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Speaker 1: This is the me Eater podcast coming in. You shirtless,

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Speaker 1: severely vote bitten and in my case, underwear listening. Don't

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Speaker 1: make even podcast. You can't predict anything, all right. Our

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Speaker 1: guest is Frank Temple. Tell us your last name Van

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Speaker 1: Mannon or von Banning, whatever you prefer. So, No, but

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Speaker 1: you're not. I was surprised you walked in. You know

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Speaker 1: you're not. You weren't born in the US. I wasn't

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Speaker 1: born in the U s No, I am a US

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Speaker 1: became a bear bile, a grizzly bear biologist or a

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Speaker 1: bear biologists here. I came to the US in Do

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Speaker 1: you remember how old you when you saw your first bear?

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Speaker 1: When I saw my first bear? Uh, that was about probably. Yeah.

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Speaker 1: So you at what age did you get into wildlife biology?

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Speaker 1: Did you get that back home? Yeah? So I did

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Speaker 1: a master's in biology at the university and in the

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Speaker 1: Netherlands at the Agricultural University and studying what like what

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Speaker 1: we're looking at their um well and again it was

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Speaker 1: actually a combined bachelor's master so in the beginning was

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Speaker 1: kind of undetermined and and towards the end UM I

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Speaker 1: got really interested in in doing an internship on a

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Speaker 1: large carnivore because there weren't any in the Netherlands. You know,

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Speaker 1: there's no wolves, there's there were no bears or anything like.

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Speaker 1: The largest carnival we had there was was red not

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Speaker 1: that they had been extra paid, but they weren't there

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Speaker 1: in the first place. Uh No, they were extrapated. So

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Speaker 1: brown bears were excapated there probably more than a thousand

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Speaker 1: years ago. Um. And wolves, I don't know exactly, but

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Speaker 1: that that that have been quite a while as well.

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Speaker 1: And interestingly wolves and now gradually returning to Western Europe.

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Speaker 1: There's have been a few sightings of wolves now in

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Speaker 1: the Netherlands. Um, how are they received their open arms

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Speaker 1: not as a controversial there as well, not not yet,

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Speaker 1: but I think it's because it's it's just been a

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Speaker 1: sighting or two. You know, they haven't dusted off someone's

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Speaker 1: landing exactly, and that once stop that starts happening, that

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Speaker 1: might change, but but so far it's mostly excitement. Actually,

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Speaker 1: that's it's amazing that in a place like the Netherlands.

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Speaker 1: You know, it's sixteen million people in in the in

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Speaker 1: the country to size that's one ninth of the state

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Speaker 1: of Montana, you know, so you do the do the math.

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Speaker 1: It's pretty amazing that you' still find rules there. So

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Speaker 1: you started looking at large carnivores in what but not

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Speaker 1: in the Netherlands. Yeah, so that there were people were

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Speaker 1: working on on brown bears and what at the time

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Speaker 1: was still Yugoslavia and in Spain, and so I was

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Speaker 1: trying to get in on some of those projects and

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Speaker 1: the timing just didn't work out, but I didn't. Through

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Speaker 1: through those contacts that I made with those projects, I

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Speaker 1: was able to get an internship at the University of

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Speaker 1: Tennessee with Dr Mike Pelton, who was a well known

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Speaker 1: black bear biologists UM in the Eastern US, and so

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Speaker 1: that's that's really where I got started with What were

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Speaker 1: you guys looking at with black bears? Well, a lot

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Speaker 1: of different things, primarily population dynamics, so population changes and

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Speaker 1: trend over time. UM looking at denning behavior, which was

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Speaker 1: really interesting over there because most of the black bears

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Speaker 1: there then in in high up in hollow trees, and

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Speaker 1: so that did a lot of lead tree climbing in

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Speaker 1: those days, and that was in a tree. Well, UM,

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Speaker 1: the highest one that we ever measured was about hundred

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Speaker 1: and ten feet up in a tree. So also not

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Speaker 1: in hollow trees. Yeah, the canopy. No, in hollow trees.

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Speaker 1: So the hell's he got a hollow big enough to

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Speaker 1: hold the black bear hunter feet in the air? Well,

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Speaker 1: there's in the smoke case. You know, you still have

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Speaker 1: some areas with with old growth. And what kind of

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Speaker 1: tree was it? Well, I think that was actually a

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Speaker 1: remember it might have been a tool of poplar um.

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Speaker 1: And there was climbing tool of poplars. Some white oaks, um,

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Speaker 1: chestnut oaks. Those were the most common trees. And so

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Speaker 1: are they up there because are they up there but

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Speaker 1: to avoid predation? Or they up there for some other reason?

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Speaker 1: Um to not not so much to avoid predation, I think,

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Speaker 1: but certainly to avoid disturbance. And you know, no better

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Speaker 1: place to beat and high up in the tree to

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Speaker 1: avoid the stones across the heck no, And so you know,

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Speaker 1: we we would climb up, we set up a climbing

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Speaker 1: rope and which was pretty tricky most of the time,

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Speaker 1: and then we get up to the to the entrance

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Speaker 1: of the den and and most of the time was

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Speaker 1: just a hollow. So it's a cavity into a hollow

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Speaker 1: part of the tree. That's uh, that was established your

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Speaker 1: years ago from a big branch breaking off, So a

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Speaker 1: wind break or a lightning strike something like that would

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Speaker 1: over to time, you know, for a fifty to a

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Speaker 1: hundred years, could create this this big hollow compartment where

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Speaker 1: bears we just uh basically hibernate for the reach in

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Speaker 1: there and touch him, right, I mean, yes, that's in

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Speaker 1: some cases they were that close. In other cases, we

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Speaker 1: you know, especially with chestnut oaks, you would go up,

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Speaker 1: you know, ft up in the tree and you look

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Speaker 1: down in bear was all the way down at the bottom.

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Speaker 1: It was hollowed out all the way to the bottom,

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Speaker 1: and the bear would actually be resting basically at the

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Speaker 1: bottom of the tree. Um. But the only way to

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Speaker 1: get there is to climb up in the tree first,

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Speaker 1: you know. So uh, some really amazing experiences to see

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Speaker 1: where those bears end up. Now, how is it that

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Speaker 1: you grew up using the metric system and you still

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Speaker 1: use it in your discipline, but you don't mind conversing

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Speaker 1: too Americans and standard. Yeah, I got to learn that.

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Speaker 1: It's like a professional skills, that's right. It took a

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Speaker 1: while because when you published work, you published it's all metric,

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Speaker 1: So just do standard Just to talk to people. They

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Speaker 1: know what you're talking abo try to Yeah, I mean,

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Speaker 1: that's appreciate, that's important. And you know, I don't have

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Speaker 1: all the stats from uh necessarily in the in in

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Speaker 1: the English system, but but yeah, you know that's something

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Speaker 1: you Yeah. Um, you also told me once and someone

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Speaker 1: told me it's not true. Would you find that those

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Speaker 1: cavities that they would take a year off between using

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Speaker 1: those cavities, that the cavities would sit empty a year

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Speaker 1: and then the bear would come back? Is that not true? Um,

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Speaker 1: they wouldn't necessarily come back to the same cavity. That

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Speaker 1: was actually pretty rare. We only saw about five to

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Speaker 1: reuse of of the same cavities. The hell they find

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Speaker 1: in the cavities, well, you know, they spent a lot

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Speaker 1: of time in trees, you know, so they're they're up

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Speaker 1: in trees eating grapes or or acorns even before they

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Speaker 1: drop on the ground. Um, so they're up and eating

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Speaker 1: out of fruits. So they're up in the trees a lot.

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Speaker 1: And and my guess is that as they they spend

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Speaker 1: a lot of time in trees, they will they will

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Speaker 1: remember places where there's a cavity, so they know them.

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Speaker 1: Their habitat like like we know our house, you know.

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Speaker 1: It's it's something that I think that that we underestimate

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Speaker 1: the capability of these animals, the spatial awareness total. They

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Speaker 1: know every every square range of their home range. And

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Speaker 1: then the females they would they would drop their litters

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Speaker 1: up in the cavities exactly. Yeah. So for for those cubs,

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Speaker 1: it's an incredibly safe place to be born. Do they

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Speaker 1: lose Uh that coke could probably would stay in a

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Speaker 1: little bit of fall, but it's gotta be dangerous getting

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Speaker 1: them back down out of there. Yeah. And and so

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Speaker 1: we've seen them come out and uh, and it's it's

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Speaker 1: pretty amazing. I mean sometimes, um, you know in April

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Speaker 1: or so, when they came out, you would seem climb

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Speaker 1: down this huge tree and you know, these tiny little cubs,

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Speaker 1: but their their claws are amazing, you know. When so

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Speaker 1: when we would do these dent visits, we if if

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Speaker 1: we had an opportunity to do it safely, we would

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Speaker 1: nobalize the female and examine that give us give us

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Speaker 1: a chance to examine the cubs and all that. And uh.

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Speaker 1: And when you take those cubs out, I mean it's

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Speaker 1: their their closet, like fell crow, they just stick to you, Yeah,

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Speaker 1: like a little kitten or something. So so it gave

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Speaker 1: him enough attraction on on the bark of the tree

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Speaker 1: to to to get down the tree. And it's it's

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Speaker 1: pretty amazing to see that how how well equipped they

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Speaker 1: are right from from that age, you know, when they

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Speaker 1: when they first come out of the den to to

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Speaker 1: spend a lot of time and trees, that's that's there

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Speaker 1: their safety place. Yeah. So is there a for a

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Speaker 1: bear guy? Like you're a bear guy right like you

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Speaker 1: you focus on barriers totally? Is there a journal you

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Speaker 1: guys used? Like, is there like a journal called like

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Speaker 1: Ursus or something? The World's Bearers? That's why I thought,

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Speaker 1: I thought, I've never seen something like that. So what

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Speaker 1: other what other species of bears around the world did

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Speaker 1: you look at before you got focused on one grizzlies?

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Speaker 1: Um so between my you know, so when I first

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Speaker 1: started on black bears, I spent quite a bit of

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Speaker 1: number of years working on black bears and eventually got

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Speaker 1: involved with some international work, um with researchers in other countries.

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Speaker 1: So in Sri Lanka, for example, I worked with with

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Speaker 1: the researcher there who was working on the sloth bears.

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Speaker 1: That was a really interesting project worked with two doing

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Speaker 1: population work on those as well. Population work also a

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Speaker 1: lot of habitat work, habitat analysis, you know, home range

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Speaker 1: sizes and things like that, because there was so little

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Speaker 1: known about there's nothing known really about sloth bears in

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Speaker 1: Sri Lankan, and they stable or they decline. Well, they

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Speaker 1: they're still holding on pretty well right now. Interestingly enough,

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Speaker 1: you know, there used to be a civil war there

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Speaker 1: and in a way that was good for for sloth

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Speaker 1: bears because the rebel um the Tamil Tigers is is

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Speaker 1: the group that that that basically had a conflict with

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Speaker 1: the national government and so um the t Aimalt Tigers

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Speaker 1: controlled a lot of the area where slot bears still existed,

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Speaker 1: and and they would not allow people to go into

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Speaker 1: those areas with guns and such. So it's actually sadly

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Speaker 1: enough that the war actually created in some ways of

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Speaker 1: protection for pho those populations. Is there a bush meat

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Speaker 1: market there or voting for parts for the asiastic like

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Speaker 1: trade and affordesiacs or what it's um most of like

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Speaker 1: what happens a lot is there's people going out sometimes

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Speaker 1: illegally into national parks, going out for for honey, but

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Speaker 1: they in the in the process, sometimes they are attacked

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Speaker 1: by by slot bears because they slot bears tend to

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Speaker 1: be relatively aggressive, and so when that happens, you know

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Speaker 1: a lot of times those bears end up and getting killed.

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Speaker 1: So it's not necessarily for for meat or anything. There's

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Speaker 1: there's really no boaching problem per se. It's just conflict

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Speaker 1: that that leads to mortalities of those bears and and

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Speaker 1: now probably illegal loggings that tie into the habitat loss

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Speaker 1: and stuff. Is that not an issue for them? Not

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Speaker 1: so much. The bigger issue right now is now that

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Speaker 1: there is a peace agreement, people are moving back into

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Speaker 1: those areas, uh, you know, because they avoided the areas

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Speaker 1: of conflict. Of course, now they're moving back and moving

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Speaker 1: into slought bear habitats. So that is um that is

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Speaker 1: probably going to have a potentially lasting effect on populations there.

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Speaker 1: So we will have to see how that how that

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Speaker 1: pans out in the future. And then have you done

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Speaker 1: work with the bears in South America? Yes, so I've

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Speaker 1: I've called sun bears right those are Indian bears or

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Speaker 1: in what's this where's the sun bear? From sun bears?

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Speaker 1: Mostly in the Southeast Asia, so Malaysia, Indonesian, Indian bear. Yes,

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Speaker 1: the South American bear, that's the only South American bearing.

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Speaker 1: I'm kind of a relatively primitive bear species if you

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Speaker 1: look at it evolutionarily. Um, they're they're quite old. Same

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Speaker 1: with with giant panda. Is also an old bear species

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Speaker 1: of the current you know, the currently the eight species

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Speaker 1: that that we have in the world. Um. And it's

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Speaker 1: uh when you say old, meaning that like the animal

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Speaker 1: hasn't changed much in a over a long period of time. Well, um,

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Speaker 1: old in the sense that that it stems from a yeah,

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Speaker 1: an older evolutionary lineage. So you know, brown bears and

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Speaker 1: polar bears are the most modern bears so to speak

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Speaker 1: in that sense, so that these are the exactly yeah

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Speaker 1: and uh, and so the giant panda and and Andian

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Speaker 1: bear are the older bear lineages so to speak. And

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Speaker 1: so yeah, and Engiine bears was a really interesting species

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Speaker 1: to work on. Um. They occur in some of the

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Speaker 1: really high elevation areas in the Indian mountains. Um, Like

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Speaker 1: they're like an alpine species. Yeah, they they had they

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Speaker 1: covered quite an elevational range. Actually, some of the work

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Speaker 1: is in in in these areas called paramou, which which

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Speaker 1: basically means no no trees. Um. So these open areas

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Speaker 1: in elevations um, you know, over fourteen thousand feet or

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Speaker 1: so and uh. And they feed primarily on bromeliads. So

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Speaker 1: they they consume the the tissue that's at the bottom

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Speaker 1: of the leaves. There is that tissue that has a

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Speaker 1: lot of sugars in it. And then so they will

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Speaker 1: rip open these these big bromeliads and and eat the

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Speaker 1: base of those leaves. It's really really neat. And how

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Speaker 1: how they've adapted to them and do they mix it

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Speaker 1: up with people or as they are they pretty docile? Um,

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Speaker 1: they they they're fairly you know, for for in terms

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Speaker 1: of calling any bear species docile, you know, it's yeah,

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Speaker 1: they're they're pretty Uh, they're they're pretty easy going. They're

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Speaker 1: they're not very aggressive species. Um. They they occasionally killed cattle.

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Speaker 1: But there's really not a not a large number of

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Speaker 1: of comps links between people and engine bears necessarily. But

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Speaker 1: if panda bears ever kill people, has that is that

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Speaker 1: knowing to happen? To my knowledge? I I'm not sure

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Speaker 1: they have no reason to kill livestock or anything. No, no, no, um.

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Speaker 1: You know panda bears are you know right? Yeah, I

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Speaker 1: mean they almost all of their diet is is bamboo,

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Speaker 1: so um yeah, of course, very unique. So they can

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Speaker 1: still can they move fast or not? Then fast? They can? Um,

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Speaker 1: but but their home ranges tend to be relatively small.

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Speaker 1: Their movement still and tend to be relatively small today.

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Speaker 1: They kind of you know, basically set up shopping in

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Speaker 1: in a good bamboo patch and basically wear it out

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Speaker 1: and move onto patch. So that's that's their their typical

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Speaker 1: mode of operations. So they like the porcupine uses his landscape,

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Speaker 1: you know, uh yeah, move on when when the resources

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Speaker 1: are you know, getting to the point where you're just

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Speaker 1: not efficient anymore? Um So then did all that? Now,

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Speaker 1: how did it come to be that you were Okay,

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Speaker 1: I guess not at this point? Explain your job now

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Speaker 1: you're titled now, Um so I'm currently um my official

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Speaker 1: title is with the U s GS is I'm a

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Speaker 1: supervisory wildlife No what is it research bottle if biologists?

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Speaker 1: Um with the inter agency. Yeah. So and that's so

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Speaker 1: I'm the team leader of the inter Agency Grizzly Best

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Speaker 1: Study Team. And by inter agency that means a Geological Service.

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Speaker 1: So where you work for U s G S. U

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Speaker 1: s G S so eight different entities altogether, So the

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Speaker 1: U S G S Fishing, Wild La Service, UM National

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Speaker 1: Park Service for Service, and then the three state agencies

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Speaker 1: for Montana, Wyoming, and Idaho. And then we also work

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Speaker 1: with the tribal agencies of the Northern Arapaho and the

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Speaker 1: Eastern show Shownee tribes. And the only thing under your

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Speaker 1: purview then is the is the grizzly bears and the

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Speaker 1: Greater yell Stone area. That's correct. Yeah, yeah, so we

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Speaker 1: this team was established back in nineteen seventy three, so

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Speaker 1: it's it's we've got a long history, more than four decades.

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Speaker 1: So well, yere did they get listed two years? And uh?

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Speaker 1: And so we were actually in existence before the the

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Speaker 1: official listing of grizzly bears in the lower forty aid

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Speaker 1: Son and A Danger Species Act. I read the Inner

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Speaker 1: Agency Group. No, I guess we should explain too. So

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Speaker 1: you're not involved in policy, in pure research, correct giving information,

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Speaker 1: providing accurate, non presumably non biased information to policymakers to

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Speaker 1: inform their decision making. And you guys got started the

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Speaker 1: right If I think I feel like I read this,

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Speaker 1: you guys got started with involved just like closing dumps

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Speaker 1: like that was sort of a strange somewhat uh, you know,

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Speaker 1: like a not very hot button topic. I'm mad at

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Speaker 1: the time, it was like the closing of dumps for

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Speaker 1: attracting bears in the park or something. Right, Yeah, so

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Speaker 1: that you know that there were open pin garbage dumps

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Speaker 1: in and throughout the Yelstone existence and including Yelstone National Park,

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Speaker 1: which is where people used to do their bear viewing.

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Speaker 1: That's right, Yeah, I mean they literally had what they

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Speaker 1: call bear counters, you know, where where bears would just

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Speaker 1: be fed the garbage from the hotels and then people

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Speaker 1: would that they would have basically a gallery for people

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Speaker 1: to sit and watch these bears feet on their lunch counter.

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Speaker 1: And and so after you know, the National Park Service

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Speaker 1: had done some had asked two for some reports and

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Speaker 1: this this famous Leopold report that that determined that that

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Speaker 1: they made a recommendation that the parkser was moved to

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Speaker 1: a more natural management of wildlife in general. No Starker, Yeah,

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Speaker 1: and after his time, well after but made a report

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Speaker 1: that that that that change policies in in the National

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Speaker 1: Parks in terms of their their management and and taking

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Speaker 1: a more um natural management approach to to all all

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Speaker 1: wildlife management, including bears and and so I think Yellstone

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Speaker 1: has been on the forefront of that right from the beginning.

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Speaker 1: And uh, and so I don't think enough people realize

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Speaker 1: some of the things about Yellowstones, like people like people

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Speaker 1: who don't have a who don't have a deep background

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Speaker 1: and wildlife and wildlife management, wildlife politics always like they

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Speaker 1: kind of like to think of Yellowstone as this sort

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Speaker 1: of thing that's always existed in this static form. Yeah,

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Speaker 1: when they don't realize that like all the buffalo or

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Speaker 1: all the bis and that are in Yellowstone used to

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Speaker 1: be in defense and we're fed hay and straw exactly.

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Speaker 1: You know that the bear viewing was, like you said,

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Speaker 1: fed bears basically you watch you would observe bait stations. Yeah,

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Speaker 1: you know what people have this image of it is

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Speaker 1: like this is like the Steen area where we all

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Speaker 1: we get to watch everything play out in its natural form,

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Speaker 1: you know, without realizing what a sort of conscious like

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Speaker 1: that there, that there was a conscious act to create

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Speaker 1: this exactly. You know what I mean, it doesn't exist

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Speaker 1: because it doesn't exist just because it's been hands off.

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Speaker 1: It exists because people have pursued a sort of vision there. Yeah.

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Speaker 1: And and you know, management in the parks has always

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Speaker 1: been pretty heavy and in some cases there there's really

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Speaker 1: no way around that. And you know, I think um

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Speaker 1: man just will have to be pretty heavy handed. And

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Speaker 1: in some instances, in some small parks for example, and

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Speaker 1: um in this way management has kind of come down

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Speaker 1: to almost non management, but like making decisions to do

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Speaker 1: these things. Yeah exactly, Yeah, because yeah, there's still even

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Speaker 1: if if you decide not to do something that's that's

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Speaker 1: you could still call that a management decision. Um. So

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Speaker 1: they got rid of the bait stations, open bit garbage dumps. Yeah.

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Speaker 1: So they got resistance to that from from park visitors

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Speaker 1: who wanted to see bears right now. Yeah. That so

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00:20:03,640 --> 00:20:07,080
Speaker 1: there was resistance from from visitors that uh that that

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Speaker 1: we're worried that they wouldn't be able to see bears anymore.

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Speaker 1: And there was resistance at that time from the two

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Speaker 1: prominent researchers on on grizzly bears and Yelstone and they

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Speaker 1: have John and Frank Craighead and of course that the

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Speaker 1: pioneers of well what was their bearers, They wouldn't be

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Speaker 1: able to find their bears very easily, know they Their

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Speaker 1: concern was that that, so the parks are was proposed

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Speaker 1: to to close the dumps basically all you know, at once,

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Speaker 1: not not phase it out. And so that was the

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Speaker 1: major disagree displacement exactly, they'd like strike off across the

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Speaker 1: country and have no idea what there and and that

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Speaker 1: did happen, um. But the parts of his argument at

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Speaker 1: the time was that if we phase it out, there's

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Speaker 1: gonna be generations and generations of bears that still know

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Speaker 1: how to use that resource, and it's better to just

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Speaker 1: wean them off right off, you know, right away, and

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Speaker 1: and deal with the consequences. And I think you know,

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Speaker 1: cold Turkey basically, yeah, yeah, we got a friend. Uh.

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Speaker 1: I don't think he's downstairs right now, but he's trying

365
00:21:07,600 --> 00:21:11,399
Speaker 1: to quit you and he needs to do like they

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Speaker 1: do with the party bears, just shut it turkey. Yeah,

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00:21:15,520 --> 00:21:18,000
Speaker 1: he needs to shut it down. Yeah. So the you

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00:21:18,040 --> 00:21:22,600
Speaker 1: know that there was a major disagreement, um, And and

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Speaker 1: the consequence of it was that that the was high

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Speaker 1: mortality of grizzly bears after that. Indeed, just like just

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Speaker 1: like you said, that's that's exactly what happened. Bears started

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Speaker 1: moving all over the place looking for foods. Then you know,

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Speaker 1: previously they got on all these easy hand nowns and

374
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Speaker 1: now they had to kind of fetch for themselves and

375
00:21:40,040 --> 00:21:46,520
Speaker 1: people's yards and roadways, camgrounds. So a large number had

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Speaker 1: to be removed because of problem issues of conflicts and

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Speaker 1: so dead probably put it dent in population at the time.

378
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Speaker 1: And the closing those open dumps happened in the park

379
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Speaker 1: and out of the part. Yeah, so they're the ones

380
00:22:01,440 --> 00:22:04,040
Speaker 1: out of the park where a little bit later, but

381
00:22:04,040 --> 00:22:06,840
Speaker 1: but by the early seventies, the ones in the park

382
00:22:06,880 --> 00:22:09,479
Speaker 1: had been closed. Now how many grizzlies were there in

383
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Speaker 1: the you know like this, I know this isn't something

384
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Speaker 1: you can you can you can't. You could probably have

385
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Speaker 1: no comment to this, but I think it was like

386
00:22:15,640 --> 00:22:18,600
Speaker 1: a strategic it's just me talking. I think it's a

387
00:22:18,640 --> 00:22:24,720
Speaker 1: strategic miscalculation to call the Greater Yellowstone ecosystem the greater

388
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Speaker 1: Yellostone ecosystem because because the name is built around the

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Speaker 1: park and people have a hard like message that can't

390
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Speaker 1: separate and they think of it as the park. I

391
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Speaker 1: would call it something totally different, like I don't know

392
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Speaker 1: name you So, I don't know the Rocky Mountain, try

393
00:22:45,720 --> 00:22:49,879
Speaker 1: state areas, the like the something, I don't know, the area,

394
00:22:50,040 --> 00:22:54,880
Speaker 1: the area with a lot of animals, because the greater area,

395
00:22:55,000 --> 00:22:57,000
Speaker 1: greater area. Because you do that, there's like we have

396
00:22:57,320 --> 00:22:59,800
Speaker 1: I think that you know, again it's just me talking personally,

397
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Speaker 1: but I think we uh collectively in the West, there's

398
00:23:02,480 --> 00:23:06,280
Speaker 1: like a thing we suffer from Yellowstone syndrome. And it's

399
00:23:06,280 --> 00:23:08,320
Speaker 1: really hard for people to like sort out the differences

400
00:23:08,320 --> 00:23:10,960
Speaker 1: of the park and the not park, you know, and

401
00:23:11,040 --> 00:23:13,800
Speaker 1: what the challenges are within the park, how those how

402
00:23:13,960 --> 00:23:16,639
Speaker 1: challenges in the park affect people surrounding the park, and

403
00:23:16,640 --> 00:23:18,439
Speaker 1: it just becomes And so when they call it the

404
00:23:18,440 --> 00:23:21,600
Speaker 1: Greater Yelostone ecosystem, I think people, you know, have a

405
00:23:21,600 --> 00:23:24,199
Speaker 1: hard time realize that that's like a little piece of

406
00:23:24,240 --> 00:23:28,560
Speaker 1: something that's the size of Indiana. Yeah, well people here, yell,

407
00:23:28,680 --> 00:23:32,639
Speaker 1: they're they're gonna the Yellowstone bears when we're talking about it,

408
00:23:32,880 --> 00:23:36,520
Speaker 1: but a fairly large region. But at that time they go,

409
00:23:36,600 --> 00:23:39,440
Speaker 1: so go back to the inception of your inter agency group,

410
00:23:40,040 --> 00:23:43,280
Speaker 1: how many how many grizzlies were there? So in the

411
00:23:43,320 --> 00:23:46,240
Speaker 1: g y e or in the actual Yellowstone or whatever. Yeah,

412
00:23:46,240 --> 00:23:48,760
Speaker 1: so that the area of occupancy at the time was

413
00:23:49,080 --> 00:23:51,159
Speaker 1: much smaller than it is now. Probably, so it was

414
00:23:51,400 --> 00:23:54,160
Speaker 1: like Yellows basically National Park and a little bit of

415
00:23:54,160 --> 00:23:57,960
Speaker 1: of area around it. Um. But they would it be

416
00:23:58,040 --> 00:24:00,240
Speaker 1: fair to say back then most bears would probably coming

417
00:24:00,320 --> 00:24:02,520
Speaker 1: in and out of the park. Yes, so at some

418
00:24:02,560 --> 00:24:05,800
Speaker 1: point in your life. Yeah, And so after all those

419
00:24:05,840 --> 00:24:08,199
Speaker 1: bears have been removed from from a system in the

420
00:24:08,200 --> 00:24:12,800
Speaker 1: early seventies because of conflicts. Um. You know, there's really

421
00:24:12,840 --> 00:24:15,560
Speaker 1: no good estimates, and and there's some numbers out there

422
00:24:15,600 --> 00:24:18,760
Speaker 1: that that people you know, have a number of like

423
00:24:18,800 --> 00:24:20,920
Speaker 1: one thirty six. Well, I don't think we can get

424
00:24:20,920 --> 00:24:24,800
Speaker 1: it that exactly. And you're specialty is dynamics looking at

425
00:24:24,800 --> 00:24:28,840
Speaker 1: population down so you're probably extra cautious. I am extra cautious.

426
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Speaker 1: So it's possible that there that there might have been

427
00:24:34,160 --> 00:24:39,359
Speaker 1: you know, around two hundred, maybe a little fewer than that.

428
00:24:39,359 --> 00:24:43,800
Speaker 1: That's the probably, um at that time. So let let's

429
00:24:43,840 --> 00:24:45,280
Speaker 1: go back, you know, let' let's do this. Let's go

430
00:24:45,320 --> 00:24:49,200
Speaker 1: to the year of the year of listing. Now when

431
00:24:49,200 --> 00:24:51,400
Speaker 1: when people say listing just for listeners. When people say listening,

432
00:24:51,400 --> 00:24:54,399
Speaker 1: what we're talking about is that the that they got

433
00:24:54,680 --> 00:24:58,120
Speaker 1: protection as a threatened species, not as an endangered species.

434
00:24:58,160 --> 00:25:01,600
Speaker 1: But the grizzly bears in the lower forty eight we're

435
00:25:01,640 --> 00:25:07,400
Speaker 1: afforded protection under the Endangered Species Act, listed as threatened,

436
00:25:07,440 --> 00:25:10,440
Speaker 1: so not listened as in endangered, but listening as threatened,

437
00:25:10,720 --> 00:25:13,800
Speaker 1: like possible for them to become endangered. Exact way to

438
00:25:13,840 --> 00:25:16,080
Speaker 1: think about it. That happened in nineteen So at that point,

439
00:25:16,080 --> 00:25:20,320
Speaker 1: how many we're in what's now the Greater Elstone ecosystem

440
00:25:20,320 --> 00:25:22,560
Speaker 1: and how many lower forty eight in total? So in

441
00:25:22,800 --> 00:25:25,240
Speaker 1: the great els in the ecosystem that probably would have

442
00:25:25,280 --> 00:25:30,240
Speaker 1: been around two hundred is our best estimate, our best

443
00:25:30,280 --> 00:25:32,880
Speaker 1: and it's it's pretty much a guess. But even uh,

444
00:25:33,080 --> 00:25:35,600
Speaker 1: you know, based some of the earlier work, um, there

445
00:25:35,680 --> 00:25:39,440
Speaker 1: were just no really really solid numbers. But that's that's

446
00:25:39,480 --> 00:25:44,360
Speaker 1: a that's a pretty reasonable assumption. Um, you know, estimates

447
00:25:44,400 --> 00:25:48,480
Speaker 1: for any of the other ecosystems were pretty poor too

448
00:25:48,520 --> 00:25:51,000
Speaker 1: at the time. So it's it's basically, you know, some

449
00:25:51,119 --> 00:25:53,480
Speaker 1: big guesswork here and I'm just throwing out a number.

450
00:25:53,480 --> 00:25:56,280
Speaker 1: It's it was probably fewer than a thousand at the time,

451
00:25:56,440 --> 00:26:00,359
Speaker 1: So how were they counting him then? Like one six

452
00:26:00,520 --> 00:26:04,399
Speaker 1: were using models. Know that a lot of it was

453
00:26:04,480 --> 00:26:09,000
Speaker 1: just based on observational work identify know, so that the

454
00:26:09,040 --> 00:26:11,320
Speaker 1: Craik has had done some works, you know, starting in

455
00:26:11,359 --> 00:26:14,600
Speaker 1: the in the you know, through the fifties and sixties

456
00:26:15,320 --> 00:26:18,879
Speaker 1: and and so based on the animals that day had

457
00:26:18,960 --> 00:26:21,920
Speaker 1: marked and observations from from those studies, you know, there

458
00:26:21,920 --> 00:26:25,160
Speaker 1: were there were some indications of of what population size

459
00:26:25,240 --> 00:26:27,679
Speaker 1: might be. But but they didn't study all bears, you know,

460
00:26:27,680 --> 00:26:30,760
Speaker 1: they studied primarily around the dump sides, so there were

461
00:26:30,880 --> 00:26:33,160
Speaker 1: probably a lot of outer bears that were never observed.

462
00:26:33,200 --> 00:26:36,199
Speaker 1: So that's why some of the low numbers that that

463
00:26:36,280 --> 00:26:39,720
Speaker 1: I've heard, you know, we kind of wonder whether that's

464
00:26:39,760 --> 00:26:42,440
Speaker 1: really whether the population really was at low or whether

465
00:26:42,520 --> 00:26:45,120
Speaker 1: a lot of bears were actually missed in those um

466
00:26:45,200 --> 00:26:48,960
Speaker 1: kind of assessments. And and that's those were I would

467
00:26:48,960 --> 00:26:52,800
Speaker 1: call them, you know, kind of qualitative population assessments. At

468
00:26:52,800 --> 00:26:55,560
Speaker 1: the time. Let's say we just knew, for whatever reason,

469
00:26:55,640 --> 00:26:57,680
Speaker 1: we knew the two D was accurate. There was two.

470
00:26:57,760 --> 00:26:59,720
Speaker 1: Let's just say there was two. Can there be two

471
00:26:59,760 --> 00:27:02,119
Speaker 1: hundre for a long time or is that like a

472
00:27:02,240 --> 00:27:04,800
Speaker 1: number that just doesn't work? Oh, that that can be

473
00:27:04,840 --> 00:27:07,040
Speaker 1: two hundred for a long time, you know, And I

474
00:27:07,280 --> 00:27:11,199
Speaker 1: don't think it was actually necessarily that long anyway, because

475
00:27:12,119 --> 00:27:14,440
Speaker 1: we'll probably talk about this getting at like they said

476
00:27:14,600 --> 00:27:18,080
Speaker 1: with passenger pigeons, right, there had to be millions. If

477
00:27:18,080 --> 00:27:20,320
Speaker 1: you don't have millions, you wouldn't have any I mean,

478
00:27:20,359 --> 00:27:23,600
Speaker 1: they really like just their whole system relied on there

479
00:27:23,600 --> 00:27:26,240
Speaker 1: being many. Yeah, and that's that's not really the case

480
00:27:26,320 --> 00:27:30,399
Speaker 1: with bare populations, you know. I'm there's some bare populations

481
00:27:30,440 --> 00:27:34,840
Speaker 1: in Europe, for example, that that for decades and now

482
00:27:35,000 --> 00:27:37,760
Speaker 1: existed in the in the range of a dozen or

483
00:27:37,800 --> 00:27:40,520
Speaker 1: so in the Pyrenees for example. Really it just like

484
00:27:40,520 --> 00:27:44,080
Speaker 1: how they mean, there are amazing animals. You know, they

485
00:27:44,119 --> 00:27:48,000
Speaker 1: can just hang on for for a long time, and

486
00:27:48,040 --> 00:27:51,080
Speaker 1: as long as you have a couple of females that's

487
00:27:51,080 --> 00:27:55,159
Speaker 1: still reproduce every now and then, it can actually you know,

488
00:27:55,320 --> 00:27:58,359
Speaker 1: stay at that level unless there's additional threats. But they

489
00:27:58,359 --> 00:28:00,200
Speaker 1: can stay at that level for a long time. It's

490
00:28:00,240 --> 00:28:02,560
Speaker 1: it's of course not those those are not by any

491
00:28:02,600 --> 00:28:07,760
Speaker 1: means sustainable viable population levels in the long run, of course. Um.

492
00:28:07,840 --> 00:28:11,040
Speaker 1: But there's there's populations in northern Italy and central Italy,

493
00:28:11,800 --> 00:28:15,760
Speaker 1: um in in western Spain and the Pyrenees that are

494
00:28:15,800 --> 00:28:19,359
Speaker 1: all around that that size of somewhere between a dozen

495
00:28:20,080 --> 00:28:23,480
Speaker 1: for some populations to forty for others, to up to

496
00:28:23,640 --> 00:28:27,080
Speaker 1: maybe eighty or so for other populations. And and they

497
00:28:27,080 --> 00:28:30,480
Speaker 1: have been like that for probably decades, and and with

498
00:28:30,640 --> 00:28:33,640
Speaker 1: no foreseeable change in the future that that those populations

499
00:28:33,680 --> 00:28:40,160
Speaker 1: would get a whole lot bigger. So variations in those populations, um,

500
00:28:40,400 --> 00:28:43,240
Speaker 1: that's been one concern for especially for the Pyrenees. So

501
00:28:43,320 --> 00:28:45,680
Speaker 1: they actually the cold quality, if I was just gonna

502
00:28:45,680 --> 00:28:51,800
Speaker 1: ask you any questions, so far, very good. So they

503
00:28:52,120 --> 00:28:56,160
Speaker 1: actually augmented the population and the Pyrenees with some bears

504
00:28:56,160 --> 00:28:59,400
Speaker 1: from Slovenia where there are very healthy populations. So and

505
00:28:59,440 --> 00:29:01,720
Speaker 1: it doesn't that's the other thing about genetics, and and

506
00:29:01,840 --> 00:29:04,000
Speaker 1: this is um I hope we get to talk about

507
00:29:04,040 --> 00:29:05,640
Speaker 1: that a little bit later on as well, But just

508
00:29:06,200 --> 00:29:09,040
Speaker 1: about the genetics of the Yelson population. There's a lot

509
00:29:09,080 --> 00:29:13,880
Speaker 1: of discussion about the genetics issues and and it it

510
00:29:14,000 --> 00:29:17,360
Speaker 1: is it is a potential concern for these really small populations,

511
00:29:17,400 --> 00:29:19,600
Speaker 1: like if you only have a dozen animals or so. Yeah,

512
00:29:19,680 --> 00:29:23,960
Speaker 1: Yet genetics is obviously a concern, but it doesn't take

513
00:29:24,040 --> 00:29:26,600
Speaker 1: much to to reverse the effects of that. You know,

514
00:29:26,640 --> 00:29:30,280
Speaker 1: so an augmentation of of moving some animals from with

515
00:29:30,720 --> 00:29:38,920
Speaker 1: another genetic background into a new area um is incredibly effective. Yeah,

516
00:29:38,920 --> 00:29:40,479
Speaker 1: did you follow the debate. I don't want to get

517
00:29:40,480 --> 00:29:42,080
Speaker 1: this too off topic, but did you follow the debate

518
00:29:42,080 --> 00:29:45,600
Speaker 1: about the Florida panther? Yeah? Well I did some work,

519
00:29:48,280 --> 00:29:50,800
Speaker 1: you know, like we're talking about dozens of animals in

520
00:29:50,840 --> 00:29:54,520
Speaker 1: Florida and they're being a debate of of Okay, well

521
00:29:54,560 --> 00:29:56,240
Speaker 1: let's bring in something from the west, where we got

522
00:29:56,280 --> 00:29:59,040
Speaker 1: plenty of from Texas. Yeah, and then and people like, yeah,

523
00:29:59,040 --> 00:30:00,600
Speaker 1: but this is the Florida p out there. We're gonna

524
00:30:00,600 --> 00:30:05,600
Speaker 1: sort of destroy, you know, genetic line. But then like, okay, sure,

525
00:30:05,680 --> 00:30:07,600
Speaker 1: but you're just gonna lose the whole damn deal if

526
00:30:07,600 --> 00:30:11,000
Speaker 1: you don't mean I mean, like the choice to me, uh,

527
00:30:11,400 --> 00:30:14,080
Speaker 1: seemed pretty pretty clear. You know, either you're bringing in

528
00:30:14,120 --> 00:30:16,720
Speaker 1: a risk the population that the only population you have

529
00:30:16,760 --> 00:30:20,360
Speaker 1: in the East, or you bring in eight Texas cougars

530
00:30:20,560 --> 00:30:23,920
Speaker 1: and introducing new genetics. And that's what they ended up doing,

531
00:30:23,960 --> 00:30:28,959
Speaker 1: of course, and that population I'm convinced that's that was

532
00:30:29,000 --> 00:30:34,560
Speaker 1: the saving grace for that population. Absolutely about the Texas cougars.

533
00:30:34,640 --> 00:30:39,720
Speaker 1: There there were super cougars. Oh yeah, yeah, like yeah,

534
00:30:39,760 --> 00:30:45,200
Speaker 1: the Livestock Interest in Florida they super coargar. No, at

535
00:30:45,200 --> 00:30:49,360
Speaker 1: the time of Texas cougar. I don't think there was

536
00:30:49,880 --> 00:30:52,680
Speaker 1: a real concern at the time because because the cattle

537
00:30:52,720 --> 00:30:55,600
Speaker 1: depredations weren't real big deal at the time. They've in

538
00:30:55,680 --> 00:31:00,200
Speaker 1: recent years they've become somewhat of an issue. But um,

539
00:31:00,840 --> 00:31:03,040
Speaker 1: it is amazing how how well that works. You know,

540
00:31:03,200 --> 00:31:06,280
Speaker 1: a lot of the genetic defects um that that we're

541
00:31:06,320 --> 00:31:09,239
Speaker 1: obvious in the resident population, you know, kink tails and

542
00:31:09,680 --> 00:31:13,640
Speaker 1: something called crypt organism. We're only one test these the

543
00:31:13,760 --> 00:31:15,560
Speaker 1: sends in the in the mail, which is you know,

544
00:31:16,000 --> 00:31:20,160
Speaker 1: definitely a sign that that these are not vigorous animals. Um,

545
00:31:20,240 --> 00:31:23,840
Speaker 1: all that really was reversed and just the introduction of

546
00:31:24,080 --> 00:31:27,320
Speaker 1: eight Texas cougars did did the jobs they bring males

547
00:31:27,360 --> 00:31:30,720
Speaker 1: or females are both? I don't know what boats as

548
00:31:30,760 --> 00:31:33,040
Speaker 1: I recall, but I don't know exactly what what the

549
00:31:33,080 --> 00:31:36,160
Speaker 1: sex ratio was. And did they pull them from a area?

550
00:31:36,520 --> 00:31:38,720
Speaker 1: Now we're seeing we're getting way off topic, but the

551
00:31:39,400 --> 00:31:44,400
Speaker 1: one last question, did they pull them from a wet area? No,

552
00:31:45,400 --> 00:31:50,160
Speaker 1: not that I remember, um, a tough situation if you're

553
00:31:50,160 --> 00:31:52,440
Speaker 1: getting about a West Texas right, and also you're like,

554
00:31:52,720 --> 00:31:57,160
Speaker 1: welcome to the Eppglades, buddy. Well true, but but I think, um,

555
00:31:57,600 --> 00:32:02,800
Speaker 1: we we underestimate how well animals can five. You know, Um,

556
00:32:02,880 --> 00:32:05,520
Speaker 1: it's the same we've we've seen that with with bears

557
00:32:05,560 --> 00:32:08,080
Speaker 1: to you know, you you reintroduce bears to new areas

558
00:32:08,200 --> 00:32:12,440
Speaker 1: and they tend to do very well, incredibly adaptable. I

559
00:32:12,480 --> 00:32:14,920
Speaker 1: got a friend who's working on a project where they're

560
00:32:14,960 --> 00:32:19,080
Speaker 1: looking at taking coyotes that live like let's say there's

561
00:32:19,080 --> 00:32:22,520
Speaker 1: a cod lives in an alpine environment, and when you

562
00:32:22,560 --> 00:32:25,160
Speaker 1: move them, where does he set up shop? Does he

563
00:32:25,240 --> 00:32:30,760
Speaker 1: travel long ways to find what he recognizes as you

564
00:32:30,800 --> 00:32:34,400
Speaker 1: know homecus you just go, well, I'm here now and

565
00:32:34,440 --> 00:32:38,320
Speaker 1: now I'm gonna figure this out, you know, displacement issues Sorright,

566
00:32:38,360 --> 00:32:41,120
Speaker 1: So back to the main subject. Now, they got yes,

567
00:32:41,200 --> 00:32:48,320
Speaker 1: a protection Right now we've got in your area, the

568
00:32:48,360 --> 00:32:52,320
Speaker 1: area under your under your review, under you know, where

569
00:32:52,320 --> 00:32:58,480
Speaker 1: you do your research. We've now got four times as many. Okay,

570
00:32:58,880 --> 00:33:00,760
Speaker 1: let's talk about that number first, because a lot of

571
00:33:00,760 --> 00:33:04,560
Speaker 1: people like to say probably many more, or they like

572
00:33:04,640 --> 00:33:07,480
Speaker 1: to say probably less. Why is it hard to tell.

573
00:33:08,120 --> 00:33:09,920
Speaker 1: Why is it hard to tell us how many there are?

574
00:33:10,200 --> 00:33:13,960
Speaker 1: And how accurate do you think whatever the fashionable number

575
00:33:14,000 --> 00:33:16,080
Speaker 1: is right now? How accurate is the number? Yeah? So

576
00:33:16,160 --> 00:33:18,760
Speaker 1: that the estimates for two thousand and sixteen is six

577
00:33:19,120 --> 00:33:22,200
Speaker 1: d ninety bears. That's down a little bit from from

578
00:33:22,280 --> 00:33:26,400
Speaker 1: last year and from the year before that, but the

579
00:33:26,440 --> 00:33:29,360
Speaker 1: population has been pretty stable at pretty much the same

580
00:33:29,440 --> 00:33:33,440
Speaker 1: level since the early two thousand's. Now that number we

581
00:33:33,600 --> 00:33:39,239
Speaker 1: know is a likely and underestimate likely and under us Yes,

582
00:33:39,440 --> 00:33:44,800
Speaker 1: And that's because the method that we use UM basically

583
00:33:44,960 --> 00:33:48,880
Speaker 1: has an underestimation bias building because you'd rather be wrong

584
00:33:48,960 --> 00:33:51,560
Speaker 1: that way than wrong. Well, it wasn't necessarily by design,

585
00:33:51,720 --> 00:33:56,840
Speaker 1: it it just so happened, UM because of the type

586
00:33:56,880 --> 00:34:01,040
Speaker 1: of methodology that we use is based on looking at

587
00:34:01,200 --> 00:34:05,400
Speaker 1: unique females with cups of the year and and separating

588
00:34:05,440 --> 00:34:09,680
Speaker 1: sightings of those individuals out from UH from one family

589
00:34:09,680 --> 00:34:12,759
Speaker 1: group to another, and and some of the criteria they

590
00:34:12,760 --> 00:34:16,320
Speaker 1: were established early on. We're a distance criteria to separate

591
00:34:16,320 --> 00:34:18,319
Speaker 1: them out. So if they were if if you have

592
00:34:18,360 --> 00:34:20,480
Speaker 1: two observations of a female with two cups, you know

593
00:34:20,719 --> 00:34:25,160
Speaker 1: you don't know, um that whether they're the same animal

594
00:34:25,239 --> 00:34:28,200
Speaker 1: or not. And so to separate them out, they in

595
00:34:28,239 --> 00:34:30,799
Speaker 1: the beginning they use the distance rule that when they

596
00:34:31,560 --> 00:34:34,759
Speaker 1: established this this technique, they use the distance rule of

597
00:34:34,760 --> 00:34:37,879
Speaker 1: thirty kilometers. And so if they were more than thirty

598
00:34:37,920 --> 00:34:41,640
Speaker 1: kilometers apart, they had to be different females well as

599
00:34:41,680 --> 00:34:48,279
Speaker 1: the not even yeah not not even then yeah, um yeah, no,

600
00:34:48,760 --> 00:34:54,240
Speaker 1: more like one point seven kilometers per mile, so say roughly,

601
00:34:54,320 --> 00:34:57,160
Speaker 1: you know, twenty miles or so. And and so what's

602
00:34:57,200 --> 00:35:02,800
Speaker 1: what happened As the population grew in densities be came higher. Um,

603
00:35:02,840 --> 00:35:07,359
Speaker 1: that that rule set wasn't necessarily as applicable anymore as

604
00:35:07,920 --> 00:35:11,399
Speaker 1: as it was in the beginning. So and so as

605
00:35:11,440 --> 00:35:16,120
Speaker 1: the population grew, that bias became stronger than underestimation bias.

606
00:35:16,440 --> 00:35:19,879
Speaker 1: And so we may be underestimating by as much as

607
00:35:19,960 --> 00:35:23,279
Speaker 1: as forty right now based on you want to know

608
00:35:23,320 --> 00:35:26,200
Speaker 1: what I think you're off because this spring we glassed

609
00:35:26,280 --> 00:35:27,960
Speaker 1: up seven in a couple of days. And I'm like,

610
00:35:28,000 --> 00:35:30,279
Speaker 1: how can we have seen such a significant percentage of

611
00:35:30,280 --> 00:35:37,239
Speaker 1: the bears just yeah, one drainage? No, absolutely, And of course,

612
00:35:37,239 --> 00:35:39,640
Speaker 1: but it is like it's I can imagine, it's difficult. Yeah,

613
00:35:39,680 --> 00:35:42,560
Speaker 1: we it is, And and so historically, you know, and

614
00:35:43,000 --> 00:35:47,239
Speaker 1: when they started developing this technique, Um, it was at

615
00:35:47,239 --> 00:35:49,359
Speaker 1: a time when when the population was in its first

616
00:35:49,400 --> 00:35:51,720
Speaker 1: stages of recovery. So there was a lot of reasons

617
00:35:51,719 --> 00:35:54,920
Speaker 1: to be conservative. Now that's that, you know, from a

618
00:35:54,920 --> 00:36:00,400
Speaker 1: biological standpoint, we've reached recovery. Um, we can it makes

619
00:36:00,719 --> 00:36:03,920
Speaker 1: sense to move to two a technique that that it's

620
00:36:03,960 --> 00:36:08,960
Speaker 1: just accurate, um, and doesn't have that built in underestimation.

621
00:36:08,960 --> 00:36:11,160
Speaker 1: But when you check, so let's say you take the number,

622
00:36:11,160 --> 00:36:13,520
Speaker 1: will tell me the number at six fifty six six

623
00:36:13,680 --> 00:36:17,560
Speaker 1: ninety four two six. But when you look at so

624
00:36:17,600 --> 00:36:19,839
Speaker 1: you got your method to use. Now when you when

625
00:36:19,880 --> 00:36:23,640
Speaker 1: you look at like hair trapping and genetics, does that

626
00:36:23,680 --> 00:36:26,840
Speaker 1: wind up giving you some other wildly off number or

627
00:36:26,840 --> 00:36:29,640
Speaker 1: does it sort of back up the figure under like

628
00:36:29,719 --> 00:36:31,759
Speaker 1: multiple ways to look at it, and if they sort

629
00:36:31,760 --> 00:36:34,880
Speaker 1: of line up and correlate. Well, so, um, yeah, a

630
00:36:34,920 --> 00:36:37,200
Speaker 1: couple of points there that so we we we have

631
00:36:37,400 --> 00:36:43,279
Speaker 1: not done a DNA sampling study for the entire ecosystem.

632
00:36:43,719 --> 00:36:46,000
Speaker 1: Um why is it expensive? And heart's you know we

633
00:36:46,080 --> 00:36:49,000
Speaker 1: calculated at the time. So what what what? Um? What

634
00:36:49,160 --> 00:36:53,000
Speaker 1: Kate Candle did in the in the northern continental divide ecosystem.

635
00:36:53,280 --> 00:36:55,800
Speaker 1: Was it was it sampling that that really covered the

636
00:36:55,920 --> 00:36:59,719
Speaker 1: entire ecosystem with DNA sampling. That was a hugely expensive.

637
00:37:00,000 --> 00:37:04,160
Speaker 1: It was a valuable information, but there was only one estimate,

638
00:37:04,160 --> 00:37:06,640
Speaker 1: you know. It's it's an estimate for the population size

639
00:37:06,680 --> 00:37:11,600
Speaker 1: that's very reliable, very refined the understanding in that area.

640
00:37:11,719 --> 00:37:16,239
Speaker 1: It's it refined the understanding of that area for two

641
00:37:16,239 --> 00:37:19,520
Speaker 1: thou four and that's what the estimate was for we

642
00:37:19,600 --> 00:37:22,400
Speaker 1: we decided in Stone not to do that. But I

643
00:37:22,400 --> 00:37:24,960
Speaker 1: got a question for you, what was the estimate before

644
00:37:25,040 --> 00:37:28,240
Speaker 1: that work, Kate Kendall, What was the estimate before that work?

645
00:37:28,280 --> 00:37:30,520
Speaker 1: And and and did that did that work make it go

646
00:37:30,640 --> 00:37:33,600
Speaker 1: up or down? You know, I'm not sure that there

647
00:37:33,680 --> 00:37:37,680
Speaker 1: was a truly a reliable estimate prior to that. It

648
00:37:37,800 --> 00:37:40,680
Speaker 1: was a question that was really kind of a benchmark number.

649
00:37:41,880 --> 00:37:45,080
Speaker 1: And so that number has now been used by by Montana,

650
00:37:45,120 --> 00:37:51,120
Speaker 1: fish wild and parks too based on the information they have.

651
00:37:51,320 --> 00:37:53,799
Speaker 1: They can make population projections on what what level of

652
00:37:53,800 --> 00:37:56,919
Speaker 1: population growth that population has experienced, and you can lose

653
00:37:57,000 --> 00:37:59,439
Speaker 1: the two thousand four number to kind of extrapolate into

654
00:37:59,440 --> 00:38:02,279
Speaker 1: the future, you know, where where the population might be now,

655
00:38:02,360 --> 00:38:05,879
Speaker 1: So people liked that work and generally accepted that work. Yes,

656
00:38:06,040 --> 00:38:10,960
Speaker 1: that number, yeah, we uh, and that I wasn't in

657
00:38:11,080 --> 00:38:12,759
Speaker 1: Jailstone at the time, but but at the time that

658
00:38:12,840 --> 00:38:17,560
Speaker 1: the study team made the decision or discussed whether they

659
00:38:17,640 --> 00:38:20,680
Speaker 1: should be pursuing something like that. And can you can

660
00:38:20,680 --> 00:38:23,080
Speaker 1: you explain the process real quick what we're talking about. Yeah, So,

661
00:38:23,160 --> 00:38:26,799
Speaker 1: what what that involves is basically setting up what we

662
00:38:26,840 --> 00:38:32,640
Speaker 1: call hair snare correl. So it's basically barbed wire. Um.

663
00:38:32,680 --> 00:38:36,439
Speaker 1: That's that basically a single or two strands of barbed

664
00:38:36,480 --> 00:38:42,279
Speaker 1: wire that stretched around four corner trees. Um, it's a

665
00:38:42,400 --> 00:38:44,279
Speaker 1: it's a pretty small area. And you know by a

666
00:38:44,320 --> 00:38:49,920
Speaker 1: little fifteen by let's see, now I'm getting my metric

667
00:38:50,000 --> 00:38:54,160
Speaker 1: and english messed up because I'm trying to be So

668
00:38:54,160 --> 00:38:59,719
Speaker 1: it's about five five by five and and so I

669
00:38:59,760 --> 00:39:01,960
Speaker 1: wait with the lure in the middle, and the idea

670
00:39:02,000 --> 00:39:05,320
Speaker 1: is that the bear will be attracted to lure. It

671
00:39:05,400 --> 00:39:08,960
Speaker 1: goes typically under the barbed wire to to get to

672
00:39:09,000 --> 00:39:11,160
Speaker 1: the lure, so that the height of the barbed wire

673
00:39:11,200 --> 00:39:13,280
Speaker 1: is pretty critical. But it goes under the barbed wire,

674
00:39:13,600 --> 00:39:15,440
Speaker 1: leaves a tuft of hair on the barbed wire. You

675
00:39:15,480 --> 00:39:17,879
Speaker 1: can collect that and if anyone who's ever walked along

676
00:39:17,960 --> 00:39:20,319
Speaker 1: barbed wire fence looking at the bottom strand or top

677
00:39:20,320 --> 00:39:21,800
Speaker 1: strand knows that there's a hell of a lot of

678
00:39:21,840 --> 00:39:25,800
Speaker 1: hair exactly. And so it's it's really it's a great technique.

679
00:39:25,800 --> 00:39:28,160
Speaker 1: You know, it's non invasive, it doesn't affect They don't

680
00:39:28,160 --> 00:39:30,520
Speaker 1: even know what happened, They don't even know who do

681
00:39:30,560 --> 00:39:33,839
Speaker 1: you use to bring them in? Blood lure typically mix

682
00:39:33,960 --> 00:39:36,160
Speaker 1: and in some cases people have mixed it up with

683
00:39:36,600 --> 00:39:40,799
Speaker 1: fish remains and and and stuff like that. So it's

684
00:39:40,800 --> 00:39:43,759
Speaker 1: it's a pretty stinky, stinking mess. And that's that's the

685
00:39:43,800 --> 00:39:47,759
Speaker 1: whole point of course. So it attracts uh, just by

686
00:39:47,800 --> 00:39:50,120
Speaker 1: stand it attracts grusy bears from you catch some of

687
00:39:50,160 --> 00:39:53,400
Speaker 1: his hair, Yeah, so we get the hair, the roots

688
00:39:53,640 --> 00:39:58,160
Speaker 1: of the hairs have DNA in him, and that's that's sufficient.

689
00:39:58,200 --> 00:40:00,720
Speaker 1: If if you get you know, five the ten hairs,

690
00:40:00,719 --> 00:40:04,760
Speaker 1: typically that's sufficient to actually get a DNA DNA sample

691
00:40:04,840 --> 00:40:08,239
Speaker 1: and and get a basically a DNA fingerprint of that individual.

692
00:40:08,800 --> 00:40:12,160
Speaker 1: And then the ideas that that you don't can calculate

693
00:40:12,719 --> 00:40:16,440
Speaker 1: not only the the number of unique individuals that have visited,

694
00:40:16,480 --> 00:40:20,080
Speaker 1: but you can also do what we call capture recapture

695
00:40:20,080 --> 00:40:23,719
Speaker 1: analysis where you catch that individual visual ones and then

696
00:40:24,239 --> 00:40:26,279
Speaker 1: how many times do you catch it in in in

697
00:40:26,400 --> 00:40:31,200
Speaker 1: future sampling periods, and that will tell you how how

698
00:40:31,800 --> 00:40:34,279
Speaker 1: effective you are at detecting them. Yeah, Like if you

699
00:40:34,360 --> 00:40:37,400
Speaker 1: catch every bear's hair twenty times, you'd probably get the

700
00:40:37,400 --> 00:40:39,200
Speaker 1: feeling that you're catching most bears. But if you have

701
00:40:39,239 --> 00:40:40,920
Speaker 1: a lot of bears you only caught your hair wants,

702
00:40:40,920 --> 00:40:43,719
Speaker 1: the assumption is probably you're missing some exactly. Yeah, And

703
00:40:43,760 --> 00:40:46,839
Speaker 1: that's and and the techniques are the statistical techniques are

704
00:40:46,880 --> 00:40:50,200
Speaker 1: based on estimating the proportion you're not you're you're you're

705
00:40:50,200 --> 00:40:53,279
Speaker 1: not sampling essentially. Yeah, that's that's the that's the hard part,

706
00:40:53,440 --> 00:40:58,319
Speaker 1: that's right. And so um, the DNA sampling is has

707
00:40:58,440 --> 00:41:01,480
Speaker 1: really been great for a lot of wildlife populations and

708
00:41:01,520 --> 00:41:04,000
Speaker 1: that that was actually invented by by some of my

709
00:41:04,040 --> 00:41:08,320
Speaker 1: colleagues in Canada working on brown Bear. So that's really there.

710
00:41:08,360 --> 00:41:12,440
Speaker 1: The the original idea came from for this DNA sampling,

711
00:41:12,480 --> 00:41:16,239
Speaker 1: and they decided against wildlife management. So why decide against it?

712
00:41:16,560 --> 00:41:18,680
Speaker 1: I mean, well, I don't care. I mean I'm not

713
00:41:18,760 --> 00:41:20,560
Speaker 1: saying like that, like I think it was the wrong decision,

714
00:41:20,600 --> 00:41:23,000
Speaker 1: but like what was the argument. That's uh, you know,

715
00:41:23,000 --> 00:41:26,479
Speaker 1: there was a lot of discussion within the study team

716
00:41:26,520 --> 00:41:29,960
Speaker 1: on that, and we calculated to do something similar to

717
00:41:30,120 --> 00:41:32,640
Speaker 1: what was done in the Northern Contental Divide. It would

718
00:41:32,680 --> 00:41:37,040
Speaker 1: cost probably close to eleven million dollars um and would

719
00:41:37,040 --> 00:41:40,479
Speaker 1: own a lot that's at Yeah, I mean that's that's

720
00:41:40,600 --> 00:41:43,560
Speaker 1: for your agency. That way, beyond any any budgets that

721
00:41:43,560 --> 00:41:48,400
Speaker 1: that we could deal with. It would require something like, um,

722
00:41:48,560 --> 00:41:54,000
Speaker 1: you know, some sort of congressional um funding source to

723
00:41:53,800 --> 00:41:56,920
Speaker 1: to really make that happen. And that's that's like like

724
00:41:57,000 --> 00:42:00,440
Speaker 1: what they did in the Northern Condental Divide, And uh,

725
00:42:00,600 --> 00:42:03,239
Speaker 1: that's I just don't see that as a as a

726
00:42:03,280 --> 00:42:07,839
Speaker 1: reasonable way to move forward. Um. So the costs was

727
00:42:07,840 --> 00:42:10,840
Speaker 1: was a major issue. The logistics of covering such a

728
00:42:10,920 --> 00:42:15,320
Speaker 1: large ecosystem were a major Asian because the area here, No,

729
00:42:16,360 --> 00:42:19,000
Speaker 1: just as we talked about this, so any part of this,

730
00:42:19,000 --> 00:42:21,400
Speaker 1: I'm wrong, Grizzly Bears and the Lower Ford the are

731
00:42:21,440 --> 00:42:27,279
Speaker 1: divided into six distinct population segments, Yeah, of which five

732
00:42:27,360 --> 00:42:31,120
Speaker 1: half bears one exactly. Yeah, yeah, one of these distinct populations.

733
00:42:31,160 --> 00:42:34,280
Speaker 1: So you have Northern Cascades. Yeah, we got Northern Cascades

734
00:42:34,360 --> 00:42:38,319
Speaker 1: has some small number bears or not, who knows how many,

735
00:42:38,680 --> 00:42:43,000
Speaker 1: probably fewer than maybe fewer than six. But that's that's

736
00:42:43,239 --> 00:42:46,520
Speaker 1: they're flirting with the border in BC exactly. And then

737
00:42:46,680 --> 00:42:54,160
Speaker 1: you have um east central Idaho. Sel Kirk's referring to

738
00:42:55,280 --> 00:42:59,839
Speaker 1: is that No, I guess are you talking about half?

739
00:43:00,000 --> 00:43:02,560
Speaker 1: What's the one that the Bitter Selway is not does

740
00:43:02,600 --> 00:43:06,480
Speaker 1: not have the correct but it's regarded as a potential location. Yeah,

741
00:43:06,520 --> 00:43:11,600
Speaker 1: and there were um uh reintroduction plans back in the

742
00:43:11,719 --> 00:43:16,640
Speaker 1: early to late nineties, early two thousands that eventually we're

743
00:43:16,680 --> 00:43:21,880
Speaker 1: not implemented, um, but they're gonna wind up there. Well,

744
00:43:22,280 --> 00:43:25,200
Speaker 1: things are, I mean, things are looking as good now

745
00:43:25,239 --> 00:43:27,319
Speaker 1: as as they have in a long time for for

746
00:43:27,440 --> 00:43:31,920
Speaker 1: bears to actually get there, um, but the reality is

747
00:43:31,960 --> 00:43:36,040
Speaker 1: that it's it's gonna take quite a bit of time. Uh.

748
00:43:36,040 --> 00:43:37,920
Speaker 1: And the first bears that would get there are probably

749
00:43:37,920 --> 00:43:42,240
Speaker 1: gonna be males only, and so for for females to actually,

750
00:43:43,000 --> 00:43:45,959
Speaker 1: you know, actually make it down there, that's it's it's

751
00:43:46,000 --> 00:43:51,360
Speaker 1: still probably gonna need some uh management and that so

752
00:43:51,480 --> 00:43:54,120
Speaker 1: that means translocating animals. And then that's for the so

753
00:43:54,239 --> 00:43:57,320
Speaker 1: seeable future. I don't see that happened in the ecosystem.

754
00:43:57,400 --> 00:44:01,680
Speaker 1: So there's one distinct populations segment in Washington when we're

755
00:44:01,719 --> 00:44:04,040
Speaker 1: just talking about that does not have bears is Idaho

756
00:44:04,400 --> 00:44:08,879
Speaker 1: Montana Northern Continental Divide yep IS, which is by all

757
00:44:08,920 --> 00:44:15,400
Speaker 1: means a healthy population. Bob, that's Bob Marshall scapegoat Glacier

758
00:44:15,480 --> 00:44:18,640
Speaker 1: National Park is really kind of the core for for

759
00:44:18,719 --> 00:44:22,440
Speaker 1: that area. And then the Greater yelso On ecosystem Idaho, Montana, Wyoming.

760
00:44:23,719 --> 00:44:27,400
Speaker 1: And then we have Cabinet Jack ecosystem which also has bears,

761
00:44:27,520 --> 00:44:30,799
Speaker 1: which also has bears. About forty is the best. That's

762
00:44:30,800 --> 00:44:36,120
Speaker 1: extreme Northwest Montana and Idaho, Idaho pay Ana, Extreme Northwest Montana. Yeah,

763
00:44:36,400 --> 00:44:41,600
Speaker 1: and you know there's reachers, fishing models. Sers are doing

764
00:44:41,600 --> 00:44:43,040
Speaker 1: a lot of research there and they have a good

765
00:44:43,040 --> 00:44:46,640
Speaker 1: handle on on the population UM and some recent DNA

766
00:44:46,719 --> 00:44:50,120
Speaker 1: work there confirmed that that that population is around forty

767
00:44:50,719 --> 00:44:53,799
Speaker 1: or so animals. So when you were looking at doing

768
00:44:53,840 --> 00:44:56,840
Speaker 1: the hair trapping count, was it just that your area

769
00:44:56,920 --> 00:44:58,920
Speaker 1: was so much bigger than the north Is it bigger

770
00:44:58,960 --> 00:45:02,880
Speaker 1: than the Northern Continental to wide area? Um? You know,

771
00:45:03,000 --> 00:45:06,279
Speaker 1: it's that currently the distribution is is a little bit

772
00:45:06,560 --> 00:45:12,200
Speaker 1: more I think UM it's it's also I think parts

773
00:45:12,239 --> 00:45:15,759
Speaker 1: of it are are more inaccessible. There's there's probably I mean,

774
00:45:16,000 --> 00:45:18,200
Speaker 1: I know we have the Bob Marshall Wilderness in northern

775
00:45:18,280 --> 00:45:21,160
Speaker 1: Continental the Vibe, but we've there's a lot of wilderness

776
00:45:21,239 --> 00:45:26,240
Speaker 1: areas on the eastern portion of of Yellowstone, So, uh cossibility.

777
00:45:27,480 --> 00:45:31,680
Speaker 1: The logistics is a big part of it. Um. The

778
00:45:31,680 --> 00:45:35,000
Speaker 1: The other issue that that led the team to decide

779
00:45:35,000 --> 00:45:37,799
Speaker 1: against it was that that it only gives you a

780
00:45:37,880 --> 00:45:40,520
Speaker 1: single estimate, you know, it just gives you an estimate

781
00:45:40,560 --> 00:45:44,160
Speaker 1: for one year. And the feeling was is that what

782
00:45:44,280 --> 00:45:47,759
Speaker 1: the team was already doing was was actually sufficient to

783
00:45:47,840 --> 00:45:50,000
Speaker 1: keep track off of the population. What do they call

784
00:45:50,080 --> 00:45:52,560
Speaker 1: the system you do use? Well, the so that the

785
00:45:53,160 --> 00:45:56,640
Speaker 1: one method that we rely on primarily is something that

786
00:45:56,680 --> 00:45:58,839
Speaker 1: we referred to as the chowd To method. It's it's

787
00:45:58,840 --> 00:46:02,520
Speaker 1: basically it's bay on those sides, chow too. It's a

788
00:46:02,960 --> 00:46:08,080
Speaker 1: it's the the chow refers to the researchers, the statistication

789
00:46:08,160 --> 00:46:13,640
Speaker 1: that who's who, whose techniques are behind some of these

790
00:46:13,960 --> 00:46:17,840
Speaker 1: the statistical techniques that we use, And so that technique

791
00:46:17,840 --> 00:46:23,759
Speaker 1: is based on on identifying unique females with cubs UM.

792
00:46:23,920 --> 00:46:26,680
Speaker 1: Then we have another one that's that we developed UM

793
00:46:27,160 --> 00:46:30,480
Speaker 1: to address the issue with an underestimation bias, and that's

794
00:46:30,719 --> 00:46:34,120
Speaker 1: what we call a mark Reside technique where we're actually

795
00:46:34,200 --> 00:46:39,080
Speaker 1: doing aerial basically systematic aerial surveys twice a year for

796
00:46:39,200 --> 00:46:42,840
Speaker 1: all the areas in the ecosystem and and try to

797
00:46:43,760 --> 00:46:47,040
Speaker 1: observe females with cubs. Again, is still based on females

798
00:46:47,080 --> 00:46:49,480
Speaker 1: with cubs. What's a good time a year to do that?

799
00:46:49,480 --> 00:46:53,640
Speaker 1: That's basically summer UM meant to mintilate summers when when

800
00:46:53,680 --> 00:46:57,160
Speaker 1: we do those surveys that got hit and miss and

801
00:46:57,440 --> 00:47:00,319
Speaker 1: it is and so that technique is bay based on

802
00:47:00,560 --> 00:47:04,480
Speaker 1: observing females with with radio colors and and it is

803
00:47:04,520 --> 00:47:06,960
Speaker 1: a little bit mitten hidden miss and so that the

804
00:47:06,960 --> 00:47:10,320
Speaker 1: problem is that on an annual basis, are sample sizes

805
00:47:10,360 --> 00:47:14,960
Speaker 1: are not really large and so the estimate is more

806
00:47:15,040 --> 00:47:19,399
Speaker 1: accurate UM, so it's it's more on target, but it's

807
00:47:19,440 --> 00:47:23,040
Speaker 1: not very precise, so you can have a confidence interval

808
00:47:23,160 --> 00:47:28,080
Speaker 1: it's way bigger than than any manager would want to use.

809
00:47:28,280 --> 00:47:32,279
Speaker 1: So it makes it really difficult to look at a

810
00:47:32,360 --> 00:47:34,959
Speaker 1: trend detection over time. And that's that's what that child

811
00:47:35,040 --> 00:47:37,600
Speaker 1: to estimator that does a better job. And so for

812
00:47:37,680 --> 00:47:40,440
Speaker 1: the time being, we're sticking with that child to estimated,

813
00:47:40,520 --> 00:47:43,080
Speaker 1: but we're still exploring ways to No one would say

814
00:47:43,200 --> 00:47:46,960
Speaker 1: that you potentially know about every female with cups, Like

815
00:47:47,080 --> 00:47:49,000
Speaker 1: if I went out and saw a female with cups, someone,

816
00:47:49,080 --> 00:47:51,279
Speaker 1: oh no, Okay, So no one's gonna say like, oh, yeah,

817
00:47:51,320 --> 00:47:54,000
Speaker 1: we know about a lot of them because they're very

818
00:47:54,040 --> 00:47:57,440
Speaker 1: you know, they people reporting to us. You know, we

819
00:47:57,520 --> 00:47:59,960
Speaker 1: have a lot of agency people out there. We observe

820
00:48:00,200 --> 00:48:04,160
Speaker 1: from our from our aerial surveys, so we do get

821
00:48:04,800 --> 00:48:09,200
Speaker 1: sightings off of the vast majority, I think, but we

822
00:48:09,200 --> 00:48:11,960
Speaker 1: we do use a statistical estimator to again estimate how

823
00:48:11,960 --> 00:48:14,560
Speaker 1: many we have not observed, but that that proportion is

824
00:48:14,640 --> 00:48:17,239
Speaker 1: is relatively small. We do observe a lot of them,

825
00:48:17,239 --> 00:48:19,520
Speaker 1: but we miss We miss a lot too. So what

826
00:48:19,680 --> 00:48:21,439
Speaker 1: is the guess, like, what's the percentage that you guys

827
00:48:21,480 --> 00:48:26,200
Speaker 1: think you're missing? Well, if yeah, and that's that actually

828
00:48:26,600 --> 00:48:29,920
Speaker 1: matches with when we do that mark recite techniques again

829
00:48:29,960 --> 00:48:33,160
Speaker 1: that's still based on females with cubs um, the total

830
00:48:33,239 --> 00:48:38,279
Speaker 1: number would actually almost double from from what we typically

831
00:48:38,320 --> 00:48:40,480
Speaker 1: have with that that out technique that the child to

832
00:48:40,719 --> 00:48:45,200
Speaker 1: estimators so um, not not quite double, but but it

833
00:48:45,239 --> 00:48:48,000
Speaker 1: would put us in the range of of ninety two

834
00:48:49,000 --> 00:48:52,560
Speaker 1: uh females with cubs for this past year, for example.

835
00:48:53,000 --> 00:48:56,120
Speaker 1: And if you extrapolate that out, you you easily come

836
00:48:56,480 --> 00:48:59,320
Speaker 1: come at a total population estimate well over a thousand

837
00:48:59,440 --> 00:49:04,480
Speaker 1: over over eleven under that actually, and so the real

838
00:49:04,640 --> 00:49:08,560
Speaker 1: estimate for the population is is certainly much higher than

839
00:49:08,760 --> 00:49:11,960
Speaker 1: what what our official estimate of six. So if you

840
00:49:12,160 --> 00:49:17,880
Speaker 1: absolutely had to say right under like this is the

841
00:49:18,040 --> 00:49:20,960
Speaker 1: imagine the most catastrophic thing that could happen to you

842
00:49:21,520 --> 00:49:25,640
Speaker 1: if you got the number wrong, okay, involving death and

843
00:49:25,719 --> 00:49:28,319
Speaker 1: injury and all every bad thing in the world, and

844
00:49:28,360 --> 00:49:32,720
Speaker 1: you had to say a number, what would you say

845
00:49:33,080 --> 00:49:35,319
Speaker 1: or just are you not comfortable saying because you just

846
00:49:35,360 --> 00:49:38,239
Speaker 1: don't know? Well, yeah, and we I would have to

847
00:49:38,280 --> 00:49:43,520
Speaker 1: still say that stick with the six nine and and

848
00:49:43,520 --> 00:49:48,480
Speaker 1: and with the caveat that that we know that that

849
00:49:48,680 --> 00:49:53,520
Speaker 1: is a suddenly by all means uh going to be

850
00:49:53,520 --> 00:49:55,640
Speaker 1: an underestimate. I'm not trying to be leading here, but

851
00:49:55,760 --> 00:49:58,680
Speaker 1: you know, you know it's not less could you be

852
00:49:58,719 --> 00:50:00,480
Speaker 1: could is there a way you're wrong? And you have

853
00:50:00,520 --> 00:50:04,319
Speaker 1: too many there there's always uncertainty with with any of

854
00:50:04,360 --> 00:50:08,439
Speaker 1: these these type of data. Um, there is always yes,

855
00:50:08,719 --> 00:50:10,400
Speaker 1: I think it's it's fair to say that there is

856
00:50:10,440 --> 00:50:15,799
Speaker 1: always a statistical probability that that is below six. But

857
00:50:16,200 --> 00:50:19,960
Speaker 1: that's not as strong as that it's above six exactly so,

858
00:50:20,000 --> 00:50:23,000
Speaker 1: because everything else is is pointing at that. And and

859
00:50:23,040 --> 00:50:26,520
Speaker 1: this is the this is the difficult situation that we're in,

860
00:50:26,560 --> 00:50:28,359
Speaker 1: you know, with with our science. We we have a

861
00:50:28,400 --> 00:50:32,960
Speaker 1: technique that that the managers still want to use and

862
00:50:32,960 --> 00:50:36,279
Speaker 1: and is being used in how we move forward with

863
00:50:36,360 --> 00:50:39,320
Speaker 1: the de listing, how the how the agencies move forward

864
00:50:39,320 --> 00:50:42,279
Speaker 1: with the de listing that is now UH language in

865
00:50:42,600 --> 00:50:44,960
Speaker 1: what it's called the conservation strategy, which will be the

866
00:50:45,040 --> 00:50:49,720
Speaker 1: guiding document after UH delisting if if that goes through.

867
00:50:50,400 --> 00:50:53,560
Speaker 1: And and so the managers have chosen to to stick

868
00:50:53,640 --> 00:50:58,560
Speaker 1: with this conservative estimator. Um that does not keep us,

869
00:50:58,600 --> 00:51:01,000
Speaker 1: of course from trying to them up with with a

870
00:51:01,080 --> 00:51:04,279
Speaker 1: better estimator in the future. And uh and and for me,

871
00:51:04,440 --> 00:51:07,239
Speaker 1: you know, that's as a scientist, I we have to

872
00:51:07,640 --> 00:51:09,600
Speaker 1: we have to have that desire to do better than

873
00:51:09,640 --> 00:51:12,279
Speaker 1: what we have now. You can always read a journalist.

874
00:51:12,480 --> 00:51:14,880
Speaker 1: You can always tell where a journalist stands on the

875
00:51:14,920 --> 00:51:20,160
Speaker 1: issues because if the journalists fails to fails to mention

876
00:51:20,200 --> 00:51:24,239
Speaker 1: the caveat that there's likely more, you know that they

877
00:51:24,239 --> 00:51:27,280
Speaker 1: have a vested interest in there being not that many.

878
00:51:27,520 --> 00:51:31,279
Speaker 1: If the journalists mentions likely more, then you know where

879
00:51:31,280 --> 00:51:34,719
Speaker 1: they stand politically. Which is a funny. It's a trick

880
00:51:34,800 --> 00:51:38,799
Speaker 1: you can use sure now. And so the one thing

881
00:51:38,840 --> 00:51:40,959
Speaker 1: that's important with that too, you know, you know we

882
00:51:40,960 --> 00:51:43,520
Speaker 1: we we have that that number of six ninety, right,

883
00:51:43,800 --> 00:51:47,279
Speaker 1: and then we have a confidence in all around that

884
00:51:47,280 --> 00:51:51,160
Speaker 1: that's a statistical estimate of how confident we are in

885
00:51:51,200 --> 00:51:54,240
Speaker 1: those numbers. So that number is is plus or minus

886
00:51:54,280 --> 00:51:58,000
Speaker 1: seventy five bears and some so some people will will

887
00:51:58,040 --> 00:52:00,479
Speaker 1: take that number and then say, so it's it's really

888
00:52:00,480 --> 00:52:03,239
Speaker 1: not six ninety, but it's six ninety minus seventy five,

889
00:52:03,640 --> 00:52:06,279
Speaker 1: and that's that's not right. You know that that all

890
00:52:06,360 --> 00:52:09,160
Speaker 1: these estimates that it's the central tendency of the data

891
00:52:09,280 --> 00:52:12,360
Speaker 1: would lead us to to six ninety more than it

892
00:52:12,360 --> 00:52:14,080
Speaker 1: would lead us to the lower end of that or

893
00:52:14,120 --> 00:52:17,960
Speaker 1: the higher end. Now, the only added thing here is

894
00:52:17,960 --> 00:52:20,880
Speaker 1: that we know, we've demonstrated in the past with simulation

895
00:52:20,920 --> 00:52:24,480
Speaker 1: studies that that that six ninety is an underestimate, that

896
00:52:24,480 --> 00:52:28,560
Speaker 1: that most likely it's it's much closer to two, probably

897
00:52:28,560 --> 00:52:32,720
Speaker 1: over a thousand individuals, and and so I'm totally confident

898
00:52:33,040 --> 00:52:38,280
Speaker 1: saying that. But but it also makes it very confusing

899
00:52:38,320 --> 00:52:40,840
Speaker 1: to the public, of course, you know, because we we

900
00:52:40,880 --> 00:52:43,480
Speaker 1: have an official estimate of six ninety, but we we

901
00:52:43,680 --> 00:52:45,480
Speaker 1: at the same time we keep saying, well, we know

902
00:52:45,600 --> 00:52:48,520
Speaker 1: that's that's an underestimate and that kind of stuff. Yeah,

903
00:52:48,600 --> 00:52:50,640
Speaker 1: they want to know how many bears. Yeah, and it's like, like,

904
00:52:50,680 --> 00:52:55,320
Speaker 1: that's the first thing I ask you. And so it's

905
00:52:55,400 --> 00:52:58,319
Speaker 1: science is messy, you know, and uh and that that's uh,

906
00:52:58,400 --> 00:53:01,160
Speaker 1: that that's frustrating for us as well. You know, we

907
00:53:01,160 --> 00:53:04,719
Speaker 1: we would like to have the perfect estimator, but you

908
00:53:04,719 --> 00:53:08,080
Speaker 1: know that just doesn't exist. What was your question, Um,

909
00:53:08,239 --> 00:53:11,279
Speaker 1: I was just wondering you'd have to know some populations,

910
00:53:11,320 --> 00:53:14,000
Speaker 1: like the park populations a lot better than I don't know.

911
00:53:14,080 --> 00:53:16,560
Speaker 1: This is just my guess as someone who doesn't know,

912
00:53:17,600 --> 00:53:19,960
Speaker 1: But would you know those park populations a lot better

913
00:53:20,000 --> 00:53:23,120
Speaker 1: than like the Madison populations and the centennial populations and

914
00:53:23,160 --> 00:53:26,120
Speaker 1: the Absorka No, not necessarily. Actually, that's that's one thing

915
00:53:26,600 --> 00:53:29,399
Speaker 1: that that I really like about how we've structured our

916
00:53:29,440 --> 00:53:35,000
Speaker 1: our research and our sampling. Um, there's really the effort

917
00:53:35,080 --> 00:53:39,480
Speaker 1: is pretty equal throughout the entire ecosystem. So it's pretty

918
00:53:39,480 --> 00:53:41,600
Speaker 1: well distributed. And it's not like that. We have a

919
00:53:41,680 --> 00:53:45,000
Speaker 1: much better handle on bare numbers for Yellstone National Park.

920
00:53:45,120 --> 00:53:47,759
Speaker 1: And in fact, we when we're asked, you know, we

921
00:53:47,760 --> 00:53:50,040
Speaker 1: were often are asking you what is their actual population

922
00:53:50,160 --> 00:53:53,120
Speaker 1: estimate for Yellowstone or for Dish National Fourth. So we

923
00:53:53,120 --> 00:53:55,600
Speaker 1: we don't provide those because we actually don't have those.

924
00:53:55,600 --> 00:53:59,640
Speaker 1: We have them truly as as a as an ecosystem number.

925
00:54:00,239 --> 00:54:03,520
Speaker 1: And uh, and that's because these these animals, you know,

926
00:54:03,600 --> 00:54:07,000
Speaker 1: they cross boundaries all the time, and so you can't

927
00:54:07,040 --> 00:54:10,240
Speaker 1: there there is really no such thing as as strictly

928
00:54:11,280 --> 00:54:16,640
Speaker 1: Yellstone National Park population. You know, they a lot of

929
00:54:16,640 --> 00:54:20,520
Speaker 1: those bears do cross the boundaries. You mentioned earlier that

930
00:54:21,200 --> 00:54:23,919
Speaker 1: the drop you've seen in the last couple of years

931
00:54:24,000 --> 00:54:26,840
Speaker 1: is that just part of a normal rise and fall

932
00:54:27,280 --> 00:54:30,839
Speaker 1: or is that is that linked to some occurrence or yeah,

933
00:54:30,920 --> 00:54:34,600
Speaker 1: So for you know, and and and you know, additional

934
00:54:34,680 --> 00:54:37,719
Speaker 1: years of data will will will tell us. But um,

935
00:54:38,120 --> 00:54:41,920
Speaker 1: you know, so some people have have argued that since um,

936
00:54:42,000 --> 00:54:46,320
Speaker 1: you know two years ago was seven fifty seven, last

937
00:54:46,719 --> 00:54:49,680
Speaker 1: last year was seven seventeen and now it's six ninety.

938
00:54:49,719 --> 00:54:51,640
Speaker 1: And some people would argue that we're in the decline,

939
00:54:51,680 --> 00:54:55,840
Speaker 1: but we don't look at at at trends and grizzly

940
00:54:55,880 --> 00:54:58,879
Speaker 1: bear populations over short time periods like that. You really

941
00:54:58,880 --> 00:55:00,399
Speaker 1: have to look at it over a long the time piece.

942
00:55:00,480 --> 00:55:02,520
Speaker 1: I mean, they can live up to thirty years. You know,

943
00:55:02,560 --> 00:55:06,799
Speaker 1: their generation time is um close to fourteen years now,

944
00:55:06,920 --> 00:55:10,120
Speaker 1: So to look at it on a on a three

945
00:55:10,200 --> 00:55:13,879
Speaker 1: year time frame is potentially dangerous because you can kind

946
00:55:13,880 --> 00:55:15,759
Speaker 1: of overreact. So you have to really look at a

947
00:55:15,760 --> 00:55:18,960
Speaker 1: longer time frame. If you look at at the variation

948
00:55:19,080 --> 00:55:22,440
Speaker 1: of that estimate since the early two thousand's um, this

949
00:55:22,560 --> 00:55:26,040
Speaker 1: still fits within that that that realm of variation that

950
00:55:26,120 --> 00:55:29,120
Speaker 1: we have observed in the past, and by all means,

951
00:55:29,120 --> 00:55:31,600
Speaker 1: all the all the data are are indicating that that

952
00:55:31,680 --> 00:55:35,640
Speaker 1: the population has has basically remained pretty constant since the

953
00:55:35,640 --> 00:55:40,560
Speaker 1: early two thousand's after after several decades of increase. What

954
00:55:40,600 --> 00:55:45,160
Speaker 1: were the what factors this is the north You can't

955
00:55:45,200 --> 00:55:48,759
Speaker 1: answer in a definitive way, but what factors allowed there

956
00:55:50,360 --> 00:55:55,799
Speaker 1: in nineties that have two nine to having you know

957
00:55:55,880 --> 00:55:59,840
Speaker 1: the six nine? Like, what factors are are are most

958
00:56:00,120 --> 00:56:04,280
Speaker 1: Is it most safe to say like thanks to blank measure,

959
00:56:04,760 --> 00:56:06,440
Speaker 1: the bears were able to increase or do you think

960
00:56:06,480 --> 00:56:10,440
Speaker 1: that would have happened outside of federal influence or did

961
00:56:10,440 --> 00:56:13,640
Speaker 1: it have to do with ESA protections or or if

962
00:56:13,680 --> 00:56:15,640
Speaker 1: you can't answer in that way, how would you answer

963
00:56:15,680 --> 00:56:18,400
Speaker 1: that question? Well, I think, um, I think E s

964
00:56:18,440 --> 00:56:21,719
Speaker 1: A protections have helped a lot. Yeah, um it led

965
00:56:21,760 --> 00:56:24,920
Speaker 1: to UM. Well, so there's a number of factors. I

966
00:56:24,960 --> 00:56:27,319
Speaker 1: think the fact that there was an independent study team

967
00:56:27,360 --> 00:56:30,320
Speaker 1: that collected all the data and had all the information

968
00:56:30,360 --> 00:56:34,479
Speaker 1: to to give managers that you know, good scientific data

969
00:56:34,520 --> 00:56:37,160
Speaker 1: to make decisions on the then like that in and

970
00:56:37,200 --> 00:56:39,800
Speaker 1: of itself is helpful, is very helpful. I think. Yeah.

971
00:56:39,960 --> 00:56:42,200
Speaker 1: Then the establishment of the inter Agency Greasy to be

972
00:56:42,200 --> 00:56:46,080
Speaker 1: a committee, which is a policy group that is informed

973
00:56:46,080 --> 00:56:49,080
Speaker 1: by our science. They make decisions based on our science

974
00:56:49,760 --> 00:56:53,920
Speaker 1: UM that was established in the early nineteen eighties and

975
00:56:54,040 --> 00:56:58,799
Speaker 1: they these were this committee still exists, UM deals with

976
00:56:58,840 --> 00:57:02,360
Speaker 1: all the local forty eight populations. But they exist of

977
00:57:02,520 --> 00:57:06,160
Speaker 1: high lanking, high ranking officials and they are the type

978
00:57:06,200 --> 00:57:08,680
Speaker 1: of people that can make changes on the ground. You know,

979
00:57:08,719 --> 00:57:10,919
Speaker 1: they can direct a national force or a national park

980
00:57:11,000 --> 00:57:14,000
Speaker 1: to do this and that to help gristly bear conservation.

981
00:57:14,040 --> 00:57:17,360
Speaker 1: And that's actually exactly what happened. Would be an example

982
00:57:17,360 --> 00:57:19,360
Speaker 1: of something they would ask, Yeah, good example. So that

983
00:57:19,440 --> 00:57:21,480
Speaker 1: the one thing that the study team identified at the

984
00:57:21,520 --> 00:57:25,160
Speaker 1: time was that that adult female survival, which is the

985
00:57:25,280 --> 00:57:30,000
Speaker 1: driving engine of of any bear population um was too low.

986
00:57:30,200 --> 00:57:32,919
Speaker 1: How did you define an adult female breeding age? Yes,

987
00:57:33,080 --> 00:57:38,000
Speaker 1: like there's actually viable and sexually mature um typically at

988
00:57:38,520 --> 00:57:42,080
Speaker 1: starting at four or five years. And really yeah, so

989
00:57:42,360 --> 00:57:46,520
Speaker 1: you know, they have relatively low reproduction because of first

990
00:57:46,560 --> 00:57:49,440
Speaker 1: of all, that there's a three year reproductive cycle, and

991
00:57:49,440 --> 00:57:52,840
Speaker 1: and they don't produce their first leader of cubs typically

992
00:57:52,880 --> 00:57:56,520
Speaker 1: on average around age five point eight. Actually, so meanwhile,

993
00:57:56,560 --> 00:57:59,479
Speaker 1: wait till deer were cranked out eight Yeah, so that

994
00:57:59,480 --> 00:58:02,400
Speaker 1: that's that that's there's a big difference there when black

995
00:58:02,400 --> 00:58:05,320
Speaker 1: bears are only two years, right, that's correct. Yeah, yeah,

996
00:58:05,360 --> 00:58:09,880
Speaker 1: so that's why black bear populations can when their numbers

997
00:58:09,880 --> 00:58:13,040
Speaker 1: are down, they can recover from from that quicker than

998
00:58:13,120 --> 00:58:17,320
Speaker 1: than grizzly bear population. That's that's just pure demographic differences.

999
00:58:17,600 --> 00:58:20,560
Speaker 1: So you identify the importance of those breeding age females, right,

1000
00:58:20,840 --> 00:58:23,480
Speaker 1: and and because the mortality of those was was really

1001
00:58:24,160 --> 00:58:27,680
Speaker 1: um too high for for sustainable levels. So what was

1002
00:58:27,720 --> 00:58:31,320
Speaker 1: causing the mortality? Well, um, in those early days, some

1003
00:58:31,400 --> 00:58:35,000
Speaker 1: of it was poaching um that that was in conflicts

1004
00:58:35,040 --> 00:58:37,640
Speaker 1: with with live stock. So one of the things that

1005
00:58:37,680 --> 00:58:41,560
Speaker 1: the Interagent Greasy Bear Committee did was to start closing

1006
00:58:41,560 --> 00:58:47,080
Speaker 1: down livestock allotments within the recovery zone and uh and

1007
00:58:47,160 --> 00:58:51,360
Speaker 1: start to deal with bears accessing garbage you know, so

1008
00:58:51,720 --> 00:58:55,800
Speaker 1: bear proof dumpsters, baarproof garbage cans. All that started in

1009
00:58:55,800 --> 00:58:59,760
Speaker 1: in those early days. The limit conflict, to limit conflict,

1010
00:59:00,120 --> 00:59:04,520
Speaker 1: and and I think those are the type of actions forcers. UM.

1011
00:59:04,920 --> 00:59:08,040
Speaker 1: You know, put a lot of effort into closing roads

1012
00:59:08,120 --> 00:59:12,320
Speaker 1: because we know that that road access typically means lower

1013
00:59:12,360 --> 00:59:15,840
Speaker 1: survival of of grizzly bears. Um. That's that's that's just

1014
00:59:15,920 --> 00:59:19,720
Speaker 1: a given like road creates a higher likelihood that that

1015
00:59:19,760 --> 00:59:22,320
Speaker 1: bear is gonna wind up mixing it up exactly the

1016
00:59:22,360 --> 00:59:23,800
Speaker 1: person and the bear is gonna be the one that

1017
00:59:23,880 --> 00:59:26,360
Speaker 1: ends up dead. Yeah, and so it's not necessarily a

1018
00:59:26,440 --> 00:59:31,440
Speaker 1: road kill situation, but it's it's just access into grizzly

1019
00:59:31,480 --> 00:59:35,520
Speaker 1: bear habitat by humans tends to reduce survival because of poaching.

1020
00:59:36,000 --> 00:59:39,080
Speaker 1: Higher likelihood of poaching or conflicts and things like that.

1021
00:59:39,200 --> 00:59:44,200
Speaker 1: So UM, closing down roads, UM and reducing road densities

1022
00:59:44,360 --> 00:59:47,439
Speaker 1: was was another big aspect of this. So all those

1023
00:59:47,480 --> 00:59:51,520
Speaker 1: actions combined, UM, you know, there's no hard data to

1024
00:59:51,560 --> 00:59:54,320
Speaker 1: show a direct cause and effect here, but there's no

1025
00:59:54,360 --> 00:59:58,480
Speaker 1: doubt in my mind that all those actions really made

1026
00:59:58,480 --> 01:00:01,560
Speaker 1: a difference. And that's what helped start to recovery of

1027
01:00:01,600 --> 01:00:07,200
Speaker 1: the population, which interestingly, after after listing in, the population

1028
01:00:07,240 --> 01:00:10,480
Speaker 1: still kept declining because some of those actions had not

1029
01:00:10,720 --> 01:00:14,520
Speaker 1: implemented been implemented at that time, didn't really start until

1030
01:00:15,000 --> 01:00:17,800
Speaker 1: the early eighties, mid eighties that they started to implement

1031
01:00:17,880 --> 01:00:20,600
Speaker 1: those and sure enough, you know, we saw the populations

1032
01:00:20,600 --> 01:00:23,800
Speaker 1: started to pick back up in the mid eighties, late eighties,

1033
01:00:23,840 --> 01:00:28,720
Speaker 1: and then started increasing through the very rigorous growth through

1034
01:00:28,720 --> 01:00:32,480
Speaker 1: the nineties, and then started leveling off in the early

1035
01:00:32,520 --> 01:00:36,160
Speaker 1: two thousand's, And some of our research recent research has

1036
01:00:37,200 --> 01:00:41,240
Speaker 1: indicated that that might simply be UM a result of

1037
01:00:41,240 --> 01:00:44,800
Speaker 1: of bears kind of reach, reaching social carrying capacity within

1038
01:00:44,840 --> 01:00:46,760
Speaker 1: their own population. I don't want to ask about, but

1039
01:00:46,800 --> 01:00:49,680
Speaker 1: just for a little background for people, So at the

1040
01:00:49,720 --> 01:00:52,800
Speaker 1: time of listening, they didn't sketch out what recovery would

1041
01:00:52,800 --> 01:00:55,440
Speaker 1: look like, if I understand right, Well, other than setting

1042
01:00:55,520 --> 01:00:59,880
Speaker 1: some recovery criterion like a minimum of five hundred crazy

1043
01:01:00,080 --> 01:01:02,880
Speaker 1: as for example, within the Yellstone ecosystem, or that at

1044
01:01:02,920 --> 01:01:04,760
Speaker 1: the time, I thought it only came later once the

1045
01:01:04,800 --> 01:01:09,560
Speaker 1: distinct population segments came into existence. Well, um, the numbers

1046
01:01:09,560 --> 01:01:13,120
Speaker 1: have changed over time. Um, but what recovery might look

1047
01:01:13,160 --> 01:01:15,360
Speaker 1: like has changed over time a little bit, you know.

1048
01:01:15,440 --> 01:01:18,360
Speaker 1: And the the the initial numbers um in the eighties, and

1049
01:01:18,960 --> 01:01:23,680
Speaker 1: we're a little bit different than the lay. The one

1050
01:01:23,880 --> 01:01:27,120
Speaker 1: was a revision and the supplement to the recovery plan,

1051
01:01:27,840 --> 01:01:32,280
Speaker 1: and that's where some of the the recovery criterion were

1052
01:01:32,320 --> 01:01:36,240
Speaker 1: ultimately based on. So one was related to population size,

1053
01:01:36,280 --> 01:01:39,040
Speaker 1: one was related to sustainable motelity limits, and one was

1054
01:01:39,240 --> 01:01:43,600
Speaker 1: one recovery criterion was related to occupancy of reproductive females,

1055
01:01:44,240 --> 01:01:46,760
Speaker 1: so not just females with cups and also females with

1056
01:01:46,840 --> 01:01:51,440
Speaker 1: dealings or two year olds. And they've met the bears

1057
01:01:51,440 --> 01:01:57,000
Speaker 1: in the Greater Yellstone have been at what's been defined

1058
01:01:57,160 --> 01:01:59,479
Speaker 1: as recovery levels. They've been at that for a number

1059
01:01:59,480 --> 01:02:01,840
Speaker 1: of years, right, yes, how long has that been? Basically

1060
01:02:01,840 --> 01:02:04,920
Speaker 1: since the early two thousand's they had early two thousands.

1061
01:02:04,960 --> 01:02:08,440
Speaker 1: They have been at at they've met all those criteria.

1062
01:02:08,640 --> 01:02:11,360
Speaker 1: And what was the first attempt that the US Fish

1063
01:02:11,400 --> 01:02:15,240
Speaker 1: and Wildlife Service made were they first proposed the listing

1064
01:02:15,280 --> 01:02:20,040
Speaker 1: was two thousand seven, That's that's correct. Yes, So that time, um,

1065
01:02:20,080 --> 01:02:23,560
Speaker 1: when they did their findings, when people sort of reviewed

1066
01:02:23,600 --> 01:02:29,240
Speaker 1: all the available data, some some suggested that they had

1067
01:02:29,280 --> 01:02:32,840
Speaker 1: not accounted for I think it was two things at

1068
01:02:32,840 --> 01:02:37,000
Speaker 1: the time. One was cut throat trout and then the

1069
01:02:37,320 --> 01:02:39,920
Speaker 1: white park pine. Academic hadn't happened yet, right or was

1070
01:02:39,960 --> 01:02:41,880
Speaker 1: it going on? It was going on going on. Yeah,

1071
01:02:41,920 --> 01:02:45,520
Speaker 1: so that was actually the bigger one, the white buck pine.

1072
01:02:45,560 --> 01:02:48,160
Speaker 1: They change, you know, changing the food resources in general,

1073
01:02:48,200 --> 01:02:50,920
Speaker 1: but especially white buck pine. Let's touch on the trout thing,

1074
01:02:50,920 --> 01:02:52,760
Speaker 1: because that always, to me has felt a little bit

1075
01:02:52,800 --> 01:02:59,560
Speaker 1: like bs it like that cannot have been enough of

1076
01:02:59,560 --> 01:03:02,360
Speaker 1: a rest source to be what was what was propping

1077
01:03:02,480 --> 01:03:06,320
Speaker 1: up grizzly bears and the lower forty eight was eating

1078
01:03:06,360 --> 01:03:09,040
Speaker 1: spawning cut throats. I'll never accept that that could have been.

1079
01:03:09,520 --> 01:03:13,680
Speaker 1: I don't disagree. I think you're here at an actually point.

1080
01:03:13,680 --> 01:03:16,800
Speaker 1: People love that story so much, but it's like it's

1081
01:03:16,800 --> 01:03:19,240
Speaker 1: just it's not if you look not like salmon runs

1082
01:03:19,240 --> 01:03:22,280
Speaker 1: on the Pacific Coast. No, it's it's not at all, um,

1083
01:03:22,320 --> 01:03:26,720
Speaker 1: I mean it's and and so in two thousand and

1084
01:03:26,760 --> 01:03:30,400
Speaker 1: thirteen we did we did this big comprehensive project. We

1085
01:03:30,520 --> 01:03:33,360
Speaker 1: called it the Food Synthesis Report. And that was in

1086
01:03:33,360 --> 01:03:37,080
Speaker 1: in response to the Ninth Circuit cord ruling, which is

1087
01:03:37,240 --> 01:03:41,800
Speaker 1: a pellet cord um that that indeed brought up that

1088
01:03:41,920 --> 01:03:44,120
Speaker 1: argument that the fish and wild lust servers had not

1089
01:03:44,240 --> 01:03:48,960
Speaker 1: adequately considered the effects of climate change and especially white

1090
01:03:49,000 --> 01:03:52,000
Speaker 1: back pine as as a food source, and and and

1091
01:03:52,000 --> 01:03:54,960
Speaker 1: and and and the effects of other changes are out

1092
01:03:54,960 --> 01:03:56,800
Speaker 1: of food resources as well. So we we had a

1093
01:03:56,880 --> 01:04:00,280
Speaker 1: very comprehensive look at that. And and I would agree

1094
01:04:00,320 --> 01:04:05,960
Speaker 1: with you because uh, it's it's one of many good

1095
01:04:06,000 --> 01:04:08,680
Speaker 1: resources that bears have access to. And and as we

1096
01:04:08,800 --> 01:04:14,479
Speaker 1: as we found out, um it sure, it's it's it's

1097
01:04:14,600 --> 01:04:19,040
Speaker 1: a resources that is high calorie, it's available to some bear,

1098
01:04:19,120 --> 01:04:21,840
Speaker 1: that is available to some bears at the time. The

1099
01:04:22,000 --> 01:04:25,440
Speaker 1: estimate was that might have been around ten of the

1100
01:04:25,440 --> 01:04:28,400
Speaker 1: population at the time might have actually been fewer if

1101
01:04:28,480 --> 01:04:31,880
Speaker 1: if you know, depending on actually had access to the fish,

1102
01:04:32,120 --> 01:04:34,959
Speaker 1: that actually had access to the fish. That's right. So

1103
01:04:35,120 --> 01:04:37,240
Speaker 1: it's one thing to keep in mind. It is we

1104
01:04:37,240 --> 01:04:39,680
Speaker 1: we refer to it as a kind of a provincial resource.

1105
01:04:39,680 --> 01:04:43,840
Speaker 1: You know, it's only those bears residing near Yellstone Lake

1106
01:04:44,480 --> 01:04:47,600
Speaker 1: that that historically took advantage of that resource. And it

1107
01:04:47,640 --> 01:04:51,240
Speaker 1: was a month or so long every yeah, um and

1108
01:04:51,240 --> 01:04:54,080
Speaker 1: and sure for those bears that had access to it,

1109
01:04:54,200 --> 01:04:57,800
Speaker 1: it's it's a great source of calories. People fall in

1110
01:04:57,840 --> 01:04:59,520
Speaker 1: love with that story. I think it's because part of

1111
01:04:59,520 --> 01:05:02,960
Speaker 1: the things like it's part of this uh, it's like

1112
01:05:03,000 --> 01:05:06,040
Speaker 1: this thinking like this, Malcolm Gladwell I and thinking where

1113
01:05:06,480 --> 01:05:10,520
Speaker 1: it's like you can always find these little surprise elements

1114
01:05:11,240 --> 01:05:15,040
Speaker 1: that actually explain the whole planet, you know, like, oh,

1115
01:05:15,080 --> 01:05:16,520
Speaker 1: if you want to understand that, all and you do

1116
01:05:16,600 --> 01:05:19,560
Speaker 1: is understand you know, these are trout and as we

1117
01:05:19,560 --> 01:05:22,400
Speaker 1: we see with anything in nature, um and and certainly

1118
01:05:22,440 --> 01:05:25,680
Speaker 1: with grizzly bears, it's not that simple. And and for

1119
01:05:25,760 --> 01:05:28,600
Speaker 1: a species that you know, we've documented more than two

1120
01:05:28,680 --> 01:05:31,480
Speaker 1: hundred and sixty different types of food that that grizzly

1121
01:05:31,480 --> 01:05:34,240
Speaker 1: bears consume in in this ecosystem. I mean that's so

1122
01:05:34,560 --> 01:05:38,640
Speaker 1: that's an astonishing number of rattle. Rattle if you are Nay,

1123
01:05:38,720 --> 01:05:41,280
Speaker 1: you name some of the more interesting Well, I mean,

1124
01:05:41,520 --> 01:05:43,520
Speaker 1: so did you know the certainly do you have the

1125
01:05:43,760 --> 01:05:48,080
Speaker 1: high calorie ones like cuts, road trout, white bug, pine, army, crust, wore, mods,

1126
01:05:48,120 --> 01:05:51,360
Speaker 1: and ungulus. Right, that those are by all means, you know,

1127
01:05:52,120 --> 01:05:56,880
Speaker 1: those are valuable resources. But but they I think somehow

1128
01:05:57,000 --> 01:06:00,200
Speaker 1: in in in the minds of some folks state they've

1129
01:06:00,240 --> 01:06:03,280
Speaker 1: been constructed as essential resources that every bear has to

1130
01:06:03,320 --> 01:06:05,880
Speaker 1: have access to those, and that's that's not true by

1131
01:06:05,880 --> 01:06:08,840
Speaker 1: any means. Um, not all bears have access to those

1132
01:06:08,840 --> 01:06:11,760
Speaker 1: four resources in every part of the ecosystem. It's it

1133
01:06:12,040 --> 01:06:16,400
Speaker 1: varies depending on where you are. So what people forget

1134
01:06:16,480 --> 01:06:24,400
Speaker 1: is that we have things like biscuit, rooten yampa um uh,

1135
01:06:24,920 --> 01:06:27,960
Speaker 1: while caraway. You know, it's an exotic species that bears

1136
01:06:27,960 --> 01:06:31,000
Speaker 1: are consuming in tom minor basin. That's that's that's where

1137
01:06:31,000 --> 01:06:32,640
Speaker 1: they're congregating. I mean, you see a hell of a

1138
01:06:32,640 --> 01:06:35,520
Speaker 1: lot of them digging for various species of ground squirrels

1139
01:06:35,560 --> 01:06:39,480
Speaker 1: and markets and whatnot. Yeah, so you've you've got that, um,

1140
01:06:39,520 --> 01:06:45,120
Speaker 1: you know, small mammals. Um, they're they're eating algae. In

1141
01:06:45,160 --> 01:06:50,160
Speaker 1: some cases they're eating mud. You know. That's uh, that

1142
01:06:50,320 --> 01:06:55,800
Speaker 1: is kind of a totally unique situation, but like a mineral,

1143
01:06:55,880 --> 01:06:58,720
Speaker 1: like a mineral rich motor. Yeah, like in some of

1144
01:06:58,760 --> 01:07:02,960
Speaker 1: the thermal areas they've the past week documented them eating,

1145
01:07:03,120 --> 01:07:07,120
Speaker 1: you know, consuming. This is just called geophagi um and

1146
01:07:07,160 --> 01:07:12,240
Speaker 1: it's it's probably yeah, for searching for particular minerals. Is

1147
01:07:12,240 --> 01:07:16,200
Speaker 1: this I was just wondering if it's true that the

1148
01:07:16,240 --> 01:07:19,400
Speaker 1: tom minor basin um, like I've heard that it's the

1149
01:07:19,480 --> 01:07:23,040
Speaker 1: largest uh like grouping of bears when they're going for

1150
01:07:23,160 --> 01:07:26,800
Speaker 1: the wildcare away in the lower forty eight UM. Actually,

1151
01:07:26,880 --> 01:07:30,560
Speaker 1: I would say that army cutform moths is probably the

1152
01:07:30,800 --> 01:07:35,200
Speaker 1: larger um grouping because some of those sites you know,

1153
01:07:35,320 --> 01:07:38,440
Speaker 1: can can have like twenty five bears at at one time.

1154
01:07:39,360 --> 01:07:42,120
Speaker 1: I've seen them tom minor a tom minor. It is

1155
01:07:42,160 --> 01:07:45,600
Speaker 1: pretty amazing. I mean, that's that's a vegetative resource that

1156
01:07:45,640 --> 01:07:49,120
Speaker 1: they're after, um, the roots of a wildcare away. And

1157
01:07:49,120 --> 01:07:51,280
Speaker 1: then they're they're digging for some out of stuff while

1158
01:07:51,320 --> 01:07:53,480
Speaker 1: Loretta too, but but that that seems to be the

1159
01:07:53,800 --> 01:07:57,160
Speaker 1: driving food source they were looking for. And yeah, you

1160
01:07:57,200 --> 01:07:59,720
Speaker 1: can you can see sometimes up to a dozen and

1161
01:07:59,760 --> 01:08:04,360
Speaker 1: more bears there and in the evening, which is pretty remarkable.

1162
01:08:04,520 --> 01:08:06,720
Speaker 1: And you know that that's pretty small basin, and for

1163
01:08:06,800 --> 01:08:10,400
Speaker 1: that many bears to show up in that one place, um,

1164
01:08:10,440 --> 01:08:13,080
Speaker 1: that leads you to believe that, yeah, there's there's maybe

1165
01:08:13,280 --> 01:08:15,840
Speaker 1: a lot more bears in the system than we think

1166
01:08:15,840 --> 01:08:18,519
Speaker 1: of the white The White Bark Pine issue did strike

1167
01:08:18,600 --> 01:08:22,320
Speaker 1: just from personal observation, did strike me as a legitimate

1168
01:08:22,360 --> 01:08:25,439
Speaker 1: concern when you because if you've spent any amount of

1169
01:08:25,439 --> 01:08:27,439
Speaker 1: time on those ridge tops that are colde in White

1170
01:08:27,439 --> 01:08:29,559
Speaker 1: Bark Pine, it's like you've never seen anything like it

1171
01:08:29,600 --> 01:08:33,240
Speaker 1: when it comes to how many animals are when you

1172
01:08:33,280 --> 01:08:38,439
Speaker 1: go from black bears, pine squirrels, grizzly bears. You know,

1173
01:08:38,560 --> 01:08:43,920
Speaker 1: Clark's not you know, Clark's not hatches. Uh, Stellar's grays, right, yeah,

1174
01:08:43,920 --> 01:08:46,519
Speaker 1: Clark's snot crackers. I mean, it's just kind of amazing

1175
01:08:46,520 --> 01:08:50,519
Speaker 1: who shows up in those places it is. I mean, yeah,

1176
01:08:50,520 --> 01:08:54,439
Speaker 1: when those trees started to die off. And also I hunted,

1177
01:08:54,439 --> 01:08:57,400
Speaker 1: I bow hunted elk a lot in White Bark pines,

1178
01:08:57,479 --> 01:08:59,320
Speaker 1: So I want up having like a view of it

1179
01:08:59,400 --> 01:09:01,280
Speaker 1: as the same way if if you spend a lot

1180
01:09:01,320 --> 01:09:03,880
Speaker 1: of time along the stream and you see bears eat

1181
01:09:03,880 --> 01:09:05,799
Speaker 1: sam and you start to think that all bears eat salmon.

1182
01:09:06,040 --> 01:09:11,599
Speaker 1: I had in my head like, man, if this collapses, um,

1183
01:09:11,680 --> 01:09:14,160
Speaker 1: it was like, it's the death of these bears, but

1184
01:09:14,160 --> 01:09:18,640
Speaker 1: against this very personal anecdotal thing. But and as it

1185
01:09:18,680 --> 01:09:21,880
Speaker 1: turns out, it's it isn't. Um. So what are they

1186
01:09:21,920 --> 01:09:23,720
Speaker 1: all like? So all those bears, like, what are they

1187
01:09:23,720 --> 01:09:27,160
Speaker 1: doing instead? And that's the one of the questions that

1188
01:09:27,200 --> 01:09:29,479
Speaker 1: we address with with that research back in two thousand

1189
01:09:29,479 --> 01:09:33,400
Speaker 1: and thirteen, in in response to the and and by

1190
01:09:33,439 --> 01:09:36,200
Speaker 1: the request of of the inter Agency Creasy Bear Committee.

1191
01:09:36,600 --> 01:09:38,400
Speaker 1: So one, you know a couple of things we looked

1192
01:09:38,400 --> 01:09:41,439
Speaker 1: at that. First of all, white back pine did decline.

1193
01:09:41,439 --> 01:09:44,600
Speaker 1: You know, there's there's there's in some of our transsex

1194
01:09:45,040 --> 01:09:47,320
Speaker 1: that we've been monitoring since the early two thousand's, we've

1195
01:09:47,360 --> 01:09:53,760
Speaker 1: we've seen seventy of the adult mature trees dying. So

1196
01:09:53,880 --> 01:09:56,360
Speaker 1: that's that's pretty substantial. That's pretty good. Nuts. You ever

1197
01:09:56,439 --> 01:09:59,439
Speaker 1: eat that nut? Um? You know it's the collective and

1198
01:09:59,560 --> 01:10:01,639
Speaker 1: rollsome it's a pain and he asked, but they're good man. Yeah,

1199
01:10:01,840 --> 01:10:05,080
Speaker 1: you can use them. I mean the humans can consumer.

1200
01:10:05,479 --> 01:10:08,840
Speaker 1: There's not a real market for it or anything. Yeah,

1201
01:10:08,880 --> 01:10:12,320
Speaker 1: it's yeah, And it's it's funny you see on the

1202
01:10:12,680 --> 01:10:15,200
Speaker 1: when we capture grizzy bears during that time period. You know,

1203
01:10:15,280 --> 01:10:18,240
Speaker 1: they have their their hair on their paws, it's kind

1204
01:10:18,240 --> 01:10:21,719
Speaker 1: of matted down and sticky from the from eating eating

1205
01:10:21,720 --> 01:10:25,759
Speaker 1: the white buck pine. UM. So, one thing we found

1206
01:10:25,840 --> 01:10:29,160
Speaker 1: was that that bears did respond and and use white

1207
01:10:29,160 --> 01:10:32,120
Speaker 1: buck pine habitat last over the over the last decade,

1208
01:10:32,120 --> 01:10:34,240
Speaker 1: so basically the decade from the early two thousands to

1209
01:10:34,360 --> 01:10:37,880
Speaker 1: the early you know, two thousand tens and uh so

1210
01:10:37,960 --> 01:10:41,719
Speaker 1: there was a response um where initially they were really

1211
01:10:41,720 --> 01:10:45,000
Speaker 1: selecting for those habitats that that selection just gradually went

1212
01:10:45,040 --> 01:10:49,080
Speaker 1: down and now they're basically using it in proportion to availability.

1213
01:10:49,120 --> 01:10:51,720
Speaker 1: But they're still using it, and so it's still in

1214
01:10:51,720 --> 01:10:55,439
Speaker 1: in in good white buck pine years, they're still taking

1215
01:10:55,479 --> 01:10:59,479
Speaker 1: advantage of that resource. And even even though you know,

1216
01:11:00,080 --> 01:11:02,840
Speaker 1: have such we've had such high mortality of white buck pine,

1217
01:11:02,880 --> 01:11:06,639
Speaker 1: there's still a large number of viable, healthy trees out

1218
01:11:06,680 --> 01:11:09,760
Speaker 1: there that that do produce for whatever reason, we're resistance.

1219
01:11:10,560 --> 01:11:14,040
Speaker 1: We don't know if they're if the resistance um. You know,

1220
01:11:14,240 --> 01:11:16,839
Speaker 1: there's a couple of factors. You know that the mortality

1221
01:11:16,840 --> 01:11:20,479
Speaker 1: of white black pine was really primarily mountain pine beetle.

1222
01:11:20,920 --> 01:11:23,800
Speaker 1: There's a little bit of blister rust um and and

1223
01:11:24,000 --> 01:11:27,760
Speaker 1: some trees may have natural resistance to that, um and

1224
01:11:28,040 --> 01:11:31,120
Speaker 1: some may may not. But it's really mount pine bal

1225
01:11:31,200 --> 01:11:33,960
Speaker 1: and also wildfire and have have something to do with it.

1226
01:11:34,080 --> 01:11:38,920
Speaker 1: So um, with mountain pine beetle I think some stands

1227
01:11:38,960 --> 01:11:43,320
Speaker 1: eventually just weren't weren't reached by mountain pine deals. So um,

1228
01:11:43,520 --> 01:11:46,400
Speaker 1: not necessarily that they were resistant to it. They got

1229
01:11:46,400 --> 01:11:52,040
Speaker 1: they got lucky basically, yeah, and so um. You know bears,

1230
01:11:54,200 --> 01:11:58,200
Speaker 1: we're used to assist them of you know, even before this,

1231
01:11:58,200 --> 01:12:01,519
Speaker 1: this this whole mount pine bal at pandemic, it was

1232
01:12:01,560 --> 01:12:07,560
Speaker 1: still a annually very unpredictable resource. So in some years

1233
01:12:07,320 --> 01:12:10,439
Speaker 1: it's you had a bumper crops. It's a masting species,

1234
01:12:10,560 --> 01:12:15,360
Speaker 1: which which means that the tree puts out throughout the ecosystem,

1235
01:12:15,439 --> 01:12:18,000
Speaker 1: just massive amounts of seeds in one year and almost

1236
01:12:18,040 --> 01:12:20,439
Speaker 1: nothing the next year. I think it's got an oak

1237
01:12:20,479 --> 01:12:25,559
Speaker 1: tree in their yard. It's like some users to yeah, yeah,

1238
01:12:25,640 --> 01:12:27,800
Speaker 1: and it's the strategy of the of the trees of course,

1239
01:12:27,840 --> 01:12:31,640
Speaker 1: to to to kind of uh make sure that they reproduce.

1240
01:12:32,000 --> 01:12:34,000
Speaker 1: And if you if you produce just a little bit

1241
01:12:34,040 --> 01:12:36,120
Speaker 1: every year, all the squirrels and everybody else is gonna

1242
01:12:36,160 --> 01:12:38,559
Speaker 1: eat all the seeds and you're not gonna not gonna

1243
01:12:38,560 --> 01:12:41,559
Speaker 1: be able to reproduce. So the strategy of the tree

1244
01:12:41,640 --> 01:12:44,880
Speaker 1: is too in one year just produce nothing and the

1245
01:12:44,960 --> 01:12:47,880
Speaker 1: next year overproduced, so that you have small populations of

1246
01:12:48,000 --> 01:12:52,000
Speaker 1: rodents that cannot hammered all the all the seeds. You know.

1247
01:12:52,000 --> 01:12:55,640
Speaker 1: It's it's pretty smart strategy, of course. But bears were

1248
01:12:55,720 --> 01:12:58,240
Speaker 1: used to that, and that's that system has been in

1249
01:12:58,280 --> 01:13:00,640
Speaker 1: existence for a long time. Of course. Um, you know,

1250
01:13:00,640 --> 01:13:03,479
Speaker 1: where you get a good crop maybe about every two

1251
01:13:03,479 --> 01:13:05,920
Speaker 1: to three years, So a thirty year old bear has

1252
01:13:05,960 --> 01:13:08,400
Speaker 1: seen a hell of a lot of years exactly. Yeah,

1253
01:13:08,680 --> 01:13:12,120
Speaker 1: we didn't start death. And so what we found was

1254
01:13:12,200 --> 01:13:15,760
Speaker 1: that by by looking at um at body condition and

1255
01:13:15,760 --> 01:13:20,400
Speaker 1: and and i'm body fat and and also consumption of

1256
01:13:20,400 --> 01:13:25,559
Speaker 1: of animal resources. Um, what we found was in year

1257
01:13:25,600 --> 01:13:29,400
Speaker 1: of poor white buck pine crops, they would simply eat

1258
01:13:29,400 --> 01:13:33,280
Speaker 1: more animal matter. And and what what we're seeing is

1259
01:13:33,320 --> 01:13:35,960
Speaker 1: that as the white bug pine as declined, they have

1260
01:13:36,200 --> 01:13:39,519
Speaker 1: switched because it's a fall resource, you know, it's it's

1261
01:13:39,560 --> 01:13:41,920
Speaker 1: the period of what we call hyper fagia where they're

1262
01:13:41,960 --> 01:13:46,000
Speaker 1: just consuming calories. You know, something like twenty tho calories

1263
01:13:46,000 --> 01:13:50,840
Speaker 1: a day, UM, and and so they just switching to

1264
01:13:50,880 --> 01:13:54,519
Speaker 1: other alternative resources. And and ungulates are one of them

1265
01:13:54,760 --> 01:13:58,760
Speaker 1: ungulates and that includes um, you know, ungulates left from

1266
01:13:58,800 --> 01:14:03,120
Speaker 1: from hunter kills, piles and things like that, at which

1267
01:14:03,120 --> 01:14:07,160
Speaker 1: they are incredibly efficient to locating those. Are they good

1268
01:14:07,200 --> 01:14:11,040
Speaker 1: at um, like in the fall months when you don't

1269
01:14:11,080 --> 01:14:14,639
Speaker 1: have you don't have young on the ground. Are they

1270
01:14:15,240 --> 01:14:18,880
Speaker 1: very adept at killing adult ungulates in the fall months

1271
01:14:18,920 --> 01:14:21,880
Speaker 1: when they're not depleted by bad weather? They're not there's

1272
01:14:21,920 --> 01:14:26,360
Speaker 1: no young around during the ruts primarily, Yeah, so during

1273
01:14:26,360 --> 01:14:29,400
Speaker 1: the bison rut and during the elk rut that bears

1274
01:14:29,439 --> 01:14:33,879
Speaker 1: do take advantage of of um. You know, the injuries

1275
01:14:33,920 --> 01:14:36,880
Speaker 1: that some of these animals I'm going to sustain during

1276
01:14:36,880 --> 01:14:41,200
Speaker 1: the rut and so UM, that's when we kind of

1277
01:14:41,240 --> 01:14:45,000
Speaker 1: see an uptick in the in in in the ungula use.

1278
01:14:45,160 --> 01:14:47,240
Speaker 1: So you're right though, and you know that what we

1279
01:14:47,320 --> 01:14:49,000
Speaker 1: see in in in the spring, of course, is that

1280
01:14:49,120 --> 01:14:53,040
Speaker 1: the cal elk having season that's black black bears and

1281
01:14:53,040 --> 01:14:56,080
Speaker 1: and and grizzy bears will take advantage of that. UM.

1282
01:14:56,280 --> 01:14:59,680
Speaker 1: They're really pretty efficient predators on on elk casts. But

1283
01:14:59,720 --> 01:15:02,240
Speaker 1: it's a pretty short season. You know, it's it's it

1284
01:15:02,360 --> 01:15:05,960
Speaker 1: only last you know, it's really only the first ten

1285
01:15:06,040 --> 01:15:08,960
Speaker 1: days of an elk calf that that a grizzly bear

1286
01:15:09,000 --> 01:15:12,720
Speaker 1: has a reasonable chance of obtaining this around Memorial Day

1287
01:15:12,760 --> 01:15:15,840
Speaker 1: into early June. It's good pick. Yeah, how many do

1288
01:15:15,920 --> 01:15:18,120
Speaker 1: you have an idea of, like how many elk calves

1289
01:15:18,360 --> 01:15:23,480
Speaker 1: one grizzly will eat in a season? Um? In a season? UM,

1290
01:15:23,520 --> 01:15:27,040
Speaker 1: I don't have any numbers right off, but I mean

1291
01:15:27,080 --> 01:15:34,439
Speaker 1: it's you know, it could it's possible for a grizzly

1292
01:15:34,479 --> 01:15:38,519
Speaker 1: bear or even a black bear population too, to affect

1293
01:15:38,520 --> 01:15:44,519
Speaker 1: population growth of of elk populations based on you know,

1294
01:15:44,640 --> 01:15:49,080
Speaker 1: predation on on elk casts. It's it's um, there can

1295
01:15:49,120 --> 01:15:52,040
Speaker 1: be an influence of that, and sometimes if it maybe

1296
01:15:52,120 --> 01:15:55,040
Speaker 1: is an in combination with having other predators there. So

1297
01:15:55,960 --> 01:15:58,559
Speaker 1: I'm glad you asked that question because what's really interesting

1298
01:15:58,640 --> 01:16:00,800
Speaker 1: if if you look at the word range, you know,

1299
01:16:00,840 --> 01:16:05,479
Speaker 1: the elk population in Yellstone's northern Range has has changed

1300
01:16:05,560 --> 01:16:08,639
Speaker 1: dramatically from I think at the peak it was somewhere

1301
01:16:08,680 --> 01:16:12,920
Speaker 1: around seventeen twenty thousand and and that number has dropped. Uh,

1302
01:16:12,920 --> 01:16:15,360
Speaker 1: And of course there were a lot more wolves out there.

1303
01:16:15,600 --> 01:16:17,280
Speaker 1: A number of years ago. Then there right now, I

1304
01:16:17,320 --> 01:16:20,680
Speaker 1: think um Yelstone Park now has has about about a

1305
01:16:20,720 --> 01:16:24,360
Speaker 1: hundred bulls now, so it looks like we might be

1306
01:16:24,400 --> 01:16:29,559
Speaker 1: reaching kind of some some sort of stable system where um,

1307
01:16:29,720 --> 01:16:32,600
Speaker 1: where you know, there's there's there was also maybe some

1308
01:16:32,720 --> 01:16:36,760
Speaker 1: over harvests in terms of hunting of that population, so

1309
01:16:37,920 --> 01:16:40,320
Speaker 1: a lot of cow hunts. Yeah, so that that number

1310
01:16:40,320 --> 01:16:44,880
Speaker 1: has dropped. The wolf population and reintroduction wolves have certainly

1311
01:16:44,920 --> 01:16:47,760
Speaker 1: have had something to do with that. Um. And then

1312
01:16:47,800 --> 01:16:51,760
Speaker 1: as the grizzly bear population grew and and and especially

1313
01:16:51,920 --> 01:16:55,400
Speaker 1: how they affected elk casts, you know, that probably has

1314
01:16:55,560 --> 01:16:59,000
Speaker 1: has added to to that mix as well. And that's

1315
01:16:59,040 --> 01:17:03,000
Speaker 1: that's probably what what let the elk population to do

1316
01:17:03,080 --> 01:17:05,760
Speaker 1: what what it's done, and and drop in numbers and

1317
01:17:05,760 --> 01:17:09,160
Speaker 1: and you know, maybe stabilizing a little bit now. And

1318
01:17:09,400 --> 01:17:12,960
Speaker 1: imagine there's also the two large beers just them learning

1319
01:17:12,960 --> 01:17:17,160
Speaker 1: how to deal with that predator. Yes, I mean like

1320
01:17:17,240 --> 01:17:19,720
Speaker 1: right now, you haven't been you know, people haven't been

1321
01:17:19,720 --> 01:17:23,840
Speaker 1: allowed to hunt Yellowstone for a hundred years. Right. If

1322
01:17:23,880 --> 01:17:26,800
Speaker 1: you all of a stud open hunting and Yellowstone, you

1323
01:17:26,880 --> 01:17:30,240
Speaker 1: can see a hellbot of elk get shot real quick.

1324
01:17:30,840 --> 01:17:32,680
Speaker 1: And then in about ten years, you're gonna find it

1325
01:17:32,680 --> 01:17:35,000
Speaker 1: becomes real hard to shoot Elk and Yellowstone. Yeah, I mean,

1326
01:17:35,720 --> 01:17:38,839
Speaker 1: and then so elkaf also responded to to the presence

1327
01:17:38,880 --> 01:17:41,640
Speaker 1: of wolves and bears everyway. Yeah, they just get used

1328
01:17:41,640 --> 01:17:46,519
Speaker 1: to Yeah in the area days you know, they might

1329
01:17:46,600 --> 01:17:49,559
Speaker 1: drop a calf out in the open, and now they're

1330
01:17:49,560 --> 01:17:52,160
Speaker 1: gonna they're gonna be more enforce it areas and they're

1331
01:17:52,200 --> 01:17:54,920
Speaker 1: gonna be hidden a little bit more because of the

1332
01:17:54,920 --> 01:17:58,880
Speaker 1: pressures of of bears for example, and the bowls as well.

1333
01:17:59,000 --> 01:18:03,479
Speaker 1: So uh yeah, elk behaviors is changing because of that. Well,

1334
01:18:03,560 --> 01:18:05,559
Speaker 1: they how do you know how long they have actively

1335
01:18:05,840 --> 01:18:08,600
Speaker 1: because they have haze bears, don't they every spring, like

1336
01:18:08,640 --> 01:18:11,559
Speaker 1: in order to essentially get them to change their behavior

1337
01:18:11,640 --> 01:18:14,120
Speaker 1: around people, because I mean, animals in the park are

1338
01:18:14,120 --> 01:18:17,160
Speaker 1: still habituated, whether like people are walking up to them

1339
01:18:17,240 --> 01:18:20,920
Speaker 1: or not. Yeah, but the typical unless bear is really

1340
01:18:20,920 --> 01:18:27,320
Speaker 1: causing problems, that the typical response to that is not hazing. Um,

1341
01:18:27,360 --> 01:18:31,960
Speaker 1: that's really kind of used and as a last resort um.

1342
01:18:32,320 --> 01:18:36,200
Speaker 1: So in terms of the hazing, what you know, the

1343
01:18:36,240 --> 01:18:39,439
Speaker 1: management agencies will will use that in some instances, but

1344
01:18:40,760 --> 01:18:44,200
Speaker 1: but it's effectiveness is really kind of limited. And and

1345
01:18:44,240 --> 01:18:47,240
Speaker 1: so what's what yells On Park, for example, has the

1346
01:18:47,320 --> 01:18:51,720
Speaker 1: policy that they have adopted with with grizzy bears near roadsides,

1347
01:18:52,600 --> 01:18:55,919
Speaker 1: is that as long as as they're showing natural behavior

1348
01:18:56,080 --> 01:18:59,400
Speaker 1: and they're they're not keying in on on human foods,

1349
01:18:59,439 --> 01:19:02,439
Speaker 1: they just let it be. And I think, um, there

1350
01:19:02,560 --> 01:19:06,519
Speaker 1: was another really good management decision, I think is to

1351
01:19:06,520 --> 01:19:10,320
Speaker 1: to to let that habituation just take place. And you

1352
01:19:10,360 --> 01:19:12,160
Speaker 1: can do that in the National Park. Now that's that's

1353
01:19:12,200 --> 01:19:14,600
Speaker 1: different in areas outside the National Park. You know, you

1354
01:19:14,680 --> 01:19:19,439
Speaker 1: may not necessarily want want that type of habituation. So

1355
01:19:19,520 --> 01:19:23,760
Speaker 1: we we make a distinct difference between food conditioning and habituation.

1356
01:19:24,439 --> 01:19:28,280
Speaker 1: And so if we're talking about habituated bears, typically hazing

1357
01:19:28,360 --> 01:19:31,759
Speaker 1: is not an uh is not a tool that's needed

1358
01:19:31,800 --> 01:19:35,519
Speaker 1: now for for food condition bears. It might be, and

1359
01:19:35,760 --> 01:19:38,840
Speaker 1: eventually if that doesn't work, then then removal of that

1360
01:19:39,000 --> 01:19:42,320
Speaker 1: individual is just really your only last choice. Over the

1361
01:19:42,400 --> 01:19:47,519
Speaker 1: last twenty years, say how many, um, how many people

1362
01:19:47,560 --> 01:19:50,320
Speaker 1: have been mauled killed by grizzly bears? Average is about

1363
01:19:50,320 --> 01:19:53,320
Speaker 1: one or two a year, right, yeah, and in recent years,

1364
01:19:53,320 --> 01:19:55,439
Speaker 1: but you know, we we went a long time without

1365
01:19:56,080 --> 01:20:01,080
Speaker 1: without any at all. Yeah. Um, well Malling's so let's

1366
01:20:01,080 --> 01:20:06,000
Speaker 1: just say death fatalities. Um yeah, in recent years we've

1367
01:20:06,040 --> 01:20:09,800
Speaker 1: we've average close to at least one a year. Yeah. Um.

1368
01:20:09,840 --> 01:20:11,599
Speaker 1: But like I said that, there was a time period

1369
01:20:11,640 --> 01:20:15,639
Speaker 1: where there really weren't any, um much at all. So

1370
01:20:16,280 --> 01:20:18,920
Speaker 1: it's kind of you know, you you you're dealing with

1371
01:20:18,960 --> 01:20:21,920
Speaker 1: such small sample size. It's always kind of hard to

1372
01:20:21,400 --> 01:20:26,880
Speaker 1: to put a specific number on it. Butily acute psychological

1373
01:20:26,920 --> 01:20:31,320
Speaker 1: feel it is. It's like everyone talking about it always

1374
01:20:31,360 --> 01:20:34,760
Speaker 1: likes to point out, um, oh you know they do.

1375
01:20:34,800 --> 01:20:38,080
Speaker 1: They always go to like what they compared to you

1376
01:20:38,200 --> 01:20:41,400
Speaker 1: dying by falling off a ladder. They compared it to

1377
01:20:41,520 --> 01:20:44,160
Speaker 1: dying by getting stung by a bee. You just like

1378
01:20:44,200 --> 01:20:46,120
Speaker 1: go to the same way you want to make something

1379
01:20:46,840 --> 01:20:53,200
Speaker 1: seems small in Land Mass you compared to Rhode Island. Yeah, right,

1380
01:20:53,880 --> 01:20:56,240
Speaker 1: or like if you want to seem big like Texas,

1381
01:20:57,000 --> 01:21:01,080
Speaker 1: it's like two Texas is um. But but I think

1382
01:21:01,120 --> 01:21:03,400
Speaker 1: that the other thing that plays into it, and I

1383
01:21:03,520 --> 01:21:06,679
Speaker 1: thought about in the past, it's like it's a psychological fear,

1384
01:21:06,680 --> 01:21:08,200
Speaker 1: and it's like when people look at what are the

1385
01:21:08,240 --> 01:21:10,360
Speaker 1: odds it's gonna happen to you? They're sort of looking

1386
01:21:10,400 --> 01:21:14,839
Speaker 1: at the human population in the g y e or wherever,

1387
01:21:15,760 --> 01:21:19,320
Speaker 1: most of whom don't engage in high risk activities. But

1388
01:21:19,400 --> 01:21:22,040
Speaker 1: when you get down to the individuals who engage in

1389
01:21:22,120 --> 01:21:27,120
Speaker 1: very high risk activities such as it winds up being

1390
01:21:28,040 --> 01:21:33,680
Speaker 1: that you know someone or your buddy knows someone who

1391
01:21:33,760 --> 01:21:36,040
Speaker 1: got scratched up by a bear, and it starts to

1392
01:21:36,160 --> 01:21:38,280
Speaker 1: feel very different. So in one hand, people are telling

1393
01:21:38,320 --> 01:21:40,800
Speaker 1: you like, oh, you've got more chance of getting killed

1394
01:21:40,800 --> 01:21:43,559
Speaker 1: by a cat, a house cat, you know, but they're

1395
01:21:43,560 --> 01:21:45,360
Speaker 1: hare like, well, you know, I have to know a

1396
01:21:45,400 --> 01:21:47,400
Speaker 1: lot of people who have been who mixed, who've been

1397
01:21:47,520 --> 01:21:50,639
Speaker 1: scratched up or run over by bears, because I belong

1398
01:21:50,720 --> 01:21:54,839
Speaker 1: to the high risk segments, like talking about verneural diseases

1399
01:21:54,880 --> 01:21:56,680
Speaker 1: with people who are who go who hang out in

1400
01:21:56,720 --> 01:21:59,200
Speaker 1: brothels all the time, like they have a very different

1401
01:21:59,280 --> 01:22:02,280
Speaker 1: view of careneerally uses the people in the monastery. Yeah,

1402
01:22:02,600 --> 01:22:05,640
Speaker 1: you know. So it's like it really is like I

1403
01:22:05,680 --> 01:22:08,160
Speaker 1: don't like the t That's an interesting analogy, but yeah,

1404
01:22:08,160 --> 01:22:10,519
Speaker 1: well I don't like the tribualized I find myself and

1405
01:22:10,600 --> 01:22:12,519
Speaker 1: sometimes I'm pointing out, you're not gonna get maulled by

1406
01:22:12,520 --> 01:22:14,640
Speaker 1: a bear. Then sometimes I'm want to point out, like

1407
01:22:14,640 --> 01:22:15,920
Speaker 1: man I should do you know a lot of people

1408
01:22:15,960 --> 01:22:18,240
Speaker 1: who have been charged by bears or run over by

1409
01:22:18,960 --> 01:22:22,719
Speaker 1: absolutely a lot of people. Yeah, the context is everything there,

1410
01:22:22,880 --> 01:22:25,240
Speaker 1: you know. No, you yourself, I mean, have you had

1411
01:22:25,280 --> 01:22:28,679
Speaker 1: some run ins? Uh? No? No, but you know, as

1412
01:22:28,760 --> 01:22:31,280
Speaker 1: I indicated in the in the in the bear spray

1413
01:22:31,640 --> 01:22:33,760
Speaker 1: and we had the discussion about you know, use of

1414
01:22:33,760 --> 01:22:37,240
Speaker 1: bear spray versus firearms. Uh. In our work, we try

1415
01:22:37,240 --> 01:22:41,240
Speaker 1: to do everything we can to to avoid encounters, and

1416
01:22:41,360 --> 01:22:43,760
Speaker 1: we can do things that hunt hunter doesn't have the

1417
01:22:43,840 --> 01:22:46,679
Speaker 1: luxury you're doing, you know, like shouting and and and

1418
01:22:46,680 --> 01:22:50,880
Speaker 1: and things like that as a prophylactic really like preventative

1419
01:22:51,240 --> 01:22:55,559
Speaker 1: preventative measure. Yeah, like shouting before it's before you're actually

1420
01:22:55,600 --> 01:22:58,519
Speaker 1: shouting at the making our presence known basically, and you know,

1421
01:22:58,800 --> 01:23:00,600
Speaker 1: as as a as a hunt or you don't have

1422
01:23:00,640 --> 01:23:03,840
Speaker 1: those options, you know, that's it's just hard to exercise it.

1423
01:23:04,320 --> 01:23:08,960
Speaker 1: Well yeah, but strategically no, yeah, So have you have

1424
01:23:09,160 --> 01:23:12,439
Speaker 1: you have you you've cut loose on with pepper sprouted

1425
01:23:12,520 --> 01:23:15,200
Speaker 1: bear though, right? Or no? No, I have people on

1426
01:23:15,240 --> 01:23:18,639
Speaker 1: our team have yeah around for you know, for example,

1427
01:23:18,680 --> 01:23:22,040
Speaker 1: when sometimes in trapping situations you're you're working up a

1428
01:23:22,080 --> 01:23:27,240
Speaker 1: bear and out of bears are you know, inevitably hanging

1429
01:23:27,280 --> 01:23:30,920
Speaker 1: around might be attracted to that and come in, what

1430
01:23:30,920 --> 01:23:34,800
Speaker 1: do you mean hand around like, because we're you know,

1431
01:23:34,880 --> 01:23:37,640
Speaker 1: in in our traps we use we use baits, and

1432
01:23:38,120 --> 01:23:42,880
Speaker 1: so there is that attracting. There's there's also any time,

1433
01:23:43,600 --> 01:23:46,160
Speaker 1: you know, I think there's there's a lot more social

1434
01:23:46,200 --> 01:23:49,599
Speaker 1: interactions among bears than we realize, and and so just

1435
01:23:49,680 --> 01:23:52,719
Speaker 1: having a bear in a trap and and and being

1436
01:23:52,760 --> 01:23:57,479
Speaker 1: handled might actually attract out of animals as well. So,

1437
01:23:58,320 --> 01:24:01,280
Speaker 1: um so we've had situations where where I field pushing

1438
01:24:01,320 --> 01:24:03,800
Speaker 1: all have had to use empty a number of of

1439
01:24:04,640 --> 01:24:08,360
Speaker 1: bear spray cans on on bears that were getting too close. Now,

1440
01:24:08,520 --> 01:24:11,519
Speaker 1: my brother is a researcher in Alaska and he works

1441
01:24:11,520 --> 01:24:15,400
Speaker 1: for a federal agency in Alaska, and they he used

1442
01:24:15,439 --> 01:24:19,200
Speaker 1: to work for university and there the policy there was,

1443
01:24:19,360 --> 01:24:23,200
Speaker 1: I believe you had to have you could you had

1444
01:24:23,240 --> 01:24:27,800
Speaker 1: the option between leath and lethal or non lethal. Because

1445
01:24:27,800 --> 01:24:31,360
Speaker 1: he spends a lot of time in aircraft, um he

1446
01:24:31,479 --> 01:24:36,040
Speaker 1: tended to carry lethal because dealing with the pepper sprays

1447
01:24:36,040 --> 01:24:39,160
Speaker 1: when you're flying on when you're flying on scheduled flights,

1448
01:24:39,320 --> 01:24:42,000
Speaker 1: so scheduled flights you can't have it, and then you

1449
01:24:42,040 --> 01:24:43,880
Speaker 1: often land in a place where you're not gonna go

1450
01:24:43,960 --> 01:24:47,040
Speaker 1: down to Walmart or whoever the hell sells bear spray,

1451
01:24:47,080 --> 01:24:52,680
Speaker 1: so you're on or if you're in a helicopter or

1452
01:24:52,720 --> 01:24:54,760
Speaker 1: in your airplane and that thing cuts, lose your debt.

1453
01:24:55,760 --> 01:24:57,920
Speaker 1: So they taped to the outside that they taped to

1454
01:24:57,960 --> 01:25:01,080
Speaker 1: the struts. Anyways, various complications has made it that he

1455
01:25:01,120 --> 01:25:05,280
Speaker 1: would generally carry the lethal means, and what he carried

1456
01:25:05,439 --> 01:25:07,920
Speaker 1: was that's probably the standards that he carried at eight

1457
01:25:08,000 --> 01:25:11,240
Speaker 1: seventy with slugs. Now where he works, he's at a

1458
01:25:11,280 --> 01:25:14,760
Speaker 1: federal agency now and it's they carry lethal and non lethal. Yes,

1459
01:25:14,840 --> 01:25:17,320
Speaker 1: we do too, So everything he does, he's got to

1460
01:25:17,360 --> 01:25:19,840
Speaker 1: have that. Yeah, you gotta go whatever you gotta go

1461
01:25:19,880 --> 01:25:22,000
Speaker 1: through to get that spray there. It's funny because him

1462
01:25:22,000 --> 01:25:26,320
Speaker 1: live in Alaska. He has boxes of spray. Because everyone

1463
01:25:26,439 --> 01:25:29,840
Speaker 1: comes to Alaska to visit, they buy spray they can't

1464
01:25:29,880 --> 01:25:33,080
Speaker 1: bring there, and so he it's like it looks like

1465
01:25:33,720 --> 01:25:37,200
Speaker 1: this area is garage, you know, bear spray. So that's

1466
01:25:37,240 --> 01:25:39,400
Speaker 1: what they do now is lethal and non lethal. And

1467
01:25:39,400 --> 01:25:42,920
Speaker 1: you're obviously encouraged to exercise the non lethal first, even

1468
01:25:42,960 --> 01:25:45,040
Speaker 1: though they're in the area where there's no serious talk

1469
01:25:45,120 --> 01:25:48,439
Speaker 1: about there being any kind of shortage of bears. I mean,

1470
01:25:48,439 --> 01:25:51,280
Speaker 1: they occupy nineties percent of their historic range in the

1471
01:25:51,320 --> 01:25:54,200
Speaker 1: state of Alaska, and here they occupy what five or

1472
01:25:54,240 --> 01:25:59,679
Speaker 1: six percent of their historic Yeah, so it's pretty low.

1473
01:26:01,000 --> 01:26:03,719
Speaker 1: But yeah, we we carry boats too, and uh and

1474
01:26:03,720 --> 01:26:06,599
Speaker 1: and like you said, you know, first lethal option, what's

1475
01:26:06,600 --> 01:26:13,320
Speaker 1: the lethal thing? You guys carry handguns or uh carry handguns? Um,

1476
01:26:13,360 --> 01:26:18,560
Speaker 1: but we also, um, you know, carry shotguns and seventies.

1477
01:26:18,880 --> 01:26:22,320
Speaker 1: How do you tote your spray? Like? Are you really

1478
01:26:22,320 --> 01:26:25,360
Speaker 1: good about just keeping it right handy? Yes? Yeah, you

1479
01:26:25,479 --> 01:26:29,400
Speaker 1: like to do personally where well, um, you know it's

1480
01:26:29,560 --> 01:26:34,919
Speaker 1: I have if you're asking about like like like, actually,

1481
01:26:34,960 --> 01:26:39,320
Speaker 1: how do you handle your bear spray? Um? I typically

1482
01:26:39,360 --> 01:26:43,439
Speaker 1: have it just on my belt. Yeah, but um, you know,

1483
01:26:43,520 --> 01:26:46,439
Speaker 1: when I'm hunting, I actually prefer to have the chests

1484
01:26:46,600 --> 01:26:49,160
Speaker 1: the chest halter, you know, where it's on your chest.

1485
01:26:49,200 --> 01:26:50,880
Speaker 1: You won't actually have to take it out. You're just

1486
01:26:51,320 --> 01:26:56,559
Speaker 1: shooting straight, which which I prefer that you can. You

1487
01:26:56,600 --> 01:26:58,800
Speaker 1: can get them at any place, you know that it's

1488
01:26:58,840 --> 01:27:01,519
Speaker 1: just espasically, you know, the the same system. But but it's

1489
01:27:01,560 --> 01:27:06,320
Speaker 1: just a chest holding well maybe his holster couldn't fit,

1490
01:27:06,880 --> 01:27:10,280
Speaker 1: you know, like on the so you but you attach

1491
01:27:10,320 --> 01:27:13,040
Speaker 1: it to your backpack strapping. No, I just I haven't.

1492
01:27:13,040 --> 01:27:14,680
Speaker 1: I put that on first and then I put my

1493
01:27:14,720 --> 01:27:17,080
Speaker 1: backpack over it. So you you always have it on you,

1494
01:27:17,280 --> 01:27:19,240
Speaker 1: not from the hip. Even if you check your back off,

1495
01:27:19,280 --> 01:27:21,240
Speaker 1: you still have the bear spray on you. And that's

1496
01:27:21,360 --> 01:27:23,160
Speaker 1: that's one thing what I like about. Have you been

1497
01:27:23,200 --> 01:27:28,160
Speaker 1: injured by bear spray? No, I've seen to, like not

1498
01:27:28,160 --> 01:27:32,120
Speaker 1: not serious, pretty, but we're getting ready to go bear hunting.

1499
01:27:32,120 --> 01:27:34,639
Speaker 1: One hunting black bears, and my brother had his pack

1500
01:27:35,560 --> 01:27:38,280
Speaker 1: laid out and he stepped down the bust of the

1501
01:27:38,280 --> 01:27:42,120
Speaker 1: nozzle holds everything down with bear spray that I got.

1502
01:27:42,120 --> 01:27:44,640
Speaker 1: I got pickpocketed going through a thicket and BC and

1503
01:27:45,040 --> 01:27:48,400
Speaker 1: holds myself down. It's not awful, but it's not good.

1504
01:27:48,439 --> 01:27:50,920
Speaker 1: Your pack is done. Yeah, you gotta replace your pack.

1505
01:27:51,439 --> 01:27:53,280
Speaker 1: I got a friend up in Alaska that had it

1506
01:27:53,360 --> 01:27:55,080
Speaker 1: cut loose in her car and it total her car.

1507
01:27:55,720 --> 01:27:58,280
Speaker 1: There's nothing you can do. It just got punctured every Yeah,

1508
01:27:58,360 --> 01:28:01,160
Speaker 1: everything in the car. There's no we've had I know

1509
01:28:01,320 --> 01:28:04,599
Speaker 1: that that. There's been instances with without a federal agencies

1510
01:28:04,600 --> 01:28:06,640
Speaker 1: where people left it on the front dash, you know,

1511
01:28:06,720 --> 01:28:09,559
Speaker 1: and the sun burst and the heat. Do you is

1512
01:28:09,600 --> 01:28:11,719
Speaker 1: it true. There's a rumor that floats around and maybe

1513
01:28:11,760 --> 01:28:15,479
Speaker 1: you know, is it true that a woman, a tourist,

1514
01:28:16,600 --> 01:28:19,320
Speaker 1: I think it was a Yellowstone, bought some spray and

1515
01:28:19,400 --> 01:28:22,720
Speaker 1: sprayed it on her kid as those mosquito repelling I

1516
01:28:22,760 --> 01:28:28,080
Speaker 1: have not heard that one. I've heard people. You have

1517
01:28:28,200 --> 01:28:32,400
Speaker 1: heard people spraying it around their tent. Yeah a repellent, Yeah,

1518
01:28:32,439 --> 01:28:37,000
Speaker 1: as a repellent. You know, that's a rough night sleep.

1519
01:28:38,800 --> 01:28:40,600
Speaker 1: So if you, let's say the goal, let's say you

1520
01:28:40,640 --> 01:28:45,800
Speaker 1: weren't dealing with a with a population that um let's

1521
01:28:45,840 --> 01:28:49,559
Speaker 1: say grizzly bears are white tailed deer. Okay, No one's

1522
01:28:49,560 --> 01:28:51,559
Speaker 1: talking about there not being enough of them. There's just

1523
01:28:51,600 --> 01:28:54,559
Speaker 1: no question about their stability. And your thing was that

1524
01:28:54,600 --> 01:28:57,120
Speaker 1: you were just going to protect yourself from bears, and

1525
01:28:57,200 --> 01:28:59,880
Speaker 1: you could pick the lethal or not leath. You could

1526
01:28:59,880 --> 01:29:02,040
Speaker 1: pick shotguns with slugs or hang on, or you can

1527
01:29:02,080 --> 01:29:05,200
Speaker 1: pick pepper spray, and nothing to do with the preserving

1528
01:29:05,200 --> 01:29:06,800
Speaker 1: the animal or helping the animal out, just had to

1529
01:29:06,800 --> 01:29:09,920
Speaker 1: do with your personal safety. Based on the people you've

1530
01:29:09,920 --> 01:29:13,559
Speaker 1: conversed with in your own personal experiences, what what would

1531
01:29:13,560 --> 01:29:17,599
Speaker 1: you pick? I would pick bear spray, Yeah, because I

1532
01:29:17,640 --> 01:29:20,519
Speaker 1: think you know that the the key thing about bear

1533
01:29:20,600 --> 01:29:24,720
Speaker 1: spray is you can be really poor at aiming and

1534
01:29:24,800 --> 01:29:27,479
Speaker 1: still have a good chance of of deterring a bear.

1535
01:29:28,360 --> 01:29:31,800
Speaker 1: And whereas with with a firearm, you really have to

1536
01:29:31,880 --> 01:29:36,080
Speaker 1: keep your composure and and hit that animal because if

1537
01:29:36,240 --> 01:29:39,840
Speaker 1: if you don't, you know, it's it's too late. And

1538
01:29:39,960 --> 01:29:42,479
Speaker 1: so with with bear spray, you you increase those chances.

1539
01:29:42,560 --> 01:29:45,200
Speaker 1: And and and the research you know that has shown that well,

1540
01:29:45,200 --> 01:29:47,479
Speaker 1: I think I think it's shown in a pretty in

1541
01:29:47,760 --> 01:29:49,680
Speaker 1: a pretty convincing way when you look at when they

1542
01:29:49,720 --> 01:29:51,400
Speaker 1: do it. But then again, you're dealing with such small

1543
01:29:51,439 --> 01:29:54,160
Speaker 1: sample sizes that it's hard to get real excited about it.

1544
01:29:54,200 --> 01:29:55,960
Speaker 1: And then people like to point out like you'll hear

1545
01:29:56,479 --> 01:29:59,360
Speaker 1: a guy's sprayed a bear with bear spray and it's

1546
01:29:59,360 --> 01:30:01,920
Speaker 1: still scratched them up. But there's a help about of

1547
01:30:01,920 --> 01:30:04,960
Speaker 1: people that shot guns at grizzlies and shot their body. Yeah,

1548
01:30:05,000 --> 01:30:06,960
Speaker 1: and people don't talk about that nearly as much as

1549
01:30:06,960 --> 01:30:09,519
Speaker 1: they talk about the speller spraying a bear is still

1550
01:30:09,520 --> 01:30:12,760
Speaker 1: getting scratched. It's like people like one story more than

1551
01:30:12,800 --> 01:30:15,320
Speaker 1: the other story. Yeah, I think there's a thing that

1552
01:30:15,360 --> 01:30:19,240
Speaker 1: there's like a thing like if you're a badass a gun,

1553
01:30:19,720 --> 01:30:21,000
Speaker 1: you know what I mean. It's just sort of this

1554
01:30:21,200 --> 01:30:24,040
Speaker 1: feeling there's there's a maybe a bit of a macho

1555
01:30:24,160 --> 01:30:27,320
Speaker 1: thing there, you know, when when in reality, um, I

1556
01:30:27,320 --> 01:30:31,120
Speaker 1: would I would feel a lot safer with bear spray alone.

1557
01:30:31,439 --> 01:30:35,000
Speaker 1: You know. For for me, I think just because that's

1558
01:30:35,160 --> 01:30:37,479
Speaker 1: so much time I spent outdoors. That spen so much

1559
01:30:37,520 --> 01:30:40,960
Speaker 1: time I spending grizzly hunt. I'm actually hunting. I'm actively hunting.

1560
01:30:41,280 --> 01:30:45,559
Speaker 1: I'm quite often carrying both anyway, you know, not counting

1561
01:30:45,560 --> 01:30:48,439
Speaker 1: bow I mean bo is you know, I mean if

1562
01:30:48,439 --> 01:30:50,320
Speaker 1: you got if you have time to shoot a bear

1563
01:30:50,360 --> 01:30:53,760
Speaker 1: with a bow, he wasn't a threat, that's right, Yeah,

1564
01:30:54,560 --> 01:30:56,640
Speaker 1: or you're war in the hell of a Yeah, are

1565
01:30:56,640 --> 01:30:59,519
Speaker 1: you're real good? Yeah, you're a long bow hunter. Maybe

1566
01:30:59,800 --> 01:31:02,479
Speaker 1: get have time to get a shot off. So what

1567
01:31:02,520 --> 01:31:04,720
Speaker 1: do you think, like, what what's the future hold? I mean,

1568
01:31:04,840 --> 01:31:06,519
Speaker 1: do you think we're there? Like do you think that

1569
01:31:06,680 --> 01:31:09,080
Speaker 1: this is about the proud number of bears we could

1570
01:31:09,080 --> 01:31:12,160
Speaker 1: hope to have and you know, barring some huge change,

1571
01:31:12,479 --> 01:31:15,439
Speaker 1: some huge societal shift that welcome bears into areas where

1572
01:31:15,439 --> 01:31:18,000
Speaker 1: they had a high risk of conflict. Exactly, we're probably

1573
01:31:18,040 --> 01:31:21,040
Speaker 1: about where we're gonna be. Yeah, I think, um, you know,

1574
01:31:21,160 --> 01:31:22,920
Speaker 1: all the all the data that we have, and and

1575
01:31:22,960 --> 01:31:25,320
Speaker 1: we actually, you know, we talked about two ways of

1576
01:31:25,600 --> 01:31:27,800
Speaker 1: estimating population. We have several lot of ways of of

1577
01:31:27,800 --> 01:31:30,439
Speaker 1: doing that. We look at a lot of other things,

1578
01:31:31,200 --> 01:31:35,240
Speaker 1: and we never look at one single data data set

1579
01:31:35,760 --> 01:31:38,559
Speaker 1: um to to to draw our conclusions. You know, all

1580
01:31:38,880 --> 01:31:42,120
Speaker 1: our conclusions are based on looking at a number of

1581
01:31:42,200 --> 01:31:45,080
Speaker 1: data sets and a number of different types of of

1582
01:31:45,200 --> 01:31:49,200
Speaker 1: indicators and you put all that together, Uh that the

1583
01:31:49,439 --> 01:31:54,360
Speaker 1: indication is that this population is at a level where

1584
01:31:54,400 --> 01:31:56,679
Speaker 1: you know, within the core of the area. We we

1585
01:31:56,680 --> 01:31:59,600
Speaker 1: we we just can't have much higher numbers than what

1586
01:31:59,680 --> 01:32:03,120
Speaker 1: we have. We're seeing these what we're called density dependent

1587
01:32:03,160 --> 01:32:07,320
Speaker 1: effects where uh, this kind of this internal population regulations

1588
01:32:07,360 --> 01:32:11,880
Speaker 1: starting to take place. So the only potential for this

1589
01:32:11,960 --> 01:32:15,200
Speaker 1: population to grow would be to expand, to keep expanding,

1590
01:32:15,240 --> 01:32:18,320
Speaker 1: and for people to allow that. And and that's that's

1591
01:32:18,360 --> 01:32:21,040
Speaker 1: so that gets exactly what you were mentioning. As as

1592
01:32:21,040 --> 01:32:24,600
Speaker 1: long as people were tolerant and and and able to accommodate,

1593
01:32:24,720 --> 01:32:27,880
Speaker 1: then uh, there could still be growth. But then you

1594
01:32:27,920 --> 01:32:30,640
Speaker 1: have to deal with the realities of bear showing up

1595
01:32:30,640 --> 01:32:34,000
Speaker 1: in people's backyards. Um. You know generally most of us,

1596
01:32:34,320 --> 01:32:37,160
Speaker 1: um would would not find that acceptable. And yes, the

1597
01:32:37,240 --> 01:32:42,599
Speaker 1: thing I brought up and something I wrote once where um,

1598
01:32:42,640 --> 01:32:45,320
Speaker 1: you know, these bear is historically occupied a range you

1599
01:32:45,400 --> 01:32:48,840
Speaker 1: might think of from where the Missouri River hooks south

1600
01:32:49,720 --> 01:32:53,640
Speaker 1: that westward to the Pacific coast. And when when the

1601
01:32:53,720 --> 01:32:56,640
Speaker 1: question of delisting in the Greater Yellostone ecosystem comes up,

1602
01:32:56,680 --> 01:32:58,839
Speaker 1: people will point out like, well, they're not recovered across

1603
01:32:58,920 --> 01:33:03,000
Speaker 1: their entire range, and I point out, well, Golden Gate

1604
01:33:03,040 --> 01:33:05,639
Speaker 1: Park is in a very different situation than ye also

1605
01:33:05,720 --> 01:33:08,600
Speaker 1: the National Park, so when we're talking about range wide recovery,

1606
01:33:10,320 --> 01:33:13,080
Speaker 1: that would include San Francisco and Los Angeles exactly. Yeah.

1607
01:33:13,120 --> 01:33:15,680
Speaker 1: So it's a it's tricky. It's like you wind up

1608
01:33:15,680 --> 01:33:17,560
Speaker 1: getting stuck in these um I think a lot of

1609
01:33:17,560 --> 01:33:21,479
Speaker 1: people get stuck in these these utopian views, and it's

1610
01:33:21,600 --> 01:33:24,880
Speaker 1: these ideologies of of you know, you know, we need

1611
01:33:24,920 --> 01:33:28,040
Speaker 1: to have grizzly bears everywhere there's suitable habitat well there

1612
01:33:28,040 --> 01:33:31,080
Speaker 1: maybe pockets of suitable habitat that and then elsewhere in

1613
01:33:31,320 --> 01:33:34,240
Speaker 1: the West, but they're not large enough. I mean, look

1614
01:33:34,280 --> 01:33:37,160
Speaker 1: at you can look at Yellstone. It's a huge area.

1615
01:33:37,320 --> 01:33:41,080
Speaker 1: Now it's we now have bears occupying more than fifty

1616
01:33:41,120 --> 01:33:45,240
Speaker 1: eight thousand square kilometers. But very personally, I would like

1617
01:33:45,240 --> 01:33:50,640
Speaker 1: to see, very personally, I would probably draw like if

1618
01:33:50,680 --> 01:33:52,639
Speaker 1: you if you mapped, if you were able to put

1619
01:33:52,760 --> 01:33:56,160
Speaker 1: people's perceptions of suitable habitat on like a number line

1620
01:33:56,240 --> 01:33:59,960
Speaker 1: or some sort, I would probably put it. I would

1621
01:34:00,040 --> 01:34:04,519
Speaker 1: probably declare more areas suitable than than you're average American

1622
01:34:05,160 --> 01:34:08,200
Speaker 1: because generally like wanting more bears mount Like I look

1623
01:34:08,240 --> 01:34:11,240
Speaker 1: at the Northern Cascades thing, and and I know there's

1624
01:34:11,400 --> 01:34:14,360
Speaker 1: varying views. And I look at the Northern Cascades area

1625
01:34:14,800 --> 01:34:17,280
Speaker 1: in the state where I live, and I'm like, yeah,

1626
01:34:17,720 --> 01:34:21,559
Speaker 1: like I get it. There's there's conflict. But in my mind,

1627
01:34:21,680 --> 01:34:24,040
Speaker 1: if if I was like king of the world, right,

1628
01:34:24,120 --> 01:34:26,240
Speaker 1: I'd be like, let's go for it. And I would

1629
01:34:26,240 --> 01:34:28,559
Speaker 1: There's a handful of places where I would say let's

1630
01:34:28,560 --> 01:34:30,719
Speaker 1: go for it. But I think that in other places

1631
01:34:30,720 --> 01:34:34,120
Speaker 1: really hard like Wyoming holds more of the bears than

1632
01:34:34,160 --> 01:34:37,040
Speaker 1: anybody else of the g y E. And I think

1633
01:34:37,080 --> 01:34:41,479
Speaker 1: that when the powers to be in Wyoming look at

1634
01:34:41,520 --> 01:34:45,400
Speaker 1: the map. I think they politically feel like they're kind

1635
01:34:45,439 --> 01:34:49,120
Speaker 1: of filled up. You know, they've got them where they

1636
01:34:49,120 --> 01:34:53,400
Speaker 1: can have them. Anywhere else is just gonna lead It's

1637
01:34:53,400 --> 01:34:56,360
Speaker 1: gonna lead to a lot of conflict. Yeah. And then

1638
01:34:56,400 --> 01:35:00,439
Speaker 1: we're seeing that, you know, the range expansion that we've seen,

1639
01:35:00,479 --> 01:35:03,680
Speaker 1: and we've seen continued range expansion even with the population

1640
01:35:03,800 --> 01:35:07,439
Speaker 1: level in the core kind of leveling out. Um. But

1641
01:35:08,040 --> 01:35:11,599
Speaker 1: we're seeing more conflicts specifically in those areas. So we're

1642
01:35:11,600 --> 01:35:14,439
Speaker 1: getting more lifestyle conflicts there. Like this year, we we

1643
01:35:14,479 --> 01:35:17,240
Speaker 1: had a number of bears um. You know that we're

1644
01:35:17,320 --> 01:35:23,439
Speaker 1: just killed through accidental in accidental type situations like um

1645
01:35:23,640 --> 01:35:26,720
Speaker 1: in one area misidentification with black bear hunters mean no

1646
01:35:27,000 --> 01:35:31,080
Speaker 1: in um in in in uh where they're encountering new

1647
01:35:32,160 --> 01:35:35,280
Speaker 1: dangerous situations. So in this case it was irrigation canal.

1648
01:35:35,360 --> 01:35:39,080
Speaker 1: We had three bears drowned in the irrigation canal that um.

1649
01:35:39,120 --> 01:35:41,320
Speaker 1: It's some of these canals and this is in the

1650
01:35:41,520 --> 01:35:45,519
Speaker 1: in Wyoming, so east of Cody, so well outside what

1651
01:35:45,600 --> 01:35:50,320
Speaker 1: we would call you know, typical suitable habitat um and

1652
01:35:50,320 --> 01:35:55,280
Speaker 1: and these irrigation canals are pretty large, really heavy high

1653
01:35:55,360 --> 01:36:02,000
Speaker 1: flow to it, and like sea walls, so steep concrete banks,

1654
01:36:03,360 --> 01:36:06,240
Speaker 1: and so bears got into it, probably because they were

1655
01:36:06,240 --> 01:36:08,599
Speaker 1: on auto. Animals in there that that they were, you know,

1656
01:36:08,760 --> 01:36:11,559
Speaker 1: ungulus or cattle that they were trying to go after

1657
01:36:12,240 --> 01:36:13,920
Speaker 1: made it in there and then got sucked by the

1658
01:36:14,000 --> 01:36:18,360
Speaker 1: current and couldn't you know, because the walls are are concrete,

1659
01:36:18,400 --> 01:36:20,599
Speaker 1: couldn't get out three of them and three of them.

1660
01:36:20,840 --> 01:36:22,680
Speaker 1: And I think it's an indication of the type of

1661
01:36:22,720 --> 01:36:26,960
Speaker 1: situations that we can expect more as their range keeps expanding.

1662
01:36:27,000 --> 01:36:30,960
Speaker 1: And it's also an example of of of that we

1663
01:36:31,000 --> 01:36:34,120
Speaker 1: should expect higher mortality rays in those areas. And that's

1664
01:36:34,200 --> 01:36:39,400
Speaker 1: that's why we make a distinction between this this central area,

1665
01:36:39,479 --> 01:36:42,719
Speaker 1: the core area where where we have a suitable habitat

1666
01:36:42,760 --> 01:36:46,040
Speaker 1: which we refer to as the demographic monitoring area, and

1667
01:36:46,120 --> 01:36:49,280
Speaker 1: areas outside, you know, more we can expect a lot

1668
01:36:49,320 --> 01:36:54,280
Speaker 1: more mortalities outside of that core area of habitat um

1669
01:36:54,320 --> 01:36:57,519
Speaker 1: simply because there's more situations where bears can get into trouble,

1670
01:36:57,600 --> 01:37:01,479
Speaker 1: even accidental deaths like that, or cattle predations and things

1671
01:37:01,560 --> 01:37:04,120
Speaker 1: like that. So, do you think it's possible that in

1672
01:37:04,240 --> 01:37:08,120
Speaker 1: fifty years, is it plausible or possible? I guess I

1673
01:37:08,200 --> 01:37:12,240
Speaker 1: mean mostly the same thing that in fifty years we

1674
01:37:12,280 --> 01:37:15,080
Speaker 1: could have a situation much like we have now that

1675
01:37:15,200 --> 01:37:20,559
Speaker 1: we have six grizzly bears that live in this area,

1676
01:37:21,479 --> 01:37:24,360
Speaker 1: and it just kind of bend that way. Yeah, I

1677
01:37:24,360 --> 01:37:27,200
Speaker 1: think that's I think that's that's very doesn't have to

1678
01:37:27,240 --> 01:37:30,720
Speaker 1: be moving in these wild oscillations. No, No, I mean

1679
01:37:30,760 --> 01:37:33,040
Speaker 1: there will be some oscillations, you know, as you as

1680
01:37:33,080 --> 01:37:36,200
Speaker 1: you can expect that for a population that's that's kind

1681
01:37:36,200 --> 01:37:41,280
Speaker 1: of you know, occupied most of the suitable habitat. Um.

1682
01:37:41,520 --> 01:37:44,160
Speaker 1: There will be years that that the population will kind

1683
01:37:44,160 --> 01:37:46,200
Speaker 1: of dip down. It will be years that it will

1684
01:37:46,360 --> 01:37:48,880
Speaker 1: it will be hired and where we are now, um,

1685
01:37:48,880 --> 01:37:53,560
Speaker 1: but with proper management, you know, scientifically based informed management,

1686
01:37:54,520 --> 01:37:57,640
Speaker 1: I'm convinced that that this population can be maintained at

1687
01:37:57,640 --> 01:38:01,400
Speaker 1: this level for the foreseeable future. Do you feel that, um,

1688
01:38:03,000 --> 01:38:06,360
Speaker 1: Do you feel that we're culturally doing a good job

1689
01:38:06,439 --> 01:38:10,640
Speaker 1: of of scientific management management from your perspective, or do

1690
01:38:10,680 --> 01:38:14,360
Speaker 1: you feel that there's a lot of pressure, um, political

1691
01:38:14,400 --> 01:38:17,679
Speaker 1: and social pressure to sort of tell people certain things

1692
01:38:17,680 --> 01:38:18,960
Speaker 1: that they want to hear, or do you feel like

1693
01:38:19,000 --> 01:38:20,600
Speaker 1: there's freedom to do your work in the way that

1694
01:38:20,640 --> 01:38:23,040
Speaker 1: you guys see fit In terms of our our work,

1695
01:38:23,080 --> 01:38:26,759
Speaker 1: I feel like we we are allowed to be completely

1696
01:38:26,760 --> 01:38:32,240
Speaker 1: independent and and I've strongly feel that two managers take

1697
01:38:32,320 --> 01:38:37,200
Speaker 1: what we say seriously. Um. They they do not question

1698
01:38:37,240 --> 01:38:40,559
Speaker 1: our our findings. Stay. I think we we have a

1699
01:38:40,560 --> 01:38:43,559
Speaker 1: lot of credibility with two managers and and the public

1700
01:38:43,560 --> 01:38:47,600
Speaker 1: at large about our data. And there's certainly individuals and

1701
01:38:47,640 --> 01:38:50,880
Speaker 1: groups that's that that are critical of our work. Um,

1702
01:38:50,880 --> 01:38:54,360
Speaker 1: that's gonna happen with with anything, uh you're doing with

1703
01:38:54,800 --> 01:38:57,920
Speaker 1: dealing with an iconic species as as greasy bears and yellstone.

1704
01:38:58,160 --> 01:39:00,479
Speaker 1: But you don't feel that someone says, you tell me

1705
01:39:00,520 --> 01:39:02,120
Speaker 1: what I need to hear. I'll find some of those

1706
01:39:02,360 --> 01:39:04,439
Speaker 1: that I would that would not I would not be

1707
01:39:04,479 --> 01:39:06,599
Speaker 1: in this position if if if that were the case,

1708
01:39:07,080 --> 01:39:09,120
Speaker 1: that would Yeah, that that would not be acceptable for

1709
01:39:09,200 --> 01:39:10,960
Speaker 1: me because as as a scientist, I need to be

1710
01:39:10,960 --> 01:39:14,400
Speaker 1: able to be completely independent of of any sort of

1711
01:39:14,400 --> 01:39:17,439
Speaker 1: political influence, and I would I would I would not

1712
01:39:17,479 --> 01:39:20,519
Speaker 1: accept that at all, um, and I would certainly let

1713
01:39:20,560 --> 01:39:23,400
Speaker 1: that be known. So Yeah, it's it's it's nice to

1714
01:39:23,400 --> 01:39:25,800
Speaker 1: be working in a situation where you are working with

1715
01:39:25,800 --> 01:39:28,559
Speaker 1: with the with the managers and the management agencies, but

1716
01:39:28,760 --> 01:39:33,800
Speaker 1: not UM. But we're not getting directors as other than

1717
01:39:34,160 --> 01:39:38,360
Speaker 1: hey investigate this particular thing because we we need more

1718
01:39:38,680 --> 01:39:41,960
Speaker 1: information on this to make decisions UH. And then and

1719
01:39:42,040 --> 01:39:44,720
Speaker 1: when we come back with with that information, that that

1720
01:39:44,800 --> 01:39:48,640
Speaker 1: information is seriously considered. And that's that's actually the for

1721
01:39:48,720 --> 01:39:51,760
Speaker 1: me as a researcher, the gratification of of working here.

1722
01:39:51,760 --> 01:39:54,000
Speaker 1: You know, a lot of times as researchers, we we

1723
01:39:54,080 --> 01:39:58,720
Speaker 1: kind of we work in isolation from from managers and UM.

1724
01:39:59,280 --> 01:40:01,439
Speaker 1: And that's certainly I would be the first to admit

1725
01:40:01,479 --> 01:40:03,120
Speaker 1: that was the case when I was working with Black

1726
01:40:03,160 --> 01:40:06,439
Speaker 1: Bears and so working with with this study team and

1727
01:40:06,479 --> 01:40:09,599
Speaker 1: working with with with the inter agency Greasy Beer Committee

1728
01:40:09,600 --> 01:40:12,200
Speaker 1: and the managers on that committee and the subcommittees of

1729
01:40:12,280 --> 01:40:17,719
Speaker 1: that UM, it's been really satisfying because for the first

1730
01:40:17,760 --> 01:40:20,679
Speaker 1: time in my career, I actually feel like the people

1731
01:40:20,800 --> 01:40:24,240
Speaker 1: that that can make changes on the ground in terms

1732
01:40:24,280 --> 01:40:29,160
Speaker 1: of management and managing the population are actually listening to

1733
01:40:29,160 --> 01:40:32,840
Speaker 1: the scientific findings, which I think that's ideal set up

1734
01:40:32,880 --> 01:40:36,080
Speaker 1: for for doing good management. And you've been at this

1735
01:40:36,280 --> 01:40:39,679
Speaker 1: how long I've been at this UH for over twenty

1736
01:40:39,680 --> 01:40:43,040
Speaker 1: five years now. It's almost almost just in the last

1737
01:40:43,479 --> 01:40:45,639
Speaker 1: deck or when did you start? Basically the last five

1738
01:40:45,720 --> 01:40:49,360
Speaker 1: years five years you feel like it's finally happening this yeah, yeah,

1739
01:40:49,439 --> 01:40:53,880
Speaker 1: where where because of the because of the system that

1740
01:40:53,920 --> 01:40:55,679
Speaker 1: it was set up, you know, with with the study

1741
01:40:55,680 --> 01:41:00,680
Speaker 1: team doing independent research, that research being um formative for

1742
01:41:00,960 --> 01:41:04,679
Speaker 1: the decision makers on the interagency greasy Beer committee who

1743
01:41:04,720 --> 01:41:07,799
Speaker 1: are able to implement it in in in the real world.

1744
01:41:08,160 --> 01:41:10,519
Speaker 1: You know, that system was set up for for that reason.

1745
01:41:11,240 --> 01:41:14,840
Speaker 1: And uh, you know, there's really not many species or

1746
01:41:14,840 --> 01:41:18,760
Speaker 1: populations where I can think of where where it's so structured.

1747
01:41:18,800 --> 01:41:21,000
Speaker 1: You know, it's by design, it was, it was done

1748
01:41:21,000 --> 01:41:24,400
Speaker 1: that way. And in most other cases, you know, research

1749
01:41:24,479 --> 01:41:27,080
Speaker 1: is kind of like I said, they you know, we

1750
01:41:27,400 --> 01:41:29,840
Speaker 1: kind of tend to work in isolation from from a

1751
01:41:29,880 --> 01:41:34,240
Speaker 1: lot of managers. Not always, but this, the way it

1752
01:41:34,320 --> 01:41:37,120
Speaker 1: was set up was was really ideal situation. What's the

1753
01:41:37,120 --> 01:41:39,600
Speaker 1: next bear you're gonna work on? This will be the

1754
01:41:39,720 --> 01:41:42,519
Speaker 1: last bear? I'll probably know what are you do? Just

1755
01:41:42,560 --> 01:41:45,360
Speaker 1: stick around? Oh yeah, absolutely, So you're into it now?

1756
01:41:45,479 --> 01:41:47,960
Speaker 1: Oh yeah, yeah, you know this, I mean if you

1757
01:41:48,000 --> 01:41:50,840
Speaker 1: look at as far as sort of what has the

1758
01:41:51,560 --> 01:41:56,840
Speaker 1: what sort of captured the popular imagination, you're there? Yeah,

1759
01:41:56,479 --> 01:42:00,320
Speaker 1: it's got man. You know, there's not a lot of

1760
01:42:00,560 --> 01:42:05,320
Speaker 1: bear jobs in the world, and for the bear scientists

1761
01:42:05,320 --> 01:42:08,280
Speaker 1: like mock me to work, um, you know, grizzly bears

1762
01:42:08,280 --> 01:42:11,040
Speaker 1: and yels, and that's kind of the epitome of of

1763
01:42:11,040 --> 01:42:13,880
Speaker 1: what what I could have ever hoped for. So uh,

1764
01:42:13,920 --> 01:42:17,280
Speaker 1: there's every reason for me to stay here until every time.

1765
01:42:17,880 --> 01:42:22,160
Speaker 1: That's my intention. Anything you want to add, well, yeah,

1766
01:42:22,240 --> 01:42:25,560
Speaker 1: it's like we're always like all you guys do, all

1767
01:42:25,600 --> 01:42:29,120
Speaker 1: of the biologists and scientists that we talk with, you

1768
01:42:29,120 --> 01:42:30,760
Speaker 1: guys do such a good job of saying that, like,

1769
01:42:30,840 --> 01:42:32,920
Speaker 1: you know, I'm in it for the research, but obviously,

1770
01:42:32,960 --> 01:42:36,320
Speaker 1: like you love the bears, right, oh, absolutely love them.

1771
01:42:36,360 --> 01:42:39,920
Speaker 1: So but I know that your goal is the first

1772
01:42:39,920 --> 01:42:42,600
Speaker 1: and foremost to get good research and not let the emotions,

1773
01:42:43,040 --> 01:42:47,760
Speaker 1: you know, muddy the waters. Um, So, like, is it

1774
01:42:47,800 --> 01:42:50,120
Speaker 1: a success now where it is? And if in fifty

1775
01:42:50,160 --> 01:42:52,479
Speaker 1: years were at the same level, would you consider a

1776
01:42:52,560 --> 01:42:54,640
Speaker 1: success or do you not even like rate what you

1777
01:42:54,720 --> 01:42:57,680
Speaker 1: do that way because you just you can't look at

1778
01:42:57,720 --> 01:43:01,640
Speaker 1: it in success and failure from a personal staying a point. Um, No,

1779
01:43:02,000 --> 01:43:04,479
Speaker 1: I think I I still look at it from a

1780
01:43:04,479 --> 01:43:09,559
Speaker 1: biological standpoint, And so uh, regardless of of what the

1781
01:43:09,640 --> 01:43:13,920
Speaker 1: legal satus of the population is now versus the future,

1782
01:43:13,960 --> 01:43:16,559
Speaker 1: in your fifty years from now, if we're if we

1783
01:43:16,600 --> 01:43:19,320
Speaker 1: are still at this at this level fifty years from now,

1784
01:43:20,240 --> 01:43:22,240
Speaker 1: I would still I would say that that is a

1785
01:43:22,280 --> 01:43:26,200
Speaker 1: total success. That would that would be an incredible success, if, if,

1786
01:43:26,280 --> 01:43:30,120
Speaker 1: if that can be done. Um, because really, we we

1787
01:43:30,280 --> 01:43:34,400
Speaker 1: have reached biological recovery in my opinion, and that's that's

1788
01:43:34,439 --> 01:43:37,760
Speaker 1: just based on scientific data and and nothing else. So

1789
01:43:37,880 --> 01:43:41,719
Speaker 1: regardless of of whether um, you know, the listing happens

1790
01:43:41,800 --> 01:43:45,479
Speaker 1: or not, it's it's the The biological fact is that

1791
01:43:46,080 --> 01:43:51,320
Speaker 1: every everything indicates that we have biologically recovered population of

1792
01:43:51,360 --> 01:43:53,880
Speaker 1: gristly bears in Yelstone. I guess that's kind of follow up.

1793
01:43:54,120 --> 01:43:56,040
Speaker 1: It might take too much time, but what's just can

1794
01:43:56,040 --> 01:43:59,080
Speaker 1: you hear us like the latest on delisting and like

1795
01:43:59,200 --> 01:44:02,800
Speaker 1: where it's well probably let me let me quick point

1796
01:44:02,800 --> 01:44:06,320
Speaker 1: out what that means. So, um, the US Wish and

1797
01:44:06,320 --> 01:44:11,040
Speaker 1: Wildife Service has for the second time proposed that grizzly

1798
01:44:11,080 --> 01:44:15,519
Speaker 1: bears be delisted, that they that their federal protection under

1799
01:44:15,520 --> 01:44:19,479
Speaker 1: the Endangered Species Act um, that that end and they

1800
01:44:19,560 --> 01:44:26,400
Speaker 1: returned to what's called state management. Now but the states, however,

1801
01:44:27,520 --> 01:44:29,400
Speaker 1: in the process of states, so we have to come

1802
01:44:29,479 --> 01:44:32,400
Speaker 1: up with management plans that are acceptable to the FEDS,

1803
01:44:33,280 --> 01:44:35,120
Speaker 1: and that that's part of the de listing process. So

1804
01:44:35,120 --> 01:44:36,920
Speaker 1: when someone says de listing, what they mean is that

1805
01:44:37,000 --> 01:44:38,920
Speaker 1: would be one of very few, would be one of

1806
01:44:38,960 --> 01:44:41,599
Speaker 1: about two percent. I think of the species that make

1807
01:44:41,640 --> 01:44:45,040
Speaker 1: it onto the Endangered Species List that are then taken

1808
01:44:45,080 --> 01:44:49,240
Speaker 1: off because of recovery. Animals get taken off in various ways.

1809
01:44:49,360 --> 01:44:51,639
Speaker 1: Some have been removed from the s A protection because

1810
01:44:51,640 --> 01:44:54,840
Speaker 1: they simply went extinct. Some have been removed from s

1811
01:44:54,880 --> 01:44:57,080
Speaker 1: A protection because they figured out that didn't they didn't

1812
01:44:57,080 --> 01:45:00,240
Speaker 1: belong there in the first place. Um, it is just

1813
01:45:00,400 --> 01:45:03,360
Speaker 1: they were operating off poor data. Some have been removed

1814
01:45:03,560 --> 01:45:08,240
Speaker 1: due to taxonomic lumping and splitting, where they had listed

1815
01:45:08,240 --> 01:45:12,240
Speaker 1: a thing thinking it was, you know, its own subspecies,

1816
01:45:12,280 --> 01:45:15,200
Speaker 1: and then realized that it's part of a of a

1817
01:45:15,200 --> 01:45:19,800
Speaker 1: different population, or that they went and found other unknown

1818
01:45:19,880 --> 01:45:22,439
Speaker 1: populations and realized that in fact the animals were not

1819
01:45:22,560 --> 01:45:26,639
Speaker 1: as hard up. And then some number uh bald eagels

1820
01:45:27,200 --> 01:45:29,360
Speaker 1: being one of them, have been removed just simply from

1821
01:45:29,360 --> 01:45:33,600
Speaker 1: recovery regators. So so yeah, there's a proposal now to

1822
01:45:33,880 --> 01:45:36,760
Speaker 1: do with grizzlies, what we do with alligators, what we

1823
01:45:36,800 --> 01:45:40,000
Speaker 1: do with bald eagles, and say the e s a worked.

1824
01:45:40,080 --> 01:45:42,000
Speaker 1: It functioned as the way it was meant to function.

1825
01:45:42,560 --> 01:45:44,960
Speaker 1: It's a two way street. Recovered species are meant to

1826
01:45:44,960 --> 01:45:47,880
Speaker 1: be removed from listing and we're facing it now, but

1827
01:45:48,360 --> 01:45:53,840
Speaker 1: that will be litigated by UM. That would be litigated

1828
01:45:54,600 --> 01:46:01,320
Speaker 1: for I don't know a decade or more unless they'll

1829
01:46:01,320 --> 01:46:05,120
Speaker 1: propose the listing and someone's gonna see Yeah, so that, Yeah,

1830
01:46:05,160 --> 01:46:09,000
Speaker 1: the the d listing has been proposed UM to line

1831
01:46:09,000 --> 01:46:15,200
Speaker 1: it up the presumably and uh, you know, the agencies

1832
01:46:15,200 --> 01:46:18,120
Speaker 1: at this point have are ready to sign what it's

1833
01:46:18,160 --> 01:46:21,639
Speaker 1: called the Conservation Strategy and so that will be the

1834
01:46:21,360 --> 01:46:28,479
Speaker 1: the post delisting uh management guidance basically and uh and

1835
01:46:28,479 --> 01:46:31,599
Speaker 1: and so most of the pieces are are in place

1836
01:46:31,640 --> 01:46:34,920
Speaker 1: at this point. Now because of the administration change, there

1837
01:46:35,000 --> 01:46:37,880
Speaker 1: might be some delay. So you know, we might be

1838
01:46:37,920 --> 01:46:41,640
Speaker 1: looking at at the middle of next year before the

1839
01:46:41,720 --> 01:46:45,800
Speaker 1: final rule to D list would come out and then

1840
01:46:45,800 --> 01:46:48,200
Speaker 1: will that go through its own comment period? Uh No,

1841
01:46:48,360 --> 01:46:51,680
Speaker 1: that will be the final one soon next year might

1842
01:46:51,680 --> 01:46:55,400
Speaker 1: be the final. Yeah, So the common periods have already occurred.

1843
01:46:56,240 --> 01:47:00,040
Speaker 1: Um and uh, and so that that the for a

1844
01:47:00,040 --> 01:47:03,160
Speaker 1: moils Service is still working on addressing those those comments

1845
01:47:03,160 --> 01:47:08,639
Speaker 1: because every every substantive comments has to be addressed. That's

1846
01:47:08,720 --> 01:47:11,360
Speaker 1: that's quite a task. And and and you know they

1847
01:47:11,479 --> 01:47:15,000
Speaker 1: tried it in the past and it was deferred, you know,

1848
01:47:15,000 --> 01:47:16,479
Speaker 1: in two thousand and seven, I guess he got deferred

1849
01:47:16,520 --> 01:47:18,280
Speaker 1: for eight or nine years while they looked into the

1850
01:47:18,320 --> 01:47:21,479
Speaker 1: answering some more questions. Yeah, well, the eventually, I think

1851
01:47:21,479 --> 01:47:24,719
Speaker 1: the Ninth Circuit Court decision came out in two thousand

1852
01:47:24,720 --> 01:47:29,439
Speaker 1: and eleven, So yeah, it was it's I expect any

1853
01:47:29,520 --> 01:47:33,559
Speaker 1: litigation on this if if a new delisting rule final

1854
01:47:33,640 --> 01:47:37,760
Speaker 1: rule does come out, UM, litigation will likely happen, and

1855
01:47:38,040 --> 01:47:40,160
Speaker 1: that we might be looking at a similar time period

1856
01:47:40,360 --> 01:47:45,919
Speaker 1: for four or five years before any any final decision

1857
01:47:45,960 --> 01:47:48,080
Speaker 1: comes out of that. I asked someone what one of

1858
01:47:48,080 --> 01:47:52,560
Speaker 1: the legal strategies might be and they pointed out that, um,

1859
01:47:52,600 --> 01:47:55,400
Speaker 1: you know, there's some technical strategies you can take where

1860
01:47:56,760 --> 01:48:04,599
Speaker 1: the um, the crew creation of distinct population segments happened

1861
01:48:04,680 --> 01:48:07,680
Speaker 1: after the listing, and so that if you're trying to

1862
01:48:07,800 --> 01:48:10,320
Speaker 1: d list because they're only like like again for listeners,

1863
01:48:10,800 --> 01:48:12,720
Speaker 1: they're not trying to delist the grizzly bear in the

1864
01:48:12,720 --> 01:48:16,400
Speaker 1: lower forty eight. They're trying to delist one population set,

1865
01:48:16,560 --> 01:48:19,479
Speaker 1: so they're trying to delist a population of grizzly bears.

1866
01:48:19,520 --> 01:48:22,559
Speaker 1: So they're trying to delist grizzy bears in a you know,

1867
01:48:22,800 --> 01:48:26,880
Speaker 1: in a define herble geographic location about you know, like

1868
01:48:26,920 --> 01:48:28,840
Speaker 1: we said earlier, like maybe I consider like the size

1869
01:48:28,840 --> 01:48:31,280
Speaker 1: of Indiana. If one of the bears that lives there

1870
01:48:31,800 --> 01:48:36,120
Speaker 1: should take a major hike and wind up safely outside

1871
01:48:36,200 --> 01:48:37,920
Speaker 1: of that thing. He's covered by the e s A

1872
01:48:38,680 --> 01:48:43,240
Speaker 1: because the s A applies to because the distinct population

1873
01:48:43,280 --> 01:48:46,840
Speaker 1: segments just one little spot. Now a strategy they're saying

1874
01:48:46,840 --> 01:48:51,600
Speaker 1: they might use to thwart this. UM. There are a

1875
01:48:51,640 --> 01:48:53,479
Speaker 1: lot of people this is just me talking persons us

1876
01:48:53,520 --> 01:48:55,880
Speaker 1: do our guests, UM, there are a lot of people

1877
01:48:55,880 --> 01:48:58,479
Speaker 1: who use the ESA as something called the Favorite Animal

1878
01:48:58,600 --> 01:49:01,280
Speaker 1: Protection Act and people who you who like to think

1879
01:49:01,320 --> 01:49:03,639
Speaker 1: of the ESA has the Favorite Animal Protection Act. One

1880
01:49:03,640 --> 01:49:06,800
Speaker 1: of the things that they'll do is they will um

1881
01:49:07,080 --> 01:49:11,519
Speaker 1: question that distinct population question the sort of legality of

1882
01:49:11,560 --> 01:49:14,559
Speaker 1: creating distinct population segments. So it might not even come

1883
01:49:14,560 --> 01:49:16,400
Speaker 1: down to like are there enough bears or whatever. It

1884
01:49:16,479 --> 01:49:19,719
Speaker 1: might just come down to legal wrangling over definitions and

1885
01:49:19,720 --> 01:49:23,800
Speaker 1: and uh, procedural stuff. And thankfully none of that, none

1886
01:49:23,800 --> 01:49:27,160
Speaker 1: of that affects you, right, Uh no, not really. Um

1887
01:49:27,000 --> 01:49:29,680
Speaker 1: I'm just gonna you do a job, provide information. We

1888
01:49:29,800 --> 01:49:33,679
Speaker 1: provide information and will continue to do so. Um and

1889
01:49:33,680 --> 01:49:36,360
Speaker 1: and the Fishing Widelo Service has addressed that that distinct

1890
01:49:36,360 --> 01:49:41,439
Speaker 1: population segment issue within their proposed rule. Um and and

1891
01:49:41,479 --> 01:49:45,120
Speaker 1: you know, use all the biological information to to make

1892
01:49:45,160 --> 01:49:47,479
Speaker 1: that argument. And I think by all means you could

1893
01:49:47,600 --> 01:49:50,120
Speaker 1: argue this is a distinct population. So I think it's

1894
01:49:50,120 --> 01:49:52,280
Speaker 1: like it's you know, it's it is still a nice

1895
01:49:52,600 --> 01:49:56,960
Speaker 1: plush management. I mean we do it all the time.

1896
01:49:57,360 --> 01:50:00,439
Speaker 1: Months like Alaska's divided up into what third d sum

1897
01:50:00,600 --> 01:50:03,040
Speaker 1: or twentysome game management. You know. It's I mean like

1898
01:50:03,120 --> 01:50:07,120
Speaker 1: it's a thing we do as humans when talking about animals,

1899
01:50:07,600 --> 01:50:10,640
Speaker 1: is that we sort of try to break up landscapes

1900
01:50:10,680 --> 01:50:13,120
Speaker 1: in the way that makes sense. Now, we drew state lines.

1901
01:50:13,160 --> 01:50:17,160
Speaker 1: We drew state lines almost arbitrarily along latitudtional longitudal lines.

1902
01:50:17,200 --> 01:50:20,320
Speaker 1: But oftentimes we're talking about animal populations, it's a little

1903
01:50:20,320 --> 01:50:23,840
Speaker 1: more informed and nuanced about landscape features. But that's all.

1904
01:50:23,960 --> 01:50:26,519
Speaker 1: That's pretty new thinking, and correct me if I'm wrong,

1905
01:50:26,560 --> 01:50:29,280
Speaker 1: But I think it wasn't until like the early eighties,

1906
01:50:29,479 --> 01:50:31,120
Speaker 1: and so we kind of said, Okay, we don't need

1907
01:50:31,160 --> 01:50:33,840
Speaker 1: to manage whit until dear By County anymore. We should

1908
01:50:33,880 --> 01:50:38,000
Speaker 1: be managing them by these landscape chunks or areas well.

1909
01:50:38,040 --> 01:50:40,479
Speaker 1: I think it happens all the time. Frederick Jackson Turner.

1910
01:50:40,560 --> 01:50:43,479
Speaker 1: He I think it was him, the environmental historian, who

1911
01:50:43,479 --> 01:50:46,360
Speaker 1: proposed that we drew states all wrong. And he thought

1912
01:50:46,360 --> 01:50:50,280
Speaker 1: we should have drawn our states as watersheds. And he said,

1913
01:50:50,320 --> 01:50:52,479
Speaker 1: like state politics would have been a lot easier if

1914
01:50:52,479 --> 01:50:53,880
Speaker 1: we had drawn him in the instead of just like

1915
01:50:55,120 --> 01:50:58,040
Speaker 1: these straight lines up and down and gritting off, you know.

1916
01:50:58,080 --> 01:51:00,680
Speaker 1: And he said that just makes it hard for for

1917
01:51:00,840 --> 01:51:04,120
Speaker 1: group cohesion, you know, different different things that YouMagine, like

1918
01:51:04,160 --> 01:51:05,960
Speaker 1: even take us like we're sitting right now in Montana

1919
01:51:05,960 --> 01:51:08,719
Speaker 1: where you have the you have part of the state

1920
01:51:09,240 --> 01:51:11,360
Speaker 1: great Planes and part of the state at the inner

1921
01:51:11,400 --> 01:51:14,920
Speaker 1: Mountain west, and just in his mind, we got it.

1922
01:51:15,240 --> 01:51:17,400
Speaker 1: We got it wrong, right, you know, we drew it

1923
01:51:17,479 --> 01:51:21,920
Speaker 1: up so that Nicole anything, last thought, does the I

1924
01:51:22,040 --> 01:51:26,200
Speaker 1: G b ST still stick around? If the bears are delisted. Yes,

1925
01:51:26,200 --> 01:51:28,880
Speaker 1: that's your agency. Yeah, so that is that is our

1926
01:51:29,040 --> 01:51:33,400
Speaker 1: our group, our your injury and is inter agency agency

1927
01:51:33,720 --> 01:51:36,920
Speaker 1: or a group of eight different agencies that work together.

1928
01:51:37,360 --> 01:51:40,320
Speaker 1: So there's um yeah, and that's that's also kind of

1929
01:51:40,360 --> 01:51:44,080
Speaker 1: written into that conservation strategy. There's you'll you'll still continue

1930
01:51:44,120 --> 01:51:46,320
Speaker 1: to we will still continue to do basically what we

1931
01:51:46,320 --> 01:51:49,120
Speaker 1: do right now and at at basically the same level.

1932
01:51:50,000 --> 01:51:52,880
Speaker 1: Um And and to my knowledge, my my agency is

1933
01:51:52,920 --> 01:51:56,000
Speaker 1: committed to to keep funding this this effort because it's

1934
01:51:56,040 --> 01:52:00,360
Speaker 1: it's such a high priority population. Well, I think it's

1935
01:52:00,360 --> 01:52:03,160
Speaker 1: really cool that you guys have so much transparency. I

1936
01:52:03,160 --> 01:52:05,320
Speaker 1: mean when I go onto your website, like I can

1937
01:52:05,320 --> 01:52:07,800
Speaker 1: look at the mortality of every single bear that you

1938
01:52:07,880 --> 01:52:10,599
Speaker 1: guys have recovered, like figure out I had read about

1939
01:52:10,640 --> 01:52:12,920
Speaker 1: those bears that have been in the canal, and then

1940
01:52:13,040 --> 01:52:15,160
Speaker 1: you actually see like how many are human cause, how

1941
01:52:15,200 --> 01:52:18,640
Speaker 1: many are like maybe bear on bear like where you

1942
01:52:18,640 --> 01:52:21,559
Speaker 1: don't know natural causes. And I would just I would

1943
01:52:21,640 --> 01:52:24,120
Speaker 1: encourage if people are interested in this, you can read

1944
01:52:24,160 --> 01:52:26,639
Speaker 1: the whole recovery plan. And I know around the time

1945
01:52:26,680 --> 01:52:28,719
Speaker 1: that the Bozeman commenting was going on, we were talking

1946
01:52:28,720 --> 01:52:31,120
Speaker 1: about it in some groups that I volunteer in, and

1947
01:52:31,240 --> 01:52:33,240
Speaker 1: so I read the whole recovery plan and it gave

1948
01:52:33,280 --> 01:52:36,879
Speaker 1: me such a better idea of how Bears would continue

1949
01:52:36,880 --> 01:52:38,920
Speaker 1: to be managed and how the states could take it over,

1950
01:52:39,160 --> 01:52:41,320
Speaker 1: and so like if you actually just go onto their website,

1951
01:52:41,400 --> 01:52:44,800
Speaker 1: there's so much just incredible information there that people don't

1952
01:52:44,800 --> 01:52:49,439
Speaker 1: want to go learn a whole bunch of ships, like

1953
01:52:49,880 --> 01:52:51,519
Speaker 1: just sit in the bar and be like they don't

1954
01:52:51,520 --> 01:52:53,439
Speaker 1: know what they're doing. But you hear so many people

1955
01:52:53,479 --> 01:52:55,840
Speaker 1: around here like just say stupid things. I mean, the

1956
01:52:55,880 --> 01:52:58,599
Speaker 1: reason that I I don't know, you mean to tell

1957
01:52:58,600 --> 01:53:02,519
Speaker 1: me that people in Montanea go spouting off about Rozzy

1958
01:53:02,560 --> 01:53:05,720
Speaker 1: Bears about knowing the full story? Come on, I think

1959
01:53:05,720 --> 01:53:11,479
Speaker 1: it's come on. Well, that's you know, for for us

1960
01:53:11,520 --> 01:53:16,320
Speaker 1: as as researchers, that's always the challenge from we when

1961
01:53:16,360 --> 01:53:22,160
Speaker 1: we get our inevitable critiques from from some directions and

1962
01:53:22,160 --> 01:53:25,080
Speaker 1: and uh, you know what people tend to focus on

1963
01:53:25,080 --> 01:53:26,960
Speaker 1: this and then then what people tend to do is

1964
01:53:27,000 --> 01:53:29,280
Speaker 1: kind of cherry pick certain things and take it out

1965
01:53:29,280 --> 01:53:31,960
Speaker 1: of context. You know. But but as a team are

1966
01:53:32,080 --> 01:53:35,000
Speaker 1: our approach has always been we look at everything combined,

1967
01:53:35,080 --> 01:53:37,240
Speaker 1: We look at the big picture and we look at

1968
01:53:37,360 --> 01:53:40,719
Speaker 1: longer time frames and things like that that are relevant

1969
01:53:40,720 --> 01:53:43,960
Speaker 1: to the species that we're studying. And and if you

1970
01:53:44,000 --> 01:53:46,160
Speaker 1: do that, you you could come to you come to

1971
01:53:46,240 --> 01:53:48,760
Speaker 1: different conclusions. Then when you look at a single data

1972
01:53:48,800 --> 01:53:52,000
Speaker 1: point like this, this idea that that the population is

1973
01:53:52,040 --> 01:53:56,439
Speaker 1: declining because it's twenty seven bears fewer than last year,

1974
01:53:56,520 --> 01:53:58,679
Speaker 1: you know, I mean when you look at the confidence intervals,

1975
01:53:58,960 --> 01:54:01,439
Speaker 1: that just doesn't that inclusion doesn't make any sense and

1976
01:54:01,479 --> 01:54:03,960
Speaker 1: it's not supported by the data. But that's those are

1977
01:54:04,000 --> 01:54:08,120
Speaker 1: the type of of ideas that that you hear people

1978
01:54:08,160 --> 01:54:10,759
Speaker 1: throw out right now. And it's and it's it's really

1979
01:54:10,800 --> 01:54:13,160
Speaker 1: not based on the on the best information that we have.

1980
01:54:13,280 --> 01:54:16,040
Speaker 1: For the best information that we have says that is

1981
01:54:16,080 --> 01:54:19,879
Speaker 1: well within the the type of variation that you expect

1982
01:54:19,880 --> 01:54:24,000
Speaker 1: over the last fifteen years. Now, you could let that

1983
01:54:24,040 --> 01:54:26,240
Speaker 1: be your concluding thought, or you could add a concluding

1984
01:54:26,280 --> 01:54:30,400
Speaker 1: thoughts anything we haven't touched on. Well, the one thing

1985
01:54:30,439 --> 01:54:33,360
Speaker 1: that we haven't touched on, which um which I think

1986
01:54:33,440 --> 01:54:35,920
Speaker 1: is an important issue, is is this whole idea of

1987
01:54:35,920 --> 01:54:39,160
Speaker 1: of genetic connectivity that's that's been brought up. Man, we

1988
01:54:39,160 --> 01:54:41,720
Speaker 1: didn't get into that. Yeah, that's that's been brought up

1989
01:54:42,440 --> 01:54:44,840
Speaker 1: a lot in the comments. One of the things being

1990
01:54:44,920 --> 01:54:48,560
Speaker 1: that the bear that we need to have corridors, ye,

1991
01:54:49,000 --> 01:54:51,960
Speaker 1: but the bears in the that these bears, these different

1992
01:54:52,040 --> 01:54:57,040
Speaker 1: population segments are able to interchange. And so you know,

1993
01:54:57,080 --> 01:55:01,120
Speaker 1: we've we've done some work genetic in recent years, and

1994
01:55:01,120 --> 01:55:03,840
Speaker 1: then we we've we've we have a huge sample size

1995
01:55:03,880 --> 01:55:06,280
Speaker 1: of bears that we have genetic samples off and we

1996
01:55:06,320 --> 01:55:10,480
Speaker 1: know the history of those bears and and so UM.

1997
01:55:10,520 --> 01:55:12,640
Speaker 1: What we were able to determine from that is that

1998
01:55:13,520 --> 01:55:16,600
Speaker 1: even though genetic diversity is a little bit lower in

1999
01:55:16,960 --> 01:55:20,879
Speaker 1: Yellstone than than other populations, because it is an isolated population.

2000
01:55:20,960 --> 01:55:24,920
Speaker 1: You cannot get more diversity in an isolated population. Your

2001
01:55:25,000 --> 01:55:29,080
Speaker 1: mutation doesn't doesn't take care of that. UM. But what

2002
01:55:29,200 --> 01:55:32,280
Speaker 1: we found was that that it hasn't declined over the

2003
01:55:32,320 --> 01:55:35,680
Speaker 1: last twenty five years. There's there's there's no indication of

2004
01:55:35,840 --> 01:55:39,600
Speaker 1: the kline in genetic diversity. There's a strong indication that

2005
01:55:40,200 --> 01:55:43,120
Speaker 1: the number of individuals that genetically are contributing to the

2006
01:55:43,120 --> 01:55:47,440
Speaker 1: population is increasing, as in fact increased three to fourfold

2007
01:55:47,520 --> 01:55:50,440
Speaker 1: over the last twenty five years. So those are really

2008
01:55:50,480 --> 01:55:56,160
Speaker 1: good indications that genetically UM there are no major concerns

2009
01:55:56,240 --> 01:56:01,120
Speaker 1: right now at this population level. And and so if

2010
01:56:01,200 --> 01:56:04,480
Speaker 1: you would ask me do we need to have connectivity

2011
01:56:04,640 --> 01:56:07,720
Speaker 1: with auto ecosystems, my answer would be yes, it might

2012
01:56:07,760 --> 01:56:12,360
Speaker 1: be desirable for for their long term future, but it's

2013
01:56:12,400 --> 01:56:14,280
Speaker 1: hard to argue based on what we know right now,

2014
01:56:14,320 --> 01:56:19,040
Speaker 1: that it's essential before delisting or any any anything like that,

2015
01:56:19,880 --> 01:56:25,160
Speaker 1: because genetically things look pretty pretty good right now. You

2016
01:56:25,160 --> 01:56:27,839
Speaker 1: know how you're talking about how people cherry pick various

2017
01:56:28,120 --> 01:56:30,680
Speaker 1: part pieces of this to paint the picture they want

2018
01:56:30,680 --> 01:56:36,840
Speaker 1: to paint. I like the connectivity argument because it services

2019
01:56:36,920 --> 01:56:40,640
Speaker 1: my greater goal, right be like, oh, sure, I love

2020
01:56:40,680 --> 01:56:44,640
Speaker 1: the idea of establishing great wildlife corridors, but we all

2021
01:56:44,720 --> 01:56:49,200
Speaker 1: do the Bob Marshall Complex and the Greater Yellstone ecosystem.

2022
01:56:49,240 --> 01:56:54,960
Speaker 1: And if that were to happen because someone pointed out

2023
01:56:55,000 --> 01:56:58,680
Speaker 1: the importance of genetic exchange, I'm like, I don't care

2024
01:56:58,720 --> 01:57:00,600
Speaker 1: how it happens. I just want into the half of

2025
01:57:00,680 --> 01:57:02,440
Speaker 1: because I think it's a step in the right direction,

2026
01:57:02,560 --> 01:57:06,600
Speaker 1: just generally for wildlife. I agree, So that's the case

2027
01:57:06,640 --> 01:57:09,880
Speaker 1: where I would be guilty of cherry picking. Yeah, And

2028
01:57:10,200 --> 01:57:13,120
Speaker 1: if if it's that reason about great, let's still find

2029
01:57:13,120 --> 01:57:16,280
Speaker 1: a different one. Well, there have been sightings in the pilars,

2030
01:57:16,440 --> 01:57:18,680
Speaker 1: and there are two settings in the big hole the summer,

2031
01:57:19,000 --> 01:57:20,920
Speaker 1: and like there haven't been bear sighted there since the

2032
01:57:20,920 --> 01:57:24,560
Speaker 1: early young young males, right, well we we don't even

2033
01:57:24,560 --> 01:57:28,920
Speaker 1: note that in some cases UM, but presumably UM males

2034
01:57:29,000 --> 01:57:31,640
Speaker 1: and and presumably younger ones. Those are those are there

2035
01:57:31,920 --> 01:57:34,520
Speaker 1: the animals that are possibly a mother with cubs was

2036
01:57:34,560 --> 01:57:37,680
Speaker 1: seen in the pilars, which around where my family is

2037
01:57:37,680 --> 01:57:39,880
Speaker 1: at a cabin for a long time. So it's a

2038
01:57:40,640 --> 01:57:44,320
Speaker 1: I don't know, it's pretty yeah, and and so I

2039
01:57:44,360 --> 01:57:47,320
Speaker 1: mean that that would actually that that would be pretty amazing,

2040
01:57:47,440 --> 01:57:52,320
Speaker 1: you know, because that's that's the thing about UM about

2041
01:57:52,400 --> 01:57:55,200
Speaker 1: range expansion and and and bears eventually getting into the

2042
01:57:55,480 --> 01:57:57,400
Speaker 1: sailway beta route. You know, it's it's it's going to

2043
01:57:57,520 --> 01:58:02,680
Speaker 1: require those females and female bears just expand their range

2044
01:58:03,320 --> 01:58:05,680
Speaker 1: pretty slowly compared to the males. You know that, So

2045
01:58:05,720 --> 01:58:09,240
Speaker 1: that much of the expansion that we've seen into eastern

2046
01:58:10,120 --> 01:58:12,600
Speaker 1: on the eastern portion of the ecosystem, for example, it's

2047
01:58:12,640 --> 01:58:15,640
Speaker 1: been really driven by by meals and and what we

2048
01:58:15,720 --> 01:58:18,760
Speaker 1: what we see is that that females will will lag behind.

2049
01:58:18,840 --> 01:58:22,680
Speaker 1: It might be as as many as ten years behind um.

2050
01:58:22,800 --> 01:58:26,000
Speaker 1: But but it's still occurring. But the point is that

2051
01:58:26,000 --> 01:58:28,880
Speaker 1: that with females is just going to take a much

2052
01:58:28,920 --> 01:58:32,800
Speaker 1: longer time period to eventually reach a place like Selway

2053
01:58:32,840 --> 01:58:36,360
Speaker 1: Bitter Route, and and so having to Certainly that's why

2054
01:58:36,400 --> 01:58:39,800
Speaker 1: I say it's desirable if if if the habitat connectivity

2055
01:58:39,840 --> 01:58:42,720
Speaker 1: was search that that was actually feasible. But if you

2056
01:58:42,800 --> 01:58:44,560
Speaker 1: ask me, and this this is where again, you know,

2057
01:58:44,600 --> 01:58:46,840
Speaker 1: where some people don't want to hear my answer simply

2058
01:58:46,880 --> 01:58:49,600
Speaker 1: because I'm just laying as scientific fact as if if

2059
01:58:49,600 --> 01:58:53,080
Speaker 1: you ask me, is it essential for this population? No,

2060
01:58:53,360 --> 01:58:56,400
Speaker 1: right now that I can't say that. I I just

2061
01:58:56,560 --> 01:58:58,320
Speaker 1: based on our data, I can't say that it is

2062
01:58:58,360 --> 01:59:01,960
Speaker 1: absolutely essential for this popular to have that connected. I'm

2063
01:59:02,000 --> 01:59:07,560
Speaker 1: surprised that some enterprising young vigilante hasn't uh culvert trapped

2064
01:59:07,680 --> 01:59:11,280
Speaker 1: a style with some coming and under the cloak of

2065
01:59:11,400 --> 01:59:15,160
Speaker 1: darkness just dumped him in the bitter Root. You'd be

2066
01:59:15,200 --> 01:59:20,800
Speaker 1: the hell out of trouble. You would be Yeah, I'm

2067
01:59:20,800 --> 01:59:25,000
Speaker 1: not gonna I'm not and it's probably hard to hide

2068
01:59:25,000 --> 01:59:28,480
Speaker 1: that sort of but you know that's you know, the

2069
01:59:28,480 --> 01:59:32,000
Speaker 1: the closest example I can come to that is that

2070
01:59:32,000 --> 01:59:36,720
Speaker 1: that links populations in Switzerland where actually reintroduced kind of

2071
01:59:36,920 --> 01:59:40,080
Speaker 1: clandestine really like bucket biologists. You didn't link I I

2072
01:59:40,160 --> 01:59:42,760
Speaker 1: don't know who actually ended up doing it, but I

2073
01:59:42,760 --> 01:59:44,960
Speaker 1: don't know if it was even biologist. But you know,

2074
01:59:45,080 --> 01:59:47,840
Speaker 1: it's a term I don't know, Ian like ab just

2075
01:59:47,880 --> 01:59:50,520
Speaker 1: be like guys who like the ice fish and you're like, hey, man,

2076
01:59:50,680 --> 01:59:53,120
Speaker 1: I like fishing northern pike. Yeah, and they put a

2077
01:59:53,160 --> 01:59:54,800
Speaker 1: couple of pike in a bucket and dumping into some

2078
01:59:54,880 --> 01:59:58,120
Speaker 1: other lake and then you know, and the major repercussions

2079
01:59:58,120 --> 02:00:02,880
Speaker 1: followed the for the ecology, like you know, you know

2080
02:00:02,960 --> 02:00:06,760
Speaker 1: that's the reintroductions in Arkansas were kind of the same.

2081
02:00:07,200 --> 02:00:12,800
Speaker 1: Um in the Washington Ozark Mountains. Um, those those are

2082
02:00:13,040 --> 02:00:16,920
Speaker 1: what animals black bears. Yeah, those are to reintroduce black

2083
02:00:16,920 --> 02:00:21,200
Speaker 1: bear populations doing great, um. But but those initial efforts

2084
02:00:21,280 --> 02:00:25,480
Speaker 1: were we're done by by the state agency at the time.

2085
02:00:25,560 --> 02:00:28,680
Speaker 1: But but you know, somewhat under or the go through

2086
02:00:28,800 --> 02:00:32,240
Speaker 1: all all the perfect process and so in those days,

2087
02:00:32,280 --> 02:00:36,000
Speaker 1: you know that you could you could do that and uh,

2088
02:00:36,120 --> 02:00:38,560
Speaker 1: you know that's that's of course not happening anymore. But

2089
02:00:38,560 --> 02:00:42,600
Speaker 1: but it's interesting that that some populations have benefited from

2090
02:00:42,960 --> 02:00:47,280
Speaker 1: basically Clantestine activities. Yeah, what's almost close to that. I

2091
02:00:47,320 --> 02:00:49,080
Speaker 1: guess this will be my last thing to add. What's

2092
02:00:49,120 --> 02:00:52,080
Speaker 1: almost close to that would be I wrote about this

2093
02:00:52,120 --> 02:00:55,240
Speaker 1: in my in my Buffalo book, where you know, they

2094
02:00:55,240 --> 02:01:00,920
Speaker 1: had they took some bison out of northwest Spontana and

2095
02:01:01,000 --> 02:01:03,800
Speaker 1: put him on a train and you know, hauled him

2096
02:01:03,800 --> 02:01:05,360
Speaker 1: out to Seattle, then put him on a boat and

2097
02:01:05,360 --> 02:01:08,080
Speaker 1: took him up to wait Tier, Alaska, and from way

2098
02:01:08,120 --> 02:01:10,080
Speaker 1: Tier put him on a train. Cut them loose in

2099
02:01:10,120 --> 02:01:13,960
Speaker 1: Delta Junction. Later they had too many running around Delta Junction.

2100
02:01:13,960 --> 02:01:17,520
Speaker 1: They put into military installation. They were causing all kinds

2101
02:01:17,520 --> 02:01:19,840
Speaker 1: of problems with landscaping, and they had come into rut

2102
02:01:19,880 --> 02:01:23,120
Speaker 1: and caused problems with people. One day they took thirteen

2103
02:01:23,200 --> 02:01:25,400
Speaker 1: of them and put him on a truck and drove

2104
02:01:25,440 --> 02:01:30,760
Speaker 1: him out to an abandoned mine, opened the door. Everyone

2105
02:01:30,760 --> 02:01:32,920
Speaker 1: assumed they were all dead because no one then saw

2106
02:01:33,000 --> 02:01:35,880
Speaker 1: him for a decade, at which point there's a hundred

2107
02:01:35,880 --> 02:01:37,720
Speaker 1: of a hundred of him turn up about a hundred

2108
02:01:37,720 --> 02:01:41,120
Speaker 1: and fifty miles from there, you know, and there was like, oh,

2109
02:01:41,200 --> 02:01:43,720
Speaker 1: that's what happened to him. You know that those kind

2110
02:01:43,720 --> 02:01:45,520
Speaker 1: of days seem to be a little bit over because

2111
02:01:45,920 --> 02:01:47,120
Speaker 1: if you look at when they just tried to do

2112
02:01:47,160 --> 02:01:49,640
Speaker 1: the wood bison reintroduction in Alaska that came on the

2113
02:01:49,640 --> 02:01:54,000
Speaker 1: tail end of about twenty five years of fighting and

2114
02:01:54,720 --> 02:01:58,240
Speaker 1: arguing and quarantines and lawsuits, you know, and used to

2115
02:01:58,360 --> 02:02:00,360
Speaker 1: just oh, it took us one guy with a truck,

2116
02:02:00,400 --> 02:02:03,160
Speaker 1: you know, and he could he could establish his own

2117
02:02:03,160 --> 02:02:06,640
Speaker 1: little population animals somewhere. You know. He just asked, like

2118
02:02:06,680 --> 02:02:08,640
Speaker 1: a guy named Bob, if it's okay, and you know,

2119
02:02:08,720 --> 02:02:10,600
Speaker 1: he says, yeah, then there you go. You got you

2120
02:02:10,680 --> 02:02:14,040
Speaker 1: gotta population. All right, Well, thanks for coming on, man,

2121
02:02:14,160 --> 02:02:19,320
Speaker 1: are you're welcome? Yeah, thank you? All right, Um, hopefully

2122
02:02:19,320 --> 02:02:21,800
Speaker 1: and the next time I shouldn't say hopefully, maybe the

2123
02:02:21,840 --> 02:02:25,200
Speaker 1: next time we talked, there'll be big bear news. That's right,

2124
02:02:25,280 --> 02:02:26,560
Speaker 1: at which point out to have you back on and

2125
02:02:26,560 --> 02:02:29,160
Speaker 1: talk to you about what that's gonna mean. All right, great,

2126
02:02:29,200 --> 02:02:29,520
Speaker 1: thank you,