1
00:00:08,960 --> 00:00:13,320
Speaker 1: This is me ea podcast coming in you shirtless, severely

2
00:00:13,480 --> 00:00:18,360
Speaker 1: vote bitten and in my case underwear listening past. You

3
00:00:18,360 --> 00:00:24,960
Speaker 1: can't predict anything. Okay, first, we're on top of where

4
00:00:24,960 --> 00:00:27,800
Speaker 1: we're at. We're gonna go micro to macro. How did

5
00:00:27,800 --> 00:00:29,480
Speaker 1: it come to be that we're in the wind River

6
00:00:29,520 --> 00:00:34,080
Speaker 1: Outdoor Company of Lander, Wyoming? Is that to me anyone?

7
00:00:34,120 --> 00:00:36,800
Speaker 1: I don't care. I mean, I don't know, honest doesn't know. Well,

8
00:00:37,640 --> 00:00:41,200
Speaker 1: I'd say it was a year ago. I wrote into um,

9
00:00:41,360 --> 00:00:44,760
Speaker 1: you guys over email on your from whatever contacts were

10
00:00:44,760 --> 00:00:46,880
Speaker 1: in the website, and I said, well, what's it gonna

11
00:00:46,880 --> 00:00:49,560
Speaker 1: take to get Steve to come out to our banquet

12
00:00:49,600 --> 00:00:53,640
Speaker 1: here for the newly Fanatics Foundation, And um, I believe

13
00:00:53,680 --> 00:00:57,120
Speaker 1: it was Michelle Jorgenson replied and said, well here's what

14
00:00:57,320 --> 00:00:59,320
Speaker 1: here's what you need to do and plan it out.

15
00:00:59,400 --> 00:01:03,240
Speaker 1: And so I think it was exactly a year ago

16
00:01:03,840 --> 00:01:06,679
Speaker 1: we kind of ink the deal and and off you

17
00:01:06,680 --> 00:01:10,679
Speaker 1: were here. So, uh, this journey and you just contact

18
00:01:10,760 --> 00:01:15,039
Speaker 1: your local hunt and fishing shop. Well yeah, so, uh,

19
00:01:15,160 --> 00:01:18,560
Speaker 1: Ron Hansen here, who's the owner of Windover Outdoor Company,

20
00:01:18,720 --> 00:01:21,200
Speaker 1: talked to him and and he was interested in being

21
00:01:21,200 --> 00:01:24,679
Speaker 1: a sponsor of the banquet of which you're speaking at tonight,

22
00:01:25,440 --> 00:01:28,959
Speaker 1: and so um he sponsored that which allows us to

23
00:01:29,280 --> 00:01:32,240
Speaker 1: financially put together a banquet that will raise a bunch

24
00:01:32,240 --> 00:01:34,800
Speaker 1: of money for mule deer as opposed to just throwing

25
00:01:34,840 --> 00:01:38,040
Speaker 1: a giant party and having people having fun. Well, they're

26
00:01:38,040 --> 00:01:40,360
Speaker 1: gonna have fun and we're gonna raise money for mule deer.

27
00:01:40,880 --> 00:01:43,880
Speaker 1: So yeah, talk talk real quick about the foundation and

28
00:01:43,920 --> 00:01:47,320
Speaker 1: how sort of where it fits into the where it

29
00:01:47,360 --> 00:01:52,400
Speaker 1: fits into the mule deer landscape. Yeah, so newly Fanatics Foundation,

30
00:01:53,160 --> 00:01:55,600
Speaker 1: I'm just gonna go ahead and and hit our our

31
00:01:55,640 --> 00:01:59,480
Speaker 1: mission specifically, our our mission is to ensure the conservation

32
00:01:59,480 --> 00:02:03,600
Speaker 1: of mule do and provide such supportive services to Sound

33
00:02:03,600 --> 00:02:08,079
Speaker 1: wildlife management and the sport of hunting to further the

34
00:02:08,639 --> 00:02:13,760
Speaker 1: UH provide supportive services to further sound wildlife management and

35
00:02:13,800 --> 00:02:16,919
Speaker 1: sport hunting. So we really work in what I would

36
00:02:16,919 --> 00:02:22,720
Speaker 1: say are three arenas. We work in um AH supporting

37
00:02:22,760 --> 00:02:25,400
Speaker 1: and funding research which the other people on on here

38
00:02:25,440 --> 00:02:28,600
Speaker 1: today we'll speak to a little bit more. Um We

39
00:02:28,680 --> 00:02:33,240
Speaker 1: also work and provide support to habitat enhancements when we

40
00:02:33,280 --> 00:02:35,680
Speaker 1: get the opportunity, and then kind of our third sector

41
00:02:36,240 --> 00:02:41,519
Speaker 1: is just recruitment and retention of new and existing conservationists.

42
00:02:41,760 --> 00:02:44,760
Speaker 1: And we had your brother on before you did. Yeah,

43
00:02:44,919 --> 00:02:49,239
Speaker 1: you know, he's the well I'm the better looking oak leaf.

44
00:02:50,040 --> 00:02:57,600
Speaker 1: Yeah yeah, yeah, he was on talking about he was

45
00:02:57,600 --> 00:03:01,200
Speaker 1: on talking about wolves because he's a research but we

46
00:03:01,240 --> 00:03:05,240
Speaker 1: talked a lot about catching them. Yeah, how to catch wolves. Yeah,

47
00:03:05,280 --> 00:03:09,520
Speaker 1: he's Uh. John and I have have really good conversations.

48
00:03:09,560 --> 00:03:12,160
Speaker 1: I mean that that's one thing that I've always enjoyed

49
00:03:12,280 --> 00:03:15,280
Speaker 1: is with my father being a biologist and of course

50
00:03:15,440 --> 00:03:18,960
Speaker 1: John as well, and then being exposed to to these guys,

51
00:03:19,040 --> 00:03:23,040
Speaker 1: the other biologists and researchers around it, it really helps

52
00:03:23,040 --> 00:03:25,280
Speaker 1: to kind of give me a bigger picture of of

53
00:03:25,320 --> 00:03:29,080
Speaker 1: what's going on ecologically, and and that makes for fun conversations.

54
00:03:30,200 --> 00:03:33,200
Speaker 1: Do you guys want to let's introduce our our other

55
00:03:33,480 --> 00:03:38,040
Speaker 1: guys here? Hit it. Kevin Montith, I'm a professor at

56
00:03:38,040 --> 00:03:41,280
Speaker 1: the University of Wyoming. And what's your How did you

57
00:03:41,280 --> 00:03:43,400
Speaker 1: come to be doing that? So? I came to be

58
00:03:43,760 --> 00:03:47,200
Speaker 1: So I'm actually just a small town redneck kid from

59
00:03:47,240 --> 00:03:52,160
Speaker 1: northeastern South Dakota. Uh. Grew up hunting and fishing, living

60
00:03:52,200 --> 00:03:55,200
Speaker 1: outdoors and didn't know anything any better. Found out there's

61
00:03:55,240 --> 00:03:58,440
Speaker 1: a wildlife school in South Dakota, and I thought, well,

62
00:03:58,480 --> 00:04:00,680
Speaker 1: surely I'll go there to be a game warden. And

63
00:04:01,040 --> 00:04:04,160
Speaker 1: because that's all I knew, I had no idea there

64
00:04:04,280 --> 00:04:07,040
Speaker 1: was a world besides being a game warden. I mean,

65
00:04:07,080 --> 00:04:09,000
Speaker 1: I grew up in a town of five people, high

66
00:04:09,000 --> 00:04:11,680
Speaker 1: school graduating classes twelve. It seems like all the kids

67
00:04:11,840 --> 00:04:13,600
Speaker 1: that hunting fish like when we were all kids, I

68
00:04:13,720 --> 00:04:15,560
Speaker 1: wanted to be No one knew what it meant, but

69
00:04:15,600 --> 00:04:17,440
Speaker 1: they all wanted to be either a game warden or

70
00:04:17,440 --> 00:04:19,680
Speaker 1: a wildlife biologist. Yeah. Well, and I didn't even know

71
00:04:19,760 --> 00:04:22,080
Speaker 1: wildlife biologists. All I knew was game war It's a

72
00:04:22,160 --> 00:04:24,000
Speaker 1: very very not you were getting checked by them all

73
00:04:24,000 --> 00:04:27,600
Speaker 1: the time or no, No, we weren't those kids. We

74
00:04:27,600 --> 00:04:32,360
Speaker 1: weren't those kids. But yeah, just so, I mean, relatively

75
00:04:32,480 --> 00:04:35,520
Speaker 1: poor family, didn't travel a lot, just wasn't exposed too

76
00:04:35,600 --> 00:04:37,839
Speaker 1: much in the outside world, and it was actually just

77
00:04:37,880 --> 00:04:40,760
Speaker 1: gonna go to tech school, the auto mechanic or something

78
00:04:40,800 --> 00:04:42,920
Speaker 1: like that. I found out there was a wildlife school,

79
00:04:42,960 --> 00:04:45,560
Speaker 1: so went there and began to learn that there's a

80
00:04:45,600 --> 00:04:48,800
Speaker 1: lot more to the story than just that, and became

81
00:04:48,920 --> 00:04:53,720
Speaker 1: involved with some research projects as an undergrad and fell

82
00:04:53,720 --> 00:04:56,360
Speaker 1: in love with research, worked really hard, was told by

83
00:04:56,360 --> 00:04:59,520
Speaker 1: many that the field is really tough and and there's

84
00:04:59,640 --> 00:05:02,000
Speaker 1: very few jobs out there, and if you want the jobs,

85
00:05:02,000 --> 00:05:04,320
Speaker 1: you gotta do this, this, and this, you gotta bust

86
00:05:04,320 --> 00:05:06,000
Speaker 1: your tail. You gotta put yourself at the top of

87
00:05:06,080 --> 00:05:08,960
Speaker 1: the list. So through that time and through the rest

88
00:05:08,960 --> 00:05:10,960
Speaker 1: of my duration there, I fell in love with research,

89
00:05:11,000 --> 00:05:12,760
Speaker 1: worked really hard to try to put myself on the

90
00:05:12,800 --> 00:05:16,320
Speaker 1: top of the list. Um, and got bachelor's and master's

91
00:05:16,360 --> 00:05:18,880
Speaker 1: degrees there, went on to Idaho State. Uh, did my

92
00:05:18,960 --> 00:05:21,400
Speaker 1: PhD there, worked on mule deer in California. For that

93
00:05:21,520 --> 00:05:25,000
Speaker 1: was your PhD. So it's unpopulation dynamics a mule deer

94
00:05:25,320 --> 00:05:27,760
Speaker 1: in this year in Nevadas of California. Yeah, and so

95
00:05:27,800 --> 00:05:30,360
Speaker 1: when I did my master's work, um, which hopefully maybe

96
00:05:30,360 --> 00:05:31,960
Speaker 1: we'll talk about that a little bit more later. But

97
00:05:32,040 --> 00:05:34,599
Speaker 1: I actually worked on captive whitetail deer. We did a

98
00:05:34,600 --> 00:05:37,320
Speaker 1: lot of nutrition related work and so and then I

99
00:05:37,600 --> 00:05:40,720
Speaker 1: it was hands on every day with deer, so literally

100
00:05:40,800 --> 00:05:43,320
Speaker 1: living with deer. And although you know it may seem

101
00:05:43,360 --> 00:05:46,680
Speaker 1: sometimes like while they're captive animals, so they're not real dear. Um,

102
00:05:47,520 --> 00:05:49,960
Speaker 1: you'd be amazed at what you can learn by literally

103
00:05:50,000 --> 00:05:53,040
Speaker 1: just interacting with animals at that level on a daily basis,

104
00:05:53,040 --> 00:05:55,080
Speaker 1: and the powerful things that you can do because of that.

105
00:05:55,160 --> 00:05:59,279
Speaker 1: So through that and instilled in me an appreciation for nutrition. Uh.

106
00:05:59,320 --> 00:06:01,720
Speaker 1: And then we took that and then basically applied a

107
00:06:01,800 --> 00:06:03,800
Speaker 1: lot of what I learned there to free ranging mule

108
00:06:03,839 --> 00:06:08,760
Speaker 1: deer in this year Nevadas of California UM long term

109
00:06:08,800 --> 00:06:12,640
Speaker 1: individual based work tracking animals through time, which has really

110
00:06:12,680 --> 00:06:14,880
Speaker 1: kind of become a foundation for a lot of what

111
00:06:15,400 --> 00:06:18,800
Speaker 1: UM we try to do in my program here within Wyoming.

112
00:06:18,839 --> 00:06:21,680
Speaker 1: And so finish my PhD there, came here as a

113
00:06:21,720 --> 00:06:25,039
Speaker 1: post talc with with Matt actually and then just begun

114
00:06:25,040 --> 00:06:28,719
Speaker 1: to sort of build some rapport in a research program

115
00:06:28,760 --> 00:06:31,400
Speaker 1: here and then and then ultimately moved up in a

116
00:06:31,440 --> 00:06:34,479
Speaker 1: couple of different positions to the UM being a professor

117
00:06:34,560 --> 00:06:36,720
Speaker 1: as am now in the hop School of Environment Natural

118
00:06:36,760 --> 00:06:39,320
Speaker 1: Resources at the University of Wyoming. Let me ask you

119
00:06:39,320 --> 00:06:44,080
Speaker 1: a quick question, UM, because you have exposure to both

120
00:06:44,160 --> 00:06:48,680
Speaker 1: Mulder and California. Is it true that if a Columbia

121
00:06:48,720 --> 00:06:52,120
Speaker 1: blacktail deer crosses I five in an eastward direction, he

122
00:06:52,120 --> 00:06:59,840
Speaker 1: becomes a Mulder? Sure? No, that's now now, I mean

123
00:07:00,240 --> 00:07:03,520
Speaker 1: because according to like the record books, that's true. Yeah, well,

124
00:07:04,120 --> 00:07:05,960
Speaker 1: so you know how we understand what I'm saying. Yeah,

125
00:07:06,080 --> 00:07:07,240
Speaker 1: I know what you're saying. You know how we are

126
00:07:07,240 --> 00:07:08,920
Speaker 1: as people. We need to be able to draw lines

127
00:07:08,960 --> 00:07:12,800
Speaker 1: and categorize things right and when those but in the

128
00:07:12,880 --> 00:07:16,640
Speaker 1: real world, those lines are very blurry. They're they're not

129
00:07:16,760 --> 00:07:20,200
Speaker 1: hard lines. Um. It's it's the same with when you know,

130
00:07:20,240 --> 00:07:24,240
Speaker 1: we sit down and have conservator conversations about subspecies. How

131
00:07:24,240 --> 00:07:26,240
Speaker 1: many subspecies of white tailed deer are there, how many

132
00:07:26,280 --> 00:07:30,040
Speaker 1: subspecies of meal they're out there? Generally over time, Generally,

133
00:07:30,080 --> 00:07:34,720
Speaker 1: over time, especially since genetic work has come into play,

134
00:07:35,200 --> 00:07:41,440
Speaker 1: the subspecies world has become less crowded in the lumpers.

135
00:07:41,520 --> 00:07:44,560
Speaker 1: I feel like the lumpers are winning, you think. So

136
00:07:46,800 --> 00:07:49,880
Speaker 1: we went from like seven Canada geese to two. We

137
00:07:49,920 --> 00:07:53,160
Speaker 1: went from like, lord knows how many bears to two.

138
00:07:53,280 --> 00:07:57,120
Speaker 1: We went from like six kinds of bison to one. Yeah,

139
00:07:57,240 --> 00:07:59,720
Speaker 1: but probably one, maybe two. I think a lot of

140
00:07:59,720 --> 00:08:03,040
Speaker 1: that is It's interesting. He depends upon what scientists you

141
00:08:03,120 --> 00:08:06,160
Speaker 1: talk to actually, and how those hairs are being split

142
00:08:06,320 --> 00:08:08,360
Speaker 1: as as we go through time, And it's amazing how

143
00:08:08,440 --> 00:08:11,360
Speaker 1: much work is done out there right now to establish

144
00:08:11,360 --> 00:08:13,720
Speaker 1: those sorts of things and and for the for so,

145
00:08:13,840 --> 00:08:17,680
Speaker 1: for example, for the animals themselves, Um, it may not

146
00:08:17,760 --> 00:08:20,280
Speaker 1: matter as much to them, it matters a lot for

147
00:08:20,360 --> 00:08:23,320
Speaker 1: us as to how we potentially define that. So if

148
00:08:23,360 --> 00:08:27,160
Speaker 1: we have something that's apparently unique, but there's not very

149
00:08:27,160 --> 00:08:29,800
Speaker 1: many of them, then we're gonna care a lot a

150
00:08:29,840 --> 00:08:33,080
Speaker 1: lot more by those few about those few, exactly. And

151
00:08:33,120 --> 00:08:35,120
Speaker 1: so that's where the importance comes in. Whereas in the

152
00:08:35,559 --> 00:08:38,839
Speaker 1: perhaps in the grand scheme of things, where there's few,

153
00:08:39,240 --> 00:08:41,800
Speaker 1: but then we we determine that, oh, well, they're just

154
00:08:41,920 --> 00:08:44,360
Speaker 1: the same as these over here. Okay, well it doesn't

155
00:08:44,400 --> 00:08:47,760
Speaker 1: matter that much. That's where taxonomy becomes weaponized. Yeah, it's

156
00:08:47,760 --> 00:08:50,960
Speaker 1: exactly right, And it's a for for me. For me,

157
00:08:51,280 --> 00:08:53,080
Speaker 1: I'm all four if it helps, if it helps, I'm

158
00:08:53,120 --> 00:08:56,120
Speaker 1: all four. Weaponizing taxonomy in the cases where it helps

159
00:08:56,120 --> 00:08:59,600
Speaker 1: what it makes what I want to happen possible? Yeah, yeah,

160
00:08:59,640 --> 00:09:04,480
Speaker 1: but then I hate it when it interferes what I

161
00:09:04,480 --> 00:09:08,040
Speaker 1: wanted to happen. Um, So okay, okay, So let me

162
00:09:08,040 --> 00:09:12,240
Speaker 1: put it a different way. Let's say a deer, a

163
00:09:12,360 --> 00:09:15,080
Speaker 1: mule deer, blacktail deer, whatever, hell it is gets hit

164
00:09:15,120 --> 00:09:18,440
Speaker 1: by a car in the center of I five. Is

165
00:09:18,440 --> 00:09:20,960
Speaker 1: there a way Is there a way for someone to

166
00:09:21,000 --> 00:09:27,280
Speaker 1: say that is an X. Well, genetically perhaps they would

167
00:09:27,280 --> 00:09:29,480
Speaker 1: be able to say, he leans black tail. Yeah, that's

168
00:09:29,520 --> 00:09:32,640
Speaker 1: exactly he leans mulder. That's exactly right. But it could

169
00:09:32,679 --> 00:09:36,640
Speaker 1: be a confused picture. Oh yeah, absolutely, yeah, certainly, yeah,

170
00:09:36,800 --> 00:09:39,000
Speaker 1: yeah yeah. And that's where like so for example, Booting

171
00:09:39,000 --> 00:09:42,079
Speaker 1: Crockett Club and actually colleagues of mine and others have

172
00:09:42,120 --> 00:09:45,360
Speaker 1: been have worked pretty hard to be able to help

173
00:09:45,360 --> 00:09:48,800
Speaker 1: in identifying those sorts of things, especially hybrids, uh, and

174
00:09:48,960 --> 00:09:50,719
Speaker 1: in trying to make sure that what gets in the

175
00:09:50,800 --> 00:09:53,680
Speaker 1: record books is actually what we all think it is.

176
00:09:54,760 --> 00:09:57,320
Speaker 1: So it's so it's really to me, it's an interesting

177
00:09:57,360 --> 00:10:00,280
Speaker 1: thing because it's a it's something that that we as

178
00:10:00,360 --> 00:10:03,120
Speaker 1: as humans have sort of um brought into that realm

179
00:10:03,960 --> 00:10:08,079
Speaker 1: to allow us to make appropriate decisions, which which is good, um,

180
00:10:08,120 --> 00:10:10,240
Speaker 1: but but it's interesting too. It just depends upon the

181
00:10:10,280 --> 00:10:12,360
Speaker 1: decision that's being made, whether it's something that where does

182
00:10:12,360 --> 00:10:14,480
Speaker 1: it go in a record book versus is it a

183
00:10:14,520 --> 00:10:17,160
Speaker 1: small population that we need to protect because they're somehow

184
00:10:17,440 --> 00:10:19,400
Speaker 1: unique in some way and you need to be able

185
00:10:19,440 --> 00:10:22,360
Speaker 1: to retain them in that way. My brother is a

186
00:10:22,480 --> 00:10:30,440
Speaker 1: ecologist and a statistician, and um, he worries that and

187
00:10:30,520 --> 00:10:33,680
Speaker 1: talking about how like in in in genetics rewriting all

188
00:10:33,679 --> 00:10:38,520
Speaker 1: of our understanding of taxonomy. He kind of he he

189
00:10:38,880 --> 00:10:41,400
Speaker 1: looks at a little bit, not professionally, but just conversate

190
00:10:41,480 --> 00:10:44,000
Speaker 1: like just for fun that It's like we're in love

191
00:10:44,040 --> 00:10:47,560
Speaker 1: with a shiny new thing and we had the we

192
00:10:47,640 --> 00:10:49,880
Speaker 1: have these systems that sort of made sense to us

193
00:10:50,240 --> 00:10:54,720
Speaker 1: about morphology, land use, like just like things where people

194
00:10:54,760 --> 00:10:57,480
Speaker 1: looking like that's different than that, but we're in love

195
00:10:57,520 --> 00:11:02,400
Speaker 1: with this shiny new object that's in some ways overcomes

196
00:11:02,400 --> 00:11:05,760
Speaker 1: our logic. We're like, oh, so, I guess it's not different,

197
00:11:05,800 --> 00:11:09,720
Speaker 1: even though everyone would agree that it is, because someone

198
00:11:09,760 --> 00:11:13,560
Speaker 1: can tell us now that you know, because this new

199
00:11:13,600 --> 00:11:16,720
Speaker 1: technology trumped all of our earlier observations about sort of

200
00:11:16,720 --> 00:11:20,440
Speaker 1: how we understood the landscape and understood creatures to be

201
00:11:20,559 --> 00:11:23,640
Speaker 1: that like a grizzly, you know, to like a grizzly

202
00:11:23,679 --> 00:11:25,679
Speaker 1: to brown bear. We do this thing, We do these

203
00:11:25,720 --> 00:11:28,680
Speaker 1: trivia questions at our live events, and we always do.

204
00:11:28,800 --> 00:11:32,400
Speaker 1: We always have people name six of the world's eight bears.

205
00:11:32,920 --> 00:11:34,880
Speaker 1: Has there every honest is there? Ever been a time

206
00:11:35,120 --> 00:11:39,880
Speaker 1: when someone didn't say, no grizzly bear, brown bear, no

207
00:11:40,000 --> 00:11:43,760
Speaker 1: kid ever Yeah never Yeah yeah, because to us they're different.

208
00:11:44,240 --> 00:11:49,439
Speaker 1: Yeah yeah, but now they're not. Now you're wrong to

209
00:11:49,520 --> 00:11:53,679
Speaker 1: think that they're different. Okay, let's move on with our introductions.

210
00:11:53,720 --> 00:11:59,679
Speaker 1: You honestly you're here, Yeah, good morning. Yeah. My name

211
00:11:59,720 --> 00:12:02,000
Speaker 1: is Matt Kaufman. So I'm a professor also at the

212
00:12:02,080 --> 00:12:07,280
Speaker 1: University of Wyoming. Um, I've been there about thirteen years

213
00:12:08,120 --> 00:12:11,679
Speaker 1: and uh, yeah and so, and my focus recently has

214
00:12:11,679 --> 00:12:16,520
Speaker 1: been big game migration, ngulate migration. Are you the guy

215
00:12:16,520 --> 00:12:24,000
Speaker 1: that made it fashionable? Um? I guess I've he's modest,

216
00:12:24,040 --> 00:12:27,600
Speaker 1: but everyone else nodded their head. Yes, I've contributed to

217
00:12:27,679 --> 00:12:31,280
Speaker 1: that for sure, But no, no, I mean there's uh

218
00:12:31,320 --> 00:12:34,880
Speaker 1: for a variety of reasons. I think you know, Wyoming

219
00:12:34,920 --> 00:12:39,120
Speaker 1: has sort of uh done a lot of migration work.

220
00:12:39,920 --> 00:12:41,800
Speaker 1: Like how did that come to be? Well, I think

221
00:12:41,840 --> 00:12:44,040
Speaker 1: it's it's it's it's partly like a sort of a

222
00:12:44,040 --> 00:12:47,320
Speaker 1: perfect storm. On the one hand, you have Wyoming is

223
00:12:47,480 --> 00:12:51,800
Speaker 1: a small state about five people a little more. Um,

224
00:12:51,840 --> 00:12:55,520
Speaker 1: it's a state in which for species like mule, deer, elk,

225
00:12:55,600 --> 00:12:59,680
Speaker 1: and pronghorn they need to migrate on this landscape. So

226
00:12:59,679 --> 00:13:03,000
Speaker 1: so migration is sort of the optimal strategy. And then

227
00:13:03,040 --> 00:13:06,679
Speaker 1: also migrations still exist because there's so few people and

228
00:13:06,880 --> 00:13:10,040
Speaker 1: such wide open spaces and so so you have a

229
00:13:10,080 --> 00:13:12,640
Speaker 1: lot of animals migrating, a lot of herds migrating to

230
00:13:12,840 --> 00:13:16,840
Speaker 1: start with. Ah, and then you've just had kind of

231
00:13:17,000 --> 00:13:22,040
Speaker 1: uh more interest in it in part because um, in

232
00:13:22,080 --> 00:13:24,600
Speaker 1: part because we have a lot of development in the state,

233
00:13:24,840 --> 00:13:28,280
Speaker 1: energy development, and so researchers and managers are kind of

234
00:13:28,400 --> 00:13:32,640
Speaker 1: racing to stay ahead of the development and understand how

235
00:13:32,679 --> 00:13:35,280
Speaker 1: animals are using the landscape. And so that that's led

236
00:13:35,280 --> 00:13:37,360
Speaker 1: to a lot of coloring studies and a lot of

237
00:13:37,400 --> 00:13:42,040
Speaker 1: discoveries of migrations um. And then and then there's also

238
00:13:42,160 --> 00:13:46,080
Speaker 1: just kind of a few iconic migrations in Wyoming that

239
00:13:46,120 --> 00:13:50,280
Speaker 1: have sort of captured the imagination of the public, like

240
00:13:50,400 --> 00:13:54,600
Speaker 1: the Path of the Prong Horn and which goes from

241
00:13:54,600 --> 00:13:57,840
Speaker 1: the Upper Green River basin down your Pinedale, and it's

242
00:13:57,880 --> 00:13:59,920
Speaker 1: kind of unique because it goes up over this mouth

243
00:14:00,240 --> 00:14:02,160
Speaker 1: range between the grove ons and the winds and down

244
00:14:02,160 --> 00:14:07,760
Speaker 1: into Jackson Hole and Grantiton National Park summer an yeah, yeah,

245
00:14:07,840 --> 00:14:09,560
Speaker 1: and and it's just a few you know, it's like

246
00:14:10,080 --> 00:14:13,120
Speaker 1: three animals or something, but they follow this really narrow path.

247
00:14:13,280 --> 00:14:16,719
Speaker 1: And and that work was really popularized by a photographer

248
00:14:16,800 --> 00:14:20,040
Speaker 1: named Joe Reese and a writer named Emily and Ascelin

249
00:14:20,120 --> 00:14:23,520
Speaker 1: and they kind of like told that story in in

250
00:14:23,520 --> 00:14:28,360
Speaker 1: in pictures and essays and emilyne followed the entire path

251
00:14:28,480 --> 00:14:31,680
Speaker 1: and and and wrote about it, and so like they

252
00:14:31,720 --> 00:14:35,680
Speaker 1: brought that migration to people's imagination. And there's a big

253
00:14:35,720 --> 00:14:38,800
Speaker 1: story in High Country News that culminated with their work.

254
00:14:38,880 --> 00:14:43,440
Speaker 1: And and then my colleague Hall Sawyer discovered this this

255
00:14:43,560 --> 00:14:46,200
Speaker 1: world's longest mule deer migration, which we call the Red

256
00:14:46,200 --> 00:14:50,720
Speaker 1: Desert to Hoback migration, um hundred fifty miles from the

257
00:14:50,760 --> 00:14:53,680
Speaker 1: Red Desert and Wyoming down near the town of Rock

258
00:14:53,760 --> 00:14:57,320
Speaker 1: Springs or a little town of Superior up almost to Jackson.

259
00:14:58,680 --> 00:15:02,600
Speaker 1: And you know, in another sort of amazing discovery, and

260
00:15:02,640 --> 00:15:06,200
Speaker 1: they had something they had kind of like evaded understanding

261
00:15:06,520 --> 00:15:10,680
Speaker 1: even though it has always gone on like yeah, yeah,

262
00:15:10,720 --> 00:15:13,480
Speaker 1: And that's the thing I think that, uh, that's sort

263
00:15:13,520 --> 00:15:17,640
Speaker 1: of we're all learning with these collering studies, is that like,

264
00:15:18,120 --> 00:15:21,000
Speaker 1: obviously there's lots of people who who pay attention to

265
00:15:21,080 --> 00:15:26,200
Speaker 1: Wyman's wildlife, and there's you know, professimals that manage our wildlife.

266
00:15:26,400 --> 00:15:29,520
Speaker 1: But it's very difficult to understand, you know, where a

267
00:15:29,640 --> 00:15:35,040
Speaker 1: migration goes from from start to finish. Ah, you know,

268
00:15:35,120 --> 00:15:39,240
Speaker 1: unless unless you either follow them with GPS callers or

269
00:15:39,280 --> 00:15:41,840
Speaker 1: you follow them on foot. And you know we don't

270
00:15:42,000 --> 00:15:46,440
Speaker 1: follow them on on foot anymore. Right, So you might

271
00:15:47,240 --> 00:15:50,000
Speaker 1: be in one place and or you're looking at the

272
00:15:50,000 --> 00:15:53,320
Speaker 1: winter range and you know, you know, uh, the fall

273
00:15:53,440 --> 00:15:55,080
Speaker 1: or early winter comes up and all of a sudden,

274
00:15:55,120 --> 00:15:57,440
Speaker 1: a lot of animals show up, and so you know

275
00:15:57,440 --> 00:16:00,520
Speaker 1: they're coming from somewhere, right, or like I've spoken to

276
00:16:00,600 --> 00:16:03,640
Speaker 1: ranchers who who sit right on this migration corridor, and

277
00:16:03,920 --> 00:16:05,960
Speaker 1: you know, they knew about the corridor. They're like, yeah,

278
00:16:06,000 --> 00:16:07,560
Speaker 1: I can sit on my porch and I can see

279
00:16:08,160 --> 00:16:11,120
Speaker 1: hundred animals a day, you know, move move across in

280
00:16:11,120 --> 00:16:12,920
Speaker 1: this quarter. I knew that there was a quarter here,

281
00:16:13,360 --> 00:16:16,280
Speaker 1: but they didn't know that, you know, from their ranch

282
00:16:16,360 --> 00:16:19,040
Speaker 1: and extended you know, sixty miles down to the Red

283
00:16:19,080 --> 00:16:23,120
Speaker 1: Desert and another ninety miles up to the upper Hoback. Right,

284
00:16:23,200 --> 00:16:25,720
Speaker 1: you don't see that full picture until you until you

285
00:16:25,720 --> 00:16:28,080
Speaker 1: put the collars on the animals and and they reveal

286
00:16:28,680 --> 00:16:31,640
Speaker 1: the you know, the length of their journey. We just

287
00:16:31,680 --> 00:16:33,880
Speaker 1: had a conversation with a guy in Colorado who had

288
00:16:33,960 --> 00:16:39,360
Speaker 1: that that localized micro understanding of mule deer movements where

289
00:16:39,360 --> 00:16:42,320
Speaker 1: he was explaining in great detail what needs to happen

290
00:16:42,360 --> 00:16:45,560
Speaker 1: with the snow, and then that a lot of them

291
00:16:45,600 --> 00:16:49,680
Speaker 1: come through. He had like a uh Matt Cook's ranch manager.

292
00:16:51,560 --> 00:16:55,560
Speaker 1: They got like a forty acre family property which sits

293
00:16:55,600 --> 00:16:57,440
Speaker 1: right by I think a home Deepot or something like that.

294
00:16:57,480 --> 00:16:59,320
Speaker 1: Remember they shot a mulder that actually died in the

295
00:16:59,400 --> 00:17:01,840
Speaker 1: home Deepot park a lot or something. But he had

296
00:17:01,880 --> 00:17:04,320
Speaker 1: this like really detailed understanding of like what needs to happen,

297
00:17:04,359 --> 00:17:05,800
Speaker 1: and then all of a sudden tons of muled your

298
00:17:05,840 --> 00:17:08,840
Speaker 1: crosses forty acre plot, but then no sort of sense

299
00:17:08,880 --> 00:17:12,680
Speaker 1: of you know, where it ends. But he just knows

300
00:17:12,720 --> 00:17:14,960
Speaker 1: like on his place, all of a sudden they all

301
00:17:15,000 --> 00:17:17,119
Speaker 1: show up and then they're all gone. Right you know,

302
00:17:18,119 --> 00:17:20,800
Speaker 1: Um what real quick though? For people explain why mulder

303
00:17:20,800 --> 00:17:22,879
Speaker 1: you have to move? You said, that's that you have

304
00:17:22,920 --> 00:17:24,920
Speaker 1: a lot of migration and wyoming. What are the factors

305
00:17:24,960 --> 00:17:29,359
Speaker 1: that make them bump around? Yeah? So um, so I

306
00:17:29,760 --> 00:17:31,800
Speaker 1: like to think of Wyoming and and actually a lot

307
00:17:31,800 --> 00:17:33,760
Speaker 1: of the West is like this. It's a it's a

308
00:17:33,800 --> 00:17:37,200
Speaker 1: habitat of mountains and plains and for for species like

309
00:17:37,280 --> 00:17:39,280
Speaker 1: mule here, that's kind of a that's kind of a

310
00:17:39,280 --> 00:17:42,639
Speaker 1: problem because, um, you know, these animals want to be

311
00:17:42,760 --> 00:17:44,639
Speaker 1: up in the mountains because that's where all the best

312
00:17:44,680 --> 00:17:47,880
Speaker 1: food is produced. That's where all the best forage is produced.

313
00:17:47,880 --> 00:17:51,080
Speaker 1: You get, um, you know that the mountains are really productive.

314
00:17:51,119 --> 00:17:55,080
Speaker 1: They're fed by massive amounts of snow melt. So, but

315
00:17:55,200 --> 00:17:57,480
Speaker 1: you can't live out their year round because you know

316
00:17:57,520 --> 00:17:59,600
Speaker 1: they would they would die if trying to move to

317
00:17:59,680 --> 00:18:01,520
Speaker 1: the span on that just a little bit more because

318
00:18:01,560 --> 00:18:03,480
Speaker 1: I know that like the ranchers too, when I worked

319
00:18:03,480 --> 00:18:06,280
Speaker 1: on a ranch guiding in Colorado, and they were like

320
00:18:06,320 --> 00:18:08,399
Speaker 1: always like wanting to get those cattle up into the

321
00:18:08,440 --> 00:18:10,119
Speaker 1: high country as soon as possible because they would just

322
00:18:10,160 --> 00:18:12,680
Speaker 1: put on the you know, the pounds faster that way.

323
00:18:12,880 --> 00:18:17,359
Speaker 1: What else is it besides a bunch of moisture up there? Yeah, well, Kevin,

324
00:18:17,560 --> 00:18:21,320
Speaker 1: maybe the nutritional experts maybe answer that question. Well, a

325
00:18:21,359 --> 00:18:23,320
Speaker 1: lot of it, of course, even as you drive through

326
00:18:23,359 --> 00:18:25,760
Speaker 1: any of this country and just look, I mean, the

327
00:18:26,160 --> 00:18:29,040
Speaker 1: habitat the assemblages. The habitats between the low country and

328
00:18:29,040 --> 00:18:32,800
Speaker 1: the high country are very different, and especially for especially

329
00:18:32,880 --> 00:18:35,840
Speaker 1: for mule deer, in particular getting them up into the

330
00:18:35,880 --> 00:18:39,080
Speaker 1: high countries where a lot of the more lush forages,

331
00:18:39,280 --> 00:18:43,000
Speaker 1: and so mule deer are a fairly small ruminant so

332
00:18:43,080 --> 00:18:47,160
Speaker 1: they're in their digestive system, uh, they're they're uniquely adapted

333
00:18:47,160 --> 00:18:50,080
Speaker 1: to have a symbiotic relationship with these bacteria in their

334
00:18:50,119 --> 00:18:53,760
Speaker 1: guts that ultimately aid them in digesting their forage. But

335
00:18:53,840 --> 00:18:56,600
Speaker 1: given how in part given how small they are, they're

336
00:18:56,600 --> 00:18:59,920
Speaker 1: what we call concentrate selectors, meaning they need higher claw

337
00:19:00,040 --> 00:19:02,840
Speaker 1: lity food. So, for example, a mule deer isn't going

338
00:19:02,880 --> 00:19:05,240
Speaker 1: to persist out on a range where you put cattle

339
00:19:05,359 --> 00:19:07,960
Speaker 1: and just eat grass the entire year round. They're not

340
00:19:07,960 --> 00:19:10,960
Speaker 1: going to make it. They just can't. They can't adjust,

341
00:19:11,119 --> 00:19:14,239
Speaker 1: they can't, they cannot digest that food as readily, and

342
00:19:14,280 --> 00:19:18,000
Speaker 1: so ideally they have access to either brows like they

343
00:19:18,040 --> 00:19:19,960
Speaker 1: do on winter range, or they get up in that

344
00:19:20,000 --> 00:19:23,480
Speaker 1: summer range habitat and and have either lush new grass

345
00:19:23,960 --> 00:19:27,880
Speaker 1: when it's early early growing phases or um for communities.

346
00:19:27,920 --> 00:19:31,280
Speaker 1: And it's really the forbes On that higher, higher elevation

347
00:19:31,359 --> 00:19:35,080
Speaker 1: country along with even the shrubs still up in that country.

348
00:19:35,119 --> 00:19:38,640
Speaker 1: That really gets them high energy, easy to digest, high

349
00:19:38,640 --> 00:19:41,680
Speaker 1: in protein, and they can really then not only finance

350
00:19:41,760 --> 00:19:44,880
Speaker 1: reproduction but put on fat um during those summer intervals.

351
00:19:45,400 --> 00:19:49,240
Speaker 1: Is there a measurement in wildlife that is like a

352
00:19:49,400 --> 00:19:54,280
Speaker 1: gut per body size ratio essentially, and so so that's

353
00:19:54,359 --> 00:19:59,760
Speaker 1: that's the relative index when we when oftentimes when we

354
00:19:59,800 --> 00:20:06,520
Speaker 1: talk about digestive morphology and their ability to maintain what

355
00:20:06,560 --> 00:20:09,520
Speaker 1: we call throughput or basically how quickly the food passes

356
00:20:09,600 --> 00:20:12,959
Speaker 1: through an animal, and so um that tends to that

357
00:20:13,000 --> 00:20:15,920
Speaker 1: tends to increase in correspondence with body size in general.

358
00:20:15,960 --> 00:20:18,480
Speaker 1: But what we generally know is that those larger animals

359
00:20:18,920 --> 00:20:21,880
Speaker 1: have a greater gut capacity relative to their body mass,

360
00:20:21,920 --> 00:20:28,440
Speaker 1: therefore allowing them to basically have have slower, longer retention time.

361
00:20:28,480 --> 00:20:31,040
Speaker 1: They can keep the food in their gut longer, which

362
00:20:31,040 --> 00:20:33,840
Speaker 1: gives them more time to digest that food, which is

363
00:20:33,880 --> 00:20:36,160
Speaker 1: important on a lower quality for it, like if you're

364
00:20:36,160 --> 00:20:38,679
Speaker 1: eating primarily grass, well, yeah, like if you look at

365
00:20:38,720 --> 00:20:40,800
Speaker 1: when you open up a cab, we were a mountain goat,

366
00:20:41,840 --> 00:20:44,640
Speaker 1: it's just like a huge damn gut compared to other

367
00:20:44,720 --> 00:20:48,960
Speaker 1: things that there's real you know, they kind of bulge

368
00:20:49,000 --> 00:20:51,400
Speaker 1: out in the middle yep, yep, and it's and it's late.

369
00:20:51,520 --> 00:20:54,880
Speaker 1: So so going from concentrate selector to what we call

370
00:20:55,000 --> 00:20:58,640
Speaker 1: intermediate foragers like an elk elk is the classic sort

371
00:20:58,680 --> 00:21:01,800
Speaker 1: of versatile you immediate forager can kind of go both

372
00:21:01,840 --> 00:21:05,800
Speaker 1: directions from subsisting on mostly grass to also eating a

373
00:21:05,800 --> 00:21:08,360
Speaker 1: lot of woody brows those sorts of things, to the

374
00:21:08,359 --> 00:21:11,120
Speaker 1: the bulk feeders or the grass ruffage eaters like our

375
00:21:11,320 --> 00:21:15,080
Speaker 1: like our bison for example, that are just ultimately like

376
00:21:15,119 --> 00:21:17,960
Speaker 1: a bison. If you put bison in a tall forb

377
00:21:18,040 --> 00:21:20,600
Speaker 1: community that a mule deer is just gonna thrive on,

378
00:21:21,280 --> 00:21:26,280
Speaker 1: they're gonna have malabsorption gut problems, um and they're not

379
00:21:26,320 --> 00:21:28,239
Speaker 1: going to be able to digest it properly because it's

380
00:21:28,240 --> 00:21:30,000
Speaker 1: going to pass through them so quickly and they're not

381
00:21:30,080 --> 00:21:33,359
Speaker 1: designed to do that. So they need that bulkier, high

382
00:21:33,400 --> 00:21:36,720
Speaker 1: biomass lots of food, but that's of generally lower quality

383
00:21:36,720 --> 00:21:38,760
Speaker 1: than on a mule deer is gonna live on. Thus,

384
00:21:39,240 --> 00:21:42,359
Speaker 1: the importance of how habitat feeds into each one of

385
00:21:42,400 --> 00:21:45,560
Speaker 1: these species somewhat uniquely or in some ways, its ultimately

386
00:21:45,600 --> 00:21:48,160
Speaker 1: ends in the same same result as far as them

387
00:21:48,200 --> 00:21:52,000
Speaker 1: garnering energy to survive, but what they need to be

388
00:21:52,000 --> 00:21:54,080
Speaker 1: able to do that differs across the board. So if

389
00:21:54,119 --> 00:21:57,080
Speaker 1: they can't be if a mule deer can't be can't

390
00:21:57,119 --> 00:21:58,760
Speaker 1: spend its whole year down in the bottom like in

391
00:21:58,800 --> 00:22:01,879
Speaker 1: the sagebrush flats, or doesn't want to, how is it

392
00:22:01,880 --> 00:22:04,199
Speaker 1: okay for it to spend four or five months down

393
00:22:04,240 --> 00:22:09,240
Speaker 1: there in the winter? Good Well, So that's also that's

394
00:22:09,240 --> 00:22:11,639
Speaker 1: a that's a great point. It's also a little bit

395
00:22:11,680 --> 00:22:15,240
Speaker 1: of a misnomer two, because deer actually can spend the

396
00:22:15,440 --> 00:22:18,159
Speaker 1: entire year down in those low elevation basins, and in

397
00:22:18,200 --> 00:22:20,800
Speaker 1: fact we do. We have lots of deer from the

398
00:22:20,800 --> 00:22:23,240
Speaker 1: Red Desert to Hoback deer that Matt was talking about,

399
00:22:23,320 --> 00:22:26,399
Speaker 1: there's a segment of that population that ultimately lives on

400
00:22:26,440 --> 00:22:29,480
Speaker 1: winter range all year round. And even in our our

401
00:22:29,560 --> 00:22:32,520
Speaker 1: deer that we work on in the Wyoming Range, the

402
00:22:32,600 --> 00:22:36,000
Speaker 1: vast majority of them migrate up into those high elevation

403
00:22:36,080 --> 00:22:38,399
Speaker 1: basins up in the high country, but we still have

404
00:22:38,480 --> 00:22:41,360
Speaker 1: a lot of them that persist on that lower elevation.

405
00:22:41,440 --> 00:22:43,359
Speaker 1: What you would just call winter range, like why in

406
00:22:43,359 --> 00:22:45,800
Speaker 1: the world are they here? But even those ones at

407
00:22:45,800 --> 00:22:48,119
Speaker 1: lower elevation, they're still catching some of that green up

408
00:22:48,119 --> 00:22:50,720
Speaker 1: early in the year. And then then in all honesty,

409
00:22:50,800 --> 00:22:52,959
Speaker 1: they're they're eating a lot of sage brush, and not

410
00:22:53,000 --> 00:22:55,560
Speaker 1: only during the winter, but all all through the summer

411
00:22:55,600 --> 00:22:58,200
Speaker 1: as well, stage brush, even though like as you drive

412
00:22:58,240 --> 00:23:01,080
Speaker 1: across Wyoming, we're kind of like, oh, the stage brushes everywhere.

413
00:23:01,119 --> 00:23:04,120
Speaker 1: It's just this crappy bush that's lives in the deserts.

414
00:23:04,160 --> 00:23:07,320
Speaker 1: And yet not only for prong horn, but mule there too,

415
00:23:07,359 --> 00:23:10,399
Speaker 1: like it's their main staple, especially all winter long, and

416
00:23:10,440 --> 00:23:13,800
Speaker 1: even for those animals that live at lower elevation. But

417
00:23:14,080 --> 00:23:17,720
Speaker 1: what we're learning as well is that those low elevation

418
00:23:17,760 --> 00:23:21,520
Speaker 1: animals perhaps have somewhat of a different strategy and a

419
00:23:21,520 --> 00:23:24,240
Speaker 1: different kind of connection to their environment as our high

420
00:23:24,280 --> 00:23:27,960
Speaker 1: elevation animals do. And these are things where beginning to

421
00:23:28,040 --> 00:23:32,280
Speaker 1: appreciate more and more. There's a huge there are multiple

422
00:23:32,359 --> 00:23:36,080
Speaker 1: solutions to the problems that animals encounter within the environments

423
00:23:36,119 --> 00:23:39,320
Speaker 1: they live in, and we're just beginning to appreciate that

424
00:23:39,400 --> 00:23:42,560
Speaker 1: more and more, as opposed to the more simplistic they

425
00:23:42,600 --> 00:23:44,399
Speaker 1: have to go here, they have to do this, that

426
00:23:44,520 --> 00:23:47,360
Speaker 1: sort of thing. No, there's actually multiple ways for them

427
00:23:47,359 --> 00:23:49,560
Speaker 1: to live in the environments that they that they actually

428
00:23:49,600 --> 00:23:51,480
Speaker 1: live in, from those that are resident live on a

429
00:23:51,520 --> 00:23:54,159
Speaker 1: winter range all year round basically to those that are

430
00:23:54,200 --> 00:23:56,640
Speaker 1: heading into the into the high elevation basis. Is there

431
00:23:56,640 --> 00:23:59,040
Speaker 1: fluidity between the two groups or do they have like

432
00:23:59,080 --> 00:24:01,240
Speaker 1: a they have a ridge and sense that I'm like

433
00:24:01,359 --> 00:24:03,639
Speaker 1: this and you're like that like the mule, like you

434
00:24:03,640 --> 00:24:05,880
Speaker 1: have the migrating population to mule, Do do do dear

435
00:24:05,960 --> 00:24:07,600
Speaker 1: be like, oh you know what, this year, I'm gonna

436
00:24:07,640 --> 00:24:10,879
Speaker 1: go with those guys on a long ass walk. Or

437
00:24:10,880 --> 00:24:13,760
Speaker 1: do they tend to stick to their own course from

438
00:24:13,760 --> 00:24:17,040
Speaker 1: generation to generation? Yeah, So with with mule dear, there's

439
00:24:17,480 --> 00:24:21,040
Speaker 1: very little fluidity. It's h I mean, they learn that

440
00:24:21,119 --> 00:24:24,560
Speaker 1: strategy from their mother and then from what we can

441
00:24:24,560 --> 00:24:27,760
Speaker 1: see in our data, that's what they do their entire life.

442
00:24:28,040 --> 00:24:32,000
Speaker 1: And even in cases like the Red Desert population that

443
00:24:32,080 --> 00:24:35,399
Speaker 1: Kevin was just describing has three different strategies kind of

444
00:24:35,400 --> 00:24:39,760
Speaker 1: the long fifty mile one, sixty mile one to the

445
00:24:39,760 --> 00:24:42,199
Speaker 1: south winds, and then kind of this resident strategy. And

446
00:24:42,920 --> 00:24:48,320
Speaker 1: we've never seen any switching between those strategies in you know,

447
00:24:48,560 --> 00:24:51,080
Speaker 1: six or seven years of of of studying them so

448
00:24:51,160 --> 00:24:54,960
Speaker 1: and and even within their strategy, you know, they make

449
00:24:55,000 --> 00:24:57,640
Speaker 1: their migration up to some arrange and then oftentimes they

450
00:24:57,680 --> 00:25:01,119
Speaker 1: walk in their same footsteps back down to winter range.

451
00:25:01,200 --> 00:25:03,240
Speaker 1: And is it reasonable to assume that if you took

452
00:25:03,280 --> 00:25:07,560
Speaker 1: all that, if you somehow removed all the mulder out

453
00:25:07,560 --> 00:25:11,040
Speaker 1: of this area and grab some new ones from somewhere

454
00:25:11,080 --> 00:25:13,760
Speaker 1: else and put the same number back, they would probably

455
00:25:13,800 --> 00:25:16,800
Speaker 1: never figure that out. They would never they would never

456
00:25:16,920 --> 00:25:20,520
Speaker 1: learn to replicate that route. Well not never, but it

457
00:25:20,520 --> 00:25:22,600
Speaker 1: would take them a very long time. So we just

458
00:25:22,720 --> 00:25:26,160
Speaker 1: we just uh, we just published a study last fall

459
00:25:27,040 --> 00:25:30,920
Speaker 1: um where we took to sort of address this question

460
00:25:30,960 --> 00:25:34,680
Speaker 1: because there's been this So there's kind of the spectrum

461
00:25:34,680 --> 00:25:37,080
Speaker 1: of how animals learned to move and to migrate, right,

462
00:25:37,119 --> 00:25:41,159
Speaker 1: and with birds, there's some genetic cues. Right With birds,

463
00:25:41,200 --> 00:25:43,200
Speaker 1: you can do the experiment you were just talking about,

464
00:25:43,560 --> 00:25:47,679
Speaker 1: and they do know the the appropriate time to migrate

465
00:25:47,760 --> 00:25:52,359
Speaker 1: and the appropriate direction based on where they the place

466
00:25:52,440 --> 00:25:55,440
Speaker 1: on the earth and where they're from. Right. But with

467
00:25:55,800 --> 00:25:57,640
Speaker 1: the idea with mammals is that it has to be learned.

468
00:25:57,680 --> 00:26:01,560
Speaker 1: And so we did this experiment where we took um

469
00:26:01,600 --> 00:26:05,879
Speaker 1: all the transplanted bighorn sheep that had been transplanted into

470
00:26:05,960 --> 00:26:10,520
Speaker 1: Idaho and Wyoming, and of course and many of those

471
00:26:10,560 --> 00:26:14,359
Speaker 1: came from places like around here up in the winds

472
00:26:14,480 --> 00:26:18,080
Speaker 1: where they were migratory, and so you know, and then

473
00:26:18,240 --> 00:26:20,119
Speaker 1: looked at whether or not they all GPS collars. You

474
00:26:20,160 --> 00:26:21,720
Speaker 1: could look at whether or not they were migratory in

475
00:26:21,760 --> 00:26:24,719
Speaker 1: their new landscape, but they have no knowledge of And

476
00:26:24,760 --> 00:26:27,920
Speaker 1: what you find is that, uh, basically all of the

477
00:26:28,720 --> 00:26:33,960
Speaker 1: transplants wouldn't couldn't migrate, didn't migrate. But the ones that

478
00:26:34,040 --> 00:26:36,359
Speaker 1: we have heards around Wyoming that have been extant that

479
00:26:36,440 --> 00:26:39,720
Speaker 1: never went through that export extirpation, so have lived in

480
00:26:39,760 --> 00:26:43,359
Speaker 1: these these mountains for two hundred years or more and

481
00:26:44,000 --> 00:26:47,280
Speaker 1: the vast majority of them migrate and then then and

482
00:26:47,359 --> 00:26:51,119
Speaker 1: so so that suggests that you have to learn how

483
00:26:51,160 --> 00:26:53,720
Speaker 1: to migrate. And then in that data set, we also

484
00:26:53,800 --> 00:26:58,720
Speaker 1: had animals that had just been recently released or other

485
00:26:58,760 --> 00:27:01,480
Speaker 1: ones that have been east into new habitats thirty or

486
00:27:01,520 --> 00:27:03,959
Speaker 1: forty or we also had some moose herds that have

487
00:27:04,080 --> 00:27:07,639
Speaker 1: recolonized habitats seventy eight years ago. And so you look

488
00:27:07,720 --> 00:27:12,040
Speaker 1: at this continuum of time since translocation, and there you

489
00:27:12,080 --> 00:27:16,080
Speaker 1: can start to see that them learning how to migrate

490
00:27:16,160 --> 00:27:19,399
Speaker 1: and learning how to use the landscape. And with with

491
00:27:19,520 --> 00:27:22,560
Speaker 1: bighorn sheep, thirty or forty years, they're starting that they're

492
00:27:22,560 --> 00:27:26,800
Speaker 1: they're trending towards migration. With moose, it takes seventy eight

493
00:27:26,960 --> 00:27:30,040
Speaker 1: years for them to learn how to migrate. So it's

494
00:27:30,040 --> 00:27:35,560
Speaker 1: not never but um, as one journalist put it, you know,

495
00:27:36,119 --> 00:27:41,240
Speaker 1: we we essentially destroyed the ancestral knowledge that species like

496
00:27:41,280 --> 00:27:45,439
Speaker 1: bighorn sheep had when we extort extirpated them across the west,

497
00:27:45,840 --> 00:27:48,439
Speaker 1: because you know, in addistant to losing the herds, we

498
00:27:48,560 --> 00:27:50,880
Speaker 1: lost all the knowledge that those animals had of how

499
00:27:50,880 --> 00:27:55,520
Speaker 1: to migrate on the landscape. And they can get it back,

500
00:27:55,560 --> 00:27:59,480
Speaker 1: but a d fifty mile migration, like what did it

501
00:27:59,560 --> 00:28:02,600
Speaker 1: take in past experience of these animals to ever have

502
00:28:02,800 --> 00:28:08,240
Speaker 1: learned how to do that? And interestingly for mule deer,

503
00:28:09,200 --> 00:28:11,399
Speaker 1: so that that work was with sheep and moose, and

504
00:28:11,440 --> 00:28:13,520
Speaker 1: at least for mule deer, with the work that we've done,

505
00:28:13,760 --> 00:28:16,920
Speaker 1: they appear to be some of the most faithful. So basically,

506
00:28:16,920 --> 00:28:19,920
Speaker 1: once they have a migratory route, that's it. They very

507
00:28:20,000 --> 00:28:22,640
Speaker 1: rarely change or do anything, and we think, we think

508
00:28:22,680 --> 00:28:26,080
Speaker 1: it comes, we think it's passed from mom to daughter,

509
00:28:26,640 --> 00:28:29,760
Speaker 1: although interestingly we've never known that for certain. We're in

510
00:28:29,800 --> 00:28:31,880
Speaker 1: the process of trying to do that right now by

511
00:28:31,920 --> 00:28:36,480
Speaker 1: following mom and daughter pairs through time. So literally, UM,

512
00:28:36,520 --> 00:28:39,000
Speaker 1: with some of the work we're doing collering newborn fawns

513
00:28:39,080 --> 00:28:43,040
Speaker 1: within one day of age, those that survive, re catching them,

514
00:28:43,040 --> 00:28:45,480
Speaker 1: putting a GPS caller on them, so we can follow

515
00:28:45,560 --> 00:28:47,600
Speaker 1: mother and daughter in the years to come to see

516
00:28:47,640 --> 00:28:51,120
Speaker 1: if those daughters ultimately stay with mom and then adopt

517
00:28:51,160 --> 00:28:54,120
Speaker 1: that same migratory route, which is what we think happens.

518
00:28:54,160 --> 00:28:57,200
Speaker 1: That's the working hypothesis, and it seems to be trending

519
00:28:57,200 --> 00:29:01,200
Speaker 1: in that direction. But it also implies this this unique

520
00:29:01,240 --> 00:29:04,640
Speaker 1: value of memory as well, and we have We had

521
00:29:04,640 --> 00:29:07,840
Speaker 1: one animal from from this past year that I think

522
00:29:07,880 --> 00:29:11,719
Speaker 1: helped helps demonstrate that in a pretty powerful way. It

523
00:29:11,760 --> 00:29:13,840
Speaker 1: was a mother daughter pair that we had followed for

524
00:29:13,880 --> 00:29:17,320
Speaker 1: a complete year. So born on summer range, migrate to

525
00:29:17,320 --> 00:29:20,200
Speaker 1: winter range, and then mom and daughter migrate back up

526
00:29:20,200 --> 00:29:24,200
Speaker 1: to summer range, and then mom gives birth again that year,

527
00:29:24,760 --> 00:29:29,520
Speaker 1: and generally mule deer around birth. UM attempt to seek solitude,

528
00:29:29,600 --> 00:29:33,480
Speaker 1: and they'll literally reject, kick away, beat the crap out

529
00:29:33,480 --> 00:29:37,640
Speaker 1: of their previous I know, I know, it's so sad.

530
00:29:38,840 --> 00:29:44,440
Speaker 1: Stand up exactly. I don't want you around anymore. So,

531
00:29:46,680 --> 00:29:51,280
Speaker 1: perhaps not coincidentally, one week after mom gave birth again,

532
00:29:51,680 --> 00:29:54,800
Speaker 1: that fawn from the previous year took off, went on

533
00:29:54,840 --> 00:29:58,360
Speaker 1: a walk about for like forty five miles and in

534
00:29:58,400 --> 00:30:00,520
Speaker 1: the right direction, no, in the wrong to action. So

535
00:30:00,640 --> 00:30:03,920
Speaker 1: winter range basically winter range to summer range was was

536
00:30:03,920 --> 00:30:07,560
Speaker 1: was south to north, and then that fawn continued going

537
00:30:07,640 --> 00:30:11,040
Speaker 1: north for like forty five miles. We thought it was

538
00:30:11,080 --> 00:30:14,280
Speaker 1: a dispersal, like, oh, Mom just kicked her off. She's

539
00:30:14,320 --> 00:30:16,200
Speaker 1: going to head to a new summer range. She's gonna

540
00:30:16,240 --> 00:30:18,720
Speaker 1: found find her place in this world, which is exciting

541
00:30:18,760 --> 00:30:21,200
Speaker 1: in and of itself if that's what she did, right.

542
00:30:21,760 --> 00:30:24,920
Speaker 1: But and this wasn't just like skirting around a mountain

543
00:30:24,960 --> 00:30:28,080
Speaker 1: and then following the foothills. We're talking going from over

544
00:30:28,200 --> 00:30:31,000
Speaker 1: nine thousand feet in elevation back down to five up

545
00:30:31,000 --> 00:30:35,720
Speaker 1: and down around around ridges, a very elaborate route. She

546
00:30:35,800 --> 00:30:38,680
Speaker 1: got to that end of that journey, and that took her.

547
00:30:38,840 --> 00:30:41,240
Speaker 1: I think that took her like nine days she turned

548
00:30:41,280 --> 00:30:45,320
Speaker 1: right days. Yeah, she turned sutably, passing all kinds of

549
00:30:45,360 --> 00:30:48,080
Speaker 1: other mulder oh mule deer, all through all of that

550
00:30:48,120 --> 00:30:50,600
Speaker 1: country and other migratory routes. And this would have been

551
00:30:50,640 --> 00:30:54,160
Speaker 1: like third week in June. So most animals are not

552
00:30:54,240 --> 00:30:57,800
Speaker 1: migrating anymore. They've set up shop. Most of the females

553
00:30:57,840 --> 00:31:00,720
Speaker 1: are giving birth. So so although we can confirm it,

554
00:31:01,120 --> 00:31:03,480
Speaker 1: there's no reason she would have been traveling with another

555
00:31:03,560 --> 00:31:05,720
Speaker 1: deer during that time. It doesn't really make any sense.

556
00:31:05,720 --> 00:31:08,280
Speaker 1: All the other deer had set up shop. But literally,

557
00:31:08,320 --> 00:31:12,200
Speaker 1: she gets there, she turned, spends one day there, turns

558
00:31:12,200 --> 00:31:16,840
Speaker 1: back around, and literally walks the exact same path all

559
00:31:16,880 --> 00:31:19,720
Speaker 1: the way back to Mom in one week's time, literally

560
00:31:19,760 --> 00:31:22,800
Speaker 1: the exact same path. And this is country she has

561
00:31:22,920 --> 00:31:26,040
Speaker 1: never seen before in her entire life. She's never set

562
00:31:26,080 --> 00:31:28,000
Speaker 1: foot in it. Mom never took her there, we've had

563
00:31:28,000 --> 00:31:31,520
Speaker 1: her collared since day one, and literally walks the exact

564
00:31:31,600 --> 00:31:33,720
Speaker 1: same path all the way back. There is no stinking

565
00:31:33,720 --> 00:31:38,080
Speaker 1: way we could ever do that. And so yeah, so

566
00:31:38,120 --> 00:31:41,640
Speaker 1: to us, what that communicates is that they may have

567
00:31:41,800 --> 00:31:46,440
Speaker 1: this just amazing ability for spatial memory. So it could

568
00:31:46,480 --> 00:31:50,080
Speaker 1: possibly be that that mother, that daughter could learn from

569
00:31:50,080 --> 00:31:53,719
Speaker 1: mother maybe walk that route once and they got it

570
00:31:53,800 --> 00:31:56,960
Speaker 1: just like that. We had one other mother daughter pair similarly,

571
00:31:57,000 --> 00:32:00,440
Speaker 1: I think a sixty mile migratory route migrated to winter

572
00:32:00,560 --> 00:32:03,400
Speaker 1: range and then at like eight months of age, mom

573
00:32:03,480 --> 00:32:05,840
Speaker 1: was killed by a mountain lion on winter range. Fawn

574
00:32:05,960 --> 00:32:09,160
Speaker 1: still lived and walk the same path all the way

575
00:32:09,200 --> 00:32:10,959
Speaker 1: back up to some range that spring. Do you ever

576
00:32:11,000 --> 00:32:14,120
Speaker 1: go have a look at that path, like physically walk it?

577
00:32:14,360 --> 00:32:16,720
Speaker 1: So we were we didn't physically walk it because you

578
00:32:16,760 --> 00:32:20,000
Speaker 1: know how the landscape funnels. Yeah, you know, for instance,

579
00:32:20,040 --> 00:32:21,880
Speaker 1: you're out in the snow, right and you cut a

580
00:32:21,920 --> 00:32:25,680
Speaker 1: set of boot tracks and you're like, oh, ship, similar

581
00:32:25,680 --> 00:32:28,200
Speaker 1: guys here, and you keep hitting the same boot tracks

582
00:32:28,240 --> 00:32:31,480
Speaker 1: all day long because that person is just sort of

583
00:32:31,520 --> 00:32:35,880
Speaker 1: has the same sense of ridgelines openings, right, and just

584
00:32:36,000 --> 00:32:38,960
Speaker 1: people would be interested to go walk it so and

585
00:32:39,000 --> 00:32:40,640
Speaker 1: be like how much does it just how much is

586
00:32:40,640 --> 00:32:43,720
Speaker 1: it the logical how much is it the logical path?

587
00:32:43,960 --> 00:32:47,320
Speaker 1: And how often did it follow like a shitty route

588
00:32:47,880 --> 00:32:50,480
Speaker 1: and then took the shitty route again? Yeah? So we

589
00:32:50,720 --> 00:32:53,280
Speaker 1: so we literally so. One of my research associates, her

590
00:32:53,320 --> 00:32:56,560
Speaker 1: name is Samantha Dunell, and a team of women actually

591
00:32:56,960 --> 00:33:01,000
Speaker 1: this year. Our aim was to to do exactly that,

592
00:33:01,320 --> 00:33:03,960
Speaker 1: to to set forth and walk one of the migratory

593
00:33:04,080 --> 00:33:06,400
Speaker 1: routes of one of our collar deer, and this animal

594
00:33:06,440 --> 00:33:09,719
Speaker 1: in particular, this to me, is what's phenomenal to her

595
00:33:09,800 --> 00:33:13,040
Speaker 1: journey is about eighty five miles. It's dear one thirty nine,

596
00:33:13,040 --> 00:33:14,880
Speaker 1: which is just a hundred and thirty nine deer that

597
00:33:14,920 --> 00:33:18,440
Speaker 1: we've had radio marked in that population. She still alive. Yeah,

598
00:33:18,480 --> 00:33:21,760
Speaker 1: in fact, she's pregnant with triplets. We just we just

599
00:33:21,960 --> 00:33:26,360
Speaker 1: we just handled her just like five days ago. I

600
00:33:26,400 --> 00:33:28,480
Speaker 1: don't know, I don't I don't know the answer to that.

601
00:33:28,520 --> 00:33:31,520
Speaker 1: We can only hope. But she's interestingly pregnant with triplets.

602
00:33:31,520 --> 00:33:34,479
Speaker 1: But she so she has about an eighty five mile journey,

603
00:33:34,760 --> 00:33:37,440
Speaker 1: and she goes up and over the Wyoming Range, drops

604
00:33:37,760 --> 00:33:40,480
Speaker 1: drops down, crosses the Grays River, up in over the

605
00:33:40,520 --> 00:33:43,240
Speaker 1: Salt Range to where her summer ranges. And so she's

606
00:33:43,320 --> 00:33:48,880
Speaker 1: literally crossing summer ranges and migratory routes of hundreds and

607
00:33:49,000 --> 00:33:51,800
Speaker 1: hundreds of other deer to stay on her route to

608
00:33:51,840 --> 00:33:54,960
Speaker 1: get to her summer range. And so it's seemingly and

609
00:33:55,120 --> 00:33:57,040
Speaker 1: and a lot of what they do is seemingly not

610
00:33:57,120 --> 00:34:01,080
Speaker 1: always that logical, but that's their out and so what

611
00:34:01,120 --> 00:34:03,920
Speaker 1: they did is they took videographers with them. We're in

612
00:34:03,960 --> 00:34:06,480
Speaker 1: the post production phases right now of working on putting

613
00:34:06,480 --> 00:34:10,480
Speaker 1: that documentary film together, but literally to experience on the

614
00:34:10,520 --> 00:34:14,000
Speaker 1: ground what the animals going through walking through that route,

615
00:34:14,040 --> 00:34:17,319
Speaker 1: the experience that they have from how they're navigating some

616
00:34:17,360 --> 00:34:19,720
Speaker 1: of the snow fields, to the foods that they're potentially

617
00:34:19,760 --> 00:34:23,239
Speaker 1: seeing and experiencing, to the to the treacherous terrain, to

618
00:34:23,320 --> 00:34:25,439
Speaker 1: the fences that are there, all those sorts of things.

619
00:34:25,440 --> 00:34:28,560
Speaker 1: And our aim was to experience that ourselves, so we

620
00:34:28,600 --> 00:34:31,799
Speaker 1: can hopefully help provide to a broader public some of

621
00:34:31,840 --> 00:34:35,040
Speaker 1: that the connection between an animal and their environment. But

622
00:34:35,120 --> 00:34:38,440
Speaker 1: in particular, if you imagine, I hope they don't disneyfy it.

623
00:34:38,440 --> 00:34:42,160
Speaker 1: It's it's not gonna be disneyfy the movie. No, no

624
00:34:42,800 --> 00:34:46,799
Speaker 1: disneyfication going on. No, I'm getting backed up on questions. Man.

625
00:34:47,320 --> 00:34:49,320
Speaker 1: Let me tell you the two questions I have, and

626
00:34:49,360 --> 00:34:51,720
Speaker 1: you can approach them. However, you guys want who pays

627
00:34:51,760 --> 00:34:56,239
Speaker 1: for all this? And and and that's great, um that

628
00:34:56,360 --> 00:34:58,600
Speaker 1: someone does. So how do you guys fund it? And

629
00:34:58,600 --> 00:35:02,000
Speaker 1: into what do you say to someone who says that

630
00:35:02,080 --> 00:35:04,719
Speaker 1: dear did that crazy little journey because you guys got

631
00:35:04,800 --> 00:35:07,600
Speaker 1: it all whacked out by catching it and messing with

632
00:35:07,680 --> 00:35:13,040
Speaker 1: it and putting a collar on it. Those are wildly diversion.

633
00:35:15,440 --> 00:35:17,640
Speaker 1: Well let's take that what I was getting backed up. Yeah,

634
00:35:17,880 --> 00:35:20,440
Speaker 1: let's take the first one first. And maybe, um, i'd

635
00:35:20,520 --> 00:35:23,960
Speaker 1: kind of pitch it over to Jared here. Um because

636
00:35:24,000 --> 00:35:26,799
Speaker 1: one of the I mean there are there are multiple

637
00:35:27,040 --> 00:35:29,640
Speaker 1: supporters of this work, but one of the important ones

638
00:35:29,640 --> 00:35:33,680
Speaker 1: are sportsman groups and UM Newly Fanatics Foundation has really

639
00:35:33,680 --> 00:35:39,359
Speaker 1: been uh remarkable in their support of research and and

640
00:35:39,440 --> 00:35:42,600
Speaker 1: kind of unique among sportsman groups in that. And I

641
00:35:42,640 --> 00:35:44,839
Speaker 1: can't really speak to the history of this or why

642
00:35:44,880 --> 00:35:49,120
Speaker 1: this got set up, but first one reason or another, UM,

643
00:35:49,120 --> 00:35:53,719
Speaker 1: sportsman groups are much more interested in funding on the

644
00:35:53,760 --> 00:36:00,120
Speaker 1: ground habitat work than they are funding research and and

645
00:36:00,120 --> 00:36:02,520
Speaker 1: and I and that I think it's a short term

646
00:36:02,520 --> 00:36:07,520
Speaker 1: and long term play. Right. Well, I fairly obvious results

647
00:36:07,600 --> 00:36:10,920
Speaker 1: right away, right, I mean, you can do habits at

648
00:36:10,960 --> 00:36:13,680
Speaker 1: work on your property and then a year later be

649
00:36:13,719 --> 00:36:17,520
Speaker 1: like holy shit, right and right and yeah, yeah, and

650
00:36:17,600 --> 00:36:20,439
Speaker 1: the and the vision is that you know, we're we're

651
00:36:21,120 --> 00:36:26,040
Speaker 1: we're cutting through all of the you know, all the middleman,

652
00:36:26,080 --> 00:36:28,960
Speaker 1: and we're just enhancing habitat and making things better for wildlife.

653
00:36:29,040 --> 00:36:32,680
Speaker 1: And you know, as as researchers, we're in the business

654
00:36:32,680 --> 00:36:35,200
Speaker 1: of like trying to figure out what's going on and

655
00:36:35,200 --> 00:36:38,200
Speaker 1: and you know why a population is declining and uh,

656
00:36:38,800 --> 00:36:42,440
Speaker 1: and that's and sometimes research is risky, right, and and

657
00:36:42,560 --> 00:36:44,319
Speaker 1: sometimes you don't. You know, sometimes you come up with

658
00:36:44,520 --> 00:36:46,680
Speaker 1: great answers. Sometimes you map a hundred and fifty mile

659
00:36:46,760 --> 00:36:50,400
Speaker 1: migration and that spurs conservation actions. But sometimes you don't,

660
00:36:50,920 --> 00:36:55,400
Speaker 1: and and then you know who invested in that research

661
00:36:55,440 --> 00:36:59,160
Speaker 1: that didn't quite pan out. It's it's risky in that

662
00:36:59,239 --> 00:37:02,759
Speaker 1: you might not we don't know the answers. But how

663
00:37:02,800 --> 00:37:06,759
Speaker 1: is it? Because how is how is there any wrong answer? Well,

664
00:37:06,800 --> 00:37:11,320
Speaker 1: I think there there's some interest and enticement in unlocking

665
00:37:11,360 --> 00:37:16,239
Speaker 1: these big migrations. For example, the Eastern Greater Eastern Yellowstone

666
00:37:16,640 --> 00:37:19,520
Speaker 1: Collar project that we are chapter helped the fund on

667
00:37:19,560 --> 00:37:22,120
Speaker 1: this side of the wind rivers. A lot of those

668
00:37:22,160 --> 00:37:25,200
Speaker 1: deer didn't migrate. In fact, the majority did not. They

669
00:37:25,280 --> 00:37:29,000
Speaker 1: just use their habitat a little different between summer and winter,

670
00:37:29,760 --> 00:37:32,239
Speaker 1: and so that lacks a little bit of the pizzaz

671
00:37:32,360 --> 00:37:35,719
Speaker 1: when compared to a hundred fifty mile migrations that go

672
00:37:35,880 --> 00:37:39,000
Speaker 1: from the desert floors to the tops of the mountain.

673
00:37:39,080 --> 00:37:42,400
Speaker 1: But it's but it is equally important because then we

674
00:37:42,480 --> 00:37:46,319
Speaker 1: have dear populations that interact with their landscape in a

675
00:37:46,360 --> 00:37:49,600
Speaker 1: way that is a lot like a stock investor, you know,

676
00:37:49,760 --> 00:37:52,719
Speaker 1: having a lot of verse versification, and how those deer

677
00:37:52,840 --> 00:37:57,160
Speaker 1: use the landscape then kind of insulates us against climatic changes,

678
00:37:57,800 --> 00:38:01,000
Speaker 1: whereas the big mountain deer probably are more susceptible to

679
00:38:01,040 --> 00:38:03,000
Speaker 1: win are killed than the ones that stay in the desert,

680
00:38:03,600 --> 00:38:07,600
Speaker 1: but they're also insulated from drought. So understanding that is important.

681
00:38:07,640 --> 00:38:11,080
Speaker 1: It just lacks the sex appeal that the big migrations has.

682
00:38:11,200 --> 00:38:13,200
Speaker 1: And and so I'd like to follow up with that, Jared,

683
00:38:13,239 --> 00:38:16,360
Speaker 1: I mean, and with respect to this this what I posed.

684
00:38:16,480 --> 00:38:20,480
Speaker 1: You know, I think newly fanatics is has been kind

685
00:38:20,480 --> 00:38:24,160
Speaker 1: of unique in funding research and and I'm curious of

686
00:38:24,280 --> 00:38:27,560
Speaker 1: your take of you know why that why that has

687
00:38:27,600 --> 00:38:30,319
Speaker 1: been the case. Well, and that's what attracted me to

688
00:38:30,400 --> 00:38:34,160
Speaker 1: the organization personally, is you know, we we can go

689
00:38:34,200 --> 00:38:35,839
Speaker 1: out and do a lot of things. We can cut

690
00:38:35,840 --> 00:38:37,680
Speaker 1: a lot of trees, we can do a lot of stuff.

691
00:38:38,640 --> 00:38:42,920
Speaker 1: There's no certainty that that's going to do anything, especially

692
00:38:42,920 --> 00:38:46,320
Speaker 1: for these wild populations. It's not you know the same

693
00:38:46,360 --> 00:38:49,720
Speaker 1: as say when when people back east or they're managing

694
00:38:49,719 --> 00:38:53,799
Speaker 1: a farm for whitetail, they do have instant results. A

695
00:38:53,880 --> 00:38:56,480
Speaker 1: lot of what we've found over the years and what

696
00:38:56,520 --> 00:38:59,520
Speaker 1: we've learned through the research is what we thought were

697
00:38:59,560 --> 00:39:05,400
Speaker 1: productive of treatments or projects really didn't benefit these wild

698
00:39:05,400 --> 00:39:07,759
Speaker 1: populations at all. And so one of the things I

699
00:39:07,800 --> 00:39:09,920
Speaker 1: always stick to, and it's something that I just kind

700
00:39:09,920 --> 00:39:12,040
Speaker 1: of keeping my head over and over and over again,

701
00:39:12,200 --> 00:39:17,800
Speaker 1: is is basically science without action. It's just research, but moreover,

702
00:39:17,920 --> 00:39:21,600
Speaker 1: action that isn't based in science is really an enterprise

703
00:39:21,640 --> 00:39:25,040
Speaker 1: of fools because you can't you can't focus your dollars

704
00:39:25,040 --> 00:39:29,279
Speaker 1: in a way that's responsive to the wildlife's need on landscape. Yeah,

705
00:39:29,280 --> 00:39:32,719
Speaker 1: that's a good point. And and so then just kind

706
00:39:32,760 --> 00:39:36,359
Speaker 1: of closing closing the loop on the on the funding question, right,

707
00:39:36,400 --> 00:39:39,400
Speaker 1: So sportsman groups, you know, the other other big funder

708
00:39:39,440 --> 00:39:41,480
Speaker 1: for our for our work has been Rocking Mountain Elk

709
00:39:41,480 --> 00:39:45,080
Speaker 1: Foundation YEA, and especially in Wyoming, they've really kind of

710
00:39:46,000 --> 00:39:50,319
Speaker 1: worked hand in hand with with our research teams at

711
00:39:50,320 --> 00:39:54,160
Speaker 1: the University of Wyoming and and have helped you know,

712
00:39:54,440 --> 00:39:57,600
Speaker 1: us discover these migrations. And now we're kind of getting

713
00:39:57,600 --> 00:40:00,920
Speaker 1: to a I think a really great place where we

714
00:40:01,000 --> 00:40:05,279
Speaker 1: recognize that those on the ground habitat projects that the

715
00:40:05,320 --> 00:40:07,399
Speaker 1: groups like Army F want to fund and have been

716
00:40:07,520 --> 00:40:11,719
Speaker 1: great champions for are now being informed by by the

717
00:40:11,760 --> 00:40:15,120
Speaker 1: research by you know, we've identified this migration cord or,

718
00:40:15,160 --> 00:40:17,680
Speaker 1: so now we can look at how to conserve a

719
00:40:17,680 --> 00:40:20,240
Speaker 1: big ranch that is in the cord or that's gonna

720
00:40:20,280 --> 00:40:22,920
Speaker 1: you know, be maximally beneficial for migrating elk. Now we

721
00:40:22,960 --> 00:40:25,960
Speaker 1: can look at modifying fences or enhancing habitat that are

722
00:40:25,960 --> 00:40:30,000
Speaker 1: on stopover sights and so uh So with Army F

723
00:40:30,080 --> 00:40:32,440
Speaker 1: there's been this sort of great investment in in the

724
00:40:32,520 --> 00:40:36,560
Speaker 1: science of of in this case elk migration, and now

725
00:40:36,560 --> 00:40:40,279
Speaker 1: we're literally they're using that science to guide their their

726
00:40:40,320 --> 00:40:43,880
Speaker 1: work on the ground and maximize like a finite budget

727
00:40:44,600 --> 00:40:48,239
Speaker 1: exactly things exactly like exactly the right place. And the

728
00:40:48,360 --> 00:40:50,600
Speaker 1: Wild Sheep Foundation is the other one that that we

729
00:40:50,719 --> 00:40:53,880
Speaker 1: sort of have to mention here. They've been huge supporters

730
00:40:53,880 --> 00:40:57,080
Speaker 1: of not only sort of like all of those reintroductions

731
00:40:57,520 --> 00:41:00,800
Speaker 1: that that I mentioned that led to that that learning

732
00:41:00,960 --> 00:41:04,320
Speaker 1: that migration and learning study, but lots of other research

733
00:41:04,400 --> 00:41:06,399
Speaker 1: and Kevin's been involved in some of that as well. Yeah,

734
00:41:06,760 --> 00:41:11,120
Speaker 1: basically it's not with any of this work, there's because

735
00:41:11,160 --> 00:41:13,040
Speaker 1: of what it takes to get it done. There's really

736
00:41:13,040 --> 00:41:15,160
Speaker 1: no one single entity that can just come to the

737
00:41:15,200 --> 00:41:17,080
Speaker 1: table and say, Okay, let's go do it. Although I

738
00:41:17,080 --> 00:41:20,360
Speaker 1: guess it's it could possibly happen, but it's very unlikely.

739
00:41:20,440 --> 00:41:23,399
Speaker 1: And so ultimately with all these things, it's a big

740
00:41:23,480 --> 00:41:25,960
Speaker 1: network in a big partnership. We may just bring the

741
00:41:26,000 --> 00:41:28,480
Speaker 1: science to the table, but all the others on the

742
00:41:28,480 --> 00:41:30,520
Speaker 1: other side of the table, we're all at the same table,

743
00:41:30,560 --> 00:41:34,080
Speaker 1: and it's all everybody's playing their role, from the nonprofits

744
00:41:34,080 --> 00:41:36,640
Speaker 1: that are helping contribute to it too. We have great

745
00:41:36,680 --> 00:41:39,320
Speaker 1: agency partnerships from the way I'm and giving Fish Department

746
00:41:39,360 --> 00:41:42,799
Speaker 1: to Bureau Land Management to Forest Service, UM, there's other

747
00:41:43,040 --> 00:41:45,920
Speaker 1: other entities within the state. You guys get hard funding

748
00:41:45,920 --> 00:41:48,000
Speaker 1: from your university or do you have to get everything

749
00:41:48,040 --> 00:41:51,680
Speaker 1: through grants? No, all through grants. We don't have any

750
00:41:51,719 --> 00:41:53,680
Speaker 1: hard funding that comes through the university, I guess except

751
00:41:53,760 --> 00:41:56,560
Speaker 1: for my salary and then Matt salaries is paid for

752
00:41:56,680 --> 00:42:01,120
Speaker 1: through us. You go out and finance all your projects. Yeah, yeah, okay,

753
00:42:01,120 --> 00:42:05,080
Speaker 1: what about whacking animals out by uh by Colum because

754
00:42:05,080 --> 00:42:07,320
Speaker 1: you know what, we were recently talking about a mortality

755
00:42:07,320 --> 00:42:10,239
Speaker 1: study in the Everglades, which is really surprising about what

756
00:42:10,320 --> 00:42:14,239
Speaker 1: kills you're in the everglades and uh a guy rolled

757
00:42:14,280 --> 00:42:18,640
Speaker 1: in and he was like you. He was saying that

758
00:42:19,680 --> 00:42:23,759
Speaker 1: by the simple fact of putting collars on them, you

759
00:42:23,840 --> 00:42:27,239
Speaker 1: change the dynamic. And he had this thing. What was

760
00:42:27,280 --> 00:42:28,680
Speaker 1: that study that came out? They were working on a

761
00:42:28,760 --> 00:42:33,720
Speaker 1: thing after they're painting certain animals. Was it actually studied?

762
00:42:34,600 --> 00:42:36,640
Speaker 1: You can't remember, they were like talking about it did,

763
00:42:36,760 --> 00:42:39,160
Speaker 1: but it definitely seen. Yeah, so that they wash you.

764
00:42:39,200 --> 00:42:42,360
Speaker 1: Why are you guys look so irritated. I was just

765
00:42:42,400 --> 00:42:46,520
Speaker 1: thinking about painting some bucks and I'm saying, by hanging

766
00:42:46,560 --> 00:42:50,360
Speaker 1: some damn thing on him, it changes like when a

767
00:42:50,400 --> 00:42:53,480
Speaker 1: predator comes into a group of them, whether it recognizes

768
00:42:53,560 --> 00:42:57,160
Speaker 1: it as an injury or whatever, that it changes the dynamic. Yeah.

769
00:42:57,880 --> 00:43:01,440
Speaker 1: My dad did a lot of research on UH pheasants

770
00:43:01,160 --> 00:43:03,680
Speaker 1: at one point in his life, and they were trying

771
00:43:03,719 --> 00:43:05,640
Speaker 1: to figure out how many of these pheasants that they

772
00:43:05,680 --> 00:43:09,920
Speaker 1: released made it, and all the pheasants were had a

773
00:43:09,960 --> 00:43:14,040
Speaker 1: little armband, wristband on their leg, and he watched several

774
00:43:14,080 --> 00:43:16,719
Speaker 1: predators key in on the ones that had the wristband

775
00:43:17,239 --> 00:43:20,400
Speaker 1: something shiny take them out, And so it was biasing

776
00:43:20,600 --> 00:43:22,880
Speaker 1: the research they were doing. Dudes, not like, I'm not

777
00:43:22,880 --> 00:43:24,360
Speaker 1: going to shoot a deer with the collar on it.

778
00:43:24,440 --> 00:43:27,840
Speaker 1: So they're doing a mortality study that factors in human mortality.

779
00:43:28,400 --> 00:43:30,200
Speaker 1: But if you're sitting on the woods and deer comes

780
00:43:30,200 --> 00:43:32,839
Speaker 1: through the collar, unless you know what's going on, most

781
00:43:32,840 --> 00:43:36,240
Speaker 1: people are gonna think like, well, I don't. These guys

782
00:43:36,280 --> 00:43:38,839
Speaker 1: just laid some callers on some really big deer, so

783
00:43:38,880 --> 00:43:40,879
Speaker 1: you might want to think twice before you say that

784
00:43:41,840 --> 00:43:45,400
Speaker 1: people shot him. Well, so we're in. I mean, definitely,

785
00:43:45,440 --> 00:43:47,680
Speaker 1: there's been maths been working on deer over in the

786
00:43:48,000 --> 00:43:50,640
Speaker 1: uh the Sierra Madres and had callers on bucks, and

787
00:43:50,680 --> 00:43:54,279
Speaker 1: we just fixed a bunch of males with callers over

788
00:43:54,280 --> 00:43:56,760
Speaker 1: in the Wyoming Range as well. And I certainly expect

789
00:43:58,080 --> 00:44:00,160
Speaker 1: there are a couple really good deer that we that

790
00:44:00,200 --> 00:44:02,600
Speaker 1: we're fortunate to put callers on, which were really excited

791
00:44:02,640 --> 00:44:04,440
Speaker 1: to see where they go and what they do. But

792
00:44:05,400 --> 00:44:08,319
Speaker 1: so in that scenario, there's but never mind, never mind

793
00:44:08,320 --> 00:44:10,080
Speaker 1: the people shooting them or not shooting. Yeah, but I'm

794
00:44:10,080 --> 00:44:12,120
Speaker 1: talking about like the idea, and I don't believe that.

795
00:44:12,160 --> 00:44:13,840
Speaker 1: I don't I don't know what this is true. But

796
00:44:13,880 --> 00:44:16,880
Speaker 1: you hear people say that when you do that, you

797
00:44:17,320 --> 00:44:20,960
Speaker 1: scramble his brain. Yeah, so whatever, Yeah, a couple of

798
00:44:20,960 --> 00:44:24,560
Speaker 1: different things associated with brain scrambling. So we did some

799
00:44:24,600 --> 00:44:26,600
Speaker 1: work in this year in Nevadas of California, and we

800
00:44:26,640 --> 00:44:29,160
Speaker 1: were doing um that deer work over the years and

801
00:44:29,160 --> 00:44:32,960
Speaker 1: and through through doing subsequent helicopter surveys and wanting to

802
00:44:33,000 --> 00:44:35,160
Speaker 1: get a good population estimate. It's important for us to

803
00:44:35,200 --> 00:44:36,799
Speaker 1: be able to know whether or not an animal has

804
00:44:36,800 --> 00:44:39,080
Speaker 1: a caller on So to do that to make sure

805
00:44:39,120 --> 00:44:41,480
Speaker 1: we didn't miss a collared animal. So for example, you

806
00:44:41,520 --> 00:44:43,360
Speaker 1: fly over a group and you didn't see the collar

807
00:44:43,480 --> 00:44:45,920
Speaker 1: that was in it, which causes some problems for bias

808
00:44:45,920 --> 00:44:48,719
Speaker 1: in your data. We have fixed orange bands around the

809
00:44:48,719 --> 00:44:50,600
Speaker 1: top of the callers to make sure that we that's

810
00:44:50,640 --> 00:44:53,240
Speaker 1: why they have those on them. Well, that's often times

811
00:44:53,239 --> 00:44:56,239
Speaker 1: the reason, and so that's why we were doing it there. Um.

812
00:44:56,280 --> 00:44:59,239
Speaker 1: But what we also learned subsequently is because of that

813
00:44:59,320 --> 00:45:03,279
Speaker 1: bright orange, it was subtle, very subtle, but it was

814
00:45:03,400 --> 00:45:07,080
Speaker 1: causing some bias immortality and in part associated with predation.

815
00:45:07,160 --> 00:45:11,000
Speaker 1: So I will admit in that scenario that yes, there

816
00:45:11,080 --> 00:45:14,319
Speaker 1: was some potential effect there with regards to that, But

817
00:45:14,800 --> 00:45:18,720
Speaker 1: we don't affix big, bright orange collars on these animals anymore.

818
00:45:18,760 --> 00:45:21,560
Speaker 1: Oftentimes they're brown or they're black. They may have a

819
00:45:21,600 --> 00:45:23,320
Speaker 1: tag on it on one side, so we can I

820
00:45:23,400 --> 00:45:26,400
Speaker 1: D them in the field, that sort of thing, um.

821
00:45:26,440 --> 00:45:30,239
Speaker 1: But ultimately there's little effect with regards to that. Now,

822
00:45:30,280 --> 00:45:34,719
Speaker 1: that's not brain scrambling. Um. The brain scrambling I made that.

823
00:45:34,960 --> 00:45:36,960
Speaker 1: I know I made. The word choice was mine, but

824
00:45:37,040 --> 00:45:39,520
Speaker 1: the sentiment the word choice is mine. But the sentiment

825
00:45:39,600 --> 00:45:42,279
Speaker 1: is that as as a relative to the effect of

826
00:45:42,320 --> 00:45:45,239
Speaker 1: affixing a collar on an animal. Now, we know from

827
00:45:45,280 --> 00:45:48,200
Speaker 1: previous work, especially before the technology got to where it

828
00:45:48,280 --> 00:45:50,040
Speaker 1: is today, that when we put a collar on an

829
00:45:50,080 --> 00:45:53,280
Speaker 1: animal that was too heavy, it did have an effect

830
00:45:53,360 --> 00:45:58,120
Speaker 1: on that animal um it. It's subsequently influenced their um

831
00:45:58,200 --> 00:46:01,680
Speaker 1: their ability to perform, reproduce um a slight effect on

832
00:46:01,680 --> 00:46:04,760
Speaker 1: survival thereafter, not huge, but it was a subtle effect.

833
00:46:05,160 --> 00:46:08,920
Speaker 1: Technology has come along where we don't have that issue anymore,

834
00:46:09,120 --> 00:46:12,680
Speaker 1: and so those effects really are not there. And even

835
00:46:12,719 --> 00:46:15,880
Speaker 1: when you step back and you consider the capture handling

836
00:46:15,960 --> 00:46:18,560
Speaker 1: process and the things that we do which we get

837
00:46:18,680 --> 00:46:21,840
Speaker 1: like brain scrambling comments or questions like that. Sometimes have

838
00:46:21,880 --> 00:46:25,239
Speaker 1: you heard other people say, I mean, maybe not use

839
00:46:25,280 --> 00:46:30,080
Speaker 1: the term brain sam Generally it's using the term stress.

840
00:46:30,320 --> 00:46:33,880
Speaker 1: You're overstressing them or or and so with with the

841
00:46:33,920 --> 00:46:38,080
Speaker 1: work that that I do, we're oftentimes UM. The way

842
00:46:38,120 --> 00:46:40,520
Speaker 1: I try to attempt to characterize it is is telling

843
00:46:40,640 --> 00:46:43,600
Speaker 1: telling an individual story. And if I can take multiple

844
00:46:43,600 --> 00:46:46,240
Speaker 1: individuals in a population and put all those stories together,

845
00:46:46,760 --> 00:46:49,480
Speaker 1: a pattern of understanding begins to emerge. And so we

846
00:46:49,560 --> 00:46:52,239
Speaker 1: do our best to follow individual animals through time, and

847
00:46:52,320 --> 00:46:55,600
Speaker 1: oftentimes what we need to be able to do is

848
00:46:55,640 --> 00:46:58,320
Speaker 1: to catch and rehandle that animal over time to assess

849
00:46:58,320 --> 00:47:01,720
Speaker 1: the reproduction, to assess how fat are, their patterns of growth,

850
00:47:01,719 --> 00:47:05,239
Speaker 1: those sorts of things. So we often catch animals UM

851
00:47:05,360 --> 00:47:07,959
Speaker 1: multiple times and so and right now in a number

852
00:47:08,000 --> 00:47:10,120
Speaker 1: of my studies, twice a year, so we can see

853
00:47:10,160 --> 00:47:13,240
Speaker 1: that picture of that seasonal change in fat and condition

854
00:47:13,320 --> 00:47:15,920
Speaker 1: and reproduction, for example, as we go through the seasons.

855
00:47:16,440 --> 00:47:19,279
Speaker 1: And so many will say, well, as you continue to

856
00:47:19,280 --> 00:47:22,000
Speaker 1: do that over time, you're just like adding more stress

857
00:47:22,080 --> 00:47:24,520
Speaker 1: to their life and eventually the cumulative stress is going

858
00:47:24,560 --> 00:47:26,280
Speaker 1: to be so high that you're gonna they're just gonna

859
00:47:26,320 --> 00:47:30,120
Speaker 1: tip over the edge. But what what the the reality

860
00:47:30,320 --> 00:47:32,759
Speaker 1: is is that, yes, I mean, I'm not gonna try

861
00:47:32,760 --> 00:47:34,920
Speaker 1: to tell you a story that those capture events are

862
00:47:34,960 --> 00:47:38,279
Speaker 1: not stressful. Certainly they are, but it's it's also it's

863
00:47:38,360 --> 00:47:41,279
Speaker 1: very acute. So it's a short tim portal window. We

864
00:47:41,360 --> 00:47:44,319
Speaker 1: do the capture, we handle the animal, we processes, they

865
00:47:44,360 --> 00:47:46,799
Speaker 1: go back on their normal way. It's not like we

866
00:47:46,880 --> 00:47:50,560
Speaker 1: continue to add the stair stepping cumulative stress that ultimately

867
00:47:50,560 --> 00:47:52,920
Speaker 1: in the end tips them over. One of my favorite

868
00:47:52,960 --> 00:47:56,840
Speaker 1: stories to tell. It's maybe seemingly anecdotable. We'd have a

869
00:47:56,920 --> 00:47:59,160
Speaker 1: number a number of other animals that I could also

870
00:47:59,160 --> 00:48:00,960
Speaker 1: tell a fairly simil their story, but to me, this

871
00:48:01,000 --> 00:48:03,560
Speaker 1: one is the most powerful. And that's an animal that

872
00:48:03,600 --> 00:48:05,840
Speaker 1: we had in our work during our study in the

873
00:48:05,880 --> 00:48:08,759
Speaker 1: year in Nevadas of California, so a mule dear. She

874
00:48:08,880 --> 00:48:11,880
Speaker 1: was part of that work from when it started to

875
00:48:12,000 --> 00:48:14,960
Speaker 1: two thousand nine when we finished, and during that window

876
00:48:14,960 --> 00:48:17,920
Speaker 1: of time, our aim through a number of the years

877
00:48:18,160 --> 00:48:20,359
Speaker 1: was to handle animals once a year, but then we

878
00:48:20,400 --> 00:48:22,279
Speaker 1: switched to twice a year, so that we could see

879
00:48:22,280 --> 00:48:25,840
Speaker 1: that seasonal change your time. So during that window of time,

880
00:48:26,360 --> 00:48:29,200
Speaker 1: we captured and handled that dear twenty one times. And

881
00:48:29,239 --> 00:48:32,040
Speaker 1: so if you tell me that there's cumulative stress effects

882
00:48:32,120 --> 00:48:35,240
Speaker 1: or whatever, that that we're affecting their viability through time

883
00:48:35,680 --> 00:48:38,400
Speaker 1: to handle that animal twenty one times over that window.

884
00:48:38,560 --> 00:48:41,600
Speaker 1: But the story gets even better. When she was twelve

885
00:48:41,680 --> 00:48:43,840
Speaker 1: years old. Now this is an animal of known age.

886
00:48:43,880 --> 00:48:47,160
Speaker 1: When she was twelve years old, she gave birth to triplets,

887
00:48:47,800 --> 00:48:50,040
Speaker 1: and I call her all three of the of her fonds.

888
00:48:50,280 --> 00:48:53,240
Speaker 1: She subsequently reared them all. At twelve years of age,

889
00:48:53,360 --> 00:48:56,080
Speaker 1: she successfully weird all of them old dry dough. The

890
00:48:56,120 --> 00:49:00,799
Speaker 1: old dry dough completely wrong, completely wrong, old dry dough,

891
00:49:00,920 --> 00:49:03,640
Speaker 1: gave birth to triplets, raised them all. She was in

892
00:49:03,800 --> 00:49:06,120
Speaker 1: very very poor shape that fall. They pretty much sucked

893
00:49:06,120 --> 00:49:08,960
Speaker 1: her dry. But and we caught her that fall, not

894
00:49:09,000 --> 00:49:11,239
Speaker 1: only in that fall, in that following spring continued to

895
00:49:11,280 --> 00:49:14,080
Speaker 1: catch her twice a year fine and I call heed

896
00:49:14,080 --> 00:49:17,359
Speaker 1: her single fond. That next spring. That faon died at

897
00:49:17,360 --> 00:49:20,400
Speaker 1: thirty days of age to a bobcat. But ultimately in

898
00:49:20,440 --> 00:49:22,799
Speaker 1: the end she lived a fifteen and a half years old.

899
00:49:22,920 --> 00:49:25,040
Speaker 1: When she was killed by a mountain lion on winter range.

900
00:49:25,160 --> 00:49:27,360
Speaker 1: And so by the time was how many fonds did

901
00:49:27,400 --> 00:49:29,840
Speaker 1: she raised successfully if you measure success as me in

902
00:49:30,200 --> 00:49:33,920
Speaker 1: how many like lifetime reproductive six I haven't done that,

903
00:49:33,960 --> 00:49:36,239
Speaker 1: but I certainly could go do that because we practically

904
00:49:36,280 --> 00:49:38,640
Speaker 1: have her whole life. But I don't. I don't know

905
00:49:38,719 --> 00:49:42,400
Speaker 1: the answer to that or whatever exactly. I'd have to

906
00:49:42,440 --> 00:49:44,080
Speaker 1: go back and look. But even in that year, she

907
00:49:44,080 --> 00:49:46,080
Speaker 1: had a kid get killed by bobcat, she got killed

908
00:49:46,080 --> 00:49:47,719
Speaker 1: by mom, She got killed by a mountain lion when

909
00:49:47,760 --> 00:49:49,440
Speaker 1: she was fifteen and a half on winter range. And

910
00:49:49,480 --> 00:49:52,160
Speaker 1: so by the time she was twelve, we had handled

911
00:49:52,160 --> 00:49:55,000
Speaker 1: her over fifteen times. And yet she gave birth to

912
00:49:55,080 --> 00:49:57,839
Speaker 1: triplets and reared them. I mean there, which is a

913
00:49:57,920 --> 00:50:01,120
Speaker 1: huge feat in and of itself, did get pretty passive

914
00:50:01,680 --> 00:50:05,880
Speaker 1: after So what's amazing is those animals that were the helicopter.

915
00:50:07,200 --> 00:50:10,319
Speaker 1: So so I I was one of the years when

916
00:50:10,320 --> 00:50:13,799
Speaker 1: she was in the year that she was thirteen, Um

917
00:50:13,960 --> 00:50:16,440
Speaker 1: I would I was gunning and I caught her, and

918
00:50:16,480 --> 00:50:19,480
Speaker 1: she's she's fairly passive. I shot a net over her

919
00:50:19,600 --> 00:50:22,040
Speaker 1: that the one the back end of the net just

920
00:50:22,120 --> 00:50:25,160
Speaker 1: touched her head and she just laid down, so she

921
00:50:25,280 --> 00:50:28,560
Speaker 1: wasn't even really caught she but she just laid down,

922
00:50:28,640 --> 00:50:30,680
Speaker 1: and I just got out of the helicopter and hobbled

923
00:50:30,680 --> 00:50:32,600
Speaker 1: her and we processed her and just super calm and

924
00:50:32,680 --> 00:50:35,719
Speaker 1: chill the entire time. And that's generally what we tend

925
00:50:35,800 --> 00:50:38,279
Speaker 1: to see. Those animals that we've handled multiple times are

926
00:50:38,360 --> 00:50:41,279
Speaker 1: just like, well, okay, we're doing this again and it's

927
00:50:41,440 --> 00:50:44,040
Speaker 1: no big deal. Which she captured the same way every time,

928
00:50:44,239 --> 00:50:47,320
Speaker 1: by a netgun from a helicopter every time, one times,

929
00:50:47,360 --> 00:50:50,960
Speaker 1: similar to being abducted by aliens. Yeah, yeah, we hear

930
00:50:51,000 --> 00:50:53,759
Speaker 1: that once in a while too. And then uh, you guys,

931
00:50:53,800 --> 00:50:56,800
Speaker 1: tranquilis or don't tranquilize it? Do you need to tranquilized year, No,

932
00:50:56,920 --> 00:51:00,359
Speaker 1: you do not know. And so generally, generally, if we

933
00:51:00,400 --> 00:51:03,400
Speaker 1: don't have to sedate or tranquilize in any way, in

934
00:51:03,440 --> 00:51:06,320
Speaker 1: my experience, it's better for the animal because they maintain

935
00:51:06,400 --> 00:51:10,480
Speaker 1: all of their abilities to thermal regulate, to respond. Um,

936
00:51:10,560 --> 00:51:13,239
Speaker 1: we don't cause that. You know that that cirin, there's

937
00:51:13,280 --> 00:51:15,279
Speaker 1: that surgeon in gentle and we just not it back

938
00:51:15,360 --> 00:51:18,480
Speaker 1: real fast again. Um, we were avoid that. And most

939
00:51:18,520 --> 00:51:22,520
Speaker 1: importantly then when we let them go, they're fully aware completely,

940
00:51:22,600 --> 00:51:25,200
Speaker 1: can do their own thing. They can navigate fences, can

941
00:51:25,200 --> 00:51:27,600
Speaker 1: deal with predators, everything else. They're back on their way.

942
00:51:27,640 --> 00:51:31,600
Speaker 1: So no drugging. Can you capture an ANTLERD Yeah, an

943
00:51:31,600 --> 00:51:34,800
Speaker 1: ANTLERD buck and not drug? Yes, so there's no safety concern.

944
00:51:35,320 --> 00:51:37,359
Speaker 1: Uh well, I mean they got bone on their head

945
00:51:37,440 --> 00:51:39,839
Speaker 1: that you have to be careful of. Um, you can

946
00:51:40,000 --> 00:51:42,839
Speaker 1: know it's can certainly be done, absolutely, and that's how

947
00:51:43,239 --> 00:51:46,919
Speaker 1: with many of them that we captured and outfitted this fall. Yeah,

948
00:51:47,120 --> 00:51:49,080
Speaker 1: they were in hard Antler and you can catch them

949
00:51:49,080 --> 00:51:51,120
Speaker 1: within that gun and do it that way. Ye. Are

950
00:51:51,120 --> 00:51:56,319
Speaker 1: you guys familiar with Valarious geist and stuff all year?

951
00:51:56,560 --> 00:52:01,360
Speaker 1: What's his reputation? So it's so in my mind valarious

952
00:52:01,360 --> 00:52:03,839
Speaker 1: guys and probably everybody would have a different opinion. But

953
00:52:04,480 --> 00:52:08,120
Speaker 1: he was, Um, he's a brilliant scientist, without a doubt.

954
00:52:08,280 --> 00:52:12,400
Speaker 1: But he's also so some and some scientists would say like,

955
00:52:12,440 --> 00:52:15,839
Speaker 1: oh man, that guy, it was crazy. But interestingly, Vow

956
00:52:15,920 --> 00:52:20,799
Speaker 1: guys to me was so creative and thoughtful that he

957
00:52:20,960 --> 00:52:25,160
Speaker 1: postulated so many ideas that have become hypotheses that we've

958
00:52:25,200 --> 00:52:27,719
Speaker 1: since tested or maybe still like some of the groundwork today.

959
00:52:27,719 --> 00:52:30,879
Speaker 1: Now admittedly some of those were pretty far out there

960
00:52:30,920 --> 00:52:33,680
Speaker 1: and pretty crazy, so yeah, OK, no way. But in

961
00:52:33,719 --> 00:52:37,040
Speaker 1: all honesty, I think it was just his creative mind

962
00:52:37,120 --> 00:52:39,560
Speaker 1: that he could come up with potential explanations to things

963
00:52:39,600 --> 00:52:42,920
Speaker 1: that ultimately lead to ideas that we could test thereafter.

964
00:52:43,040 --> 00:52:46,719
Speaker 1: Remember someone saying that he, um, we'll kind of like

965
00:52:46,920 --> 00:52:50,880
Speaker 1: light a fuse and then leave the room by by

966
00:52:50,960 --> 00:52:55,439
Speaker 1: tossing something that yeah, right, like here's the idea. Here's

967
00:52:55,440 --> 00:53:02,799
Speaker 1: an idea. But before we of the brain scrambling, well,

968
00:53:02,840 --> 00:53:05,480
Speaker 1: I mean all uh, I mean, I think Kevin gave

969
00:53:05,520 --> 00:53:09,000
Speaker 1: a great description about the ways in which you know,

970
00:53:09,120 --> 00:53:13,480
Speaker 1: these type of coloring activity activities don't or minimally disrupt

971
00:53:13,480 --> 00:53:16,160
Speaker 1: the animals. But I don't want to lose the larger

972
00:53:16,360 --> 00:53:20,319
Speaker 1: point here, which is that this is the way that

973
00:53:20,360 --> 00:53:24,120
Speaker 1: we have advanced modern wildlife management for the last half

974
00:53:24,200 --> 00:53:28,520
Speaker 1: a century. Right, So, almost everything that we know about

975
00:53:29,120 --> 00:53:34,200
Speaker 1: how animals respond to roads, how animals respond to human hunting,

976
00:53:35,080 --> 00:53:41,920
Speaker 1: predator prey interactions, disease interactions, competitive interactions, population dynamics, Capturing

977
00:53:41,960 --> 00:53:44,560
Speaker 1: and coloring animals is the tools of the trade. This

978
00:53:44,640 --> 00:53:47,400
Speaker 1: is this is the tool that has led to most

979
00:53:47,440 --> 00:53:50,279
Speaker 1: of the sort of modern advancements in our understanding of

980
00:53:50,280 --> 00:53:54,600
Speaker 1: wildlife management and wildlife biology, and so you know, and

981
00:53:54,680 --> 00:53:58,480
Speaker 1: as we go forward, right like, we need these tools

982
00:53:58,560 --> 00:54:03,240
Speaker 1: even more than ever, right because we are in most

983
00:54:03,239 --> 00:54:06,280
Speaker 1: of the species we're talking about are hunted, but um,

984
00:54:06,320 --> 00:54:09,000
Speaker 1: but we are in a biodversity crisis here and for

985
00:54:09,040 --> 00:54:12,719
Speaker 1: the you know, for a lot of the game species. Yes, um,

986
00:54:12,760 --> 00:54:16,320
Speaker 1: we're not worried about losing mule dear, but we are

987
00:54:16,360 --> 00:54:20,280
Speaker 1: worried about losing mule dear migrations or you know, severing

988
00:54:20,280 --> 00:54:23,440
Speaker 1: those migrations, and for and for lots of other species.

989
00:54:23,920 --> 00:54:26,560
Speaker 1: We can think of ungulates in in Africa another species.

990
00:54:26,600 --> 00:54:30,919
Speaker 1: You know, we are concerned about losing these these herds.

991
00:54:31,000 --> 00:54:33,799
Speaker 1: And it's not going to because when we lose them,

992
00:54:33,800 --> 00:54:37,640
Speaker 1: it's not going to be because we somehow, you know,

993
00:54:38,040 --> 00:54:40,440
Speaker 1: stress them out a little bit too much by capturing

994
00:54:40,560 --> 00:54:43,200
Speaker 1: one too many animals. It's it's going to be because

995
00:54:43,239 --> 00:54:47,520
Speaker 1: we didn't understand what these animals needed in terms of

996
00:54:47,560 --> 00:54:50,839
Speaker 1: their habitat requirements and their movement or requirements to live

997
00:54:50,920 --> 00:54:54,480
Speaker 1: on this landscape. And we didn't understand what we were

998
00:54:54,480 --> 00:54:58,120
Speaker 1: doing to alter that. So you know, that's what research

999
00:54:58,160 --> 00:55:01,319
Speaker 1: gives us. Research gives us that understand ending and and

1000
00:55:02,640 --> 00:55:05,480
Speaker 1: you know, to me, that's to me, that's the big picture,

1001
00:55:05,480 --> 00:55:07,600
Speaker 1: and that's that's why we do this research. And there's

1002
00:55:07,880 --> 00:55:11,400
Speaker 1: lots of examples of that type of understanding being applied

1003
00:55:11,800 --> 00:55:15,279
Speaker 1: to better manage and conserve these species. And so you know,

1004
00:55:15,480 --> 00:55:17,359
Speaker 1: that's the flip side. You know, that's what I think

1005
00:55:17,400 --> 00:55:20,520
Speaker 1: about when we get criticized about, you know, the the

1006
00:55:20,560 --> 00:55:23,680
Speaker 1: acute stress that that we're putting these animals under. Yeah,

1007
00:55:23,719 --> 00:55:25,200
Speaker 1: I wanted to return to that, but I'm glad you

1008
00:55:25,280 --> 00:55:27,839
Speaker 1: got that taken care of. You're welcome. I was devil.

1009
00:55:28,160 --> 00:55:32,239
Speaker 1: I was definitely davocate. I was being the devil's advocate there. Um,

1010
00:55:32,320 --> 00:55:34,319
Speaker 1: I understand that well, and we'll even revisit that. Let

1011
00:55:34,360 --> 00:55:38,200
Speaker 1: you uh emphasize that point again. But the reason I'm

1012
00:55:38,200 --> 00:55:42,279
Speaker 1: bringing up val Geist is, you know, his thing, like

1013
00:55:42,400 --> 00:55:45,440
Speaker 1: his idea about how muleteer came into existence? Is that

1014
00:55:45,760 --> 00:55:49,160
Speaker 1: is that does that have? Is there academic consensus and

1015
00:55:49,239 --> 00:55:52,440
Speaker 1: his idea of how the sort of evolutionary path of

1016
00:55:52,440 --> 00:55:56,200
Speaker 1: the mule deer, you know, let me let me approach

1017
00:55:56,239 --> 00:55:57,719
Speaker 1: it is a completely different way. Let me approach a

1018
00:55:57,719 --> 00:56:00,600
Speaker 1: completely different way. How did mulder come to be what's

1019
00:56:00,600 --> 00:56:04,480
Speaker 1: through evolution, the evolutionary path that led to Mulder Well,

1020
00:56:04,480 --> 00:56:07,879
Speaker 1: and I just was curious to the same thing because

1021
00:56:07,920 --> 00:56:12,000
Speaker 1: I've read Val guys book and there was a yeah,

1022
00:56:12,040 --> 00:56:17,160
Speaker 1: he talks about basically it muled there across between blacktail

1023
00:56:17,200 --> 00:56:21,040
Speaker 1: and whitetailed deer and a really new species. And I

1024
00:56:21,040 --> 00:56:23,399
Speaker 1: even texted the biologists about it last night. He said

1025
00:56:23,440 --> 00:56:25,719
Speaker 1: he's like familiar with it, but wanted to go revisit

1026
00:56:25,760 --> 00:56:31,239
Speaker 1: the idea. So the under Geiss theory, they're basically been

1027
00:56:31,280 --> 00:56:37,080
Speaker 1: around ten thousand years splices Holli scene transition, and when

1028
00:56:37,080 --> 00:56:40,480
Speaker 1: I read that, I instantly thought, wow, I was skeptical.

1029
00:56:41,480 --> 00:56:46,520
Speaker 1: Um Also he has within anything with science, there's usually

1030
00:56:46,640 --> 00:56:50,359
Speaker 1: some counterpoints, and I got digging around and there's other

1031
00:56:50,480 --> 00:56:54,880
Speaker 1: theories of maybe glaciers separated deer and then they evolved

1032
00:56:54,920 --> 00:56:59,440
Speaker 1: into those those different different species that we know today.

1033
00:56:59,520 --> 00:57:02,600
Speaker 1: So there is too kind of counter theories. And I

1034
00:57:02,600 --> 00:57:05,320
Speaker 1: don't know if you guys know, it is pretty commonly

1035
00:57:05,360 --> 00:57:10,080
Speaker 1: held that they're fairly new species to the planet, um say,

1036
00:57:10,200 --> 00:57:13,520
Speaker 1: especially compared to white tailed deer who've been around, as

1037
00:57:13,560 --> 00:57:16,760
Speaker 1: I'm told a lot long like fairly unchanged for a

1038
00:57:16,800 --> 00:57:20,880
Speaker 1: long long time. I think that's the general consensus is

1039
00:57:20,920 --> 00:57:23,880
Speaker 1: that what is exactly what you said, being a fairly

1040
00:57:24,000 --> 00:57:28,640
Speaker 1: new species whatever whatever new actually means. Um. But with

1041
00:57:28,680 --> 00:57:31,920
Speaker 1: regards to that, yeah, and then historically the notions with

1042
00:57:32,920 --> 00:57:35,320
Speaker 1: you know, my the initials some of the mitochondrial DNA

1043
00:57:35,440 --> 00:57:39,760
Speaker 1: indicating that mule deer were more closely related to whitetailed

1044
00:57:39,800 --> 00:57:42,200
Speaker 1: deer as opposed to blacktail deer, and then more recent

1045
00:57:42,240 --> 00:57:45,000
Speaker 1: analyzes indicating that well, no, mule deer and blacktail deer

1046
00:57:45,000 --> 00:57:47,920
Speaker 1: are probably more closely related than our deer. It's just

1047
00:57:48,000 --> 00:57:51,480
Speaker 1: like and that's like our referenced our conversation before. As

1048
00:57:51,480 --> 00:57:54,120
Speaker 1: we begin to like, you know, split all these things up,

1049
00:57:54,120 --> 00:57:59,040
Speaker 1: it's like, okay, well fine, And interestingly, as far as like,

1050
00:57:59,160 --> 00:58:02,840
Speaker 1: ecologists tend to not dwell on those sorts of things

1051
00:58:02,880 --> 00:58:05,280
Speaker 1: a lot um in the arena of the geneticists and

1052
00:58:05,280 --> 00:58:07,200
Speaker 1: those sorts of things, there's lots of you know, those

1053
00:58:07,280 --> 00:58:10,400
Speaker 1: talk taxonomic related questions, how do these animals come about?

1054
00:58:10,440 --> 00:58:12,560
Speaker 1: Where do we where do we draw the lines? But

1055
00:58:12,800 --> 00:58:14,960
Speaker 1: as ecologists, we tend to we tend to dwell on

1056
00:58:15,000 --> 00:58:17,760
Speaker 1: some of those aspects um a lot less and focus

1057
00:58:17,800 --> 00:58:20,640
Speaker 1: more on the ecology connections of animals to their environment

1058
00:58:21,040 --> 00:58:24,320
Speaker 1: as as opposed to what should be split, what should

1059
00:58:24,320 --> 00:58:26,920
Speaker 1: be split, and where the splitters versus you become curious

1060
00:58:26,920 --> 00:58:33,080
Speaker 1: about it, I'm not that curious. Yeah, I just And

1061
00:58:33,120 --> 00:58:35,840
Speaker 1: I think it's because I think it's because just our level,

1062
00:58:35,920 --> 00:58:38,240
Speaker 1: our level, as we talked before, I mean, there's definitely

1063
00:58:38,240 --> 00:58:42,440
Speaker 1: some value within that relative to how unique is this

1064
00:58:42,600 --> 00:58:44,600
Speaker 1: versus that? And where do we draw these lines to

1065
00:58:44,680 --> 00:58:47,400
Speaker 1: seek to conserve and protect? And I guess I like

1066
00:58:47,480 --> 00:58:49,160
Speaker 1: to think of it more bigger picture, and I'm like,

1067
00:58:49,200 --> 00:58:52,240
Speaker 1: I think we want to be able to conserve essentially

1068
00:58:52,280 --> 00:58:54,840
Speaker 1: every everything we got, even those that even those that

1069
00:58:54,920 --> 00:58:59,959
Speaker 1: are not um small or isolated or or seemingly they

1070
00:59:00,000 --> 00:59:02,920
Speaker 1: are unique. Um, I guess I like, I think a

1071
00:59:02,920 --> 00:59:05,760
Speaker 1: little bit more big picture and in a bit differently

1072
00:59:05,800 --> 00:59:08,320
Speaker 1: of just the connection between an animal and their environments

1073
00:59:08,320 --> 00:59:11,440
Speaker 1: and within within those ways as opposed to the splits

1074
00:59:11,520 --> 00:59:14,400
Speaker 1: or the lumps or But if if say, like a

1075
00:59:14,480 --> 00:59:18,120
Speaker 1: mule deer, it's been on the planet much shorter period

1076
00:59:18,440 --> 00:59:22,480
Speaker 1: pretty much agreed to on that, does that then ecologically

1077
00:59:22,600 --> 00:59:27,320
Speaker 1: make them more sensitive to change and less able to adapt?

1078
00:59:31,200 --> 00:59:35,160
Speaker 1: That's a really good question. Well, it depends, it would

1079
00:59:35,200 --> 00:59:40,400
Speaker 1: depends upon how the various traits that that exists there

1080
00:59:40,440 --> 00:59:44,280
Speaker 1: have become potentially fixed and thus la and thus are

1081
00:59:44,320 --> 00:59:47,880
Speaker 1: more lacked in diversity. So something that's potentially newer as

1082
00:59:47,880 --> 00:59:50,800
Speaker 1: so long as there's traits for adaptation to act on

1083
00:59:51,440 --> 00:59:54,680
Speaker 1: that could lead to a viable strategy, then it's fine.

1084
00:59:54,760 --> 00:59:58,440
Speaker 1: That part doesn't really matter. Whereas if if various traits

1085
00:59:58,440 --> 01:00:03,280
Speaker 1: have become fixed and then something changes, then there's there's

1086
01:00:03,560 --> 01:00:07,560
Speaker 1: no there's no potential beneficial trait for that change to

1087
01:00:07,640 --> 01:00:10,440
Speaker 1: be able to operate on that leads to some benefit

1088
01:00:10,480 --> 01:00:14,360
Speaker 1: over time. Um So whether or not there's the diversity

1089
01:00:14,400 --> 01:00:16,360
Speaker 1: in those traits to allow that to happen, depending upon

1090
01:00:16,400 --> 01:00:18,120
Speaker 1: how long one has been around or not. I don't

1091
01:00:18,160 --> 01:00:20,960
Speaker 1: know that it necessarily makes them one more sensitive than

1092
01:00:21,560 --> 01:00:24,040
Speaker 1: than the other, but others could certainly argue it in

1093
01:00:24,040 --> 01:00:27,200
Speaker 1: the opposite direction. I'm gonna layout the damn geist flock.

1094
01:00:28,360 --> 01:00:30,080
Speaker 1: Are you familiar enough with it? The fact? Check me

1095
01:00:30,080 --> 01:00:34,360
Speaker 1: out it. We'll see. Okay, he's got this idea right,

1096
01:00:35,160 --> 01:00:42,600
Speaker 1: probably untestable. Are had this this long standing like very

1097
01:00:42,720 --> 01:00:46,160
Speaker 1: versatile population of white tailed deer that had always been

1098
01:00:46,160 --> 01:00:50,400
Speaker 1: and what's now the Southeast United States and climatic conditions

1099
01:00:50,400 --> 01:00:54,720
Speaker 1: were such that had enjoyed this westward expansion um and

1100
01:00:54,760 --> 01:00:59,720
Speaker 1: then climatic conditions were such that the middle ground faded

1101
01:00:59,760 --> 01:01:02,560
Speaker 1: out and you had this remnant population on the west coast,

1102
01:01:03,640 --> 01:01:07,480
Speaker 1: and this this in that expanding population retracted back to

1103
01:01:07,640 --> 01:01:11,040
Speaker 1: east and they enjoyed this long period of separation and

1104
01:01:11,040 --> 01:01:14,800
Speaker 1: eventually emerged the black tail deer in the white tail deer.

1105
01:01:15,080 --> 01:01:18,360
Speaker 1: And then climatic conditions were such that those populations were

1106
01:01:18,360 --> 01:01:22,080
Speaker 1: brought back together eastward and westward expansion. There was a

1107
01:01:22,160 --> 01:01:28,440
Speaker 1: hybridization event. It's very beautiful in its details. There's a

1108
01:01:28,520 --> 01:01:33,800
Speaker 1: hybridization event around the Rocky Mountain front or so, and

1109
01:01:33,800 --> 01:01:38,560
Speaker 1: then and then the retraction again, and it left this

1110
01:01:38,640 --> 01:01:42,880
Speaker 1: population of yeah, it's tidy, it's tidy, tidy, tidy, neat

1111
01:01:43,080 --> 01:01:46,600
Speaker 1: So tell me then, no, no, listen, I don't know, no,

1112
01:01:46,640 --> 01:01:49,480
Speaker 1: I no, no, Okay, So it's not it's not a question.

1113
01:01:49,520 --> 01:01:51,400
Speaker 1: It's not a question for you. Perhaps it's more of

1114
01:01:51,480 --> 01:01:57,000
Speaker 1: no hybrids, okay, white tail deer, meal deer hybrids. How

1115
01:01:57,000 --> 01:01:58,960
Speaker 1: do they do? I don't think they do very good? Right,

1116
01:01:59,000 --> 01:02:02,080
Speaker 1: they do terrible. Then they're not sexually they're not sexually

1117
01:02:02,120 --> 01:02:04,360
Speaker 1: viol generally not. And in fact I had one in

1118
01:02:04,720 --> 01:02:07,160
Speaker 1: captivity that we that because we have both meal deer

1119
01:02:07,240 --> 01:02:09,520
Speaker 1: and if you are white till the end a few

1120
01:02:09,520 --> 01:02:12,800
Speaker 1: meal there in captivity and we had um but got

1121
01:02:12,800 --> 01:02:14,720
Speaker 1: in with their white tail mule deer buck broke through

1122
01:02:14,720 --> 01:02:16,600
Speaker 1: the fence, got in with our white tailed does the

1123
01:02:16,640 --> 01:02:18,560
Speaker 1: one year and happened to knock one of them up

1124
01:02:18,600 --> 01:02:20,240
Speaker 1: until we ended up with a hybrid. Is that how

1125
01:02:20,320 --> 01:02:22,360
Speaker 1: hybridization occurs? It's a mule your buck in a white

1126
01:02:22,360 --> 01:02:24,760
Speaker 1: tailed dough or couldn't go the other way? Yeah, there's

1127
01:02:24,760 --> 01:02:26,760
Speaker 1: lots of speculation on that it can go the other way.

1128
01:02:26,920 --> 01:02:29,320
Speaker 1: So people, I mean, but like, so you've seen it happen.

1129
01:02:29,560 --> 01:02:33,320
Speaker 1: Have you seen have you seen both happen? We have

1130
01:02:33,400 --> 01:02:36,600
Speaker 1: evidence of that happening. I haven't seen it myself happened

1131
01:02:36,640 --> 01:02:39,320
Speaker 1: that way, we have evidence is known to have happened. Yeah.

1132
01:02:39,360 --> 01:02:43,200
Speaker 1: And the general theory, which probably comes from geist to um,

1133
01:02:43,320 --> 01:02:46,040
Speaker 1: is that because of the way white tailed deer rut

1134
01:02:46,600 --> 01:02:49,480
Speaker 1: and that they travel lots of country and then find

1135
01:02:49,520 --> 01:02:51,560
Speaker 1: one female and stick with it. That sort of thing,

1136
01:02:51,600 --> 01:02:55,200
Speaker 1: as opposed to the maybe more sometimes storm hair harem

1137
01:02:55,240 --> 01:02:58,000
Speaker 1: oriented a little bit, although they're not really lot, not

1138
01:02:58,080 --> 01:03:00,360
Speaker 1: quite a lot within meal there it's little bit of

1139
01:03:00,360 --> 01:03:03,280
Speaker 1: attending bond, but that it's more likely for a white

1140
01:03:03,280 --> 01:03:05,880
Speaker 1: tail to happen to encounter muleedr dough and then ultimately

1141
01:03:06,640 --> 01:03:11,000
Speaker 1: but yeah, yeah, but regardless like that hybrid even in captivity,

1142
01:03:11,200 --> 01:03:14,880
Speaker 1: it's actually pretty hilarious watching if it, if it got

1143
01:03:14,880 --> 01:03:17,240
Speaker 1: spooked a little bit and took off, it would like

1144
01:03:17,400 --> 01:03:19,720
Speaker 1: stop once and then sort of run and then like

1145
01:03:19,760 --> 01:03:23,080
Speaker 1: start again and then try to run again. Almost it's

1146
01:03:23,120 --> 01:03:26,040
Speaker 1: completely true. You hear those stories, but explain stotting because

1147
01:03:26,080 --> 01:03:27,360
Speaker 1: a lot of people are gonna know what that work is.

1148
01:03:27,480 --> 01:03:30,320
Speaker 1: So startting is where it is very common and mule

1149
01:03:30,400 --> 01:03:32,720
Speaker 1: dear and it's where the animal lands on all four

1150
01:03:32,800 --> 01:03:35,640
Speaker 1: feet at once and then pops up again. And the

1151
01:03:35,680 --> 01:03:38,560
Speaker 1: idea behind that, which probably comes from Val guys To,

1152
01:03:40,840 --> 01:03:44,439
Speaker 1: is that within within the more the more in his book,

1153
01:03:44,760 --> 01:03:47,560
Speaker 1: Oh yeah, no, I know he did. Yeah. Um, as

1154
01:03:47,640 --> 01:03:51,160
Speaker 1: they as they live in the more like shrubby ruggedy

1155
01:03:51,200 --> 01:03:54,200
Speaker 1: country and with some of the predators that they encounter there,

1156
01:03:54,240 --> 01:03:57,160
Speaker 1: that them bounding in between as opposed to the flat

1157
01:03:57,200 --> 01:04:00,160
Speaker 1: out open wide open run in open country that white

1158
01:04:00,200 --> 01:04:02,880
Speaker 1: till dere are that it's a better adaptation for them

1159
01:04:02,920 --> 01:04:05,080
Speaker 1: to aid predator by that popping up and down and

1160
01:04:05,080 --> 01:04:08,280
Speaker 1: so you don't see exactly and so it's that all

1161
01:04:08,480 --> 01:04:11,040
Speaker 1: all four fet bouncing. But yeah, what that hybrid would

1162
01:04:11,080 --> 01:04:13,480
Speaker 1: do is he would literally do that. He'd like stop

1163
01:04:13,560 --> 01:04:15,440
Speaker 1: once and then open up and then start again and

1164
01:04:15,440 --> 01:04:17,960
Speaker 1: then open up. Really bizarre. And so you can imagine

1165
01:04:18,000 --> 01:04:21,640
Speaker 1: that an adaptation like that is not really fit on

1166
01:04:21,720 --> 01:04:24,960
Speaker 1: either side of the spectrum where do you live and

1167
01:04:24,960 --> 01:04:27,360
Speaker 1: and be viable that way? But in general, to get

1168
01:04:27,400 --> 01:04:31,640
Speaker 1: back to your question, if that happened to me, those

1169
01:04:31,760 --> 01:04:34,720
Speaker 1: hybrids should be way more viable than they are. And

1170
01:04:34,800 --> 01:04:38,120
Speaker 1: the point is is they're not. And and that's one

1171
01:04:38,120 --> 01:04:41,680
Speaker 1: of the greatest challenges with regards to that idea. Um

1172
01:04:41,720 --> 01:04:46,520
Speaker 1: And unless reproductive isolation for whatever hundreds of thousands of

1173
01:04:46,600 --> 01:04:49,120
Speaker 1: years or whatever it is, was long enough to cause

1174
01:04:49,200 --> 01:04:53,360
Speaker 1: hybrids to not be viable from from that relationship, maybe,

1175
01:04:53,440 --> 01:04:55,160
Speaker 1: but it seems to me that they should be more

1176
01:04:55,240 --> 01:04:57,400
Speaker 1: viable than they are. If if that was the case,

1177
01:04:57,720 --> 01:05:03,160
Speaker 1: so good job, man, is it. At what point in

1178
01:05:03,200 --> 01:05:07,160
Speaker 1: time are you leaving val guys? Because I got oh no, yeah,

1179
01:05:07,160 --> 01:05:09,520
Speaker 1: but go ahead, man, you're not gonna go ahead. Yeah,

1180
01:05:09,520 --> 01:05:11,520
Speaker 1: I'm gonna go. I wanna know if they If they

1181
01:05:11,640 --> 01:05:14,440
Speaker 1: what about when they think about the shirking or shirk

1182
01:05:14,520 --> 01:05:20,320
Speaker 1: or idea that val guys has big into this. Yeah,

1183
01:05:20,680 --> 01:05:24,400
Speaker 1: that's the super How he turns into a super buck

1184
01:05:24,720 --> 01:05:31,320
Speaker 1: is that he removes himself from the breeding. Everybody else

1185
01:05:31,320 --> 01:05:34,240
Speaker 1: has burned up their fat reserves. He just kicks back,

1186
01:05:34,360 --> 01:05:37,960
Speaker 1: keeps eating and like just for years. He takes himself

1187
01:05:37,960 --> 01:05:40,440
Speaker 1: out for years of this, and so one day he

1188
01:05:40,880 --> 01:05:44,360
Speaker 1: emerges just the man. Yeah, nobody can mess with him.

1189
01:05:44,400 --> 01:05:46,400
Speaker 1: And at that point he can just spread his jeans

1190
01:05:46,560 --> 01:05:49,439
Speaker 1: everywhere so I can speak. You guys got a collar

1191
01:05:49,560 --> 01:05:55,320
Speaker 1: one of them suck? Well we should, yeah, we should

1192
01:05:55,360 --> 01:05:59,000
Speaker 1: potentially see that. So so back with the captive deer

1193
01:05:59,040 --> 01:06:03,040
Speaker 1: work that I did years ago, um, we we sort

1194
01:06:03,080 --> 01:06:05,200
Speaker 1: of we angled at a question along those lines, but

1195
01:06:05,280 --> 01:06:07,720
Speaker 1: not exactly like that. We're in. Our interests was in

1196
01:06:07,840 --> 01:06:12,040
Speaker 1: looking at how yearling males so kind of their first

1197
01:06:12,160 --> 01:06:16,560
Speaker 1: year coming into that age where they could potentially reproduce

1198
01:06:16,680 --> 01:06:19,760
Speaker 1: or participate in the rut, whether or not when a

1199
01:06:19,960 --> 01:06:23,920
Speaker 1: big adult male was around, if that suppressive effect of

1200
01:06:23,960 --> 01:06:26,840
Speaker 1: that hierarchy caused them to not engage in the rut,

1201
01:06:27,200 --> 01:06:30,240
Speaker 1: whereas in a scenario where we had yearling males with

1202
01:06:30,280 --> 01:06:32,760
Speaker 1: no big males around, just two yearling males and their

1203
01:06:32,800 --> 01:06:35,040
Speaker 1: access to females all by themselves, they can be the

1204
01:06:35,080 --> 01:06:38,000
Speaker 1: top dogs. And whether or not they then expended more

1205
01:06:38,000 --> 01:06:41,080
Speaker 1: resources in the rut. And so during the during that

1206
01:06:41,160 --> 01:06:43,480
Speaker 1: those windows of time, we separated them out into those

1207
01:06:43,480 --> 01:06:46,840
Speaker 1: groups like that, and we monitored their food intake, their

1208
01:06:46,920 --> 01:06:49,680
Speaker 1: changing body mass during the rut, and then also they're

1209
01:06:49,760 --> 01:06:52,680
Speaker 1: changing fat during the rut. And what was amazing to

1210
01:06:52,720 --> 01:06:55,320
Speaker 1: me is those yearling males, regardless of whether or not

1211
01:06:55,400 --> 01:06:57,760
Speaker 1: they had big males around, and by the way, they

1212
01:06:57,800 --> 01:07:00,800
Speaker 1: acted very differently those yearling males where they didn't have

1213
01:07:00,800 --> 01:07:03,200
Speaker 1: a big male around, you knew they were top dog.

1214
01:07:03,400 --> 01:07:05,840
Speaker 1: They acted like they were topped do oh yeah, oh

1215
01:07:05,880 --> 01:07:09,680
Speaker 1: crap yeah. Behaviorally it was very obvious. But interestingly, their

1216
01:07:09,800 --> 01:07:13,040
Speaker 1: forage intake in the mass they lost during the rut

1217
01:07:13,320 --> 01:07:16,720
Speaker 1: was no different, which to me was completely phenomenal. And

1218
01:07:16,800 --> 01:07:20,160
Speaker 1: in those big adult males, they were expending a lot

1219
01:07:20,200 --> 01:07:22,760
Speaker 1: of energy during the rut. Fact, those adult males like

1220
01:07:22,960 --> 01:07:26,080
Speaker 1: ford It four to seven years old, they could lose

1221
01:07:26,200 --> 01:07:28,800
Speaker 1: eight percent of their body mass in a week like,

1222
01:07:29,000 --> 01:07:32,280
Speaker 1: and they're literally and these are in a We're literally

1223
01:07:32,280 --> 01:07:34,920
Speaker 1: putting males in a four ft by eight foot what

1224
01:07:34,960 --> 01:07:37,720
Speaker 1: we call metabolic box during the day, allowing them out

1225
01:07:37,760 --> 01:07:39,960
Speaker 1: to interact in the mornings and evenings, putting them back

1226
01:07:39,960 --> 01:07:42,680
Speaker 1: in that box, food right in front of their face,

1227
01:07:42,720 --> 01:07:44,320
Speaker 1: as much of it as they want, and they eat

1228
01:07:44,360 --> 01:07:47,880
Speaker 1: almost nothing. So most of the mass loss associated with

1229
01:07:47,920 --> 01:07:49,960
Speaker 1: the rut, even though we think it's because they're running

1230
01:07:50,000 --> 01:07:54,640
Speaker 1: all over the place, is actually because it's it's voluntary hypophagia.

1231
01:07:54,760 --> 01:07:57,959
Speaker 1: They're not eating. They're simply not eating, and that's where

1232
01:07:58,000 --> 01:08:00,200
Speaker 1: most of the change occurs during the rut. But we

1233
01:08:00,320 --> 01:08:03,200
Speaker 1: learned from that work as well, so not only those

1234
01:08:03,280 --> 01:08:06,520
Speaker 1: dynamics with those yearling males, but also the bigger males,

1235
01:08:07,200 --> 01:08:10,200
Speaker 1: is that it's largely what we call state dependent. So

1236
01:08:10,240 --> 01:08:12,880
Speaker 1: a big male that has more fat reserves at the

1237
01:08:12,920 --> 01:08:15,680
Speaker 1: beginning of the year is going to expend more of

1238
01:08:15,720 --> 01:08:19,280
Speaker 1: those reserves in the rut than a male that is

1239
01:08:19,320 --> 01:08:22,080
Speaker 1: simply did not pack on as many reserves early in

1240
01:08:22,080 --> 01:08:25,160
Speaker 1: the year, or is younger and thus still growing. Therefore

1241
01:08:25,200 --> 01:08:27,640
Speaker 1: it doesn't have the fat reserve to expend because it

1242
01:08:27,680 --> 01:08:30,439
Speaker 1: was putting most of its energy into growth and body

1243
01:08:30,439 --> 01:08:33,880
Speaker 1: structure and body mass, and then it's going to expend less.

1244
01:08:33,920 --> 01:08:36,840
Speaker 1: So while the Shirker mail it could have been a

1245
01:08:36,880 --> 01:08:39,760
Speaker 1: big male that year and he went all out, but

1246
01:08:39,840 --> 01:08:43,479
Speaker 1: that's because he had it. It doesn't necessarily imply that

1247
01:08:43,520 --> 01:08:45,800
Speaker 1: he's been saving it up for years and then going

1248
01:08:45,840 --> 01:08:48,080
Speaker 1: all in and in all honesty with regards to a

1249
01:08:48,240 --> 01:08:53,080
Speaker 1: tactic wherein you would you know, attempt to contribute your

1250
01:08:53,160 --> 01:08:57,320
Speaker 1: genes to subsequent generations. If you wait that long, there

1251
01:08:57,360 --> 01:09:00,599
Speaker 1: you also could die and then contribute nothing as well.

1252
01:09:00,680 --> 01:09:03,240
Speaker 1: And so if you do that in a manner wherein

1253
01:09:03,320 --> 01:09:06,120
Speaker 1: in the years you have the resources to expend towards it,

1254
01:09:06,160 --> 01:09:09,360
Speaker 1: you by all means should probably engage to take advantage

1255
01:09:09,360 --> 01:09:11,960
Speaker 1: of that opportunity. Yeah, I don't I don't remember the

1256
01:09:11,960 --> 01:09:16,559
Speaker 1: Shirkert male, but it does seem like it's a little

1257
01:09:16,560 --> 01:09:21,160
Speaker 1: bit counterintuitive of the way. You know, he used it

1258
01:09:21,200 --> 01:09:24,240
Speaker 1: as like a way to explain, but you get like

1259
01:09:24,520 --> 01:09:27,960
Speaker 1: the biggest you want him to save up and then yeah,

1260
01:09:28,120 --> 01:09:31,160
Speaker 1: but the but the problem is that like an individual

1261
01:09:31,200 --> 01:09:37,000
Speaker 1: animal like, they don't know that that strategy is possible, right,

1262
01:09:37,080 --> 01:09:39,280
Speaker 1: So it's it's kind of a hard So you know,

1263
01:09:39,320 --> 01:09:40,760
Speaker 1: all the sort of evolution. He's like, you know how,

1264
01:09:40,800 --> 01:09:45,599
Speaker 1: I'm gonna play it boys, right, right, and for five

1265
01:09:45,680 --> 01:09:48,599
Speaker 1: years when I come back, and then at year four,

1266
01:09:48,680 --> 01:09:53,200
Speaker 1: you're dead, right, So you know, animals discount the uncertainty

1267
01:09:53,240 --> 01:09:55,280
Speaker 1: of the future, like they don't know what the future,

1268
01:09:55,320 --> 01:09:58,479
Speaker 1: their future reproductive possibilities are going to be. And so

1269
01:09:58,600 --> 01:10:00,600
Speaker 1: but I don't think he's proposing that there game in it.

1270
01:10:00,680 --> 01:10:02,160
Speaker 1: I think it's just a thing that like it's got

1271
01:10:02,160 --> 01:10:04,960
Speaker 1: low tea or whatever. I don't know. We should have

1272
01:10:04,960 --> 01:10:09,800
Speaker 1: the guy on, yes, but I'm ready to leave him.

1273
01:10:09,800 --> 01:10:11,600
Speaker 1: I'm GOODA we touched on that because that brought up

1274
01:10:11,600 --> 01:10:14,880
Speaker 1: some interesting stuff to listen to. This podcast is gonna

1275
01:10:14,880 --> 01:10:21,120
Speaker 1: be a good one. Wait, you haven't been listening so again? Yeah?

1276
01:10:21,640 --> 01:10:25,120
Speaker 1: Is it good that we move on? Please? Okay? UM,

1277
01:10:25,160 --> 01:10:27,760
Speaker 1: are you guys familiar with the idea that that at

1278
01:10:27,800 --> 01:10:32,680
Speaker 1: the time, like at the moment of European contact, um

1279
01:10:32,720 --> 01:10:39,679
Speaker 1: that shortly thereafter, we probably enjoyed the highest buffalo constant

1280
01:10:39,840 --> 01:10:42,680
Speaker 1: buffalo bison. I'm gonna use bison because you guys professionals,

1281
01:10:42,680 --> 01:10:47,600
Speaker 1: the highest bison population perhaps that ever existed on the

1282
01:10:47,600 --> 01:10:53,160
Speaker 1: continent because you had, you know, lost of the indigenous

1283
01:10:53,200 --> 01:10:55,519
Speaker 1: hunters on the landscape and their other landscape changes. And

1284
01:10:55,520 --> 01:10:59,080
Speaker 1: so we came in and sought us perhaps saw this

1285
01:10:59,200 --> 01:11:04,519
Speaker 1: like very momentary artificial thing of the you know, the

1286
01:11:05,400 --> 01:11:08,280
Speaker 1: much cited like thirty two or forty two or whatever

1287
01:11:08,360 --> 01:11:12,760
Speaker 1: fashionable number of bison that around the landscape, when we

1288
01:11:12,800 --> 01:11:16,880
Speaker 1: took it to be like, uh, yeah, yeah, used to

1289
01:11:16,880 --> 01:11:19,760
Speaker 1: be the fashionable number, used to be sixty millions gone down. Um.

1290
01:11:20,000 --> 01:11:21,960
Speaker 1: But anyways, we looked like holy smokes, is a ton

1291
01:11:22,000 --> 01:11:23,880
Speaker 1: of these things. But there's no reason to think that

1292
01:11:23,920 --> 01:11:25,519
Speaker 1: it had been like that for a long time. And

1293
01:11:25,560 --> 01:11:29,280
Speaker 1: there could have been factors that allowed this explosion and

1294
01:11:29,280 --> 01:11:32,040
Speaker 1: that allowed the animals to be in places they weren't,

1295
01:11:32,360 --> 01:11:35,240
Speaker 1: such as like the mound builders in the Ohio and

1296
01:11:35,240 --> 01:11:38,760
Speaker 1: Mississippi Valley. Um, they made effigy mounds to all the

1297
01:11:38,800 --> 01:11:42,519
Speaker 1: animals around. They never made an effigy mound to buffalo.

1298
01:11:42,680 --> 01:11:45,240
Speaker 1: Yet when the English came into those areas, they're all

1299
01:11:45,240 --> 01:11:48,559
Speaker 1: over to damn place. So people wonder what happened there.

1300
01:11:48,560 --> 01:11:51,120
Speaker 1: Had they moved into these places, why were they not

1301
01:11:51,200 --> 01:11:56,440
Speaker 1: represented an art um? Was this just a temporary phenomenon

1302
01:11:56,479 --> 01:11:59,599
Speaker 1: that they witnessed, So what what point do you feel

1303
01:12:00,640 --> 01:12:09,280
Speaker 1: we had the most mule deer X. Yeah, I mean

1304
01:12:10,120 --> 01:12:12,960
Speaker 1: it seems like it seems like that was that was

1305
01:12:13,000 --> 01:12:15,080
Speaker 1: probably the hey day. So that is a that's a

1306
01:12:15,160 --> 01:12:17,920
Speaker 1: legit idea. I mean, it seems like it's a you know,

1307
01:12:18,000 --> 01:12:20,439
Speaker 1: we don't have great uh data going back that far,

1308
01:12:20,520 --> 01:12:23,080
Speaker 1: but it definitely seems like that's kind of the the

1309
01:12:23,120 --> 01:12:26,640
Speaker 1: conventional wisdom because you had cultures, like you had indigenous

1310
01:12:26,680 --> 01:12:29,720
Speaker 1: cultures even that that focus like it's it's hard to

1311
01:12:29,760 --> 01:12:32,120
Speaker 1: imagine not you had indigenous cultures that seem to have

1312
01:12:32,200 --> 01:12:36,839
Speaker 1: focused on hunting big horns m m. Had indigenous cultures

1313
01:12:36,840 --> 01:12:42,160
Speaker 1: that focused on hunting doll sheep, which seems yeah, wild right,

1314
01:12:42,280 --> 01:12:44,240
Speaker 1: And you had people that like very much focused on

1315
01:12:44,280 --> 01:12:46,360
Speaker 1: all the things. But there's no sort of like mule

1316
01:12:46,400 --> 01:12:50,599
Speaker 1: deer society. Right well, you know it's uh, I mean,

1317
01:12:50,640 --> 01:12:53,639
Speaker 1: of course, like our our understanding of these things gets

1318
01:12:54,160 --> 01:12:56,960
Speaker 1: dimmer and dimmer the farther we go back, right, but

1319
01:12:58,200 --> 01:13:02,439
Speaker 1: they you've probably read that journal The Trapper by Osborne Russell, right,

1320
01:13:02,760 --> 01:13:05,320
Speaker 1: and and you know, so this was this was a

1321
01:13:05,360 --> 01:13:09,280
Speaker 1: trapper that was moving through the Greater Yellowstone Region in

1322
01:13:09,360 --> 01:13:12,760
Speaker 1: the eighteen thirties and he was you know, hunting beaver

1323
01:13:12,960 --> 01:13:16,759
Speaker 1: and and supplying beaver pelts uh to the regional markets,

1324
01:13:16,840 --> 01:13:20,599
Speaker 1: and and it's striking and and he was a fairly

1325
01:13:20,640 --> 01:13:24,679
Speaker 1: remarkable guy because he basically wrote down in enough detail

1326
01:13:24,800 --> 01:13:29,679
Speaker 1: his journeys every day that historians and could go back

1327
01:13:29,840 --> 01:13:33,479
Speaker 1: and and trace his path right of of where he

1328
01:13:33,600 --> 01:13:36,840
Speaker 1: was the entire season. Even he's been a couple of

1329
01:13:36,840 --> 01:13:41,240
Speaker 1: different years moving through that landscape, and and he wrote

1330
01:13:41,240 --> 01:13:45,360
Speaker 1: down every time they shot something. And what's remarkable is

1331
01:13:45,479 --> 01:13:48,080
Speaker 1: they're moving through the you know, the greater Yellowstone landscape,

1332
01:13:48,160 --> 01:13:52,719
Speaker 1: and whenever they need food, it's either bison or bighorn sheet.

1333
01:13:53,720 --> 01:13:57,800
Speaker 1: You know, yeah, needed, you know, needed to stop and

1334
01:13:57,840 --> 01:14:01,160
Speaker 1: make something shot of bison shot. He he has an

1335
01:14:01,200 --> 01:14:05,160
Speaker 1: observation somewhere up in the in the Gravant Rivers, which

1336
01:14:05,200 --> 01:14:07,960
Speaker 1: is the Gravant drainage, which is kind of would be

1337
01:14:08,040 --> 01:14:11,760
Speaker 1: sort of south of of the southeastern corner of Yellowstone

1338
01:14:12,280 --> 01:14:16,240
Speaker 1: where he's at Camp. And he and he makes an observation.

1339
01:14:16,240 --> 01:14:20,639
Speaker 1: He counts on the cliffs around Camp a thousand big

1340
01:14:20,640 --> 01:14:25,879
Speaker 1: horn sheep, which is just unimaginable today right from France's apartment,

1341
01:14:26,479 --> 01:14:29,080
Speaker 1: I'm not familiar. He's the historian he traveled with the

1342
01:14:29,120 --> 01:14:31,920
Speaker 1: Oglala Sioux in the forties and they go into the

1343
01:14:31,920 --> 01:14:38,559
Speaker 1: Black Hills and kill big horns with rocks to get

1344
01:14:38,600 --> 01:14:40,760
Speaker 1: above them. They get above them and roll rocks down

1345
01:14:40,800 --> 01:14:45,760
Speaker 1: to kill him. Crazy go Yeah, So I mean like

1346
01:14:46,400 --> 01:14:52,400
Speaker 1: like there, you wouldn't occasionally, you know, occasionally Russell reports

1347
01:14:52,479 --> 01:14:57,760
Speaker 1: killing a meal deer, occasionally an elk, and so I like, yeah,

1348
01:14:57,800 --> 01:14:59,559
Speaker 1: I mean that, and that's what if you're traveling through

1349
01:14:59,560 --> 01:15:02,360
Speaker 1: there now, would be like your main right, that's what

1350
01:15:02,360 --> 01:15:06,320
Speaker 1: you're gonna run into. Yeah, yeah, and so yeah, I's that,

1351
01:15:06,640 --> 01:15:09,920
Speaker 1: you know. It counts like that make me curious how

1352
01:15:10,000 --> 01:15:15,120
Speaker 1: much bison shaped the ecosystem and you know, modifying the

1353
01:15:15,160 --> 01:15:19,200
Speaker 1: habitat and and extantially competed with species like like mule

1354
01:15:19,280 --> 01:15:22,760
Speaker 1: deer and elk you know, and you know, interactions and

1355
01:15:22,880 --> 01:15:27,040
Speaker 1: dynamics that we have no way to really understand today.

1356
01:15:28,240 --> 01:15:31,280
Speaker 1: The school I found a school, a bison school at

1357
01:15:32,520 --> 01:15:36,880
Speaker 1: like literally going towards an elk bugle at nine thousand

1358
01:15:36,960 --> 01:15:43,519
Speaker 1: ft in heavy timber in the Madison Range, and you

1359
01:15:43,600 --> 01:15:46,760
Speaker 1: just you cannot picture it now, like what what that

1360
01:15:46,760 --> 01:15:50,280
Speaker 1: looked like when that thing died? And you know, it's

1361
01:15:50,280 --> 01:15:55,559
Speaker 1: just it's it's so confusing, right, um so yeah, when

1362
01:15:55,600 --> 01:16:00,479
Speaker 1: were there a bunch of them like the you know

1363
01:16:00,600 --> 01:16:02,760
Speaker 1: the idea are you familiar with the idea that they're

1364
01:16:02,800 --> 01:16:04,960
Speaker 1: like like that? The everyone talks about the mule dear

1365
01:16:05,040 --> 01:16:07,120
Speaker 1: heyday of the nineteen sixties that might have been, like,

1366
01:16:07,160 --> 01:16:08,639
Speaker 1: what were the factors that could have led to something

1367
01:16:08,720 --> 01:16:12,960
Speaker 1: like that? Yes, yes, don't like speculate about old timey stuff.

1368
01:16:14,320 --> 01:16:17,719
Speaker 1: It's because the speculation just tough for scientists, Like, Okay,

1369
01:16:17,720 --> 01:16:21,000
Speaker 1: I'm gonna go there. Well, because you get skittish, Well,

1370
01:16:21,040 --> 01:16:23,559
Speaker 1: you get skittish, and it's I mean, we're our job

1371
01:16:23,680 --> 01:16:26,680
Speaker 1: is to Our job is to um in our in,

1372
01:16:26,720 --> 01:16:29,360
Speaker 1: our passion ultimately is to talk science, use the evidence

1373
01:16:29,400 --> 01:16:32,799
Speaker 1: that we have to talk about that, and to hopefully

1374
01:16:32,920 --> 01:16:37,160
Speaker 1: help make sound decisions. No, it's true, I'm gonna I'm

1375
01:16:37,160 --> 01:16:41,240
Speaker 1: gonna speculate. Please let me speculate. So sixteen seventies, so

1376
01:16:41,360 --> 01:16:45,320
Speaker 1: many of referenced that as a potential eruption, right, interruptive

1377
01:16:45,439 --> 01:16:50,120
Speaker 1: dynamics sixties or sixties, seventies, sixties and seven, Yeah, in

1378
01:16:50,120 --> 01:16:52,639
Speaker 1: in that window and they're um in like the notion

1379
01:16:52,680 --> 01:16:56,600
Speaker 1: behind eruptive dynamics and ungulate populations it's not it's not

1380
01:16:56,640 --> 01:16:59,599
Speaker 1: a new idea. We know that that thing that that

1381
01:16:59,640 --> 01:17:02,320
Speaker 1: type of dynamic happens. And where it's most definitely in

1382
01:17:02,360 --> 01:17:06,080
Speaker 1: the word so eruptive dynamics, is just simply this notion

1383
01:17:06,200 --> 01:17:10,840
Speaker 1: where a great example is you you take some some ungulates, um,

1384
01:17:11,080 --> 01:17:13,360
Speaker 1: put them on an island, and they grow and grow,

1385
01:17:13,400 --> 01:17:16,280
Speaker 1: they just explode to great abundance, and then they subsequently

1386
01:17:16,320 --> 01:17:19,320
Speaker 1: crash and then we never see them recover to that

1387
01:17:19,520 --> 01:17:21,840
Speaker 1: abundance again, um, which is what you see when you

1388
01:17:21,840 --> 01:17:24,960
Speaker 1: introduce wild turkeys somewhere. Yeah, yeah, certainly. Yeah, it's the

1389
01:17:25,000 --> 01:17:28,200
Speaker 1: same sort of notion. It's this explosion, make use of

1390
01:17:28,240 --> 01:17:31,280
Speaker 1: this like brand new habitat that's that's been you know,

1391
01:17:31,360 --> 01:17:34,320
Speaker 1: unpioneered before you see them reach great abundance and then

1392
01:17:34,320 --> 01:17:38,160
Speaker 1: they subsequently predators aren't used to you yet. Lots of things. Yeah,

1393
01:17:38,200 --> 01:17:42,160
Speaker 1: potential ideal scenario. And so the notion of the sixties

1394
01:17:42,160 --> 01:17:44,479
Speaker 1: and seventies, which we're all potentially fond of. And I

1395
01:17:44,479 --> 01:17:48,599
Speaker 1: think there's a general like desire and thirst to have

1396
01:17:48,720 --> 01:17:51,639
Speaker 1: that great abundance of of mule deer again most most

1397
01:17:51,640 --> 01:17:57,679
Speaker 1: certainly UM and and but also for us as people,

1398
01:17:57,760 --> 01:17:59,479
Speaker 1: I think we all, we often look in the past

1399
01:17:59,520 --> 01:18:01,280
Speaker 1: and think, why wanted the way it was back then?

1400
01:18:01,439 --> 01:18:04,040
Speaker 1: We should have that number again. But the only way

1401
01:18:04,040 --> 01:18:06,439
Speaker 1: to potentially get that number again is for everything to

1402
01:18:06,479 --> 01:18:08,680
Speaker 1: be the exact same way it was back then. And

1403
01:18:08,720 --> 01:18:11,200
Speaker 1: things are very different that then. UM. Our forests were

1404
01:18:11,240 --> 01:18:14,719
Speaker 1: at different successional stages. UM. So forest management has definitely

1405
01:18:14,800 --> 01:18:19,040
Speaker 1: progressed through time. Um we've seen successful changes in those forests.

1406
01:18:19,439 --> 01:18:23,559
Speaker 1: Livestock grazing was potentially different, Predators were potentially different, climate

1407
01:18:23,560 --> 01:18:27,599
Speaker 1: regimes were different, our presence on the landscape was certainly different.

1408
01:18:27,640 --> 01:18:32,280
Speaker 1: Our use of habitat ourselves was different. Agriculture was different. So,

1409
01:18:32,320 --> 01:18:34,760
Speaker 1: like you, you begin to think through each one of

1410
01:18:34,800 --> 01:18:38,200
Speaker 1: those things that are different now than they were back then,

1411
01:18:38,760 --> 01:18:41,840
Speaker 1: and you begin and reflecting on that, you potentially begin

1412
01:18:41,920 --> 01:18:44,720
Speaker 1: to realize that, Okay, well maybe there were a lot

1413
01:18:44,720 --> 01:18:47,759
Speaker 1: of different things that potentially contributed to that great abundance

1414
01:18:47,800 --> 01:18:49,559
Speaker 1: at that point in time. And another one is even

1415
01:18:50,200 --> 01:18:53,400
Speaker 1: other other species of ungulus present on the landscape. I mean,

1416
01:18:53,439 --> 01:18:56,320
Speaker 1: we didn't have near the elk abundance back then as

1417
01:18:56,360 --> 01:19:00,800
Speaker 1: we do as we do now. Most certainly, and regardless,

1418
01:19:00,840 --> 01:19:04,360
Speaker 1: there's you know, the way the way you ultimately get

1419
01:19:04,400 --> 01:19:06,759
Speaker 1: that you get in abundance like that is, it starts

1420
01:19:06,760 --> 01:19:08,880
Speaker 1: from the ground level. The only way that you can

1421
01:19:08,880 --> 01:19:11,080
Speaker 1: get there and to maintain that much is to ultimately

1422
01:19:11,080 --> 01:19:14,360
Speaker 1: have that have the habitat and that nutrition and fundamental

1423
01:19:14,400 --> 01:19:17,960
Speaker 1: building block for populations. Now, maybe other things like presence

1424
01:19:18,000 --> 01:19:21,040
Speaker 1: of predators and other things interact to influence those things,

1425
01:19:21,120 --> 01:19:24,120
Speaker 1: but you ultimately do not get there unless you have

1426
01:19:24,280 --> 01:19:27,959
Speaker 1: that fundamental building block. And so for me most certainly

1427
01:19:28,000 --> 01:19:31,599
Speaker 1: that fundamental building block had to be different predators aside,

1428
01:19:31,600 --> 01:19:33,519
Speaker 1: all those other sort of human harvest, all those other

1429
01:19:33,560 --> 01:19:36,600
Speaker 1: things aside you had You had to have that fundamental

1430
01:19:36,600 --> 01:19:38,400
Speaker 1: building block to be able to get there. And I

1431
01:19:38,439 --> 01:19:41,599
Speaker 1: think that building block that be food, that being food,

1432
01:19:41,760 --> 01:19:46,000
Speaker 1: and use of that building block um today is is

1433
01:19:46,040 --> 01:19:48,800
Speaker 1: different than it was than it wasn't a success. I

1434
01:19:48,800 --> 01:19:52,400
Speaker 1: think you think about what led into that nineteen sixties eruptions,

1435
01:19:52,479 --> 01:19:57,360
Speaker 1: So you had probably landscape level disturbances occurring, and then

1436
01:19:57,680 --> 01:20:00,200
Speaker 1: you kind of swing in the mid nine nine team

1437
01:20:00,280 --> 01:20:03,840
Speaker 1: thirties or so, and then there's this relax and and

1438
01:20:03,960 --> 01:20:07,479
Speaker 1: the disturbance kind of stop slows down, and that habitat

1439
01:20:07,520 --> 01:20:11,080
Speaker 1: is allowed to mature to state that's very desirable for deer,

1440
01:20:11,680 --> 01:20:14,479
Speaker 1: and they just chase that habitat would be I'll go

1441
01:20:14,479 --> 01:20:17,959
Speaker 1: ahead and speculate because I'm not bound by the Shackles science,

1442
01:20:18,080 --> 01:20:21,000
Speaker 1: but um, I agree. I think it had to have

1443
01:20:21,040 --> 01:20:25,519
Speaker 1: been a disturbance regime followed by a relax And the

1444
01:20:25,640 --> 01:20:29,040
Speaker 1: great thing about the disturbance regime that happened that time

1445
01:20:29,120 --> 01:20:32,200
Speaker 1: is you didn't have all the external stressors that we

1446
01:20:32,280 --> 01:20:36,320
Speaker 1: have now, um be at cheat grass or other demands

1447
01:20:36,320 --> 01:20:41,320
Speaker 1: on the landscape. You had historically low predator populations. Yeah,

1448
01:20:41,600 --> 01:20:44,360
Speaker 1: I mean that that can could contribute to it. But

1449
01:20:44,439 --> 01:20:46,960
Speaker 1: like Kevin was saying, you still need the groceries to

1450
01:20:47,080 --> 01:20:52,280
Speaker 1: recruit the baby. So what what is an acceptable number

1451
01:20:52,280 --> 01:20:54,720
Speaker 1: of milder? I mean, because you want to having like

1452
01:20:54,720 --> 01:20:59,000
Speaker 1: a fatalism problem, right, Like if you ask me, if

1453
01:20:59,000 --> 01:21:01,960
Speaker 1: you asked me, like, what's the benchmark of what's the

1454
01:21:02,000 --> 01:21:05,880
Speaker 1: benchmark of what we should strive towards? If I never

1455
01:21:05,880 --> 01:21:07,599
Speaker 1: really had to come up with like, oh, okay, what's

1456
01:21:07,640 --> 01:21:14,680
Speaker 1: the ideal, I would say fourteen nine two? Is that

1457
01:21:14,760 --> 01:21:23,200
Speaker 1: a number a year, a year, a hundred years before

1458
01:21:24,479 --> 01:21:27,040
Speaker 1: you know, a hundred years before European contact. I don't know,

1459
01:21:27,240 --> 01:21:29,400
Speaker 1: Like what is the number if we say like, oh,

1460
01:21:29,400 --> 01:21:33,000
Speaker 1: we want wildlife, we want to make room for wildlife.

1461
01:21:33,000 --> 01:21:35,920
Speaker 1: We want to Like if you just say, like, my

1462
01:21:36,040 --> 01:21:37,599
Speaker 1: job is just to tell you what's going on and

1463
01:21:37,600 --> 01:21:40,599
Speaker 1: what's here. At some point, it's gonna lean into advocacy here,

1464
01:21:40,600 --> 01:21:44,840
Speaker 1: at some point it's gonna lean into preservation. So are

1465
01:21:44,880 --> 01:21:47,280
Speaker 1: you always just chasing the idea that I want to

1466
01:21:47,360 --> 01:21:51,439
Speaker 1: maintain what we have right now and that's what I

1467
01:21:51,479 --> 01:21:53,920
Speaker 1: would like to see happen, or you're are you trying

1468
01:21:53,960 --> 01:21:59,080
Speaker 1: to um? Are you trying to be to go back

1469
01:21:59,160 --> 01:22:06,600
Speaker 1: and and hit some like retroactive point to say like, no,

1470
01:22:06,840 --> 01:22:10,439
Speaker 1: we pushed it too far. Now it's we we've messed

1471
01:22:10,439 --> 01:22:13,080
Speaker 1: it up too much. We need to fix things. Or

1472
01:22:13,160 --> 01:22:15,120
Speaker 1: is it just I just want to capture what's here

1473
01:22:15,160 --> 01:22:19,800
Speaker 1: now and maintain that, or I'm willing to see us

1474
01:22:19,880 --> 01:22:22,519
Speaker 1: lose a bunch more and then come to some point

1475
01:22:22,600 --> 01:22:25,879
Speaker 1: when we want to stop the loss of wildlife. Generally,

1476
01:22:26,760 --> 01:22:29,519
Speaker 1: I know it's not your job, but as a human being,

1477
01:22:30,080 --> 01:22:34,080
Speaker 1: do you think about that? Does that motivate your thoughts

1478
01:22:34,160 --> 01:22:39,800
Speaker 1: as a scientist? Well, this is sort of uh, well,

1479
01:22:40,120 --> 01:22:42,439
Speaker 1: this is a difficult question, right there is the most

1480
01:22:42,439 --> 01:22:46,519
Speaker 1: difficult question I mean the right. So, I mean, I mean,

1481
01:22:46,640 --> 01:22:50,960
Speaker 1: one answer obviously is like for us, I mean, we're researchers, right,

1482
01:22:51,000 --> 01:22:55,000
Speaker 1: so our job is not to articulate how many meals

1483
01:22:54,960 --> 01:22:57,760
Speaker 1: here there should be on the landscape. Okay, so that's

1484
01:22:57,800 --> 01:23:00,559
Speaker 1: that's what he's here's the problem. Here's the problem. Here's

1485
01:23:00,560 --> 01:23:03,040
Speaker 1: the problem. I didn't say. It was that if you

1486
01:23:03,120 --> 01:23:05,559
Speaker 1: didn't care, if you didn't care, you wouldn't do what

1487
01:23:05,600 --> 01:23:08,320
Speaker 1: you did. I respect, I understand, I respect, I respect

1488
01:23:08,360 --> 01:23:10,160
Speaker 1: what you're saying, and I have because both my brothers

1489
01:23:10,160 --> 01:23:11,640
Speaker 1: are in the business here, and I'll always be like,

1490
01:23:11,920 --> 01:23:17,559
Speaker 1: what do you hope happens? Like, I don't hope anything happens. Um,

1491
01:23:17,680 --> 01:23:19,960
Speaker 1: you wouldn't be motivated if you didn't care about dear,

1492
01:23:19,960 --> 01:23:21,639
Speaker 1: you wouldn't be messing with them all the time. You'd

1493
01:23:21,640 --> 01:23:27,360
Speaker 1: be doing something different, right, right. Yeah, So that's one answer, right, obviously,

1494
01:23:27,439 --> 01:23:31,800
Speaker 1: one that you don't like. But I mean the other

1495
01:23:31,840 --> 01:23:36,400
Speaker 1: answer is that, um, right, we have to. So I

1496
01:23:36,439 --> 01:23:39,839
Speaker 1: think it's as when we when we manage these systems,

1497
01:23:40,040 --> 01:23:43,680
Speaker 1: it's just really hard. It's this sort of shifting baseline problem, right,

1498
01:23:44,080 --> 01:23:47,880
Speaker 1: It's it's really hard to go back. It's really hard

1499
01:23:48,080 --> 01:23:53,559
Speaker 1: to get the public too, imagine, you know, to get

1500
01:23:53,600 --> 01:23:56,200
Speaker 1: the public. And I've tried this to get the public

1501
01:23:56,240 --> 01:24:01,439
Speaker 1: to think about what Osborne Russell saw in those mountains

1502
01:24:01,439 --> 01:24:05,920
Speaker 1: outside of Yellowstone, Right, we don't nobody thinks about Nobody

1503
01:24:05,920 --> 01:24:08,920
Speaker 1: can even imagine a world in which someone at their

1504
01:24:08,960 --> 01:24:11,280
Speaker 1: camp could see a thousand big horn sheep on the

1505
01:24:11,320 --> 01:24:14,880
Speaker 1: cliffs about them. No, that nobody can imagine that. And

1506
01:24:14,920 --> 01:24:17,639
Speaker 1: that is not in the discussion when we think about

1507
01:24:17,640 --> 01:24:19,560
Speaker 1: how many big horn sheep we should have on the

1508
01:24:19,640 --> 01:24:25,960
Speaker 1: landscape today, right, and that ship is sailed, right, and

1509
01:24:25,960 --> 01:24:29,400
Speaker 1: and and so so I think that we so I mean,

1510
01:24:29,479 --> 01:24:32,160
Speaker 1: when I think about it practically, and when I think

1511
01:24:32,160 --> 01:24:35,960
Speaker 1: about conservation, I think about, you know, uh, how many?

1512
01:24:35,960 --> 01:24:38,240
Speaker 1: And it's for me, it's not so much how many

1513
01:24:38,560 --> 01:24:43,639
Speaker 1: mule deer, it's you know, where do we have mule deer? Where?

1514
01:24:43,640 --> 01:24:47,240
Speaker 1: Will will we have migratory mule deer? Where where? You know? Where?

1515
01:24:47,240 --> 01:24:50,440
Speaker 1: Where will we sort of continue to have these animals

1516
01:24:51,160 --> 01:24:55,080
Speaker 1: um making their best living by moving across the big

1517
01:24:55,160 --> 01:24:58,920
Speaker 1: landscapes of the American West. And when you think about that,

1518
01:24:59,400 --> 01:25:03,280
Speaker 1: like I think, you have to start in practical terms,

1519
01:25:03,320 --> 01:25:06,360
Speaker 1: you have to start with what we have now and

1520
01:25:06,360 --> 01:25:09,960
Speaker 1: and the conservation discussion, Like, we can argue about what

1521
01:25:10,040 --> 01:25:12,559
Speaker 1: it should be, but I think in practical terms, the

1522
01:25:12,680 --> 01:25:16,400
Speaker 1: only place that we can start with is conserving what

1523
01:25:16,479 --> 01:25:19,679
Speaker 1: we have now, right, I mean, it's it's it's called

1524
01:25:19,720 --> 01:25:25,320
Speaker 1: conservation for a reason, right, it's conserving. You can't conserve

1525
01:25:25,400 --> 01:25:27,800
Speaker 1: what you don't have, right, So we're conserving what we

1526
01:25:27,880 --> 01:25:31,920
Speaker 1: have now. And the notion, you know, I think that

1527
01:25:32,000 --> 01:25:34,800
Speaker 1: the big horn sheep people have been um. Of course,

1528
01:25:35,720 --> 01:25:40,880
Speaker 1: they have been tremendously successful in restoring big horn sheep, right,

1529
01:25:40,920 --> 01:25:44,360
Speaker 1: and so maybe that that's an example where they've been

1530
01:25:44,400 --> 01:25:48,160
Speaker 1: able to get the public and get sportsmen to imagine

1531
01:25:49,240 --> 01:25:52,559
Speaker 1: what the West used to look like and work towards

1532
01:25:53,080 --> 01:25:56,040
Speaker 1: you know, getting sheep back on those mountains again. And

1533
01:25:56,080 --> 01:25:59,200
Speaker 1: that's you know, that's been successful. They've been successful in

1534
01:25:59,240 --> 01:26:04,160
Speaker 1: restoring you know, bighorn sheep in lots of places where

1535
01:26:04,320 --> 01:26:07,040
Speaker 1: we used to have them but lost them, you know,

1536
01:26:07,200 --> 01:26:12,720
Speaker 1: went during European settlement. But with mule deer, yeah, I

1537
01:26:12,720 --> 01:26:14,920
Speaker 1: think it's a bit more. It's yeah, I think you

1538
01:26:14,960 --> 01:26:17,040
Speaker 1: have to start with with what you have now and

1539
01:26:17,040 --> 01:26:21,679
Speaker 1: and hope you don't slip slip further back. Two Then

1540
01:26:21,880 --> 01:26:25,479
Speaker 1: twenty years from now, we're having the same conservation question

1541
01:26:25,680 --> 01:26:29,320
Speaker 1: in this restoration about or about what we have now,

1542
01:26:29,680 --> 01:26:32,320
Speaker 1: which is far less than what we had twenty years prior.

1543
01:26:32,960 --> 01:26:38,880
Speaker 1: Um Anyways, that's that's how I think about it. My

1544
01:26:38,920 --> 01:26:43,040
Speaker 1: brother who works in Alaska, he talks about that they're

1545
01:26:43,080 --> 01:26:46,760
Speaker 1: they're still in uh he works with fisheries, and he

1546
01:26:46,800 --> 01:26:49,640
Speaker 1: says that there were still in the sort of the

1547
01:26:49,720 --> 01:26:53,600
Speaker 1: descriptive phase, just trying to understand what's here. There's a

1548
01:26:53,600 --> 01:26:54,920
Speaker 1: lot of things. They don't know what's there yet, no

1549
01:26:54,960 --> 01:26:58,519
Speaker 1: one's measured it. And he talks about how here he

1550
01:26:58,560 --> 01:27:00,880
Speaker 1: sees so much where we're in the rest down in

1551
01:27:00,960 --> 01:27:04,000
Speaker 1: Lower forty eight, we live a lot in the restoration space,

1552
01:27:05,040 --> 01:27:08,880
Speaker 1: the restoration phase, because we know what's there. Yeah, there's

1553
01:27:08,880 --> 01:27:11,320
Speaker 1: a lot of work here, like you know, Atlantic Atlantic

1554
01:27:11,360 --> 01:27:13,080
Speaker 1: sturge and whatever. There's a lot of work down here

1555
01:27:13,080 --> 01:27:19,519
Speaker 1: trying to restore populations. Which is kind of interesting because

1556
01:27:19,560 --> 01:27:21,559
Speaker 1: I don't know about you guys, but I feel like

1557
01:27:21,600 --> 01:27:24,240
Speaker 1: we're very much still in that descriptive phase when we're

1558
01:27:24,280 --> 01:27:29,760
Speaker 1: still discussing fifty mile migrations. It feels like we're we're

1559
01:27:29,840 --> 01:27:32,920
Speaker 1: learning as we're going along, and but we're also we

1560
01:27:33,000 --> 01:27:35,600
Speaker 1: have now all these societal pressures on these animals, and

1561
01:27:35,640 --> 01:27:37,479
Speaker 1: so we don't have the luxury maybe that we had

1562
01:27:37,560 --> 01:27:40,680
Speaker 1: before of just kind of unknowingly making mistakes and then

1563
01:27:40,720 --> 01:27:43,360
Speaker 1: I'm you know, I'm doing those mistakes later. Yeah, that's

1564
01:27:43,360 --> 01:27:47,200
Speaker 1: a good counterpoint to to his like casual observation is

1565
01:27:47,200 --> 01:27:50,320
Speaker 1: that people just found out about some of these you know,

1566
01:27:50,600 --> 01:27:52,479
Speaker 1: some of these things that we didn't know about. Yeah.

1567
01:27:52,520 --> 01:27:54,559
Speaker 1: The more I talked with these guys, the more lost

1568
01:27:54,640 --> 01:27:57,719
Speaker 1: I actually feel on the older and what I thought

1569
01:27:57,760 --> 01:28:00,760
Speaker 1: I knew. But it's a good of a lost man

1570
01:28:01,640 --> 01:28:05,400
Speaker 1: finding yourself lost. Can you touch on the idea real

1571
01:28:05,479 --> 01:28:12,160
Speaker 1: quick that that what happens to a fawn in utero

1572
01:28:13,200 --> 01:28:15,960
Speaker 1: is that's the right term. What happens to a fawn

1573
01:28:16,160 --> 01:28:20,559
Speaker 1: in utero will be then realized throughout its entire life,

1574
01:28:20,920 --> 01:28:23,000
Speaker 1: including whether or not it might turn into a big, huge,

1575
01:28:23,000 --> 01:28:28,360
Speaker 1: giant buck. I'd love to. Yeah, So the easiest way

1576
01:28:28,439 --> 01:28:30,040
Speaker 1: for me to do that is to actually tell a

1577
01:28:30,040 --> 01:28:32,519
Speaker 1: little bit of a story behind some some work that

1578
01:28:32,560 --> 01:28:34,960
Speaker 1: we did. And of course it ties back to like

1579
01:28:35,600 --> 01:28:37,760
Speaker 1: the size that animals ultimately tained, which a lot of

1580
01:28:37,840 --> 01:28:40,720
Speaker 1: us are are interested in as well. Um, and so

1581
01:28:40,840 --> 01:28:45,879
Speaker 1: we in South Dakota, there's two two different primary regions

1582
01:28:45,920 --> 01:28:48,320
Speaker 1: and habitats, So eastern South Dakota where I grew up,

1583
01:28:48,720 --> 01:28:53,920
Speaker 1: um crop agriculture dominated landscape, and then the beautiful Black

1584
01:28:53,960 --> 01:28:58,679
Speaker 1: Hills in southwestern South Dakota. And during that that, during

1585
01:28:58,720 --> 01:29:00,920
Speaker 1: that time, there was this general observation that and I

1586
01:29:00,960 --> 01:29:02,720
Speaker 1: don't know if you are you spent some time in

1587
01:29:02,760 --> 01:29:04,840
Speaker 1: the Black Hills, perhaps, but those deer are tiny. They

1588
01:29:04,880 --> 01:29:07,439
Speaker 1: look like little mini deer compared with deer in eastern

1589
01:29:07,520 --> 01:29:09,320
Speaker 1: South Dakota. No, I didn't know that, like maybe a

1590
01:29:09,360 --> 01:29:13,040
Speaker 1: hundred pound difference at adult white tailed I'm sorry white

1591
01:29:13,040 --> 01:29:15,760
Speaker 1: tailed deer story, but but it's the best story. It's

1592
01:29:15,760 --> 01:29:18,480
Speaker 1: probably the best example that we have that clearly demonstrates

1593
01:29:18,640 --> 01:29:22,400
Speaker 1: um this phenomena. And so the question was, and this

1594
01:29:22,479 --> 01:29:25,200
Speaker 1: is I think what's so powerful about this is I

1595
01:29:25,240 --> 01:29:28,240
Speaker 1: think as as people and as as as hunters and

1596
01:29:28,280 --> 01:29:31,080
Speaker 1: folks that appreciate the outdoors and think about big males

1597
01:29:31,080 --> 01:29:34,080
Speaker 1: and those sorts of things. When when we see big

1598
01:29:34,120 --> 01:29:36,120
Speaker 1: deer over here and we don't see dear, big big

1599
01:29:36,120 --> 01:29:38,720
Speaker 1: deer over here, well, it's because it's genetics. We got

1600
01:29:38,720 --> 01:29:40,760
Speaker 1: great genetics over here. For big bucks, and we don't

1601
01:29:40,760 --> 01:29:42,800
Speaker 1: have it over here. And so that was one of

1602
01:29:42,880 --> 01:29:45,519
Speaker 1: the questions with regards to deer in the Black Hills, Well,

1603
01:29:45,560 --> 01:29:48,240
Speaker 1: it must just be genetics that's making them that much smaller.

1604
01:29:48,360 --> 01:29:50,760
Speaker 1: And so we did what's called a common garden experiment

1605
01:29:51,160 --> 01:29:55,040
Speaker 1: where we took common garden common garden experiment where you

1606
01:29:55,120 --> 01:29:59,200
Speaker 1: take individuals from two different places, bring them into the

1607
01:29:59,280 --> 01:30:03,040
Speaker 1: same place, and raised them under the exact same environmental conditions.

1608
01:30:03,040 --> 01:30:06,080
Speaker 1: And in this scenario, we took newborn white tailed deer

1609
01:30:06,080 --> 01:30:08,799
Speaker 1: from the Black Hills, newborn white tailed deer from eastern

1610
01:30:08,800 --> 01:30:13,120
Speaker 1: South Dakota. UH. We raised them in captivity, hand raised them,

1611
01:30:13,240 --> 01:30:15,240
Speaker 1: offered them a high quality diet, and watched them grow

1612
01:30:15,280 --> 01:30:18,559
Speaker 1: all the way through to adulthood. And so we focused

1613
01:30:18,560 --> 01:30:22,240
Speaker 1: on males because of the questions UM. But we raised

1614
01:30:22,240 --> 01:30:24,719
Speaker 1: those newborn males all the way up to like seven

1615
01:30:24,760 --> 01:30:27,280
Speaker 1: eight years of age and watched their changes in body

1616
01:30:27,360 --> 01:30:31,000
Speaker 1: mass UH and antler size and low and behold, even

1617
01:30:31,000 --> 01:30:35,040
Speaker 1: though they were raised under identical conditions, they were radically

1618
01:30:35,080 --> 01:30:38,360
Speaker 1: different in body mass and antler size, like hundred pound

1619
01:30:38,400 --> 01:30:41,800
Speaker 1: difference over a hundred pound difference in body mass, and

1620
01:30:42,040 --> 01:30:45,479
Speaker 1: like fifty plus inches and antler size, huge, huge difference,

1621
01:30:45,760 --> 01:30:50,120
Speaker 1: um once they reached that peak size. So initially we thought, huh, okay,

1622
01:30:50,120 --> 01:30:53,880
Speaker 1: well maybe it is genetics. Then, because we had both

1623
01:30:53,920 --> 01:30:56,439
Speaker 1: males and females that we had hand raised, we then

1624
01:30:56,600 --> 01:30:59,800
Speaker 1: allowed them to breed in captivity, so we had black Hills,

1625
01:30:59,800 --> 01:31:04,000
Speaker 1: may else and females, males and females. Okay, yeah, you

1626
01:31:04,040 --> 01:31:06,519
Speaker 1: want to tell the rest of the story. I just

1627
01:31:06,560 --> 01:31:09,479
Speaker 1: got excited, dude. I love this story because to me,

1628
01:31:09,520 --> 01:31:13,960
Speaker 1: it's so powerful. So we allowed them to breed in captivity,

1629
01:31:14,040 --> 01:31:15,960
Speaker 1: and then we did the exact same thing again. We

1630
01:31:16,080 --> 01:31:19,479
Speaker 1: hand raised all of those back the ones. Okay, we

1631
01:31:19,560 --> 01:31:21,760
Speaker 1: didn't cross, no, no, I got you. I just want

1632
01:31:21,760 --> 01:31:23,519
Speaker 1: to make sure I'm clear on something. The ones that

1633
01:31:23,600 --> 01:31:26,120
Speaker 1: you took, Okay, the ones you took and took them

1634
01:31:26,120 --> 01:31:29,160
Speaker 1: for two different areas in raisins staying conditions. At What

1635
01:31:29,200 --> 01:31:31,720
Speaker 1: age were they? Sorry? Oh? So we we watched those

1636
01:31:31,760 --> 01:31:33,160
Speaker 1: males grow all the way up to seven eight years.

1637
01:31:33,560 --> 01:31:36,360
Speaker 1: What age did you bring them together? Oh? Newborns? We

1638
01:31:36,360 --> 01:31:39,559
Speaker 1: we literally we collected them from the wild as brand

1639
01:31:39,560 --> 01:31:42,320
Speaker 1: new babies, so like two days of age, that's one

1640
01:31:42,360 --> 01:31:44,840
Speaker 1: of two days we bottled bottle fed the funds. They're

1641
01:31:44,840 --> 01:31:47,400
Speaker 1: already weaned, and they weren't even weaned yet. No, no, no,

1642
01:31:47,520 --> 01:31:49,920
Speaker 1: literally right out of the gate. So the only the

1643
01:31:49,920 --> 01:31:55,040
Speaker 1: only influence before was basically mom's influence in utero. And

1644
01:31:55,040 --> 01:31:58,080
Speaker 1: that's why I wanted to understand, just like brand, spanking brand.

1645
01:31:58,720 --> 01:32:01,559
Speaker 1: And then they realized these different, that's right, that's right,

1646
01:32:01,960 --> 01:32:05,639
Speaker 1: that's exactly right. So bread them. We didn't cross East

1647
01:32:05,800 --> 01:32:08,280
Speaker 1: River Eastern South Dakota with Black Hills. We kept we

1648
01:32:08,360 --> 01:32:10,879
Speaker 1: kept them apart. So Black Hills males, Black Hills females,

1649
01:32:10,960 --> 01:32:13,519
Speaker 1: Eastern South Dakota males, Eastern South Dakota females, and then

1650
01:32:13,560 --> 01:32:17,800
Speaker 1: hand raised those offspring, the Eastern South Dakota males, so

1651
01:32:17,880 --> 01:32:20,840
Speaker 1: which means we now have first generation and second generation. Right,

1652
01:32:20,880 --> 01:32:23,559
Speaker 1: first generation came from the wild. The second generation were

1653
01:32:23,560 --> 01:32:28,160
Speaker 1: born in captivity. Right, the eastern South Dakota animals that

1654
01:32:28,200 --> 01:32:31,760
Speaker 1: we watched grow, those males were like exactly like their

1655
01:32:31,760 --> 01:32:36,160
Speaker 1: father's same body mass, same antler size, literally identical trajectory

1656
01:32:36,160 --> 01:32:40,680
Speaker 1: and growth. But the offspring from those Black Hills males

1657
01:32:40,720 --> 01:32:43,800
Speaker 1: at at peak body size and antler size, so like

1658
01:32:43,840 --> 01:32:48,240
Speaker 1: that five to six year age mark, those those male

1659
01:32:48,400 --> 01:32:53,439
Speaker 1: offspring were seventy pounds heavier than their dads and grew

1660
01:32:53,479 --> 01:32:57,200
Speaker 1: thirty two inches more antler than their dads, same diet

1661
01:32:57,240 --> 01:33:01,280
Speaker 1: as their dad's, oh, exact same environmental conditions. And I

1662
01:33:01,320 --> 01:33:04,639
Speaker 1: mean literally mom, mom and their mom and dad came

1663
01:33:04,720 --> 01:33:08,120
Speaker 1: from the wild, were small, but then got that much

1664
01:33:08,160 --> 01:33:11,120
Speaker 1: bigger and and under the exact same scenario. And we

1665
01:33:11,120 --> 01:33:13,679
Speaker 1: didn't see any change in those eastern South Dakota animals

1666
01:33:13,720 --> 01:33:17,680
Speaker 1: that your growth trajectory was identical. So literally like overt

1667
01:33:18,200 --> 01:33:22,400
Speaker 1: increase in antler size, over increase in body mass over

1668
01:33:22,439 --> 01:33:26,519
Speaker 1: that one generation within within captivity. Now the notion is,

1669
01:33:26,760 --> 01:33:29,800
Speaker 1: so all the animals we collected from the wild, where

1670
01:33:29,800 --> 01:33:34,720
Speaker 1: all the maternal environment they experienced were from wild mom, right,

1671
01:33:35,200 --> 01:33:37,280
Speaker 1: and that wild mom being in the Black Hills Black

1672
01:33:37,360 --> 01:33:42,719
Speaker 1: Hills Ponderosa pine dominated forest, pretty pretty crappy food source, yeah,

1673
01:33:42,840 --> 01:33:48,160
Speaker 1: mostly pine needles. Yeah. But then in captivity, once, once

1674
01:33:48,320 --> 01:33:51,840
Speaker 1: that fawn had grown up in captivity, it had realized

1675
01:33:51,840 --> 01:33:54,360
Speaker 1: the high high plane and nutrition. Now, although it never

1676
01:33:54,479 --> 01:33:58,200
Speaker 1: changed its pattern of growth then basically and we saw

1677
01:33:58,240 --> 01:34:00,960
Speaker 1: this with regards to birth masses all, it then began

1678
01:34:01,040 --> 01:34:03,879
Speaker 1: to pump what we call like the silver spoon effect

1679
01:34:04,240 --> 01:34:07,160
Speaker 1: into their offspring, and we've seen that saw that radical

1680
01:34:07,280 --> 01:34:11,799
Speaker 1: changing growth within that subsequent generations, which means it connects

1681
01:34:11,840 --> 01:34:13,960
Speaker 1: it all the way back to the maternal environment. We

1682
01:34:14,000 --> 01:34:16,800
Speaker 1: call it a negative maternal effect. Maybe it's related to

1683
01:34:16,840 --> 01:34:20,519
Speaker 1: epigenetics associated with like basically turning on or off jeanes,

1684
01:34:20,600 --> 01:34:24,519
Speaker 1: those sorts of things, but regardless, it ultimately stems from

1685
01:34:24,560 --> 01:34:28,360
Speaker 1: the nutrition that mom experienced. And and what that means

1686
01:34:28,400 --> 01:34:30,360
Speaker 1: is so even though we took those fawns from the

1687
01:34:30,400 --> 01:34:34,799
Speaker 1: wild brought them into captivity, because mom had basically set

1688
01:34:34,840 --> 01:34:37,840
Speaker 1: that trajectory for growth, it didn't matter how good it

1689
01:34:37,880 --> 01:34:39,960
Speaker 1: got later in those years, because it was as good

1690
01:34:40,000 --> 01:34:42,840
Speaker 1: as it's gonna get. Their Their growth was still quote

1691
01:34:42,920 --> 01:34:45,840
Speaker 1: unquote stunted. It still followed the trajectory that mom had

1692
01:34:45,880 --> 01:34:50,760
Speaker 1: set it on, like multiple generations down the line, right,

1693
01:34:50,800 --> 01:34:54,519
Speaker 1: And we oh, yeah, exactly. And so we suspect that

1694
01:34:54,560 --> 01:34:57,000
Speaker 1: if we had kept doing that, maybe over two or

1695
01:34:57,080 --> 01:35:00,679
Speaker 1: three generations, those Black Hills animals would have actually gotten

1696
01:35:00,720 --> 01:35:03,360
Speaker 1: to the size of those eastern South Dakota animals. But

1697
01:35:03,479 --> 01:35:08,960
Speaker 1: even with one subsequent generation after that improved conditions, they

1698
01:35:09,000 --> 01:35:11,479
Speaker 1: made up over seventy of the difference in antler and

1699
01:35:11,560 --> 01:35:14,960
Speaker 1: body size that occurred between animals from those two regions.

1700
01:35:15,000 --> 01:35:18,240
Speaker 1: So no genetic related influence. And I think, and I

1701
01:35:18,280 --> 01:35:20,599
Speaker 1: mean I probably I certainly did it historically. And you

1702
01:35:20,600 --> 01:35:22,360
Speaker 1: you hear folks say it all the time while we

1703
01:35:22,439 --> 01:35:24,360
Speaker 1: got genetics for big bucks over here, but we just

1704
01:35:24,400 --> 01:35:27,760
Speaker 1: don't have those genetics over here now. Our growing appreciation

1705
01:35:27,800 --> 01:35:32,080
Speaker 1: now is that is almost most certainly largely an effective

1706
01:35:32,160 --> 01:35:36,400
Speaker 1: nutrition and nutrition that's lasted over over many, many generations.

1707
01:35:36,400 --> 01:35:38,320
Speaker 1: I mean, we've done that work on white tailed deer,

1708
01:35:38,760 --> 01:35:41,800
Speaker 1: We've done work on sheep in the Sierra Nevadas of California,

1709
01:35:41,840 --> 01:35:45,880
Speaker 1: where over six different populations, we can explain over eighty

1710
01:35:45,960 --> 01:35:49,320
Speaker 1: percent of the differences in horn size across those six

1711
01:35:49,360 --> 01:35:53,080
Speaker 1: populations just by how fat females are, which is powerful,

1712
01:35:53,800 --> 01:35:56,680
Speaker 1: Oh yeah, no joke, Yeah yeah, over eighty percent of

1713
01:35:56,680 --> 01:35:59,800
Speaker 1: the difference. And and horn size varies markedly across this

1714
01:36:00,000 --> 01:36:03,880
Speaker 1: its populations, we can explain eighty percent of those difference

1715
01:36:03,960 --> 01:36:06,759
Speaker 1: just by basically how fat the moms are. And certainly

1716
01:36:06,800 --> 01:36:11,639
Speaker 1: that's just like a broad indicator of nutrition across those ranges. Yeah,

1717
01:36:11,680 --> 01:36:15,879
Speaker 1: physiological stress associated from nutrition and and those nutritional dynamics,

1718
01:36:15,880 --> 01:36:18,880
Speaker 1: And so I think I think over time, as as

1719
01:36:18,920 --> 01:36:22,599
Speaker 1: these stories begin to pile on that. For example, I've

1720
01:36:22,640 --> 01:36:25,400
Speaker 1: I've started saying that you know when we handle animals

1721
01:36:25,479 --> 01:36:28,479
Speaker 1: or we look at body mass. Body mass to me,

1722
01:36:29,439 --> 01:36:31,680
Speaker 1: isn't we think of that, Well, that's the condition of

1723
01:36:31,720 --> 01:36:34,240
Speaker 1: that animal. Well no, not really, body fat is the

1724
01:36:34,280 --> 01:36:37,559
Speaker 1: condition that animal. But how big it is is literally

1725
01:36:38,160 --> 01:36:42,479
Speaker 1: this long term signature of nutritional dynamics within that place

1726
01:36:42,640 --> 01:36:44,639
Speaker 1: on the landscape. And so as you go from one

1727
01:36:44,680 --> 01:36:47,840
Speaker 1: place to the next, Man, there's big animals here, they're

1728
01:36:47,880 --> 01:36:51,439
Speaker 1: smaller animals here. Well, that's part of how they're in

1729
01:36:51,520 --> 01:36:54,200
Speaker 1: tune and adapted to the environment that they live in.

1730
01:36:54,360 --> 01:36:56,679
Speaker 1: And if they were if they were trying to get

1731
01:36:56,720 --> 01:37:00,200
Speaker 1: as large as they are in in the better environ mean,

1732
01:37:00,280 --> 01:37:02,200
Speaker 1: they may not ever get there, or they may not

1733
01:37:02,320 --> 01:37:04,680
Speaker 1: ever it's gonna be another year or two before they

1734
01:37:04,680 --> 01:37:07,719
Speaker 1: get the chance to reproduce because they're focused on growing bigger.

1735
01:37:08,160 --> 01:37:11,880
Speaker 1: So the adaptation in is in nutritionally limited environment will

1736
01:37:11,920 --> 01:37:14,839
Speaker 1: be smaller and as a consequence of that, you can

1737
01:37:14,880 --> 01:37:18,040
Speaker 1: continue to be viable and you demand less resources through

1738
01:37:18,080 --> 01:37:20,479
Speaker 1: the year as well. So it reflects not only the

1739
01:37:20,600 --> 01:37:24,479
Speaker 1: underlying fundamental process of nutrition and how it feeds into

1740
01:37:24,520 --> 01:37:27,639
Speaker 1: growth and dynamics within population. But it's also a cool

1741
01:37:28,200 --> 01:37:30,160
Speaker 1: a cool way to think about how these animals are

1742
01:37:30,200 --> 01:37:32,720
Speaker 1: just uniquely adapted to the environments that they live in.

1743
01:37:33,320 --> 01:37:36,080
Speaker 1: We got a buddy who's uh, he manages a big

1744
01:37:36,120 --> 01:37:41,640
Speaker 1: white tailed property in South Texas, and he feels that

1745
01:37:41,880 --> 01:37:44,880
Speaker 1: like he likes to keep it's a little bit off topic,

1746
01:37:45,000 --> 01:37:50,240
Speaker 1: he likes to keep his buck numbers really low because

1747
01:37:50,240 --> 01:37:54,759
Speaker 1: he feels that bucks stressed, dear out. Mm hmm, buck stressed.

1748
01:37:54,800 --> 01:37:56,960
Speaker 1: You're out having a bunch of males running around stresses,

1749
01:37:57,000 --> 01:37:59,599
Speaker 1: and he feels they get fatter and healthier the less

1750
01:37:59,640 --> 01:38:02,840
Speaker 1: that's going on around him. Huh. Would Wouldn't that just

1751
01:38:02,880 --> 01:38:06,479
Speaker 1: be a factor of competition? Yes, that to me is

1752
01:38:06,479 --> 01:38:09,479
Speaker 1: certainly it's it's just more milds feeding and just so

1753
01:38:09,520 --> 01:38:12,200
Speaker 1: it's just more more individuals. You probably see the same thing.

1754
01:38:12,240 --> 01:38:14,519
Speaker 1: If he pulled females out of it, he pile those

1755
01:38:14,560 --> 01:38:17,080
Speaker 1: out too. Wow. Yeah, So I suspect it probably has

1756
01:38:17,080 --> 01:38:18,960
Speaker 1: more to do with that than anything. There's there's a

1757
01:38:19,040 --> 01:38:22,320
Speaker 1: number of ideas out there associated with particularly during the

1758
01:38:22,400 --> 01:38:26,000
Speaker 1: rut and rutting behavior and how that feeds into like

1759
01:38:26,640 --> 01:38:29,120
Speaker 1: buck ratio and how many big males you have and

1760
01:38:29,200 --> 01:38:33,519
Speaker 1: our young young males less experienced and therefore pushed females

1761
01:38:33,560 --> 01:38:36,320
Speaker 1: that much harder because their immature and don't know what

1762
01:38:36,360 --> 01:38:39,120
Speaker 1: they're doing, that sort of thing. And those results are

1763
01:38:39,680 --> 01:38:42,559
Speaker 1: a bit equivocal. There's not really a clear pattern that

1764
01:38:42,560 --> 01:38:44,599
Speaker 1: that emerges from that. So I have a feeling it's

1765
01:38:44,800 --> 01:38:47,679
Speaker 1: mostly associated with density and just more minds being there.

1766
01:38:47,880 --> 01:38:49,720
Speaker 1: Have you guys looked at well that that kind of

1767
01:38:49,760 --> 01:38:52,560
Speaker 1: ties into this. Have you guys looked at the effect

1768
01:38:52,840 --> 01:38:58,320
Speaker 1: of exposure two predators, not even mortality, but effects of

1769
01:38:58,360 --> 01:39:03,000
Speaker 1: exposure to predators on nutrition and on fat because I know,

1770
01:39:03,120 --> 01:39:07,840
Speaker 1: like like cattle ranchers will observe that even in the

1771
01:39:07,920 --> 01:39:12,160
Speaker 1: absence of wolves killing cattle cattle, you know, it's just

1772
01:39:12,320 --> 01:39:15,040
Speaker 1: anecdotal observation, they'll say that they don't get as fat

1773
01:39:15,080 --> 01:39:17,680
Speaker 1: as quickly because they're living with this constant stress and

1774
01:39:17,760 --> 01:39:20,200
Speaker 1: moving in unpredictable patterns that do you notice that in

1775
01:39:20,439 --> 01:39:24,280
Speaker 1: game animals, deer, elk whatever. Yeah, So this was a

1776
01:39:24,320 --> 01:39:28,040
Speaker 1: big question with when when when wolves were reintroduced into

1777
01:39:28,120 --> 01:39:31,719
Speaker 1: Yellowstone and and we did a big project on this

1778
01:39:32,040 --> 01:39:36,480
Speaker 1: um that you know, same type of question that um

1779
01:39:36,520 --> 01:39:39,960
Speaker 1: that wolves were causing elk to you know, be more

1780
01:39:39,960 --> 01:39:44,080
Speaker 1: alert on the landscape, be more vigilant, not forage in

1781
01:39:44,320 --> 01:39:48,080
Speaker 1: risky places that might that might have higher food value

1782
01:39:48,800 --> 01:39:51,840
Speaker 1: and forage and less risky places that where there's not

1783
01:39:51,880 --> 01:39:56,040
Speaker 1: as much to eat. And um and this was this

1784
01:39:56,120 --> 01:39:58,800
Speaker 1: was sort of a big idea. Um we call it

1785
01:39:58,880 --> 01:40:05,479
Speaker 1: the you know, sort of the landscape of fear, you know, yeah,

1786
01:40:05,600 --> 01:40:08,760
Speaker 1: yeah uh. And so that that so there was this

1787
01:40:08,880 --> 01:40:12,400
Speaker 1: idea that elk were responding to this new landscape of

1788
01:40:12,439 --> 01:40:17,480
Speaker 1: fear that wolves had created in places like Yellowstone and

1789
01:40:17,479 --> 01:40:21,160
Speaker 1: and of course wolves do eat elk, but there there

1790
01:40:21,240 --> 01:40:24,439
Speaker 1: was this idea that there was this larger effect, a

1791
01:40:24,439 --> 01:40:28,400
Speaker 1: so called non consumptive effect, that this sort of wolf

1792
01:40:28,479 --> 01:40:31,920
Speaker 1: jitters kind of idea that elk just weren't weren't weren't

1793
01:40:32,000 --> 01:40:34,719
Speaker 1: living as well, weren't finding as much food, weren't spending

1794
01:40:34,760 --> 01:40:37,000
Speaker 1: as much time feeding because they're always alert to wolves

1795
01:40:37,600 --> 01:40:41,400
Speaker 1: and um uh. That idea got a lot of traction

1796
01:40:41,439 --> 01:40:45,280
Speaker 1: in the literature without very much empirical evidence to support it.

1797
01:40:45,720 --> 01:40:47,680
Speaker 1: And then we did a test which I think was

1798
01:40:48,560 --> 01:40:53,160
Speaker 1: fairly definitive. We basically had GPS colored elk, GPS colored

1799
01:40:53,160 --> 01:40:57,759
Speaker 1: wolves we could score for every elk um how often

1800
01:40:58,120 --> 01:41:01,000
Speaker 1: it came into contact with wolf, so sort of like

1801
01:41:01,479 --> 01:41:06,439
Speaker 1: it's encounter rate, right And and obviously the prediction is

1802
01:41:06,479 --> 01:41:09,720
Speaker 1: if you're coming into contact with wolves more frequently than

1803
01:41:09,800 --> 01:41:13,519
Speaker 1: and and this wolf jitters is a thing, then those

1804
01:41:13,560 --> 01:41:16,640
Speaker 1: animals should have less fat, that they should burn more

1805
01:41:16,680 --> 01:41:18,639
Speaker 1: fat through the winter, and it should be less pregnant.

1806
01:41:19,080 --> 01:41:21,960
Speaker 1: And uh we we captured all the all the animals

1807
01:41:22,400 --> 01:41:26,040
Speaker 1: assess their their rump fat and body condition through ultrasound

1808
01:41:26,520 --> 01:41:31,040
Speaker 1: and absolutely no effect. Can you go down to Colorado

1809
01:41:32,160 --> 01:41:37,880
Speaker 1: and test the theory that the massive increase in summer

1810
01:41:38,200 --> 01:41:44,439
Speaker 1: recreation is affecting here year round really now is affecting

1811
01:41:44,439 --> 01:41:49,800
Speaker 1: the health and well being of mungulates. Uh well we should.

1812
01:41:49,840 --> 01:41:51,240
Speaker 1: I bet you'd be able to find money to do

1813
01:41:51,280 --> 01:41:56,120
Speaker 1: that in Colorado, so we could. We did um basically

1814
01:41:56,160 --> 01:41:59,160
Speaker 1: something along those lines with with mule deer, but with

1815
01:41:59,200 --> 01:42:01,599
Speaker 1: an eye towards end energy development, which is not all

1816
01:42:01,640 --> 01:42:05,200
Speaker 1: that different. It's a human presence and at times somewhat

1817
01:42:05,240 --> 01:42:07,880
Speaker 1: unpredictable human presence those sorts of things. That's something we've

1818
01:42:07,880 --> 01:42:11,240
Speaker 1: been concerned with for some time. UM, and we we

1819
01:42:11,320 --> 01:42:14,679
Speaker 1: have known based on GPS caller data and a lot

1820
01:42:14,680 --> 01:42:17,400
Speaker 1: of work that Hal Sawyer, a colleague of ours here's

1821
01:42:17,439 --> 01:42:20,559
Speaker 1: in here in Wyoming, have done that the presence of

1822
01:42:21,120 --> 01:42:25,440
Speaker 1: human presence within those energy fields results in behavioral displacements.

1823
01:42:25,479 --> 01:42:27,559
Speaker 1: So they're using those areas next to well pads and

1824
01:42:27,640 --> 01:42:31,479
Speaker 1: roads um less on on their winter range. Uh. And

1825
01:42:31,560 --> 01:42:34,800
Speaker 1: so we we took that a step further, and we

1826
01:42:34,880 --> 01:42:37,240
Speaker 1: aim to address that question of is it like this

1827
01:42:37,400 --> 01:42:41,000
Speaker 1: chronic stressor that So, for example, animals that are exposed

1828
01:42:41,040 --> 01:42:44,160
Speaker 1: to more energy development are losing more fat over the winter.

1829
01:42:44,680 --> 01:42:46,640
Speaker 1: And so we did that with that too, you know,

1830
01:42:46,760 --> 01:42:49,400
Speaker 1: twice this twice a year capture to look at change

1831
01:42:49,400 --> 01:42:52,000
Speaker 1: and body fat over winter, those sorts of things, and

1832
01:42:52,040 --> 01:42:55,120
Speaker 1: then related that to exposure to energy development. And so

1833
01:42:55,200 --> 01:42:58,200
Speaker 1: interestingly there was nothing there which maybe speaks to some

1834
01:42:59,640 --> 01:43:04,640
Speaker 1: well let me go, let me keep going, what what

1835
01:43:04,720 --> 01:43:06,720
Speaker 1: I think is powerful? So the other thing. So we

1836
01:43:06,800 --> 01:43:09,280
Speaker 1: did not only that, but we took it one step further.

1837
01:43:09,320 --> 01:43:12,720
Speaker 1: And when you see this behavioral displacement, the question then

1838
01:43:12,800 --> 01:43:15,840
Speaker 1: and ultimately how that potentially links to the population could

1839
01:43:15,880 --> 01:43:19,120
Speaker 1: be through like this chronic stressor or it could be

1840
01:43:19,120 --> 01:43:23,400
Speaker 1: because they're functionally losing food and habitat on the landscape,

1841
01:43:23,400 --> 01:43:26,680
Speaker 1: and it's that food that's ultimately determining the carring capacity

1842
01:43:26,680 --> 01:43:28,519
Speaker 1: of their winter range. So what we did is we

1843
01:43:28,600 --> 01:43:33,519
Speaker 1: literally went on the ground and and measured measured sage brush,

1844
01:43:33,840 --> 01:43:36,880
Speaker 1: measured growth of stage brush as well as subsequent youth

1845
01:43:37,160 --> 01:43:39,360
Speaker 1: use of sage brush at the end of the winter.

1846
01:43:39,880 --> 01:43:42,320
Speaker 1: What's really interesting is that dear the way in which

1847
01:43:42,360 --> 01:43:45,599
Speaker 1: they select habitat and use habitat across those winter ranges.

1848
01:43:45,920 --> 01:43:48,599
Speaker 1: They're keying in on stage brush where we're getting more

1849
01:43:48,720 --> 01:43:51,280
Speaker 1: leader growth, and it's it's those new leaders each year

1850
01:43:51,360 --> 01:43:54,280
Speaker 1: that is really what's their primary staple if they can

1851
01:43:54,320 --> 01:43:57,080
Speaker 1: have it. So that's one thing that's powerful that tells

1852
01:43:57,080 --> 01:43:59,880
Speaker 1: either queuing and queuing in the food. But the other

1853
01:44:00,040 --> 01:44:02,120
Speaker 1: aspect of that is what was happening is that in

1854
01:44:02,160 --> 01:44:04,960
Speaker 1: those areas adjacent to or near the well pads or

1855
01:44:05,000 --> 01:44:07,920
Speaker 1: the roads where we were getting that disturbance, they were

1856
01:44:07,960 --> 01:44:10,920
Speaker 1: not using the food that was there as much as

1857
01:44:10,960 --> 01:44:14,599
Speaker 1: they were in areas um that didn't have that level

1858
01:44:14,640 --> 01:44:17,880
Speaker 1: of exposure. So what that means is that there's ultimately

1859
01:44:18,439 --> 01:44:21,679
Speaker 1: residual food that's left on the landscape that's not being

1860
01:44:21,800 --> 01:44:25,280
Speaker 1: used because of our our presence in that human disturbance,

1861
01:44:25,360 --> 01:44:27,720
Speaker 1: which means a functional loss in the carying capacity of

1862
01:44:27,760 --> 01:44:31,679
Speaker 1: that winter range. So with that displacement, not necessarily stressed,

1863
01:44:31,720 --> 01:44:34,080
Speaker 1: not necessarily stressed. So it's a food it's a food

1864
01:44:34,080 --> 01:44:37,040
Speaker 1: based link to the change in population and within that

1865
01:44:37,080 --> 01:44:40,200
Speaker 1: one herd in particular, we've observed um I think it's

1866
01:44:40,200 --> 01:44:44,120
Speaker 1: a thirty thirty six decrease in population size on that

1867
01:44:44,160 --> 01:44:46,800
Speaker 1: winter range as that energy development has come into play.

1868
01:44:46,880 --> 01:44:49,320
Speaker 1: Does it take a long time to realize it? No,

1869
01:44:49,439 --> 01:44:53,040
Speaker 1: I was over like at decade, So it didn't. Yeah, yeah,

1870
01:44:53,120 --> 01:44:54,960
Speaker 1: but it didn't. It really didn't take that long. But

1871
01:44:55,000 --> 01:44:58,560
Speaker 1: what that indicates to us is that based on that displacement,

1872
01:44:58,600 --> 01:45:01,759
Speaker 1: you're resulting in increase in density in the other adjacent

1873
01:45:01,840 --> 01:45:04,320
Speaker 1: areas where there's already dear and there's only so much

1874
01:45:04,320 --> 01:45:06,479
Speaker 1: food to go around. And so if you you have

1875
01:45:06,520 --> 01:45:08,760
Speaker 1: a grocery store that's feeding so many people and you

1876
01:45:08,840 --> 01:45:11,719
Speaker 1: take out one whole corner of the grocery store, there's

1877
01:45:11,800 --> 01:45:14,519
Speaker 1: you're not going to support as as many people. And

1878
01:45:14,560 --> 01:45:16,400
Speaker 1: in that instance, we're not going to support as many

1879
01:45:16,439 --> 01:45:18,960
Speaker 1: animals based on the groceries that are stored there is

1880
01:45:19,040 --> 01:45:21,840
Speaker 1: ultimately what it means. So it's not a stressor link,

1881
01:45:21,920 --> 01:45:24,120
Speaker 1: but it's a food based link. Do you translate that

1882
01:45:24,200 --> 01:45:29,080
Speaker 1: to recommendations? That's well, yes, we translate that into here's

1883
01:45:29,080 --> 01:45:32,320
Speaker 1: the realities of this, and so we've with that effort.

1884
01:45:32,360 --> 01:45:36,439
Speaker 1: We've also um because of the analyzes and the modeling

1885
01:45:36,520 --> 01:45:38,599
Speaker 1: that we did, we've been able to place that into

1886
01:45:38,640 --> 01:45:42,160
Speaker 1: hypothetical scenarios. So for example, if you based on a

1887
01:45:42,240 --> 01:45:44,600
Speaker 1: modeling of food distribution on the landscape and what we

1888
01:45:44,640 --> 01:45:47,639
Speaker 1: know about how dear use that food. If we put

1889
01:45:47,640 --> 01:45:50,080
Speaker 1: a road here in a well pad here, here's what

1890
01:45:50,200 --> 01:45:52,719
Speaker 1: that's gonna mean as far as an indirect food loss.

1891
01:45:53,240 --> 01:45:55,679
Speaker 1: Or if you if you place one big one here,

1892
01:45:56,120 --> 01:45:58,800
Speaker 1: or you have three other one three you know, three

1893
01:45:58,840 --> 01:46:01,160
Speaker 1: smaller ones as opposed to one big one. So we've

1894
01:46:01,200 --> 01:46:05,479
Speaker 1: taken that and translated that into those relationships to to

1895
01:46:05,600 --> 01:46:08,160
Speaker 1: derive a direct expectation as to what that's going to

1896
01:46:08,240 --> 01:46:10,880
Speaker 1: mean for food loss depending upon a build out plan,

1897
01:46:11,040 --> 01:46:13,639
Speaker 1: that sort of thing. And the hope is to simply

1898
01:46:13,640 --> 01:46:16,120
Speaker 1: be able to communicate the realities of it. I mean,

1899
01:46:16,160 --> 01:46:19,599
Speaker 1: we are humans living in this landscape. We're gonna affect

1900
01:46:19,640 --> 01:46:22,080
Speaker 1: it in some way, but ideally however we are, we're

1901
01:46:22,080 --> 01:46:24,360
Speaker 1: at least informed as to the effects that we're going

1902
01:46:24,439 --> 01:46:26,519
Speaker 1: to bring to the table, and that we can we

1903
01:46:26,560 --> 01:46:28,880
Speaker 1: can do it in a wise way, and when we can't,

1904
01:46:28,920 --> 01:46:31,880
Speaker 1: we can at least speak the realities. Okay, well, if

1905
01:46:31,920 --> 01:46:34,080
Speaker 1: we're going to do this, this is what this is

1906
01:46:34,120 --> 01:46:36,479
Speaker 1: gonna mean, and are we willing to accept that? And

1907
01:46:36,479 --> 01:46:39,320
Speaker 1: then if we are, we are. But ideally it's it's

1908
01:46:39,400 --> 01:46:42,280
Speaker 1: less walking around of that. Oh, it'll probably be okay

1909
01:46:42,320 --> 01:46:45,240
Speaker 1: sort of thing. Um, here's here's what's liable to happen.

1910
01:46:45,400 --> 01:46:49,519
Speaker 1: And I was gonna say that. So that work has

1911
01:46:49,560 --> 01:46:53,240
Speaker 1: been translated into yea, So how do we manage these fields? Right?

1912
01:46:53,280 --> 01:46:56,479
Speaker 1: So the result that Kevin was just describing as basically

1913
01:46:56,640 --> 01:47:00,439
Speaker 1: means that meal deer avoid human disturbance, and when you

1914
01:47:00,479 --> 01:47:02,560
Speaker 1: develop a gas field you can there are ways to

1915
01:47:02,600 --> 01:47:06,960
Speaker 1: minimize human disturbance. So the most disturbances when you're actively

1916
01:47:07,040 --> 01:47:11,720
Speaker 1: drilling the well. Then, um, we have wells that are producing,

1917
01:47:12,040 --> 01:47:15,639
Speaker 1: but but have trucks coming into constantly to call off

1918
01:47:15,680 --> 01:47:18,760
Speaker 1: the content sate, and we have wells where that condensate

1919
01:47:18,840 --> 01:47:23,320
Speaker 1: is being um taken off underground. And so this has

1920
01:47:23,400 --> 01:47:27,120
Speaker 1: led to a shift in the way that wells are managed,

1921
01:47:27,160 --> 01:47:29,719
Speaker 1: that the oil and gas wells are managed. We limit

1922
01:47:29,760 --> 01:47:33,040
Speaker 1: the time of drilling, and we've shifted from you know,

1923
01:47:33,080 --> 01:47:36,120
Speaker 1: pulling that condensate off underground so we don't have the

1924
01:47:36,120 --> 01:47:40,040
Speaker 1: truck traffic. And so you know, it's the it's the

1925
01:47:40,080 --> 01:47:43,680
Speaker 1: human activity in those in those fields that that that

1926
01:47:43,800 --> 01:47:46,720
Speaker 1: the animals are responding to. And so we so we

1927
01:47:46,840 --> 01:47:49,280
Speaker 1: now we now know that if we can reduce that

1928
01:47:49,360 --> 01:47:52,880
Speaker 1: human activity, we can reduce the impact on on wintering deer.

1929
01:47:53,240 --> 01:47:57,559
Speaker 1: And just to close the loop on the cattle wolf

1930
01:47:57,640 --> 01:48:02,639
Speaker 1: jitters question, we're sort of talked about three different cases here, right.

1931
01:48:02,680 --> 01:48:06,640
Speaker 1: So on the one hand, cattle not bred to to

1932
01:48:07,400 --> 01:48:10,680
Speaker 1: deal with predators right there. They're bred to either to

1933
01:48:10,960 --> 01:48:14,880
Speaker 1: put on fat and and grow grow fast, unlimited food

1934
01:48:14,920 --> 01:48:18,800
Speaker 1: to go to market, right and and so but and

1935
01:48:18,840 --> 01:48:21,080
Speaker 1: we kind of made this mistake when when wolves are

1936
01:48:21,080 --> 01:48:25,960
Speaker 1: reintroduced to Yellowstone, researchers and certainly the public thought well,

1937
01:48:26,120 --> 01:48:29,240
Speaker 1: this is this huge change that now now wolves are

1938
01:48:29,240 --> 01:48:32,200
Speaker 1: on landscape. Now there's a landscape of fear, right, But

1939
01:48:32,360 --> 01:48:36,240
Speaker 1: elk have other landscapes, like they have a landscape nutrition

1940
01:48:36,560 --> 01:48:38,519
Speaker 1: and they have a landscape of starvation that they also

1941
01:48:38,600 --> 01:48:40,800
Speaker 1: have to respond to and so you know, the reason

1942
01:48:40,840 --> 01:48:42,639
Speaker 1: you we didn't see any effect. One of the main

1943
01:48:42,680 --> 01:48:45,679
Speaker 1: reasons is that there's a risk of starving to death

1944
01:48:45,880 --> 01:48:49,200
Speaker 1: every winter for an elk and Yellowstone, and they need

1945
01:48:49,240 --> 01:48:51,720
Speaker 1: to make decisions that minimize that risk. They need to

1946
01:48:51,760 --> 01:48:54,519
Speaker 1: feed where you know, where they can still find food,

1947
01:48:54,560 --> 01:48:57,000
Speaker 1: and they need to not spend time and three foot

1948
01:48:57,000 --> 01:48:58,400
Speaker 1: of snow where they're going to burn a bunch of

1949
01:48:58,400 --> 01:49:00,360
Speaker 1: calories and then end up starving at the end of

1950
01:49:00,400 --> 01:49:04,560
Speaker 1: the winter. And wolves are new to us in Yellowstone,

1951
01:49:04,560 --> 01:49:06,839
Speaker 1: but they're not really new to elk. They still contain

1952
01:49:07,000 --> 01:49:12,240
Speaker 1: all the adaptations of living with wolves for millennia. So um,

1953
01:49:12,680 --> 01:49:16,400
Speaker 1: we think of wolves as being this novel um stress

1954
01:49:16,439 --> 01:49:22,000
Speaker 1: in this novel predation for elk, but in reality, you know,

1955
01:49:22,000 --> 01:49:25,160
Speaker 1: they're adapted to to live with these predators, but they're

1956
01:49:25,200 --> 01:49:29,559
Speaker 1: not adapted to live with energy development. And that's a

1957
01:49:29,680 --> 01:49:33,160
Speaker 1: very different kind of disturbance, right. That's always in the

1958
01:49:33,200 --> 01:49:37,160
Speaker 1: same place, like you know, the footprint and the human

1959
01:49:37,200 --> 01:49:39,519
Speaker 1: activity is at that well and at those roads. It's

1960
01:49:39,560 --> 01:49:42,640
Speaker 1: always in the same place. So it it can it

1961
01:49:42,720 --> 01:49:46,320
Speaker 1: can send a more common, more consistent que that that

1962
01:49:46,360 --> 01:49:49,719
Speaker 1: animals respond to and there you do see this result

1963
01:49:49,800 --> 01:49:51,960
Speaker 1: of you know, the mule that you're leaving the food

1964
01:49:52,200 --> 01:49:54,920
Speaker 1: behind that's close to the well pads. I don't think

1965
01:49:54,920 --> 01:49:57,400
Speaker 1: elk are leaving well I've tested this. I know that

1966
01:49:57,479 --> 01:49:59,960
Speaker 1: elk are not leaving food behind that are in play

1967
01:50:00,080 --> 01:50:03,519
Speaker 1: sis where where wolves frequent and where it's risky to

1968
01:50:03,840 --> 01:50:08,280
Speaker 1: forage because of wolves. They still find it because yeah,

1969
01:50:08,360 --> 01:50:11,720
Speaker 1: that that's a that's a stress, that's a sort of

1970
01:50:11,960 --> 01:50:15,800
Speaker 1: source of of of risk that they're adapted to to

1971
01:50:16,040 --> 01:50:19,720
Speaker 1: work with on the landscape. I just recently shared a

1972
01:50:20,640 --> 01:50:25,599
Speaker 1: um photograph of a graphic that was in the mule

1973
01:50:25,640 --> 01:50:28,839
Speaker 1: Deer Migration Assessment that was put out by the Wyoming

1974
01:50:28,880 --> 01:50:34,200
Speaker 1: Migration Initiative, and it's a it's a graphic that shows

1975
01:50:34,280 --> 01:50:39,360
Speaker 1: mule deer using winter range near Rock Springs, Wyoming, north

1976
01:50:39,400 --> 01:50:45,960
Speaker 1: of I eight and I A d literally formed, so

1977
01:50:46,080 --> 01:50:49,559
Speaker 1: it's like an The northern end of showing all the

1978
01:50:49,640 --> 01:50:53,360
Speaker 1: uth patterns is a morphous. It's just like, as you know,

1979
01:50:54,680 --> 01:50:57,880
Speaker 1: like how do you describe it? It looks like a

1980
01:50:57,920 --> 01:51:00,280
Speaker 1: head of cauliflower, right, It's just they're kind of going

1981
01:51:00,479 --> 01:51:03,600
Speaker 1: along natural land use patterns. The southern edge of the

1982
01:51:03,600 --> 01:51:08,840
Speaker 1: winter range is a straight line formed by a four

1983
01:51:08,920 --> 01:51:11,720
Speaker 1: lane divided highway. Yeah. Like it's just like it's like

1984
01:51:11,720 --> 01:51:13,639
Speaker 1: if you took a pair of scissors and cut off

1985
01:51:15,320 --> 01:51:20,080
Speaker 1: the landscape, what are the like in your mind? Like,

1986
01:51:20,120 --> 01:51:24,759
Speaker 1: what is it about that highway that they don't like? Yeah?

1987
01:51:24,800 --> 01:51:27,880
Speaker 1: So in that case, you know, uh, and and you

1988
01:51:27,920 --> 01:51:30,040
Speaker 1: know what you can't see in that graphic that you

1989
01:51:30,200 --> 01:51:33,640
Speaker 1: just described is that you know those animals travel a

1990
01:51:33,720 --> 01:51:37,519
Speaker 1: hundred and fifty miles from the north down to that

1991
01:51:37,560 --> 01:51:41,679
Speaker 1: winter range two. Then um, you know have have part

1992
01:51:41,680 --> 01:51:44,559
Speaker 1: of it truncated by interstates, have to come a hundred

1993
01:51:44,560 --> 01:51:47,840
Speaker 1: fifty miles. But then be like, but that I don't like. Right,

1994
01:51:47,880 --> 01:51:51,639
Speaker 1: So so Interstate eighty is, um there are um right

1995
01:51:51,680 --> 01:51:57,040
Speaker 1: away fences which are maybe eight inches high meal you

1996
01:51:57,280 --> 01:51:59,920
Speaker 1: jumpable by a mule deer. That's not the problem. The

1997
01:52:00,040 --> 01:52:05,120
Speaker 1: problem is that Interstate eighty has an incredible level of traffic,

1998
01:52:06,000 --> 01:52:10,200
Speaker 1: and so the animals have just learned that like, this

1999
01:52:10,280 --> 01:52:14,160
Speaker 1: is a this is a risky endeavor, and they don't

2000
01:52:14,200 --> 01:52:18,400
Speaker 1: for the most part. Uh, they don't try to cross

2001
01:52:18,439 --> 01:52:21,519
Speaker 1: Interstate eight because they're the traffic levels are just so high.

2002
01:52:21,560 --> 01:52:24,839
Speaker 1: So if you if you ceased traffic, they would obviously

2003
01:52:24,880 --> 01:52:28,000
Speaker 1: just walk right through. Oh yeah, because I was looking

2004
01:52:28,040 --> 01:52:30,439
Speaker 1: at photographs of because at first when I saw that picture,

2005
01:52:30,479 --> 01:52:32,400
Speaker 1: I was like, there has been another explanation, Like I

2006
01:52:32,439 --> 01:52:35,320
Speaker 1: thought that the south it was maybe like that, it's

2007
01:52:35,400 --> 01:52:38,280
Speaker 1: following the course of a large river. There's a giant

2008
01:52:38,400 --> 01:52:41,760
Speaker 1: bluff like you find in places. But then I and

2009
01:52:41,800 --> 01:52:44,800
Speaker 1: I voiced this to Jana's and he's like no, and

2010
01:52:44,840 --> 01:52:49,519
Speaker 1: he pulls up a photograph. There's no damn difference. Yeah. Yeah,

2011
01:52:49,560 --> 01:52:52,000
Speaker 1: So I mean they would, uh, they might not do

2012
01:52:52,040 --> 01:52:54,479
Speaker 1: it immediately, they have, you know, a bit of memory,

2013
01:52:54,520 --> 01:52:56,640
Speaker 1: but they would eventually, Yes, they would. They would cross it.

2014
01:52:57,240 --> 01:53:02,719
Speaker 1: Um if if the traffic the war of trucks or yeah, yeah, yeah,

2015
01:53:02,760 --> 01:53:05,559
Speaker 1: and that I mean, and if you if you travel

2016
01:53:05,640 --> 01:53:08,240
Speaker 1: that interstate, you you you know what I'm talking about.

2017
01:53:08,240 --> 01:53:10,839
Speaker 1: I mean, and oftentimes when you're driving on that interscape,

2018
01:53:10,880 --> 01:53:12,479
Speaker 1: you can just look down the road and it's just

2019
01:53:12,560 --> 01:53:14,960
Speaker 1: a line of semis in front of you and on

2020
01:53:15,200 --> 01:53:18,920
Speaker 1: in the other lane. What would a cost to do

2021
01:53:18,960 --> 01:53:25,120
Speaker 1: you have some familiarity with um overpasses, underpasses, what would

2022
01:53:25,240 --> 01:53:28,519
Speaker 1: cost to like if you just take that isolated spot. Okay,

2023
01:53:30,240 --> 01:53:31,920
Speaker 1: let's let's see you let me put it this way.

2024
01:53:32,000 --> 01:53:33,439
Speaker 1: Let's say you had all the morning in the world,

2025
01:53:34,080 --> 01:53:37,800
Speaker 1: what would you do to fix that spot? Yeah, so

2026
01:53:37,920 --> 01:53:42,479
Speaker 1: you can put in, uh, you can you can put

2027
01:53:42,520 --> 01:53:47,160
Speaker 1: in so you can put in underpasses or overpasses, and

2028
01:53:47,600 --> 01:53:49,599
Speaker 1: you know, and we've had a couple of those in

2029
01:53:49,720 --> 01:53:54,599
Speaker 1: Wyoming that have been really successful, uh underpass and scared

2030
01:53:54,640 --> 01:53:57,400
Speaker 1: the hell out of them. Well, so, yeah, it's interesting

2031
01:53:57,560 --> 01:54:00,560
Speaker 1: that you say that. So when so inter eight was

2032
01:54:00,600 --> 01:54:03,680
Speaker 1: created in the seventies, and there was there was like

2033
01:54:03,720 --> 01:54:06,719
Speaker 1: a smaller road. But then when they built the Interstate

2034
01:54:07,240 --> 01:54:11,679
Speaker 1: UM in the seventies, they knew that they were um

2035
01:54:11,760 --> 01:54:14,000
Speaker 1: that they were going to disrupt migrations. You know, they

2036
01:54:14,000 --> 01:54:16,000
Speaker 1: didn't have the maps of the migrations that we have now,

2037
01:54:16,040 --> 01:54:17,720
Speaker 1: but they knew that they were going to disrupt it,

2038
01:54:18,200 --> 01:54:25,439
Speaker 1: and so proactively they put in these tunnels underneath the interstate.

2039
01:54:26,040 --> 01:54:29,240
Speaker 1: But the tunnels are like I call them tunnels because

2040
01:54:29,280 --> 01:54:31,280
Speaker 1: I think that's what they look like to a mule deer.

2041
01:54:31,479 --> 01:54:36,040
Speaker 1: They're like ten ft wide by ten ft high. Yeah.

2042
01:54:36,160 --> 01:54:39,920
Speaker 1: And and and the Interstate, you know, is is two

2043
01:54:40,000 --> 01:54:43,360
Speaker 1: lanes here the big median and then two lanes. So

2044
01:54:43,880 --> 01:54:46,120
Speaker 1: they're long. And when you when you look through them,

2045
01:54:46,320 --> 01:54:52,680
Speaker 1: you know, you the afterlife. Yeah, you see this tiny

2046
01:54:52,840 --> 01:54:55,560
Speaker 1: light at the end of it, and uh yeah. So

2047
01:54:56,200 --> 01:54:58,920
Speaker 1: mule deer have not used those. You can imagine a

2048
01:54:58,960 --> 01:55:01,720
Speaker 1: bobcat might be like, yeah, go through there, right right,

2049
01:55:01,840 --> 01:55:04,720
Speaker 1: So so there's been monitoring that mule they have not

2050
01:55:04,960 --> 01:55:08,800
Speaker 1: used those. Um so underpasses, you know, but but there

2051
01:55:08,840 --> 01:55:11,240
Speaker 1: are options, right Like you could have a smaller underpass

2052
01:55:11,320 --> 01:55:14,640
Speaker 1: that goes through the eastbound lane, then it opens up

2053
01:55:14,880 --> 01:55:17,600
Speaker 1: into a into a fenced opening in the in the median,

2054
01:55:17,800 --> 01:55:19,360
Speaker 1: then you go through this. You know, that would be

2055
01:55:19,480 --> 01:55:25,000
Speaker 1: much more effective or overpasses. Those overpasses, um you know,

2056
01:55:25,000 --> 01:55:28,840
Speaker 1: I don't have exact numbers, but there four to five,

2057
01:55:29,400 --> 01:55:33,840
Speaker 1: eight to ten million um per probably to go all

2058
01:55:33,880 --> 01:55:35,800
Speaker 1: the way over. And why does it have to be

2059
01:55:35,800 --> 01:55:39,080
Speaker 1: before it ceases to be spooky to them? It doesn't

2060
01:55:39,120 --> 01:55:42,600
Speaker 1: have to be that wide. I think the a the

2061
01:55:42,640 --> 01:55:44,440
Speaker 1: one that I don't know if either you know, the

2062
01:55:44,480 --> 01:55:46,720
Speaker 1: one at Trappers Point. We have one over We have

2063
01:55:46,760 --> 01:55:49,800
Speaker 1: two overpasses news since two thousand twelve in Wyoming and

2064
01:55:49,800 --> 01:55:53,080
Speaker 1: they're both on that path of the Prong Horn migration

2065
01:55:53,080 --> 01:55:55,840
Speaker 1: that I mentioned earlier, and also a mule deer migration,

2066
01:55:56,040 --> 01:56:02,440
Speaker 1: and I'd say it's probably fifty or sixty feet wide

2067
01:56:02,760 --> 01:56:06,120
Speaker 1: feet feet, but it has but it also has burms

2068
01:56:06,160 --> 01:56:08,440
Speaker 1: on on the overpath, so if you're a prong horner

2069
01:56:08,520 --> 01:56:11,800
Speaker 1: meal deer, you can't really see the traffic on either

2070
01:56:11,840 --> 01:56:14,360
Speaker 1: side as you're going over it. I read somewhere too

2071
01:56:14,400 --> 01:56:18,440
Speaker 1: that when you burm it too steep, they don't like

2072
01:56:18,520 --> 01:56:20,600
Speaker 1: it too. Yeah, I could probably start making it goes

2073
01:56:20,600 --> 01:56:22,640
Speaker 1: out of some findings out of Europe where it when

2074
01:56:22,640 --> 01:56:25,640
Speaker 1: it's burned too steep, they feel like they're just afraid

2075
01:56:25,720 --> 01:56:28,840
Speaker 1: of they're afraid of like amber. It needs to I can't.

2076
01:56:28,840 --> 01:56:30,520
Speaker 1: I wish I remembered it better, but there's like a

2077
01:56:30,560 --> 01:56:34,320
Speaker 1: way that to make them feel at ease where they

2078
01:56:34,360 --> 01:56:36,360
Speaker 1: have sort of awareness of what's to the right most

2079
01:56:36,560 --> 01:56:38,960
Speaker 1: left as they pass into it. But fifty ft will

2080
01:56:39,000 --> 01:56:42,560
Speaker 1: do it. I thought you're gonna say, like fifty yards. Well,

2081
01:56:42,600 --> 01:56:44,920
Speaker 1: I mean, I'm I'm kind of guessing. I've never measured it,

2082
01:56:44,960 --> 01:56:48,000
Speaker 1: but but being up on those that feels like about

2083
01:56:48,000 --> 01:56:50,360
Speaker 1: what it is. It's not it's not like a football field.

2084
01:56:50,880 --> 01:56:54,480
Speaker 1: They're they're they're relatively small. But but the challenge with

2085
01:56:54,560 --> 01:56:58,000
Speaker 1: Interstate eight and you got to vegetate the thing. Uh,

2086
01:56:58,080 --> 01:57:00,520
Speaker 1: well they do in in like they're some up in

2087
01:57:00,560 --> 01:57:03,720
Speaker 1: Canada near Banff National Park. Those are vegetated. The ones

2088
01:57:04,400 --> 01:57:07,280
Speaker 1: are those are like stunning. Yeah. Yeah. The ones that

2089
01:57:07,360 --> 01:57:10,000
Speaker 1: we have are not. Um, I mean they're re vegetated.

2090
01:57:10,000 --> 01:57:11,520
Speaker 1: So there's some grass on it, but it's not like

2091
01:57:11,560 --> 01:57:14,000
Speaker 1: you got. But they're coming across the open country though

2092
01:57:14,000 --> 01:57:16,320
Speaker 1: it's not they're not coming out of the forest. And

2093
01:57:16,400 --> 01:57:18,960
Speaker 1: I mean he's not walking on concrete. No, No, it's

2094
01:57:19,000 --> 01:57:22,360
Speaker 1: got dirt and grass. Yeah. But just to circle back

2095
01:57:22,400 --> 01:57:25,240
Speaker 1: to the inter State Ada that so yes, where that

2096
01:57:25,320 --> 01:57:28,360
Speaker 1: red desert to hop back migration comes down the winter

2097
01:57:28,480 --> 01:57:30,680
Speaker 1: range that you were describing. You know, we could put

2098
01:57:30,680 --> 01:57:33,680
Speaker 1: a crossing structure there and I think those animals over time,

2099
01:57:34,320 --> 01:57:37,440
Speaker 1: difficult to say how long would discover it, move across

2100
01:57:37,480 --> 01:57:40,960
Speaker 1: it and discover probably what was a historical winter range

2101
01:57:41,400 --> 01:57:44,720
Speaker 1: that was lost when near State Ada was built. But

2102
01:57:45,120 --> 01:57:48,920
Speaker 1: for Pronghorn and elsewhere along the interstate at a quarter

2103
01:57:48,960 --> 01:57:52,760
Speaker 1: which cuts across the whole southern half of Wyoming, it's

2104
01:57:53,480 --> 01:57:57,640
Speaker 1: we have lost those migrations. They've been severed. And so

2105
01:57:58,280 --> 01:58:02,200
Speaker 1: now now and we a project looking at this, it's

2106
01:58:02,360 --> 01:58:07,280
Speaker 1: very difficult for us to identify where the where the

2107
01:58:07,280 --> 01:58:10,480
Speaker 1: animals used you know, where the ghost migrations are. Where

2108
01:58:10,480 --> 01:58:13,120
Speaker 1: do they used to cross the interstate? And where now

2109
01:58:13,160 --> 01:58:15,560
Speaker 1: if we put a crossing structure, will they rediscover it

2110
01:58:15,600 --> 01:58:18,640
Speaker 1: and red and restore those migrations. That's a good point, man.

2111
01:58:19,040 --> 01:58:22,440
Speaker 1: Everywhere else where we've done crossing structures in Wyoming, at

2112
01:58:22,480 --> 01:58:25,160
Speaker 1: least they've been places where the animals are still migrating,

2113
01:58:26,000 --> 01:58:28,760
Speaker 1: so they're still crossing the road. Mortalities are piling up

2114
01:58:28,760 --> 01:58:31,360
Speaker 1: there on the road, so they're showing us this is

2115
01:58:31,520 --> 01:58:33,960
Speaker 1: this is where we cross. And you put the crossing

2116
01:58:34,000 --> 01:58:36,880
Speaker 1: structure there and they learn how to use it really quickly.

2117
01:58:36,920 --> 01:58:39,880
Speaker 1: And those have been wildly successful. Tens of thousands of

2118
01:58:39,880 --> 01:58:44,080
Speaker 1: animals moved across those crossing structures. Is there an element

2119
01:58:44,120 --> 01:58:48,320
Speaker 1: of your work where you interface with historians who are

2120
01:58:48,320 --> 01:58:53,240
Speaker 1: familiar with oral tradition to try to piece together lost

2121
01:58:53,280 --> 01:58:59,880
Speaker 1: bits of knowledge about animal movements. Uh, We've we've been

2122
01:59:00,080 --> 01:59:03,280
Speaker 1: interested in doing that, and especially and and on that

2123
01:59:03,360 --> 01:59:08,800
Speaker 1: interstate ad project where we're trying to uh, you know,

2124
01:59:09,000 --> 01:59:11,360
Speaker 1: trying we can't use sort of we can call o

2125
01:59:11,440 --> 01:59:15,080
Speaker 1: the animals today, but those animals can't show us, right,

2126
01:59:15,480 --> 01:59:18,600
Speaker 1: So we're trying to get a hold of old timers

2127
01:59:18,600 --> 01:59:21,760
Speaker 1: who might have known where, you know, where some of

2128
01:59:21,800 --> 01:59:25,800
Speaker 1: those movements were. UM. We've also done we've done some

2129
01:59:25,840 --> 01:59:28,520
Speaker 1: work here on the Wind River Reservation and have done

2130
01:59:28,560 --> 01:59:32,920
Speaker 1: some UM interviews with tribal elders trying to understand what

2131
01:59:32,960 --> 01:59:37,280
Speaker 1: they knew about historical migrations. UM. And as you can imagine,

2132
01:59:37,280 --> 01:59:40,760
Speaker 1: it's it's challenging, which I guess. We don't have any

2133
01:59:40,960 --> 01:59:46,160
Speaker 1: examples yet where and I'd love, I'd love to to

2134
01:59:46,240 --> 01:59:49,960
Speaker 1: stumble upon this, right, I can tell you, Yeah, even

2135
01:59:50,040 --> 01:59:54,200
Speaker 1: with Pompey's Pillar along the Old Stone River east of Billings, Montana, No,

2136
01:59:54,440 --> 01:59:56,960
Speaker 1: people would always run into stuff there. But you go

2137
01:59:57,080 --> 01:59:59,240
Speaker 1: look and it makes sense. Yeah, people always run into

2138
01:59:59,240 --> 02:00:02,920
Speaker 1: elkom bison there because the north side of the rivers

2139
02:00:02,920 --> 02:00:05,960
Speaker 1: just giant sandstone bluffs and there's a creek comes down

2140
02:00:07,200 --> 02:00:10,480
Speaker 1: that forms a pass through the bluffs. And it was

2141
02:00:10,560 --> 02:00:15,080
Speaker 1: like people having shootouts there, people hunting there. People get

2142
02:00:15,120 --> 02:00:16,680
Speaker 1: there and they describe like as far as I can

2143
02:00:16,680 --> 02:00:18,720
Speaker 1: see you to the right and the left, you know,

2144
02:00:18,880 --> 02:00:20,560
Speaker 1: and even though that stuffs not that they're still they're

2145
02:00:20,560 --> 02:00:22,720
Speaker 1: not doing it now. But it's like very definite. But

2146
02:00:22,720 --> 02:00:24,360
Speaker 1: it's funny because you go there and look and you're like, oh,

2147
02:00:24,400 --> 02:00:27,360
Speaker 1: I can totally see it. Right. So it's it's reflected

2148
02:00:27,400 --> 02:00:31,640
Speaker 1: again and again in in the journals people who traveled

2149
02:00:31,680 --> 02:00:35,360
Speaker 1: through that this is like the spot right right, yeah,

2150
02:00:35,400 --> 02:00:37,400
Speaker 1: And so what we what what would be nice would

2151
02:00:37,400 --> 02:00:41,320
Speaker 1: be to you know, uncover some of that information which

2152
02:00:41,360 --> 02:00:45,640
Speaker 1: points to a historical corridor that we can then work

2153
02:00:45,880 --> 02:00:49,040
Speaker 1: they can increase our confidence in knowing that that's the

2154
02:00:49,120 --> 02:00:53,640
Speaker 1: right place to restore. And uh. A related example is

2155
02:00:54,120 --> 02:00:57,240
Speaker 1: at this, uh that path of the pronghorn where where

2156
02:00:57,320 --> 02:00:59,920
Speaker 1: that overpass was built. Is that the is that a

2157
02:01:00,000 --> 02:01:03,600
Speaker 1: place called Trapper's Point which was a historical rendezvous site.

2158
02:01:04,160 --> 02:01:09,040
Speaker 1: And and also um, when they widened that highway, uh

2159
02:01:10,120 --> 02:01:14,160
Speaker 1: two thousand, eight thousand nine, I think, um, now it

2160
02:01:14,240 --> 02:01:17,080
Speaker 1: was earlier than that. They anyways, they had to do

2161
02:01:17,080 --> 02:01:21,120
Speaker 1: an archaeological survey and they discovered a pronghorn kill site.

2162
02:01:22,480 --> 02:01:25,720
Speaker 1: I'm not and uh uh and what was unique? So

2163
02:01:25,760 --> 02:01:29,320
Speaker 1: they basically started finding bones after bones after bones, and

2164
02:01:29,400 --> 02:01:32,200
Speaker 1: they all had you know, butchering marks on them. And

2165
02:01:32,240 --> 02:01:34,920
Speaker 1: it was and it was right on the current. It's

2166
02:01:34,920 --> 02:01:38,160
Speaker 1: at the bottleneck where they migrate there. And in addition

2167
02:01:38,680 --> 02:01:42,720
Speaker 1: they found fetal bones, and the size of the fetal

2168
02:01:42,760 --> 02:01:46,320
Speaker 1: bones indicated that those aren't those prong horn would have

2169
02:01:46,360 --> 02:01:50,839
Speaker 1: been killed um during the spring migration when the winds

2170
02:01:50,960 --> 02:01:55,800
Speaker 1: were pregnant and those and those so so that suggests,

2171
02:01:55,840 --> 02:01:58,480
Speaker 1: you know, an an ancient kill site where early humans

2172
02:01:58,480 --> 02:02:02,080
Speaker 1: were ambushing prong horn, killing them butchering them in the spring,

2173
02:02:02,440 --> 02:02:05,000
Speaker 1: in the spring, right on the migration corridor. And they

2174
02:02:05,160 --> 02:02:09,520
Speaker 1: date um from five to eight thousand years ago. Wow,

2175
02:02:09,520 --> 02:02:11,720
Speaker 1: that's cool. Yeah, they have any idea how they were

2176
02:02:11,800 --> 02:02:15,640
Speaker 1: killing them? Well, the archaelogists probably have a sense. Feels

2177
02:02:15,640 --> 02:02:19,880
Speaker 1: like projectile points or if there's like net materials or what. Yeah,

2178
02:02:19,920 --> 02:02:24,560
Speaker 1: I we probably have to get their archologists archaeologists in here.

2179
02:02:24,640 --> 02:02:28,120
Speaker 1: You know. So what do we not hit? Johnnie? What

2180
02:02:28,200 --> 02:02:33,320
Speaker 1: have we not hit? Yeah? I know there's a there's

2181
02:02:33,360 --> 02:02:35,560
Speaker 1: a lot um. I've got a couple of follow up

2182
02:02:35,640 --> 02:02:38,560
Speaker 1: questions if you can't if we can affectively hit on

2183
02:02:38,600 --> 02:02:43,880
Speaker 1: those while we think of what we've missed. But um,

2184
02:02:44,000 --> 02:02:46,040
Speaker 1: all the way back to the when we're talking about

2185
02:02:46,080 --> 02:02:48,720
Speaker 1: the different types of like the migrate the different groups

2186
02:02:48,720 --> 02:02:50,520
Speaker 1: and mule do that migrate in different ways or don't

2187
02:02:50,560 --> 02:02:53,120
Speaker 1: migrate at all, do you guys? And without speculating if

2188
02:02:53,120 --> 02:02:56,200
Speaker 1: you guys looked into it or have any ideas on

2189
02:02:56,840 --> 02:03:00,640
Speaker 1: is that just like a greater species tactic to because

2190
02:03:00,760 --> 02:03:02,600
Speaker 1: I'm just thinking in my head like, well, of course

2191
02:03:02,600 --> 02:03:05,840
Speaker 1: it makes sense because if then one population gets wiped

2192
02:03:05,840 --> 02:03:08,360
Speaker 1: out because they all got stuck in the mountains, you

2193
02:03:08,400 --> 02:03:11,120
Speaker 1: still have all these other mule deer, these other five

2194
02:03:11,160 --> 02:03:16,440
Speaker 1: different migratory patterns that are going to survive. Like you

2195
02:03:16,440 --> 02:03:19,680
Speaker 1: guys thinking that way? Or what good question? So to

2196
02:03:19,760 --> 02:03:23,640
Speaker 1: be the dorky scientist, Um, is there such a thing?

2197
02:03:24,680 --> 02:03:27,280
Speaker 1: H imagine there probably is. You guys have been sitting

2198
02:03:27,280 --> 02:03:30,840
Speaker 1: here thinking that that whole time. So that notion. So

2199
02:03:30,880 --> 02:03:33,760
Speaker 1: that notion is what we would call a group selectionist argument,

2200
02:03:34,440 --> 02:03:39,080
Speaker 1: which means that where in natural selection and how processes operate,

2201
02:03:39,160 --> 02:03:43,879
Speaker 1: ultimately work at the individual level. So individuals don't generally

2202
02:03:43,920 --> 02:03:48,120
Speaker 1: have the greater species in their mind right, it's their

2203
02:03:48,200 --> 02:03:51,360
Speaker 1: their mode of operation is to survive, reproduce, pass on

2204
02:03:51,400 --> 02:03:54,080
Speaker 1: their genes to their progeny and so forth, as opposed

2205
02:03:54,120 --> 02:03:56,720
Speaker 1: to the like, well you go here and then I'll

2206
02:03:56,760 --> 02:04:04,360
Speaker 1: go up here and our species will survive. Yeah, yeah, yeah, yeah,

2207
02:04:04,720 --> 02:04:07,640
Speaker 1: So that's that's what's called a group selection argument, which

2208
02:04:07,840 --> 02:04:11,760
Speaker 1: that that contradicts directly the notion of natural selection and

2209
02:04:11,800 --> 02:04:15,280
Speaker 1: how these processes operate. And it's pretty much been disproven,

2210
02:04:15,880 --> 02:04:18,480
Speaker 1: so it's more of But but the angle you're headed

2211
02:04:18,520 --> 02:04:21,320
Speaker 1: down is more of this kind of what we mentioned earlier,

2212
02:04:21,440 --> 02:04:25,320
Speaker 1: or the notion of a um a portfolio effect, wherein

2213
02:04:25,480 --> 02:04:29,960
Speaker 1: for the greater good of of the species or the population.

2214
02:04:30,040 --> 02:04:33,520
Speaker 1: That yes, when you have a number of viable tactics

2215
02:04:33,560 --> 02:04:38,320
Speaker 1: that are occurring as things change, there's some potential still

2216
02:04:38,480 --> 02:04:43,520
Speaker 1: viable tactic even if others become non viable, therefore maintaining

2217
02:04:43,560 --> 02:04:47,240
Speaker 1: the greater diversity, like in our minds from a conservation perspective,

2218
02:04:47,240 --> 02:04:51,600
Speaker 1: maintaining the greater diversity. This whole portfolio ensures that we

2219
02:04:51,680 --> 02:04:56,520
Speaker 1: have potential traits and going forward or behavioral tactics, those

2220
02:04:56,560 --> 02:04:58,839
Speaker 1: sorts of things that are potentially going to be viable

2221
02:04:58,880 --> 02:05:03,520
Speaker 1: in the future, what as it works for the animals themselves.

2222
02:05:04,120 --> 02:05:08,400
Speaker 1: It's clearly more of an individual tactic of this is

2223
02:05:08,480 --> 02:05:10,920
Speaker 1: what I do. Here's where I'm going to go. I'm

2224
02:05:10,920 --> 02:05:14,360
Speaker 1: gonna do my best given my environment um to do

2225
02:05:14,440 --> 02:05:17,280
Speaker 1: the sorts of things that that I do. And and

2226
02:05:17,320 --> 02:05:20,280
Speaker 1: if you think about that, and so if if migration

2227
02:05:20,360 --> 02:05:23,040
Speaker 1: is really inherited, especially mule deer, from mother to daughter,

2228
02:05:23,560 --> 02:05:26,960
Speaker 1: and it becomes functionally fixed once they inherit that this

2229
02:05:27,040 --> 02:05:28,520
Speaker 1: is what I'm gonna do, and I'm gonna do it

2230
02:05:28,560 --> 02:05:32,120
Speaker 1: every year, which is interesting. Clearly, it clearly relates to

2231
02:05:32,240 --> 02:05:36,839
Speaker 1: us a tactic or a strategy that has been viable

2232
02:05:36,880 --> 02:05:41,000
Speaker 1: for many generations and in hundreds of years. Therefore, clearly,

2233
02:05:41,520 --> 02:05:44,680
Speaker 1: you know, inheriting a route and doing what mom did,

2234
02:05:44,720 --> 02:05:46,920
Speaker 1: if that's the way it works, must be something that's

2235
02:05:47,680 --> 02:05:51,000
Speaker 1: allowed the species to persist all these many generations and

2236
02:05:51,080 --> 02:05:55,960
Speaker 1: is therefore really important and intuitively, although it seems like okay,

2237
02:05:56,040 --> 02:05:59,680
Speaker 1: then they must be less adaptable to change, well maybe

2238
02:06:00,000 --> 02:06:02,920
Speaker 1: at it. Also, if if mom has been successful and

2239
02:06:02,960 --> 02:06:07,440
Speaker 1: survived and she successfully raised you as as an offspring,

2240
02:06:07,840 --> 02:06:11,160
Speaker 1: well clearly that's been a viable strategy, so perhaps why

2241
02:06:11,160 --> 02:06:14,320
Speaker 1: once you adapt it? So that's that's perhaps one of

2242
02:06:14,320 --> 02:06:18,520
Speaker 1: the arguments behind this cross generational potential inheritance of a

2243
02:06:18,560 --> 02:06:21,080
Speaker 1: migratory route, and you're just doing that, you're in and

2244
02:06:21,120 --> 02:06:24,040
Speaker 1: you're out. It's a known thing. It worked for mom,

2245
02:06:24,080 --> 02:06:26,120
Speaker 1: it should work for me. It's worked for my mom's

2246
02:06:26,160 --> 02:06:30,200
Speaker 1: mom's might you know, in in many generations beyond. Assuming

2247
02:06:30,240 --> 02:06:32,880
Speaker 1: that that's how that process has come about, and as

2248
02:06:32,880 --> 02:06:36,520
Speaker 1: a consequence of that you end up which is multiple

2249
02:06:36,640 --> 02:06:40,240
Speaker 1: different tactics that exist within a single population that creates

2250
02:06:40,280 --> 02:06:44,240
Speaker 1: this grander portfolio. Are you guys from we talked about

2251
02:06:44,280 --> 02:06:48,120
Speaker 1: your night, but are you familiar with the like the

2252
02:06:48,520 --> 02:06:53,400
Speaker 1: southern resident killer whales in the migratory killer whales in

2253
02:06:53,440 --> 02:06:57,000
Speaker 1: the Puget Sound area? Not super familiar. So you have

2254
02:06:57,120 --> 02:07:00,520
Speaker 1: this this kind of interesting thing where you have there's

2255
02:07:00,560 --> 02:07:03,600
Speaker 1: a resident population around pus It Sound of killer whales

2256
02:07:03,680 --> 02:07:08,880
Speaker 1: or some folks calm or because that they're they're chinook specialists,

2257
02:07:09,960 --> 02:07:14,120
Speaker 1: and they're literally starving to death right now. Meanwhile, there's

2258
02:07:14,120 --> 02:07:17,480
Speaker 1: a population that rolls up and down the coast and

2259
02:07:17,480 --> 02:07:20,400
Speaker 1: their marine mammals, well they're more generals, but eat a

2260
02:07:20,400 --> 02:07:24,600
Speaker 1: lot of marine mammals and they're thriving. They have different languages,

2261
02:07:25,880 --> 02:07:31,280
Speaker 1: they avoid each other, and one is got fell into

2262
02:07:31,320 --> 02:07:35,720
Speaker 1: the trap, you know, and they won't they won't eat seals.

2263
02:07:37,720 --> 02:07:41,920
Speaker 1: So there it's also the situation where people treat them

2264
02:07:41,960 --> 02:07:47,400
Speaker 1: like they treat him as like humans regard them as

2265
02:07:47,440 --> 02:07:51,760
Speaker 1: this very separate thing, you know. And it's like this

2266
02:07:51,880 --> 02:07:55,959
Speaker 1: idea that uh I see, like I see a semblance

2267
02:07:55,960 --> 02:07:57,480
Speaker 1: of that, and what we're talking about with mule deer

2268
02:07:57,680 --> 02:08:00,120
Speaker 1: or some like figure out how to survive without need

2269
02:08:00,120 --> 02:08:02,760
Speaker 1: to move, and then some need to move, and at

2270
02:08:02,800 --> 02:08:04,360
Speaker 1: some point in time, the ones that need to move

2271
02:08:04,400 --> 02:08:09,520
Speaker 1: are going to be possibly become the vulnerable ones exactly.

2272
02:08:09,720 --> 02:08:12,400
Speaker 1: So it's nice to have different there's a pathways. Like

2273
02:08:12,440 --> 02:08:16,840
Speaker 1: people celebrate salmon for their fidelity to their natal stream,

2274
02:08:16,880 --> 02:08:18,840
Speaker 1: but one of the things that allows salmon to do

2275
02:08:18,920 --> 02:08:22,120
Speaker 1: well is that some don't have fidelity to the natal stream.

2276
02:08:22,840 --> 02:08:26,800
Speaker 1: They pioneer new rivers and like rivers change and they

2277
02:08:26,800 --> 02:08:29,360
Speaker 1: find new spots because some of them just screw up

2278
02:08:29,480 --> 02:08:33,720
Speaker 1: or whatever. Yeah, it's that diversity of tactics. And one

2279
02:08:33,720 --> 02:08:36,160
Speaker 1: of the things that I think about in this context

2280
02:08:36,200 --> 02:08:40,960
Speaker 1: is is climate change, right, and so like if you

2281
02:08:41,000 --> 02:08:45,600
Speaker 1: think about it, uh, you know, a resident animal when

2282
02:08:45,640 --> 02:08:48,040
Speaker 1: the when the climate change is like, they don't have

2283
02:08:48,240 --> 02:08:53,680
Speaker 1: many options, right. All they know is this this small

2284
02:08:53,800 --> 02:08:59,440
Speaker 1: landscape that they live on and as spring comes earlier

2285
02:08:59,720 --> 02:09:03,880
Speaker 1: there there's more snow or whatever climate change brings. The

2286
02:09:03,960 --> 02:09:07,040
Speaker 1: only place that they can adapt to that is within

2287
02:09:07,320 --> 02:09:10,600
Speaker 1: this small range that they know. But if you're a

2288
02:09:10,800 --> 02:09:15,040
Speaker 1: meal there makes a hundred fifty mile migration right well

2289
02:09:15,880 --> 02:09:18,240
Speaker 1: over that hundred fifty miles. I mean, you can you

2290
02:09:18,280 --> 02:09:20,800
Speaker 1: can almost choose whatever climate you want depending on where

2291
02:09:20,840 --> 02:09:23,400
Speaker 1: you want to be on that So they have a

2292
02:09:23,480 --> 02:09:27,880
Speaker 1: template that that they have detailed knowledge about, and they

2293
02:09:27,920 --> 02:09:33,640
Speaker 1: can they can exploit that landscape template um to their advantage.

2294
02:09:34,000 --> 02:09:36,480
Speaker 1: You know, when it's a drought, when it's a when

2295
02:09:36,480 --> 02:09:38,040
Speaker 1: it's a really you know, they can make advantage of

2296
02:09:38,080 --> 02:09:40,040
Speaker 1: when it's a really lush summer or when it's a

2297
02:09:40,040 --> 02:09:43,160
Speaker 1: really harsh winter. They've got they have a hundred fifty

2298
02:09:43,200 --> 02:09:46,960
Speaker 1: miles of options to choose from versus you know, the

2299
02:09:47,000 --> 02:09:50,000
Speaker 1: three or four square miles at the resident animals has

2300
02:09:50,120 --> 02:09:53,400
Speaker 1: and I think and to me, that's sort of one

2301
02:09:53,400 --> 02:09:56,760
Speaker 1: of you know, that's one of the reasons to maintain migration.

2302
02:09:56,840 --> 02:09:58,960
Speaker 1: It's also a reason to maintain sort of these diverse

2303
02:09:59,560 --> 02:10:02,080
Speaker 1: stratag geez like you sort of alluded to it in

2304
02:10:02,160 --> 02:10:07,000
Speaker 1: your in your in your question. Yeah, yeah, um, but

2305
02:10:07,120 --> 02:10:10,240
Speaker 1: I know it's very interesting reading that assessment was how

2306
02:10:10,400 --> 02:10:15,000
Speaker 1: narrow like parts of that migration got is that, I mean,

2307
02:10:15,080 --> 02:10:19,960
Speaker 1: it obviously is partially of just like what it is today. Yeah,

2308
02:10:20,000 --> 02:10:22,480
Speaker 1: and you guys have just figured it out recently and

2309
02:10:22,560 --> 02:10:25,200
Speaker 1: are looking at it. Do you think historically it might

2310
02:10:25,240 --> 02:10:28,600
Speaker 1: have been much wider? Is there any evidence for that?

2311
02:10:28,760 --> 02:10:31,400
Speaker 1: I mean, is it just because there is it's parallels

2312
02:10:32,120 --> 02:10:35,240
Speaker 1: a river and a highway and that's where the most

2313
02:10:35,320 --> 02:10:38,760
Speaker 1: more development is or do you think even you know,

2314
02:10:39,520 --> 02:10:42,000
Speaker 1: years ago, they were crossing right there at the same

2315
02:10:42,040 --> 02:10:45,880
Speaker 1: spot where the outlet is of the lake. Yeah. I

2316
02:10:45,880 --> 02:10:48,480
Speaker 1: I suspect that that that one. It probably has been

2317
02:10:48,560 --> 02:10:52,680
Speaker 1: like that for a really long time. And the you know,

2318
02:10:52,720 --> 02:10:55,800
Speaker 1: we don't so that's you know, very common for mule

2319
02:10:55,840 --> 02:10:59,280
Speaker 1: here that they we see them following along the same path.

2320
02:10:59,440 --> 02:11:05,160
Speaker 1: And uh, you know, there's there's a there's a h

2321
02:11:05,280 --> 02:11:06,960
Speaker 1: a sort of a lot of interest right now to

2322
02:11:07,040 --> 02:11:09,800
Speaker 1: understand sort of the benefit of sort of collective you know,

2323
02:11:09,800 --> 02:11:14,720
Speaker 1: how animals move together and and you can imagine that

2324
02:11:14,720 --> 02:11:17,840
Speaker 1: that there's benefits in a migration like that of the

2325
02:11:17,880 --> 02:11:21,520
Speaker 1: animals following within each other's footsteps. I mean sometimes when

2326
02:11:21,520 --> 02:11:24,800
Speaker 1: they go through, um, they're going over a little passes

2327
02:11:24,840 --> 02:11:28,200
Speaker 1: that in the spring still might have snow in the fall.

2328
02:11:28,240 --> 02:11:31,040
Speaker 1: When they come down they're basically playing this game to

2329
02:11:31,480 --> 02:11:33,360
Speaker 1: stay you know, they want to stay up in the

2330
02:11:33,360 --> 02:11:35,920
Speaker 1: mountains as long as possible. And they're playing this game too.

2331
02:11:36,240 --> 02:11:39,520
Speaker 1: They don't just rush down to winter range. They make

2332
02:11:39,560 --> 02:11:42,960
Speaker 1: their way down with each little snowstorm, with each drop

2333
02:11:42,960 --> 02:11:45,480
Speaker 1: in temperature, and then and they're trying to avoid getting

2334
02:11:45,520 --> 02:11:49,840
Speaker 1: stuck behind a big snowstorm. But if you do get stuck, um,

2335
02:11:49,880 --> 02:11:52,360
Speaker 1: having a hundred or two hundred animals go through that

2336
02:11:52,400 --> 02:11:55,360
Speaker 1: spot before you on that day makes it a lot

2337
02:11:55,800 --> 02:12:02,600
Speaker 1: less energetically costly. Have you're observed a caribou migration? Uh?

2338
02:12:02,840 --> 02:12:06,800
Speaker 1: You know only on YouTube. What's interesting about it is

2339
02:12:06,840 --> 02:12:09,920
Speaker 1: that depending on where you go, you can go into

2340
02:12:09,960 --> 02:12:16,960
Speaker 1: places where they are walking through like they're walking through

2341
02:12:17,000 --> 02:12:19,280
Speaker 1: someone where they're somewhere where they've never walked through before.

2342
02:12:20,000 --> 02:12:22,280
Speaker 1: They had this like very big they necessary big macro

2343
02:12:22,400 --> 02:12:24,360
Speaker 1: sense of where they're moving. But they take different routes

2344
02:12:25,320 --> 02:12:27,200
Speaker 1: and in some years will be like that. There would

2345
02:12:27,200 --> 02:12:28,880
Speaker 1: be places where they hadn't gone through in a decade

2346
02:12:28,880 --> 02:12:31,040
Speaker 1: and then they go through the area. But you'll watch

2347
02:12:31,080 --> 02:12:34,440
Speaker 1: them and you wake up one day and they're all

2348
02:12:34,560 --> 02:12:39,400
Speaker 1: using a pass for whatever reason, like ones that they're

2349
02:12:39,440 --> 02:12:41,640
Speaker 1: spread out so far apart that you might watch four

2350
02:12:42,080 --> 02:12:43,360
Speaker 1: comes through through all the course of the day, and

2351
02:12:43,400 --> 02:12:46,480
Speaker 1: they really tend to like some little pass and the

2352
02:12:46,560 --> 02:12:49,600
Speaker 1: next day you wake up and the whole line seems

2353
02:12:49,600 --> 02:12:52,960
Speaker 1: to have kind of shifted a mile, and then in

2354
02:12:53,040 --> 02:12:56,440
Speaker 1: the bulk of them seems to be taking some other

2355
02:12:56,480 --> 02:13:00,200
Speaker 1: thing where they're just like going by smell, and they

2356
02:13:00,600 --> 02:13:02,520
Speaker 1: like to go where the other ones have gone. But

2357
02:13:02,600 --> 02:13:06,080
Speaker 1: it's not fixed year to year. It's just a general

2358
02:13:06,120 --> 02:13:08,760
Speaker 1: sense of must be the those ones made it through,

2359
02:13:08,840 --> 02:13:11,160
Speaker 1: nothing bad happened to them, so I want to go

2360
02:13:11,200 --> 02:13:14,160
Speaker 1: that way. But then it meanders yeah, you know, yeah,

2361
02:13:14,200 --> 02:13:16,640
Speaker 1: and that's you know, of course, a very different landscape,

2362
02:13:17,680 --> 02:13:22,440
Speaker 1: much broader, sort of less topographically diverse than the sort

2363
02:13:22,480 --> 02:13:25,200
Speaker 1: of mountains and plains landscapes that the Meal they are

2364
02:13:25,320 --> 02:13:28,520
Speaker 1: migrating through. I think one point that's a good one

2365
02:13:28,560 --> 02:13:32,800
Speaker 1: to your question, your honest is, historically I think the

2366
02:13:32,840 --> 02:13:36,640
Speaker 1: common knowledge was really focused on winter range for Meal

2367
02:13:36,720 --> 02:13:40,000
Speaker 1: there and maintaining that winter range. And one thing that's

2368
02:13:40,040 --> 02:13:43,520
Speaker 1: been discovered with this migration route mapping is all the

2369
02:13:43,560 --> 02:13:49,760
Speaker 1: attributes of that migration are important to long term species viability,

2370
02:13:50,400 --> 02:13:52,680
Speaker 1: one being the bottlenext which you were speaking to. But

2371
02:13:52,720 --> 02:13:55,400
Speaker 1: the other one are when you're looking at those migration routes,

2372
02:13:55,440 --> 02:13:58,400
Speaker 1: all of a sudden things kinda stop and slow down

2373
02:13:58,400 --> 02:14:01,520
Speaker 1: and they spread out, and that's called stop over areas.

2374
02:14:01,560 --> 02:14:03,520
Speaker 1: And if you one of you guys could talk about

2375
02:14:03,520 --> 02:14:05,880
Speaker 1: the importance of stop over areas and what we've learned

2376
02:14:05,920 --> 02:14:09,520
Speaker 1: about that as far as the importance especially for me

2377
02:14:09,560 --> 02:14:12,480
Speaker 1: older but also all on gillets, that would be I

2378
02:14:12,480 --> 02:14:16,400
Speaker 1: think it's interesting thing to add to that question. Sure, yeah,

2379
02:14:16,440 --> 02:14:19,960
Speaker 1: So just in thinking about how animals move across the landscape,

2380
02:14:19,960 --> 02:14:22,840
Speaker 1: and in particular in the spring, we think about a

2381
02:14:22,880 --> 02:14:25,880
Speaker 1: migratory route and whether it's twenty miles or it's a

2382
02:14:25,920 --> 02:14:28,440
Speaker 1: hundred and fifty miles or two d plus miles, we

2383
02:14:28,560 --> 02:14:30,760
Speaker 1: just think it's well, animals are just going from winter

2384
02:14:30,880 --> 02:14:34,120
Speaker 1: to summer range. But interestingly, the vast majority of the

2385
02:14:34,160 --> 02:14:38,800
Speaker 1: time that they're quote unquote migrating, they're actually not just

2386
02:14:39,040 --> 02:14:42,280
Speaker 1: moving and walking on a path. They're actually held up

2387
02:14:42,360 --> 02:14:46,040
Speaker 1: on what what we've called stopovers, which are areas where

2388
02:14:46,080 --> 02:14:49,720
Speaker 1: they're largely spending time feeding. And we know the attributes

2389
02:14:49,720 --> 02:14:54,320
Speaker 1: associated with those stop stopovers are also areas that helped

2390
02:14:54,320 --> 02:14:57,080
Speaker 1: facilitate feeding. So over the long term. If you if

2391
02:14:57,080 --> 02:15:00,240
Speaker 1: you take and look at the landscape and you get

2392
02:15:00,240 --> 02:15:03,360
Speaker 1: how green up, for example, occurs every year. The stopovers

2393
02:15:03,360 --> 02:15:06,240
Speaker 1: that these mule deer are using. Our places that tend

2394
02:15:06,280 --> 02:15:09,880
Speaker 1: to consistently green up early every year typically don't have

2395
02:15:09,960 --> 02:15:13,960
Speaker 1: the level of snow deposition, are largely dry south southwesterly slopes.

2396
02:15:14,000 --> 02:15:17,480
Speaker 1: So it's those sweetheart spots on their way where they

2397
02:15:17,480 --> 02:15:21,000
Speaker 1: can stop and grab that lush new food that's coming along,

2398
02:15:21,200 --> 02:15:25,160
Speaker 1: and then they pace their migration, especially in the spring,

2399
02:15:25,200 --> 02:15:27,920
Speaker 1: in correspondence with that new wave of food as it

2400
02:15:27,960 --> 02:15:31,040
Speaker 1: comes up progressively along the landscape. So if you imagine

2401
02:15:31,480 --> 02:15:35,920
Speaker 1: staying in one spot, experiencing spring, getting that really great food,

2402
02:15:36,360 --> 02:15:38,760
Speaker 1: and then just it's gone right if you stay in

2403
02:15:38,760 --> 02:15:42,000
Speaker 1: that one spot, but if you follow it, you experience

2404
02:15:42,080 --> 02:15:44,640
Speaker 1: spring for a really long time as you move across

2405
02:15:44,640 --> 02:15:47,760
Speaker 1: the landscape, thus simply enhancing your energetic game. But I

2406
02:15:47,760 --> 02:15:50,720
Speaker 1: think what's interesting is, as you're alluding to, is that

2407
02:15:50,720 --> 02:15:53,880
Speaker 1: that migratory route isn't just a path they walk on.

2408
02:15:54,240 --> 02:15:58,480
Speaker 1: It's functional habitat as well well, where they're gleaning resources.

2409
02:15:58,560 --> 02:16:01,800
Speaker 1: And the other element that I think is really important

2410
02:16:01,880 --> 02:16:05,040
Speaker 1: that maybe kind of gets lost and all the you know, my,

2411
02:16:05,800 --> 02:16:08,600
Speaker 1: the the excitement and the phenomena associated with migration and

2412
02:16:08,640 --> 02:16:12,520
Speaker 1: walking across the long landscapes is the other really important

2413
02:16:12,520 --> 02:16:15,840
Speaker 1: thing to comprehend is that by functionally moving across the

2414
02:16:15,920 --> 02:16:19,839
Speaker 1: landscape and going to a different place and thus garnering

2415
02:16:19,880 --> 02:16:25,400
Speaker 1: different resources, it's a it's functionally for the population increasing

2416
02:16:25,440 --> 02:16:29,720
Speaker 1: that population's carrying capacity. So for example, if you put

2417
02:16:29,760 --> 02:16:32,040
Speaker 1: in I eight something new new, I A d somewhere

2418
02:16:32,040 --> 02:16:34,720
Speaker 1: and you clip off a migratory route and you remove

2419
02:16:34,959 --> 02:16:38,240
Speaker 1: all that summer range that those animals were using that

2420
02:16:38,440 --> 02:16:41,480
Speaker 1: summer range because those animals were going there, we're walking

2421
02:16:41,480 --> 02:16:43,760
Speaker 1: there using that food for how many months out of

2422
02:16:43,760 --> 02:16:46,959
Speaker 1: the year. That's part of the capacity of their range.

2423
02:16:47,480 --> 02:16:50,400
Speaker 1: So for example, as as we if we've lost migratory

2424
02:16:50,480 --> 02:16:55,120
Speaker 1: routes in some places, we have potentially created vacant habitat

2425
02:16:55,240 --> 02:16:58,720
Speaker 1: as in the food is there, which ultimately determines carrying capacity,

2426
02:16:58,800 --> 02:17:01,600
Speaker 1: but nobody knows to go there and use it. Therefore,

2427
02:17:01,760 --> 02:17:04,720
Speaker 1: what we can potentially sustain as far as the population

2428
02:17:04,800 --> 02:17:09,400
Speaker 1: level thereafter, is not as many animals because they're functional

2429
02:17:09,760 --> 02:17:14,720
Speaker 1: carrying capacity, their food based has been diminished because behaviorally

2430
02:17:14,720 --> 02:17:16,720
Speaker 1: they're not making use of it anymore. And to me,

2431
02:17:16,840 --> 02:17:20,520
Speaker 1: from a migratory perspective, like that's ultimately where the rubber

2432
02:17:20,560 --> 02:17:23,480
Speaker 1: meets the road. That's why we can help maintain robust

2433
02:17:23,480 --> 02:17:28,720
Speaker 1: populations is because by moving and integrating this huge landscape

2434
02:17:29,200 --> 02:17:32,560
Speaker 1: with into into their behavior and into their nutritional dynamics,

2435
02:17:32,680 --> 02:17:36,000
Speaker 1: they're functionally increasing the carrying capacity for the population by

2436
02:17:36,040 --> 02:17:38,640
Speaker 1: them doing that. In the moment we clip that off,

2437
02:17:39,480 --> 02:17:41,920
Speaker 1: there's no way we can sustain as many animals because

2438
02:17:41,959 --> 02:17:44,000
Speaker 1: we don't have the food based because they're not going

2439
02:17:44,040 --> 02:17:47,400
Speaker 1: there and using it, Which is really why you can

2440
02:17:47,400 --> 02:17:50,720
Speaker 1: think about migration and it's connected and it's connecting it

2441
02:17:50,800 --> 02:17:55,720
Speaker 1: to large viable populations because it's the food resources that

2442
02:17:55,760 --> 02:17:59,760
Speaker 1: they're that they're garnering by going and moving there. You wonder,

2443
02:18:00,320 --> 02:18:02,400
Speaker 1: it's a funny thing about migration because it kind of

2444
02:18:02,400 --> 02:18:05,120
Speaker 1: almost sets in your head like the wrong idea about it.

2445
02:18:05,160 --> 02:18:09,080
Speaker 1: But we hunt turkeys in areas where you could basically

2446
02:18:09,120 --> 02:18:14,800
Speaker 1: say the turkey's migrate uphill, but in his head, he's

2447
02:18:14,800 --> 02:18:19,200
Speaker 1: probably he's chasing young growth and as he does that

2448
02:18:19,280 --> 02:18:20,920
Speaker 1: for six weeks, he winds up at the top of

2449
02:18:20,920 --> 02:18:24,120
Speaker 1: the mountain and then things frost off. And start to

2450
02:18:24,120 --> 02:18:27,200
Speaker 1: die and fin Angles is way back down and he

2451
02:18:27,240 --> 02:18:28,959
Speaker 1: probably never like was like I'm going to head up

2452
02:18:28,959 --> 02:18:30,760
Speaker 1: to the top of that mountain. He's just like every

2453
02:18:30,840 --> 02:18:34,240
Speaker 1: day I'm maximizing my thing, or just making small jumps

2454
02:18:34,240 --> 02:18:37,080
Speaker 1: to the next place. I know, without really having this

2455
02:18:37,920 --> 02:18:42,240
Speaker 1: idea that like tomorrow we leave for the far away place. Right,

2456
02:18:42,240 --> 02:18:48,000
Speaker 1: It's like, well, okay, but in in Meal Deer, they

2457
02:18:48,080 --> 02:18:52,440
Speaker 1: do like this is something that we have shown they have. Yes,

2458
02:18:52,480 --> 02:18:55,600
Speaker 1: maybe they're not thinking tomorrow. You know, we're we're headed

2459
02:18:55,840 --> 02:18:58,520
Speaker 1: on this hundred and fifty mile journey. But they have

2460
02:18:58,600 --> 02:19:02,800
Speaker 1: a mental map, yes, have a mental map, and and

2461
02:19:02,840 --> 02:19:05,920
Speaker 1: the way we've and we've done sort of simulations, so

2462
02:19:06,280 --> 02:19:09,800
Speaker 1: the describe the behavior you were just describing of them,

2463
02:19:09,840 --> 02:19:12,680
Speaker 1: of the turkey, you know, following that fresh green grass

2464
02:19:12,920 --> 02:19:17,080
Speaker 1: and eventually making a migration. Right, We've asked that question

2465
02:19:17,120 --> 02:19:20,039
Speaker 1: of mule Deer, like, if you have perfect knowledge of

2466
02:19:20,400 --> 02:19:22,400
Speaker 1: what we call it the green wave, and in fact

2467
02:19:22,400 --> 02:19:25,200
Speaker 1: we call it surfing the green wave, I don't really

2468
02:19:25,240 --> 02:19:32,080
Speaker 1: like that. It's just it. Yeah, it doesn't really matter

2469
02:19:32,080 --> 02:19:37,720
Speaker 1: if you like it or not. Already entrenched in the literature. Um,

2470
02:19:37,800 --> 02:19:43,920
Speaker 1: so it sounds a little too zippy, we're battle boarding

2471
02:19:43,920 --> 02:19:48,680
Speaker 1: the green wave again, this ship is already sailed. H

2472
02:19:50,560 --> 02:19:54,920
Speaker 1: So uh, mule deer can't recreate a d fifty mile

2473
02:19:55,000 --> 02:19:58,760
Speaker 1: migration even when they know exactly where the green wave is.

2474
02:19:59,800 --> 02:20:03,400
Speaker 1: They have to have memory of that of that migration

2475
02:20:03,560 --> 02:20:05,320
Speaker 1: in order to do it. And and and the way you

2476
02:20:05,320 --> 02:20:08,800
Speaker 1: can think about that is like, if you just imagine

2477
02:20:08,800 --> 02:20:11,520
Speaker 1: a mule deer and a hundred fifty mile landscape, even

2478
02:20:11,520 --> 02:20:14,120
Speaker 1: if they know sort of where it's the best place

2479
02:20:14,160 --> 02:20:17,520
Speaker 1: to be at the best time during spring, that doesn't

2480
02:20:17,560 --> 02:20:20,600
Speaker 1: get them to where, you know, to this hundred fifty

2481
02:20:20,600 --> 02:20:23,720
Speaker 1: mile migration. What gets them to a fifty mile migration

2482
02:20:24,360 --> 02:20:28,680
Speaker 1: is the trials and heirs of their ancestors, who for

2483
02:20:28,760 --> 02:20:33,000
Speaker 1: generations and generations have done this. And and at some

2484
02:20:33,160 --> 02:20:36,600
Speaker 1: point one animal figured, hey, if you go this way,

2485
02:20:36,959 --> 02:20:40,440
Speaker 1: it's awesome, and there's all this green grass, or maybe

2486
02:20:40,440 --> 02:20:42,520
Speaker 1: it's worked the other way. There was a harsh winter,

2487
02:20:42,600 --> 02:20:46,760
Speaker 1: and they pushed further down to the red desert and

2488
02:20:46,800 --> 02:20:49,800
Speaker 1: then that and then they discovered that they learned that

2489
02:20:49,920 --> 02:20:53,400
Speaker 1: migration and discovered like, this is a great tactic, and

2490
02:20:53,400 --> 02:20:55,879
Speaker 1: then of course passed that onto their young and did

2491
02:20:55,920 --> 02:20:59,800
Speaker 1: well and and now we have the memory that now

2492
02:20:59,840 --> 02:21:02,199
Speaker 1: that heard has the knowledge of that hundred and fifty

2493
02:21:02,200 --> 02:21:06,840
Speaker 1: mile migration. So that's been an active sort of area

2494
02:21:06,920 --> 02:21:09,440
Speaker 1: of research and we and we initially asked that question

2495
02:21:09,520 --> 02:21:12,280
Speaker 1: that you sort of posed with the turkey. Um. I

2496
02:21:12,320 --> 02:21:13,880
Speaker 1: don't think you put it this way, but you had

2497
02:21:13,920 --> 02:21:23,160
Speaker 1: asked if the if the turkey serfed and it's O man.

2498
02:21:23,200 --> 02:21:25,520
Speaker 1: I was going to talk about I'm not going to

2499
02:21:25,640 --> 02:21:27,920
Speaker 1: get into it because we're gonna move into our we're

2500
02:21:27,920 --> 02:21:31,640
Speaker 1: gonna move into our closers. But I was going to

2501
02:21:31,720 --> 02:21:37,320
Speaker 1: bring up the idea of human migrations. Okay, so humans

2502
02:21:37,440 --> 02:21:41,000
Speaker 1: coming into the Western hemisphere and imagine bring you like

2503
02:21:41,040 --> 02:21:47,960
Speaker 1: the bearing Land Bridge. The first clan of people did

2504
02:21:47,959 --> 02:21:50,040
Speaker 1: not have the luxury of saying, hey, we're gonna go

2505
02:21:50,080 --> 02:21:54,160
Speaker 1: over there because they'll be sweet. They probably were born,

2506
02:21:54,920 --> 02:22:00,640
Speaker 1: lived and died on the bearing Land Bridge, probably not

2507
02:22:00,800 --> 02:22:05,679
Speaker 1: very aware that they were, over the course of generations

2508
02:22:05,720 --> 02:22:10,320
Speaker 1: heading to Patagonia. But it was just and then they

2509
02:22:10,360 --> 02:22:14,320
Speaker 1: would come to glaciers as they traveled the coast, presumably,

2510
02:22:14,760 --> 02:22:16,520
Speaker 1: and you had no notion of what was on the

2511
02:22:16,560 --> 02:22:19,840
Speaker 1: other side of the glacier, and you weren't being driven

2512
02:22:19,840 --> 02:22:24,879
Speaker 1: by warfare. You weren't being driven by starvation, but you

2513
02:22:25,000 --> 02:22:27,920
Speaker 1: one day I was like, man, you know, I gotta

2514
02:22:28,040 --> 02:22:33,000
Speaker 1: check it out. And so there is that little I

2515
02:22:33,040 --> 02:22:34,840
Speaker 1: don't even know where the connection is, but there is

2516
02:22:34,879 --> 02:22:38,680
Speaker 1: that little sense of of pioneering. And so you have

2517
02:22:38,800 --> 02:22:43,000
Speaker 1: the idea that that um, that species now today would

2518
02:22:43,760 --> 02:22:45,680
Speaker 1: maybe come up with some cool new way of using

2519
02:22:45,720 --> 02:22:51,240
Speaker 1: the landscape. Yeah, yeah, absolutely, and she kind of yea yeah.

2520
02:22:51,240 --> 02:22:53,360
Speaker 1: And so what we're really so she's still alive. We

2521
02:22:53,480 --> 02:22:57,720
Speaker 1: just saw her the other day, and how she's old.

2522
02:22:57,800 --> 02:23:00,720
Speaker 1: Winner Winner has been tough, So that young exter Winner

2523
02:23:00,760 --> 02:23:02,520
Speaker 1: Winner has been tough. We sure hope that she can

2524
02:23:02,560 --> 02:23:04,360
Speaker 1: make it through the winter in that country. It's been

2525
02:23:04,680 --> 02:23:06,840
Speaker 1: it's been pretty hard on them. Um, they're they're in

2526
02:23:06,879 --> 02:23:09,960
Speaker 1: pretty rough shape. But we're super excited to see what

2527
02:23:10,040 --> 02:23:13,040
Speaker 1: she does. Is she not in this year? So that

2528
02:23:13,200 --> 02:23:16,920
Speaker 1: she's now she'll be two here in June. And interestingly,

2529
02:23:17,120 --> 02:23:19,720
Speaker 1: she's pregnant, so she bred is a yearling, which is

2530
02:23:19,760 --> 02:23:22,280
Speaker 1: really cool. It's not it doesn't always happen for mule deer,

2531
02:23:22,600 --> 02:23:25,160
Speaker 1: but so she's gonna give birth this year. Um for

2532
02:23:25,200 --> 02:23:27,880
Speaker 1: the first time. Mom is still alive. We saw her

2533
02:23:27,920 --> 02:23:30,680
Speaker 1: as well, um, and so we'll be really curious to

2534
02:23:30,720 --> 02:23:34,080
Speaker 1: see if she's gonna go and set up shopping your

2535
02:23:34,120 --> 02:23:37,440
Speaker 1: mom to give birth, or would she happen to go

2536
02:23:37,600 --> 02:23:41,400
Speaker 1: on that forty plus mile journey again, that's what she's gonna.

2537
02:23:41,520 --> 02:23:44,600
Speaker 1: She's gonna she kicks her phone out, fawn walks forty

2538
02:23:44,640 --> 02:23:46,680
Speaker 1: five and comes back again. Then you'll be honest something,

2539
02:23:46,760 --> 02:23:50,280
Speaker 1: yeah exactly, then you can publish a cool paper. So

2540
02:23:50,640 --> 02:23:52,960
Speaker 1: we're really excited to see what she's gonna do. Because

2541
02:23:53,160 --> 02:23:55,160
Speaker 1: by and large, what we know is that mule deer

2542
02:23:55,440 --> 02:23:57,879
Speaker 1: are not We don't think they're very good pioneers the

2543
02:23:57,920 --> 02:24:01,040
Speaker 1: evidence that we have, but we've been missing at early

2544
02:24:01,120 --> 02:24:03,600
Speaker 1: part of their life yet we really, we really have

2545
02:24:03,720 --> 02:24:06,280
Speaker 1: not explored that adequately yet. And so we're trying to

2546
02:24:06,280 --> 02:24:08,879
Speaker 1: do that now to see if is that their pioneering

2547
02:24:08,920 --> 02:24:11,480
Speaker 1: phase or is it really just this entrenched thing where

2548
02:24:11,520 --> 02:24:14,040
Speaker 1: they're just they're doing what mom is doing, uh, and

2549
02:24:14,120 --> 02:24:17,280
Speaker 1: gonna adopt that tactic. And then in reference to you

2550
02:24:17,320 --> 02:24:20,800
Speaker 1: know the notion earlier, it's not even it is learning,

2551
02:24:21,120 --> 02:24:25,360
Speaker 1: and it's learning what's viable, but it's also surviving and reproducing.

2552
02:24:25,480 --> 02:24:28,160
Speaker 1: If you happen to go do something and it turns

2553
02:24:28,160 --> 02:24:30,520
Speaker 1: out not to work. You're gone and so you don't

2554
02:24:30,560 --> 02:24:34,480
Speaker 1: have subsequent progeny that are gonna adopt that because because

2555
02:24:34,520 --> 02:24:37,480
Speaker 1: you're gone, bitch an idea, but you got dusted off,

2556
02:24:37,720 --> 02:24:39,880
Speaker 1: and then it's it's done. It's it's not gonna happen,

2557
02:24:39,879 --> 02:24:41,959
Speaker 1: and we're not going to see it in subsequent generation

2558
02:24:42,120 --> 02:24:44,800
Speaker 1: because because you're gone and you had no progeny to

2559
02:24:44,879 --> 02:24:48,240
Speaker 1: continue to adopt that adopt that tactic. So okay, we're

2560
02:24:48,240 --> 02:24:51,200
Speaker 1: gonna move into our concluders. A concluder is where you

2561
02:24:51,200 --> 02:24:54,720
Speaker 1: get to say whatever you want. Um, yeah, you want

2562
02:24:54,720 --> 02:24:58,480
Speaker 1: to start us off, I'm just gonna say thank you guys.

2563
02:24:58,560 --> 02:25:01,200
Speaker 1: You guys really just crushed it. Man, it's gonna be

2564
02:25:01,200 --> 02:25:04,840
Speaker 1: an awesome podcast. At this moment in my life, I'm

2565
02:25:04,840 --> 02:25:07,280
Speaker 1: like as interested in the science of mule here as

2566
02:25:07,320 --> 02:25:11,560
Speaker 1: I am killing big Bucks, which I don't think that.

2567
02:25:12,000 --> 02:25:14,080
Speaker 1: The whole time, I've been wondering if these guys would

2568
02:25:14,080 --> 02:25:16,960
Speaker 1: be good muleed your hunters or not. Oh gosh, I

2569
02:25:17,040 --> 02:25:20,760
Speaker 1: think they would be here. Yeah, I've been really wondering that. Well,

2570
02:25:20,800 --> 02:25:23,360
Speaker 1: that's what the guy told me, the guy that Vincent

2571
02:25:23,440 --> 02:25:27,240
Speaker 1: that gave me this copy of their migration assessment. He goes, uh,

2572
02:25:27,360 --> 02:25:28,720
Speaker 1: I don't even know if I should say this on

2573
02:25:28,959 --> 02:25:31,080
Speaker 1: remember Pat Dirk and talking about some of the cold

2574
02:25:31,160 --> 02:25:34,240
Speaker 1: blooded his killing his white tail hunters. You know, can't

2575
02:25:34,280 --> 02:25:35,760
Speaker 1: tell you what you're kind of tree they're sitting in.

2576
02:25:36,000 --> 02:25:41,039
Speaker 1: Yeah that's true. But no he said he was using

2577
02:25:41,040 --> 02:25:44,520
Speaker 1: this uh paper as a way to sort of figure

2578
02:25:44,520 --> 02:25:48,240
Speaker 1: out where he was going to go hunting. Good good luck.

2579
02:25:48,360 --> 02:25:52,760
Speaker 1: It doesn't work. It doesn't work. So yeah, that's my concluder,

2580
02:25:54,400 --> 02:25:57,080
Speaker 1: I think for me, and it speaks to your point. Honest.

2581
02:25:57,240 --> 02:25:58,560
Speaker 1: You know, I said, you get to talk about whatever

2582
02:25:58,560 --> 02:26:00,920
Speaker 1: you want, you're gonna delimit me. Yeah, can you make

2583
02:26:00,959 --> 02:26:03,160
Speaker 1: sure to talk about how people can support you and

2584
02:26:03,160 --> 02:26:06,440
Speaker 1: then that will support research. But I will, I will

2585
02:26:06,480 --> 02:26:10,879
Speaker 1: go into Meuly fanatics first, UM log Onto merely fanatics

2586
02:26:10,920 --> 02:26:14,160
Speaker 1: dot org. There's many ways to support us. We have

2587
02:26:14,200 --> 02:26:17,680
Speaker 1: a lot of raffles going on for commissioners, tags that

2588
02:26:17,800 --> 02:26:20,800
Speaker 1: go out to certain projects that we unfortunately didn't mention,

2589
02:26:20,959 --> 02:26:24,200
Speaker 1: but the Deer Alcohology Research Project was, which is a

2590
02:26:24,240 --> 02:26:28,800
Speaker 1: big project we're pushing UM that looks at all the

2591
02:26:28,879 --> 02:26:34,199
Speaker 1: multifaceted ways in which environments are influencing Meal deer nutritions specifically,

2592
02:26:34,600 --> 02:26:36,560
Speaker 1: and it's also taken a very hard look at the

2593
02:26:36,840 --> 02:26:40,480
Speaker 1: competition issues. So some of our raffle, our commissioner tag

2594
02:26:40,560 --> 02:26:43,959
Speaker 1: raffles are going towards that. We have one locally that

2595
02:26:44,080 --> 02:26:46,520
Speaker 1: we haven't allocated directly to a project, but it will

2596
02:26:46,560 --> 02:26:50,080
Speaker 1: go to our main three core mission areas UH, so

2597
02:26:50,120 --> 02:26:53,080
Speaker 1: they can get onto meuly fanatics dot org and support us.

2598
02:26:53,160 --> 02:26:56,320
Speaker 1: I think another one that I should mention that anybody

2599
02:26:56,320 --> 02:27:00,440
Speaker 1: in Wyoming can do to support migrations is by the

2600
02:27:00,600 --> 02:27:04,760
Speaker 1: Wylming Wildlife Migration UH Conservation license plate, which is a

2601
02:27:04,760 --> 02:27:08,760
Speaker 1: new license plate that we have in Wyoming where the

2602
02:27:09,400 --> 02:27:14,480
Speaker 1: purchaser pays eighty dollars extra on top of their usual

2603
02:27:14,560 --> 02:27:18,960
Speaker 1: licensing fees. Hundred fifty of that goes into a pot

2604
02:27:19,520 --> 02:27:22,279
Speaker 1: that will then be doled out to reduce and alleviate

2605
02:27:22,360 --> 02:27:24,600
Speaker 1: migration problems such as the I A DY one that

2606
02:27:24,680 --> 02:27:26,840
Speaker 1: you mentioned. So there's a lot of ways to not

2607
02:27:26,959 --> 02:27:31,080
Speaker 1: only support the organization but support migration also. The Migration

2608
02:27:31,080 --> 02:27:35,160
Speaker 1: Initiative has fundraisers, we do some sometimes we sell some

2609
02:27:35,160 --> 02:27:38,800
Speaker 1: commissioners tags or raffle commissioner tags to support them. So

2610
02:27:38,840 --> 02:27:41,840
Speaker 1: a lot of ways to get involved, not just coming

2611
02:27:41,840 --> 02:27:44,000
Speaker 1: to a banquet, which we're gonna have a lot of

2612
02:27:44,000 --> 02:27:46,760
Speaker 1: fun at tonight. Uh, the other way, and it's one

2613
02:27:46,800 --> 02:27:49,240
Speaker 1: that I want to speak to. That's a very important

2614
02:27:49,280 --> 02:27:52,600
Speaker 1: part and and and has been extremely rewarding for me

2615
02:27:53,400 --> 02:27:57,720
Speaker 1: is getting involved in starting a chapter locally. I get

2616
02:27:57,760 --> 02:28:00,640
Speaker 1: to meet these guys, I get to have my horizons

2617
02:28:00,680 --> 02:28:04,720
Speaker 1: expanded beyond just thinking big buck killers are cool. But

2618
02:28:04,920 --> 02:28:07,360
Speaker 1: really the biology is that where it's at and and

2619
02:28:07,400 --> 02:28:11,000
Speaker 1: that's the cool part. And that's really our responsibility within

2620
02:28:11,040 --> 02:28:15,080
Speaker 1: the North American Conservation models as hunters is to actually

2621
02:28:15,080 --> 02:28:17,760
Speaker 1: take time to learn some of the science before we

2622
02:28:17,760 --> 02:28:21,200
Speaker 1: we advocate and and participate in that role. But but

2623
02:28:21,280 --> 02:28:23,680
Speaker 1: there's just a lot of ways where people can get involved.

2624
02:28:23,720 --> 02:28:26,320
Speaker 1: And starting a chapter would be a great one. Where

2625
02:28:26,680 --> 02:28:31,320
Speaker 1: we have fourteen chapters across Wyoming, Colorado, Idaho, and Utah

2626
02:28:31,680 --> 02:28:35,200
Speaker 1: and always our our guys in in the headquarters in

2627
02:28:35,200 --> 02:28:37,320
Speaker 1: Green River are always willing to support a new chapter

2628
02:28:37,360 --> 02:28:41,520
Speaker 1: and it's it is a rewarding endeavor. UM. Just an example,

2629
02:28:41,560 --> 02:28:43,480
Speaker 1: and I'll just go for no reason, couldn't be one

2630
02:28:43,480 --> 02:28:47,600
Speaker 1: in New Mexico, Absolutely not UM. And I'll just close

2631
02:28:47,640 --> 02:28:51,720
Speaker 1: with with what I find extremely rewarding is our chapter

2632
02:28:52,080 --> 02:28:54,600
Speaker 1: has gone through four funding cycles and we put nine

2633
02:28:54,840 --> 02:29:00,560
Speaker 1: grand back to Mulder specifically in the local area. I

2634
02:29:00,640 --> 02:29:03,959
Speaker 1: could have never done that by myself. So it's extremely

2635
02:29:04,000 --> 02:29:07,480
Speaker 1: rewarding to be able to as we work with the researchers,

2636
02:29:07,600 --> 02:29:10,560
Speaker 1: identify needs and put money back to those needs and

2637
02:29:10,920 --> 02:29:17,720
Speaker 1: be part of the solution. Great. I kind of feel

2638
02:29:17,800 --> 02:29:21,560
Speaker 1: like we forgot to talk about conservation. No I tried to.

2639
02:29:21,600 --> 02:29:25,120
Speaker 1: You didn't want to. I didn't because you're a scientist.

2640
02:29:27,160 --> 02:29:32,160
Speaker 1: Oh maybe, okay, maybe I maybe I missed your cues. Um. Well,

2641
02:29:32,240 --> 02:29:36,800
Speaker 1: so I I want to mention some of the conservation efforts,

2642
02:29:37,280 --> 02:29:39,760
Speaker 1: um before we end here. I mean we' we've talked

2643
02:29:39,760 --> 02:29:43,360
Speaker 1: a lot about different things and uh, and we talked

2644
02:29:43,360 --> 02:29:46,840
Speaker 1: a lot about migration. I think, um, what we've seen,

2645
02:29:48,040 --> 02:29:50,880
Speaker 1: you know, we we sort of started out talking about,

2646
02:29:51,160 --> 02:29:53,640
Speaker 1: you know, why are there so many migrations in Wyoming,

2647
02:29:53,720 --> 02:29:58,360
Speaker 1: right and U And one of those reasons is that

2648
02:29:58,600 --> 02:30:01,080
Speaker 1: is that you know Whyoming still a small state. But

2649
02:30:01,080 --> 02:30:05,080
Speaker 1: but it's changing and we're seeing you know, increased energy development.

2650
02:30:05,080 --> 02:30:09,440
Speaker 1: We're seeing growth of towns that literally are growing to

2651
02:30:09,879 --> 02:30:14,680
Speaker 1: sort of spill over into migration corridors. Um, and we're

2652
02:30:14,720 --> 02:30:18,080
Speaker 1: just starting to map those things. And I think, uh,

2653
02:30:18,120 --> 02:30:20,600
Speaker 1: one of the things, you know, for a variety of reasons,

2654
02:30:20,600 --> 02:30:23,039
Speaker 1: Wyoming has sort of been at the forefront of this

2655
02:30:23,720 --> 02:30:27,440
Speaker 1: and we've we've not one of the most exciting things

2656
02:30:27,480 --> 02:30:30,520
Speaker 1: about I think this whole sort of area of research

2657
02:30:31,320 --> 02:30:36,400
Speaker 1: is that we've now sort of proven up that we

2658
02:30:36,600 --> 02:30:40,080
Speaker 1: can maintain these migrations. Right. So, there have been examples

2659
02:30:40,080 --> 02:30:42,480
Speaker 1: in Wyoming of we talked a bit about the underpasses

2660
02:30:42,520 --> 02:30:46,640
Speaker 1: and overpasses those have been really success successful, have reduced

2661
02:30:46,840 --> 02:30:50,760
Speaker 1: mortality road mortality by eight to nine in some of

2662
02:30:50,800 --> 02:30:56,120
Speaker 1: these In some of these bottlenecks, there's h fencing projects,

2663
02:30:56,560 --> 02:30:58,800
Speaker 1: especially in the western part of the state, that are

2664
02:30:58,800 --> 02:31:03,440
Speaker 1: now being guided by by the science by where where

2665
02:31:03,480 --> 02:31:07,240
Speaker 1: the migration corridors are so limited resources, but if you

2666
02:31:07,240 --> 02:31:09,680
Speaker 1: know where the corridors are, you can focus your attention

2667
02:31:09,720 --> 02:31:12,520
Speaker 1: on modifying or moving fences that are within the cord

2668
02:31:12,600 --> 02:31:16,320
Speaker 1: or um. We're involved in a project with the Nature

2669
02:31:16,360 --> 02:31:20,920
Speaker 1: Conservancy and other land trusts that are bringing ten million

2670
02:31:20,959 --> 02:31:26,279
Speaker 1: dollars to conserve big private ranches in the Greater Yellowstone ecosystem,

2671
02:31:26,280 --> 02:31:30,960
Speaker 1: with the Wyoming portion that exclusively our ranches that fall

2672
02:31:31,040 --> 02:31:36,199
Speaker 1: within migration corridors, mapped migration corridors, and you know, those

2673
02:31:36,240 --> 02:31:40,080
Speaker 1: are the places that it's where it's most important that

2674
02:31:40,160 --> 02:31:46,600
Speaker 1: we limit, you know, residential development. Um that bottleneck that

2675
02:31:46,680 --> 02:31:50,680
Speaker 1: you mentioned, the Conservation Fund raised two million dollars after

2676
02:31:50,720 --> 02:31:54,160
Speaker 1: the after that magazine was put out and we identified

2677
02:31:54,160 --> 02:31:56,920
Speaker 1: the cord or the conservation that we we listed that

2678
02:31:57,000 --> 02:32:00,440
Speaker 1: as the number one risk for that herd forty five thousand,

2679
02:32:00,959 --> 02:32:04,520
Speaker 1: uh meal there squeezing through a quarter mile bottleneck between

2680
02:32:04,560 --> 02:32:08,000
Speaker 1: the town of Pinedale and Fremont, like this deep glacial

2681
02:32:08,080 --> 02:32:13,039
Speaker 1: lake complicated by a parcel of private land with a

2682
02:32:13,280 --> 02:32:17,480
Speaker 1: eight foot high woven wire elk fence on it. Yeah. Yeah,

2683
02:32:17,560 --> 02:32:19,600
Speaker 1: I didn't know. I had no idea until reading that

2684
02:32:19,720 --> 02:32:23,040
Speaker 1: about how many of these eight foot high fences Wyoming

2685
02:32:23,120 --> 02:32:25,520
Speaker 1: has to keep I'm getting I think, I don't know

2686
02:32:25,520 --> 02:32:27,760
Speaker 1: if it didn't explain that well, but from I get

2687
02:32:27,760 --> 02:32:29,840
Speaker 1: from it is just to keep elk off of agg

2688
02:32:30,600 --> 02:32:33,560
Speaker 1: and off of hay bales and whatnot. Is that and

2689
02:32:33,640 --> 02:32:35,920
Speaker 1: kind of funnel them into the feed grounds that they

2690
02:32:35,920 --> 02:32:39,440
Speaker 1: have established on that side of the state. Yeah. Yeah,

2691
02:32:39,440 --> 02:32:43,120
Speaker 1: so in that case, you have, yeah, the eight foot

2692
02:32:43,200 --> 02:32:46,920
Speaker 1: high fences there to keep elk where they're supplementally fed

2693
02:32:46,959 --> 02:32:50,039
Speaker 1: from spill on the forest from spilling down into private land.

2694
02:32:50,600 --> 02:32:54,200
Speaker 1: But then that then four to five thousand meal there

2695
02:32:54,760 --> 02:32:58,840
Speaker 1: migrating a hundred fifty miles have to navigate that fence

2696
02:32:58,879 --> 02:33:02,879
Speaker 1: as well. And uh. Anyways, that the identification of that

2697
02:33:02,920 --> 02:33:07,400
Speaker 1: bottleneck led to the Conservation Fund raising two million dollars

2698
02:33:07,400 --> 02:33:13,160
Speaker 1: to buy that plot of private uh land that was

2699
02:33:13,200 --> 02:33:17,280
Speaker 1: slated for lakeside development and turn it into a wildlife

2700
02:33:17,280 --> 02:33:21,720
Speaker 1: habitat management area, take down the fence, basically uncork the

2701
02:33:21,760 --> 02:33:28,720
Speaker 1: bottleneck and uh and now it's it exists in perpetuity. Yep. Yeah,

2702
02:33:28,720 --> 02:33:31,800
Speaker 1: the the land was was on the market. Yeah, that's

2703
02:33:31,840 --> 02:33:36,960
Speaker 1: great man. Yeah. So so you know, for me, um, like,

2704
02:33:37,760 --> 02:33:42,520
Speaker 1: we're sort of in a unique, uh a unique time

2705
02:33:42,760 --> 02:33:45,720
Speaker 1: in sort of the history of wildlife conservation in the

2706
02:33:45,720 --> 02:33:50,040
Speaker 1: American West, because this isn't this isn't a thorny problem

2707
02:33:50,080 --> 02:33:56,120
Speaker 1: like climate change. This is a relatively simple problem. We

2708
02:33:56,200 --> 02:33:59,080
Speaker 1: know how to map migration corridors, we know how to

2709
02:33:59,120 --> 02:34:04,440
Speaker 1: identify through and we know how to implement solutions. It's

2710
02:34:04,800 --> 02:34:06,720
Speaker 1: it's just a matter of you know, having this sort

2711
02:34:06,720 --> 02:34:10,720
Speaker 1: of political will and and conservation attention to getting it done.

2712
02:34:10,840 --> 02:34:14,520
Speaker 1: So so to me, that's sort of like that's happening

2713
02:34:14,520 --> 02:34:18,120
Speaker 1: in Wyoming and starting to happen in other parts of

2714
02:34:18,200 --> 02:34:21,120
Speaker 1: the West, and it's sort of a great example of

2715
02:34:21,160 --> 02:34:25,680
Speaker 1: sort of science based conservation. You guys have the coolest

2716
02:34:25,720 --> 02:34:30,800
Speaker 1: state in the lower forty eight. We like to think, well,

2717
02:34:31,080 --> 02:34:32,920
Speaker 1: I think it is. And I often tell people, if

2718
02:34:32,920 --> 02:34:35,080
Speaker 1: you want to understand wildlife in America, all you have

2719
02:34:35,120 --> 02:34:39,560
Speaker 1: to do is watch Wyoming. Like every major wildlife issue

2720
02:34:39,600 --> 02:34:45,520
Speaker 1: and from yes, a issues, migration is like like a

2721
02:34:46,640 --> 02:34:50,320
Speaker 1: it's a case study where you can look at energy

2722
02:34:50,440 --> 02:34:52,920
Speaker 1: like everything. Yeah, and a lot of it's still sort

2723
02:34:52,959 --> 02:34:56,800
Speaker 1: of functioning the way it used to um because because

2724
02:34:56,800 --> 02:34:59,480
Speaker 1: we have I mean, and it's not that Wyoming has

2725
02:34:59,520 --> 02:35:05,160
Speaker 1: done you know, has has been way advanced in its

2726
02:35:05,160 --> 02:35:08,520
Speaker 1: wildlife conservation or management. I think we've gotten a little

2727
02:35:08,520 --> 02:35:10,440
Speaker 1: bit of a free pass because there are so few

2728
02:35:10,480 --> 02:35:14,600
Speaker 1: people in Wyoming, half a million people in the entire state.

2729
02:35:14,800 --> 02:35:18,000
Speaker 1: That's the size of you know, most many metropolitan areas.

2730
02:35:19,560 --> 02:35:23,240
Speaker 1: It doesn't hurt. Okay, you can let your concluder rip.

2731
02:35:23,320 --> 02:35:26,560
Speaker 1: Now my concluder, Okay, I've just been waiting patiently. You

2732
02:35:26,640 --> 02:35:29,680
Speaker 1: got a good one. No, but we'll see so well.

2733
02:35:29,720 --> 02:35:31,680
Speaker 1: First off, I just want to say thanks guys. This

2734
02:35:31,720 --> 02:35:35,840
Speaker 1: has been great. Appreciate you guys taking the time allowing

2735
02:35:35,920 --> 02:35:38,280
Speaker 1: us to visit for a bit. And also I think

2736
02:35:38,840 --> 02:35:41,240
Speaker 1: I just wanna as well be able to say that,

2737
02:35:41,520 --> 02:35:43,240
Speaker 1: you know, we get to be here talking about some

2738
02:35:43,280 --> 02:35:46,400
Speaker 1: of the things that we've been doing, that we've been learning,

2739
02:35:46,440 --> 02:35:48,600
Speaker 1: some of the science in our professional opinions and those

2740
02:35:48,600 --> 02:35:51,440
Speaker 1: sorts of things. But at the same time, UM, there's

2741
02:35:51,440 --> 02:35:54,400
Speaker 1: no way that we would be here without UM the network,

2742
02:35:54,480 --> 02:35:56,840
Speaker 1: the partners UM that have ultimately made a lot of

2743
02:35:56,840 --> 02:35:59,840
Speaker 1: the work that we've been doing possible, like Jared's greup

2744
02:35:59,840 --> 02:36:03,519
Speaker 1: in really Fanatic Foundation and all the other various nonprofits

2745
02:36:03,520 --> 02:36:06,000
Speaker 1: and agency folks that are willing to UM see the

2746
02:36:06,080 --> 02:36:08,480
Speaker 1: value and research and allow us to go out there

2747
02:36:08,520 --> 02:36:11,000
Speaker 1: and do our best to help learn what what makes

2748
02:36:11,000 --> 02:36:14,080
Speaker 1: these animals tick, which to me is really important. I

2749
02:36:14,080 --> 02:36:16,280
Speaker 1: feel I'm very humbled and I feel very fortunate to

2750
02:36:16,320 --> 02:36:18,000
Speaker 1: be in the position that I'm in to be able

2751
02:36:18,040 --> 02:36:21,119
Speaker 1: to do that. But I'm fully aware that without everybody

2752
02:36:21,160 --> 02:36:24,600
Speaker 1: else that UM are maybe seemingly behind the scenes, but

2753
02:36:24,640 --> 02:36:27,120
Speaker 1: I don't really want them to be. UM. We we

2754
02:36:27,160 --> 02:36:29,560
Speaker 1: wouldn't be here having these conversations, and I feel very

2755
02:36:29,600 --> 02:36:32,440
Speaker 1: fortunate to be able to UM, to be able to

2756
02:36:32,480 --> 02:36:34,120
Speaker 1: do that. So I just want to thank all those

2757
02:36:34,160 --> 02:36:36,760
Speaker 1: that are out there that have have contributed in those

2758
02:36:36,800 --> 02:36:40,720
Speaker 1: ways and see the value and research UM and to

2759
02:36:40,879 --> 02:36:44,120
Speaker 1: channel Josh Corsi, President Meally Fanatic Foundation a little bit

2760
02:36:44,160 --> 02:36:45,840
Speaker 1: and he always says that we're only as good as

2761
02:36:45,879 --> 02:36:48,840
Speaker 1: the information that we have, and arguably, then as a

2762
02:36:48,879 --> 02:36:51,280
Speaker 1: consequence of that, the decisions we make are only as

2763
02:36:51,280 --> 02:36:53,120
Speaker 1: good as the information we have, which I think is

2764
02:36:53,560 --> 02:36:55,720
Speaker 1: a really powerful way to think about that, and hopefully

2765
02:36:55,760 --> 02:36:58,000
Speaker 1: we're getting there one step at a time of getting

2766
02:36:58,000 --> 02:37:01,200
Speaker 1: more of that information UM. But then as far as

2767
02:37:01,240 --> 02:37:04,640
Speaker 1: just other other thoughts, I think for me and in

2768
02:37:04,680 --> 02:37:08,840
Speaker 1: my career and in UM learning the things that that

2769
02:37:08,959 --> 02:37:11,800
Speaker 1: we've been able to learn, as well as just the

2770
02:37:11,879 --> 02:37:15,560
Speaker 1: perspectives that are out there, I think interestingly, and in

2771
02:37:15,600 --> 02:37:18,520
Speaker 1: the hunting industry too, we've our culture is changing. It

2772
02:37:18,520 --> 02:37:21,920
Speaker 1: seems like it's changing a lot. UM we've in in

2773
02:37:21,920 --> 02:37:25,199
Speaker 1: in weird ways sometimes too, I think UM we've characterized

2774
02:37:25,200 --> 02:37:28,080
Speaker 1: it as it becoming progressively more of a hornographic culture.

2775
02:37:28,400 --> 02:37:31,680
Speaker 1: It's been focused on the head gear as opposed to

2776
02:37:32,320 --> 02:37:36,240
Speaker 1: um and and maybe at sometimes in some instances losing

2777
02:37:36,280 --> 02:37:41,120
Speaker 1: touch a little bit with our true like naturalist hunting heritage,

2778
02:37:41,160 --> 02:37:45,440
Speaker 1: appreciating the outdoors and the open spaces for what they are. Um.

2779
02:37:45,480 --> 02:37:48,160
Speaker 1: And I mean, I love to kill a big deer

2780
02:37:48,680 --> 02:37:50,760
Speaker 1: or you know, a big elk or whatever, just as

2781
02:37:50,879 --> 02:37:54,320
Speaker 1: much as anybody. But I think sometimes it's, um, we've

2782
02:37:54,360 --> 02:37:57,000
Speaker 1: gone so far in that direction. We've just come by

2783
02:37:57,240 --> 02:38:00,840
Speaker 1: become so myopically focused on what's on the head and

2784
02:38:00,920 --> 02:38:03,080
Speaker 1: kind of lost the big picture as to what's behind

2785
02:38:03,080 --> 02:38:05,840
Speaker 1: the scenes that's even allowing that animal to exist in

2786
02:38:05,840 --> 02:38:09,800
Speaker 1: in that landscape. Uh and and just kind of um

2787
02:38:09,800 --> 02:38:13,360
Speaker 1: creating a culture. Perhaps that's losing touch a little bit. Um.

2788
02:38:13,400 --> 02:38:16,360
Speaker 1: And then along those same lines, as we've become I

2789
02:38:16,400 --> 02:38:19,680
Speaker 1: think sometimes myopically focused on that, it's also caused us

2790
02:38:19,680 --> 02:38:22,840
Speaker 1: to focus on genetics, which for me, unfortunately I'm I

2791
02:38:22,840 --> 02:38:27,200
Speaker 1: am the nutritional ecologist, but I think so so I'm

2792
02:38:27,200 --> 02:38:29,720
Speaker 1: perhaps biased, but I also know the realities from the

2793
02:38:29,760 --> 02:38:32,240
Speaker 1: work that we've done. And it's like, if we're gonna

2794
02:38:32,280 --> 02:38:35,440
Speaker 1: if we're just going to focus on genetics, like there's

2795
02:38:35,480 --> 02:38:39,199
Speaker 1: few links to the population itself, there's few links to reality,

2796
02:38:39,280 --> 02:38:41,560
Speaker 1: there's a few things that we can actually even do.

2797
02:38:42,200 --> 02:38:43,920
Speaker 1: And so if that's all that we can think about,

2798
02:38:44,040 --> 02:38:48,119
Speaker 1: is that you know that that individual had some fantastic genetics.

2799
02:38:48,160 --> 02:38:50,760
Speaker 1: I mean, even when the um the new world record

2800
02:38:50,800 --> 02:38:53,240
Speaker 1: big horn was found on wild Horse Island, there's a

2801
02:38:53,280 --> 02:38:56,040
Speaker 1: flurry of who sweet genetics coming from this country, and

2802
02:38:56,040 --> 02:38:58,680
Speaker 1: all I think is, man, No, it's an it's an

2803
02:38:58,720 --> 02:39:02,119
Speaker 1: island system, food phenomenal from out of the gate today one.

2804
02:39:02,160 --> 02:39:03,879
Speaker 1: I mean, if it's just genetics, and why didn't we

2805
02:39:03,879 --> 02:39:06,240
Speaker 1: produce a world record the first first year or two,

2806
02:39:06,800 --> 02:39:08,840
Speaker 1: you know, within the first decade that those animals were

2807
02:39:08,840 --> 02:39:12,360
Speaker 1: on that island. I'm virtually certain it's it's nutrition. I mean,

2808
02:39:12,400 --> 02:39:14,840
Speaker 1: sure the genetics needed to be there, but it's not

2809
02:39:14,879 --> 02:39:17,120
Speaker 1: just genetics that made that animal so huge. And so

2810
02:39:17,200 --> 02:39:20,800
Speaker 1: to me, I think that if there's a slight shift

2811
02:39:20,959 --> 02:39:25,600
Speaker 1: in angle to acknowledging for me, like what I've often

2812
02:39:25,640 --> 02:39:28,879
Speaker 1: said is I will have made an impact in my

2813
02:39:29,040 --> 02:39:34,400
Speaker 1: career broadly, just just beyond science. If someday our our

2814
02:39:34,600 --> 02:39:38,039
Speaker 1: our culture or hunting culture. When you know, somebody kills

2815
02:39:38,040 --> 02:39:40,760
Speaker 1: a giant mule deer out of the Wyoming range, that

2816
02:39:40,879 --> 02:39:43,600
Speaker 1: rather than having to see the articles that are referencing, Oh,

2817
02:39:43,640 --> 02:39:46,680
Speaker 1: it's some impressive genetics out of the wyoming range, impressive

2818
02:39:46,720 --> 02:39:50,000
Speaker 1: migration corridors well, or like that dude must have had

2819
02:39:50,040 --> 02:39:53,200
Speaker 1: an incredibly fat mom like that, that's it, you know,

2820
02:39:53,720 --> 02:39:57,280
Speaker 1: let's let's focus on fat moms. I mean seriously, like

2821
02:39:57,600 --> 02:40:00,160
Speaker 1: so to me too, because if if we're if we

2822
02:40:00,200 --> 02:40:03,040
Speaker 1: succeed in doing that, and it just taking the shift,

2823
02:40:03,080 --> 02:40:05,960
Speaker 1: like we can appreciate those things. But if we take

2824
02:40:06,000 --> 02:40:09,119
Speaker 1: the shift, the shift goes away from just the genetics

2825
02:40:09,120 --> 02:40:11,199
Speaker 1: and the myopic focus on the headgear, and it shifts

2826
02:40:11,200 --> 02:40:13,000
Speaker 1: it to the ground. It shifts us to the food,

2827
02:40:13,040 --> 02:40:17,000
Speaker 1: to the habitat, to nutrition, which is ultimately that building block,

2828
02:40:17,040 --> 02:40:20,080
Speaker 1: and I think can help get us places in a

2829
02:40:20,200 --> 02:40:23,000
Speaker 1: much quicker manner. And by appreciating those sorts of things

2830
02:40:23,040 --> 02:40:26,800
Speaker 1: from from that bottom up, that habitat driven perspective, because

2831
02:40:26,800 --> 02:40:30,360
Speaker 1: we're gonna make more conservation advances if we if we

2832
02:40:30,400 --> 02:40:33,240
Speaker 1: are thinking about that from the from the migratory routes

2833
02:40:33,280 --> 02:40:35,720
Speaker 1: to the value of the summer range to making sure

2834
02:40:35,760 --> 02:40:39,000
Speaker 1: we can maintain solid stage brush and pristine winter range,

2835
02:40:39,040 --> 02:40:43,160
Speaker 1: and we can acknowledge like whether climate changes affects the

2836
02:40:43,200 --> 02:40:45,440
Speaker 1: food and thus feeds into the population, or we have

2837
02:40:45,520 --> 02:40:49,320
Speaker 1: a bad winter and animals are burning fat reserves to survive, Like, well,

2838
02:40:49,560 --> 02:40:52,840
Speaker 1: we'll have a greater appreciation understanding for what's there and

2839
02:40:52,879 --> 02:40:56,240
Speaker 1: I think can have a better conversation at the table

2840
02:40:56,320 --> 02:40:58,600
Speaker 1: that that leads to more advances over the long term

2841
02:40:58,640 --> 02:41:00,640
Speaker 1: if if we're able to succeed eat in doing that.

2842
02:41:00,840 --> 02:41:04,920
Speaker 1: So it's all about fat moms. I think that horniography,

2843
02:41:05,120 --> 02:41:08,480
Speaker 1: or like the thirst for big giant bucks, um, does

2844
02:41:08,600 --> 02:41:14,520
Speaker 1: lead some people in down the path of being curious

2845
02:41:14,520 --> 02:41:20,120
Speaker 1: about ecology and being interested in these things, which is

2846
02:41:20,400 --> 02:41:22,600
Speaker 1: it's not the most direct path, but I have seen

2847
02:41:22,680 --> 02:41:29,360
Speaker 1: it can it can get there. I've seen that happen

2848
02:41:29,360 --> 02:41:35,080
Speaker 1: a lot. My final question, Okay, crystal ball game, right, Um,

2849
02:41:35,120 --> 02:41:38,480
Speaker 1: an hundred years, Like this is not a this is

2850
02:41:38,760 --> 02:41:42,240
Speaker 1: this is just speculation from you in just for you

2851
02:41:42,240 --> 02:41:45,120
Speaker 1: guys to think about, like in a hundred years, Like,

2852
02:41:45,200 --> 02:41:47,560
Speaker 1: do you think it's a given that things will be

2853
02:41:47,600 --> 02:41:51,520
Speaker 1: a lot worse in a hundred years for just for

2854
02:41:51,640 --> 02:41:53,800
Speaker 1: mulders specifically, do you think it's a given that there'll

2855
02:41:53,840 --> 02:41:55,840
Speaker 1: be a lot worse or do you think that in

2856
02:41:55,879 --> 02:42:02,520
Speaker 1: a hundred years, Um, that's too far years and fifty

2857
02:42:02,560 --> 02:42:05,440
Speaker 1: years will invariably be that, like things are just shitty

2858
02:42:05,680 --> 02:42:07,720
Speaker 1: or do you think in fifty years it could be like, Wow, man,

2859
02:42:08,160 --> 02:42:15,360
Speaker 1: mule deer kicking ass h. I mean, I hate to

2860
02:42:15,400 --> 02:42:20,959
Speaker 1: say it, but I two thousand and sixty nine, I

2861
02:42:21,000 --> 02:42:23,560
Speaker 1: think it's going to be worse. I think, I mean,

2862
02:42:24,000 --> 02:42:33,039
Speaker 1: we are we I mean, we we scrape away, cut up,

2863
02:42:35,200 --> 02:42:40,160
Speaker 1: erect barriers over you know, mule deer habitat every day, right,

2864
02:42:40,400 --> 02:42:44,879
Speaker 1: We're just we're on the slippery slope and were we're

2865
02:42:44,879 --> 02:42:49,040
Speaker 1: continuing to make the lives of meal deer and especially

2866
02:42:49,160 --> 02:42:54,680
Speaker 1: migratory meal deer harder, and so you know, for me,

2867
02:42:54,959 --> 02:42:57,560
Speaker 1: I mean that's what sort of motivates my research, is

2868
02:42:57,600 --> 02:43:00,959
Speaker 1: that is that I think we're to me, it's it's

2869
02:43:01,080 --> 02:43:04,359
Speaker 1: stopped bleeding. Yeah, like we're we're heading in that direction.

2870
02:43:04,520 --> 02:43:07,199
Speaker 1: And I don't know, you know what I was thinking

2871
02:43:07,200 --> 02:43:11,440
Speaker 1: about this the other day, I wonder if we like it.

2872
02:43:12,200 --> 02:43:18,200
Speaker 1: I'm often frustrated by the fact that we don't recognize that.

2873
02:43:19,240 --> 02:43:21,720
Speaker 1: I'm often frustrated by the fact that I think a

2874
02:43:21,720 --> 02:43:26,560
Speaker 1: lot of sportsmen don't recognize that, um that you know,

2875
02:43:26,720 --> 02:43:29,320
Speaker 1: there's seems to be more of a mentality of like, well,

2876
02:43:29,879 --> 02:43:33,000
Speaker 1: these mulity have always been here. I I hunted them,

2877
02:43:33,000 --> 02:43:34,879
Speaker 1: and I'm a kid, and you know, I'm still going

2878
02:43:34,920 --> 02:43:39,199
Speaker 1: to the same places. Maybe there's maybe there's less hard

2879
02:43:39,240 --> 02:43:43,040
Speaker 1: to say, hard to its hard to say, but you know,

2880
02:43:43,080 --> 02:43:45,280
Speaker 1: I think when when we look at when we look

2881
02:43:45,360 --> 02:43:48,240
Speaker 1: you know, migration is a great lens to look at

2882
02:43:48,280 --> 02:43:52,120
Speaker 1: these things. Those migrations are all getting harder. Everything we

2883
02:43:52,160 --> 02:43:56,200
Speaker 1: do to the landscape makes them harder. Um and so

2884
02:43:56,560 --> 02:43:58,760
Speaker 1: and and and and and and I think I wonder

2885
02:43:58,800 --> 02:44:03,920
Speaker 1: if we kind of as a as wildlife managers, I

2886
02:44:04,000 --> 02:44:07,920
Speaker 1: wonder if we kind of have this false sense of

2887
02:44:08,120 --> 02:44:15,560
Speaker 1: optimism generated from the success of the North American model. Right, Like,

2888
02:44:16,400 --> 02:44:21,199
Speaker 1: we went through this bottleneck. We we almost hunted all

2889
02:44:21,240 --> 02:44:25,240
Speaker 1: of these big game critters out of existence in North America.

2890
02:44:25,720 --> 02:44:29,640
Speaker 1: And then we developed policies and ways to regulate our

2891
02:44:29,720 --> 02:44:35,480
Speaker 1: harvest and we and we and we, yeah, we we

2892
02:44:35,640 --> 02:44:41,520
Speaker 1: brought them back right. Um, But that was you know,

2893
02:44:42,400 --> 02:44:45,880
Speaker 1: the turn of the century. There were far less people

2894
02:44:46,320 --> 02:44:48,560
Speaker 1: on this planet, and far less people in North America,

2895
02:44:48,600 --> 02:44:52,800
Speaker 1: and far less demands on these landscapes that that these

2896
02:44:52,879 --> 02:44:58,640
Speaker 1: that this wildlife um require. And I I think we

2897
02:44:59,040 --> 02:45:01,960
Speaker 1: I wonder if if we have a false sense of

2898
02:45:01,959 --> 02:45:05,920
Speaker 1: optimism that we we we we got through that. So

2899
02:45:06,240 --> 02:45:08,920
Speaker 1: you know, all of these sort of changes that we're

2900
02:45:08,920 --> 02:45:11,800
Speaker 1: seeing now are sort of temporary and we can we

2901
02:45:11,840 --> 02:45:16,000
Speaker 1: can come up with fixes of them. UM. But I'm

2902
02:45:16,000 --> 02:45:19,800
Speaker 1: not terribly optimistic. What and what motivates me is is

2903
02:45:19,840 --> 02:45:23,680
Speaker 1: that you know, we that we are on the slippery

2904
02:45:23,680 --> 02:45:28,880
Speaker 1: slope of making uh, making these habitats more more difficult,

2905
02:45:29,320 --> 02:45:35,400
Speaker 1: less profitable. Um. And so you know, in my view,

2906
02:45:35,600 --> 02:45:37,240
Speaker 1: we have a lot of work to do to figure

2907
02:45:37,280 --> 02:45:41,320
Speaker 1: out how how to how to stop the bleeding and

2908
02:45:41,480 --> 02:45:45,879
Speaker 1: how to sort of keep all this threaded together and

2909
02:45:45,959 --> 02:45:50,120
Speaker 1: stitched together as we go forward. Fifty years was a

2910
02:45:50,160 --> 02:45:55,960
Speaker 1: long time. I think the noteworthy part of that my

2911
02:45:56,080 --> 02:45:59,520
Speaker 1: mind is you think about this downward decline from the

2912
02:45:59,600 --> 02:46:03,360
Speaker 1: ninety sixties population whatever whatever you want to refer to

2913
02:46:03,440 --> 02:46:07,520
Speaker 1: that as UM. But to know that in Wyoming, the

2914
02:46:07,680 --> 02:46:12,800
Speaker 1: decline in Mulder since been about that says to me,

2915
02:46:12,840 --> 02:46:16,800
Speaker 1: there's an acceleration that's at starting to move at a

2916
02:46:16,840 --> 02:46:20,120
Speaker 1: breakneck pace. You don't have to be a mathematician to

2917
02:46:20,160 --> 02:46:23,360
Speaker 1: recognize that that can be a zero sum game pretty

2918
02:46:23,400 --> 02:46:27,560
Speaker 1: quickly and UH, from a conservation organization perspective. I think

2919
02:46:27,640 --> 02:46:31,720
Speaker 1: it's important to recognize that maybe we aren't in capable

2920
02:46:31,760 --> 02:46:34,200
Speaker 1: of reaching peaks, but we're definitely capable of drawn a

2921
02:46:34,240 --> 02:46:38,000
Speaker 1: line at the basement. And um, that's that's where we

2922
02:46:38,040 --> 02:46:41,439
Speaker 1: want to fit in, is to help stop that downturn

2923
02:46:42,040 --> 02:46:49,160
Speaker 1: and at least start to maintain hopefully a gradual increase later. Yeah,

2924
02:46:49,200 --> 02:46:51,200
Speaker 1: And I think I think that's a great point. And

2925
02:46:51,240 --> 02:46:54,080
Speaker 1: I still I'll fall back again to the how do

2926
02:46:54,160 --> 02:46:57,000
Speaker 1: we stop that downturn, which is ultimately going to come

2927
02:46:57,120 --> 02:46:58,920
Speaker 1: come down to the science side of it anyway, and

2928
02:46:58,920 --> 02:47:02,440
Speaker 1: there's there's still lots of questions associated with that and

2929
02:47:02,480 --> 02:47:04,879
Speaker 1: if that was an eruption, how far are we going

2930
02:47:04,920 --> 02:47:06,800
Speaker 1: to go down? And what are we gonna do? And

2931
02:47:07,120 --> 02:47:10,160
Speaker 1: I'm still I don't necessarily disagree with Matt either, but

2932
02:47:10,440 --> 02:47:13,280
Speaker 1: maybe maybe I'm a little more optimistic too, and that

2933
02:47:13,480 --> 02:47:17,160
Speaker 1: I think from from a sign. So for mule deer,

2934
02:47:17,160 --> 02:47:19,879
Speaker 1: although they've been declining, there's still they're still sort of

2935
02:47:19,879 --> 02:47:23,080
Speaker 1: a fairly common species, right, And so I think in

2936
02:47:23,160 --> 02:47:25,760
Speaker 1: some instances, as decisions are made there may be viewed

2937
02:47:25,800 --> 02:47:27,680
Speaker 1: a little bit as a common species. Well, if we

2938
02:47:27,720 --> 02:47:30,000
Speaker 1: give some here, if we give give away some here,

2939
02:47:30,000 --> 02:47:32,200
Speaker 1: it's okay because we have them in all these other places,

2940
02:47:32,240 --> 02:47:36,640
Speaker 1: whereas we can, I think, help gain that appreciation, helping

2941
02:47:36,680 --> 02:47:39,760
Speaker 1: still some of that appreciation by by also increase in

2942
02:47:39,879 --> 02:47:44,600
Speaker 1: understanding and how uniquely connected they're amazingly connected to the landscape,

2943
02:47:44,600 --> 02:47:46,800
Speaker 1: in the environment that they live in, and by helping

2944
02:47:46,920 --> 02:47:49,840
Speaker 1: understand that better and being able to relay that, I

2945
02:47:49,879 --> 02:47:52,560
Speaker 1: would hope that I still kind of think we're we're

2946
02:47:52,600 --> 02:47:56,440
Speaker 1: just pushing through almost to a breakthrough wherein we're getting

2947
02:47:56,480 --> 02:47:59,080
Speaker 1: to the point where mule deer and some of our

2948
02:47:59,120 --> 02:48:02,400
Speaker 1: other ungulates have much much more traction at the table

2949
02:48:02,520 --> 02:48:06,160
Speaker 1: when decisions are being made. And I truly think that

2950
02:48:06,160 --> 02:48:08,840
Speaker 1: that has a lot to do with the science and

2951
02:48:08,879 --> 02:48:11,680
Speaker 1: the communication of it. And we're we're beginning to to

2952
02:48:11,760 --> 02:48:14,760
Speaker 1: get to a point where we're learning these little nuanced

2953
02:48:15,320 --> 02:48:18,440
Speaker 1: mechanistic things that can make a huge different yet our

2954
02:48:18,800 --> 02:48:21,959
Speaker 1: difference yet are really interesting too and will help inspire

2955
02:48:22,040 --> 02:48:26,520
Speaker 1: people that to garner an appreciation for them and not

2956
02:48:26,640 --> 02:48:30,080
Speaker 1: just focus on economics, even though they're very economically valuable

2957
02:48:30,080 --> 02:48:32,160
Speaker 1: as well. So I still think we're getting to a

2958
02:48:32,200 --> 02:48:33,720
Speaker 1: point where maybe we're going to get a little bit

2959
02:48:33,720 --> 02:48:36,120
Speaker 1: of a breakthrough doesn't mean we're not gonna have some

2960
02:48:36,200 --> 02:48:39,440
Speaker 1: more bleeding um in the interim. But at the same time,

2961
02:48:39,480 --> 02:48:41,360
Speaker 1: I think as we have that little breakthrough as far

2962
02:48:41,400 --> 02:48:44,920
Speaker 1: as public sentiment appreciation those sorts of things, that there's

2963
02:48:44,959 --> 02:48:48,920
Speaker 1: also going to be additional lines of information from a

2964
02:48:48,959 --> 02:48:51,800
Speaker 1: scientific perspective that's going to help us help us do

2965
02:48:51,879 --> 02:48:53,640
Speaker 1: better to the benefit of meal there two and that

2966
02:48:53,680 --> 02:48:57,800
Speaker 1: could be managing other species, to managing habitat to even

2967
02:48:57,840 --> 02:49:02,760
Speaker 1: more motivation associate with um, conserving protecting migratory corridors, and

2968
02:49:02,840 --> 02:49:05,240
Speaker 1: the sorts of things. So I think it's one of

2969
02:49:05,240 --> 02:49:07,360
Speaker 1: the challenges and being human is to be able to

2970
02:49:07,400 --> 02:49:12,640
Speaker 1: have like a pessimistic, maybe you know, realistic, borderline pessimistic

2971
02:49:12,800 --> 02:49:16,920
Speaker 1: perception of what's going on, but then still wake up

2972
02:49:16,959 --> 02:49:19,520
Speaker 1: and do what you need to do. Remember the first

2973
02:49:19,560 --> 02:49:21,200
Speaker 1: time I ever sat in a meeting about making a

2974
02:49:21,240 --> 02:49:23,240
Speaker 1: TV show, the first thing out of someone's mouth as

2975
02:49:23,280 --> 02:49:26,600
Speaker 1: they were saying, like, it's impossible, it's almost never happens.

2976
02:49:27,520 --> 02:49:32,800
Speaker 1: Let's get started. You guys, thank you very much for

2977
02:49:32,879 --> 02:49:33,199
Speaker 1: joining