they're anesthesia has taken effect, and it will it

901
00:49:44,840 --> 00:49:48,440
Speaker 1: will slow down the effects of the drugs. So we

902
00:49:48,560 --> 00:49:50,239
Speaker 1: get a head cover on them as soon as it's

903
00:49:50,280 --> 00:49:52,279
Speaker 1: kind of safe right and there and their head can

904
00:49:52,280 --> 00:49:54,600
Speaker 1: still be up. They really want to get away from

905
00:49:54,640 --> 00:49:56,319
Speaker 1: you at that point. They're not like trying to fight

906
00:49:56,400 --> 00:49:58,279
Speaker 1: or anything. They just want to walk away. Um. So

907
00:49:58,320 --> 00:49:59,880
Speaker 1: we get a head cover on and that really seems

908
00:49:59,880 --> 00:50:02,440
Speaker 1: to subdue them, and they relax pretty fast once their

909
00:50:02,440 --> 00:50:06,520
Speaker 1: eyes are covered. So we call her the cat. We're

910
00:50:06,600 --> 00:50:09,240
Speaker 1: using the regular survey caller that you see on every

911
00:50:09,280 --> 00:50:13,400
Speaker 1: wildlife project, right, the big GPS collar weighs like whatever.

912
00:50:13,920 --> 00:50:16,319
Speaker 1: I don't know how many grams, but they're big. And

913
00:50:16,400 --> 00:50:20,480
Speaker 1: on that we're we're fixing the garment unit. So we're

914
00:50:20,480 --> 00:50:25,279
Speaker 1: putting that that other garment UM T five tracking unit,

915
00:50:25,640 --> 00:50:29,080
Speaker 1: which basically the same thing you get from a UM

916
00:50:29,480 --> 00:50:31,920
Speaker 1: same thing for a dog. Right, we're fixing that to

917
00:50:31,960 --> 00:50:37,000
Speaker 1: the Vectronics survey caller and with that unit were able

918
00:50:37,080 --> 00:50:40,719
Speaker 1: to get two second data on the cat. And then

919
00:50:41,280 --> 00:50:45,920
Speaker 1: what does so on a survey caller, I'm getting data

920
00:50:45,960 --> 00:50:48,280
Speaker 1: every four hours, so I kind of have an idea

921
00:50:48,320 --> 00:50:51,279
Speaker 1: where that cat lives. On a four hour rippet, you know,

922
00:50:51,320 --> 00:50:53,760
Speaker 1: every four hours I get a point. So over time

923
00:50:53,800 --> 00:50:55,879
Speaker 1: you get really good data. But I only have a month.

924
00:50:55,960 --> 00:50:57,759
Speaker 1: I'm not trying to determine home range or any of

925
00:50:57,800 --> 00:50:59,600
Speaker 1: that stuff. I just want to know where that cats

926
00:50:59,600 --> 00:51:03,240
Speaker 1: at right now out. So with a with this garment,

927
00:51:03,640 --> 00:51:05,160
Speaker 1: it's the same thing I use on a dog. I

928
00:51:05,160 --> 00:51:08,440
Speaker 1: have a handheld that shows that cat's location, shows my location,

929
00:51:08,440 --> 00:51:12,640
Speaker 1: it shows the dog's location. So when I'm in the field,

930
00:51:13,600 --> 00:51:15,600
Speaker 1: I have this real time data on the cat and

931
00:51:15,640 --> 00:51:18,799
Speaker 1: I know within two seconds where that cat's at and

932
00:51:18,840 --> 00:51:21,480
Speaker 1: when it's moving and how fast, so you basically, I mean,

933
00:51:21,600 --> 00:51:24,480
Speaker 1: you know right where he is, that everything I know exactly.

934
00:51:24,680 --> 00:51:28,239
Speaker 1: It's UM so, to my knowledge, is the first time

935
00:51:28,280 --> 00:51:30,960
Speaker 1: anybody's done this with cats anyway, with that kind of

936
00:51:31,040 --> 00:51:34,520
Speaker 1: data and that kind of field capability. But bart why

937
00:51:34,600 --> 00:51:40,480
Speaker 1: even attached the regular GPS collar because battery life is

938
00:51:40,480 --> 00:51:43,879
Speaker 1: an issue with the garments. We're still working on how long.

939
00:51:43,960 --> 00:51:46,319
Speaker 1: We don't really know how long a battery will last.

940
00:51:46,320 --> 00:51:48,040
Speaker 1: In the winter time, it seems like we're getting about

941
00:51:48,080 --> 00:51:50,680
Speaker 1: a month out of one of those batteries right now.

942
00:51:50,719 --> 00:51:54,279
Speaker 1: It's extended out to about six weeks, I think. But

943
00:51:54,360 --> 00:51:56,160
Speaker 1: I just didn't want to have a cat running around

944
00:51:56,160 --> 00:51:58,360
Speaker 1: with a dead caller on it and have no way

945
00:51:58,400 --> 00:52:01,440
Speaker 1: to go recapture the cat. So that normal survey caller

946
00:52:01,520 --> 00:52:06,799
Speaker 1: has the four hour data and um VHF capability, so

947
00:52:06,840 --> 00:52:09,320
Speaker 1: it lasts. You know, I get two years of battery

948
00:52:09,360 --> 00:52:12,239
Speaker 1: life out of that unit, UM compared to that other

949
00:52:12,239 --> 00:52:14,080
Speaker 1: collar where I'm getting two second data and it's just

950
00:52:14,160 --> 00:52:17,400
Speaker 1: going through the battery fast. When this cat wakes up

951
00:52:17,440 --> 00:52:20,680
Speaker 1: from getting tranquilized. I know this isn't the object of

952
00:52:20,680 --> 00:52:25,279
Speaker 1: your study, but when when you tranquilize the mountainlin. Um,

953
00:52:25,440 --> 00:52:27,680
Speaker 1: you put the collar on it, it's effective immediately right,

954
00:52:27,680 --> 00:52:33,480
Speaker 1: you can just start tracking him instantaneously. How far does

955
00:52:33,520 --> 00:52:38,080
Speaker 1: he go and how long? How much time goes by

956
00:52:38,160 --> 00:52:41,560
Speaker 1: before he's back to like acting like a mountline. I

957
00:52:42,080 --> 00:52:46,160
Speaker 1: with this, with the ban, the recovery is fast. I

958
00:52:46,200 --> 00:52:51,680
Speaker 1: think they start acting normal far more quickly than they

959
00:52:51,719 --> 00:52:56,239
Speaker 1: do with the ketamine capture. I think that that ketamine

960
00:52:56,239 --> 00:52:59,799
Speaker 1: capture really it causes you know, for lack of a

961
00:52:59,800 --> 00:53:02,400
Speaker 1: better terms, I hangover for that cat that lasts a

962
00:53:02,480 --> 00:53:06,280
Speaker 1: day or two with the BAM, we see movement pretty quick. Um,

963
00:53:06,320 --> 00:53:09,839
Speaker 1: once they recover because we reverse it. Remember, So we

964
00:53:09,920 --> 00:53:12,920
Speaker 1: reverse that cat and within about three minutes that cat

965
00:53:12,960 --> 00:53:14,880
Speaker 1: is on its feet and running away from us. And

966
00:53:14,920 --> 00:53:17,040
Speaker 1: how far will he run before he goes? Like? How

967
00:53:17,080 --> 00:53:19,640
Speaker 1: far does the hall ask before he like chills out again?

968
00:53:20,160 --> 00:53:23,160
Speaker 1: Not far? Maybe a hundred or two yards. I mean

969
00:53:23,840 --> 00:53:28,839
Speaker 1: it doesn't just run for miles just nope. So they

970
00:53:28,880 --> 00:53:30,520
Speaker 1: move away from us and we get out of there.

971
00:53:30,560 --> 00:53:34,200
Speaker 1: And so before I leave, I put that garment caller

972
00:53:34,280 --> 00:53:37,480
Speaker 1: to sleep mode, so that saves the battery life. And

973
00:53:37,760 --> 00:53:39,440
Speaker 1: when I want to go find that cat again, I

974
00:53:39,680 --> 00:53:42,880
Speaker 1: use the VHF on the electronics collar. I get close

975
00:53:43,040 --> 00:53:46,120
Speaker 1: and I used my handheld garment unit to just wake

976
00:53:46,160 --> 00:53:48,359
Speaker 1: that collar up so it's in wake mode, and then

977
00:53:48,360 --> 00:53:50,759
Speaker 1: I started generating that two seconds data, so I'm not

978
00:53:50,840 --> 00:53:53,200
Speaker 1: logging data all the time. It's only when it's awake.

979
00:53:53,480 --> 00:53:55,640
Speaker 1: So it's kind of a nice function to save battery

980
00:53:55,680 --> 00:53:58,120
Speaker 1: on that unit. So how much time will elapse before

981
00:53:58,120 --> 00:54:00,520
Speaker 1: you go back to look at it. I've been trying

982
00:54:00,560 --> 00:54:02,719
Speaker 1: to give him at least five days after capture to

983
00:54:02,760 --> 00:54:05,040
Speaker 1: go re to go have another look at that cat

984
00:54:05,040 --> 00:54:08,080
Speaker 1: to recapture it. And typically how far away are they

985
00:54:08,200 --> 00:54:11,279
Speaker 1: from where you've originally caught them. You know, we've had

986
00:54:11,320 --> 00:54:13,760
Speaker 1: a couple of cats that live on the same mountain

987
00:54:13,840 --> 00:54:16,960
Speaker 1: the whole time we we've studied them, and then we've

988
00:54:16,960 --> 00:54:19,560
Speaker 1: had others that bounce around, you know, six, eight, ten

989
00:54:19,600 --> 00:54:22,520
Speaker 1: miles over the course of that week. So it just

990
00:54:22,600 --> 00:54:25,040
Speaker 1: really depends on the cat and also the time of year.

991
00:54:25,560 --> 00:54:28,200
Speaker 1: You know, the cat that lived on one mountain the

992
00:54:28,200 --> 00:54:30,640
Speaker 1: whole project was in the middle of winters up north,

993
00:54:31,520 --> 00:54:34,719
Speaker 1: but by Calville, Washington, deep Snow had a nice piece

994
00:54:34,719 --> 00:54:37,319
Speaker 1: of winter range for deer, and that cat was just

995
00:54:37,400 --> 00:54:40,799
Speaker 1: living right above the deer. I had no reason to leave.

996
00:54:42,200 --> 00:54:45,960
Speaker 1: And then after you tranquilize them and collar them, what's

997
00:54:46,000 --> 00:54:47,919
Speaker 1: as soon as you've seen him actually kill a big

998
00:54:47,920 --> 00:54:51,799
Speaker 1: game animal or kill a large animal after that, I'm

999
00:54:51,800 --> 00:54:53,759
Speaker 1: not sure. I haven't really paid attention to that. I

1000
00:54:53,800 --> 00:54:59,560
Speaker 1: know one cat we collared her, so we were approaching

1001
00:54:59,600 --> 00:55:04,680
Speaker 1: her to call her, and she had just killed a deer.

1002
00:55:04,840 --> 00:55:08,200
Speaker 1: The deer was actually hadn't even been dragged too a

1003
00:55:08,320 --> 00:55:10,800
Speaker 1: cash It was laying in an open field basically or

1004
00:55:10,920 --> 00:55:13,799
Speaker 1: meadow up in the mountain, so she had just made

1005
00:55:13,800 --> 00:55:17,360
Speaker 1: a kill. We callared her, and later that day she

1006
00:55:17,480 --> 00:55:19,840
Speaker 1: was on that deer and drug it into the brush

1007
00:55:19,880 --> 00:55:22,279
Speaker 1: and and ate on it for about a week. So

1008
00:55:23,080 --> 00:55:26,239
Speaker 1: so pretty quick turnaround to going back to normal. Yeah,

1009
00:55:26,239 --> 00:55:30,120
Speaker 1: she started acting normal pretty quick. Oh yeah, that's amazing.

1010
00:55:30,400 --> 00:55:32,600
Speaker 1: Like you did that to a person, do it, they'd

1011
00:55:32,600 --> 00:55:35,600
Speaker 1: be whacked out for the longest time. Man, Yeah, the

1012
00:55:35,719 --> 00:55:39,879
Speaker 1: BC A counselor. Yeah, yeah, well we would be thinking

1013
00:55:39,880 --> 00:55:42,600
Speaker 1: about it too much. She's just her belly started growling

1014
00:55:42,640 --> 00:55:45,439
Speaker 1: and she said, oh yeah, that's right, killed the deer

1015
00:55:45,480 --> 00:55:48,640
Speaker 1: this morning. All right, I think I can ask a

1016
00:55:48,680 --> 00:55:51,640
Speaker 1: good prompting question, um kind of what are the like

1017
00:55:51,680 --> 00:55:54,879
Speaker 1: the data points then that you're actually looking for now

1018
00:55:54,960 --> 00:55:57,440
Speaker 1: that you've you've you've probably got the data point of

1019
00:55:57,440 --> 00:56:01,279
Speaker 1: like where you caught it right at your start, and

1020
00:56:01,320 --> 00:56:03,879
Speaker 1: then so what are the data points? And then next

1021
00:56:03,880 --> 00:56:07,719
Speaker 1: time you go in that you're capturing. So there's really

1022
00:56:07,920 --> 00:56:11,200
Speaker 1: there's three important data points that I'm gathering. And that's

1023
00:56:11,960 --> 00:56:14,920
Speaker 1: more important than the actual point is that the distances

1024
00:56:14,960 --> 00:56:18,879
Speaker 1: in between them. So when a week after we put

1025
00:56:18,880 --> 00:56:21,200
Speaker 1: a collar on a cat, we go, we approach that cat,

1026
00:56:21,520 --> 00:56:25,640
Speaker 1: and when I'm within about four hundred yards, I start

1027
00:56:26,000 --> 00:56:29,920
Speaker 1: the podcast on an ad decibel little bluetooth speaker. Do

1028
00:56:29,960 --> 00:56:32,480
Speaker 1: you play the intro because it's gonna think it's tree

1029
00:56:32,480 --> 00:56:36,200
Speaker 1: has fallen down. I don't play the intro. It's actually

1030
00:56:36,760 --> 00:56:39,000
Speaker 1: I've only used like two podcasts, don't I don't. I'm

1031
00:56:39,000 --> 00:56:42,200
Speaker 1: not out there that long, so um so you don't

1032
00:56:42,239 --> 00:56:45,440
Speaker 1: pick your favorite parts. Now. I've just got one podcast

1033
00:56:45,480 --> 00:56:47,400
Speaker 1: that's been playing for the same for the cats for

1034
00:56:47,520 --> 00:56:49,600
Speaker 1: quite a while. Okay, okay, what do you mind me

1035
00:56:49,680 --> 00:56:51,879
Speaker 1: asking real quick? Like what's going on in the show.

1036
00:56:52,760 --> 00:56:57,480
Speaker 1: It was the Polar Expedition show, all people eating each

1037
00:56:57,520 --> 00:57:01,200
Speaker 1: other and everything that's turns a cat man. Yeah, they

1038
00:57:01,200 --> 00:57:03,920
Speaker 1: don't like that. And then the other one was that

1039
00:57:04,000 --> 00:57:08,200
Speaker 1: gentleman that wrote the book about Davy Crockett. Yeah, which, yeah,

1040
00:57:08,280 --> 00:57:11,160
Speaker 1: I listened to. I mean, I want to read that

1041
00:57:11,160 --> 00:57:14,040
Speaker 1: book now. After listening to you at eighty decibels for

1042
00:57:14,080 --> 00:57:16,160
Speaker 1: an hour and a half talk about it, it's like,

1043
00:57:16,200 --> 00:57:20,040
Speaker 1: I feel like I have to read it. Um. So

1044
00:57:20,120 --> 00:57:22,800
Speaker 1: I approached the cat from about four yards I'll start

1045
00:57:23,160 --> 00:57:25,360
Speaker 1: that podcast, and I started approaching the cat on it

1046
00:57:25,400 --> 00:57:28,680
Speaker 1: basically a direct line, and I know exactly where it's at.

1047
00:57:28,720 --> 00:57:30,480
Speaker 1: I've got it on the handheld and i know where

1048
00:57:30,480 --> 00:57:32,280
Speaker 1: I'm at, and I don't have dogs with me or

1049
00:57:32,320 --> 00:57:34,280
Speaker 1: anything else. I'm just walking up to the cat with

1050
00:57:34,280 --> 00:57:39,480
Speaker 1: this human voice playing the important data is back backup,

1051
00:57:39,880 --> 00:57:42,280
Speaker 1: and he's like in a predictable place. Are you like, oh,

1052
00:57:42,440 --> 00:57:44,760
Speaker 1: he's he's in a cliff, or he's in you know

1053
00:57:44,800 --> 00:57:47,120
Speaker 1: what I mean, or they just like our in just

1054
00:57:47,240 --> 00:57:50,800
Speaker 1: weird places. If it's in the middle of the day,

1055
00:57:51,160 --> 00:57:54,560
Speaker 1: which we're trying to trying to capture cats or recapture

1056
00:57:54,600 --> 00:57:56,280
Speaker 1: cats in the middle of the day, it's a pretty

1057
00:57:56,280 --> 00:57:59,520
Speaker 1: predictable place. And we're starting to make some connections that

1058
00:57:59,560 --> 00:58:02,760
Speaker 1: way about. Um, wind cats are on the move compared

1059
00:58:02,800 --> 00:58:05,000
Speaker 1: to wind cats are in a bed. How they respond.

1060
00:58:06,280 --> 00:58:10,800
Speaker 1: But there's a little bit of individuality that way too. Um.

1061
00:58:10,840 --> 00:58:13,040
Speaker 1: Some cats are a little more comfortable laying out in

1062
00:58:13,080 --> 00:58:16,479
Speaker 1: a less of a thicket, and some cats really hole

1063
00:58:16,600 --> 00:58:19,680
Speaker 1: into some gnarly little thickets or rock overhangs and things

1064
00:58:19,720 --> 00:58:21,040
Speaker 1: like that, so they like it. They like to lay

1065
00:58:21,040 --> 00:58:24,000
Speaker 1: in the thicket. We Yeah, there are some cats that

1066
00:58:24,080 --> 00:58:29,040
Speaker 1: lay in a thicket that's like impenetrable thickets, And when

1067
00:58:29,080 --> 00:58:30,720
Speaker 1: they're in one of those, they feel very safe. You

1068
00:58:30,720 --> 00:58:33,880
Speaker 1: could walk right up to them, got you, Okay, So

1069
00:58:34,560 --> 00:58:35,920
Speaker 1: I don't mean to derail you there. I was just

1070
00:58:35,920 --> 00:58:41,800
Speaker 1: curious about. No, but what's the more open location look like? Uh,

1071
00:58:42,080 --> 00:58:45,600
Speaker 1: penetrable thicket? Okay, So it's never like you just laid

1072
00:58:45,640 --> 00:58:47,200
Speaker 1: out in the sun in the midle of a hundred

1073
00:58:47,240 --> 00:58:50,840
Speaker 1: yard grassy meadow. No, they we do find them laying

1074
00:58:50,880 --> 00:58:54,560
Speaker 1: an open forest on occasion, but tip if they are,

1075
00:58:54,680 --> 00:58:57,600
Speaker 1: it's like at the base of a tree with heavy

1076
00:58:57,640 --> 00:59:00,200
Speaker 1: with branches. I feel like when we chase those first

1077
00:59:00,280 --> 00:59:02,920
Speaker 1: day we chased lines with you in northern Idaho, didn't

1078
00:59:02,960 --> 00:59:05,000
Speaker 1: we find two beds that were in a pretty open

1079
00:59:05,560 --> 00:59:08,760
Speaker 1: for forest. Yeah, that was open forest, are up up

1080
00:59:08,800 --> 00:59:11,360
Speaker 1: against you know, old growth cedar trees. I mean they

1081
00:59:11,440 --> 00:59:14,680
Speaker 1: had some cover. They're still so they like some covered.

1082
00:59:14,680 --> 00:59:18,920
Speaker 1: They like a thicket, they're like a rocky spot. Yeah,

1083
00:59:19,000 --> 00:59:21,240
Speaker 1: we find them in all of those things. Over hung rocks.

1084
00:59:21,280 --> 00:59:23,720
Speaker 1: We've had a couple of cats that we walked right

1085
00:59:23,800 --> 00:59:25,960
Speaker 1: up to that we're in over hung rocks. Um, we

1086
00:59:26,120 --> 00:59:30,400
Speaker 1: actually had one Monday that was we got within thirty

1087
00:59:30,480 --> 00:59:33,680
Speaker 1: yards of she was laid up in a rock pile. Okay,

1088
00:59:33,720 --> 00:59:35,600
Speaker 1: so you start playing this sing and walking towards him,

1089
00:59:35,600 --> 00:59:39,000
Speaker 1: and you start playing it at four yards, Yeah, four yards.

1090
00:59:39,000 --> 00:59:41,040
Speaker 1: I turned the podcast on. I approached them in the

1091
00:59:41,080 --> 00:59:44,720
Speaker 1: most direct line. How loud is it? How how loud

1092
00:59:44,800 --> 00:59:47,920
Speaker 1: is it for you? Like you're playing it to It's like,

1093
00:59:48,200 --> 00:59:52,280
Speaker 1: that's an annoying level for you. Yeah, it's it's annoying.

1094
00:59:52,320 --> 00:59:56,520
Speaker 1: It's an outside voice for sure. Um, it's not a

1095
00:59:56,680 --> 00:59:59,440
Speaker 1: it's not a shout necessarily, but it's a loud talk

1096
00:59:59,600 --> 01:00:01,920
Speaker 1: for sure. If we were talking, if we were walking

1097
01:00:01,960 --> 01:00:04,680
Speaker 1: in the woods talking at that level, it would be annoying,

1098
01:00:04,720 --> 01:00:08,800
Speaker 1: probably it. Um, So they're hearing it from quite a ways.

1099
01:00:09,880 --> 01:00:13,960
Speaker 1: So as we approached this cat. The important data that

1100
01:00:14,040 --> 01:00:17,160
Speaker 1: I grab is how close do I get to the

1101
01:00:17,200 --> 01:00:20,320
Speaker 1: cat before it gets to its feet and leaves? So

1102
01:00:20,360 --> 01:00:23,680
Speaker 1: I grabbed at that point, I stopped, I get my location,

1103
01:00:23,760 --> 01:00:26,520
Speaker 1: pin that I get the cat's location. I pined that,

1104
01:00:26,880 --> 01:00:28,480
Speaker 1: and then I just watched that cat, and I keep

1105
01:00:28,520 --> 01:00:31,800
Speaker 1: the speaker playing and I just stand still, and I'll

1106
01:00:31,880 --> 01:00:35,800
Speaker 1: let that cat move away however it chooses. Whatever that

1107
01:00:36,040 --> 01:00:38,560
Speaker 1: flight looks like, however much energy it wants to put

1108
01:00:38,600 --> 01:00:43,120
Speaker 1: into escaping my approach, and the cat moves away and

1109
01:00:43,160 --> 01:00:46,640
Speaker 1: I get Typically that happens within about oh ten minutes.

1110
01:00:47,160 --> 01:00:49,600
Speaker 1: And once that cat stops moving for you know, I

1111
01:00:49,600 --> 01:00:51,160
Speaker 1: don't have a set amount of time, but once it's

1112
01:00:51,200 --> 01:00:53,600
Speaker 1: hanging out in an area and not really going anywhere,

1113
01:00:54,160 --> 01:00:55,960
Speaker 1: I called at the end of the mobilization, and I

1114
01:00:56,000 --> 01:00:59,760
Speaker 1: grabbed that distance. So the two important measurements really are

1115
01:00:59,800 --> 01:01:03,840
Speaker 1: the distance from me the stimuli, and the distance of

1116
01:01:03,880 --> 01:01:07,600
Speaker 1: the flight. And then I hop on the radio. I

1117
01:01:07,640 --> 01:01:09,400
Speaker 1: walk up to where that cat was laying around. I

1118
01:01:09,440 --> 01:01:12,040
Speaker 1: hop on the radio, and uh, tell, Bruce kicked the

1119
01:01:12,080 --> 01:01:14,760
Speaker 1: dogs loose. The dogs are now trained to like track me.

1120
01:01:14,800 --> 01:01:16,280
Speaker 1: If I ever get lost, of dogs are gonna find

1121
01:01:16,280 --> 01:01:19,480
Speaker 1: me in a second. They they know what's going on now,

1122
01:01:19,520 --> 01:01:23,600
Speaker 1: so they track me to the site of the cat's bed,

1123
01:01:23,920 --> 01:01:25,680
Speaker 1: and once they get there, they take off and go

1124
01:01:25,840 --> 01:01:28,600
Speaker 1: treat that cat. What are the distances, like the cats

1125
01:01:28,640 --> 01:01:32,880
Speaker 1: responding at what distance typically, so we are seeing those

1126
01:01:32,920 --> 01:01:35,840
Speaker 1: distances extend over the course of the project for all

1127
01:01:35,840 --> 01:01:39,200
Speaker 1: pretty much every cat the first time, the first time

1128
01:01:39,200 --> 01:01:41,800
Speaker 1: we approach a cat. So Monday was an exciting day.

1129
01:01:42,200 --> 01:01:44,000
Speaker 1: Monday was the first cat that I've walked up on

1130
01:01:44,080 --> 01:01:47,080
Speaker 1: and actually looked at before it ran away from me.

1131
01:01:47,440 --> 01:01:49,840
Speaker 1: And it's the first time. It's the first hazing. That's

1132
01:01:49,880 --> 01:01:52,480
Speaker 1: kind of what we're calling this, the first hazing event. Um.

1133
01:01:52,520 --> 01:01:55,240
Speaker 1: The cat had been captured once. It was pretty calm

1134
01:01:55,560 --> 01:01:59,000
Speaker 1: at capture. UM. So last Monday, I went out and

1135
01:01:59,040 --> 01:02:00,560
Speaker 1: I walked up to that cat and I'm watching the

1136
01:02:00,560 --> 01:02:04,600
Speaker 1: GPS and it's pretty thick. They're not terribly thick. You

1137
01:02:04,640 --> 01:02:07,440
Speaker 1: can certainly see, you know, forty or fifty yards in

1138
01:02:07,480 --> 01:02:10,000
Speaker 1: the forest. And I'm watching the GPS and like, man,

1139
01:02:10,000 --> 01:02:11,920
Speaker 1: I'm getting pretty close to this cat. He ought to be.

1140
01:02:12,040 --> 01:02:13,880
Speaker 1: It's like that scene. It's like that scene in Red

1141
01:02:13,960 --> 01:02:16,240
Speaker 1: Don when the dudes in the white suits the rooskis

1142
01:02:16,280 --> 01:02:21,080
Speaker 1: in the white suits start tracking that kid down right. Yeah,

1143
01:02:20,680 --> 01:02:23,960
Speaker 1: it was unnerving. I'm looking at my GPS and it like,

1144
01:02:24,040 --> 01:02:26,240
Speaker 1: is this thing updating? Right? Like, what's going on here?

1145
01:02:26,440 --> 01:02:30,120
Speaker 1: Do you get nervous? Do you get nervous around mountainlines? Um?

1146
01:02:30,160 --> 01:02:33,040
Speaker 1: I had my gun out, I had my gun out

1147
01:02:33,040 --> 01:02:38,040
Speaker 1: of its holster. Yeah, I was nervous. Um. I mean

1148
01:02:38,040 --> 01:02:40,280
Speaker 1: when I was close, I got within nineteen feet of him.

1149
01:02:40,560 --> 01:02:42,920
Speaker 1: Ho he smokes. He's just as laying there listening to

1150
01:02:42,960 --> 01:02:46,440
Speaker 1: the podcast. He's like, dude, this is fascinating. Yeah, I

1151
01:02:46,480 --> 01:02:52,160
Speaker 1: can't get enough of this guy. So he was bedded

1152
01:02:52,200 --> 01:02:54,320
Speaker 1: underneath the kind of a leaning log, and I hopped

1153
01:02:54,360 --> 01:02:55,880
Speaker 1: up on top of the log to get a better

1154
01:02:55,960 --> 01:02:58,960
Speaker 1: view and get a little bit of elevation. And when

1155
01:02:58,960 --> 01:03:00,760
Speaker 1: I did, the end of the log moved, and I

1156
01:03:00,760 --> 01:03:03,800
Speaker 1: think that unnerved him and he ran out from underneath that. Um.

1157
01:03:03,880 --> 01:03:06,040
Speaker 1: So that's the first one that I've actually watched run away.

1158
01:03:07,200 --> 01:03:10,360
Speaker 1: And then he sorry to be clear, this was the

1159
01:03:10,400 --> 01:03:14,240
Speaker 1: first time you had gone in right after this cat,

1160
01:03:14,320 --> 01:03:17,120
Speaker 1: after this cat. This is the first time it's been

1161
01:03:17,160 --> 01:03:20,200
Speaker 1: exposed to this stimuli. So it run it ran off

1162
01:03:20,240 --> 01:03:23,240
Speaker 1: and only went seventy five and stopped and would not move.

1163
01:03:23,240 --> 01:03:26,360
Speaker 1: And I'm like, really, now I'm unnerved at this point.

1164
01:03:26,360 --> 01:03:28,520
Speaker 1: So I'm holler and I'm watching the GPS very closely

1165
01:03:28,560 --> 01:03:30,200
Speaker 1: to make sure it's not circling me or coming back

1166
01:03:30,200 --> 01:03:32,800
Speaker 1: to me or something, because it knows I'm there. It

1167
01:03:32,840 --> 01:03:35,160
Speaker 1: has seen me. It's here and me and now here's me,

1168
01:03:35,280 --> 01:03:39,160
Speaker 1: holler and added over the podcast. Um. And it sat

1169
01:03:39,200 --> 01:03:42,880
Speaker 1: there for a long time, I mean several minutes at

1170
01:03:42,920 --> 01:03:46,680
Speaker 1: seventy and finally I'm like, well, I guess we have

1171
01:03:46,720 --> 01:03:49,080
Speaker 1: to turn the dogs loose. It's that's that's it's flight.

1172
01:03:49,120 --> 01:03:51,000
Speaker 1: That's what it's gonna do. It's not gonna run from me.

1173
01:03:51,280 --> 01:03:53,600
Speaker 1: Oh yeah, yeah, I got just so you marked down

1174
01:03:53,800 --> 01:03:59,280
Speaker 1: his his his flight. Yeah, that's pretty close. So the

1175
01:03:59,320 --> 01:04:02,560
Speaker 1: dogs treat it within about four hundred yards, and UM,

1176
01:04:02,720 --> 01:04:05,600
Speaker 1: at the first at the first hazing event, we shoot

1177
01:04:05,680 --> 01:04:07,440
Speaker 1: him with a paintball gun. We tie all the dogs back,

1178
01:04:07,480 --> 01:04:09,440
Speaker 1: take him back to the truck, and we shoot that

1179
01:04:09,480 --> 01:04:11,440
Speaker 1: cat with a paintball gun to give it a to

1180
01:04:11,600 --> 01:04:14,120
Speaker 1: reinforce that negative stimuli, and holler at it, you know,

1181
01:04:14,160 --> 01:04:16,200
Speaker 1: and kind of try to stress it out a little bit.

1182
01:04:16,240 --> 01:04:18,920
Speaker 1: That first capture event. So I'll see what he does.

1183
01:04:18,920 --> 01:04:20,520
Speaker 1: We're gonna go after him again next week, and I'll

1184
01:04:20,520 --> 01:04:24,200
Speaker 1: probably take somebody with me this time, just because and

1185
01:04:24,280 --> 01:04:28,680
Speaker 1: presumably he'll I think he's gonna I think he's going

1186
01:04:28,720 --> 01:04:30,720
Speaker 1: to get out of there. Yeah, that has been our

1187
01:04:30,800 --> 01:04:34,439
Speaker 1: experience the first the first time we approach a cat,

1188
01:04:34,920 --> 01:04:39,560
Speaker 1: they let us get quite close. Um. I we have

1189
01:04:39,720 --> 01:04:43,760
Speaker 1: not had one flea from outside of a hundred yards yet,

1190
01:04:44,280 --> 01:04:47,280
Speaker 1: so we're within a hundreds of anything right now the

1191
01:04:47,280 --> 01:04:51,600
Speaker 1: first time. And we have complete data I think on

1192
01:04:51,720 --> 01:04:55,680
Speaker 1: eight cats now and pretty much across the boarder complete me.

1193
01:04:55,920 --> 01:04:58,640
Speaker 1: You've done it four or five weeks in a row. Yes,

1194
01:04:59,120 --> 01:05:02,320
Speaker 1: So that's mats that have been collared, hazed four or

1195
01:05:02,320 --> 01:05:06,360
Speaker 1: five times and then uncollared and released. And are you

1196
01:05:06,400 --> 01:05:10,960
Speaker 1: seeing that it grow every time? Not every time? UM.

1197
01:05:11,000 --> 01:05:13,880
Speaker 1: We do have a couple of negative data points. We

1198
01:05:13,960 --> 01:05:15,760
Speaker 1: have a couple of cats that let us get a

1199
01:05:15,800 --> 01:05:19,520
Speaker 1: little bit closer, you know, on the third or fourth event. Um.

1200
01:05:19,560 --> 01:05:22,160
Speaker 1: Some of those are easily explained. Some one of those

1201
01:05:22,160 --> 01:05:24,160
Speaker 1: cats was sitting down by a creek in a thicket.

1202
01:05:24,200 --> 01:05:26,439
Speaker 1: Probably didn't hear us with that running water right next

1203
01:05:26,440 --> 01:05:29,480
Speaker 1: to it. Um. Let us get pretty close. Another one

1204
01:05:29,600 --> 01:05:32,400
Speaker 1: was up with them. It was a tom a big

1205
01:05:32,440 --> 01:05:36,160
Speaker 1: tom ud seventy pound cat that had a female and

1206
01:05:36,200 --> 01:05:38,720
Speaker 1: they were laid up in a rock pile together. And

1207
01:05:38,760 --> 01:05:42,160
Speaker 1: he let me get quite close before they mobilized, which

1208
01:05:42,160 --> 01:05:43,760
Speaker 1: I want to say that, which is to say as well,

1209
01:05:43,760 --> 01:05:46,240
Speaker 1: that she let you get quite close. That was an

1210
01:05:46,240 --> 01:05:49,840
Speaker 1: interesting deal for sure, because he left first. He was

1211
01:05:49,880 --> 01:05:53,600
Speaker 1: in a little town called Ion up north, and he

1212
01:05:53,720 --> 01:05:57,120
Speaker 1: was in a wood shed when we darted him. Actually

1213
01:05:57,160 --> 01:05:59,640
Speaker 1: he was in the woodshed and we couldn't get him out.

1214
01:06:00,000 --> 01:06:01,760
Speaker 1: He shot him with a slingshot to get him to

1215
01:06:01,840 --> 01:06:03,320
Speaker 1: run out of the woodshed, and I couldn't get a

1216
01:06:03,400 --> 01:06:06,080
Speaker 1: dart in him when he was running out, and he

1217
01:06:06,160 --> 01:06:10,640
Speaker 1: went into a car port and was hiding under a boat,

1218
01:06:11,560 --> 01:06:13,000
Speaker 1: and I tried to get a dart in in there,

1219
01:06:13,000 --> 01:06:14,600
Speaker 1: and he squirted out the back of that and went

1220
01:06:14,680 --> 01:06:16,680
Speaker 1: under a deck and we ended up having to dig

1221
01:06:17,800 --> 01:06:20,640
Speaker 1: deep snow. It's chest deep snow. We had to dig

1222
01:06:20,640 --> 01:06:23,480
Speaker 1: a tunnel kind of down underneath the deck and he

1223
01:06:24,080 --> 01:06:26,240
Speaker 1: I got a dart in him there. Um, So that

1224
01:06:26,320 --> 01:06:28,960
Speaker 1: guy had some experience with people, and he lived in

1225
01:06:29,120 --> 01:06:32,120
Speaker 1: very close proximity to a little town and the first

1226
01:06:32,160 --> 01:06:34,960
Speaker 1: time we when after him, he was up in these

1227
01:06:35,080 --> 01:06:39,080
Speaker 1: rocky cliffy, this terrible spot, walk around and hunt and

1228
01:06:39,720 --> 01:06:42,040
Speaker 1: you know in the GPS and you're looking at data

1229
01:06:42,720 --> 01:06:46,560
Speaker 1: from overhead, you know, so fifteen yards might mean a

1230
01:06:46,640 --> 01:06:49,240
Speaker 1: hundred or two hundred vertical feet if it's cliffs, which

1231
01:06:49,280 --> 01:06:51,280
Speaker 1: is in this case is what it was, so that

1232
01:06:51,360 --> 01:06:56,640
Speaker 1: data is difficult to explain. Um. Yeah, I got within

1233
01:06:56,760 --> 01:07:00,520
Speaker 1: forty of him, but he was uphill of me in

1234
01:07:00,600 --> 01:07:03,440
Speaker 1: this overhang with a with a female that you know,

1235
01:07:04,080 --> 01:07:07,080
Speaker 1: line of sight would have been a hundred meters or something,

1236
01:07:07,120 --> 01:07:10,640
Speaker 1: so difficult. So he left and I circled around and

1237
01:07:10,680 --> 01:07:11,960
Speaker 1: I got up on top of the cliff and I

1238
01:07:11,960 --> 01:07:14,160
Speaker 1: found his tracks, and I was sitting on his tracks,

1239
01:07:14,520 --> 01:07:16,000
Speaker 1: and at this point I didn't know there was another

1240
01:07:16,040 --> 01:07:18,120
Speaker 1: cat there. So I was sitting on his tracks, just

1241
01:07:18,200 --> 01:07:21,280
Speaker 1: kind of watching the garment unit and filling out my

1242
01:07:21,360 --> 01:07:24,280
Speaker 1: daddas sheet and grabbing data that I needed. And he

1243
01:07:24,360 --> 01:07:26,400
Speaker 1: got out a few hundred yards and I still had

1244
01:07:26,440 --> 01:07:31,640
Speaker 1: the podcast playing, and I hear something to my side

1245
01:07:31,720 --> 01:07:34,280
Speaker 1: and I look over and there that female is walking

1246
01:07:34,320 --> 01:07:37,400
Speaker 1: on his tracks straight at me and maybe fifteen ft

1247
01:07:37,440 --> 01:07:40,680
Speaker 1: from me, and slips down through that same little crack

1248
01:07:40,720 --> 01:07:43,640
Speaker 1: in the rocks that that mail had just used to escape.

1249
01:07:44,320 --> 01:07:47,000
Speaker 1: It kind of seemed like without a care in the

1250
01:07:47,040 --> 01:07:50,480
Speaker 1: world about me, and you're making noise because you're still

1251
01:07:50,480 --> 01:07:54,680
Speaker 1: playing the noise she's walking toward. Yeah, she walked right

1252
01:07:54,760 --> 01:07:57,840
Speaker 1: up close to me, but it was Yeah, her desire

1253
01:07:57,880 --> 01:08:00,760
Speaker 1: to follow that Tom's tracks was pretty strong. She pretty

1254
01:08:00,800 --> 01:08:03,959
Speaker 1: much ignored me to do that. And we got eyes

1255
01:08:04,000 --> 01:08:06,919
Speaker 1: on him twice before he went in a tree that day.

1256
01:08:07,080 --> 01:08:20,519
Speaker 1: He was just pretty bold. Cat. I'm surprised that I

1257
01:08:20,560 --> 01:08:25,400
Speaker 1: don't picture you're gonna have a continued problem, uh, funding

1258
01:08:25,439 --> 01:08:28,760
Speaker 1: your work. I don't think so. A lot of people

1259
01:08:28,760 --> 01:08:30,920
Speaker 1: will be like really interested in this and how to

1260
01:08:31,160 --> 01:08:33,519
Speaker 1: because the thing about it's not like rats, right, It's

1261
01:08:33,520 --> 01:08:35,400
Speaker 1: not like where you have just thousands and thousands of

1262
01:08:35,439 --> 01:08:37,400
Speaker 1: them and there's nothing you can do, Like what do

1263
01:08:37,439 --> 01:08:40,360
Speaker 1: you have mountain lion problems or mountain lions that you

1264
01:08:40,439 --> 01:08:43,439
Speaker 1: want in areas where they're imperiled and not doing well.

1265
01:08:44,600 --> 01:08:47,800
Speaker 1: You're talking about like a small number of animals, so

1266
01:08:47,880 --> 01:08:51,479
Speaker 1: you could actually go through and give them some white

1267
01:08:51,479 --> 01:08:53,960
Speaker 1: glove of service, so to speak, and and and and

1268
01:08:54,000 --> 01:08:56,639
Speaker 1: be targeted about it. And it will still make sense,

1269
01:08:56,680 --> 01:09:00,000
Speaker 1: you know, I mean, because there's so few of them.

1270
01:09:00,040 --> 01:09:02,559
Speaker 1: It's not like it's not insurmountable to go into an

1271
01:09:02,600 --> 01:09:04,920
Speaker 1: area that has a lot of lion problems and and

1272
01:09:05,000 --> 01:09:10,160
Speaker 1: adjust some lion behavior, right. Yeah, And had a biologist

1273
01:09:10,160 --> 01:09:12,320
Speaker 1: from California reach out to me and he's got some

1274
01:09:12,400 --> 01:09:16,640
Speaker 1: interest in replicating the project down near San Francisco on

1275
01:09:16,720 --> 01:09:19,360
Speaker 1: a one of the big reserves that they have a

1276
01:09:19,400 --> 01:09:22,800
Speaker 1: lot of what they believe are habituated cougars. Cougars that

1277
01:09:22,840 --> 01:09:25,519
Speaker 1: are kind of just laying around in day use areas,

1278
01:09:25,760 --> 01:09:28,880
Speaker 1: um and sort of don't have a care in the

1279
01:09:28,880 --> 01:09:31,880
Speaker 1: world about people. And I don't tend to make a

1280
01:09:31,880 --> 01:09:33,479
Speaker 1: whole bunch of promises about what we can do. But

1281
01:09:33,520 --> 01:09:35,400
Speaker 1: I can, like I did promise him, Like if there's

1282
01:09:35,400 --> 01:09:37,719
Speaker 1: a cat laying in a day use area, I promise

1283
01:09:37,800 --> 01:09:39,960
Speaker 1: I can change that cat's behavior to not do that anymore.

1284
01:09:40,000 --> 01:09:43,000
Speaker 1: Like we can we can adjust that cat's attitude really

1285
01:09:43,080 --> 01:09:47,600
Speaker 1: quickly with dogs and paintballs. Have you messed with I

1286
01:09:47,640 --> 01:09:50,800
Speaker 1: know that? Like I support using the podcast, think it's

1287
01:09:50,800 --> 01:09:53,639
Speaker 1: a great idea, it's a great touch. But have you

1288
01:09:54,000 --> 01:10:00,599
Speaker 1: um experimented with other stimul others stimuli, like do they

1289
01:10:00,720 --> 01:10:03,519
Speaker 1: hear free like like a dog for instance? Do they

1290
01:10:03,560 --> 01:10:11,720
Speaker 1: hear frequencies that humans can't hear? I don't know that. Um.

1291
01:10:11,760 --> 01:10:14,880
Speaker 1: I have considered using the sound of dogs barking rather

1292
01:10:14,920 --> 01:10:17,040
Speaker 1: than a human voice to approach the cat and see

1293
01:10:17,080 --> 01:10:19,840
Speaker 1: how they respond, not like a not like a hounds bang,

1294
01:10:19,880 --> 01:10:22,120
Speaker 1: but like you know, a border collie happen around, or

1295
01:10:22,160 --> 01:10:26,439
Speaker 1: something that's going to hear I mean fairly regularly. If

1296
01:10:26,479 --> 01:10:28,720
Speaker 1: we get to a point in the project where we

1297
01:10:28,840 --> 01:10:31,640
Speaker 1: have good enough data and solid enough data with the

1298
01:10:31,720 --> 01:10:34,760
Speaker 1: human voice stimuli, we will switch to another sound, but

1299
01:10:34,840 --> 01:10:38,960
Speaker 1: it will probably be either like the sound of equipment

1300
01:10:39,040 --> 01:10:43,000
Speaker 1: droning on like a four wheeler, or like dog barking,

1301
01:10:43,080 --> 01:10:45,120
Speaker 1: or something that cats are going to hear that would

1302
01:10:45,160 --> 01:10:49,000
Speaker 1: have a management It would be a management action, right,

1303
01:10:49,040 --> 01:10:51,720
Speaker 1: like something you could tell people. You could tell people like, YEA,

1304
01:10:51,920 --> 01:10:53,559
Speaker 1: once we do this, this cat is probably not going

1305
01:10:53,600 --> 01:10:56,840
Speaker 1: to go close to your barking dogs or whatever. I

1306
01:10:56,880 --> 01:11:00,479
Speaker 1: had a friend who uses to design soundscapes and films,

1307
01:11:00,760 --> 01:11:04,439
Speaker 1: and when you're watching a movie, you know, and there's

1308
01:11:04,520 --> 01:11:07,439
Speaker 1: people sort of driving around and you're hearing the sounds

1309
01:11:07,520 --> 01:11:13,400
Speaker 1: of the city, he would like design those just just

1310
01:11:13,439 --> 01:11:16,880
Speaker 1: the ambiance sound you know in films. So yeah, like

1311
01:11:16,920 --> 01:11:19,280
Speaker 1: it just that that you'd make a soundscape of just

1312
01:11:19,400 --> 01:11:24,559
Speaker 1: human activity, you know, doors shutting, kids whatever, people yelling

1313
01:11:24,600 --> 01:11:27,519
Speaker 1: at their kids would be like my house, yeah, and

1314
01:11:27,560 --> 01:11:30,400
Speaker 1: get him use of that noise, right, And then the

1315
01:11:30,439 --> 01:11:32,760
Speaker 1: obvious thing that you know, if you let's say you

1316
01:11:32,800 --> 01:11:37,440
Speaker 1: did this over the course of time in an area. Um,

1317
01:11:37,600 --> 01:11:39,800
Speaker 1: it's a small area. This isn't something that I expect

1318
01:11:39,840 --> 01:11:42,760
Speaker 1: to happen across the whole region or something. But it

1319
01:11:42,800 --> 01:11:46,439
Speaker 1: would be interesting then to see how cats responded to

1320
01:11:46,720 --> 01:11:49,800
Speaker 1: just a speaker at a house or out in the woods, say,

1321
01:11:49,880 --> 01:11:52,840
Speaker 1: see how they moved around the landscape, to see if

1322
01:11:52,840 --> 01:11:55,120
Speaker 1: they avoided that or just passed through like they always have.

1323
01:11:56,200 --> 01:11:57,519
Speaker 1: And is there a thing you can do to them

1324
01:11:57,560 --> 01:12:01,960
Speaker 1: that they really really hate? Touch them? Yeah, if you

1325
01:12:02,000 --> 01:12:06,120
Speaker 1: can get contact with them that paintball or anything like

1326
01:12:06,160 --> 01:12:08,280
Speaker 1: that just drives them crazy. They hate that. They don't

1327
01:12:08,320 --> 01:12:12,280
Speaker 1: like it. No, you change your shot placement when you're

1328
01:12:12,280 --> 01:12:15,960
Speaker 1: doing the negative reinforcement a game, for like point of

1329
01:12:16,000 --> 01:12:18,080
Speaker 1: the shoulder or something. Something that's gonna chip of the

1330
01:12:18,080 --> 01:12:23,200
Speaker 1: nose bruise. Yeah. Um, when I'm when I'm shooting a paintball.

1331
01:12:23,240 --> 01:12:26,200
Speaker 1: So that's I was kind of experimenting with this permanent

1332
01:12:26,240 --> 01:12:29,960
Speaker 1: paint that foresters use. They mark trees with this stuff,

1333
01:12:30,000 --> 01:12:32,840
Speaker 1: and it lasts quite a while on a tree. Um.

1334
01:12:33,520 --> 01:12:37,000
Speaker 1: So I bought some of these fancy paintballs with permanent

1335
01:12:37,040 --> 01:12:41,360
Speaker 1: paint in them, and I wanted to see how long

1336
01:12:41,439 --> 01:12:44,600
Speaker 1: that would last on a cat. Just so if we

1337
01:12:44,680 --> 01:12:47,200
Speaker 1: had a cat that was causing trouble in an area

1338
01:12:47,240 --> 01:12:49,400
Speaker 1: of sighting, I didn't have a caller available or whatever.

1339
01:12:49,400 --> 01:12:51,400
Speaker 1: All right, we'll go. We'll go mark this thing and

1340
01:12:52,160 --> 01:12:54,200
Speaker 1: if it you know, without having to drug it and

1341
01:12:54,240 --> 01:12:55,639
Speaker 1: handle it. We're not going to put an ear tag

1342
01:12:55,640 --> 01:12:57,800
Speaker 1: in it or anything else. If that cat shows up,

1343
01:12:57,880 --> 01:12:59,720
Speaker 1: hopefully the people that see it would be able to

1344
01:12:59,760 --> 01:13:01,519
Speaker 1: say yeah. As it turns out, I had a bunch

1345
01:13:01,520 --> 01:13:03,960
Speaker 1: of blue paint on its side, so I wanted to

1346
01:13:03,960 --> 01:13:05,800
Speaker 1: see how long that paint would last. And it turns

1347
01:13:05,800 --> 01:13:08,599
Speaker 1: out it's not that effective. It comes off fairly quickly.

1348
01:13:09,160 --> 01:13:12,080
Speaker 1: But I was trying to shoot the cat forward in

1349
01:13:12,120 --> 01:13:13,960
Speaker 1: the body where it wouldn't be able to reach back

1350
01:13:13,960 --> 01:13:16,880
Speaker 1: and lick that stuff off. So I was shooting him

1351
01:13:16,880 --> 01:13:18,880
Speaker 1: in the front of the shoulder, neck and back. It's

1352
01:13:18,920 --> 01:13:21,840
Speaker 1: kind of where I was aiming. But I don't know

1353
01:13:21,880 --> 01:13:24,559
Speaker 1: if that. I mean, anybody that's played paintball and knows

1354
01:13:24,600 --> 01:13:27,240
Speaker 1: the things they staying. They're not gonna break skin on

1355
01:13:27,240 --> 01:13:29,280
Speaker 1: a cougar for sure, but they do sting and making

1356
01:13:29,320 --> 01:13:32,160
Speaker 1: contact with that cat drives him crazy. Have any of

1357
01:13:32,160 --> 01:13:35,560
Speaker 1: the cats that you got right now? How often you

1358
01:13:35,600 --> 01:13:36,840
Speaker 1: have a cat that has a collar on and then

1359
01:13:36,880 --> 01:13:40,320
Speaker 1: it gets killed somehow or another while like unrelated to

1360
01:13:40,360 --> 01:13:43,920
Speaker 1: your activity. So that big tom cat that I was

1361
01:13:43,960 --> 01:13:46,240
Speaker 1: talking about in the cliff just a minute ago, he

1362
01:13:46,320 --> 01:13:50,120
Speaker 1: got killed. He took off after that day and walked

1363
01:13:50,160 --> 01:13:53,479
Speaker 1: all the way into Canada about ten miles north of

1364
01:13:53,479 --> 01:13:55,599
Speaker 1: the border, and got himself killed by a legal hunter

1365
01:13:55,720 --> 01:14:01,439
Speaker 1: up there. Yeah, so I got that collar back. Um.

1366
01:14:01,479 --> 01:14:06,000
Speaker 1: And then another cat was down here close to Spokane.

1367
01:14:06,080 --> 01:14:08,640
Speaker 1: It was a cat that Were you pissed at the

1368
01:14:08,640 --> 01:14:11,400
Speaker 1: guy shot your collared lion? No, I don't care. It

1369
01:14:11,479 --> 01:14:15,680
Speaker 1: was a whatever. That's the that's the deal. He's was

1370
01:14:15,720 --> 01:14:18,280
Speaker 1: he aware that it had a collar? Oh yeah, now

1371
01:14:18,280 --> 01:14:19,720
Speaker 1: he knew I had a caller. I think he's a

1372
01:14:19,760 --> 01:14:22,200
Speaker 1: little bit nervous. He was like, he's a little bit

1373
01:14:22,200 --> 01:14:25,000
Speaker 1: resisent to to call me, and you know, on the

1374
01:14:25,040 --> 01:14:27,200
Speaker 1: caller has my name and phone number and says whatever,

1375
01:14:27,240 --> 01:14:29,799
Speaker 1: please call it. Found it took a little I actually

1376
01:14:29,840 --> 01:14:33,479
Speaker 1: saw it on Facebook before I think I actually I

1377
01:14:33,479 --> 01:14:37,360
Speaker 1: think I actually reached out to him first. Um, but

1378
01:14:37,479 --> 01:14:42,840
Speaker 1: I think I'm not mad. Just by any chance that

1379
01:14:42,880 --> 01:14:45,840
Speaker 1: color lying you shot right, Well, that's got you know,

1380
01:14:45,920 --> 01:14:50,360
Speaker 1: that's a caller plus the garment caller plus all the

1381
01:14:50,439 --> 01:14:52,599
Speaker 1: data that's on it, which is you know, that's three

1382
01:14:52,720 --> 01:14:54,920
Speaker 1: or four days of work worth a data that's stored

1383
01:14:54,960 --> 01:14:58,080
Speaker 1: on that garment. You know that I really want. So yeah,

1384
01:14:58,160 --> 01:15:01,000
Speaker 1: I called him and I'm like, hey, look, congratulations, that's

1385
01:15:01,000 --> 01:15:02,920
Speaker 1: a big cat. I can I can send you some

1386
01:15:03,160 --> 01:15:06,040
Speaker 1: pictures of them if you want, you know, pictures of

1387
01:15:06,120 --> 01:15:07,760
Speaker 1: him in a wood shed and pictures of him in

1388
01:15:07,800 --> 01:15:11,439
Speaker 1: a tree and whatever, pictures of him with anesthetized and

1389
01:15:11,479 --> 01:15:14,640
Speaker 1: whatever else. So once he knew that I was not

1390
01:15:14,760 --> 01:15:17,439
Speaker 1: mad and I was also a hunter, and you know,

1391
01:15:17,680 --> 01:15:19,960
Speaker 1: congratulated him on such an awesome cat he was, he

1392
01:15:20,080 --> 01:15:21,760
Speaker 1: softened up a little bit and sent me my collar

1393
01:15:21,800 --> 01:15:24,880
Speaker 1: back and that was a Yeah, it was a really

1394
01:15:24,960 --> 01:15:27,920
Speaker 1: nice cat. Man. It's cooler for him too, because he

1395
01:15:27,960 --> 01:15:30,920
Speaker 1: can be like I can tell you exactly what that

1396
01:15:30,960 --> 01:15:33,600
Speaker 1: cat's been doing. You don't have to. Yeah, he'd be like,

1397
01:15:33,680 --> 01:15:35,200
Speaker 1: and he ran over to this tree, and then he

1398
01:15:35,320 --> 01:15:38,559
Speaker 1: ran over this that's right. Yeah, I had an ear

1399
01:15:38,600 --> 01:15:40,559
Speaker 1: tag into I don't know if they I'm sure they

1400
01:15:40,640 --> 01:15:43,320
Speaker 1: pulled that out for the taxi ermist. But get a

1401
01:15:43,320 --> 01:15:47,000
Speaker 1: little jewelry too. Wow, And and remind me getting how

1402
01:15:47,000 --> 01:15:49,160
Speaker 1: many cats you got running around right now? I got

1403
01:15:49,200 --> 01:15:52,240
Speaker 1: five on their right now. So I haven't really got

1404
01:15:52,240 --> 01:15:54,160
Speaker 1: into a couple of parts of the project that are

1405
01:15:54,320 --> 01:15:57,479
Speaker 1: fairly important one of them. Um. So once that cat runs,

1406
01:15:58,240 --> 01:16:00,840
Speaker 1: I'm getting the I'm getting screwed route on this deal

1407
01:16:00,880 --> 01:16:03,960
Speaker 1: as far as seeing cougars other than happening upon one

1408
01:16:04,000 --> 01:16:07,400
Speaker 1: here and there. Um, I don't understand what do you

1409
01:16:07,400 --> 01:16:09,519
Speaker 1: mean by that. I'm not even going to the tree anymore.

1410
01:16:09,600 --> 01:16:12,240
Speaker 1: So Bruce and the other gang walks to the tree,

1411
01:16:12,800 --> 01:16:17,479
Speaker 1: and I am now measuring the habitat. So I'm grabbing

1412
01:16:17,479 --> 01:16:20,080
Speaker 1: all this data from the flight of the cat. So

1413
01:16:20,160 --> 01:16:23,120
Speaker 1: once that cat runs away. Once that cat runs, I

1414
01:16:23,360 --> 01:16:25,760
Speaker 1: measure the distance. You know, let's say it goes two

1415
01:16:26,200 --> 01:16:30,320
Speaker 1: yards or whatever meters. Um. I then lay out a

1416
01:16:30,360 --> 01:16:33,320
Speaker 1: tape and I measure the slope, So did that cat

1417
01:16:33,400 --> 01:16:35,920
Speaker 1: run uphill or downhill? I grab a slope measurement, and

1418
01:16:35,920 --> 01:16:39,720
Speaker 1: then I grab um the basil area measurement. So it's

1419
01:16:39,720 --> 01:16:42,360
Speaker 1: a forestry measurement that kind of tells you the forest type,

1420
01:16:42,360 --> 01:16:44,680
Speaker 1: will give you a rough idea how wooded it is,

1421
01:16:45,000 --> 01:16:48,000
Speaker 1: and then also shrub component, and you know if it's

1422
01:16:48,000 --> 01:16:50,439
Speaker 1: a thicket or if it's running through fairly open stuff

1423
01:16:50,520 --> 01:16:52,640
Speaker 1: or down the road or whatever. So I'm trying to describe,

1424
01:16:52,680 --> 01:16:57,240
Speaker 1: then how much energy that cat used to escape? Are

1425
01:16:57,320 --> 01:17:01,800
Speaker 1: you taking note of the wind to wind direction? I

1426
01:17:01,840 --> 01:17:05,759
Speaker 1: have a wind noise factor that's just one through five.

1427
01:17:05,840 --> 01:17:09,200
Speaker 1: And does he play the wind when he runs? Oh?

1428
01:17:09,240 --> 01:17:14,160
Speaker 1: I haven't. No, I haven't paid attention to that man. Yeah,

1429
01:17:14,640 --> 01:17:17,160
Speaker 1: because maybe he always he always goes into the wind.

1430
01:17:18,720 --> 01:17:22,120
Speaker 1: It would be an interesting thing to start to test

1431
01:17:22,160 --> 01:17:24,240
Speaker 1: some of that, as far as circling up wind or

1432
01:17:24,280 --> 01:17:26,840
Speaker 1: downwind of him too. I should have been a scientist, man,

1433
01:17:27,360 --> 01:17:28,960
Speaker 1: You should have been a scientist. You should come along

1434
01:17:29,240 --> 01:17:32,799
Speaker 1: to have all kinds of science things I'd figure out. Yeah,

1435
01:17:33,240 --> 01:17:35,280
Speaker 1: that'd be fun to circle give him, give him the

1436
01:17:35,320 --> 01:17:40,080
Speaker 1: wind advantage and see if they mobilize more quickly or not. Yeah,

1437
01:17:40,240 --> 01:17:42,400
Speaker 1: So I'm grabbing all of this habitat data for the

1438
01:17:42,560 --> 01:17:45,400
Speaker 1: escape path, and I'll use that UM to kind of

1439
01:17:45,400 --> 01:17:50,240
Speaker 1: help describe distance with you know, capital D distance then,

1440
01:17:50,400 --> 01:17:53,759
Speaker 1: so rather than just a linear measurement, it will include

1441
01:17:53,760 --> 01:17:55,680
Speaker 1: all of this other stuff like if it ran up

1442
01:17:55,680 --> 01:17:58,439
Speaker 1: and over a cliff, that might only be a couple

1443
01:17:58,560 --> 01:18:01,559
Speaker 1: hundred yards, but it's a definitely more energy to escape

1444
01:18:01,920 --> 01:18:05,400
Speaker 1: than just jogging down a two track road. UM. And

1445
01:18:05,439 --> 01:18:07,640
Speaker 1: then I'm also grabbing some data when we get to

1446
01:18:07,680 --> 01:18:11,679
Speaker 1: the tree, uh, kind of measuring the behavior of that cat.

1447
01:18:11,760 --> 01:18:15,040
Speaker 1: And we have we've kind of had to develop this scale,

1448
01:18:15,120 --> 01:18:17,760
Speaker 1: but I don't I think it'll be useful when we're done.

1449
01:18:17,760 --> 01:18:20,360
Speaker 1: We'll have a lot of it. How that cat's responding

1450
01:18:20,360 --> 01:18:23,760
Speaker 1: in the tree. How is it snarling, is it urinating?

1451
01:18:23,880 --> 01:18:26,960
Speaker 1: Is it jump bailing out and finding a different tree,

1452
01:18:27,760 --> 01:18:30,200
Speaker 1: How high is it in the tree, is it moving around? Whatever?

1453
01:18:30,200 --> 01:18:32,400
Speaker 1: So we measure all that stuff for a number of

1454
01:18:32,439 --> 01:18:36,719
Speaker 1: times per five minutes that it's tree, and we're seeing

1455
01:18:36,760 --> 01:18:39,639
Speaker 1: that number change as well. The cats tend to by

1456
01:18:39,680 --> 01:18:41,799
Speaker 1: the end of the project tree quite a bit higher

1457
01:18:41,960 --> 01:18:45,719
Speaker 1: and be quite a bit calmer where they're not dancing

1458
01:18:45,760 --> 01:18:48,080
Speaker 1: around in the tree. They kind of know the routine

1459
01:18:48,160 --> 01:18:50,720
Speaker 1: by about the fourth or fifth time we're capturing them.

1460
01:18:50,760 --> 01:18:53,719
Speaker 1: It's like they just grab a tree before the dogs

1461
01:18:53,760 --> 01:18:55,519
Speaker 1: are very close to them, and they climb way up

1462
01:18:55,560 --> 01:18:57,760
Speaker 1: high and they're just like laid out, relaxed when we

1463
01:18:57,800 --> 01:19:00,559
Speaker 1: get there. That's not going to bowl well for them

1464
01:19:00,560 --> 01:19:02,800
Speaker 1: when they're traveled like that one, when they're traveling into

1465
01:19:02,880 --> 01:19:05,920
Speaker 1: hunted areas, they're gonna make them. They're gonna make them

1466
01:19:05,920 --> 01:19:10,000
Speaker 1: easy pickings. Maybe. I mean it might also have implications

1467
01:19:10,000 --> 01:19:12,960
Speaker 1: in wolf country, which almost everything in northeast Washington is

1468
01:19:13,000 --> 01:19:15,960
Speaker 1: wolf country now. So I don't know how that'll, if

1469
01:19:15,960 --> 01:19:18,559
Speaker 1: that will play out, or how it would. Oh yeah,

1470
01:19:18,600 --> 01:19:20,800
Speaker 1: because you were mentioned that that that you find that

1471
01:19:20,960 --> 01:19:23,439
Speaker 1: cats that have exposure to wolves. You were wondering if

1472
01:19:23,479 --> 01:19:25,519
Speaker 1: it changes their attitude about whether or not they need

1473
01:19:25,560 --> 01:19:28,639
Speaker 1: to get into a tree or not. Yeah. Maybe. Um,

1474
01:19:29,680 --> 01:19:33,400
Speaker 1: I think no, that's well documented that a single wolf

1475
01:19:33,560 --> 01:19:36,840
Speaker 1: is trouble for a cat, like if is in trouble

1476
01:19:36,840 --> 01:19:38,679
Speaker 1: if it encounters a cat. A cat'll kill a wolf

1477
01:19:38,720 --> 01:19:41,200
Speaker 1: when it gets the opportunity, as long as there's not

1478
01:19:41,280 --> 01:19:44,519
Speaker 1: a bunch of them. Um, And I think it's not

1479
01:19:44,560 --> 01:19:48,439
Speaker 1: a far stretch to imagine those cats learning that on

1480
01:19:48,479 --> 01:19:50,639
Speaker 1: a single wolf a couple of times in their life,

1481
01:19:50,640 --> 01:19:52,439
Speaker 1: and then all of a sudden, this single dog shows

1482
01:19:52,520 --> 01:19:54,120
Speaker 1: up that's half as big as a wolf. You happen

1483
01:19:54,160 --> 01:19:56,400
Speaker 1: and they're just like, well, I'll just kill that because

1484
01:19:56,400 --> 01:19:58,600
Speaker 1: I've done this before. So a lion will kill a

1485
01:19:58,680 --> 01:20:03,479
Speaker 1: single wolf, yes, not vice versa. Oh, one on one,

1486
01:20:03,520 --> 01:20:06,799
Speaker 1: it's a no contest lions or yeah, they're deadly at

1487
01:20:06,840 --> 01:20:11,360
Speaker 1: all five ends. Huh, yeah, that wolf doesn't have a chance. Well,

1488
01:20:11,400 --> 01:20:14,280
Speaker 1: it's interesting. I wouldn't think, I guess, I don't know.

1489
01:20:14,439 --> 01:20:17,639
Speaker 1: I mean, unless it's a subadult line or something else.

1490
01:20:17,680 --> 01:20:20,720
Speaker 1: An adult lion versus adult wolf, I'd say lion every

1491
01:20:20,720 --> 01:20:26,240
Speaker 1: single time. So what's next? Man? Um, Well, hold on,

1492
01:20:26,240 --> 01:20:28,200
Speaker 1: I gotta I feel like we You told us about

1493
01:20:28,240 --> 01:20:33,679
Speaker 1: the negative changes in in the couple of the data

1494
01:20:33,720 --> 01:20:36,800
Speaker 1: points right when you went back subsequently, But what on

1495
01:20:36,960 --> 01:20:41,759
Speaker 1: average is the change after the hazing for the third, fourth,

1496
01:20:41,800 --> 01:20:44,520
Speaker 1: and fifth times you come in there with the podcast

1497
01:20:44,600 --> 01:20:48,400
Speaker 1: going yeah, um, good question. The biggest The biggest change

1498
01:20:48,479 --> 01:20:53,240
Speaker 1: almost always occurs on the second hazing, So that cat

1499
01:20:53,680 --> 01:20:55,759
Speaker 1: the second time it happens, I think they're still pretty

1500
01:20:55,800 --> 01:21:02,280
Speaker 1: freaked out. And it's been two of both the flight

1501
01:21:02,320 --> 01:21:05,600
Speaker 1: initiation and the flight distance. And we've had some that

1502
01:21:05,760 --> 01:21:09,840
Speaker 1: like don't stop running. We've had some cats once they mobilized,

1503
01:21:09,880 --> 01:21:14,160
Speaker 1: they run like nine hundred yards along ways. Um, so

1504
01:21:14,200 --> 01:21:16,320
Speaker 1: they're getting out of there by the end of the project.

1505
01:21:16,479 --> 01:21:19,920
Speaker 1: These cats, without exception, we haven't had any cat let

1506
01:21:20,000 --> 01:21:22,040
Speaker 1: us get closer over the course of the project. We've

1507
01:21:22,040 --> 01:21:23,600
Speaker 1: just had a couple. When I say a couple of

1508
01:21:23,640 --> 01:21:26,840
Speaker 1: data points, I mean like outliers, Like we caught this

1509
01:21:26,920 --> 01:21:30,800
Speaker 1: cat four times and one of them was a negative distance. Yeah,

1510
01:21:31,240 --> 01:21:33,840
Speaker 1: so of the you know, let's say we have eight

1511
01:21:33,880 --> 01:21:36,599
Speaker 1: cats with full data, you know, four to five points each.

1512
01:21:37,320 --> 01:21:42,960
Speaker 1: You know those points are positive and two is a

1513
01:21:42,960 --> 01:21:47,240
Speaker 1: pretty standard increase. So that's why you feel so confident

1514
01:21:47,280 --> 01:21:49,639
Speaker 1: if you you had a problem line that was hanging

1515
01:21:49,640 --> 01:21:51,720
Speaker 1: out in a day used area, that you you could

1516
01:21:51,760 --> 01:21:54,320
Speaker 1: go in there and do this for a month and

1517
01:21:54,360 --> 01:21:58,080
Speaker 1: that would cure his that habit of his or s.

1518
01:21:58,600 --> 01:22:01,000
Speaker 1: And I think you know the other reason that second

1519
01:22:01,360 --> 01:22:04,720
Speaker 1: might be so significant, that second hazing event might that

1520
01:22:04,800 --> 01:22:07,080
Speaker 1: increase might be so significant because at the first hazing

1521
01:22:07,120 --> 01:22:09,200
Speaker 1: event we shoot him with paintballs. If we did that

1522
01:22:09,280 --> 01:22:12,880
Speaker 1: every time, that distance might just contendue to increase until

1523
01:22:12,880 --> 01:22:15,640
Speaker 1: as soon as that cat heart Steve's voice at hauled ass.

1524
01:22:15,960 --> 01:22:18,680
Speaker 1: Oh so you guys don't continue that the paintball. Now

1525
01:22:18,720 --> 01:22:22,720
Speaker 1: we just shoot at the one time? Was that? I

1526
01:22:22,760 --> 01:22:25,240
Speaker 1: don't know. I kind of feel bad shooting him every time,

1527
01:22:25,280 --> 01:22:28,160
Speaker 1: Like I don't want I don't know. I just need

1528
01:22:28,160 --> 01:22:29,760
Speaker 1: to learn whether or not it works. I don't need

1529
01:22:29,800 --> 01:22:32,200
Speaker 1: to torture a cat. Yeah, you don want to make

1530
01:22:32,200 --> 01:22:35,240
Speaker 1: a man hater out of what? Yeah? So again this

1531
01:22:35,400 --> 01:22:37,680
Speaker 1: I want this to have management implications where when a

1532
01:22:37,720 --> 01:22:39,800
Speaker 1: cat's hanging around a farm and it gets sited, they

1533
01:22:39,840 --> 01:22:42,000
Speaker 1: can call somebody with dogs and say, all right, go

1534
01:22:42,080 --> 01:22:43,880
Speaker 1: treat this cat and shoot it with paintballs, and we

1535
01:22:44,000 --> 01:22:47,439
Speaker 1: have a and have a reasonable expectation of how that

1536
01:22:47,479 --> 01:22:50,200
Speaker 1: cat is going to respond in the future. If you

1537
01:22:50,280 --> 01:22:53,040
Speaker 1: give it a known if you give it a stimuli

1538
01:22:53,160 --> 01:22:57,639
Speaker 1: that it can associate right well, and then replicate that,

1539
01:22:57,880 --> 01:22:59,920
Speaker 1: then then you have to turn around and replicate that

1540
01:23:00,080 --> 01:23:03,400
Speaker 1: stimuli to get the desired effect out of it. Well,

1541
01:23:03,439 --> 01:23:06,000
Speaker 1: I don't think you would have to. I mean I

1542
01:23:06,040 --> 01:23:10,240
Speaker 1: don't think let's say whatever, after this project said and done,

1543
01:23:10,280 --> 01:23:12,800
Speaker 1: and um we get a call, say, there's a cat

1544
01:23:13,160 --> 01:23:14,800
Speaker 1: on the edge of a neighborhood, can you go chase

1545
01:23:14,800 --> 01:23:16,519
Speaker 1: it out of there. I don't think we would have

1546
01:23:16,600 --> 01:23:20,439
Speaker 1: to go approach that cat with a voice recording or

1547
01:23:20,479 --> 01:23:22,920
Speaker 1: anything else. I think we could go capture that cat,

1548
01:23:23,880 --> 01:23:25,519
Speaker 1: bang on the tree, shoot it with paintballs, and it

1549
01:23:25,560 --> 01:23:28,559
Speaker 1: would make the association between our because we're talking while

1550
01:23:28,560 --> 01:23:30,559
Speaker 1: we're at the tree. I mean, it's hearing human voices

1551
01:23:30,560 --> 01:23:34,360
Speaker 1: the whole time, right, It would make that association that way.

1552
01:23:34,560 --> 01:23:36,840
Speaker 1: It is interesting though, because I mean you said in

1553
01:23:36,880 --> 01:23:38,960
Speaker 1: the beginning that some of these cats were hanging out

1554
01:23:39,160 --> 01:23:44,040
Speaker 1: so close to these you know, urban interface kill sites

1555
01:23:44,080 --> 01:23:46,880
Speaker 1: where they're likely hearing a lot of human voices too.

1556
01:23:46,960 --> 01:23:51,879
Speaker 1: So you're fighting against this kind of maybe not a positive,

1557
01:23:53,000 --> 01:23:56,840
Speaker 1: uh interaction, but it's kind of like this background noise

1558
01:23:56,920 --> 01:24:01,240
Speaker 1: of human ranch work, farmwork. But I can still go

1559
01:24:01,280 --> 01:24:04,639
Speaker 1: down and pick off alpaca or a dog or something

1560
01:24:04,680 --> 01:24:08,600
Speaker 1: like that, right, Yeah, And you know that's one of

1561
01:24:08,640 --> 01:24:10,760
Speaker 1: the real that's one of the misconceptions of having a

1562
01:24:10,840 --> 01:24:12,639
Speaker 1: terrible time with on this project. A lot of people

1563
01:24:12,680 --> 01:24:15,519
Speaker 1: think that I'm going out and trying to chase his

1564
01:24:15,600 --> 01:24:17,760
Speaker 1: cat away from a farm with the expectation that it's

1565
01:24:17,800 --> 01:24:20,920
Speaker 1: not going to come back to the farm or that area,

1566
01:24:21,080 --> 01:24:22,760
Speaker 1: and that's not really it. What I want is that

1567
01:24:22,840 --> 01:24:28,800
Speaker 1: cat to avoid all of these human associations. Um And

1568
01:24:28,840 --> 01:24:30,720
Speaker 1: I don't know if we'll get there or not, not

1569
01:24:30,800 --> 01:24:32,479
Speaker 1: for all of the cats, not for all the time,

1570
01:24:32,520 --> 01:24:35,320
Speaker 1: but I think there is an argument to be made

1571
01:24:35,320 --> 01:24:39,880
Speaker 1: that the cats being messed with are going to behave better.

1572
01:24:41,200 --> 01:24:44,320
Speaker 1: Is there a version of this you could attempt on grizzlies?

1573
01:24:45,360 --> 01:24:50,800
Speaker 1: Not with my dogs. They do this. They tell on

1574
01:24:51,000 --> 01:24:52,559
Speaker 1: dogs if you go, if you try to run a

1575
01:24:52,560 --> 01:24:54,960
Speaker 1: grizzly with dogs, they're just hard on the dogs. I've

1576
01:24:55,000 --> 01:24:57,320
Speaker 1: never done it, but I have heard stories about people

1577
01:24:57,560 --> 01:25:00,960
Speaker 1: getting on them. Um And I guess they're long runners.

1578
01:25:00,960 --> 01:25:03,920
Speaker 1: They're straight line long runners like a black barrel tend

1579
01:25:03,960 --> 01:25:06,519
Speaker 1: to circle, and a grizzly barrel just lying out and go.

1580
01:25:06,800 --> 01:25:08,639
Speaker 1: And then of course they don't climb, they just bay

1581
01:25:08,680 --> 01:25:12,080
Speaker 1: and fight. So I mean people certainly do it. There's

1582
01:25:12,200 --> 01:25:14,880
Speaker 1: like famous hound hunters from back in the day that

1583
01:25:15,040 --> 01:25:18,479
Speaker 1: collected bounties on grizzly bears by using dogs, and I

1584
01:25:18,479 --> 01:25:20,599
Speaker 1: don't think we have good records of how many dogs

1585
01:25:20,680 --> 01:25:22,559
Speaker 1: they went through over the course of that time, but

1586
01:25:22,720 --> 01:25:26,200
Speaker 1: probably a lot. Yeah, I mean black bears are tough

1587
01:25:26,200 --> 01:25:28,800
Speaker 1: on dogs. Mean, black bear is a tough thing for

1588
01:25:28,840 --> 01:25:33,280
Speaker 1: a dog to handle. Have you lost the dogs lately? No,

1589
01:25:33,640 --> 01:25:36,080
Speaker 1: We've had good luck with dogs. Actually, I've still got Whisper.

1590
01:25:36,160 --> 01:25:41,519
Speaker 1: She's twelve. Nosies nine Radar die to cancer two years ago.

1591
01:25:41,880 --> 01:25:43,400
Speaker 1: And what's the one who got what's the one who

1592
01:25:43,400 --> 01:25:46,960
Speaker 1: got mauled up? Pretty good? Nosy? Yeah, she's hanging in there.

1593
01:25:46,960 --> 01:25:49,360
Speaker 1: She was with me to listen Monday. She'd treated two

1594
01:25:49,360 --> 01:25:53,200
Speaker 1: cats with me. Does she Oh, she hates him, Yeah,

1595
01:25:53,200 --> 01:25:55,200
Speaker 1: she's She was kind of hateful little dog before that

1596
01:25:55,240 --> 01:25:57,599
Speaker 1: whole reck and now she's now she's full of it.

1597
01:25:58,920 --> 01:26:02,479
Speaker 1: I got and oh, sorry, go ahead, go on with

1598
01:26:02,520 --> 01:26:06,679
Speaker 1: the dogs. I got a question for you. It's out

1599
01:26:06,680 --> 01:26:08,960
Speaker 1: there though. Okay. Yeah, we had and we had a

1600
01:26:08,960 --> 01:26:12,080
Speaker 1: litter of pups. Actually, remember Bruce has had that big

1601
01:26:12,080 --> 01:26:15,120
Speaker 1: red dog, Gus. We've read him back to Tipsy who

1602
01:26:15,400 --> 01:26:17,240
Speaker 1: those were the two cats that were at your tree,

1603
01:26:17,320 --> 01:26:20,120
Speaker 1: Steve Guss and Tipsy Um, So we've read those two

1604
01:26:20,160 --> 01:26:23,000
Speaker 1: dogs together. Yeah, they were awesome. We have a good

1605
01:26:23,040 --> 01:26:24,599
Speaker 1: litter of pups out of them, and they've been on

1606
01:26:25,439 --> 01:26:28,679
Speaker 1: a lot of cats like, um, those pups are doing

1607
01:26:28,720 --> 01:26:30,599
Speaker 1: most of the work right now. I think they've seen

1608
01:26:31,360 --> 01:26:36,160
Speaker 1: well sixty three cats so far since January one um

1609
01:26:36,200 --> 01:26:38,920
Speaker 1: that we've treated, So they're getting a lot of exercise.

1610
01:26:39,560 --> 01:26:41,200
Speaker 1: Cal what's your question you have to fire you have

1611
01:26:41,280 --> 01:26:45,360
Speaker 1: to fire out question. Yeah, this this would fall firmly

1612
01:26:45,360 --> 01:26:50,320
Speaker 1: in the final thoughts type of I got this new neighbor,

1613
01:26:51,400 --> 01:26:53,800
Speaker 1: California guy, just the guy that throws all the good

1614
01:26:53,800 --> 01:26:58,559
Speaker 1: stuff in the dumpster. Okay, um, real, real, nice guy.

1615
01:27:00,040 --> 01:27:05,559
Speaker 1: He's got a place that butts up to the Hearst Castle,

1616
01:27:05,680 --> 01:27:12,320
Speaker 1: which is now California State Park and Hurst William Randolph Hurst.

1617
01:27:13,120 --> 01:27:17,040
Speaker 1: Tons of cash through the twenties thirties built up this

1618
01:27:17,600 --> 01:27:23,600
Speaker 1: incredible place. I had this giant zoo and wildlife menagerie,

1619
01:27:24,720 --> 01:27:30,360
Speaker 1: and there's all these stories going around that when the

1620
01:27:30,560 --> 01:27:36,360
Speaker 1: financial hard times of the thirties caught up to him Uh,

1621
01:27:36,680 --> 01:27:39,920
Speaker 1: one solution that they came across was to release a

1622
01:27:40,000 --> 01:27:43,280
Speaker 1: bunch of animals, the animals that they couldn't give away.

1623
01:27:44,000 --> 01:27:49,280
Speaker 1: And my neighbor claims that they have trail camera pictures

1624
01:27:50,080 --> 01:27:57,400
Speaker 1: of the descendants of hers Uh jaguar population that have

1625
01:27:57,520 --> 01:28:02,040
Speaker 1: now crossbread with the California mountain lions in the area,

1626
01:28:02,520 --> 01:28:07,799
Speaker 1: and they're getting black mountain lions that are noticeably larger

1627
01:28:08,400 --> 01:28:12,360
Speaker 1: than your typical mountain lion. H I have my doubts.

1628
01:28:12,760 --> 01:28:15,719
Speaker 1: Come on, I mean, if they're getting trail camera pictures,

1629
01:28:15,760 --> 01:28:22,840
Speaker 1: just ask them to produce those. Let's see it. Um,

1630
01:28:22,960 --> 01:28:28,839
Speaker 1: does that exist though, Jaguar mountain lion hybrids or cross

1631
01:28:28,840 --> 01:28:31,200
Speaker 1: breeding of any sort. Have you ever heard of that?

1632
01:28:31,960 --> 01:28:33,720
Speaker 1: I've never heard of that. And if I mean they

1633
01:28:33,720 --> 01:28:39,439
Speaker 1: share range, they share habitat and the southwest right, Yeah,

1634
01:28:39,680 --> 01:28:43,200
Speaker 1: I mean they we would probably know if that happened. Yeah,

1635
01:28:43,280 --> 01:28:46,880
Speaker 1: they share habitat like across the I think the entirety

1636
01:28:46,920 --> 01:28:51,080
Speaker 1: of the jaguars range. Yeah. Probably. I mean, no, they do,

1637
01:28:51,160 --> 01:28:54,240
Speaker 1: I know they do now I think about it. But anyway,

1638
01:28:54,240 --> 01:29:01,400
Speaker 1: I give this guy your cellphone number. So yeah, alright, Bart, George,

1639
01:29:01,600 --> 01:29:04,280
Speaker 1: you got any more questions for Bart? No, I'm good, Bart.

1640
01:29:04,600 --> 01:29:08,320
Speaker 1: Thanks for chatting with us. Yeah, man, I hope you.

1641
01:29:08,360 --> 01:29:10,639
Speaker 1: I hope your money comes through because it's interesting. But see,

1642
01:29:10,680 --> 01:29:12,880
Speaker 1: I'm trying to figure out what it means, uh, what

1643
01:29:12,880 --> 01:29:16,200
Speaker 1: it means in general, because I don't know if it

1644
01:29:16,240 --> 01:29:23,320
Speaker 1: means um like doesn't mean that this will be like

1645
01:29:23,600 --> 01:29:29,519
Speaker 1: a thing that winds up being used against people hunting

1646
01:29:29,600 --> 01:29:34,839
Speaker 1: mountain lions with dogs. Well, this will national geographic reports

1647
01:29:34,880 --> 01:29:36,920
Speaker 1: on this. Will they be able to twist it into

1648
01:29:37,000 --> 01:29:39,960
Speaker 1: a you see, people shouldn't be able to hunt mountain

1649
01:29:40,000 --> 01:29:42,599
Speaker 1: lions with dogs. I gotta think this through for a minute.

1650
01:29:43,560 --> 01:29:46,920
Speaker 1: I hope not. I mean, I think it's Ultimately I

1651
01:29:46,960 --> 01:29:50,519
Speaker 1: wanted to lead to another tool for wildlife managers that

1652
01:29:50,560 --> 01:29:57,160
Speaker 1: are dealing with potential public safety conflicts and depredation issues,

1653
01:29:58,080 --> 01:30:02,080
Speaker 1: you know, like that that park or reserve down in California.

1654
01:30:02,120 --> 01:30:04,519
Speaker 1: I want them to have a tool to be able

1655
01:30:04,560 --> 01:30:08,960
Speaker 1: to say with some level of certainty, like, look, we

1656
01:30:08,960 --> 01:30:12,880
Speaker 1: can save these cats lives in the future. If these

1657
01:30:12,920 --> 01:30:14,639
Speaker 1: cats get in trouble, if they get in a mix

1658
01:30:14,720 --> 01:30:17,720
Speaker 1: up with a person, they're gonna die. Like that's that's

1659
01:30:17,720 --> 01:30:21,799
Speaker 1: a certainty. UM. In Northeast Washington, when a cat kills livestock,

1660
01:30:21,880 --> 01:30:24,759
Speaker 1: it dies. So if we can keep that from happening,

1661
01:30:24,800 --> 01:30:27,479
Speaker 1: and you know, let's say it only works of the time,

1662
01:30:27,520 --> 01:30:30,640
Speaker 1: that's still that's a lot of cats, um that we

1663
01:30:30,680 --> 01:30:33,479
Speaker 1: can keep out there on the landscape and um, you know,

1664
01:30:33,520 --> 01:30:37,080
Speaker 1: available to sport hunters and available for whatever other purpose.

1665
01:30:37,240 --> 01:30:38,400
Speaker 1: That was good. Little I liked that, man, It was

1666
01:30:38,400 --> 01:30:40,400
Speaker 1: a good little twist in the end. That was good.

1667
01:30:40,920 --> 01:30:43,120
Speaker 1: All right, Bart George, How long is it gonna be

1668
01:30:43,840 --> 01:30:46,160
Speaker 1: till you come back on again and tells what you're

1669
01:30:46,160 --> 01:30:47,559
Speaker 1: out your next how long you've be doing this for

1670
01:30:47,640 --> 01:30:51,680
Speaker 1: it and go on to something else that might be interesting. Um, well,

1671
01:30:51,720 --> 01:30:55,320
Speaker 1: I'll be working on this probably for another year or so. Um.

1672
01:30:55,360 --> 01:30:57,320
Speaker 1: Still doing some cariboo work if you want to have

1673
01:30:57,320 --> 01:30:59,599
Speaker 1: a talk about cariboo. There's still some in the Central

1674
01:30:59,600 --> 01:31:03,799
Speaker 1: Selk Works and working with International Cariboo Foundation trying to

1675
01:31:03,840 --> 01:31:06,519
Speaker 1: help out a group up at the Arrow Lakes um

1676
01:31:06,840 --> 01:31:10,080
Speaker 1: save a herd that's down to about twenty four animals.

1677
01:31:10,680 --> 01:31:13,120
Speaker 1: Got some callers out on that heard this year, so

1678
01:31:13,160 --> 01:31:15,320
Speaker 1: we know that there's at least nine cows up there

1679
01:31:15,680 --> 01:31:18,200
Speaker 1: and a couple of calves alive, but the census wasn't

1680
01:31:18,240 --> 01:31:22,800
Speaker 1: complete because damn shut down stuff Canada. Also, so what

1681
01:31:22,840 --> 01:31:26,320
Speaker 1: about personal hunting? You hunt in spring bear? Is here? No?

1682
01:31:26,439 --> 01:31:28,479
Speaker 1: I usually hunt Idaho spring bears and they won't let

1683
01:31:28,520 --> 01:31:31,680
Speaker 1: us come across the border. Nowadays they cut off all

1684
01:31:31,720 --> 01:31:36,759
Speaker 1: the non resident taxies Washington. Our season, yeah, our season

1685
01:31:36,800 --> 01:31:39,080
Speaker 1: will be open here in a couple of days for turkeys.

1686
01:31:39,080 --> 01:31:40,920
Speaker 1: I'll probably go try to pick a couple of them

1687
01:31:40,960 --> 01:31:42,679
Speaker 1: up on my way to work. Are they letting non

1688
01:31:42,720 --> 01:31:49,360
Speaker 1: residents come to Washington for what? Turkeys? Extremely late turkey

1689
01:31:49,400 --> 01:31:55,679
Speaker 1: season that you told? Yeah, probably it's that's it's turkeys

1690
01:31:55,720 --> 01:32:00,160
Speaker 1: in June. June. Man, that's crazy. I an't. I I

1691
01:32:00,160 --> 01:32:02,520
Speaker 1: don't like hunting turkeys after May first, once some mosquitoes

1692
01:32:02,520 --> 01:32:03,920
Speaker 1: are out and the ticks are out, which I've already

1693
01:32:03,920 --> 01:32:06,320
Speaker 1: found some of those. I kind of shy away from

1694
01:32:06,360 --> 01:32:08,360
Speaker 1: turkey on but I'll probably try to get out. It's

1695
01:32:08,360 --> 01:32:10,360
Speaker 1: the only show in town right now for us. The

1696
01:32:10,840 --> 01:32:15,920
Speaker 1: last question, you're married, right? Oh? Yeah, two kids? I

1697
01:32:15,960 --> 01:32:17,280
Speaker 1: was getting to is where you're at with the kid

1698
01:32:17,360 --> 01:32:20,120
Speaker 1: thing right now? Yeah? We Uh, my wife and I

1699
01:32:20,200 --> 01:32:23,479
Speaker 1: have two boys, two and a half and seven months,

1700
01:32:23,520 --> 01:32:26,519
Speaker 1: so we're pretty busy. And ever, the older boy has

1701
01:32:26,560 --> 01:32:29,200
Speaker 1: been able to tag along on a few cougar hunts.

1702
01:32:29,240 --> 01:32:30,760
Speaker 1: He was in a backpack with me the other day

1703
01:32:30,760 --> 01:32:34,120
Speaker 1: when we treate a cat for work. Um so he

1704
01:32:34,160 --> 01:32:37,519
Speaker 1: knows the routine. He'll be has year old say or

1705
01:32:37,560 --> 01:32:41,519
Speaker 1: do when they see a cat in a tree. Um. Well,

1706
01:32:41,560 --> 01:32:44,599
Speaker 1: the more concerning part was before we saw the cat.

1707
01:32:45,360 --> 01:32:47,760
Speaker 1: I was approaching the cat, had the podcast playing, and

1708
01:32:47,800 --> 01:32:49,479
Speaker 1: he's got a stick back there, and he's banging around

1709
01:32:49,520 --> 01:32:53,920
Speaker 1: hitting everything with a stick as we're walking in the backpack. Um,

1710
01:32:54,000 --> 01:32:56,680
Speaker 1: and he knows we're he knows kind of what we're doing.

1711
01:32:56,720 --> 01:32:58,360
Speaker 1: He has an idea that we're out looking for a cougar,

1712
01:32:58,400 --> 01:33:00,639
Speaker 1: but he doesn't know the routine yet. So we march

1713
01:33:00,720 --> 01:33:03,840
Speaker 1: along and he starts saying, I see a cougar. I

1714
01:33:03,880 --> 01:33:06,000
Speaker 1: see a cougar. And I'm I'm close to the cat,

1715
01:33:06,080 --> 01:33:08,000
Speaker 1: you know, I'm within about sixty yards. I'm like, ship,

1716
01:33:08,160 --> 01:33:09,800
Speaker 1: is this call or not working? Does he actually see

1717
01:33:09,800 --> 01:33:11,679
Speaker 1: a cougar? And I'm like trying to ask him where

1718
01:33:11,720 --> 01:33:16,479
Speaker 1: and I can't see where he's pointing. But he hadn't

1719
01:33:16,479 --> 01:33:19,040
Speaker 1: he was just telling stories. So when he when we

1720
01:33:19,080 --> 01:33:20,720
Speaker 1: get to the tree, he's excited about I keep a

1721
01:33:20,760 --> 01:33:25,479
Speaker 1: distance obviously, I'm not handling cats with him, Um, But

1722
01:33:25,600 --> 01:33:27,320
Speaker 1: we get to the tree, will stay a little ways

1723
01:33:27,320 --> 01:33:29,920
Speaker 1: back and let Bruce to all the heavy lifting at

1724
01:33:29,960 --> 01:33:31,600
Speaker 1: the tree and then just call the dogs to us

1725
01:33:31,600 --> 01:33:36,760
Speaker 1: when we're done. He's excited about it. Good future scientists. Yeah,

1726
01:33:36,880 --> 01:33:39,719
Speaker 1: maybe all right? Man, thanks again for joining us Bart

1727
01:33:40,640 --> 01:33:45,719
Speaker 1: we'll talk later hecks later.