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Speaker 1: Welcome back to cutting the distance. Today, I'm in a

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Speaker 1: cabin about twenty seven kilometers from a satan in the

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Speaker 1: Blue Mountains of Washington, helping try to wrestle newborn elk

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Speaker 1: calves in an effort to put callers on them, ear tagum,

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Speaker 1: and let fish and wildlife monitor them to see how

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Speaker 1: they're doing. I'm here with Poulwick, who has been here

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Speaker 1: for the last twenty years and currently serves as a

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Speaker 1: district Wildlife biologist. He completed his undergrad at Central Washington

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Speaker 1: and received his masters from the University of Idaho. After

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Speaker 1: completion of his masters, he went on to work at

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Speaker 1: the DNR as the Northeast Regional bio prior to working

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Speaker 1: here in the Blues. The Blues, for those of you

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Speaker 1: that don't know, at one time could have been said

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Speaker 1: the rival any elk cutting anywhere in the world, And

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Speaker 1: I would say that's based on both trophy potential and opportunity.

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Speaker 1: But in just the last i would say ten short years,

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Speaker 1: a unit seemed to have taken a little downward turn,

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Speaker 1: and two years ago they had only a thirteen percent

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Speaker 1: survival rate of their calves. So I'm here to talk

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Speaker 1: with Paul to see what he thinks is going on,

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Speaker 1: and to talk about any other factors that the elker

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Speaker 1: facing that could improve or affect their survivability, and how

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Speaker 1: maybe we're going to get those herds back or if

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Speaker 1: we have to accept the fact they may never get

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Speaker 1: back to where they're at, but how we're gonna to

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Speaker 1: repair those So welcome to.

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Speaker 2: The show, Paul, thank you for having me.

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Speaker 1: We're here. I got here this morning. We went out

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Speaker 1: and looked for calves a little bit. Found one that

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Speaker 1: you've already wrangled up, had an ear take in it

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Speaker 1: had a collar on it. But you're in the thicket

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Speaker 1: calving season. How's it going so far?

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Speaker 2: It's going well. Right now, we've been out here for

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Speaker 2: about three weeks. We've caught twenty seven calves in the

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Speaker 2: area we're currently in. We also have two other groups

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Speaker 2: working in the Dayton and two Cannon areas that are

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Speaker 2: catching calves. Our goal is to have one hundred and

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Speaker 2: twenty five by the end of next week. And although

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Speaker 2: the numbers I just gave you aren't gonna say we're

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Speaker 2: gonna get there, but we have a hell copter showing

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Speaker 2: up in two days that is gonna catch calves and

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Speaker 2: the reason we use a helicopter is they're much more effective,

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Speaker 2: but they're not very effective at finding the zero to

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Speaker 2: three day old calves which are still in a hiding pace.

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Speaker 2: So that's why we're on the ground trying to catch

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Speaker 2: the really young ones to not miss any sources of

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Speaker 2: mortality that would be important.

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Speaker 1: Okay, yeah, and we're gonna get into it here in

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Speaker 1: a little bit exactly what capturing calves looks like, because

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Speaker 1: to me, it's all brand new too. So I was

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Speaker 1: asking you questions like how do I glass form? Where

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Speaker 1: should they be? You know, because this is the time

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Speaker 1: of the year, most elk hunters aren't thinking about elk

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Speaker 1: or they're not necessary out in the wood unless they're spring

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Speaker 1: bear hunting or picking mushrooms or doing some other stuff.

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Speaker 1: But even then, like I'm not glassing. So we're gonna

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Speaker 1: jump into that a little bit more. But we're gonna

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Speaker 1: start this cutting the Distance episode like we do every episode.

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Speaker 1: We're gonna start with some listener questions, and once again,

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Speaker 1: if you have questions of your own for me or

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Speaker 1: my guests, feel free to email them to us at

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Speaker 1: CTD at Phelps game Calls dot com or send us

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Speaker 1: a social a message on social and we'll do our

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Speaker 1: best to get them on the show. So the first

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Speaker 1: one comes from Alan Roberts. What predator reaks the most

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Speaker 1: havoc on elk?

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Speaker 2: So, for calves, which we have the most data right now,

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Speaker 2: it's cougars, at least in the Blue Mountains. We're finding

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Speaker 2: that cougars are taking sixty two percent of the mortalities

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Speaker 2: occurring from the calves that we're marking, which really dwarfs

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Speaker 2: any other predator on the landscape.

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Speaker 1: Do you feel that and I know we talked about

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Speaker 1: this before, We've had a couple hour long conversations on this,

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Speaker 1: do you feel that that changes with matui el ca?

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Speaker 1: And I know you don't have data, but give me

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Speaker 1: your opinion or maybe what you can speculate is is

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Speaker 1: the cougar the predominant predator on maturialk or does it

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Speaker 1: balance out a little bit with bears or wolves or

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Speaker 1: anything else in the area.

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Speaker 2: So bears really aren't very effective on adult elk. Cougars

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Speaker 2: make up a majority of the predators on the landscape,

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Speaker 2: so they should make up a majority of the predation

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Speaker 2: that's occurring on adult elk. Can kind of go back

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Speaker 2: to some previous work we did when I was originally

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Speaker 2: started in the Blues and three, we were marking adult

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Speaker 2: bulls and some adult cows, but mostly adult balls and

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Speaker 2: cougar's made up a majority of the source of mortality

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Speaker 2: for predation on those animals. That was pre wolf, so

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Speaker 2: we didn't have wolf data at that time. There were

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Speaker 2: not wolves in the Blue Mountains at least in numbers

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Speaker 2: that were meaningful in any way. A couple of dispersers

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Speaker 2: coming from Idaho and Oregon at that time. Right now

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Speaker 2: there are six wolf packs in the Blue Mountains. What

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Speaker 2: effect they might be having on adult elk we don't

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Speaker 2: have a good answer for. So they probably are having

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Speaker 2: more of an effect than when we did the study,

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Speaker 2: which makes sense.

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Speaker 1: Yeah, yeah, I mean the one thing I know, and

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Speaker 1: I'm not a biologist, is I know what They're not

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Speaker 1: having a positive effect on the elk population. That's one

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Speaker 1: thing we could probably confidently say is it's not helping

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Speaker 1: the elk by any means. But it sounds like from

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Speaker 1: the data from the science cougars are the predator that

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Speaker 1: at least has the biggest effect on caves, and from

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Speaker 1: old data it had the most effect negative effect on

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Speaker 1: mature balls or adult balls.

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Speaker 2: Yes, And there are places in the West that wolves

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Speaker 2: are not keeping the population in check, and there's places

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Speaker 2: that they are, and it's hard to predict what where

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Speaker 2: those places are and what the circumstances are that why

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Speaker 2: that happens. I mean, there's places in western Montana with

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Speaker 2: wolves that the population still they're still having a hard

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Speaker 2: time keeping the population in check. In the Blues with

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Speaker 2: poor recruitment, additional mortality is probably not going to help

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Speaker 2: us get to where we want to.

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Speaker 1: Be, for sure. So amongst your colleagues in the West,

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Speaker 1: and hopefull I don't put you on the spot here.

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Speaker 1: Are there units aside from the Blues where there are shifts?

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Speaker 1: Is it terrain? Is it vegetation? Is there are there

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Speaker 1: places where bears will have more of an effect or

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Speaker 1: maybe cougar aren't so much and wolves are heavier. Like

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Speaker 1: you know, you always hear about the fame Lolo area

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Speaker 1: in Idaho. Do you have much insight to if the

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Speaker 1: cougar's being the apex predator at least as far as

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Speaker 1: Elk are concerned. Is that a Blues thing or is

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Speaker 1: it typical amongst all western Elk areas?

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Speaker 2: Or do you, It's really hard for me to expand

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Speaker 2: outside of the Blues, but that does include Northeast Oregon

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Speaker 2: because the Blue Mountains, approximately ninety percent of the Blue

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Speaker 2: Mountains are an Oregon and Oregon's done some similar work

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Speaker 2: with calves in the early two thousands. They've done a

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Speaker 2: lot of elk research and they found very similar numbers

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Speaker 2: for cougar predation in Northeast Oregon as they have in

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Speaker 2: southeast Washington. So it's hard for me to say how

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Speaker 2: we compare to Central Idaho, you know, kind of the Rockies.

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Speaker 2: We're kind of on the edge of the Rockies here.

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Speaker 2: We're kind of a unique our own ecosystem in some

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Speaker 2: our ecoregion in some respects. So there are different factors

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Speaker 2: that are going to affect the other herds as you

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Speaker 2: go further west from you know, the elevation, the level,

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Speaker 2: the distance they migrate, the habitat that they have available,

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Speaker 2: and climate and fire history all play a pretty big, important,

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Speaker 2: important contributing factors to this.

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Speaker 1: Gotcha, Yeah, thanks for that one. The next question we've

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Speaker 1: got comes from Cody Stein. It was carrying capacity and

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Speaker 1: how do biologists or departments know how do they determine

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Speaker 1: that and then what factors are included in that in

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Speaker 1: your opinion, Like, how do you is it? You know

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Speaker 1: when we talked about this a little bit when you

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Speaker 1: know it's the same things we talk about, but pose

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Speaker 1: a question a little bit different there.

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Speaker 2: So carrying capacity is one of the most difficult things

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Speaker 2: to measure. Pretty much you know it when you've gotten

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Speaker 2: there because the population starts performing very poorly. Calves don't survive,

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Speaker 2: pregnancy rates drop, winter mortality goes up. But to know

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Speaker 2: what that number is ahead of time is pretty been

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Speaker 2: almost impossible to measure for these herds because it's also

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Speaker 2: a moving target. Carrying capacity one year with a lot

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Speaker 2: of say summer precipitation, a wet spring, lots of forage

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Speaker 2: on the landscape is going to be very different in

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Speaker 2: a drought year, where the percentage forage might be thirty

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Speaker 2: forty percent less. So they all have a lot less

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Speaker 2: available to them. So it's a question that we've actually

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Speaker 2: gotten for the Blues, are we near or at carrying capacity?

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Speaker 2: And if we look at the density of elk on

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Speaker 2: the sides of the landscape they're in, we're at a

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Speaker 2: pretty low density for elk at this point in time,

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Speaker 2: So I don't think we are. We actually manage more

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Speaker 2: towards what we call social carrying capacity in the Blues,

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Speaker 2: and that's because of the interface we have with agriculture,

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Speaker 2: and if we have too many elk, they're getting into

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Speaker 2: the peas, the winter wheat, the summer wheat, and we

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Speaker 2: know that it's socially unacceptable and causes financial hardship for

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Speaker 2: the farmers that are in the foothills of the Blues.

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Speaker 2: So we try to manage to keep the elk at

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Speaker 2: a level that reduces that which in our mind keeps

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Speaker 2: us well below the carrying capacity as well.

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Speaker 1: That makes sense. So in the Blues, there was a

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Speaker 1: time frame where there was no hunting, correct as we

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Speaker 1: tried to rebuild these populations.

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Speaker 2: Only there's always been hunting in the Blues. There's been

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Speaker 2: one game management unit of the Lick Creek unit where

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Speaker 2: we weren't issuing branch bulltags for a period of time,

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Speaker 2: but there was still the spike only season. There still

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Speaker 2: was cow tags at times when the population was doing well.

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Speaker 1: Even during like the late nineties early two thousands, there

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Speaker 1: was always hunting, just very reduced.

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Speaker 2: Yes, there's always been hunting in the Blues. We've never.

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Speaker 2: I think it was since like nineteen thirties. I think

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Speaker 2: they were brought back in the nineteen twenties from Yellowstone,

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Speaker 2: but on a train brought to Dayton, brought to Pomeroy,

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Speaker 2: and from what I've been told from some of the locals,

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Speaker 2: within five years we were having egg damage.

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Speaker 1: I I was just kind of I was digging it.

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Speaker 1: I didn't know if that was like a unique way

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Speaker 1: to maybe study carrying capacity. Why there were so few

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Speaker 1: tags given? Were we able to watch those hurt umbers

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Speaker 1: because there was I mean we we've talked about it back,

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Speaker 1: but probably twenty ten to fourteen maybe you might be

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Speaker 1: able to envelope those dates better. But the Blues were

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Speaker 1: maybe an all time high or or a level where

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Speaker 1: the elk hunting seemed to be good. You know, you

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Speaker 1: always based off what elk hunting looks like and the

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Speaker 1: opportunity mature bowls, you know, all these things kind of

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Speaker 1: add into these little factors, and it seemed to be

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Speaker 1: and you know, maybe two thousand and five twenty fifteen

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Speaker 1: was like the high. Did we get to a point

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Speaker 1: where you thought we were close to carrying capacity? Or

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Speaker 1: has had always been off of that a little bit?

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Speaker 2: No, and the reason I'd say that is two thousand

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Speaker 2: and fifteen was roughly our recent high and elk numbers

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Speaker 2: that for modern data, uh, And that's kind of a

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Speaker 2: relative term because we've been doing elk population estimates through

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Speaker 2: aerial survey since nineteen ninety six and the highest counts

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Speaker 2: we got were roughly around twenty fifteen. And we also

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Speaker 2: had some of the higher calf ratios during that time,

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Speaker 2: and if we were approaching carrying capacity, we should have

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Speaker 2: seen our calf ratios really declining if we were anywhere

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Speaker 2: near carrying capacity.

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Speaker 1: So it's a good indicator just how many calves are

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Speaker 1: being born and of age, because that has to do

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Speaker 1: with nutrition that's available, and those things will start to

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Speaker 1: affect you know, just like our last podcast with Brock,

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Speaker 1: we we get into you know, you know, the cow's

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Speaker 1: ability to go into estrus after she you know, had

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Speaker 1: a successful calf, and then you're rolling it in. So

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Speaker 1: that all makes sense and plays right into you wouldn't

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Speaker 1: see those calf successes that high if if you were

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Speaker 1: close to carrying capacity.

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Speaker 2: Yeah, at no point in the last twenty years have

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Speaker 2: we seen any of the indicators that would indicate carrying

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Speaker 2: capacities even being approached.

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Speaker 1: Okay, yeah, thanks, thanks for the answer, and once again

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Speaker 1: you have your own question for me or my guests.

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Speaker 1: Feel free to email those to us at CTD at

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Speaker 1: Phelps game Calls dot com, or send us a message

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Speaker 1: on social and we'll do our best to get them

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Speaker 1: on here. So now we're gonna jump into my discussion

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Speaker 1: with you. One of the reasons i'm here. I was

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Speaker 1: fortunate to have a Blues tag last year. I love

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Speaker 1: the Blues. I've been able to elk hunt here three

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Speaker 1: or four times now. You know, it seems to be

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Speaker 1: a real treat when you do get to come here.

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Speaker 1: You know, my tag was as early as last year.

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Speaker 1: My wife drew, I believe in twenty thirteen, so I've

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Speaker 1: got to I believe I was here like at the

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Speaker 1: high point and then got to hunt the same unit

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Speaker 1: and see a little bit of contrast. You know, still

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Speaker 1: able to get it done. But there's just something growing

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Speaker 1: up in Washington, like you always dream of hunting the Blues,

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Speaker 1: hunting it one time. I figured if I can hunt

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Speaker 1: it one time before I die, I'll be happy. But

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Speaker 1: I'm very interested in the Blues. You know, we kind

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Speaker 1: of kicked this off with some uh, you know, I'd

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Speaker 1: even heard numbers of worse than the thirteen percent survival

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Speaker 1: immortality above ninety percent. The blues from a hunter's perspective

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Speaker 1: or perspective seems to be deteriorating. And so I got

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Speaker 1: a hold of you and just wanted to come talk

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Speaker 1: to you. And you know what you're seeing, You know,

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Speaker 1: a guy that's out here with these elk deer sheep,

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Speaker 1: you know every day, and kind of what your opinions are,

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Speaker 1: what your professional opinions are, what the research shows. And

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Speaker 1: I think we can all play armchair biologists from our chairs,

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Speaker 1: but I think every all the biologists I've got to meet,

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Speaker 1: you know, gett to hang out with you so far

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Speaker 1: for just a couple of hours, Like you care about

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Speaker 1: the olk, You're a hunting yourself, you want to see

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Speaker 1: it do good. You're not making recommendations that hurt the population.

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Speaker 1: So I really want to just kind of jump into

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Speaker 1: that talk with you and then let all the listeners

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Speaker 1: kind of know what what you're seeing on the ground

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Speaker 1: and shed some light on that. So we're going to

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Speaker 1: jump back into what we're what you're doing right now

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Speaker 1: that that calf study, can you give us a little

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Speaker 1: background on what kicked off the calf study and where

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Speaker 1: it's going to go, what the data is going to

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Speaker 1: be used for, and some of that.

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Speaker 2: Yeah, so you're right. You know, two thousand through twenty sixteen,

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Speaker 2: we had pretty healthy calf ratios. You know, we really

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Speaker 2: want a minimum of twenty five calves per hundred for

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Speaker 2: our population just to remain stable. About twenty sixteen, right

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Speaker 2: after twenty sixteen, the numbers started dropping. The numbers have

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Speaker 2: been consistently below twenty five since twenty sixteen, which indicates

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Speaker 2: a problem. Something changed. We didn't know exactly what. We

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Speaker 2: have some ideas that you know, climate played apart. Through

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Speaker 2: we had a couple of severe winter events. We've had

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Speaker 2: some summer droughts that have definitely played a part affecting

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Speaker 2: the nutrition of the elk and the pregnancy rates, the

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Speaker 2: ability to nurse and lactate, which is a huge demand

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Speaker 2: upon elk. So really about twenty sixteen twenty seventeen, we

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Speaker 2: noticed something changing in the population. We started making some

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Speaker 2: small changes. We started reducing cow tags where it was appropriate.

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Speaker 2: We started seeing less bowls on the landscape, so we

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Speaker 2: started reducing the bull tags to try and let it

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Speaker 2: balance itself, hoping that it would rebound within two three years,

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Speaker 2: and it really hasn't had the ability to rebound. So

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Speaker 2: we're see about two thousand and nineteen twenty twenty, we

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Speaker 2: started having some internal discussions on what does this mean?

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Speaker 2: Why is it happening. We did some internal reviews and

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Speaker 2: had some good discussions internally in the agency, and it

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Speaker 2: came down to the calf recruitment seemed to be the

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Speaker 2: limiting factor. So the agency started a calf coloring effort.

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Speaker 2: In twenty twenty one was our first year of doing that.

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Speaker 2: We caught one hundred and twenty five calves in twenty

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Speaker 2: twenty one. In twenty twenty two we were able to

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Speaker 2: catch one hundred and two. We weren't even able to

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Speaker 2: catch all one hundred and twenty five. The helicopter couldn't

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Speaker 2: locate enough calves to keep going at that point, and

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Speaker 2: it is a bit of a cost prohibitive effort. Their

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Speaker 2: helicopter capture rates are extremely expensive and it is stressful

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Speaker 2: on the animals. So we made a call to stop

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Speaker 2: at that point. And this is the third and what

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Speaker 2: we think is our final year of doing this uh

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Speaker 2: to look at calf survival estimates and is there options

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Speaker 2: for the agency to make management actions that can improve

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Speaker 2: calf survival?

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Speaker 1: So with with that said, make management decisions what in

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Speaker 1: your opinion, like you is there and we're going to

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Speaker 1: get into this more. I maybe jumping ahead on my

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Speaker 1: own my own topics here, but what so I look

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Speaker 1: at it from like if if maybe causes aren't getting pregnant,

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Speaker 1: like do you need to have more balls on the

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Speaker 1: landscape or you know, so some of this like what

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Speaker 1: can be made based on calf survival? I mean, we

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Speaker 1: we know what's happening by predators. Does it need to

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Speaker 1: be different predator seasons or is there anything within what

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Speaker 1: I would consider like agency decisions, Is there anything that

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Speaker 1: can be done to actually help that number?

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Speaker 2: So there are a number of things that can be done.

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Speaker 2: I'll start with the A biotic factors, climate, how good

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Speaker 2: a shape is the range in? You know, are there

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Speaker 2: things that we can do habitat wise, is there is

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Speaker 2: the data indicating there might be a habitat factor? Are

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Speaker 2: we seeing calves starve to death in the winter? Are

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Speaker 2: we seeing calves starve to death in the summer because

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Speaker 2: the cows can't get enough nutrition to have enough lactation

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Speaker 2: ability to feed the calves. If we're seeing predation as

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Speaker 2: a limiting factor, can we change predator numbers for a

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Speaker 2: short period of time to try and get the population boosted.

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Speaker 2: So those are things the agency could consider. Our data

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Speaker 2: at this point is really pointing towards calves not surviving

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Speaker 2: because of predation. We're seeing predation be account for seventy

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Speaker 2: eight percent of the mortalities that are occurring, and that's

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Speaker 2: the first two year average, and of that, sixty two

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Speaker 2: of the calves dying or dying from cougars. So it

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Speaker 2: really points that cougar predation is the leading cause of mortality. Now,

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Speaker 2: the one thing we can't determined is are they predisposed

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Speaker 2: for some reason to cougar predation, And you know that's

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Speaker 2: a really difficult thing to address. Are the calves in

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Speaker 2: poor condition or are they not. Calves really don't have

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Speaker 2: much body fat the first couple of months of life,

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Speaker 2: so we can't look at them like an adult elk,

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Speaker 2: where if it died in the winter, we can look

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Speaker 2: at bone marrow or percent body fat and see if

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Speaker 2: they were not likely going to make it anyway, and

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Speaker 2: that's why they were predated and that's where wolves, you know,

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Speaker 2: they tend to take the old and the young and

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Speaker 2: the susceptible on a lot of a lot of their

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Speaker 2: kills are animals that wouldn't have made it, not all

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Speaker 2: by any means. But we can't look at that with

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Speaker 2: elk calves. So all we can say is sixty two

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Speaker 2: percent of the calves are dying from cougars at this point.

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Speaker 1: Gotcha, So what percentage of calves? I mean you may

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Speaker 1: have alluded to it. You need twenty five percent? Here

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Speaker 1: in the blues is the determinate, you know, So what

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Speaker 1: what percentage of calves do you need to make it

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Speaker 1: through in order to maintain which you've said twenty five?

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Speaker 1: I believe it? Correct me if i'm.

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Speaker 2: Well, we use a ratio of caves per hundred cows.

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Speaker 2: So if you have twenty five cows per hundred cows,

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Speaker 2: break it down fifty to fifty by sexes, so you

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Speaker 2: have twelve and a half male twelve and a half

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Speaker 2: female per hundred cows, and average adult cow survival. You know,

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Speaker 2: good cow survivals ninety percent, So annually across your heart,

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Speaker 2: you know you can see as low eighty five to

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Speaker 2: ninety percent is kind of average, So you need to

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Speaker 2: replace ten to fifteen per hundred, so that gets you

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Speaker 2: ballpark needing twenty five and that'll maintain maintain your population stable.

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Speaker 1: Okay, And then is there a point in which those calves,

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Speaker 1: I mean you you we talked earlier, and we'll get

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Speaker 1: into the story of you going up finding the mortality

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Speaker 1: calf yesterday. But is there a point in which they

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Speaker 1: have a better chance of surviving because it sounds like

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Speaker 1: a lot of these calves are very susceptible very early

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Speaker 1: in life when they you know, the mom beds them down.

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Speaker 1: They're they're basically cougar bait at that point. But what's

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Speaker 1: the data show as far as caller tracking that they've

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Speaker 1: got a good chance at making it.

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Speaker 2: So for our data, we're seeing the highest mortality in

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Speaker 2: the first three to four months. We have not documented

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Speaker 2: a calf dying after one hundred and fifty days of

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Speaker 2: being alive in the last two years, so our winner

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Speaker 2: survival has been one hundred percent. So we really see

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Speaker 2: that zero to three months is when a majority of

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Speaker 2: them are dying. It's really consistent. We haven't I mean,

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Speaker 2: the numbers tape are off pretty quick. If you're losing

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Speaker 2: the percent we're losing the first three months. But the

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Speaker 2: first year we were doing this, you know, we were

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Speaker 2: running sometimes two to three mortalities a day after we

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Speaker 2: were done capture.

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Speaker 1: That's crazy. So yeah, we looked at the charts a

402
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Speaker 1: little bit. I think it's crazy. It's a reverse bill

403
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Speaker 1: curve and you lose about seventy I don't know what

404
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Speaker 1: the data is. You lose the majority of your calves

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Speaker 1: in those firste hundred twenty hundred fifty days, and after

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Speaker 1: that the line's straight. You don't lose any more that

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Speaker 1: are colored.

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Speaker 2: Yeah, which is really surprising because there still should be

409
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Speaker 2: some sources of mortality occurring. And one possible explanation is

410
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Speaker 2: is we've lost so many calves by that time. Hits

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Speaker 2: are sample sizes pretty small through winter. I think we

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Speaker 2: had like thirteen calves going into the winter the first year,

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Speaker 2: and last year we had a problem with callers, so

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Speaker 2: in September October we lost thirty six collars defenses. So

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Speaker 2: these are expandable colors with just pleats that have a

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Speaker 2: couple of stitches in them, so as the calf grows,

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Speaker 2: it grows with the calf, so we don't cause harm

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Speaker 2: to them. But if they catch on a stick or

419
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Speaker 2: if barberire fence or something like that, they can pull

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Speaker 2: all the pleats and the collar becomes too big and

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Speaker 2: just falls off the calf. So we lost a lot

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Speaker 2: last year.

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Speaker 1: Yeah, And another thing to get some of your predation number,

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Speaker 1: It sounds like you guys are doing something where you're

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Speaker 1: sending in saliva swabs now on the study. And what

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Speaker 1: that is, my understanding is so that you guys can

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Speaker 1: confirm what your visual determining was the cause of death.

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Speaker 1: Is that there's maybe some saliva that matches up with

429
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Speaker 1: the biologists and their confirmation of what killed the calf.

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Speaker 2: Yeah, So we still do a full knee cropsy in

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Speaker 2: the field, which we can go into that here in

432
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Speaker 2: a minute if you want. But we're identifying the bite

433
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Speaker 2: marks or the attack sites, not the feeding sites, but

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Speaker 2: the attack sites, and we are swabbing for DNA on

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Speaker 2: there and sending it to the University of Washington, and

436
00:22:23,640 --> 00:22:26,800
Speaker 2: they're identifying the species of animal that bit at those

437
00:22:26,800 --> 00:22:30,240
Speaker 2: sites we identified, and they can actually take it down

438
00:22:30,280 --> 00:22:33,640
Speaker 2: to the individual animal level. So we're hoping to look

439
00:22:33,680 --> 00:22:38,800
Speaker 2: and see the intent kind of is one cougar killing

440
00:22:38,840 --> 00:22:42,119
Speaker 2: three or four calves or is it a unique cougar

441
00:22:42,200 --> 00:22:45,560
Speaker 2: for every calf and that kind of it definitely comes

442
00:22:45,600 --> 00:22:48,320
Speaker 2: back to the behavior of the cougars and the territoriality.

443
00:22:48,400 --> 00:22:50,840
Speaker 2: So this distribution of where the calves die.

444
00:22:51,000 --> 00:22:53,720
Speaker 1: That's yeah, that'd be interesting, just if nothing else is

445
00:22:53,760 --> 00:22:56,080
Speaker 1: to look at, Like is one cougar killing five calves

446
00:22:56,119 --> 00:22:58,560
Speaker 1: on a ridge or is it five different cougars and

447
00:22:58,680 --> 00:23:01,080
Speaker 1: you know they're their dominant home range, Like how does

448
00:23:01,119 --> 00:23:04,280
Speaker 1: that the social you know, the social all that stuff

449
00:23:04,280 --> 00:23:06,080
Speaker 1: that we think we know about cougars, But are there

450
00:23:06,119 --> 00:23:10,120
Speaker 1: multiple cougars one area that calving season they maybe disrupt

451
00:23:10,200 --> 00:23:12,439
Speaker 1: their home territory or you know, I don't know what

452
00:23:12,480 --> 00:23:13,720
Speaker 1: you're going to gather out of that, but it'd be

453
00:23:13,720 --> 00:23:17,479
Speaker 1: definitely interesting to see the data on that.

454
00:23:17,800 --> 00:23:19,879
Speaker 2: Yeah, and I've seen the first year, but we definitely

455
00:23:19,880 --> 00:23:23,600
Speaker 2: need to overlay it spatially to It was for the

456
00:23:23,640 --> 00:23:25,960
Speaker 2: most part, all unique cougars the first year, but if

457
00:23:25,960 --> 00:23:28,800
Speaker 2: they're all a certain distance apart, you would expect them

458
00:23:28,840 --> 00:23:31,520
Speaker 2: to be unique cougars based on their own territorial behavior.

459
00:23:31,600 --> 00:23:33,720
Speaker 1: Yeah, I mean, because around home, I don't know if

460
00:23:33,800 --> 00:23:36,159
Speaker 1: blues is different or the densities higher on cougars, I

461
00:23:36,200 --> 00:23:38,359
Speaker 1: obviously compared to where we're out at home. But you know,

462
00:23:38,359 --> 00:23:40,439
Speaker 1: we always hear people talk about like twenty five to

463
00:23:40,440 --> 00:23:43,040
Speaker 1: fifty square miles for an adult male tom and that

464
00:23:43,320 --> 00:23:46,160
Speaker 1: I can't imagine that would work here because it would encompass,

465
00:23:46,240 --> 00:23:48,320
Speaker 1: you know, two or three male tom for this entire

466
00:23:48,359 --> 00:23:49,080
Speaker 1: area that we're in.

467
00:23:49,400 --> 00:23:51,760
Speaker 2: Yeah, and we did do cougar work here in the Blues,

468
00:23:51,840 --> 00:23:53,959
Speaker 2: I think in two thousand and nine through twenty twelve.

469
00:23:54,440 --> 00:23:56,960
Speaker 2: And the numbers are probably not correct on the top

470
00:23:57,000 --> 00:23:58,359
Speaker 2: of my head, but I thought it was about forty

471
00:23:58,400 --> 00:24:01,720
Speaker 2: five to sixty square kilometer for a female and roughly

472
00:24:01,720 --> 00:24:03,640
Speaker 2: one hundred and fifty square kilometers for a male.

473
00:24:04,720 --> 00:24:07,119
Speaker 1: Is a sot I have to make a joke on kilometers?

474
00:24:07,200 --> 00:24:09,800
Speaker 1: Is it Sultin County the only county in the state

475
00:24:09,840 --> 00:24:11,040
Speaker 1: that still uses kilometers?

476
00:24:11,600 --> 00:24:14,960
Speaker 2: Yes, since the nineteen seventies, is what I was told.

477
00:24:15,119 --> 00:24:17,840
Speaker 1: Yeah, that was my joke. On the intro, I was

478
00:24:17,920 --> 00:24:20,800
Speaker 1: following it's not mile posts here, it's kilometer posts, and

479
00:24:21,320 --> 00:24:23,359
Speaker 1: the cabin's twenty seven kilometers. So I found that a

480
00:24:23,359 --> 00:24:27,960
Speaker 1: little funny fact about a Sultin County. So let's get

481
00:24:28,000 --> 00:24:30,000
Speaker 1: back to how you would normally, let's say, without the

482
00:24:30,040 --> 00:24:33,440
Speaker 1: slava swabs confirming what do you I want to get

483
00:24:33,480 --> 00:24:35,439
Speaker 1: too gruesome. But we talked about a little bit like

484
00:24:35,520 --> 00:24:39,200
Speaker 1: being able to visually identify what predator you believe has

485
00:24:40,040 --> 00:24:43,240
Speaker 1: killed the animals. So if you go through cougars, bears,

486
00:24:43,240 --> 00:24:45,160
Speaker 1: and wolves and how you would identify those ones.

487
00:24:45,359 --> 00:24:47,840
Speaker 2: Yeah, I'll start with cougar just because it's the one

488
00:24:47,880 --> 00:24:50,879
Speaker 2: we see the most of. You know, when we're walking

489
00:24:50,920 --> 00:24:52,959
Speaker 2: in on a scene, you know, we're looking for tracks,

490
00:24:52,960 --> 00:24:56,840
Speaker 2: if there's a potential for tracks, we're looking for casing

491
00:24:56,920 --> 00:24:59,960
Speaker 2: of the animal. So cougars definitely tend to scrape grass

492
00:25:00,080 --> 00:25:02,880
Speaker 2: and brush and stuff and bury their their kill after

493
00:25:02,880 --> 00:25:08,000
Speaker 2: they're done with their feeding. Then we're gonna skin the animal,

494
00:25:08,040 --> 00:25:10,359
Speaker 2: so we're gonna you know, examine the outside look for bite,

495
00:25:10,359 --> 00:25:13,680
Speaker 2: wound scratches, something like that, and then we do skin

496
00:25:13,720 --> 00:25:18,639
Speaker 2: the entire carcass. We're ignoring the feeding site for the

497
00:25:18,680 --> 00:25:21,879
Speaker 2: most part. We're actually looking for hemorrhaging underneath the skin

498
00:25:22,440 --> 00:25:25,520
Speaker 2: that shows the animal was still alive when it was attacked,

499
00:25:25,560 --> 00:25:30,440
Speaker 2: so you're gonna see bruising. We use the term grape

500
00:25:30,480 --> 00:25:32,560
Speaker 2: jelly in terms of wolves because they bite so hard

501
00:25:32,600 --> 00:25:35,000
Speaker 2: that it causes the muscles and the cells to break apart.

502
00:25:35,400 --> 00:25:39,040
Speaker 2: So we want to see pre mortem are before death

503
00:25:39,080 --> 00:25:42,880
Speaker 2: bleeding as an indicator that the animal was actually killed.

504
00:25:43,240 --> 00:25:45,199
Speaker 2: If we can't find that, we can't confirm that the

505
00:25:45,240 --> 00:25:49,480
Speaker 2: animal is actually alive and it's not scavenging. So cougars,

506
00:25:49,480 --> 00:25:51,520
Speaker 2: you know, we skin it out these Some of these

507
00:25:51,560 --> 00:25:54,920
Speaker 2: calves are so small, you know, they're roughly thirty to

508
00:25:54,960 --> 00:25:58,600
Speaker 2: fifty pounds at this zero to seven day age. You

509
00:25:58,600 --> 00:25:59,840
Speaker 2: can skin it out a lot of times in the

510
00:25:59,840 --> 00:26:01,720
Speaker 2: whole to hide up to the sun and you can

511
00:26:01,920 --> 00:26:05,720
Speaker 2: see the scratch marks from the four talents down the back.

512
00:26:07,640 --> 00:26:10,840
Speaker 2: You can see that that was actually bruising occurred before alive,

513
00:26:11,119 --> 00:26:13,840
Speaker 2: but you won't see that from the outside. It's kind

514
00:26:13,880 --> 00:26:16,760
Speaker 2: of been a unique thing to see that you can

515
00:26:16,760 --> 00:26:19,120
Speaker 2: look at a calf and it looks like you can't

516
00:26:19,119 --> 00:26:21,600
Speaker 2: see from the outside that it was actually physically attacked,

517
00:26:22,000 --> 00:26:24,199
Speaker 2: but if you skin it out and hold it up

518
00:26:24,200 --> 00:26:26,240
Speaker 2: to the sun, you can see the claw marks going

519
00:26:26,280 --> 00:26:31,120
Speaker 2: down its back. Inside. Cougars are really quick and effective

520
00:26:31,119 --> 00:26:33,080
Speaker 2: at killing these things. We're not seeing a lot of

521
00:26:33,200 --> 00:26:37,200
Speaker 2: damage to these things, and then they typically feed around

522
00:26:37,240 --> 00:26:40,720
Speaker 2: the paunch their first feeding is near the paunch. We're

523
00:26:40,760 --> 00:26:44,120
Speaker 2: getting to these things, so the callers are set. If

524
00:26:44,119 --> 00:26:46,119
Speaker 2: that doesn't move for four hours, we get a text

525
00:26:46,119 --> 00:26:48,719
Speaker 2: in an email that something's not right. And we've been

526
00:26:48,760 --> 00:26:51,760
Speaker 2: getting to these things within twenty four hours a lot

527
00:26:51,760 --> 00:26:54,560
Speaker 2: of times the same day, so they haven't had time

528
00:26:54,600 --> 00:26:58,320
Speaker 2: to be scavenged yet, and you know, those are the

529
00:26:58,320 --> 00:27:02,840
Speaker 2: indicators we're looking for. With cougars, bears tend to attack

530
00:27:02,920 --> 00:27:04,840
Speaker 2: on the back of the animal, bite the back of

531
00:27:04,880 --> 00:27:07,400
Speaker 2: the neck, the back of the shoulders, and when they

532
00:27:07,400 --> 00:27:09,840
Speaker 2: feed on it, they pretty much open it up in

533
00:27:09,840 --> 00:27:14,520
Speaker 2: one spot and skin it out extremely cleanly, and they

534
00:27:14,520 --> 00:27:18,240
Speaker 2: skin it out until it's inside out. There's a number

535
00:27:18,240 --> 00:27:20,880
Speaker 2: of times we find they look like an elk calf

536
00:27:20,960 --> 00:27:24,680
Speaker 2: sock puppet turned inside out, so that's a really good

537
00:27:24,680 --> 00:27:27,439
Speaker 2: indicator of a bear. Bears also leave a lot of

538
00:27:27,480 --> 00:27:30,760
Speaker 2: scat in the area they can cash, but we haven't

539
00:27:30,760 --> 00:27:32,520
Speaker 2: seen that nearly as much, and they tend to eat

540
00:27:32,520 --> 00:27:36,120
Speaker 2: a lot more of it initially. Wolves we've only had

541
00:27:36,119 --> 00:27:39,720
Speaker 2: three wolf kills on calves in two years, and they've

542
00:27:40,160 --> 00:27:43,880
Speaker 2: in essence disarticulated the animal and spread it across the hillside.

543
00:27:44,320 --> 00:27:47,240
Speaker 2: There's just little pieces left, but they also usually tend

544
00:27:47,280 --> 00:27:49,000
Speaker 2: to leave a lot of tracks and sign as well.

545
00:27:50,040 --> 00:27:54,000
Speaker 2: We have had one bobcat, a couple of coyotes, but

546
00:27:54,040 --> 00:27:56,440
Speaker 2: for the most part, you know, the big three or

547
00:27:56,760 --> 00:27:58,439
Speaker 2: what we're seeing more of.

548
00:27:59,200 --> 00:28:03,320
Speaker 1: Yeah, and then so to accomplish this, we talked about

549
00:28:03,359 --> 00:28:04,639
Speaker 1: me being you know, I always like to know what

550
00:28:04,640 --> 00:28:07,399
Speaker 1: I'm doing in glass night spots. You're going out on

551
00:28:07,400 --> 00:28:09,760
Speaker 1: a high ridge, glassing into these pockets that are known

552
00:28:09,840 --> 00:28:12,560
Speaker 1: to be nurseries or where cows are going to take

553
00:28:12,600 --> 00:28:15,159
Speaker 1: their calves to be comfortable. You're glassing them and then

554
00:28:15,160 --> 00:28:19,280
Speaker 1: you wait for that cow to stash the calf and leave,

555
00:28:19,320 --> 00:28:21,360
Speaker 1: and then you you're able to just walk in. And

556
00:28:21,520 --> 00:28:23,280
Speaker 1: you had mentioned and I'm going to get the term wrong.

557
00:28:23,320 --> 00:28:25,919
Speaker 1: I'm not even gonna mention it that some people believe

558
00:28:25,960 --> 00:28:30,239
Speaker 1: that the elk calve kind of pre wired for their

559
00:28:30,280 --> 00:28:32,760
Speaker 1: heart rate to actually go down to maybe as they

560
00:28:32,760 --> 00:28:33,400
Speaker 1: get spooked.

561
00:28:33,440 --> 00:28:36,439
Speaker 2: So yeah, that term is a fright bray of cardia.

562
00:28:37,040 --> 00:28:41,080
Speaker 2: And we've definitely noticed it on these you know, zero

563
00:28:41,160 --> 00:28:44,000
Speaker 2: to two day old calves. You walk in and they

564
00:28:44,000 --> 00:28:46,880
Speaker 2: don't even move for you. They're really quiet, really mellow.

565
00:28:46,920 --> 00:28:49,680
Speaker 2: Their breathing is quite slow and it's not going to

566
00:28:49,760 --> 00:28:52,160
Speaker 2: last very long. You can stimulate them out of it too,

567
00:28:53,200 --> 00:28:56,040
Speaker 2: But the those one day old calves are extremely easy

568
00:28:56,080 --> 00:28:59,360
Speaker 2: to handle. We don't have to hobble home, we don't

569
00:28:59,480 --> 00:29:01,080
Speaker 2: put a blind fold on them, and we can do

570
00:29:01,120 --> 00:29:04,600
Speaker 2: everything except ear tag them without holding them down. We

571
00:29:04,680 --> 00:29:08,280
Speaker 2: get weight, sex look for things to help us age them,

572
00:29:08,600 --> 00:29:12,040
Speaker 2: such as their insize, their growth of their teeth, the

573
00:29:12,040 --> 00:29:15,479
Speaker 2: color and softness of their dew claws and hoofs, and

574
00:29:15,800 --> 00:29:20,480
Speaker 2: the umbilical cord attachment point umbilicus, whether it's still bloody

575
00:29:20,680 --> 00:29:24,520
Speaker 2: scab dried, and being able to see them walk a

576
00:29:24,520 --> 00:29:26,720
Speaker 2: lot of times as a good indicator too. They're pretty

577
00:29:26,760 --> 00:29:30,080
Speaker 2: bow legged and bent knees and unstable the first two days.

578
00:29:31,680 --> 00:29:34,240
Speaker 2: By day six, day seven, they're starting to run around

579
00:29:34,280 --> 00:29:37,960
Speaker 2: the hill pretty fast, far faster than we are able

580
00:29:37,960 --> 00:29:38,560
Speaker 2: to catch them.

581
00:29:39,400 --> 00:29:41,880
Speaker 1: Gotchi, I know you had mentioned if we see one

582
00:29:41,880 --> 00:29:44,120
Speaker 1: walking pretty well behind its mom, we're not going after

583
00:29:44,160 --> 00:29:45,560
Speaker 1: that one because we're not going to catch it.

584
00:29:45,640 --> 00:29:48,600
Speaker 2: So yeah, we've learned a lot in the three years

585
00:29:48,600 --> 00:29:50,760
Speaker 2: of doing this, and there's times that we can try.

586
00:29:51,240 --> 00:29:53,560
Speaker 2: I mean, mom still will bed them down somewhere and

587
00:29:53,680 --> 00:29:56,400
Speaker 2: leave for hours at a time, and if they're in

588
00:29:56,440 --> 00:29:58,560
Speaker 2: thick enough brush, we can sneak in and try and

589
00:29:58,640 --> 00:30:01,240
Speaker 2: catch them by we definitely miss occasionally.

590
00:30:01,480 --> 00:30:05,560
Speaker 1: And then just to coordinate, you correlate your data with

591
00:30:05,600 --> 00:30:07,760
Speaker 1: what we interviewed Brock with We're about a week apart

592
00:30:07,760 --> 00:30:10,360
Speaker 1: actually recording these two podcasts, and he had mentioned you

593
00:30:10,440 --> 00:30:12,640
Speaker 1: mentioned a week ago we were in our hot point

594
00:30:12,680 --> 00:30:14,800
Speaker 1: and he was about a week ago on Thursday. So

595
00:30:15,160 --> 00:30:17,120
Speaker 1: to kind of answer our question on latitude and how

596
00:30:17,800 --> 00:30:19,720
Speaker 1: you know it should have an effect but doesn't seem

597
00:30:19,760 --> 00:30:21,280
Speaker 1: to have much of an effect on ELK. So at

598
00:30:21,360 --> 00:30:25,080
Speaker 1: least from southeast Washington down to most of Utah, it

599
00:30:25,080 --> 00:30:27,760
Speaker 1: doesn't seem to be much effect at all when that

600
00:30:28,600 --> 00:30:32,040
Speaker 1: like peak of Calves is hitting. Which is a little

601
00:30:32,080 --> 00:30:35,640
Speaker 1: bit interesting that there's no real change as you move,

602
00:30:36,280 --> 00:30:39,360
Speaker 1: you know, down latitude or up latitude, at least within

603
00:30:39,600 --> 00:30:43,840
Speaker 1: the data we have from me hunting New Mexico to Washington,

604
00:30:43,880 --> 00:30:45,960
Speaker 1: the UT seems to go incide at the same time

605
00:30:46,000 --> 00:30:46,680
Speaker 1: in the same dates.

606
00:30:46,800 --> 00:30:49,560
Speaker 2: Yeah, roughly September nineteenth to twenty first, is going to

607
00:30:49,600 --> 00:30:51,080
Speaker 2: be your pig of breeding.

608
00:30:51,280 --> 00:31:08,959
Speaker 1: Yep. So one thing I want to jump into. Uh,

609
00:31:09,560 --> 00:31:12,800
Speaker 1: we talked a little bit about carrying capacity in the blues,

610
00:31:13,560 --> 00:31:15,800
Speaker 1: especially where we're at. I don't think we need to

611
00:31:16,480 --> 00:31:18,360
Speaker 1: disclod you know, hide the location. But we're here in

612
00:31:18,400 --> 00:31:20,600
Speaker 1: the Lick Creek unit currently, and I know you had

613
00:31:20,640 --> 00:31:24,120
Speaker 1: mentioned earlier that the data shows about five hundred plus

614
00:31:24,160 --> 00:31:26,560
Speaker 1: or minus. Maybe a little bit of a lack of

615
00:31:26,680 --> 00:31:29,760
Speaker 1: mature bowls is really what's limiting the you know what

616
00:31:29,800 --> 00:31:32,040
Speaker 1: people would call the big bowl tags, the quality tags.

617
00:31:32,840 --> 00:31:35,800
Speaker 1: Where there's a unit, you know, across adjacent to it

618
00:31:35,800 --> 00:31:37,880
Speaker 1: that seems to have double the carrying capacity for a

619
00:31:37,920 --> 00:31:42,320
Speaker 1: similar you know type unit is there in your research

620
00:31:42,400 --> 00:31:44,800
Speaker 1: and you just being in the area. Is there any

621
00:31:44,800 --> 00:31:48,200
Speaker 1: good explanation for why adjacent units one will be doing

622
00:31:48,200 --> 00:31:50,200
Speaker 1: better than the other have double the elk? Is it

623
00:31:50,320 --> 00:31:53,560
Speaker 1: historically always carried more? What are we seeing here on

624
00:31:53,600 --> 00:31:55,880
Speaker 1: why maybe one unit is doing so much better than

625
00:31:55,880 --> 00:31:56,840
Speaker 1: an adjacent unit.

626
00:31:58,440 --> 00:32:02,680
Speaker 2: That's a tough question to answer. Historically, the Lick Creek

627
00:32:02,760 --> 00:32:05,760
Speaker 2: unit had a thousand elk in it. Eight years ago

628
00:32:05,840 --> 00:32:08,760
Speaker 2: we had a thousand elk here. We noticed half the

629
00:32:08,840 --> 00:32:12,479
Speaker 2: unit start to decline pretty quick around twenty sixteen. We

630
00:32:12,560 --> 00:32:16,280
Speaker 2: missed our opportunity to start collaring animals at that time

631
00:32:16,360 --> 00:32:19,120
Speaker 2: to figure out why, and there's a good reason. The

632
00:32:19,120 --> 00:32:21,040
Speaker 2: agency only has so much money to go around. It's

633
00:32:21,040 --> 00:32:24,960
Speaker 2: hard to in expensive you go start capturing elk with

634
00:32:25,000 --> 00:32:29,160
Speaker 2: the hope that it was a short term thing. The

635
00:32:29,200 --> 00:32:32,360
Speaker 2: Mountain View unit to the south of US has remained

636
00:32:32,400 --> 00:32:35,880
Speaker 2: relatively stable at a thousand elk, but they still don't

637
00:32:35,880 --> 00:32:38,320
Speaker 2: have good calf survival down there, but the adults seem

638
00:32:38,360 --> 00:32:40,960
Speaker 2: to be doing at least we're not losing the adults

639
00:32:41,040 --> 00:32:44,040
Speaker 2: or they're remaining relatively stable. It's a much slower decline.

640
00:32:45,240 --> 00:32:47,480
Speaker 2: I don't think it's a caring capacity issue. It's a

641
00:32:47,520 --> 00:32:51,960
Speaker 2: survival issue. What's causing survival to be higher down there

642
00:32:52,480 --> 00:32:55,680
Speaker 2: than here? And I don't have a good answer for

643
00:32:55,840 --> 00:33:00,160
Speaker 2: why survival is better down there. The elk here have

644
00:33:00,520 --> 00:33:03,560
Speaker 2: actually pretty good winter range, and unfortunately a lot of

645
00:33:03,640 --> 00:33:05,440
Speaker 2: our elk in the Lick Creek unit are going to

646
00:33:05,440 --> 00:33:09,719
Speaker 2: private land throughout the winter. It's hard to compete with

647
00:33:10,080 --> 00:33:13,600
Speaker 2: farmers and winter wheat and canolae even with a nice

648
00:33:13,640 --> 00:33:17,280
Speaker 2: healthy bunch grass ecosystem that we have here. The Mountain

649
00:33:17,360 --> 00:33:19,960
Speaker 2: View elk, a good percentage of them go to a

650
00:33:20,000 --> 00:33:23,400
Speaker 2: feed lot in Oregon and get fed all winter. But

651
00:33:23,440 --> 00:33:27,400
Speaker 2: there's still predation happening down there. There's still probably similar

652
00:33:27,480 --> 00:33:29,920
Speaker 2: numbers of cougar down there. There's wolves on the landscape

653
00:33:29,920 --> 00:33:34,160
Speaker 2: in both places. The winners are probably more severe actually

654
00:33:34,160 --> 00:33:37,200
Speaker 2: down in Mountain View. So I don't have a good

655
00:33:37,240 --> 00:33:41,280
Speaker 2: answer as to why some units performing differently than others.

656
00:33:41,440 --> 00:33:44,240
Speaker 1: And calf survival you mentioned, it's just the adult survival

657
00:33:44,280 --> 00:33:49,239
Speaker 1: of calf, calf recruitment or you know, calf mortalities are

658
00:33:49,280 --> 00:33:51,080
Speaker 1: real similar in those two units. You're just able to

659
00:33:51,080 --> 00:33:53,720
Speaker 1: hold that higher number due to adult elk making it through.

660
00:33:54,080 --> 00:33:56,640
Speaker 2: Yeah, and it's there is a difference, but it's not

661
00:33:56,720 --> 00:33:59,200
Speaker 2: a huge difference. I mean we're still below twenty five

662
00:33:59,280 --> 00:34:01,280
Speaker 2: down there. I think a couple of years in a

663
00:34:01,360 --> 00:34:03,360
Speaker 2: row the feed lot was with six hundred elk on

664
00:34:03,400 --> 00:34:07,959
Speaker 2: it was averaging twelve calves per hundred. Yeah, it's it's

665
00:34:08,000 --> 00:34:10,800
Speaker 2: a tough one. You know, we have private land adjacent

666
00:34:10,800 --> 00:34:12,440
Speaker 2: where these things are on private land, and you know

667
00:34:12,440 --> 00:34:14,280
Speaker 2: they're doing a little bit better. There's probably a different

668
00:34:14,320 --> 00:34:17,000
Speaker 2: risk on the landscape in private land where there's a

669
00:34:17,040 --> 00:34:22,480
Speaker 2: lot more people available for everything to interact with. So

670
00:34:24,080 --> 00:34:26,400
Speaker 2: it's I don't have a good answer. The Dayton unit's

671
00:34:26,440 --> 00:34:29,280
Speaker 2: doing really poorly. I mean that's gone from a thousand

672
00:34:29,280 --> 00:34:31,080
Speaker 2: elk down to like three hundred and fifty elk and

673
00:34:31,120 --> 00:34:33,120
Speaker 2: we had only nine calves per hundred in there this

674
00:34:33,200 --> 00:34:34,840
Speaker 2: year when we did our aerial survey.

675
00:34:35,560 --> 00:34:39,400
Speaker 1: And it's yeah, it's just it's tough, you know, what's happening.

676
00:34:39,960 --> 00:34:42,760
Speaker 1: And how come. One of the things we talked about

677
00:34:42,800 --> 00:34:47,600
Speaker 1: maybe affecting it is you know, climate change, and what

678
00:34:47,640 --> 00:34:50,080
Speaker 1: I mean by that is the extreme weather swings. We

679
00:34:50,200 --> 00:34:53,759
Speaker 1: may have you know, a bad an extreme winner every

680
00:34:53,760 --> 00:34:55,960
Speaker 1: third year, but in between those, you mix in an

681
00:34:55,960 --> 00:34:59,120
Speaker 1: extreme drought at the same time. And so while you're

682
00:34:59,200 --> 00:35:02,319
Speaker 1: maybe struggling for this CAFF recruitment, which may it may

683
00:35:02,360 --> 00:35:06,440
Speaker 1: play into it, we're not being helped at all by

684
00:35:06,440 --> 00:35:08,560
Speaker 1: the weather r It's not given us a favor. So

685
00:35:08,600 --> 00:35:13,000
Speaker 1: we're trying to comp compound poor CAF survivability in between this,

686
00:35:13,080 --> 00:35:15,640
Speaker 1: but then get dished out some crappy winters in some

687
00:35:15,880 --> 00:35:16,680
Speaker 1: dry summers.

688
00:35:16,880 --> 00:35:20,160
Speaker 2: Yeah. I mean, in the last eight years, we've had

689
00:35:20,400 --> 00:35:24,200
Speaker 2: two severe winners, the winner of sixteen seventeen and eighteen nineteen,

690
00:35:26,000 --> 00:35:29,640
Speaker 2: probably one of the worst winters in fifty years that

691
00:35:29,680 --> 00:35:31,960
Speaker 2: people recall in the Blue mountains, and there's some metrics

692
00:35:31,960 --> 00:35:35,480
Speaker 2: to support that through the National Weather Service. We've also

693
00:35:35,520 --> 00:35:39,920
Speaker 2: had the drought of twenty fifteen. We also had a

694
00:35:40,040 --> 00:35:43,680
Speaker 2: twenty one drought that was some of those temperatures that

695
00:35:43,760 --> 00:35:45,440
Speaker 2: set all time records. It was like one hundred and

696
00:35:45,480 --> 00:35:50,560
Speaker 2: twenty two degrees in Clarkston in June. Really affects the

697
00:35:51,200 --> 00:35:55,279
Speaker 2: nutrition of the landscape, the available forage. You know, it

698
00:35:55,280 --> 00:35:58,359
Speaker 2: affects pregnancy rates of cows. So if you had a

699
00:35:58,480 --> 00:36:02,200
Speaker 2: drought in twenty fifteen, in a severe winner of sixteen seventeen,

700
00:36:02,719 --> 00:36:04,839
Speaker 2: so if they're bread in the fall of fifteen, they're

701
00:36:04,840 --> 00:36:08,480
Speaker 2: born in sixteen, we count them as calves in seventeen

702
00:36:09,000 --> 00:36:12,680
Speaker 2: following a bad winter. What was the main effect We

703
00:36:12,719 --> 00:36:15,439
Speaker 2: probably will never know, but we do know that those

704
00:36:15,480 --> 00:36:18,520
Speaker 2: things all are cumulative effects. And it's not given this

705
00:36:18,600 --> 00:36:19,880
Speaker 2: population a break right now.

706
00:36:20,160 --> 00:36:23,640
Speaker 1: Yep. Yeah, it's tough. You need you know, it's to

707
00:36:23,719 --> 00:36:25,759
Speaker 1: rebuild this You need all that ideal. You know, you

708
00:36:25,760 --> 00:36:27,840
Speaker 1: don't want your cows going in in bad shape and

709
00:36:27,880 --> 00:36:30,040
Speaker 1: then your calves having to survive that bad winter. It's

710
00:36:30,080 --> 00:36:33,840
Speaker 1: just a bunch of compounding issues that are ultimately not

711
00:36:33,960 --> 00:36:35,760
Speaker 1: helping us out. With the oak population.

712
00:36:36,120 --> 00:36:38,600
Speaker 2: Yeah, in our calf study we did, we started in

713
00:36:38,640 --> 00:36:42,359
Speaker 2: twenty one in a severe drought. Twenty two was one

714
00:36:42,360 --> 00:36:46,680
Speaker 2: of the more wet springs in summers in the Blue Mountains,

715
00:36:46,719 --> 00:36:48,920
Speaker 2: and we don't know what twenty three is going to be.

716
00:36:49,120 --> 00:36:51,799
Speaker 2: I mean, we did see higher survival in twenty two.

717
00:36:52,000 --> 00:36:53,839
Speaker 2: Is it related to the weather? You know? Those are

718
00:36:53,920 --> 00:36:55,640
Speaker 2: really tough questions to tease apart.

719
00:36:55,800 --> 00:37:04,520
Speaker 1: Yep, yep, it's tough. So within management and management strategies,

720
00:37:04,680 --> 00:37:06,799
Speaker 1: what are the bowl to cow ratios here in the

721
00:37:06,800 --> 00:37:09,359
Speaker 1: Blues or does it very tremendously from unit to unit

722
00:37:09,480 --> 00:37:10,839
Speaker 1: or is it fairly.

723
00:37:11,840 --> 00:37:15,680
Speaker 2: It does vary unit to unit. Our goal is twenty

724
00:37:15,840 --> 00:37:19,319
Speaker 2: to twenty five bulls per hundred cows, and twenty five

725
00:37:19,320 --> 00:37:20,759
Speaker 2: bulls per hundred cows is going to give you a

726
00:37:20,760 --> 00:37:25,839
Speaker 2: pretty diverse age ratio and some good quality opportunity. Some

727
00:37:25,920 --> 00:37:28,600
Speaker 2: of our units are running in the low teens. The

728
00:37:28,960 --> 00:37:33,960
Speaker 2: Lick Creek units typically low, you know, the Winnaha typically high,

729
00:37:34,400 --> 00:37:38,080
Speaker 2: much tougher place to hunt. We're fairly conservative with tags

730
00:37:38,120 --> 00:37:41,160
Speaker 2: in there. We also share it with Oregon for management purposes,

731
00:37:42,400 --> 00:37:45,520
Speaker 2: So there is the bowl ratio really is an effect

732
00:37:45,520 --> 00:37:49,040
Speaker 2: of our hunting and we can manipulate that to some degree,

733
00:37:49,080 --> 00:37:51,440
Speaker 2: assuming that there's normal recruitment.

734
00:37:51,640 --> 00:37:54,640
Speaker 1: And so I have to assume that the Blues is

735
00:37:54,640 --> 00:37:58,400
Speaker 1: still being managed for trophy quality. Is that still the intent?

736
00:37:58,680 --> 00:38:00,960
Speaker 1: Or are we getting ourselves to a point where we

737
00:38:01,040 --> 00:38:04,279
Speaker 1: might actually need to be managing for quantity with some

738
00:38:04,320 --> 00:38:08,120
Speaker 1: of the lower numbers, or do you get what I'm saying?

739
00:38:08,160 --> 00:38:10,200
Speaker 1: Like direct me there, Like are we managing for quality

740
00:38:10,239 --> 00:38:11,239
Speaker 1: still or are we gonna have to get to a

741
00:38:11,239 --> 00:38:12,920
Speaker 1: point where, hey, we may just need to manage for

742
00:38:13,280 --> 00:38:15,760
Speaker 1: quantity and you know, opportunity at some point.

743
00:38:16,440 --> 00:38:19,840
Speaker 2: We definitely still are managing for a little bit higher

744
00:38:19,840 --> 00:38:23,120
Speaker 2: bowl ratio than other units in the state, with the

745
00:38:23,120 --> 00:38:27,319
Speaker 2: result being quality, quality, opportunity, quality bowls in terms of

746
00:38:27,360 --> 00:38:31,719
Speaker 2: age structure, you know, mature bowls for big antlers. We

747
00:38:31,760 --> 00:38:34,920
Speaker 2: still do have the spike only general season, so everybody

748
00:38:34,960 --> 00:38:38,160
Speaker 2: gets to hunt. Are we approaching a time to change?

749
00:38:39,200 --> 00:38:41,880
Speaker 2: You know? We rerode our ELK Plan three three to

750
00:38:41,960 --> 00:38:46,680
Speaker 2: five years ago, went through public process, got you know,

751
00:38:46,800 --> 00:38:49,840
Speaker 2: majority support for how we're managing Elk in the Blue Mountains.

752
00:38:50,480 --> 00:38:53,399
Speaker 2: If the constituents of Washington want to change, we can,

753
00:38:53,520 --> 00:38:55,799
Speaker 2: We can definitely you know, go through that process and

754
00:38:55,840 --> 00:38:59,440
Speaker 2: evaluate it and let the majority, you know, rule as

755
00:38:59,440 --> 00:39:01,560
Speaker 2: long as we stay within bounds of you know, a

756
00:39:01,600 --> 00:39:04,680
Speaker 2: healthy herd biologically, so there's enough cows to be bred.

757
00:39:04,719 --> 00:39:07,000
Speaker 2: And you know, I'm a big fan of having some

758
00:39:07,120 --> 00:39:09,160
Speaker 2: bulls diye of old age on the landscape. I think

759
00:39:09,160 --> 00:39:12,320
Speaker 2: that's healthy for an elk population or any hunted population.

760
00:39:13,200 --> 00:39:15,879
Speaker 2: So I don't want to necessarily go down the road

761
00:39:15,920 --> 00:39:19,399
Speaker 2: of having average age of three to five year old

762
00:39:19,400 --> 00:39:21,399
Speaker 2: in a harvest. But if that's what the hunters want,

763
00:39:21,440 --> 00:39:24,560
Speaker 2: we could do that. But there's repercussions for any change

764
00:39:24,560 --> 00:39:25,120
Speaker 2: we make.

765
00:39:25,160 --> 00:39:27,600
Speaker 1: And we're gonna that's my last bullet here at the bottom.

766
00:39:27,640 --> 00:39:30,040
Speaker 1: We've talked about this, I talked about with brock Is.

767
00:39:31,000 --> 00:39:32,200
Speaker 1: As a matter of fact, we probably had a twenty

768
00:39:32,239 --> 00:39:34,080
Speaker 1: minute conversation the day on the hill. Is how do

769
00:39:34,160 --> 00:39:37,880
Speaker 1: you manage for everybody? And I think it's impossible. We're

770
00:39:37,920 --> 00:39:40,080
Speaker 1: going to save that that little bit here for the end,

771
00:39:41,480 --> 00:39:44,799
Speaker 1: So back, let's rewind back to predators. I think that's

772
00:39:44,840 --> 00:39:48,520
Speaker 1: the main purpose of the calf study. There. There's no

773
00:39:48,640 --> 00:39:51,800
Speaker 1: denying apex predators on the ground. You showed me a

774
00:39:51,840 --> 00:39:55,840
Speaker 1: little video yesterday where you thought you had got a beacon.

775
00:39:56,040 --> 00:39:58,400
Speaker 1: You got a signal from a dead calf that you

776
00:39:58,520 --> 00:40:02,120
Speaker 1: had just colored what within the last week. Yes, got

777
00:40:02,120 --> 00:40:04,239
Speaker 1: a caller and that you showed me the video and

778
00:40:04,280 --> 00:40:05,719
Speaker 1: I thought the same thing. You can see the calf

779
00:40:05,719 --> 00:40:08,839
Speaker 1: in the video, and I'll let you take it from there. Yeah.

780
00:40:08,880 --> 00:40:11,680
Speaker 2: So I walked in on this dead calf, and the

781
00:40:11,760 --> 00:40:15,640
Speaker 2: dead calf was there. And as I'm getting close and

782
00:40:16,080 --> 00:40:19,680
Speaker 2: these callers give you a GPS point, I'm using telemetry

783
00:40:19,719 --> 00:40:21,719
Speaker 2: to listen to the calf and I can tell I'm

784
00:40:21,880 --> 00:40:24,239
Speaker 2: probably within ten twenty feet of it at this point.

785
00:40:25,160 --> 00:40:27,239
Speaker 2: Can't see the calf for sure, but I see this

786
00:40:27,239 --> 00:40:31,640
Speaker 2: little tan patch in the shrub and I immediately conclude

787
00:40:31,640 --> 00:40:34,279
Speaker 2: that that's the calf laying there under the shrub, and

788
00:40:34,360 --> 00:40:36,879
Speaker 2: I go walking up to it. I'm maybe six feet

789
00:40:36,920 --> 00:40:39,839
Speaker 2: away at this point, and it starts to move, and

790
00:40:39,880 --> 00:40:42,239
Speaker 2: I'm still thinking, elk calf in my mind that I

791
00:40:42,280 --> 00:40:46,040
Speaker 2: got a false mortality signal from the collar. I'm wondering

792
00:40:46,080 --> 00:40:47,759
Speaker 2: if something's wrong with the calf. And this is all

793
00:40:47,800 --> 00:40:51,239
Speaker 2: happening in probably two three seconds, and I'm debating whether

794
00:40:51,280 --> 00:40:53,920
Speaker 2: I want to grab the calf to check the collar

795
00:40:53,960 --> 00:40:57,120
Speaker 2: fit see if there's something wrong with it when I

796
00:40:57,200 --> 00:41:00,839
Speaker 2: realized it was a cougar or that I was done

797
00:41:00,840 --> 00:41:03,560
Speaker 2: about four feet from at that point in time, and

798
00:41:03,800 --> 00:41:07,560
Speaker 2: it just stood up and slowly walked away through the brush.

799
00:41:07,640 --> 00:41:11,239
Speaker 2: And I quickly backed up a few feet and it

800
00:41:11,280 --> 00:41:14,080
Speaker 2: walked about twenty feet and laid back down, and the

801
00:41:14,160 --> 00:41:16,440
Speaker 2: calf was laying about six feet away, buried under a

802
00:41:16,440 --> 00:41:17,240
Speaker 2: bunch of grass.

803
00:41:17,600 --> 00:41:19,120
Speaker 1: It was about that point you probably wish you had

804
00:41:19,160 --> 00:41:22,480
Speaker 1: some bear spray, maybe brought just to, you know, something

805
00:41:22,520 --> 00:41:26,000
Speaker 1: with you to defend yourself aside from your little pocket knife.

806
00:41:26,080 --> 00:41:28,160
Speaker 2: I wished I had something at that point in time,

807
00:41:28,200 --> 00:41:30,240
Speaker 2: but it was it was one of those things where

808
00:41:30,520 --> 00:41:33,000
Speaker 2: how cool is this? And also am I in a

809
00:41:33,000 --> 00:41:33,560
Speaker 2: bad spot?

810
00:41:34,200 --> 00:41:37,600
Speaker 1: It looked that mature cat. Maybe this is what I

811
00:41:37,640 --> 00:41:40,000
Speaker 1: was guessing, maybe a little bigger in the video.

812
00:41:39,880 --> 00:41:41,960
Speaker 2: I was guessing a little bigger, a little bit. I

813
00:41:42,000 --> 00:41:44,200
Speaker 2: was guessing it was, you know, a tom that it

814
00:41:44,280 --> 00:41:46,759
Speaker 2: might have been over one thirty one forty. But you know,

815
00:41:47,640 --> 00:41:49,799
Speaker 2: it is a really difficult thing though. Look at a cat.

816
00:41:49,880 --> 00:41:51,320
Speaker 1: It was just a big It wasn't It wasn't a

817
00:41:51,360 --> 00:41:54,879
Speaker 1: juvenile by any means. It was an adult cat, you know, tough,

818
00:41:54,920 --> 00:41:56,560
Speaker 1: But I was. It was kind of cool just for

819
00:41:56,600 --> 00:41:58,960
Speaker 1: you to capture that and that cat running away, and

820
00:41:59,000 --> 00:42:02,560
Speaker 1: it's you know, literally really in such a short time frame.

821
00:42:02,920 --> 00:42:05,160
Speaker 1: You you were the ones to put a collar and

822
00:42:05,200 --> 00:42:07,600
Speaker 1: an ear tag, and yet within that seven days that

823
00:42:07,600 --> 00:42:10,520
Speaker 1: calf's dead by a cougar. And it's just it's happening

824
00:42:10,520 --> 00:42:13,200
Speaker 1: that quick. And it kind of goes back to those numbers.

825
00:42:13,239 --> 00:42:14,640
Speaker 1: You know that firs one hundred and twenty days is

826
00:42:14,680 --> 00:42:16,960
Speaker 1: really really hard on a calf, you know, almost We

827
00:42:17,000 --> 00:42:18,960
Speaker 1: do a lot of turkey biology and it's like, you know,

828
00:42:19,440 --> 00:42:21,719
Speaker 1: you're lucky your ninety percent chance of dying in that

829
00:42:21,760 --> 00:42:23,560
Speaker 1: first year and then the seventy percent chance of dying

830
00:42:23,560 --> 00:42:25,719
Speaker 1: in your second year. It's just it's almost, you know,

831
00:42:25,920 --> 00:42:27,760
Speaker 1: it's a little eye opening to me. I didn't realize

832
00:42:27,800 --> 00:42:31,080
Speaker 1: that calf mortality was maybe so bad. And it may

833
00:42:31,120 --> 00:42:32,680
Speaker 1: not be like this in every area, but at least

834
00:42:32,719 --> 00:42:34,880
Speaker 1: the Blues has got a cat problem.

835
00:42:36,400 --> 00:42:39,160
Speaker 2: We do have a healthy cougar population in the Blues. Yeah.

836
00:42:39,320 --> 00:42:42,040
Speaker 1: Yeah, So back to my question, I wanted to share

837
00:42:42,040 --> 00:42:43,640
Speaker 1: that little story. I forgot about it earlier when we

838
00:42:43,640 --> 00:42:46,920
Speaker 1: were talking about the predators on the ground. In your opinion,

839
00:42:47,680 --> 00:42:51,120
Speaker 1: can can we as hunters make a difference and put

840
00:42:51,120 --> 00:42:54,040
Speaker 1: a dent into those cats. And I I'm not going

841
00:42:54,120 --> 00:42:57,040
Speaker 1: to get into baiting or hounds or any of that,

842
00:42:57,160 --> 00:43:00,120
Speaker 1: just as hunters with the weapons were given now and

843
00:43:00,200 --> 00:43:02,560
Speaker 1: the tactics that we can implore, Like, is there any

844
00:43:02,560 --> 00:43:05,480
Speaker 1: way we can make a difference on the predators to

845
00:43:05,520 --> 00:43:06,600
Speaker 1: maybe help out.

846
00:43:07,560 --> 00:43:09,960
Speaker 2: I think it's a you have to revert back to

847
00:43:09,960 --> 00:43:14,319
Speaker 2: the biology, and you can't ignore the social constraints and

848
00:43:14,920 --> 00:43:19,680
Speaker 2: the season structure that Washington currently has cougars. You know,

849
00:43:19,880 --> 00:43:23,480
Speaker 2: Washington's currently targeting twelve to sixteen percent is our harvest

850
00:43:23,520 --> 00:43:26,960
Speaker 2: guideline of cougars, and that's what the science shows that

851
00:43:27,000 --> 00:43:30,160
Speaker 2: they reproduce with an excess of say average of fourteen percent,

852
00:43:30,680 --> 00:43:33,080
Speaker 2: So you can harvest fourteen percent a year and the

853
00:43:33,160 --> 00:43:38,080
Speaker 2: population will remain stable. To reduce a cougar population, you

854
00:43:38,160 --> 00:43:41,799
Speaker 2: actually have to hit the cougars quite hard. You know,

855
00:43:42,280 --> 00:43:44,120
Speaker 2: more than thirty percent of the cougars would have to

856
00:43:44,160 --> 00:43:47,440
Speaker 2: be harvested a year just to account for the dispersers

857
00:43:47,440 --> 00:43:52,400
Speaker 2: on the landscape, the territorial nature of it, and boot

858
00:43:52,480 --> 00:43:55,760
Speaker 2: hunters alone, even if we had a year round season

859
00:43:55,800 --> 00:44:00,440
Speaker 2: and multiple tags. They're a secret of animal. It's hunters

860
00:44:00,480 --> 00:44:04,000
Speaker 2: probably can't kill that many cats a year without the

861
00:44:04,080 --> 00:44:07,759
Speaker 2: other tools that you know aren't currently available to us.

862
00:44:09,680 --> 00:44:11,959
Speaker 2: Harvesting cats is not a bad thing in any way.

863
00:44:12,040 --> 00:44:13,840
Speaker 2: I mean, I have a cougar tag in my pocket,

864
00:44:15,440 --> 00:44:23,200
Speaker 2: but we're without political support and commission support, we probably

865
00:44:23,200 --> 00:44:26,520
Speaker 2: wouldn't open our season up to that structure without those

866
00:44:26,520 --> 00:44:29,680
Speaker 2: people buying into it. We used to have a cougar

867
00:44:29,719 --> 00:44:33,160
Speaker 2: season year round season in the Blues for one tag

868
00:44:33,200 --> 00:44:36,879
Speaker 2: per person, and we actually killed less cats pre two

869
00:44:36,920 --> 00:44:39,320
Speaker 2: thousand and eight when we started adjusting our season structure

870
00:44:39,880 --> 00:44:43,719
Speaker 2: than we did once we adjusted it. And there's not

871
00:44:43,760 --> 00:44:45,120
Speaker 2: a lot of people that want to kill a bunch

872
00:44:45,160 --> 00:44:49,400
Speaker 2: of cats. Taxi Ermy's expensive times, expensive gas is expensive,

873
00:44:50,040 --> 00:44:52,080
Speaker 2: and you've got to spend a lot of time.

874
00:44:52,600 --> 00:44:55,120
Speaker 1: Yeah, there's not a lot of proven tactics right to

875
00:44:55,239 --> 00:44:57,520
Speaker 1: come out and be successful like gear and elk glass

876
00:44:57,560 --> 00:45:00,319
Speaker 1: do this, do that? Cats are you know, maybe cut

877
00:45:00,320 --> 00:45:02,879
Speaker 1: a track and start walking it down in your boots.

878
00:45:02,680 --> 00:45:04,960
Speaker 2: And we have people that do that every year successfully.

879
00:45:05,280 --> 00:45:07,920
Speaker 2: We have people that call every year and are successful.

880
00:45:07,920 --> 00:45:11,959
Speaker 2: Some of the times. They're a very difficult animal. Most

881
00:45:12,000 --> 00:45:14,359
Speaker 2: of our harvest is deer, and elk hunters out there

882
00:45:14,400 --> 00:45:17,640
Speaker 2: during modern firearm just encountering an animal, and I think

883
00:45:17,640 --> 00:45:23,160
Speaker 2: it's incidental take to actually target a cougar. Very challenging, Yeah.

884
00:45:23,120 --> 00:45:27,440
Speaker 1: Very tough, So we can't forget about. In my opinion,

885
00:45:27,520 --> 00:45:30,200
Speaker 1: hunters are a predator as well. Belk right, we take them.

886
00:45:30,239 --> 00:45:34,399
Speaker 1: We've got spike season over the counter here in all

887
00:45:34,480 --> 00:45:37,319
Speaker 1: these blues units. We do have the quality bull tags.

888
00:45:37,360 --> 00:45:40,960
Speaker 1: We have some cow tags to In your opinion, our

889
00:45:41,040 --> 00:45:44,920
Speaker 1: hunters having a drastic effect on the population or is

890
00:45:44,960 --> 00:45:48,840
Speaker 1: it a small enough population that it it doesn't calculate

891
00:45:48,880 --> 00:45:50,600
Speaker 1: in I gets if that makes any sense, Like.

892
00:45:50,560 --> 00:45:53,080
Speaker 2: It totally does. In terms of bull as long as

893
00:45:53,120 --> 00:45:56,920
Speaker 2: there's enough bulls to breed the cows. And there's been

894
00:45:56,920 --> 00:46:00,040
Speaker 2: some work done at Starkey and Northeast Oregon. The breeding

895
00:46:00,080 --> 00:46:04,800
Speaker 2: efficiency you know, was still increasing up to age five

896
00:46:04,880 --> 00:46:08,440
Speaker 2: for bulls. So you want enough bulls age five plus

897
00:46:08,800 --> 00:46:11,720
Speaker 2: to actually efficiently breed your cows so they get bread

898
00:46:11,719 --> 00:46:15,880
Speaker 2: and first estis But if you have that on the landscape,

899
00:46:15,960 --> 00:46:18,480
Speaker 2: killing surplus bulls on top of that isn't really a

900
00:46:18,480 --> 00:46:21,319
Speaker 2: population effect. So where hunters have an effect on the

901
00:46:21,320 --> 00:46:24,880
Speaker 2: population is killing cows and in the Blues. You know,

902
00:46:24,960 --> 00:46:28,799
Speaker 2: we've historically issued a lot of antlerless elk opportunities when

903
00:46:28,840 --> 00:46:32,600
Speaker 2: the population can support it. We're down to zero antlerless

904
00:46:32,640 --> 00:46:36,000
Speaker 2: opportunities on public land, and the tags we issue now

905
00:46:36,040 --> 00:46:38,719
Speaker 2: are on the egg area, so we're you know, trying

906
00:46:38,719 --> 00:46:42,120
Speaker 2: to protect the farmers from damage and still balance the

907
00:46:42,280 --> 00:46:46,120
Speaker 2: elk population. But that's kind of outside the core public

908
00:46:46,200 --> 00:46:48,960
Speaker 2: land portion of the Blues. So there's some places that

909
00:46:49,200 --> 00:46:51,440
Speaker 2: I'm still happy to issue lots of cow tags, and

910
00:46:51,480 --> 00:46:54,960
Speaker 2: I say out by Tri Cities, the Burbank area, you know,

911
00:46:54,960 --> 00:46:57,240
Speaker 2: it's not an area we want the population to really

912
00:46:57,320 --> 00:47:00,440
Speaker 2: grow or establish anymore than it already is. But in

913
00:47:00,480 --> 00:47:02,840
Speaker 2: the core Blues, in the public land areas, you know,

914
00:47:02,960 --> 00:47:06,200
Speaker 2: we've taken that opportunity away from hunters a couple of

915
00:47:06,239 --> 00:47:09,880
Speaker 2: years ago, and if hunters were having an effect, we

916
00:47:09,960 --> 00:47:12,440
Speaker 2: should have seen some kind of change in the population.

917
00:47:13,320 --> 00:47:16,440
Speaker 2: We haven't seen a change, and that would really indicate

918
00:47:16,480 --> 00:47:19,040
Speaker 2: that what we think is happening is, you know, hunters

919
00:47:19,080 --> 00:47:21,360
Speaker 2: are not having that population effect.

920
00:47:21,800 --> 00:47:26,600
Speaker 1: Gotch So the reduction in tags is really just a

921
00:47:26,760 --> 00:47:30,040
Speaker 1: result of the population as an overall doing poorly. There's

922
00:47:30,080 --> 00:47:33,840
Speaker 1: just that age class of bulls are there like missing

923
00:47:34,000 --> 00:47:37,960
Speaker 1: segments or missing age classes that we're seeing. Is it?

924
00:47:38,000 --> 00:47:40,879
Speaker 1: Is it age class? Like, you know, not necessarily trying

925
00:47:40,920 --> 00:47:43,160
Speaker 1: to dig it why there's a reduction in tags. Obviously

926
00:47:43,239 --> 00:47:45,920
Speaker 1: I'm in full support of definitely cow, you know, and

927
00:47:45,960 --> 00:47:48,920
Speaker 1: then you ultimately bulls, bull tags coming down if you

928
00:47:48,960 --> 00:47:53,360
Speaker 1: can't support that quality and that that potential. But is

929
00:47:53,400 --> 00:47:57,440
Speaker 1: that I don't want to twist what you said, but

930
00:47:57,560 --> 00:48:00,080
Speaker 1: if bulls, if the quality of bulls and the number

931
00:48:00,080 --> 00:48:06,799
Speaker 1: of bulls are there to basically regenerate and repopulate, what's

932
00:48:06,800 --> 00:48:10,240
Speaker 1: the tag recommendation based on I just did a big

933
00:48:10,360 --> 00:48:12,439
Speaker 1: roundabout and I ended up back right to my same place.

934
00:48:12,480 --> 00:48:14,880
Speaker 1: But it's trying to get that answer of, you know,

935
00:48:14,920 --> 00:48:18,080
Speaker 1: like what dictates that tag number.

936
00:48:18,400 --> 00:48:21,200
Speaker 2: Yeah, So we do what's called an aerial sideability survey.

937
00:48:21,400 --> 00:48:24,279
Speaker 2: So we fly in a helicopter typically the first two

938
00:48:24,280 --> 00:48:28,520
Speaker 2: weeks of March in the Blues. We fly. The Blues

939
00:48:28,600 --> 00:48:33,279
Speaker 2: is broken into thirty seven different survey zones. We fly

940
00:48:33,400 --> 00:48:37,080
Speaker 2: a percentage of those survey zones based on what we

941
00:48:37,200 --> 00:48:40,440
Speaker 2: determine our how many ELK are in each one, And

942
00:48:40,520 --> 00:48:44,000
Speaker 2: that's really based on twenty five thirty years of doing

943
00:48:44,040 --> 00:48:46,240
Speaker 2: this at this point, so we have a good idea

944
00:48:46,280 --> 00:48:50,160
Speaker 2: where they'll like to be for winter, and that accounts

945
00:48:50,160 --> 00:48:55,319
Speaker 2: for elkie don't see based on group size, behavior, snow cover,

946
00:48:56,080 --> 00:48:59,480
Speaker 2: and it gives us a population estimate with confidence intervals

947
00:48:59,480 --> 00:49:01,800
Speaker 2: that we can say we have this ninety five percent

948
00:49:01,840 --> 00:49:04,440
Speaker 2: probability of having this mini elk with a range of this.

949
00:49:05,760 --> 00:49:09,080
Speaker 2: With that, I actually have a formula that we calculate

950
00:49:09,080 --> 00:49:11,080
Speaker 2: how many bull tags we want to issue. So we

951
00:49:11,120 --> 00:49:14,759
Speaker 2: calculate how many bulls we want to take accounting for

952
00:49:14,800 --> 00:49:18,440
Speaker 2: the bull ratio the bulls per hundred cows broken out

953
00:49:18,440 --> 00:49:22,319
Speaker 2: by GMU, and then that is further broken down by

954
00:49:22,320 --> 00:49:26,520
Speaker 2: a percentage of weapon type archers muzzleowders, modern firearm and

955
00:49:27,200 --> 00:49:29,960
Speaker 2: runs off an average success rate of three years of

956
00:49:30,000 --> 00:49:32,759
Speaker 2: the previous tag holders. So we can calculate how many

957
00:49:32,840 --> 00:49:35,640
Speaker 2: tags based on that. So the current reduction that people

958
00:49:35,680 --> 00:49:37,960
Speaker 2: are seeing as a result of us counting less bulls

959
00:49:37,960 --> 00:49:42,120
Speaker 2: in these units and trying to maintain our you know,

960
00:49:42,160 --> 00:49:45,480
Speaker 2: our objective of how many bulls per hundred cows and

961
00:49:46,120 --> 00:49:48,080
Speaker 2: what opportunity there is for harvest.

962
00:49:49,120 --> 00:49:51,120
Speaker 1: Gotcha? So I don't know if I missed it in

963
00:49:51,160 --> 00:49:55,120
Speaker 1: there as far as manager is are you guys managing

964
00:49:55,160 --> 00:49:59,319
Speaker 1: to like a certain amount of like size or is

965
00:49:59,320 --> 00:50:01,759
Speaker 1: it age or what are we using to determine like,

966
00:50:03,520 --> 00:50:06,680
Speaker 1: you know, because we are managing for trophy, but are

967
00:50:06,719 --> 00:50:09,480
Speaker 1: we managing on all right? You've got ten bowls over

968
00:50:09,640 --> 00:50:11,319
Speaker 1: three fifty? I don't know if that's the that's a

969
00:50:11,360 --> 00:50:14,080
Speaker 1: horrible metric, but I'm throwing it out there. Or we've

970
00:50:14,080 --> 00:50:16,799
Speaker 1: got tooth data back on the four bowls that were

971
00:50:16,880 --> 00:50:18,719
Speaker 1: killed out of this unit and they're all averaging this.

972
00:50:18,840 --> 00:50:21,200
Speaker 1: We need to you know, we like, how do you

973
00:50:21,239 --> 00:50:24,560
Speaker 1: come up with the trophy versus the number of tags?

974
00:50:25,560 --> 00:50:27,839
Speaker 2: Part of the trophy is not really considered in how

975
00:50:27,840 --> 00:50:30,360
Speaker 2: we're issuing tags. It's almost more towards a bull ratio.

976
00:50:30,640 --> 00:50:32,319
Speaker 2: But I don't want to ignore that because we do

977
00:50:32,360 --> 00:50:35,640
Speaker 2: collect that in the helicopter. We collect yearling's rag horns,

978
00:50:36,200 --> 00:50:38,480
Speaker 2: what we call like three to four year old bulls,

979
00:50:38,480 --> 00:50:40,879
Speaker 2: which you know probably are in that that two point

980
00:50:40,880 --> 00:50:44,040
Speaker 2: fifty to three hundred range three ten range, and then

981
00:50:44,040 --> 00:50:47,920
Speaker 2: what we call adult bulls that are you know, likely

982
00:50:47,960 --> 00:50:51,400
Speaker 2: over three hundred. Judging a bull from the helicopter is

983
00:50:51,440 --> 00:50:54,759
Speaker 2: not easy. It's really hard, but you can tell when

984
00:50:54,760 --> 00:50:56,600
Speaker 2: it's a big bowl versus a small bull. That's pretty

985
00:50:56,640 --> 00:50:59,800
Speaker 2: easy to do. But if we run an average of

986
00:51:00,239 --> 00:51:02,960
Speaker 2: just twenty to twenty five bulls per hundred cows, you

987
00:51:03,000 --> 00:51:05,000
Speaker 2: know there is going to be a percentage of those

988
00:51:05,280 --> 00:51:07,719
Speaker 2: in there that are mature. And we can look at

989
00:51:07,760 --> 00:51:10,000
Speaker 2: our data to see if that trend from our counts

990
00:51:10,040 --> 00:51:13,080
Speaker 2: is declining or increasing, and we're going to issue a

991
00:51:13,120 --> 00:51:15,560
Speaker 2: percentage of the bulls in the unit. And if our

992
00:51:15,600 --> 00:51:18,600
Speaker 2: bull ratio is high, we issue a higher percentage of

993
00:51:18,680 --> 00:51:21,799
Speaker 2: the number of bulls in the unit for harvest, and

994
00:51:21,840 --> 00:51:24,000
Speaker 2: if our bull ratio is dropping, we issue a much

995
00:51:24,040 --> 00:51:27,279
Speaker 2: smaller percentage of the total counted bulls in the unit

996
00:51:27,480 --> 00:51:30,960
Speaker 2: for what the harvest opportunity is. So somewhat a self

997
00:51:31,000 --> 00:51:34,320
Speaker 2: correcting model to try and get us to our target.

998
00:51:34,600 --> 00:51:38,759
Speaker 1: Okay, that makes that makes sense. So everything we've talked

999
00:51:38,760 --> 00:51:42,759
Speaker 1: about some struggles, you know, historically, what I would I

1000
00:51:42,800 --> 00:51:45,560
Speaker 1: would even great, great elk cutting, right, it's every we've

1001
00:51:45,600 --> 00:51:47,239
Speaker 1: talked about this. It's got everything you need. It's got

1002
00:51:47,280 --> 00:51:49,600
Speaker 1: the genetics, it's got the potential for the food. If

1003
00:51:49,600 --> 00:51:53,279
Speaker 1: we're not dealing with drought and winter. The blues, I

1004
00:51:53,280 --> 00:51:55,319
Speaker 1: would say, has the potential. But I almost feel where

1005
00:51:55,320 --> 00:51:57,080
Speaker 1: at a point where we almost need to accept that

1006
00:51:57,120 --> 00:51:59,719
Speaker 1: it may not ever get back to where it was

1007
00:52:00,200 --> 00:52:02,600
Speaker 1: the historic eyes. Maybe maybe not. Maybe you're going to

1008
00:52:02,640 --> 00:52:05,279
Speaker 1: disagree with me here, but let's roll all this up

1009
00:52:05,320 --> 00:52:07,920
Speaker 1: here to kind of to close this up. You have

1010
00:52:08,000 --> 00:52:10,279
Speaker 1: like an ultimate fix for this area, and I know,

1011
00:52:10,760 --> 00:52:15,359
Speaker 1: being being science driven myself, and there's there's multiple factors, right,

1012
00:52:15,360 --> 00:52:20,080
Speaker 1: there's this equations probably you know a yard long by

1013
00:52:20,120 --> 00:52:22,680
Speaker 1: time you lay out all the variables and all the factors.

1014
00:52:22,960 --> 00:52:25,280
Speaker 1: But but in your opinion, like what are some things

1015
00:52:25,320 --> 00:52:28,000
Speaker 1: that that would fix or start to fix this area

1016
00:52:28,040 --> 00:52:30,960
Speaker 1: and kind of turn that corner and either let it

1017
00:52:31,040 --> 00:52:32,480
Speaker 1: level out or start to improve.

1018
00:52:33,120 --> 00:52:36,319
Speaker 2: So there's a lot of things we can't control as managers.

1019
00:52:37,520 --> 00:52:41,040
Speaker 2: Weather patterns is going to be something that needs to

1020
00:52:41,120 --> 00:52:43,840
Speaker 2: align for three to five years in a row that

1021
00:52:43,880 --> 00:52:48,239
Speaker 2: are favorable for elk to reproduce and grow calves. And

1022
00:52:48,280 --> 00:52:51,480
Speaker 2: that's a huge one. H you know, the habitats in

1023
00:52:51,520 --> 00:52:55,520
Speaker 2: good condition in the Blue Mountains, we've had numerous landscape

1024
00:52:55,600 --> 00:52:59,480
Speaker 2: level fires since two thousand and five. A huge percentage

1025
00:52:59,480 --> 00:53:02,120
Speaker 2: of the Blue mounta says burnt in the last eighteen years,

1026
00:53:02,880 --> 00:53:06,120
Speaker 2: creating what should be good el habitat. You know, fire

1027
00:53:06,239 --> 00:53:09,560
Speaker 2: has a you know, a ten to twenty year benefit,

1028
00:53:09,719 --> 00:53:12,719
Speaker 2: sometimes less depending on the habitat type. If you're talking grasslands,

1029
00:53:12,719 --> 00:53:16,040
Speaker 2: you're probably talking three to five year benefit. But overall,

1030
00:53:16,080 --> 00:53:19,879
Speaker 2: we think our habitat's in good shape. What dials can

1031
00:53:19,920 --> 00:53:23,440
Speaker 2: an agency turn? I mean we can change harvest, We

1032
00:53:23,480 --> 00:53:26,520
Speaker 2: can change harvest of the carnivores, we can change harvest

1033
00:53:26,560 --> 00:53:30,680
Speaker 2: of the elk themselves. Those are the few things that

1034
00:53:30,719 --> 00:53:33,920
Speaker 2: we can actually change when things are not aligning for

1035
00:53:33,960 --> 00:53:37,879
Speaker 2: a population. Our hands really don't have a lot of opportunities.

1036
00:53:38,360 --> 00:53:42,359
Speaker 2: And you know, turning some of these dials comes with

1037
00:53:42,520 --> 00:53:46,920
Speaker 2: social feedback, and the hunters may think one thing and

1038
00:53:47,040 --> 00:53:49,960
Speaker 2: other groups in the members of the public might think

1039
00:53:49,960 --> 00:53:52,319
Speaker 2: another thing in terms of what value they want to

1040
00:53:52,360 --> 00:53:57,960
Speaker 2: place on this, And that's probably where Washington's struggling lately.

1041
00:53:58,280 --> 00:54:00,960
Speaker 2: So are there dials we can turn the ad but

1042
00:54:01,000 --> 00:54:04,319
Speaker 2: they all come with some kind of feedback that the

1043
00:54:04,440 --> 00:54:05,920
Speaker 2: decision makers have to balance.

1044
00:54:06,280 --> 00:54:08,480
Speaker 1: Yeah, and the decision makers and then you know the

1045
00:54:09,600 --> 00:54:14,680
Speaker 1: very opinionated hunters. Right. We talked about this on the

1046
00:54:14,719 --> 00:54:17,480
Speaker 1: mountain already this morning. Is I'm going to point my

1047
00:54:17,560 --> 00:54:21,320
Speaker 1: life where I want an opportunity at a mature animal.

1048
00:54:21,440 --> 00:54:23,520
Speaker 1: I want to go challenge myself. I want to see

1049
00:54:23,520 --> 00:54:25,359
Speaker 1: if I can outsmart that animal. But I've also got

1050
00:54:25,400 --> 00:54:28,000
Speaker 1: a thirteen year old son that's starting to hunt. I

1051
00:54:28,000 --> 00:54:29,759
Speaker 1: don't necessarily want him to have to go out and

1052
00:54:29,800 --> 00:54:32,400
Speaker 1: challenge himself to kill trophy. I want him to have opportunity.

1053
00:54:32,440 --> 00:54:35,560
Speaker 1: My old man, you know where he was where I'm at, like, yeah,

1054
00:54:35,600 --> 00:54:39,040
Speaker 1: he was getting Now maybe he's kind of transitioning out

1055
00:54:39,040 --> 00:54:41,600
Speaker 1: of that trophy and wanting to go just have opportunity.

1056
00:54:42,000 --> 00:54:43,960
Speaker 1: And then you take that and dice it up by

1057
00:54:44,160 --> 00:54:48,040
Speaker 1: archery hunters wanting this season or that many tags versus

1058
00:54:48,160 --> 00:54:51,359
Speaker 1: muzzloader versus rifle, and they all start bickering about why

1059
00:54:51,360 --> 00:54:54,239
Speaker 1: their success is better and theirs is worse, and why

1060
00:54:54,280 --> 00:54:56,719
Speaker 1: they should get more tags because they're less successful, and

1061
00:54:57,760 --> 00:54:59,560
Speaker 1: you know, and then you hear the rifle hunters argue

1062
00:54:59,560 --> 00:55:02,040
Speaker 1: about how of the archery hunters actually wound, so we

1063
00:55:02,040 --> 00:55:04,239
Speaker 1: shouldn't even have any and you know, it just it

1064
00:55:04,239 --> 00:55:07,040
Speaker 1: turns into this big infighting. Right, nobody can decide whether

1065
00:55:07,440 --> 00:55:10,160
Speaker 1: you know, I might want trophy, you might want opportunity.

1066
00:55:10,200 --> 00:55:12,839
Speaker 1: Neither of us are wrong, but we're gonna voice our

1067
00:55:12,880 --> 00:55:15,760
Speaker 1: opinion differently when we go talk to the rule setters

1068
00:55:15,880 --> 00:55:18,440
Speaker 1: or the and then like I say, you add it

1069
00:55:18,440 --> 00:55:23,600
Speaker 1: with different hunters, or there's a mindset of maybe somebody

1070
00:55:23,640 --> 00:55:25,120
Speaker 1: just wants to put a cow in their freezer because

1071
00:55:25,120 --> 00:55:29,319
Speaker 1: they eat better, versus Joe over here just wants an

1072
00:55:29,360 --> 00:55:32,120
Speaker 1: opportunity at any legal bull. He's not interested in a cow,

1073
00:55:32,120 --> 00:55:34,080
Speaker 1: but he doesn't necessarily care about going and chasing the

1074
00:55:34,080 --> 00:55:36,399
Speaker 1: biggest bull in the unit. And then there's and it's

1075
00:55:36,440 --> 00:55:38,960
Speaker 1: just like, I don't know the right answer. Me and

1076
00:55:39,040 --> 00:55:41,200
Speaker 1: Brock ended our podcast with it. We're gonna end ours

1077
00:55:41,239 --> 00:55:44,760
Speaker 1: with it, just because I don't think we can all

1078
00:55:44,880 --> 00:55:48,040
Speaker 1: sit back and be the armchair biologists. Yeah. We I

1079
00:55:48,080 --> 00:55:50,359
Speaker 1: would say most people on the landscape got good ideas.

1080
00:55:50,400 --> 00:55:52,960
Speaker 1: They're out there, they're observing. We're all part. We're an

1081
00:55:53,000 --> 00:55:56,200
Speaker 1: intricate part of this balance, right, and so we're all involved.

1082
00:55:56,400 --> 00:55:58,960
Speaker 1: But I don't necessarily think that that it's as easy

1083
00:55:59,000 --> 00:56:02,280
Speaker 1: as saying I'm an archery hunter that want trophy bowls.

1084
00:56:02,320 --> 00:56:04,640
Speaker 1: This is the way it should be. You know, there

1085
00:56:04,800 --> 00:56:07,920
Speaker 1: there's so much more. There's the winter range, there's nutrition,

1086
00:56:08,080 --> 00:56:11,080
Speaker 1: there's there's all of this, and then you throw us

1087
00:56:11,080 --> 00:56:13,400
Speaker 1: in our opportunity in and what we're after. It just

1088
00:56:13,440 --> 00:56:17,399
Speaker 1: becomes a very very complex decision. And I don't envy

1089
00:56:17,920 --> 00:56:21,799
Speaker 1: the biologists the rules setters at all. But the more

1090
00:56:21,840 --> 00:56:24,080
Speaker 1: I get to hang out with the biologists, the more

1091
00:56:24,120 --> 00:56:26,480
Speaker 1: I get to talk with you, you know, all of you,

1092
00:56:26,680 --> 00:56:29,799
Speaker 1: I honestly feel you guys are doing what's right by

1093
00:56:29,800 --> 00:56:34,960
Speaker 1: the herd and making good decisions. Just what do we

1094
00:56:35,040 --> 00:56:38,160
Speaker 1: do about this? Like opportunity versus quality? Is it ever?

1095
00:56:38,320 --> 00:56:39,920
Speaker 1: Is it ever going to be a clear decision from

1096
00:56:39,920 --> 00:56:41,759
Speaker 1: here on out? Or is it always just going to

1097
00:56:41,800 --> 00:56:44,120
Speaker 1: be I mean, it seems like you can't make a

1098
00:56:44,160 --> 00:56:47,200
Speaker 1: decision without being wrong at this point, Like there is

1099
00:56:47,200 --> 00:56:48,480
Speaker 1: no right decision at this point.

1100
00:56:49,120 --> 00:56:51,120
Speaker 2: Change is hard, no doubt about it. I'm in the

1101
00:56:51,120 --> 00:56:53,879
Speaker 2: same boat as you. I've been working here twenty years,

1102
00:56:53,880 --> 00:56:56,760
Speaker 2: haven't drawn a branch bull tag yet, sitting on twenty points.

1103
00:56:57,280 --> 00:57:00,640
Speaker 2: I really would like to pursue an adult ball while

1104
00:57:00,680 --> 00:57:02,520
Speaker 2: I work here. Is part of my career. But I

1105
00:57:02,520 --> 00:57:05,120
Speaker 2: also have two teenagers that I want them to be

1106
00:57:05,120 --> 00:57:08,160
Speaker 2: able to hunt every year at this age when they're

1107
00:57:08,440 --> 00:57:10,719
Speaker 2: you know, receptive to it, make it part of their lifestyle.

1108
00:57:11,560 --> 00:57:15,360
Speaker 2: It's hard to have both. It's hard for success rates

1109
00:57:15,360 --> 00:57:18,080
Speaker 2: to be managed in a way that people are happy.

1110
00:57:18,200 --> 00:57:20,760
Speaker 2: I mean, if we run two percent success on our

1111
00:57:20,800 --> 00:57:23,440
Speaker 2: general season spike hunt, when you know one in fifty

1112
00:57:23,440 --> 00:57:27,400
Speaker 2: people are getting to shoot a spike, Hunters need some

1113
00:57:27,480 --> 00:57:30,040
Speaker 2: kind of positive feedback occasionally. I mean, not everybody's out

1114
00:57:30,080 --> 00:57:33,280
Speaker 2: there just for the meat or the kill. But that's

1115
00:57:33,320 --> 00:57:39,920
Speaker 2: pretty bad success. I can manage a population either the

1116
00:57:39,920 --> 00:57:43,000
Speaker 2: way we're doing it now. We could go permit only

1117
00:57:43,760 --> 00:57:46,960
Speaker 2: people don't get a hunt every year. Draw odds in

1118
00:57:47,080 --> 00:57:52,400
Speaker 2: Washington are not that good. Our current system is not

1119
00:57:52,600 --> 00:57:57,600
Speaker 2: favorable to drawing tags very often. So it'd be great

1120
00:57:57,640 --> 00:58:02,880
Speaker 2: if our hunters could get together, not fight over weapon type,

1121
00:58:04,680 --> 00:58:07,439
Speaker 2: fight for the resource and maybe fights the wrong word here,

1122
00:58:07,480 --> 00:58:10,840
Speaker 2: but you know, work together to make sure that hunting

1123
00:58:10,880 --> 00:58:13,760
Speaker 2: remains part of our tradition in Washington, because there are

1124
00:58:13,800 --> 00:58:17,080
Speaker 2: people that want to take that away, yep. And in

1125
00:58:17,080 --> 00:58:19,880
Speaker 2: this day and age with the Internet and social media,

1126
00:58:20,560 --> 00:58:24,040
Speaker 2: those voices are definitely being heard more so the hunters

1127
00:58:24,080 --> 00:58:27,320
Speaker 2: need to work together to you know, keep working towards

1128
00:58:27,520 --> 00:58:32,920
Speaker 2: keeping this lifestyle, keeping this recreational opportunity, and coming to

1129
00:58:32,960 --> 00:58:35,560
Speaker 2: consensus is never going to be easy, but we can

1130
00:58:35,640 --> 00:58:38,240
Speaker 2: manage this herd in a lot of ways for recreational

1131
00:58:38,280 --> 00:58:42,320
Speaker 2: opportunity and still stay within the bounds of what's biologically

1132
00:58:42,640 --> 00:58:46,520
Speaker 2: you know, feasible and correct to keep the system functioning

1133
00:58:46,560 --> 00:58:47,280
Speaker 2: the way it should be.

1134
00:58:47,480 --> 00:58:49,440
Speaker 1: Yep, No, I'm I'm in full agreement. I think we

1135
00:58:49,480 --> 00:58:51,800
Speaker 1: need to take like you know, you as a biologist,

1136
00:58:51,840 --> 00:58:53,800
Speaker 1: you've got data, you've got the research. Me as kind

1137
00:58:53,840 --> 00:58:57,720
Speaker 1: of an engineer who bases everything off of science data calculations.

1138
00:58:58,400 --> 00:58:59,640
Speaker 1: I think we just need to look at it and

1139
00:58:59,640 --> 00:59:04,480
Speaker 1: not be selfish. It's easy to figure out success ratios, opportunities,

1140
00:59:04,960 --> 00:59:07,840
Speaker 1: and I think we should just you know, it may

1141
00:59:07,880 --> 00:59:09,560
Speaker 1: we may have to take a deep look into ourselves,

1142
00:59:09,600 --> 00:59:11,520
Speaker 1: make sure we're not being greedy, make sure it's not

1143
00:59:11,600 --> 00:59:12,960
Speaker 1: all for us. It might be for our kids, it

1144
00:59:13,040 --> 00:59:15,040
Speaker 1: might be for my dad, might be for my grandpa,

1145
00:59:15,200 --> 00:59:17,480
Speaker 1: whatever it may be. And I think we need to

1146
00:59:17,520 --> 00:59:21,840
Speaker 1: find something that worked for everybody. And I think we

1147
00:59:21,880 --> 00:59:23,320
Speaker 1: just need to look at it and like I say,

1148
00:59:23,320 --> 00:59:26,280
Speaker 1: I think greed and maybe a little selfishness, and just

1149
00:59:26,320 --> 00:59:28,680
Speaker 1: look at if we put the elk, the deer, whatever

1150
00:59:28,720 --> 00:59:30,760
Speaker 1: animal is that we're trying to manage to a point

1151
00:59:30,800 --> 00:59:34,040
Speaker 1: that's sustainable and provides the opportunity is what should be

1152
00:59:34,040 --> 00:59:36,480
Speaker 1: at the forefront, and maybe not so much our own

1153
00:59:36,520 --> 00:59:39,840
Speaker 1: personal wish upon getting a tag or whatever it may be.

1154
00:59:40,520 --> 00:59:42,880
Speaker 2: Yeah, well said, Yeah, we'll.

1155
00:59:42,800 --> 00:59:44,680
Speaker 1: Close it with that. I really appreciate you inviting me here.

1156
00:59:44,680 --> 00:59:47,200
Speaker 1: Hopefully we can go wrestle some calves to night, and

1157
00:59:47,880 --> 00:59:49,960
Speaker 1: really looking forward to that. But I appreciate having you out.

1158
00:59:50,040 --> 00:59:52,800
Speaker 1: Thank you for being on the podcast. I really, like,

1159
00:59:52,840 --> 00:59:57,120
Speaker 1: I say, me be in semi numerical driven, science driven.

1160
00:59:57,160 --> 01:00:00,120
Speaker 1: I love being able to interview biologists because it let

1161
01:00:00,200 --> 01:00:02,080
Speaker 1: us know exactly what's happening, not just a bunch of

1162
01:00:02,120 --> 01:00:05,640
Speaker 1: speculation and guessing. So really appreciate you having me here, Paul,

1163
01:00:05,720 --> 01:00:08,400
Speaker 1: and good luck on what seems to be maybe a

1164
01:00:08,400 --> 01:00:11,200
Speaker 1: little bit of an uphill battle here with the Elk

1165
01:00:11,240 --> 01:00:11,760
Speaker 1: and the Blues.

1166
01:00:12,440 --> 01:00:15,520
Speaker 2: Well, thank you and hopefully we can see some positive

1167
01:00:15,600 --> 01:00:16,960
Speaker 2: change in the next couple of years here.

1168
01:00:17,040 --> 01:00:19,200
Speaker 1: Yeah, like I said, I was very very fortunate last

1169
01:00:19,240 --> 01:00:21,000
Speaker 1: year to have a tag. I don't know if you

1170
01:00:21,040 --> 01:00:22,920
Speaker 1: deserve the credit, but there was. I mean, there are

1171
01:00:23,040 --> 01:00:25,400
Speaker 1: large mature bulls that are still out here. It takes

1172
01:00:25,400 --> 01:00:27,400
Speaker 1: a little more work than when my wife had the

1173
01:00:27,400 --> 01:00:30,000
Speaker 1: tag in twenty thirteen, but the opportunity is still there,

1174
01:00:31,200 --> 01:00:34,160
Speaker 1: and so no, thank you, thanks for being on and

1175
01:00:34,200 --> 01:00:35,760
Speaker 1: good luck and everything in the future.

1176
01:00:36,160 --> 01:00:37,040
Speaker 2: Thank you for having me