1 00:00:01,360 --> 00:00:05,200 Speaker 1: Welcome to the Wired to Hunt podcast, home of the 2 00:00:05,320 --> 00:00:10,639 Speaker 1: modern white tail hunter and now your host, Mark Kenyon. 3 00:00:13,560 --> 00:00:16,200 Speaker 1: Welcome to the Wire to Hunt podcast. I'm your guest 4 00:00:16,239 --> 00:00:20,280 Speaker 1: host Tony Peterson, and today I'm speaking with wildlife researcher 5 00:00:20,400 --> 00:00:39,360 Speaker 1: and white tail expert Dr Carl Miller. All Right, folks, 6 00:00:39,640 --> 00:00:41,760 Speaker 1: welcome to the Wire to Hunt podcast, which is brought 7 00:00:41,800 --> 00:00:45,040 Speaker 1: to you by First Light. You might notice that this 8 00:00:45,120 --> 00:00:49,000 Speaker 1: is not the voice of the well mustachioed Mark Kenyon. 9 00:00:49,520 --> 00:00:52,199 Speaker 1: Mark actually sent me an email last week saying that 10 00:00:52,240 --> 00:00:55,760 Speaker 1: he was going to work on his extreme ironing skills 11 00:00:56,400 --> 00:00:58,680 Speaker 1: and was really going to test his limits with his 12 00:00:58,840 --> 00:01:03,080 Speaker 1: latest challenge. I don't I don't know anymore, you guys. 13 00:01:03,600 --> 00:01:06,360 Speaker 1: I don't have the energy to care. If you're wondering 14 00:01:06,400 --> 00:01:09,080 Speaker 1: if Marcus as strange as he sounds, trust me, it's 15 00:01:09,240 --> 00:01:12,640 Speaker 1: orders of magnitude worse when you get to know him. Anyway. 16 00:01:12,880 --> 00:01:14,960 Speaker 1: I hope he cheats death with his latest hobby of 17 00:01:15,040 --> 00:01:19,720 Speaker 1: his And moving on today, I'm speaking with Dr Carl Miller. 18 00:01:20,600 --> 00:01:23,720 Speaker 1: There is no one out there who has contributed more 19 00:01:23,840 --> 00:01:27,520 Speaker 1: to the body of white tail research than this guy. 20 00:01:27,680 --> 00:01:30,720 Speaker 1: He's an absolute wealth of knowledge who has given back 21 00:01:30,800 --> 00:01:33,680 Speaker 1: an awful lot to the white tail community. And on 22 00:01:33,720 --> 00:01:36,679 Speaker 1: this show we dive pretty deep into how dear here 23 00:01:37,120 --> 00:01:39,800 Speaker 1: and how good they are actually at hearing, which is 24 00:01:39,840 --> 00:01:42,600 Speaker 1: going to surprise you, and then we get into how 25 00:01:42,640 --> 00:01:45,800 Speaker 1: well dear can actually smell and why that's so tough 26 00:01:45,920 --> 00:01:49,960 Speaker 1: to study. This episode is pretty heavy into science, but 27 00:01:50,040 --> 00:01:52,440 Speaker 1: it will make you a better deer hunter. I promise. 28 00:01:52,560 --> 00:01:55,360 Speaker 1: I think you're really gonna like it. Carl Miller, it 29 00:01:55,440 --> 00:02:00,960 Speaker 1: is an honor to get to talk to you made 30 00:02:00,960 --> 00:02:04,880 Speaker 1: me nervous by saying that honor. I've read your stuff 31 00:02:04,920 --> 00:02:07,320 Speaker 1: for a long long time, and I gotta ask you. 32 00:02:07,360 --> 00:02:10,760 Speaker 1: Are you the most published white tail researcher on the planet. 33 00:02:11,280 --> 00:02:12,920 Speaker 1: We're telling you. First of all, you said you regret 34 00:02:12,960 --> 00:02:15,000 Speaker 1: it for a long time. That makes me sound really old, 35 00:02:15,040 --> 00:02:18,960 Speaker 1: but which I met the last couple of years. Actually 36 00:02:19,000 --> 00:02:21,720 Speaker 1: I am, and I think right now I have public 37 00:02:21,880 --> 00:02:24,640 Speaker 1: more in the scientific literature than anybody else has on 38 00:02:24,720 --> 00:02:28,799 Speaker 1: white tails. After taking a look at the list of 39 00:02:28,800 --> 00:02:31,079 Speaker 1: of stuff that you have out there, I would believe 40 00:02:31,120 --> 00:02:34,640 Speaker 1: that because I've gotter viewed quite a few dear biologists 41 00:02:34,639 --> 00:02:38,440 Speaker 1: and wildlife researchers and you have a resume. Man, I'm 42 00:02:38,480 --> 00:02:41,440 Speaker 1: so I'm so glad to have you on. There's a 43 00:02:41,440 --> 00:02:42,639 Speaker 1: couple of things that I really want to talk to 44 00:02:42,720 --> 00:02:46,080 Speaker 1: you about. But first, where where did the white tail 45 00:02:46,400 --> 00:02:50,120 Speaker 1: obsession come from? You know, one of the things I 46 00:02:50,160 --> 00:02:52,560 Speaker 1: wanted to comment on what you just said earlier about, 47 00:02:52,639 --> 00:02:56,880 Speaker 1: you know, having such a a long list of publication 48 00:02:56,919 --> 00:02:59,440 Speaker 1: and stuff in my in my career and stuff. And 49 00:03:00,000 --> 00:03:02,000 Speaker 1: other reason for that is I've just absolutely been blessed 50 00:03:02,000 --> 00:03:04,000 Speaker 1: by the people I've had the opportunity to work with, 51 00:03:04,600 --> 00:03:06,200 Speaker 1: you know, a lot of good colleagues, with a lot 52 00:03:06,240 --> 00:03:09,600 Speaker 1: of good mentors, and particularly a lot of great graduate students, 53 00:03:09,639 --> 00:03:11,560 Speaker 1: and those of the workhorses that got a lot of 54 00:03:11,600 --> 00:03:13,760 Speaker 1: this stuff done. You know, I came up, come up 55 00:03:13,800 --> 00:03:15,799 Speaker 1: with some ideas and just turn a loose off stuff 56 00:03:15,800 --> 00:03:19,240 Speaker 1: and we put together a really good team at Georgia, 57 00:03:19,280 --> 00:03:21,840 Speaker 1: and that that that team is continuing on with the 58 00:03:22,320 --> 00:03:27,640 Speaker 1: people who replaced me since I retired. So, but my obsession. 59 00:03:28,360 --> 00:03:31,120 Speaker 1: I grew up in northern Pennsylvania in the Big Woods 60 00:03:31,440 --> 00:03:34,280 Speaker 1: with the traditional hunting deer hunting camp up there, lived 61 00:03:34,320 --> 00:03:37,360 Speaker 1: and breathed whitetailed deer from probably the time I came 62 00:03:37,360 --> 00:03:40,640 Speaker 1: out of the womb. I guess a long tradition. It 63 00:03:40,800 --> 00:03:44,720 Speaker 1: may have actually come back. Um my birthdays in August, 64 00:03:44,800 --> 00:03:47,720 Speaker 1: so I kind of backdated nine months. And I think 65 00:03:47,760 --> 00:03:50,440 Speaker 1: I was a product of Hello, honey, I'm home from 66 00:03:50,440 --> 00:03:57,000 Speaker 1: deer camp. You know, Uh, you're the first I've interviewed 67 00:03:57,000 --> 00:03:59,600 Speaker 1: a lot of people. Nobody's ever traced back their conception 68 00:03:59,680 --> 00:04:03,640 Speaker 1: date that quite that way. Well, I shouldn't say that, 69 00:04:03,640 --> 00:04:07,200 Speaker 1: nobody's ever openly admitted it. So I guess my dad 70 00:04:07,200 --> 00:04:11,960 Speaker 1: got his buck that year. Yeah, oh man, that's awesome. 71 00:04:11,960 --> 00:04:14,839 Speaker 1: So it was. It was a it was a family thing. 72 00:04:14,920 --> 00:04:17,160 Speaker 1: It was the social thing. You know. You you just 73 00:04:17,200 --> 00:04:20,000 Speaker 1: grew up with it, and you you just parlay it 74 00:04:20,120 --> 00:04:24,400 Speaker 1: into a really really long career. Huh yeah, you know. 75 00:04:24,520 --> 00:04:27,440 Speaker 1: And I, you know, starting out in college, I wanted 76 00:04:27,480 --> 00:04:29,560 Speaker 1: to go into wildlife, but you know, listen to guidence 77 00:04:29,560 --> 00:04:31,960 Speaker 1: concerts and parents and stuff. You know, they said, well, 78 00:04:31,960 --> 00:04:33,400 Speaker 1: you're not going to get a job in wildlife, so 79 00:04:33,440 --> 00:04:35,600 Speaker 1: you gotta go into something else. So I I started 80 00:04:35,600 --> 00:04:39,560 Speaker 1: off in in another field, and ultimately, by the time 81 00:04:39,640 --> 00:04:41,880 Speaker 1: I was working on my master's degree, I thought, you 82 00:04:41,880 --> 00:04:45,400 Speaker 1: know what, there's somebody getting jobs in wildlife and wildlife management. Dear, 83 00:04:45,800 --> 00:04:47,839 Speaker 1: if you're good you'll get the jobs. So that's where 84 00:04:47,839 --> 00:04:50,520 Speaker 1: I made my switch, and I haven't looked back since. 85 00:04:50,560 --> 00:04:54,599 Speaker 1: I mean, it's been it's been an incredible opportunity, you know, 86 00:04:54,640 --> 00:04:56,680 Speaker 1: over forty years to work with the animal you love 87 00:04:56,720 --> 00:04:58,880 Speaker 1: to work with. So when you were when you were 88 00:04:58,920 --> 00:05:01,040 Speaker 1: kind of kicking around the idea of going into this field, 89 00:05:01,080 --> 00:05:03,400 Speaker 1: you had people in your life who said, that's there's 90 00:05:03,440 --> 00:05:05,440 Speaker 1: there's no money, there's there's no jobs there. You gotta 91 00:05:05,440 --> 00:05:09,160 Speaker 1: look somewhere else, right, Yeah, But you know, like I said, 92 00:05:09,160 --> 00:05:12,720 Speaker 1: you know somebody has those jobs, and you know you 93 00:05:12,839 --> 00:05:14,839 Speaker 1: might be a little more competition out there, but you know, 94 00:05:14,839 --> 00:05:17,280 Speaker 1: if you're good, you're gonna find one. Yeah. I had 95 00:05:17,320 --> 00:05:19,200 Speaker 1: the same thing happened to me. I always wanted to 96 00:05:19,240 --> 00:05:22,120 Speaker 1: be an outdoor writer, and when I went to college, 97 00:05:22,400 --> 00:05:25,280 Speaker 1: a lot of people were like, listen, man, you gotta 98 00:05:25,279 --> 00:05:27,400 Speaker 1: you gotta get get a real job. You know, there's 99 00:05:27,440 --> 00:05:29,839 Speaker 1: like ten guys in the world making a living doing that. 100 00:05:30,320 --> 00:05:32,520 Speaker 1: And you know, lucky for me, I was just young 101 00:05:32,560 --> 00:05:34,960 Speaker 1: and dumb enough and didn't have enough holding me down 102 00:05:35,000 --> 00:05:37,440 Speaker 1: to not give it a shot. And it, you know, 103 00:05:37,600 --> 00:05:41,000 Speaker 1: changed the ark of my life. Just just pursuing that, uh, right, 104 00:05:41,000 --> 00:05:44,000 Speaker 1: and you've done well with it. It's been it's been fun. 105 00:05:44,040 --> 00:05:47,560 Speaker 1: I've been lucky. I feel lucky. Man. Uh, I want 106 00:05:47,560 --> 00:05:51,359 Speaker 1: to talk to you. You've studied so much different stuff 107 00:05:51,360 --> 00:05:53,520 Speaker 1: in the white tailed world. But there's two things I 108 00:05:53,600 --> 00:05:56,000 Speaker 1: got to talk to you about. And it's it's how 109 00:05:56,040 --> 00:05:59,240 Speaker 1: well a deer can hear, or I should probably rephrase that, 110 00:05:59,520 --> 00:06:04,680 Speaker 1: how how dear here compared to us, and how how 111 00:06:04,720 --> 00:06:07,880 Speaker 1: we kind of should be leaving behind what we know 112 00:06:07,960 --> 00:06:10,440 Speaker 1: about hearing because of how we hear and how we 113 00:06:10,480 --> 00:06:13,479 Speaker 1: should be thinking about how they have evolved here. And 114 00:06:13,520 --> 00:06:16,520 Speaker 1: I want to do the same thing with with their noses. Uh. 115 00:06:16,600 --> 00:06:18,960 Speaker 1: So let's let's start with deer hearing, because I know 116 00:06:19,080 --> 00:06:22,440 Speaker 1: you've you've bumped into this a lot where we just 117 00:06:22,520 --> 00:06:25,560 Speaker 1: as humans, we bring into this this biases of like, Okay, 118 00:06:26,000 --> 00:06:28,440 Speaker 1: we know what hearing is, like, we know how we hear, 119 00:06:28,480 --> 00:06:29,840 Speaker 1: and so we look at deer and go, well, they 120 00:06:29,920 --> 00:06:32,320 Speaker 1: must be way better at it because we give him 121 00:06:32,360 --> 00:06:35,039 Speaker 1: a lot of credit. But that's not necessarily the case 122 00:06:35,080 --> 00:06:37,840 Speaker 1: of your research. Yeah, and you know that this is 123 00:06:37,880 --> 00:06:41,080 Speaker 1: the easy one, dear hearing, because to tell you the truth, 124 00:06:41,200 --> 00:06:43,960 Speaker 1: dear hearing is really not that much different than ours, 125 00:06:44,400 --> 00:06:48,240 Speaker 1: which which really surprises a lot of people. There's been 126 00:06:48,720 --> 00:06:50,840 Speaker 1: two different studies that have done that that have been 127 00:06:50,839 --> 00:06:52,280 Speaker 1: done that looked at that. We did one at the 128 00:06:52,360 --> 00:06:55,000 Speaker 1: University of Georgia had a graduate student named Gino D'Angelo 129 00:06:55,040 --> 00:06:57,960 Speaker 1: who's now on the faculty there, and he did one 130 00:06:58,000 --> 00:07:00,800 Speaker 1: where we looked at auditory brain samoris wants, which actually 131 00:07:00,800 --> 00:07:03,520 Speaker 1: measures the signal in the deer's brain in response to 132 00:07:03,760 --> 00:07:08,279 Speaker 1: different sounds, you know, different frequencies and different intensities of sound. 133 00:07:09,360 --> 00:07:13,360 Speaker 1: And that study was done basically physiologically. And then there 134 00:07:13,400 --> 00:07:16,280 Speaker 1: was a study done out of the University of Toledo 135 00:07:16,640 --> 00:07:20,360 Speaker 1: by a father's son parents named Heffner, and they actually 136 00:07:20,400 --> 00:07:24,120 Speaker 1: did what it's called a behavioral behavioral that's hard word 137 00:07:24,120 --> 00:07:27,480 Speaker 1: to say, an audiogram where they actually trained the deer 138 00:07:27,520 --> 00:07:31,080 Speaker 1: to respond if they heard a sound. And interestingly, both 139 00:07:31,080 --> 00:07:35,200 Speaker 1: studies found essentially the same thing. The deer hearing range 140 00:07:35,400 --> 00:07:39,360 Speaker 1: is very similar to ours. Their best hearing is probably 141 00:07:39,400 --> 00:07:42,560 Speaker 1: a little bit higher frequencies than ours. We probably hear 142 00:07:42,600 --> 00:07:45,240 Speaker 1: in the you know, two to four killing hers range. 143 00:07:45,280 --> 00:07:47,680 Speaker 1: They hear in the four to kill her range at 144 00:07:47,680 --> 00:07:50,800 Speaker 1: their peak. But if you relay overlay the two curves 145 00:07:50,840 --> 00:07:54,800 Speaker 1: they are almost identical, which you know, so they may 146 00:07:54,840 --> 00:07:56,920 Speaker 1: hear a little higher frequency better. We may hear low 147 00:07:57,000 --> 00:08:00,600 Speaker 1: frequently better, but it's not that much different. But the 148 00:08:00,640 --> 00:08:04,360 Speaker 1: surprising thing that people don't realize is that their ability 149 00:08:04,440 --> 00:08:07,240 Speaker 1: to hear is not that much better than ours, essentially 150 00:08:07,320 --> 00:08:10,400 Speaker 1: about the same. So how do you test that? So 151 00:08:10,440 --> 00:08:12,720 Speaker 1: when you say, when you guys did the study and 152 00:08:12,760 --> 00:08:15,640 Speaker 1: you're looking at the brain scan, I'm assuming it's that's 153 00:08:15,640 --> 00:08:17,920 Speaker 1: a that's a great way to not have any false 154 00:08:17,960 --> 00:08:20,600 Speaker 1: positives or any kind of ambiguous data, right, Like the 155 00:08:20,640 --> 00:08:23,120 Speaker 1: brain lights up. You know they heard it. And then 156 00:08:23,160 --> 00:08:26,240 Speaker 1: they do this study down in Ohio and you know 157 00:08:26,280 --> 00:08:28,760 Speaker 1: there it's a conditioned response thing, probably a food thing 158 00:08:28,840 --> 00:08:31,640 Speaker 1: or something. I'm guessing. Uh So how do you how 159 00:08:31,680 --> 00:08:34,200 Speaker 1: do you figure out like because I think when you 160 00:08:34,480 --> 00:08:36,160 Speaker 1: if you ask the average hunter, they would say, well, 161 00:08:36,160 --> 00:08:38,720 Speaker 1: of course a deer can hear something farther away than us, 162 00:08:38,720 --> 00:08:40,760 Speaker 1: and of course they can pinpoint it better like it 163 00:08:40,800 --> 00:08:42,960 Speaker 1: would be for most people would be a given. But 164 00:08:42,960 --> 00:08:45,320 Speaker 1: how do you test? How do you test to figure 165 00:08:45,400 --> 00:08:46,960 Speaker 1: that out? To go? You know, their hearing is not 166 00:08:47,000 --> 00:08:49,920 Speaker 1: that much better than ours. Okay, now you're going into 167 00:08:49,960 --> 00:08:51,400 Speaker 1: a place that I want to get. There are some 168 00:08:51,440 --> 00:08:55,040 Speaker 1: caveats for this, okay, And that's related to the external 169 00:08:55,080 --> 00:08:57,240 Speaker 1: ears that a deer has. You know, this is what 170 00:08:57,280 --> 00:08:59,439 Speaker 1: the deer is hearing capability is when the sound is 171 00:08:59,480 --> 00:09:04,440 Speaker 1: played into the deer's ear and um and both of 172 00:09:04,480 --> 00:09:07,640 Speaker 1: it has done with different frequencies and you use different intensities, 173 00:09:07,960 --> 00:09:10,480 Speaker 1: you know, different volumes of the sound to get their response. 174 00:09:11,320 --> 00:09:14,199 Speaker 1: So but the difference is the deer have these large 175 00:09:14,240 --> 00:09:17,760 Speaker 1: external ears called you know, they're called pena, which have 176 00:09:18,040 --> 00:09:21,960 Speaker 1: multiple functions um, one of which is to accentuate sound 177 00:09:22,080 --> 00:09:24,960 Speaker 1: is entering into the ear. So it's like when you 178 00:09:25,000 --> 00:09:26,720 Speaker 1: put your hand to your ear when you're trying to 179 00:09:26,760 --> 00:09:30,320 Speaker 1: hear something, it actually funnels more of that sound into 180 00:09:30,360 --> 00:09:32,400 Speaker 1: your ear. So when a deer has this ears cut 181 00:09:32,480 --> 00:09:36,240 Speaker 1: towards an object that's producing that sound, it actually accentuates 182 00:09:36,240 --> 00:09:39,120 Speaker 1: that sound. But on the flip side, when they're turned 183 00:09:39,160 --> 00:09:42,240 Speaker 1: to wait way from something, it decreases the sounds. So 184 00:09:42,240 --> 00:09:44,720 Speaker 1: when the deer's ears are pointed forward, it's not hearing 185 00:09:44,800 --> 00:09:48,559 Speaker 1: is well behind, or when it's ears are backwards is 186 00:09:48,559 --> 00:09:51,080 Speaker 1: not hearing it well forward. So when a deer's ears 187 00:09:51,080 --> 00:09:52,640 Speaker 1: are cup towards you, that's when it's going to have 188 00:09:52,679 --> 00:09:56,199 Speaker 1: the best ability to pick up that sound. That also 189 00:09:56,280 --> 00:10:01,240 Speaker 1: helps a lot with the ability to u localize the 190 00:10:01,520 --> 00:10:05,319 Speaker 1: source of the sound, define the directionality because that extends 191 00:10:05,360 --> 00:10:08,480 Speaker 1: the distance between the two years out further, and the 192 00:10:08,520 --> 00:10:11,720 Speaker 1: distance between the ears is what helps you to identify 193 00:10:11,800 --> 00:10:16,360 Speaker 1: where the sound came from. M So one other thing 194 00:10:16,400 --> 00:10:19,280 Speaker 1: to think about this is though, and I know a 195 00:10:19,320 --> 00:10:21,439 Speaker 1: lot of hunter will say, oh, you know what, I've 196 00:10:21,480 --> 00:10:23,520 Speaker 1: been sitting on deer stand and I've been watching deer 197 00:10:23,559 --> 00:10:25,400 Speaker 1: right under my stand, and all of a sudden it 198 00:10:25,679 --> 00:10:28,600 Speaker 1: you're picked up his head and looked in a different direction, 199 00:10:28,640 --> 00:10:31,960 Speaker 1: like it hurts something. I never heard something. So why 200 00:10:32,080 --> 00:10:34,880 Speaker 1: is that? Well, you know the way I kind of 201 00:10:36,080 --> 00:10:41,000 Speaker 1: uh explain that is deer spend their entire life twenty 202 00:10:41,040 --> 00:10:43,000 Speaker 1: four hours a day, seven days a week, three and 203 00:10:43,080 --> 00:10:45,280 Speaker 1: sixty five days in the in the woods. They know 204 00:10:45,360 --> 00:10:47,360 Speaker 1: what they're supposed to hear and what they're not supposed 205 00:10:47,400 --> 00:10:49,840 Speaker 1: to hear. You know what you're supposed to hear in 206 00:10:49,880 --> 00:10:52,080 Speaker 1: your house or not supposed to hear in your house. 207 00:10:52,120 --> 00:10:54,000 Speaker 1: If you drop the nickel versus a dime on the 208 00:10:54,080 --> 00:10:57,240 Speaker 1: kitchen floor, you know the difference, right, or if you're 209 00:10:57,280 --> 00:11:01,360 Speaker 1: driving your truck and it starts to make a weird sound, 210 00:11:01,400 --> 00:11:04,240 Speaker 1: you know, you say, that's not normal. You know, most 211 00:11:04,320 --> 00:11:06,360 Speaker 1: of my wife can't do that, but most people can 212 00:11:06,760 --> 00:11:10,720 Speaker 1: can do that. So, uh so they know what they're 213 00:11:10,720 --> 00:11:12,960 Speaker 1: supposed to hear. How many times have you been on 214 00:11:13,040 --> 00:11:14,800 Speaker 1: a deer stand and had a deer in front of 215 00:11:14,840 --> 00:11:17,680 Speaker 1: you and you heard a rustle behind the behind you 216 00:11:17,720 --> 00:11:19,360 Speaker 1: and you turn around and it's a squirrel And the 217 00:11:19,400 --> 00:11:21,920 Speaker 1: deer never picked its head up. The deer knew what 218 00:11:21,960 --> 00:11:24,360 Speaker 1: it was supposed to hear, and knew what was natural 219 00:11:24,520 --> 00:11:26,760 Speaker 1: and what was unnatural. And that's what they're picking up on, 220 00:11:26,880 --> 00:11:29,480 Speaker 1: is the unnatural sound out in the woods. That thing 221 00:11:30,679 --> 00:11:33,959 Speaker 1: what this reminds me of. I think it was Steve 222 00:11:34,280 --> 00:11:36,200 Speaker 1: Rinella that was talking about this when he spent some 223 00:11:36,280 --> 00:11:40,800 Speaker 1: time down in the Amazon with with natives who you know, 224 00:11:40,840 --> 00:11:44,920 Speaker 1: these indigenous indigenous people, their life is condensed down to 225 00:11:45,000 --> 00:11:48,000 Speaker 1: like twenty square miles of jungle, and so they are 226 00:11:48,120 --> 00:11:52,439 Speaker 1: just tuned into that. That's you know, that's it's an 227 00:11:52,440 --> 00:11:55,280 Speaker 1: area that we you know, like living in the modern 228 00:11:55,320 --> 00:11:57,520 Speaker 1: world where we live, we don't think of things that way, right, 229 00:11:57,520 --> 00:11:58,880 Speaker 1: Like you think of the city, you know, and you 230 00:11:58,960 --> 00:12:00,560 Speaker 1: go to the grocery store or what ever. Maybe you 231 00:12:00,559 --> 00:12:03,560 Speaker 1: go to your farm, but their entire existence is boiled 232 00:12:03,559 --> 00:12:06,960 Speaker 1: down to that whatever twenty square miles it is. So 233 00:12:07,000 --> 00:12:08,640 Speaker 1: they know the trees, they know the plants, they know 234 00:12:08,679 --> 00:12:11,200 Speaker 1: the animals, they know, you know, there's new tracks coming 235 00:12:11,200 --> 00:12:13,120 Speaker 1: in here or whatever. And I always thinking about that 236 00:12:13,160 --> 00:12:15,600 Speaker 1: with deer, when you look at if you use like 237 00:12:15,880 --> 00:12:18,400 Speaker 1: a square mile is just like a generic home range, 238 00:12:18,960 --> 00:12:23,520 Speaker 1: and then you think about that's their entire existence. And 239 00:12:23,520 --> 00:12:25,679 Speaker 1: and and we look at time a little bit differently, right, 240 00:12:25,679 --> 00:12:28,520 Speaker 1: we have long lives compared to them, So they spend 241 00:12:28,559 --> 00:12:31,280 Speaker 1: one year there, and we think that's you know, like 242 00:12:31,320 --> 00:12:34,200 Speaker 1: a young whatever, a little forky or something, a year 243 00:12:34,240 --> 00:12:36,520 Speaker 1: and a half old out there, but he spent his 244 00:12:36,679 --> 00:12:39,640 Speaker 1: entire existence and maybe you know, maybe he's dispersed or something, 245 00:12:39,679 --> 00:12:44,160 Speaker 1: but you've got just a little piece of ground that 246 00:12:44,320 --> 00:12:47,880 Speaker 1: everything that's there is what they encounter every day, and 247 00:12:47,920 --> 00:12:50,240 Speaker 1: it's it's a different thing. So they're so in tune 248 00:12:50,800 --> 00:12:55,040 Speaker 1: to that environment compared to how we probably perceive it. Yeah, 249 00:12:55,040 --> 00:12:57,959 Speaker 1: I think you're exactly right. You can almost see it 250 00:12:57,960 --> 00:13:00,920 Speaker 1: in your kids. And when I was teaching at the university. 251 00:13:01,400 --> 00:13:04,240 Speaker 1: You could tell kids who grew up with a you know, 252 00:13:04,480 --> 00:13:06,319 Speaker 1: spending a lot of time out of the wood versus 253 00:13:06,320 --> 00:13:08,200 Speaker 1: once and one once, we didn't to spend most of 254 00:13:08,200 --> 00:13:10,880 Speaker 1: their time on a game boy or something. You know, 255 00:13:11,600 --> 00:13:13,400 Speaker 1: because the kids have spent a lot of times in 256 00:13:13,440 --> 00:13:16,720 Speaker 1: the woods could see things, They picked up on things. 257 00:13:16,880 --> 00:13:18,640 Speaker 1: They just knew what was supposed to be there, and 258 00:13:18,679 --> 00:13:21,199 Speaker 1: what if something unusual they picked up on it, where 259 00:13:21,240 --> 00:13:24,560 Speaker 1: the other kids didn't, they didn't have the appreciation for it. Yeah, well, 260 00:13:24,559 --> 00:13:26,160 Speaker 1: I I ran into that. I take a lot of 261 00:13:26,200 --> 00:13:28,560 Speaker 1: kids turkey hunting, and you know, I have twin ten 262 00:13:28,640 --> 00:13:32,280 Speaker 1: year old daughters. One of them is phenomenal at details 263 00:13:32,280 --> 00:13:34,160 Speaker 1: and paying attention in the woods, and one of them 264 00:13:34,200 --> 00:13:38,559 Speaker 1: is oblivious. And I mean that's partially they're wiring, but 265 00:13:38,920 --> 00:13:41,240 Speaker 1: I and I'm used to them. But I took one 266 00:13:41,280 --> 00:13:45,080 Speaker 1: of my nephews out, who's I think he's eleven or twelve, 267 00:13:45,679 --> 00:13:48,200 Speaker 1: his first turkey hunt. He's never done it. He had 268 00:13:48,240 --> 00:13:50,040 Speaker 1: deer hunted with his dad, a little bit rifle hunted, 269 00:13:50,679 --> 00:13:53,199 Speaker 1: and I'm telling you, every sound we heard in the woods, 270 00:13:53,240 --> 00:13:55,440 Speaker 1: he was like, what's that, what's that? What's that? They 271 00:13:55,480 --> 00:13:58,000 Speaker 1: He had no you know, it's stuff that we hear, 272 00:13:58,440 --> 00:14:01,280 Speaker 1: you know, you don't even acknowledge, like blue jays flying overhead, 273 00:14:01,440 --> 00:14:04,200 Speaker 1: or affiliated woodpecker or whatever flying by. It's just like 274 00:14:04,280 --> 00:14:08,280 Speaker 1: so part of the landscape. To him, he was just 275 00:14:08,840 --> 00:14:10,760 Speaker 1: holy cow, I don't know what any And it was 276 00:14:10,760 --> 00:14:12,920 Speaker 1: like kind of an eye opener for me. Yeah, And 277 00:14:12,960 --> 00:14:14,640 Speaker 1: we only spend a few hours a week out in 278 00:14:14,640 --> 00:14:19,320 Speaker 1: the woods compared with deer spending hours a day out there. Yeah, yeah, 279 00:14:19,400 --> 00:14:21,840 Speaker 1: I mean it's a different thing. So when you talk 280 00:14:21,880 --> 00:14:26,520 Speaker 1: about you know, their ears and and and pinpointing stuff. 281 00:14:26,520 --> 00:14:28,120 Speaker 1: When you when you say that, what it reminds me 282 00:14:28,200 --> 00:14:30,640 Speaker 1: of is when you deer hunt a lot, you sort 283 00:14:30,680 --> 00:14:34,240 Speaker 1: of intuitively learn and you probably learned just from your 284 00:14:34,240 --> 00:14:36,920 Speaker 1: mistakes that if if a deer is looking in your direction, 285 00:14:36,960 --> 00:14:39,960 Speaker 1: you don't call, if you know, like when you're bow hunting, 286 00:14:40,000 --> 00:14:42,800 Speaker 1: because they're gonna be on you in a second. But 287 00:14:42,920 --> 00:14:45,320 Speaker 1: you wait till they turn their head and there those 288 00:14:45,360 --> 00:14:47,960 Speaker 1: ears are are, you know, turned the other way. Then 289 00:14:48,000 --> 00:14:50,680 Speaker 1: you call, and it just feels like that's the right move, 290 00:14:51,280 --> 00:14:53,800 Speaker 1: right or even if they're looking your in your direction 291 00:14:53,840 --> 00:14:55,880 Speaker 1: and the ears or turned back, which they have the 292 00:14:55,880 --> 00:14:59,080 Speaker 1: ability here, you know, listening behind them. That's another thing 293 00:14:59,120 --> 00:15:00,960 Speaker 1: to think about what you're deer hunt is watching the 294 00:15:01,000 --> 00:15:03,480 Speaker 1: deer is the ear because you know, if you're watching 295 00:15:03,480 --> 00:15:05,920 Speaker 1: the deer's ear, you can tell what that deer's thinking 296 00:15:05,960 --> 00:15:08,640 Speaker 1: about it as well. And you know if they all 297 00:15:08,680 --> 00:15:10,760 Speaker 1: of a sudden they're they're cupping their ears behind them, 298 00:15:10,880 --> 00:15:13,280 Speaker 1: they're hearing something that they want more information on. It 299 00:15:13,360 --> 00:15:15,920 Speaker 1: might be another deer, might be something they're they're trying 300 00:15:15,960 --> 00:15:18,080 Speaker 1: to identify what that is. So it's time for you 301 00:15:18,120 --> 00:15:20,920 Speaker 1: to pay attention to what's going on behind the deer. Yeah, well, 302 00:15:20,920 --> 00:15:23,600 Speaker 1: that's that's what I was curious about with you, is 303 00:15:23,640 --> 00:15:26,600 Speaker 1: how much you know, like how many ideas for your 304 00:15:26,600 --> 00:15:28,800 Speaker 1: research do you get just from sitting in the woods 305 00:15:28,800 --> 00:15:30,560 Speaker 1: and hunting deer, Because like when you when you talk 306 00:15:30,600 --> 00:15:33,320 Speaker 1: about like that, that right there, one of the things 307 00:15:33,320 --> 00:15:35,080 Speaker 1: I always think is so cool. If you watch a deer, 308 00:15:35,760 --> 00:15:38,280 Speaker 1: you know that's that's chomping on some acorns or maybe 309 00:15:38,280 --> 00:15:40,400 Speaker 1: eating a corn cob in the field or something, and 310 00:15:40,440 --> 00:15:42,520 Speaker 1: they think they hear something, you watch them stop chewing, 311 00:15:43,160 --> 00:15:45,080 Speaker 1: and it's the same thing people do you know, like 312 00:15:45,120 --> 00:15:46,800 Speaker 1: if somebody speaks to you and you've got a mouthful 313 00:15:46,840 --> 00:15:49,600 Speaker 1: of cheerios or whatever, you stop chewing for a second 314 00:15:49,600 --> 00:15:51,800 Speaker 1: so you can hear and you see those little behaviors 315 00:15:51,800 --> 00:15:53,760 Speaker 1: like how often are you just have you been in 316 00:15:53,800 --> 00:15:55,400 Speaker 1: your life just sitting on the woods and saw like 317 00:15:55,400 --> 00:15:56,960 Speaker 1: a little thing like that and went, you know what? 318 00:15:58,160 --> 00:16:00,800 Speaker 1: And then and it sparks some kind of idea. Yeah, 319 00:16:00,840 --> 00:16:04,840 Speaker 1: but I think that happens, you know, quite often. Um, 320 00:16:04,960 --> 00:16:06,960 Speaker 1: I don't know how if I can give you a 321 00:16:07,000 --> 00:16:10,320 Speaker 1: specific instance on, but you know, just just watching dear behavior, 322 00:16:10,680 --> 00:16:13,240 Speaker 1: I'm always asking the question why is that dear doing 323 00:16:13,280 --> 00:16:15,840 Speaker 1: what that dear is doing instead of just accepting the 324 00:16:15,880 --> 00:16:18,080 Speaker 1: fact that it is doing that, And I think that 325 00:16:18,160 --> 00:16:20,200 Speaker 1: makes the difference and try to answer and trying to 326 00:16:20,200 --> 00:16:23,760 Speaker 1: figure out you know a lot of the questions we asked. Yeah, 327 00:16:23,840 --> 00:16:26,480 Speaker 1: I mean I tell people, you know, we get hit 328 00:16:26,520 --> 00:16:30,480 Speaker 1: with questions constantly on like you know, what what scent 329 00:16:30,560 --> 00:16:32,760 Speaker 1: should I buy? What call should I buy? Like it's 330 00:16:32,760 --> 00:16:35,360 Speaker 1: always like how what what can you tell me to 331 00:16:35,440 --> 00:16:38,160 Speaker 1: get me closer to dead bucks? And I'm always like 332 00:16:38,160 --> 00:16:40,720 Speaker 1: pay attention to deer, figure out where they like to walk, 333 00:16:40,840 --> 00:16:42,520 Speaker 1: figure out why they like to walk there, and then 334 00:16:42,560 --> 00:16:46,520 Speaker 1: go shoot them. They don't want to hear it, they 335 00:16:46,560 --> 00:16:48,280 Speaker 1: don't want to hear. They want to buy a crush 336 00:16:48,320 --> 00:16:50,960 Speaker 1: instead of going out there and actually learning the animal. 337 00:16:51,040 --> 00:16:54,200 Speaker 1: And that's what I You know, I'm a minimalist when 338 00:16:54,200 --> 00:16:57,120 Speaker 1: it comes to hunting. I generally have either my bowing 339 00:16:57,160 --> 00:16:58,960 Speaker 1: an arrow or a gun in a couple of shells, 340 00:16:59,000 --> 00:17:00,920 Speaker 1: and that's about all I carry to the woods with me. 341 00:17:01,000 --> 00:17:02,440 Speaker 1: And you know, I use my mouth to make any 342 00:17:02,440 --> 00:17:04,560 Speaker 1: calls that I want to make. But I don't like 343 00:17:04,600 --> 00:17:07,040 Speaker 1: to carry a lot of gear because it kind of 344 00:17:07,080 --> 00:17:10,240 Speaker 1: takes away from the hunt to me. Yeah, I'm a 345 00:17:10,400 --> 00:17:13,119 Speaker 1: I'm a minimalist too. I like I like simple. I 346 00:17:13,200 --> 00:17:15,520 Speaker 1: like I like just trying to figure out what makes 347 00:17:15,560 --> 00:17:18,960 Speaker 1: them tick and try to capitalize on that. Um let's 348 00:17:19,000 --> 00:17:20,760 Speaker 1: let's back up one second. There's something I want to 349 00:17:20,760 --> 00:17:23,119 Speaker 1: ask you about. So when you when you mentioned, you know, 350 00:17:23,160 --> 00:17:26,040 Speaker 1: the frequency in which we hear versus the frequency in 351 00:17:26,040 --> 00:17:31,000 Speaker 1: which we think, dear here, it's a little bit different. Yeah, why, 352 00:17:31,400 --> 00:17:35,959 Speaker 1: I mean, where's the evolutionary divergence there? You you know 353 00:17:36,040 --> 00:17:40,359 Speaker 1: what that might be. It might be the you know, 354 00:17:40,400 --> 00:17:43,160 Speaker 1: the deer that we tested were you know, five six 355 00:17:43,320 --> 00:17:45,600 Speaker 1: four or five six years old or something. You know, 356 00:17:45,720 --> 00:17:48,159 Speaker 1: most times humans when they're being tested, they're you know, 357 00:17:48,359 --> 00:17:50,200 Speaker 1: twenty years old, and you know, we lose our some 358 00:17:50,280 --> 00:17:53,080 Speaker 1: of our ability to hear higher frequencies. And it might 359 00:17:53,119 --> 00:17:55,880 Speaker 1: be just something that over time that the subjects themselves 360 00:17:55,880 --> 00:17:58,400 Speaker 1: have lost their ability to hear high frequencies. I can't 361 00:17:58,400 --> 00:18:00,399 Speaker 1: hear high pitches like I used to do any or 362 00:18:00,920 --> 00:18:03,320 Speaker 1: so that might be part of it, whether or not 363 00:18:03,359 --> 00:18:09,320 Speaker 1: there's an evolutionary thing significance to it. Um heck, I 364 00:18:09,359 --> 00:18:10,840 Speaker 1: don't know. You know, some of the some of the 365 00:18:10,880 --> 00:18:13,639 Speaker 1: calls that they dare make that like the call an alarm, 366 00:18:13,760 --> 00:18:17,719 Speaker 1: that that's a higher pitch, higher frequency sound, it might 367 00:18:17,760 --> 00:18:19,800 Speaker 1: be a little bit easier for them to hear that sound, 368 00:18:19,960 --> 00:18:22,680 Speaker 1: you know, to fall because but you know, I don't 369 00:18:22,680 --> 00:18:27,359 Speaker 1: think it's a it's not such a major difference between 370 00:18:27,400 --> 00:18:30,520 Speaker 1: the deer and the human that there's really that much 371 00:18:30,560 --> 00:18:35,400 Speaker 1: to really, I don't know, there's not that much. You're 372 00:18:35,440 --> 00:18:38,680 Speaker 1: really trying to put together an evolutionary picture for it. 373 00:18:38,680 --> 00:18:42,000 Speaker 1: It might not mean anything. Yeah, it's not that different. Yeah, 374 00:18:42,040 --> 00:18:44,399 Speaker 1: I mean, or it might. When I when I hear 375 00:18:44,400 --> 00:18:46,240 Speaker 1: stuff like that, I always wonder, you know, you mentioned 376 00:18:46,240 --> 00:18:49,199 Speaker 1: like a you know, a deer snorting and alarm, or 377 00:18:49,280 --> 00:18:53,360 Speaker 1: you know, maybe it's tied to coyote howells or some 378 00:18:53,359 --> 00:18:56,320 Speaker 1: some predator the pitch of some predators call or something 379 00:18:56,359 --> 00:18:57,879 Speaker 1: at some point, or it just could be just the 380 00:18:57,880 --> 00:19:01,679 Speaker 1: way things shook out. You know. Well that's a that's 381 00:19:01,720 --> 00:19:02,800 Speaker 1: that's a good way to say it. I wish I 382 00:19:02,840 --> 00:19:04,800 Speaker 1: would have said it that way. Yeah, I mean, it's 383 00:19:04,800 --> 00:19:10,320 Speaker 1: just it's it's interesting, it is, yeah, yeah, Um, it's interesting. 384 00:19:10,480 --> 00:19:13,600 Speaker 1: Has anybody so when you when you talk about how 385 00:19:13,640 --> 00:19:15,880 Speaker 1: a white tail you know, we we we know how 386 00:19:15,880 --> 00:19:18,080 Speaker 1: they use their ears. You know, we know what they're 387 00:19:18,080 --> 00:19:20,280 Speaker 1: doing with them. Has anybody ever studied meal deer in 388 00:19:20,320 --> 00:19:24,840 Speaker 1: the same capacity? Uh, not that I know of, But 389 00:19:24,960 --> 00:19:27,800 Speaker 1: it would make sense obviously with those larger ears, they 390 00:19:27,840 --> 00:19:30,399 Speaker 1: have a good you know, and it's a greater enhanced 391 00:19:30,400 --> 00:19:32,639 Speaker 1: ability to capture some of that sound and maybe the 392 00:19:32,720 --> 00:19:36,080 Speaker 1: localize that sound as well. And do you think that 393 00:19:36,080 --> 00:19:39,679 Speaker 1: would come from just maybe being in a more open, bigger, 394 00:19:39,800 --> 00:19:44,719 Speaker 1: bigger terrain or something. Yeah, you know, distances distances are 395 00:19:44,800 --> 00:19:48,960 Speaker 1: much greater in those situations, and sounds carries further. Uh 396 00:19:49,000 --> 00:19:52,760 Speaker 1: you know, you know, a closed forest situation, white till 397 00:19:52,800 --> 00:19:56,360 Speaker 1: lives up close and personal whereas a meal near lives, 398 00:19:56,520 --> 00:20:00,199 Speaker 1: you know, his his experiences as much broader of what 399 00:20:00,280 --> 00:20:04,959 Speaker 1: he's visualizing for hearing. Yeah, yeah, it's just a different thing. Um, 400 00:20:05,119 --> 00:20:09,040 Speaker 1: anything besides the obvious you know, metal on metal sounds. 401 00:20:09,080 --> 00:20:12,080 Speaker 1: Maybe it's like, you know, a two tight tree strap 402 00:20:12,160 --> 00:20:15,560 Speaker 1: on the wrong tree bar can really make a nasty sound. 403 00:20:15,960 --> 00:20:19,119 Speaker 1: Anything anything that Like you personally when you're you're hunting. 404 00:20:19,160 --> 00:20:22,000 Speaker 1: Because of your research on dear hearing, have you ever 405 00:20:22,160 --> 00:20:24,120 Speaker 1: are you just like I can't make that noise all right? 406 00:20:24,160 --> 00:20:26,439 Speaker 1: Just when you make it, you're like, oh, that's terrible. 407 00:20:27,840 --> 00:20:30,200 Speaker 1: You know. One of the sounds that kind of falls 408 00:20:30,359 --> 00:20:32,959 Speaker 1: right into the peak of the deers hearing capability is 409 00:20:33,400 --> 00:20:36,240 Speaker 1: kind of the Russell sound you make when you're your 410 00:20:36,520 --> 00:20:40,199 Speaker 1: clothing moves you move against a tree that you know 411 00:20:40,240 --> 00:20:44,320 Speaker 1: that that brush um and some types of fabrics make 412 00:20:44,359 --> 00:20:47,840 Speaker 1: an awful lot of noise. Uh. And that's one of 413 00:20:47,840 --> 00:20:50,879 Speaker 1: the things I worked with was Sick on a camera 414 00:20:50,920 --> 00:20:52,920 Speaker 1: and is developing some of this, you know, helping them 415 00:20:52,960 --> 00:20:56,120 Speaker 1: develop some of their camera based on deers hearing capabilities, 416 00:20:56,520 --> 00:20:59,359 Speaker 1: and they're trying to minimize the sounds of those camels made. 417 00:20:59,720 --> 00:21:02,399 Speaker 1: And you know, I think that's critically important. You know. 418 00:21:02,520 --> 00:21:05,000 Speaker 1: You just don't hear nylon naturally out in the woods, 419 00:21:05,040 --> 00:21:08,080 Speaker 1: you know, or bell crow or bell crow, you know, 420 00:21:08,119 --> 00:21:10,800 Speaker 1: any of any of those types of things. Any unnatural 421 00:21:10,880 --> 00:21:16,159 Speaker 1: sound is going to be picked up pretty quickly. Yeah. Yeah, 422 00:21:16,920 --> 00:21:22,600 Speaker 1: it's just it's interesting when you see. It's interesting when 423 00:21:22,600 --> 00:21:25,000 Speaker 1: you could like walk down the aisle of a Cabelas 424 00:21:25,080 --> 00:21:27,000 Speaker 1: or whatever and look at the different camo and look 425 00:21:27,000 --> 00:21:29,159 Speaker 1: at a you know, all the different vinyl harnesses and 426 00:21:29,160 --> 00:21:31,959 Speaker 1: the different class and vel crow and all the stuff. 427 00:21:32,359 --> 00:21:34,359 Speaker 1: And then over the years you go out and hunt 428 00:21:34,760 --> 00:21:37,280 Speaker 1: and you just realize, like, if I'm wearing this kind 429 00:21:37,320 --> 00:21:40,600 Speaker 1: of jacket, I can't turn with my back to a 430 00:21:40,640 --> 00:21:43,760 Speaker 1: tree when the deer close. Well, if I stand up, 431 00:21:43,800 --> 00:21:45,600 Speaker 1: I gotta stand up with my back away. Or you 432 00:21:45,640 --> 00:21:47,280 Speaker 1: have some stuff that you can kind of ride that 433 00:21:47,400 --> 00:21:49,719 Speaker 1: right up that bark and it's not gonna make any noise. 434 00:21:49,760 --> 00:21:53,040 Speaker 1: And it's little stuff like that. I think when you 435 00:21:53,080 --> 00:21:54,880 Speaker 1: get a lot of bow hunting experience, you just start 436 00:21:54,920 --> 00:21:57,879 Speaker 1: to intuitively go, you know, I'm in a green like that, 437 00:21:57,920 --> 00:21:59,840 Speaker 1: and I'm never gonna wear this again kind of stuff, 438 00:22:00,359 --> 00:22:03,280 Speaker 1: right and not you know that that fabric choice and 439 00:22:03,320 --> 00:22:06,000 Speaker 1: the noise of that fabric makes might be more important 440 00:22:06,000 --> 00:22:09,880 Speaker 1: than the actual camel that you're using. You know, picked 441 00:22:09,920 --> 00:22:13,360 Speaker 1: your camel up with your ears, not your eyes. Um, 442 00:22:13,400 --> 00:22:14,600 Speaker 1: I want to I want to ask you one more 443 00:22:14,600 --> 00:22:17,160 Speaker 1: thing about deer hearing before we move on. So when 444 00:22:17,240 --> 00:22:19,640 Speaker 1: when I asked you about the frequency in which they 445 00:22:19,680 --> 00:22:21,679 Speaker 1: hear and in which we hear, and we kind of 446 00:22:21,720 --> 00:22:25,119 Speaker 1: decided it might not mean anything at all. Um, you 447 00:22:25,200 --> 00:22:28,360 Speaker 1: mentioned you know as you get older. You know, we're 448 00:22:28,440 --> 00:22:32,560 Speaker 1: like very aware that you know, our hearing can be 449 00:22:32,960 --> 00:22:35,560 Speaker 1: can go downhill, right, Like we're very aware that our 450 00:22:35,600 --> 00:22:37,359 Speaker 1: eyesight at a certain age, like you're gonna need your 451 00:22:37,400 --> 00:22:40,920 Speaker 1: cheater glasses. Like we don't think about animals in terms 452 00:22:40,960 --> 00:22:44,040 Speaker 1: like that. They I don't. I don't ever think about 453 00:22:44,080 --> 00:22:48,119 Speaker 1: an old buck, you know, having any diminished sense of 454 00:22:48,359 --> 00:22:51,560 Speaker 1: hearing or any diminished sense of vision. Do you think 455 00:22:51,600 --> 00:22:56,040 Speaker 1: that happens? I mean, you know, none of the studies 456 00:22:56,080 --> 00:22:58,960 Speaker 1: have looked in an age relationship to that, or have 457 00:22:59,080 --> 00:23:01,440 Speaker 1: found an age related and shift to that. We didn't 458 00:23:01,440 --> 00:23:03,639 Speaker 1: notice any different, any difference in any of our study 459 00:23:03,680 --> 00:23:07,400 Speaker 1: animals as related to age. You know, it's it's possible, 460 00:23:07,440 --> 00:23:09,720 Speaker 1: but you know, think about this you're dealing with a 461 00:23:09,760 --> 00:23:13,160 Speaker 1: six year span between a fawn and a mature book 462 00:23:13,720 --> 00:23:16,880 Speaker 1: versus a seventy year span between you know, a young 463 00:23:16,960 --> 00:23:20,400 Speaker 1: child and a rich older man. Well, and you would 464 00:23:20,840 --> 00:23:22,560 Speaker 1: you would have to account for the fact that, even 465 00:23:22,560 --> 00:23:25,879 Speaker 1: if there was some level of diminishment there, you're also 466 00:23:26,160 --> 00:23:29,520 Speaker 1: they're also layering in an extra year of survival experience 467 00:23:29,560 --> 00:23:33,640 Speaker 1: and encounters, and so they would probably more than offset 468 00:23:33,680 --> 00:23:35,719 Speaker 1: anything like that. I just I've never thought about it 469 00:23:35,760 --> 00:23:38,040 Speaker 1: that way. Yeah. Well, I also think about you know, 470 00:23:38,119 --> 00:23:40,440 Speaker 1: where do we lose our hearing from listening to too 471 00:23:40,440 --> 00:23:43,160 Speaker 1: many chainsaws? Rock and rolls are gunshots? You're right? Yeah, 472 00:23:43,240 --> 00:23:47,159 Speaker 1: for our vision is based on watching computers screens, we 473 00:23:47,240 --> 00:23:49,520 Speaker 1: lose it. You don't do any of that stuff, right, Yeah, 474 00:23:49,560 --> 00:23:52,720 Speaker 1: Well that's true. And in one very specific instance, I 475 00:23:52,720 --> 00:23:54,439 Speaker 1: can say you can lose some hearing if your dipshit 476 00:23:54,480 --> 00:23:56,720 Speaker 1: buddy shoots a twelve gage right by your ear when 477 00:23:56,720 --> 00:24:00,400 Speaker 1: a grouse flies over, and your ears will never stop 478 00:24:00,560 --> 00:24:03,920 Speaker 1: ringing again. Uh So that's that's a bummer. And they 479 00:24:03,960 --> 00:24:08,720 Speaker 1: don't have that happened very often to them. I guess, Okay, 480 00:24:08,800 --> 00:24:10,760 Speaker 1: occasionally I'll have a gun shot that you know, if 481 00:24:10,800 --> 00:24:13,560 Speaker 1: if somebody happens to miss. But if they're lucky, they 482 00:24:13,680 --> 00:24:16,560 Speaker 1: got they got that gun shot, right, yep, yep. Um, 483 00:24:16,840 --> 00:24:21,320 Speaker 1: let's talk about so I think if you if you 484 00:24:21,359 --> 00:24:23,800 Speaker 1: were gonna just like rank them, if you're gonna ask 485 00:24:23,920 --> 00:24:27,119 Speaker 1: hunters like, okay, what's the what's the sense or you know, 486 00:24:27,160 --> 00:24:30,280 Speaker 1: what's the sense you want to avoid the most? You know, 487 00:24:30,320 --> 00:24:32,880 Speaker 1: I think you people would waffle back and forth between 488 00:24:32,920 --> 00:24:34,280 Speaker 1: eyesight and hearing, you know, like you don't want to 489 00:24:34,280 --> 00:24:36,080 Speaker 1: get spotted, you don't want dear to hear you're doing 490 00:24:36,119 --> 00:24:38,400 Speaker 1: something dumb. But the nose is the one you want 491 00:24:38,400 --> 00:24:41,000 Speaker 1: to beat, and the nose is the one that's probably 492 00:24:41,040 --> 00:24:44,520 Speaker 1: the hardest for us to understand, wouldn't you say? Yeah? 493 00:24:44,640 --> 00:24:48,119 Speaker 1: But you know what I've thought at I've asked asked 494 00:24:48,119 --> 00:24:50,480 Speaker 1: that question, and had that question asked me. I think 495 00:24:50,480 --> 00:24:52,960 Speaker 1: that probably the most important one that I would want 496 00:24:52,960 --> 00:24:55,879 Speaker 1: to beat is their sense of vision, and particularly with 497 00:24:55,960 --> 00:25:00,520 Speaker 1: their ability to detect movement, and that is the office one. 498 00:25:00,840 --> 00:25:03,960 Speaker 1: The sense of smell is easier to beat. All you 499 00:25:04,040 --> 00:25:05,840 Speaker 1: have to be is down wind of the deer and 500 00:25:06,600 --> 00:25:09,720 Speaker 1: you beat it right. So that that's the trick is 501 00:25:09,720 --> 00:25:11,480 Speaker 1: not how do you how do you mask your oder. 502 00:25:11,480 --> 00:25:14,119 Speaker 1: It's just present your order into a place where you 503 00:25:14,160 --> 00:25:17,960 Speaker 1: expected deer to be. M hmm, well so I would 504 00:25:18,000 --> 00:25:22,320 Speaker 1: look at that maybe a little bit differently. So, yes, 505 00:25:22,720 --> 00:25:25,080 Speaker 1: if you're sitting in your tree stand and you're sent 506 00:25:25,119 --> 00:25:28,199 Speaker 1: blows to that deer, you're in trouble. But we're also like, 507 00:25:28,480 --> 00:25:31,520 Speaker 1: I'm I'm super into training bird dogs and and working 508 00:25:31,560 --> 00:25:35,280 Speaker 1: with dogs, and like, I'm fascinated by their ability to 509 00:25:35,320 --> 00:25:40,240 Speaker 1: smell stuff, right, no question, it's it's it's it's we 510 00:25:40,280 --> 00:25:43,840 Speaker 1: can't even fathom what they appreciate through their nose. No, not, 511 00:25:44,200 --> 00:25:45,760 Speaker 1: we don't. I don't think we could even in a 512 00:25:45,840 --> 00:25:48,359 Speaker 1: millionaire were not even anywhere near it. But so what 513 00:25:48,480 --> 00:25:50,399 Speaker 1: I think about is, yes, we have to we have 514 00:25:50,400 --> 00:25:53,000 Speaker 1: two tasks with white tails a lot of times, right 515 00:25:53,480 --> 00:25:55,480 Speaker 1: the immediate moment of the hunt where you're like I 516 00:25:55,520 --> 00:25:57,879 Speaker 1: don't want him to smell me, or it's over or whoever, 517 00:25:58,040 --> 00:25:59,920 Speaker 1: you know, whatever deer is going by, But we're also 518 00:26:00,080 --> 00:26:03,280 Speaker 1: leaving residual scent out there, and I feel like we 519 00:26:03,480 --> 00:26:07,679 Speaker 1: educate them that way, uh, to a level that we 520 00:26:07,800 --> 00:26:14,879 Speaker 1: probably can't really appreciate. That's that's conceivable. But you know, 521 00:26:14,920 --> 00:26:17,440 Speaker 1: there's also a pout using everything else walking through those 522 00:26:17,480 --> 00:26:20,119 Speaker 1: wizard are potential predators for them, that's leaving. That's you know, 523 00:26:20,160 --> 00:26:23,560 Speaker 1: they're sent in different places. You know, is it is 524 00:26:23,840 --> 00:26:26,439 Speaker 1: how important is it for a deer to know that 525 00:26:26,600 --> 00:26:30,359 Speaker 1: something was there versus something is there? And I think 526 00:26:30,440 --> 00:26:33,359 Speaker 1: the most important aspect is something is there? Right, So 527 00:26:33,400 --> 00:26:35,639 Speaker 1: if it's an older scent that we've left, and you know, 528 00:26:35,760 --> 00:26:39,200 Speaker 1: you're can very likely tell the age of sense as well. 529 00:26:40,080 --> 00:26:42,720 Speaker 1: And it's clearly they can tell the age of the 530 00:26:43,080 --> 00:26:45,480 Speaker 1: difference in the age of attracted another deer left, or 531 00:26:45,520 --> 00:26:49,360 Speaker 1: that the predator left very likely human as well. Well. 532 00:26:49,840 --> 00:26:53,080 Speaker 1: So I wonder about that because you know, the evaporation 533 00:26:53,200 --> 00:26:56,120 Speaker 1: level of the scent, you know, like knowing which direction 534 00:26:56,160 --> 00:26:58,040 Speaker 1: you were going, how long ago you were there. Like 535 00:26:58,080 --> 00:26:59,840 Speaker 1: they're they're really figuring out a lot of stuff with 536 00:27:00,040 --> 00:27:03,320 Speaker 1: dogs on that front, which is fascinating. Like everybody's like, 537 00:27:03,320 --> 00:27:05,359 Speaker 1: how does the dog know when I'm coming home from work? 538 00:27:06,000 --> 00:27:09,160 Speaker 1: And it's like it's a condition from you know, being 539 00:27:09,240 --> 00:27:11,520 Speaker 1: left at home every day and they know You're scent 540 00:27:11,920 --> 00:27:15,239 Speaker 1: smells at this strength now at five o'clock at night, 541 00:27:15,240 --> 00:27:18,000 Speaker 1: so you're coming home, you know, And when I hear 542 00:27:18,000 --> 00:27:19,280 Speaker 1: stuff like that. I always try to think about it 543 00:27:19,280 --> 00:27:22,800 Speaker 1: in like a deer's in a deer's world, but there's 544 00:27:22,840 --> 00:27:25,160 Speaker 1: just there's just so much going on there. But when 545 00:27:25,160 --> 00:27:28,159 Speaker 1: I when I heard you on on bows podcast, I 546 00:27:28,200 --> 00:27:30,560 Speaker 1: would if you would asked me before I listen to 547 00:27:30,600 --> 00:27:33,159 Speaker 1: you say this, I would have said, scent, you know, 548 00:27:33,200 --> 00:27:37,400 Speaker 1: they're their old factory abilities. That is predator detection, that's 549 00:27:37,480 --> 00:27:40,000 Speaker 1: that's like a major thing for him. But you actually 550 00:27:40,080 --> 00:27:45,119 Speaker 1: said that it's it's more about food and communication. Yeah, 551 00:27:45,240 --> 00:27:48,040 Speaker 1: and I would I would still say that that, you know, predators, 552 00:27:48,640 --> 00:27:52,600 Speaker 1: you know, the sense of smell is tremendous for predator 553 00:27:52,680 --> 00:27:55,760 Speaker 1: detection if the predator is in the right place, you know, 554 00:27:55,760 --> 00:27:58,119 Speaker 1: if the predators approaching from down wind, the sense of 555 00:27:58,160 --> 00:28:01,920 Speaker 1: smells is totally useless for the deer. Right. So, but 556 00:28:02,080 --> 00:28:04,840 Speaker 1: you know the other aspect is, you know the sense 557 00:28:04,840 --> 00:28:07,080 Speaker 1: of smell that people don't appreciate is you know, do 558 00:28:07,119 --> 00:28:10,320 Speaker 1: you have to find food? You know? Uh, you know, 559 00:28:10,400 --> 00:28:13,200 Speaker 1: basically that's what they're doing is all day long is forging. 560 00:28:13,560 --> 00:28:15,320 Speaker 1: And how do they find that food. They don't find 561 00:28:15,320 --> 00:28:17,800 Speaker 1: that food by looking for it. They don't pick up 562 00:28:17,840 --> 00:28:19,360 Speaker 1: an acorn to look and see if it's a good 563 00:28:19,359 --> 00:28:22,280 Speaker 1: acorn versus a bad acorn. They smell the difference. Right. 564 00:28:22,640 --> 00:28:24,560 Speaker 1: If you ever sat on a deer stand and you know, 565 00:28:24,640 --> 00:28:28,159 Speaker 1: wash meat and acorns, you don't see them looking for acorns. 566 00:28:28,200 --> 00:28:30,160 Speaker 1: There's snuffing, you can hear, you can actually hear them, 567 00:28:30,280 --> 00:28:32,840 Speaker 1: you know, snuffing in the leaves for the acorns. So 568 00:28:32,960 --> 00:28:35,520 Speaker 1: they're they're detecting what they're gonna pick on, what they're 569 00:28:35,520 --> 00:28:39,280 Speaker 1: gonna eat based on the sense of smell, you know. Rankly, 570 00:28:39,320 --> 00:28:42,120 Speaker 1: their their eyes for that up closed vision are not 571 00:28:42,160 --> 00:28:44,520 Speaker 1: that good anyways, So they probably have a harder time 572 00:28:44,640 --> 00:28:48,520 Speaker 1: finding an acorn with their eyes than we would, yeah, 573 00:28:48,560 --> 00:28:51,720 Speaker 1: because they're not there. Their eyesight's not wired for that. 574 00:28:52,400 --> 00:28:53,960 Speaker 1: And then and then on top of that, you know, 575 00:28:54,000 --> 00:28:57,400 Speaker 1: they don't go around with a with you know, a 576 00:28:57,440 --> 00:29:01,240 Speaker 1: dendrology book trying to identify dick plants and different leaves 577 00:29:01,240 --> 00:29:03,120 Speaker 1: based on the shape of their leaves and stuff like that. 578 00:29:03,200 --> 00:29:04,600 Speaker 1: They know what it's supposed to eat and what they're 579 00:29:04,600 --> 00:29:06,680 Speaker 1: not supposed to eat by the smell of those plants, 580 00:29:06,760 --> 00:29:09,400 Speaker 1: you know, and some plants they have different compounds. They 581 00:29:09,400 --> 00:29:12,880 Speaker 1: don't call it antire beervery compounds like tannins and so 582 00:29:13,040 --> 00:29:16,040 Speaker 1: forth that deer can pick up on with their sense 583 00:29:16,080 --> 00:29:18,920 Speaker 1: of smell or and or their sense of taste, and 584 00:29:19,000 --> 00:29:22,959 Speaker 1: they can reject those in difference to other plants. And 585 00:29:23,000 --> 00:29:25,120 Speaker 1: you've probably even noticed that, you know, deer can tell 586 00:29:25,240 --> 00:29:28,520 Speaker 1: something They typically pick the young growth, or deer can 587 00:29:28,600 --> 00:29:30,960 Speaker 1: even pick out something that's been fertilized versus something that's 588 00:29:30,960 --> 00:29:34,120 Speaker 1: not been fertilized, because there's something different about that plant, 589 00:29:34,880 --> 00:29:37,440 Speaker 1: and that's not by vision, that's by the sense of smell. 590 00:29:38,280 --> 00:29:40,440 Speaker 1: That sense of smell is incredibly important for them to 591 00:29:40,640 --> 00:29:45,320 Speaker 1: do for foraging. They can probably they can probably differentiate 592 00:29:45,400 --> 00:29:49,400 Speaker 1: like levels of ripeness and fruit. Uh. They can probably 593 00:29:49,720 --> 00:29:57,280 Speaker 1: differentiate like to some extent, like micronutrient levels in different stuff. Huh, certainly. Yeah, 594 00:29:57,320 --> 00:30:00,320 Speaker 1: I don't think there's any question about that. How do 595 00:30:00,360 --> 00:30:04,440 Speaker 1: you study that? Uh, I don't know that should do. 596 00:30:06,000 --> 00:30:08,360 Speaker 1: So we're just yeah, I'll tell you what you know. 597 00:30:08,600 --> 00:30:11,240 Speaker 1: I started out my career looking a lot with your 598 00:30:11,320 --> 00:30:13,600 Speaker 1: scent communication and do the sense of smell and stuff 599 00:30:13,640 --> 00:30:15,640 Speaker 1: like that. And that's why we've switched to hearing vision 600 00:30:15,720 --> 00:30:17,960 Speaker 1: because it's got a lot easier to do those studies 601 00:30:18,400 --> 00:30:21,920 Speaker 1: because the sense of smell, there's so many aspects of this, 602 00:30:22,840 --> 00:30:25,600 Speaker 1: so many variables that come into play without you know, 603 00:30:25,640 --> 00:30:27,880 Speaker 1: working with the sense of smell, and we really have 604 00:30:28,000 --> 00:30:33,040 Speaker 1: no appreciation for what the deer smells, you know, for 605 00:30:33,120 --> 00:30:37,920 Speaker 1: even a comparison studies. So you know, how how do 606 00:30:37,960 --> 00:30:40,040 Speaker 1: you study those things that you know you sometimes you 607 00:30:40,200 --> 00:30:42,040 Speaker 1: maybe you not even need to study them. Just good 608 00:30:42,040 --> 00:30:44,800 Speaker 1: observation can tell you some of this stuff what's going on. Right. 609 00:30:45,320 --> 00:30:47,720 Speaker 1: You don't have to have a controlled scientific experiment to 610 00:30:48,120 --> 00:30:50,120 Speaker 1: know that if you fertilize your as alius in your 611 00:30:50,120 --> 00:30:53,760 Speaker 1: front yard and deals end up eating your ass aliens, right, 612 00:30:54,320 --> 00:30:56,320 Speaker 1: which would tell you that deer can tell the difference. 613 00:30:57,400 --> 00:31:01,000 Speaker 1: So and you know, it's even interesting, you know, just observations, 614 00:31:01,040 --> 00:31:04,640 Speaker 1: like there are times of year when deer will forage 615 00:31:04,640 --> 00:31:07,360 Speaker 1: on different types of plants. There's a lot of plants 616 00:31:07,400 --> 00:31:10,920 Speaker 1: that in the fall time they dear will you know, 617 00:31:10,960 --> 00:31:13,640 Speaker 1: they won't use utilize those plants all summer long, and 618 00:31:13,680 --> 00:31:17,400 Speaker 1: then comes sometime in later August or early into September, 619 00:31:17,440 --> 00:31:20,880 Speaker 1: they'll start really hitting those types of plants. What's changed 620 00:31:20,920 --> 00:31:23,200 Speaker 1: are the deer hungrier? I don't think so. I think 621 00:31:23,200 --> 00:31:26,240 Speaker 1: what what has changed is something's changed about those plants, 622 00:31:27,320 --> 00:31:30,320 Speaker 1: and as those plants are going to go into sin escence, 623 00:31:30,360 --> 00:31:32,760 Speaker 1: you know, they're going to their decisionous plants. Maybe they're 624 00:31:32,800 --> 00:31:35,800 Speaker 1: losing their you know, those secondary compounds that are those 625 00:31:35,840 --> 00:31:39,560 Speaker 1: toxic compounds that you're you know, causing deer to avoid them. 626 00:31:39,720 --> 00:31:42,840 Speaker 1: Or maybe the sugar levels are going up because of 627 00:31:42,880 --> 00:31:45,760 Speaker 1: their their transporting some of those sugars you know, into 628 00:31:45,760 --> 00:31:48,680 Speaker 1: the root system. So there's there's differences that are occurring 629 00:31:48,680 --> 00:31:52,360 Speaker 1: physiologically in those plants that you are able to pick 630 00:31:52,440 --> 00:31:55,160 Speaker 1: up with, and they're doing that with their senses. Now 631 00:32:08,880 --> 00:32:11,560 Speaker 1: do you do you think because we you know, when 632 00:32:11,560 --> 00:32:13,440 Speaker 1: you talk about like a sugar content of you know, 633 00:32:13,480 --> 00:32:17,160 Speaker 1: certain plants after the first hard frost or something like that, 634 00:32:17,280 --> 00:32:19,200 Speaker 1: or you know, a ripe apple versus of you know, 635 00:32:20,040 --> 00:32:22,080 Speaker 1: an early green one, that's just not the same thing. 636 00:32:22,680 --> 00:32:26,280 Speaker 1: Do do you think that we because you know, we 637 00:32:26,400 --> 00:32:29,560 Speaker 1: eat off of taste, right, like we we just do, 638 00:32:29,640 --> 00:32:32,560 Speaker 1: Like we're not most of us aren't paying attention to 639 00:32:32,760 --> 00:32:35,120 Speaker 1: like micronutrient levels and stuff like that while we're eating 640 00:32:35,120 --> 00:32:38,000 Speaker 1: and trying to have like a truly balanced diet that way. 641 00:32:38,560 --> 00:32:42,920 Speaker 1: But we do also kind of like inherently sometimes crave 642 00:32:43,000 --> 00:32:44,640 Speaker 1: things that our body needs, you know, like if you 643 00:32:44,640 --> 00:32:48,280 Speaker 1: go into the Elk Mountains for ten days, yeah, you know, 644 00:32:48,320 --> 00:32:50,440 Speaker 1: on day eight you have a different like you you 645 00:32:50,480 --> 00:32:53,560 Speaker 1: have a different desire for food. Like even if you've 646 00:32:53,640 --> 00:32:56,560 Speaker 1: kept yourself well fed with whatever calorie amount you needed 647 00:32:56,560 --> 00:32:58,520 Speaker 1: and you weren't in a big deficit, you're still like, man, 648 00:32:58,560 --> 00:33:01,000 Speaker 1: I want a salad in a bacon cheeseburgers, Like there's 649 00:33:01,000 --> 00:33:06,160 Speaker 1: something your body or pregnant women like they yeah, those crazy, 650 00:33:06,200 --> 00:33:08,160 Speaker 1: Like there's a reason they're eating chalk, And it's not 651 00:33:08,200 --> 00:33:11,280 Speaker 1: because they're sitting here going like I really like the 652 00:33:11,320 --> 00:33:13,680 Speaker 1: taste of chalk. Just once in a while, they're going 653 00:33:13,840 --> 00:33:16,320 Speaker 1: the body is saying something. So I wonder about deer 654 00:33:16,360 --> 00:33:19,520 Speaker 1: because we always focus on like the the taste aspect. 655 00:33:19,640 --> 00:33:21,240 Speaker 1: It's like, oh, they're gonna they're gonna hit the brass 656 00:33:21,240 --> 00:33:24,400 Speaker 1: because now because it got to ten degrees last night, 657 00:33:24,440 --> 00:33:27,040 Speaker 1: and the sugar content or whatever. But I wonder how 658 00:33:27,160 --> 00:33:29,200 Speaker 1: much of that is just tied to them inherently or 659 00:33:29,240 --> 00:33:33,640 Speaker 1: instinctively knowing I need you know, I need more of this, 660 00:33:33,760 --> 00:33:35,800 Speaker 1: I need more of that. I need my you know, 661 00:33:35,840 --> 00:33:38,000 Speaker 1: like I need this to be healthy. And they're probably 662 00:33:38,000 --> 00:33:41,400 Speaker 1: not aware of it. Yeah, and it could be I think, 663 00:33:41,440 --> 00:33:44,080 Speaker 1: you know that is it either or or both? And 664 00:33:44,280 --> 00:33:46,440 Speaker 1: I think it's a both. And you know, you know, 665 00:33:46,680 --> 00:33:49,640 Speaker 1: the change in the taste probably affects them as well. 666 00:33:49,920 --> 00:33:53,040 Speaker 1: We'll think about one that's you know, very clear, they're 667 00:33:53,080 --> 00:33:56,480 Speaker 1: craving for salt, and they only create salt during certain 668 00:33:56,480 --> 00:33:58,680 Speaker 1: times of year. That's during the spring and early summer 669 00:33:58,720 --> 00:34:02,320 Speaker 1: when vegetation is very succulent. They're taking in a lot 670 00:34:02,360 --> 00:34:05,280 Speaker 1: of water content, which means they're urinating allots, which means 671 00:34:05,320 --> 00:34:07,600 Speaker 1: they're losing a lot of sodium. And that's what the 672 00:34:07,800 --> 00:34:10,920 Speaker 1: that's what's driving their salt uses, the sodium deficit that 673 00:34:10,960 --> 00:34:15,800 Speaker 1: most herbivores are running running against just just because of 674 00:34:15,400 --> 00:34:19,879 Speaker 1: the seasonal timing, and they're flushing out their system more right, right, 675 00:34:19,920 --> 00:34:22,480 Speaker 1: because you know, there there's so much they're taking in, 676 00:34:22,560 --> 00:34:25,080 Speaker 1: so much of that moisture in that young, succulent vegetation. 677 00:34:25,440 --> 00:34:28,680 Speaker 1: They're not drinking that much water. They're just they're just 678 00:34:28,719 --> 00:34:30,359 Speaker 1: having to get rid of a lot of water, which 679 00:34:30,440 --> 00:34:31,759 Speaker 1: means you're going to get rid of a lot of 680 00:34:31,760 --> 00:34:33,880 Speaker 1: sodium as well. So that's that's not tied to antler 681 00:34:33,880 --> 00:34:37,800 Speaker 1: growth or anything like that. Huh, not necessarily, yeah, m 682 00:34:39,080 --> 00:34:41,040 Speaker 1: you know, and it's probably you know, there's also some 683 00:34:41,200 --> 00:34:43,440 Speaker 1: aspect of fetal growth in there as well, you know, 684 00:34:43,480 --> 00:34:47,520 Speaker 1: in that springtime. So but you know, we've seen some 685 00:34:47,640 --> 00:34:50,480 Speaker 1: really neat movement patterns of deer in response to the 686 00:34:50,520 --> 00:34:53,839 Speaker 1: sodium drive as well. Where we had a study up 687 00:34:53,840 --> 00:34:55,600 Speaker 1: in West Virginia a number of years ago where we 688 00:34:55,640 --> 00:34:58,719 Speaker 1: had radio colored a bunch of radio colored does and 689 00:34:58,800 --> 00:35:01,040 Speaker 1: on this study or in these when for about a 690 00:35:01,120 --> 00:35:05,280 Speaker 1: week or two would just disappear and it generally occurs 691 00:35:05,360 --> 00:35:07,520 Speaker 1: sometime in May or June, and then about two weeks 692 00:35:07,600 --> 00:35:09,399 Speaker 1: later they'd come back. And this was before the days 693 00:35:09,400 --> 00:35:12,479 Speaker 1: of GPS, so we ended up having to go around, 694 00:35:12,520 --> 00:35:14,560 Speaker 1: you know, really dry around and find these doughs. And 695 00:35:15,000 --> 00:35:17,160 Speaker 1: it turned out a lot of these doughs were going 696 00:35:17,200 --> 00:35:21,280 Speaker 1: to some old gas wells where there was a pool 697 00:35:21,280 --> 00:35:23,640 Speaker 1: of water out in front of these old gas wells, 698 00:35:23,840 --> 00:35:25,520 Speaker 1: and we took some measurements of that water and it 699 00:35:25,640 --> 00:35:29,040 Speaker 1: had really high sodium levels in it. So these doughs 700 00:35:29,120 --> 00:35:32,319 Speaker 1: would go and sometimes they would go miles to pick 701 00:35:32,400 --> 00:35:34,080 Speaker 1: up you know and camp out for a week or 702 00:35:34,080 --> 00:35:37,000 Speaker 1: two at these sodium sources and then come back and 703 00:35:37,719 --> 00:35:40,520 Speaker 1: to their home range. So you know, it's kind of 704 00:35:40,520 --> 00:35:43,040 Speaker 1: interesting that there's two aspects of that are kind of need. 705 00:35:43,040 --> 00:35:45,200 Speaker 1: Is one of the deer have an ability to recycle 706 00:35:45,280 --> 00:35:48,320 Speaker 1: sodium in their body, so they're picking up that sodium 707 00:35:48,400 --> 00:35:50,040 Speaker 1: load and they're you know, and keeping it. But the 708 00:35:50,120 --> 00:35:52,200 Speaker 1: question is how did they find it? Well, that's what 709 00:35:52,280 --> 00:35:54,279 Speaker 1: I was gonna ask you, how how the hell did 710 00:35:54,280 --> 00:35:57,440 Speaker 1: they find that? Well? As well as interesting that that 711 00:35:57,600 --> 00:35:59,040 Speaker 1: some of the deer did this and some of the 712 00:35:59,080 --> 00:36:02,439 Speaker 1: dear dnt And you know, we don't know this for sure, 713 00:36:02,520 --> 00:36:04,800 Speaker 1: but it's very likely that some of the deer that 714 00:36:05,080 --> 00:36:08,560 Speaker 1: actually went to these sodium sources must have been deer 715 00:36:08,600 --> 00:36:12,120 Speaker 1: that have dispersed it sometimes or their ancestors dispersed it 716 00:36:12,239 --> 00:36:16,399 Speaker 1: sometime into this new area. But when these dose went 717 00:36:16,520 --> 00:36:18,640 Speaker 1: a lot of times in June or even into July, 718 00:36:18,840 --> 00:36:20,640 Speaker 1: a lot of times they took their yearlings with them, 719 00:36:20,640 --> 00:36:23,040 Speaker 1: and sometimes they took their phones with them. So they 720 00:36:23,080 --> 00:36:25,880 Speaker 1: were learning this like a migration pattern. They learned this 721 00:36:25,920 --> 00:36:28,200 Speaker 1: from and so it becomes a cultural knowledge in that 722 00:36:28,320 --> 00:36:31,040 Speaker 1: deer herd amongst some of the deer that hey, we 723 00:36:31,120 --> 00:36:35,480 Speaker 1: go get our sodium and at this place, So do 724 00:36:35,520 --> 00:36:39,560 Speaker 1: you do you think there's any way it's gonna be 725 00:36:39,560 --> 00:36:43,560 Speaker 1: super wool, there's any way that there's like an ancestral 726 00:36:44,160 --> 00:36:48,239 Speaker 1: sort of memory or something there that a deer. Let's 727 00:36:48,239 --> 00:36:50,120 Speaker 1: say that you take a two year old oo that's 728 00:36:50,120 --> 00:36:54,160 Speaker 1: never been shown one of those spots. Could she know, 729 00:36:54,520 --> 00:36:56,759 Speaker 1: like I mean, we we could never know, right, Yeah, 730 00:36:57,000 --> 00:36:58,839 Speaker 1: And I don't I don't think she could know, Yeah, 731 00:36:58,960 --> 00:37:01,479 Speaker 1: because they did. You know, it doesn't make that sense 732 00:37:01,520 --> 00:37:03,120 Speaker 1: of that sheet knew because why didn't some of the 733 00:37:03,160 --> 00:37:05,279 Speaker 1: other deer do it? But you know, you think about 734 00:37:05,480 --> 00:37:09,319 Speaker 1: back in the you know, pre Columbian days, before you know, 735 00:37:09,400 --> 00:37:13,359 Speaker 1: the white man got here, there are places where they 736 00:37:13,400 --> 00:37:16,120 Speaker 1: were kind of mass migrations of herbivores to some of 737 00:37:16,120 --> 00:37:18,960 Speaker 1: these sodium areas that you know, they call them salt licks. 738 00:37:18,960 --> 00:37:21,160 Speaker 1: And there's a number of different places you hear about 739 00:37:21,239 --> 00:37:24,440 Speaker 1: deer run lick or some something you know, Salt Creek 740 00:37:24,520 --> 00:37:26,719 Speaker 1: or something like that, you know, all times kinds of 741 00:37:26,760 --> 00:37:29,719 Speaker 1: place to particularly across the eastern United States, and those 742 00:37:29,760 --> 00:37:31,759 Speaker 1: were placed where the Indians and later the you know, 743 00:37:31,840 --> 00:37:35,040 Speaker 1: the early settlers set up to go kill deer because 744 00:37:35,040 --> 00:37:36,480 Speaker 1: they knew the deer were going to be coming to 745 00:37:36,600 --> 00:37:38,680 Speaker 1: these things, So it must be something that they learned 746 00:37:38,680 --> 00:37:42,759 Speaker 1: through time, and there were migrations to these sodium sources. 747 00:37:42,960 --> 00:37:46,440 Speaker 1: Well yeah, I mean we we bring sort of a 748 00:37:46,440 --> 00:37:49,360 Speaker 1: modern spin to that study you're talking about, where you 749 00:37:49,400 --> 00:37:52,840 Speaker 1: think about the source. You know, it's a man made source, 750 00:37:53,360 --> 00:37:55,640 Speaker 1: and you don't think about it the same way as 751 00:37:55,640 --> 00:37:57,600 Speaker 1: you do a natural salt lick out there somewhere where 752 00:37:57,600 --> 00:38:01,000 Speaker 1: generations of deer would find it or be around it, 753 00:38:01,280 --> 00:38:04,160 Speaker 1: or be aware of it in proximity to where their 754 00:38:04,200 --> 00:38:07,640 Speaker 1: home ranges or whatever. But it's really like it doesn't 755 00:38:07,680 --> 00:38:11,759 Speaker 1: the deer don't care that that was not natural. They 756 00:38:11,800 --> 00:38:14,040 Speaker 1: don't give a ship if it was. Whoever put it 757 00:38:14,080 --> 00:38:16,120 Speaker 1: there doesn't matter to them. They just that's like a 758 00:38:16,200 --> 00:38:19,160 Speaker 1: thing that there they find and they know they need 759 00:38:19,160 --> 00:38:21,279 Speaker 1: in a certain percentage of them past that knowledge on. 760 00:38:21,360 --> 00:38:23,960 Speaker 1: And that's just how it happens, right, And you know, 761 00:38:24,120 --> 00:38:27,560 Speaker 1: in most places we don't see that that that drive anymore, 762 00:38:27,640 --> 00:38:30,480 Speaker 1: or those movements in response to salt, because they're sold everywhere, 763 00:38:31,160 --> 00:38:34,600 Speaker 1: hundreds of putting out salt. You know, every got you know, 764 00:38:34,640 --> 00:38:37,440 Speaker 1: a couple of salt blocks on it, and they're sold 765 00:38:37,480 --> 00:38:41,400 Speaker 1: on roadsides, you know, after winter times, so you know, 766 00:38:41,640 --> 00:38:44,480 Speaker 1: there's just lots of salt available for deer. Now, yeah, 767 00:38:44,680 --> 00:38:49,279 Speaker 1: it's not. It's just not a limited resource for them anymore. Right, So, 768 00:38:49,640 --> 00:38:54,640 Speaker 1: how did you when you when you mentioned earlier that 769 00:38:54,760 --> 00:38:57,600 Speaker 1: studying how a deer you know, how good does the 770 00:38:57,640 --> 00:39:00,640 Speaker 1: deer's knows and how does it work? You mentioned how 771 00:39:00,760 --> 00:39:03,600 Speaker 1: challenging that was, and it kind of kind of motivates 772 00:39:03,600 --> 00:39:05,600 Speaker 1: you to go study some other senses a little bit more. 773 00:39:05,960 --> 00:39:08,680 Speaker 1: But one of the things we didn't study how good 774 00:39:08,680 --> 00:39:10,759 Speaker 1: a deer's knows is. We we studied more on the 775 00:39:10,840 --> 00:39:12,960 Speaker 1: different glands that deer head and how they utilize those 776 00:39:12,960 --> 00:39:15,520 Speaker 1: glands for communication and such, you know, and that and 777 00:39:15,640 --> 00:39:20,120 Speaker 1: identifying some of the chemical analysis of those glands. But 778 00:39:20,200 --> 00:39:22,200 Speaker 1: you know, there's been a just a couple of studies 779 00:39:22,239 --> 00:39:25,359 Speaker 1: that actually kind of tried to approach this idea of 780 00:39:25,400 --> 00:39:27,560 Speaker 1: how well a deer can smell compared to a human. 781 00:39:28,239 --> 00:39:30,400 Speaker 1: And one of the ways they did that by actually 782 00:39:30,560 --> 00:39:33,960 Speaker 1: counting the number of sensory receptors in the nose. You know, 783 00:39:34,040 --> 00:39:36,560 Speaker 1: you can and you can find widely different estimates on 784 00:39:36,600 --> 00:39:39,319 Speaker 1: this thing, basically in the in the scientific literature and 785 00:39:39,480 --> 00:39:43,799 Speaker 1: on you know, in the popular magazines as well, and 786 00:39:43,840 --> 00:39:45,200 Speaker 1: it was one. You know, one time, I believe that 787 00:39:45,320 --> 00:39:47,719 Speaker 1: humans only had about five million receptors, you know, and 788 00:39:47,760 --> 00:39:50,440 Speaker 1: now now the fostsl are that somewhere between ten and 789 00:39:50,960 --> 00:39:54,440 Speaker 1: ten and fifty million receptors. But you know, there was 790 00:39:54,440 --> 00:39:57,680 Speaker 1: a study done on white tails back in the nineties 791 00:39:57,719 --> 00:40:00,280 Speaker 1: that I think they came up with something like million 792 00:40:00,280 --> 00:40:02,920 Speaker 1: back there, but that was that's probably a low estimate, 793 00:40:02,960 --> 00:40:05,920 Speaker 1: and that number very likely if it's comparable to you know, 794 00:40:05,960 --> 00:40:07,560 Speaker 1: some of the stuff we know about dogs now, it 795 00:40:07,800 --> 00:40:11,120 Speaker 1: might be over a billion yea of different receptors. So 796 00:40:11,280 --> 00:40:14,920 Speaker 1: you know, just the structure of the nose of the 797 00:40:15,040 --> 00:40:17,600 Speaker 1: human compared to the structure of the nose of the deer, 798 00:40:17,680 --> 00:40:21,080 Speaker 1: you know, speaks to the how well they can smell. Yeah. Well, 799 00:40:21,400 --> 00:40:24,399 Speaker 1: one thing I don't think people really appreciate that much though, 800 00:40:24,520 --> 00:40:28,000 Speaker 1: is that, you know, to smell a particular smell, you 801 00:40:28,080 --> 00:40:32,640 Speaker 1: need a receptor site that's keyed to that scent. It's 802 00:40:32,640 --> 00:40:35,080 Speaker 1: not just there's just a random any set any receptor 803 00:40:35,160 --> 00:40:38,040 Speaker 1: site can pick up any any odor. It's gonna happen 804 00:40:38,480 --> 00:40:42,640 Speaker 1: a mash between the receptor side and the odor. So there, 805 00:40:42,680 --> 00:40:46,160 Speaker 1: you know, the receptor sides are are probably built around 806 00:40:46,320 --> 00:40:49,919 Speaker 1: what a deer what sense are important to dear too, 807 00:40:51,080 --> 00:40:53,399 Speaker 1: versus you know, a human receptors might be built around 808 00:40:53,400 --> 00:40:57,880 Speaker 1: what's important to human. So it's it's actually conceivable, but 809 00:40:58,000 --> 00:41:00,560 Speaker 1: there are there may be some odors that humans could 810 00:41:00,560 --> 00:41:05,480 Speaker 1: spell better than deer. It's conceivable, you know, I don't 811 00:41:05,480 --> 00:41:08,040 Speaker 1: know what that might be. But the you know, the 812 00:41:08,120 --> 00:41:10,919 Speaker 1: point is that there are so many receptors sites on 813 00:41:10,920 --> 00:41:14,200 Speaker 1: on deer, and very likely a lot of them are 814 00:41:14,600 --> 00:41:18,839 Speaker 1: keyed into some of these highly bottled compounds that are 815 00:41:18,880 --> 00:41:23,600 Speaker 1: dispersed by the wind that are for you know, for 816 00:41:23,680 --> 00:41:26,840 Speaker 1: predator avoidance as well as some of the things associated 817 00:41:26,880 --> 00:41:30,359 Speaker 1: with with the frous resources too. So deer very likely 818 00:41:30,400 --> 00:41:32,399 Speaker 1: able to pick up on some sense that we can't 819 00:41:32,400 --> 00:41:37,600 Speaker 1: even smell period, regardless of their concentration. Yeah, well, wouldn't 820 00:41:37,640 --> 00:41:40,880 Speaker 1: it be, I mean, doesn't it make sense to say 821 00:41:40,920 --> 00:41:47,080 Speaker 1: then that sure, like we we smell of gasoline, right, 822 00:41:47,120 --> 00:41:48,719 Speaker 1: you get gasoline in your hands, We smell. We know 823 00:41:48,800 --> 00:41:51,560 Speaker 1: that scent, we know what it means. A deer, without question, 824 00:41:51,640 --> 00:41:54,280 Speaker 1: could probably smell gasoline. It probably just doesn't matter to them. 825 00:41:54,320 --> 00:41:56,319 Speaker 1: It might just be like like a curious scent. But 826 00:41:56,400 --> 00:42:01,000 Speaker 1: in their worldview, they probably really don't like it, probably 827 00:42:01,000 --> 00:42:04,719 Speaker 1: doesn't register as much of like, probably doesn't matter. Yeah, 828 00:42:04,760 --> 00:42:06,799 Speaker 1: that that kind of brings up the the idea. You know, 829 00:42:06,920 --> 00:42:10,840 Speaker 1: if if you smell something, do you have an innate 830 00:42:10,880 --> 00:42:13,239 Speaker 1: response to it or is it a learned response to it? 831 00:42:14,000 --> 00:42:16,719 Speaker 1: And most smells, you know, you you you respond to 832 00:42:16,760 --> 00:42:20,080 Speaker 1: that based on a learned behavior. So deer learns what's 833 00:42:20,200 --> 00:42:23,160 Speaker 1: what smells maybe bad or what smells are good. They 834 00:42:23,239 --> 00:42:25,239 Speaker 1: must learn the smell of a predator from their mother. 835 00:42:25,320 --> 00:42:28,719 Speaker 1: Do they inherently fear a predator? You know, there might 836 00:42:28,760 --> 00:42:30,880 Speaker 1: be some aspect of that, but I think you know 837 00:42:31,160 --> 00:42:34,160 Speaker 1: how many times you can train a deer to walk 838 00:42:34,239 --> 00:42:36,279 Speaker 1: up to a human. We train them all the time. 839 00:42:36,320 --> 00:42:38,080 Speaker 1: You know, they're not afraid of the smell of a human. 840 00:42:38,239 --> 00:42:41,960 Speaker 1: Inherently they learn that, and you look at it, you know, 841 00:42:42,120 --> 00:42:44,319 Speaker 1: suburban urban deer, they're not afraid of the smell of 842 00:42:44,320 --> 00:42:48,000 Speaker 1: a human. You know. So it's it's much more of 843 00:42:48,040 --> 00:42:51,120 Speaker 1: a learned behavior which which things are important, in which 844 00:42:51,160 --> 00:42:54,719 Speaker 1: things are not important to them? Yeah, um, you know, 845 00:42:54,719 --> 00:42:56,600 Speaker 1: I mean you think about that from the perspective of 846 00:42:56,640 --> 00:43:00,880 Speaker 1: a you know, if a fawn out there when mom 847 00:43:00,960 --> 00:43:03,279 Speaker 1: bumps into the scent of you know, a coyote just 848 00:43:03,320 --> 00:43:05,279 Speaker 1: went through her, a wolf or something just went through 849 00:43:05,400 --> 00:43:08,680 Speaker 1: her reaction that. You know, we don't think about communication 850 00:43:08,960 --> 00:43:11,239 Speaker 1: in body language nearly as much as we probably should. 851 00:43:11,280 --> 00:43:13,799 Speaker 1: You do a lot when you train dogs, but you know, 852 00:43:13,920 --> 00:43:17,520 Speaker 1: there's an entire lesson with every encounter that they're just 853 00:43:17,800 --> 00:43:21,400 Speaker 1: throughout osmosis. Man, they're just taking that in and we 854 00:43:21,400 --> 00:43:23,319 Speaker 1: we don't think about it that way. We think of 855 00:43:23,360 --> 00:43:25,520 Speaker 1: it as something inherent. But you're right, I mean it 856 00:43:25,560 --> 00:43:28,960 Speaker 1: probably all of that stuff is learned right right, And 857 00:43:29,000 --> 00:43:30,520 Speaker 1: I think a lot of it, like you said, is 858 00:43:30,680 --> 00:43:33,640 Speaker 1: learned through body language. How the how the damn responds 859 00:43:33,680 --> 00:43:37,080 Speaker 1: to something that the phone learner responded that way as well. 860 00:43:37,680 --> 00:43:40,319 Speaker 1: And there are just so many subtle things that, dear 861 00:43:40,400 --> 00:43:42,520 Speaker 1: dude that you know, most humans, if you don't train, 862 00:43:42,640 --> 00:43:44,319 Speaker 1: your eyes are not trained to it. You don't see 863 00:43:44,480 --> 00:43:47,680 Speaker 1: that you're actually doing that. You know, I learned a 864 00:43:47,680 --> 00:43:50,240 Speaker 1: lot from my wife who she's trained horses all her life, 865 00:43:50,239 --> 00:43:52,640 Speaker 1: and she can tell what horses thinking. Just buy what 866 00:43:52,719 --> 00:43:55,920 Speaker 1: that horses. It's posture, ear posture and stuff like that. 867 00:43:56,239 --> 00:43:57,759 Speaker 1: So you know, I translate a lot of that and 868 00:43:58,200 --> 00:44:03,600 Speaker 1: deer as well. Yeah, that that world. It's so fascinating. 869 00:44:03,600 --> 00:44:06,600 Speaker 1: I mean when you when you talk about you know, 870 00:44:07,280 --> 00:44:08,920 Speaker 1: dog training. I hate to keep going back to this, 871 00:44:08,960 --> 00:44:13,040 Speaker 1: but you know, we have years of coevolution and we 872 00:44:13,080 --> 00:44:15,600 Speaker 1: haven't been able to speak to each other, and yet 873 00:44:15,640 --> 00:44:17,680 Speaker 1: we can do a lot of really cool stuff, you know, 874 00:44:17,680 --> 00:44:19,319 Speaker 1: I mean, we we can speak to a dog, right, 875 00:44:19,360 --> 00:44:22,120 Speaker 1: but it's not it's not you and I having a conversation. 876 00:44:22,560 --> 00:44:24,600 Speaker 1: And yet they can figure out a hell of a 877 00:44:24,640 --> 00:44:27,280 Speaker 1: lot about what we want just by how we move 878 00:44:27,400 --> 00:44:30,080 Speaker 1: and how we look and I you know, eye contact 879 00:44:30,120 --> 00:44:32,239 Speaker 1: and all that stuff. And then you think about these 880 00:44:32,239 --> 00:44:34,640 Speaker 1: animals out there, and you know, you know, social animals 881 00:44:34,680 --> 00:44:37,920 Speaker 1: like deer living together. There's there's so much going on 882 00:44:38,480 --> 00:44:41,839 Speaker 1: and and so much behavior that we really don't get 883 00:44:41,880 --> 00:44:44,280 Speaker 1: to observe like we we think we do as hunters. 884 00:44:44,280 --> 00:44:46,560 Speaker 1: We're like, oh, we're pretty clued into this scrape behavior 885 00:44:46,680 --> 00:44:49,440 Speaker 1: or fighting, you know, And then you think, well, how 886 00:44:49,440 --> 00:44:51,880 Speaker 1: often do you actually watch two bucks really fight or 887 00:44:51,920 --> 00:44:54,200 Speaker 1: two does you know, get up on their hind legs 888 00:44:54,200 --> 00:44:57,239 Speaker 1: and squat each other. It's pretty rare, and when when 889 00:44:57,239 --> 00:45:00,440 Speaker 1: you do, you're getting a lesson in body language every time. Right, 890 00:45:01,000 --> 00:45:03,799 Speaker 1: But a lot of times what you see there's precursors 891 00:45:03,840 --> 00:45:06,640 Speaker 1: to that in all types of body language that's being used, 892 00:45:06,640 --> 00:45:10,759 Speaker 1: and many times it's avoiding because of those precursors. Yeah, 893 00:45:10,840 --> 00:45:13,319 Speaker 1: you see him, You see him kind of bump up 894 00:45:13,360 --> 00:45:16,640 Speaker 1: a little bit, But they don't. You know, some somebody 895 00:45:16,680 --> 00:45:20,120 Speaker 1: wins without having to win, win, right because you know 896 00:45:20,280 --> 00:45:22,040 Speaker 1: that if you're going to get into a fight that 897 00:45:22,160 --> 00:45:24,799 Speaker 1: that becomes risky for you. Why take that risk if 898 00:45:24,800 --> 00:45:26,719 Speaker 1: you can just use and you know, and an ear 899 00:45:26,800 --> 00:45:30,319 Speaker 1: drop or something like that that avoid Yeah, and that 900 00:45:30,560 --> 00:45:32,640 Speaker 1: I mean this kind of goes back to what we 901 00:45:32,680 --> 00:45:35,640 Speaker 1: talked about earlier about you know, calling to a deer 902 00:45:35,640 --> 00:45:38,640 Speaker 1: that's looking away versus calling towards you, and and just 903 00:45:38,680 --> 00:45:42,839 Speaker 1: paying attention to dear behavior. I mean, every time every 904 00:45:42,840 --> 00:45:45,160 Speaker 1: time you have two bucks square up around you, or 905 00:45:45,200 --> 00:45:47,440 Speaker 1: you know, kind of bump up against one another, and 906 00:45:47,480 --> 00:45:49,680 Speaker 1: don't they're not gonna fight, but they're doing something. They're 907 00:45:50,280 --> 00:45:51,840 Speaker 1: you know, like they're they're working on each other a 908 00:45:51,920 --> 00:45:54,680 Speaker 1: little bit. Or you watch that dominant dough in the 909 00:45:54,719 --> 00:45:57,000 Speaker 1: little Kill plot and that year and a half old 910 00:45:57,040 --> 00:45:58,880 Speaker 1: dough tries to come in and you see just the 911 00:45:59,600 --> 00:46:03,000 Speaker 1: just to suttle a hunch you know, like she's saying something. 912 00:46:03,600 --> 00:46:05,440 Speaker 1: It teaches you so much about how you need to 913 00:46:05,480 --> 00:46:08,000 Speaker 1: interact with them, like if you like to call or 914 00:46:08,239 --> 00:46:11,040 Speaker 1: like what you know, like what level of interaction you're 915 00:46:11,080 --> 00:46:13,520 Speaker 1: gonna have with that specific deer that morning as he's 916 00:46:13,560 --> 00:46:15,359 Speaker 1: walking by, like it, does he look like a deer 917 00:46:15,360 --> 00:46:17,120 Speaker 1: who's gonna be callable? Like is he going to be 918 00:46:17,120 --> 00:46:18,880 Speaker 1: susceptible to the snort wheel er? Is he going to 919 00:46:18,960 --> 00:46:22,880 Speaker 1: be gonzo? Yeah? Or even you know, attending grunt is 920 00:46:23,239 --> 00:46:26,759 Speaker 1: you know, I've had I've had bucks that I knew 921 00:46:26,760 --> 00:46:30,000 Speaker 1: were there. I learned this a long time ago that 922 00:46:30,040 --> 00:46:31,759 Speaker 1: there are time to use attending grunt and tome not 923 00:46:31,880 --> 00:46:33,160 Speaker 1: to And the peak of the right is not the 924 00:46:33,200 --> 00:46:35,760 Speaker 1: time to use attending grunt because most of the bucks 925 00:46:35,760 --> 00:46:38,839 Speaker 1: out there have a dough with them. So if they're 926 00:46:38,880 --> 00:46:40,920 Speaker 1: just if they're already on on a hot no, you 927 00:46:41,000 --> 00:46:43,439 Speaker 1: use it, you know, an aggressive grunt or attending grunt 928 00:46:43,520 --> 00:46:45,120 Speaker 1: or something like that. They're they're not going to come 929 00:46:45,160 --> 00:46:47,200 Speaker 1: to fight. They're going to take that do and turn 930 00:46:47,239 --> 00:46:50,759 Speaker 1: her away and go the other direction. Right, So you know, 931 00:46:50,920 --> 00:46:52,799 Speaker 1: I use a grunt before and after the peak of 932 00:46:52,800 --> 00:46:54,640 Speaker 1: the rep but not actually during the peak of the Red. 933 00:46:54,880 --> 00:46:59,920 Speaker 1: Yeah me too. I Actually I really like early season 934 00:47:00,320 --> 00:47:02,279 Speaker 1: when you when you see the right situation. Like I 935 00:47:02,360 --> 00:47:04,480 Speaker 1: killed a buck in Minnesota, I don't know, five or 936 00:47:04,480 --> 00:47:06,759 Speaker 1: six years ago, and he came out and there was 937 00:47:06,760 --> 00:47:09,440 Speaker 1: already two little bucks out in the field and just 938 00:47:09,520 --> 00:47:11,080 Speaker 1: kind of the way he was around him. I was like, 939 00:47:11,080 --> 00:47:13,200 Speaker 1: I'm gonna grunt this dude and kill him. And it 940 00:47:13,320 --> 00:47:16,520 Speaker 1: took like five contact grunts and you could just see 941 00:47:16,600 --> 00:47:19,279 Speaker 1: him just he was just like I don't want to 942 00:47:19,360 --> 00:47:21,760 Speaker 1: be I'm not gonna let anybody who's in this field 943 00:47:21,760 --> 00:47:23,680 Speaker 1: talk to me that way, like you could just see. 944 00:47:24,160 --> 00:47:26,040 Speaker 1: And part of it was the way he was interacting 945 00:47:26,080 --> 00:47:27,600 Speaker 1: with those other bucks. And he came in and I 946 00:47:27,600 --> 00:47:29,719 Speaker 1: shot him, and it was like almost it was just 947 00:47:29,760 --> 00:47:31,239 Speaker 1: one of those moments where you just look at it 948 00:47:31,239 --> 00:47:32,920 Speaker 1: and you go, this is the guy. Like it's like 949 00:47:32,960 --> 00:47:35,000 Speaker 1: that hot two year old turkey that you run into 950 00:47:35,320 --> 00:47:37,040 Speaker 1: that hits your you know, as soon as you yell 951 00:47:37,040 --> 00:47:42,520 Speaker 1: pee gobbles, you're like, oh, y, yeah, this one's coming. Man. Uh. 952 00:47:42,600 --> 00:47:46,160 Speaker 1: Let's let's switch gears here a little bit and talk 953 00:47:46,239 --> 00:47:49,520 Speaker 1: about deer management. What what what's going on? What do 954 00:47:49,520 --> 00:47:52,680 Speaker 1: you what's what's the future of of the white tail. 955 00:47:52,719 --> 00:47:54,719 Speaker 1: I'm gonna give you a really hard question, because there's 956 00:47:54,760 --> 00:47:57,040 Speaker 1: a million things we could go But how do you 957 00:47:57,080 --> 00:48:01,200 Speaker 1: feel about the future of white tails? Uh? Well, that's 958 00:48:01,239 --> 00:48:06,520 Speaker 1: that's that's a well, that's a thousand dollar questions. Oh hey, 959 00:48:06,560 --> 00:48:09,239 Speaker 1: let me rephrase that. Are we gonna be hunting white 960 00:48:09,239 --> 00:48:12,600 Speaker 1: tales in fifty years? Yeah? Here here? You know, I've 961 00:48:12,640 --> 00:48:15,279 Speaker 1: I've often thought of this, this question, you know, and 962 00:48:15,400 --> 00:48:18,280 Speaker 1: if I how I respond if I was asked this question. 963 00:48:18,760 --> 00:48:23,960 Speaker 1: You know, the white tail, from the time we started 964 00:48:23,960 --> 00:48:26,600 Speaker 1: restoring white tailed deer over a hundreds some years ago, 965 00:48:27,520 --> 00:48:30,719 Speaker 1: has always had issues that biologists and hunters have had 966 00:48:30,760 --> 00:48:33,399 Speaker 1: to get go get through to get us to where 967 00:48:33,480 --> 00:48:35,919 Speaker 1: we are. So, you know, at one time there weren't 968 00:48:35,920 --> 00:48:39,320 Speaker 1: any here. Then we went through the restoration phase, and 969 00:48:39,360 --> 00:48:41,879 Speaker 1: then we ended up with in many places, we went 970 00:48:41,880 --> 00:48:46,040 Speaker 1: in through the popular overpopulation phase, you know, in Pennsylvania, 971 00:48:46,120 --> 00:48:49,600 Speaker 1: West Virginia, Michigan, all throughout the New England states. And 972 00:48:49,680 --> 00:48:52,000 Speaker 1: you know, there's always been a different phase that we've 973 00:48:52,000 --> 00:48:55,480 Speaker 1: gone through. Someone been easier to surmountain some of them. 974 00:48:55,480 --> 00:48:58,240 Speaker 1: Having the white tale is facing a number of challenges 975 00:48:58,360 --> 00:49:01,080 Speaker 1: right now, and one of the bigest challenges, of course 976 00:49:01,080 --> 00:49:03,160 Speaker 1: the c w D, which I know you've done a 977 00:49:03,160 --> 00:49:08,680 Speaker 1: podcast on, but that one is a major, a major issue, 978 00:49:08,760 --> 00:49:12,239 Speaker 1: because there's no right now, there's nothing in the course 979 00:49:12,719 --> 00:49:15,880 Speaker 1: sitable future that's going to stop. So it's gonna be 980 00:49:16,000 --> 00:49:20,160 Speaker 1: ubiquitous across the white tails range someday, very likely it 981 00:49:20,200 --> 00:49:23,200 Speaker 1: will be, and it's not it's going to affect dear populations, 982 00:49:23,239 --> 00:49:26,360 Speaker 1: and it's gonna affect deer hunters and the number of hunters. 983 00:49:26,360 --> 00:49:28,120 Speaker 1: And I think that the number of hunters is a 984 00:49:28,200 --> 00:49:31,680 Speaker 1: very important aspect of this. We've already had seen over 985 00:49:31,719 --> 00:49:34,000 Speaker 1: the last couple of decades of decline in the numbers 986 00:49:34,000 --> 00:49:37,080 Speaker 1: of hunters. The recruitment and retention of hunters has gone 987 00:49:37,120 --> 00:49:40,480 Speaker 1: down pretty dramatically over the last couple of decades, which 988 00:49:40,880 --> 00:49:44,200 Speaker 1: that supports the funding free agencies and so forth. So 989 00:49:44,400 --> 00:49:46,440 Speaker 1: you know, when the money starts to dry up, then 990 00:49:46,440 --> 00:49:48,759 Speaker 1: the management starts to decrease, and you know, you get 991 00:49:48,920 --> 00:49:52,000 Speaker 1: a vicious cycle here. So that's a tough one to 992 00:49:52,040 --> 00:49:54,879 Speaker 1: deal with. You know, although you know that if there 993 00:49:54,960 --> 00:49:57,720 Speaker 1: was a bright spot to COVID, you know that COVID 994 00:49:57,719 --> 00:49:59,600 Speaker 1: actually put a bunch of people in the woods. When 995 00:50:00,160 --> 00:50:02,880 Speaker 1: you couldn't do you know, anything else. So there was 996 00:50:02,920 --> 00:50:04,920 Speaker 1: a bump up. And then you know this idea with 997 00:50:05,000 --> 00:50:08,920 Speaker 1: the local order, you know, the hunters, you recruiting new hunters. 998 00:50:09,160 --> 00:50:10,759 Speaker 1: You know, that's a it's a good movement in the 999 00:50:10,840 --> 00:50:12,759 Speaker 1: right direction, you know. So I think we're you know, 1000 00:50:13,120 --> 00:50:16,040 Speaker 1: that one is being addressed. The c w D issue. 1001 00:50:16,200 --> 00:50:21,040 Speaker 1: That's also gonna have affect populations. Um, you know, areas 1002 00:50:21,040 --> 00:50:23,080 Speaker 1: where you have high productivity, like where it is in 1003 00:50:23,080 --> 00:50:26,440 Speaker 1: Wisconsin and stuff like that. In places like that high productivity, 1004 00:50:26,480 --> 00:50:29,160 Speaker 1: you can manage around it, although we unite might not 1005 00:50:29,239 --> 00:50:32,799 Speaker 1: be managed for the mature buses because those are the 1006 00:50:32,800 --> 00:50:35,759 Speaker 1: ones that have the highest prevalence rates. Right. But in 1007 00:50:35,800 --> 00:50:40,480 Speaker 1: some areas where um you got inherently low productivity deer 1008 00:50:40,560 --> 00:50:43,200 Speaker 1: hers throwing c w ding on top of that, could 1009 00:50:43,480 --> 00:50:47,879 Speaker 1: could be you know, pretty strong population influence driving those 1010 00:50:47,920 --> 00:50:52,480 Speaker 1: populations down. Yeah. So that's that's that's one major living much. 1011 00:50:52,560 --> 00:50:55,240 Speaker 1: One other major issue is well that I see, particularly 1012 00:50:55,239 --> 00:50:57,680 Speaker 1: in the East United States, is the changing predator context 1013 00:50:58,200 --> 00:51:01,160 Speaker 1: that the white tail losing. Now, uh, you know, we 1014 00:51:01,200 --> 00:51:05,000 Speaker 1: didn't have coyotes, we do have kiots. Beer populations where 1015 00:51:05,040 --> 00:51:07,960 Speaker 1: bears occur are at an all time higher or a 1016 00:51:08,000 --> 00:51:11,560 Speaker 1: stort high UH. And both of those are tremendous predators 1017 00:51:11,560 --> 00:51:14,799 Speaker 1: on pawns, and in some areas, like the Southern Appalachians, 1018 00:51:14,880 --> 00:51:18,359 Speaker 1: they're driving deer populations down to the point where right 1019 00:51:18,360 --> 00:51:20,719 Speaker 1: now we just are finishing up a study in North 1020 00:51:20,800 --> 00:51:26,560 Speaker 1: Georgia where the survival rate is incredibly lower fawds UH, 1021 00:51:26,560 --> 00:51:30,600 Speaker 1: and it's most mostly due to predation by bears and coyotes, 1022 00:51:31,040 --> 00:51:36,560 Speaker 1: to the point where even uh, even eliminating antler harvest 1023 00:51:36,640 --> 00:51:40,160 Speaker 1: antler list harvest completely, those populations aren't going to recover 1024 00:51:41,320 --> 00:51:44,040 Speaker 1: unless there's something that can be done to increase bonding recruitment. 1025 00:51:44,840 --> 00:51:47,640 Speaker 1: You know, in most areas where we have a good productivity, 1026 00:51:47,680 --> 00:51:50,400 Speaker 1: we can deal with fond you know, faw predation aspects 1027 00:51:50,760 --> 00:51:53,480 Speaker 1: by predators by reducing ant lost harvest. But there are 1028 00:51:53,520 --> 00:51:55,359 Speaker 1: some areas where we're not gonna be able to do that, 1029 00:51:55,640 --> 00:52:00,000 Speaker 1: and those deer populations are going to be no, They're 1030 00:52:00,000 --> 00:52:03,160 Speaker 1: will be difficult to deal with. Places in Georgia North 1031 00:52:03,160 --> 00:52:05,560 Speaker 1: Groorgan Mountains where we historically have you know, some more 1032 00:52:06,239 --> 00:52:09,640 Speaker 1: earliest deer hunting was done up there. Well, the number 1033 00:52:09,680 --> 00:52:12,160 Speaker 1: of hunters is incredibly old and now be just because 1034 00:52:12,280 --> 00:52:17,200 Speaker 1: the deer are gone, and so this this is probably 1035 00:52:17,400 --> 00:52:20,799 Speaker 1: I'm guessing there's some people listening to this who are 1036 00:52:20,880 --> 00:52:24,080 Speaker 1: young enough or they don't really understand that what you're 1037 00:52:24,120 --> 00:52:28,680 Speaker 1: talking about here is their deer herds that haven't historically 1038 00:52:28,880 --> 00:52:32,920 Speaker 1: had coyotes uh to deal with like this, this is 1039 00:52:32,920 --> 00:52:34,439 Speaker 1: a new thing. Like if you look at the history 1040 00:52:34,440 --> 00:52:36,640 Speaker 1: of coyotes where they came from in the Southwest and 1041 00:52:37,160 --> 00:52:40,320 Speaker 1: have been pushed farther out into the country and they've 1042 00:52:40,360 --> 00:52:42,840 Speaker 1: expanded and they're there. You know, we talked about the 1043 00:52:42,840 --> 00:52:46,800 Speaker 1: adaptability of white tails, which is incredible in the diverse 1044 00:52:46,920 --> 00:52:49,360 Speaker 1: environments they can take hold of and and and be 1045 00:52:49,400 --> 00:52:51,760 Speaker 1: pretty facund in there. When you look at the story 1046 00:52:51,760 --> 00:52:55,120 Speaker 1: of the coyote, that's incredible. That's that's like, think of 1047 00:52:55,160 --> 00:52:58,040 Speaker 1: them what you will. They are an incredible survival machine. 1048 00:52:58,520 --> 00:53:00,200 Speaker 1: And you know that you look at their die had 1049 00:53:00,520 --> 00:53:03,040 Speaker 1: they eat everything from scorpions on up, you know, and 1050 00:53:03,040 --> 00:53:05,480 Speaker 1: and you'll see them eating apples and stuff sometimes off 1051 00:53:05,560 --> 00:53:08,719 Speaker 1: your stand like there crazy. And now you've got these 1052 00:53:08,760 --> 00:53:11,880 Speaker 1: populations of deer you know, did have been there for 1053 00:53:12,080 --> 00:53:14,520 Speaker 1: generations and generations and they're dealing with a predator that 1054 00:53:14,560 --> 00:53:18,719 Speaker 1: they don't have that ancestral kind of uh, you know 1055 00:53:18,719 --> 00:53:23,680 Speaker 1: knowledge or you know, survivors survival skills to deal with. 1056 00:53:23,719 --> 00:53:26,879 Speaker 1: And then you throw in bear. You know, bear populations 1057 00:53:26,960 --> 00:53:29,520 Speaker 1: on the rise, and how good they are at chomping 1058 00:53:29,520 --> 00:53:31,719 Speaker 1: fonds for the first you know, x amount of weeks 1059 00:53:31,719 --> 00:53:35,480 Speaker 1: that they're dropped. And it's a sort of a rough picture. Yeah, 1060 00:53:35,640 --> 00:53:38,120 Speaker 1: when you when you think about I don't think until 1061 00:53:38,160 --> 00:53:41,000 Speaker 1: some of the last decade or two when the research 1062 00:53:41,080 --> 00:53:43,160 Speaker 1: was done in Pennsylvania, here a Georgia in the number 1063 00:53:43,160 --> 00:53:47,560 Speaker 1: of places across you know, across the country, how effective 1064 00:53:47,560 --> 00:53:49,759 Speaker 1: both bears and kyles are when it comes to take 1065 00:53:49,800 --> 00:53:52,160 Speaker 1: a fawns. But when you think about it, for seven 1066 00:53:52,200 --> 00:53:54,520 Speaker 1: or fourteen days or more, you got these little protein 1067 00:53:54,560 --> 00:53:57,400 Speaker 1: package laying out all over the woods. Just all they 1068 00:53:57,400 --> 00:53:59,759 Speaker 1: have to do is pick them up. I remember what 1069 00:54:00,000 --> 00:54:03,080 Speaker 1: one time a few years ago up in Pennsylvania, I 1070 00:54:03,120 --> 00:54:06,000 Speaker 1: was up there during right during the pick of the fawning, 1071 00:54:06,080 --> 00:54:09,000 Speaker 1: and in some of the fourth there's a lot of 1072 00:54:09,120 --> 00:54:11,000 Speaker 1: hasten and fern, a lot of fern cover on the 1073 00:54:11,160 --> 00:54:13,640 Speaker 1: on the ground, and I saw a place where a 1074 00:54:13,680 --> 00:54:15,920 Speaker 1: bear had gone through and had were actually worked a 1075 00:54:15,960 --> 00:54:19,360 Speaker 1: grid pattern back and forth back and forth, back and forth, 1076 00:54:19,480 --> 00:54:21,800 Speaker 1: you know, looking for fawns at that time, you know, 1077 00:54:21,840 --> 00:54:24,120 Speaker 1: and that bear could do that all day long. How 1078 00:54:24,120 --> 00:54:26,480 Speaker 1: many funds is he going to encounter? Certainly is going 1079 00:54:26,520 --> 00:54:29,480 Speaker 1: to be start below you know. Yeah, well that's why 1080 00:54:29,600 --> 00:54:31,759 Speaker 1: I think that I've talked about this on here a 1081 00:54:31,760 --> 00:54:34,920 Speaker 1: few times. They studied uh fawn mortality in the in 1082 00:54:35,000 --> 00:54:38,480 Speaker 1: the county I hunt over in northern Wisconsin, and it 1083 00:54:38,600 --> 00:54:41,000 Speaker 1: was pretty dismal. It was nine of the funds were 1084 00:54:41,040 --> 00:54:44,040 Speaker 1: killed in the first eleven months, and like two thirds 1085 00:54:44,040 --> 00:54:46,839 Speaker 1: of those were predation and the bears were the number 1086 00:54:46,920 --> 00:54:50,120 Speaker 1: one predator. And you're talking a place with a high 1087 00:54:50,200 --> 00:54:53,800 Speaker 1: prevalence of you've got bears, coyotes, you've got some wolves 1088 00:54:53,880 --> 00:54:57,360 Speaker 1: they're not a not a ton, but you've got bob cats. 1089 00:54:57,360 --> 00:55:00,279 Speaker 1: You you've got things that can chomp on fawns, and 1090 00:55:00,360 --> 00:55:03,640 Speaker 1: bears are so good at it. When you said that 1091 00:55:04,480 --> 00:55:09,080 Speaker 1: of the fonds that were born died or the mortality 1092 00:55:09,120 --> 00:55:12,399 Speaker 1: of fawnds based on predators, they were dead. So no, no, 1093 00:55:12,560 --> 00:55:15,080 Speaker 1: So of the funds they studied were dead within the 1094 00:55:15,080 --> 00:55:20,719 Speaker 1: first eleven months, and two thirds of those were predator, right, yeah, So, 1095 00:55:20,800 --> 00:55:22,680 Speaker 1: and I mean it's one County. It's you know it. 1096 00:55:23,800 --> 00:55:26,040 Speaker 1: But to me it was an eye opener because we 1097 00:55:26,239 --> 00:55:27,839 Speaker 1: you know, it's the only place I ever hunt where 1098 00:55:27,880 --> 00:55:31,560 Speaker 1: I just incidentally see bears quite often, Like there's there's 1099 00:55:31,560 --> 00:55:34,400 Speaker 1: a lot of them there, and you know, we've always 1100 00:55:34,400 --> 00:55:36,759 Speaker 1: had trouble with deer, like you just don't have very 1101 00:55:36,760 --> 00:55:39,279 Speaker 1: many of them, and you start seeing that connection and 1102 00:55:39,280 --> 00:55:42,799 Speaker 1: then you think about, you know, it's something I want 1103 00:55:42,800 --> 00:55:45,680 Speaker 1: to ask you about. Is like you take that just 1104 00:55:45,760 --> 00:55:47,000 Speaker 1: for what it is. If you're hunt in the big 1105 00:55:47,000 --> 00:55:50,080 Speaker 1: woods up north, you're like, Okay, I'm around these predators 1106 00:55:50,080 --> 00:55:52,000 Speaker 1: that have just kind of always been here, right Like, 1107 00:55:52,040 --> 00:55:54,480 Speaker 1: I mean, you know, the wolf populations have changed and 1108 00:55:54,520 --> 00:55:57,480 Speaker 1: we know that story, but generally they've had a rough 1109 00:55:57,680 --> 00:55:59,839 Speaker 1: the deer have a rough go of it up there. 1110 00:56:00,160 --> 00:56:02,640 Speaker 1: But now you talk about you know, you're talking about 1111 00:56:02,640 --> 00:56:04,600 Speaker 1: Georgia and you're talking about the southeast, and you're talking 1112 00:56:04,640 --> 00:56:10,120 Speaker 1: about places where you know, like these predators haven't been 1113 00:56:10,840 --> 00:56:12,840 Speaker 1: you know, the counties haven't been there. The bears probably 1114 00:56:12,880 --> 00:56:14,920 Speaker 1: been there, but not to the extent they are so 1115 00:56:15,160 --> 00:56:16,880 Speaker 1: and now you know, you know how people like to 1116 00:56:16,920 --> 00:56:19,720 Speaker 1: talk about, well, there's bobcats everywhere, or there's mountain lions 1117 00:56:19,760 --> 00:56:22,520 Speaker 1: making it into this spot. Why why are the predators 1118 00:56:22,560 --> 00:56:26,480 Speaker 1: doing so well in these places we go? Bear populations 1119 00:56:26,560 --> 00:56:28,920 Speaker 1: are doing well because the bear harvest has been restricted 1120 00:56:29,000 --> 00:56:32,040 Speaker 1: enough to allow the bear populaces to recover. To the 1121 00:56:32,080 --> 00:56:34,520 Speaker 1: point is, you know, actually in some place like North 1122 00:56:34,560 --> 00:56:38,560 Speaker 1: Rodian Mountains you can take multiple bears. Now the point 1123 00:56:38,560 --> 00:56:41,520 Speaker 1: it was after you shoot one bear, particularly in those mountains, 1124 00:56:41,560 --> 00:56:42,799 Speaker 1: and you have to get it out of the humming 1125 00:56:42,840 --> 00:56:45,120 Speaker 1: bears you need to to kill in your lifetime. It's 1126 00:56:45,160 --> 00:56:47,600 Speaker 1: not like you're going out and killing deer for the freezer. 1127 00:56:47,760 --> 00:56:52,400 Speaker 1: So so you know, the harvest has not matched the 1128 00:56:52,440 --> 00:56:55,319 Speaker 1: population growth, and the population growth, based on what our 1129 00:56:55,320 --> 00:56:58,120 Speaker 1: research has shown over the last several decades, it's continuing 1130 00:56:58,160 --> 00:57:01,960 Speaker 1: to increase in the bear population. So the kyots, you know, 1131 00:57:02,320 --> 00:57:05,920 Speaker 1: Kyle has been extremely successful, particularly here in the southeast, 1132 00:57:06,000 --> 00:57:10,160 Speaker 1: because you know, we have such varied habitats from urban, 1133 00:57:10,200 --> 00:57:13,400 Speaker 1: suburban and agricultural. You know, different types of forest types 1134 00:57:13,960 --> 00:57:16,880 Speaker 1: all interspersed together. There's plenty for a kyote because I'll 1135 00:57:16,880 --> 00:57:20,680 Speaker 1: eat just about anything, and we probably have higher kyoud 1136 00:57:20,760 --> 00:57:24,160 Speaker 1: densities here than you know then most places you would 1137 00:57:24,160 --> 00:57:27,960 Speaker 1: ever expect to find Kyos maybe maybe similar to South Texas. 1138 00:57:28,040 --> 00:57:32,480 Speaker 1: You know. Um, so when you got that high density 1139 00:57:32,520 --> 00:57:35,919 Speaker 1: to Kyle's out there, and then just for a few 1140 00:57:36,200 --> 00:57:39,080 Speaker 1: brief time you have all these sponsor at the ground, 1141 00:57:39,320 --> 00:57:41,120 Speaker 1: they're going to key in on those things because it's 1142 00:57:41,120 --> 00:57:44,240 Speaker 1: a very easy, easy thing for them to find, and 1143 00:57:44,320 --> 00:57:47,440 Speaker 1: particularly it's easy for them to catch some of that 1144 00:57:47,480 --> 00:57:50,080 Speaker 1: stuff and carry it back to the the pups in 1145 00:57:50,120 --> 00:57:52,600 Speaker 1: the den. Because those things are happening at the same 1146 00:57:52,640 --> 00:57:54,760 Speaker 1: time they're raising pups. They can carry the little protein 1147 00:57:54,800 --> 00:57:58,439 Speaker 1: package back. You know, it's not picture of that versus 1148 00:57:58,440 --> 00:57:59,840 Speaker 1: going out and trying to catch a bunch of mice 1149 00:57:59,880 --> 00:58:02,320 Speaker 1: and carrying them back. Do your puffs. You know, they're 1150 00:58:02,320 --> 00:58:05,640 Speaker 1: going to key on those fawnds. Yeah, you're when you're 1151 00:58:05,680 --> 00:58:09,320 Speaker 1: you know, when we're really hungry, we've we've gravitate towards 1152 00:58:09,320 --> 00:58:12,360 Speaker 1: calorie dense food, which is why we eat a lot 1153 00:58:12,360 --> 00:58:13,880 Speaker 1: of junk food. But we don't think about that the 1154 00:58:13,920 --> 00:58:15,440 Speaker 1: same way. If you're a coyote out there and you 1155 00:58:15,440 --> 00:58:17,320 Speaker 1: can work all day to catch a bunch of grasshoppers, 1156 00:58:17,400 --> 00:58:19,240 Speaker 1: or you can work all day and find one fawn, 1157 00:58:20,160 --> 00:58:24,320 Speaker 1: you know what you're gonna do. Yeah, Yeah, If there 1158 00:58:24,440 --> 00:58:27,320 Speaker 1: was a market for kyotes or if there's a you 1159 00:58:27,360 --> 00:58:30,720 Speaker 1: know right now, you know the highest mortality of kyles 1160 00:58:30,720 --> 00:58:33,480 Speaker 1: in Georgia and most of the southeast or hunter harps 1161 00:58:33,520 --> 00:58:36,400 Speaker 1: to kylets. It's a lot of people hunting kylets. Uh. 1162 00:58:36,440 --> 00:58:39,200 Speaker 1: And then the road kills are the other major influence. 1163 00:58:39,720 --> 00:58:44,880 Speaker 1: But you think of one kyle producing you know plast six, seven, eight, 1164 00:58:45,200 --> 00:58:47,560 Speaker 1: eight pubs, persons of fawn are dope producing one and 1165 00:58:47,600 --> 00:58:52,720 Speaker 1: maybe two fawns. Uh. It's kind of disproportionate there. Yeah, 1166 00:58:52,760 --> 00:58:56,440 Speaker 1: And you know, we don't have we don't have the 1167 00:58:56,520 --> 00:58:59,600 Speaker 1: trapping that we used to, you know, I mean it's 1168 00:58:59,600 --> 00:59:02,360 Speaker 1: just yet we just generally don't. That's that's such a 1169 00:59:02,760 --> 00:59:06,040 Speaker 1: such an effective way to control coyotes and some of 1170 00:59:06,040 --> 00:59:10,000 Speaker 1: the other predators. And I think, you know, I don't know, 1171 00:59:10,600 --> 00:59:12,000 Speaker 1: I don't know about where you live it. So I 1172 00:59:12,040 --> 00:59:13,400 Speaker 1: live in the I live in the suburbs of the 1173 00:59:13,400 --> 00:59:17,680 Speaker 1: Twin Cities, and these people around me, like my wife's friends, 1174 00:59:17,720 --> 00:59:21,280 Speaker 1: they have no clue about nature, like they just they 1175 00:59:21,320 --> 00:59:24,920 Speaker 1: just don't. So when everybody started getting like ring doorbell 1176 00:59:25,040 --> 00:59:27,560 Speaker 1: cameras and you know, like the surveillance cameras, that a 1177 00:59:27,560 --> 00:59:29,000 Speaker 1: lot of people have or you know a lot of 1178 00:59:29,000 --> 00:59:31,960 Speaker 1: people will put out a trail camera on their bird 1179 00:59:32,000 --> 00:59:35,200 Speaker 1: feeder or whatever now, you know, so it's like they're seeing, 1180 00:59:35,640 --> 00:59:37,800 Speaker 1: oh my god, there's coyotes everywhere. I'm like, yeah, you 1181 00:59:37,800 --> 00:59:40,280 Speaker 1: can freaking hear them. I hear coyotes all the time 1182 00:59:40,360 --> 00:59:43,160 Speaker 1: right at my front door. And now the bear thing, 1183 00:59:43,680 --> 00:59:45,400 Speaker 1: you know, it's like, oh my god, we we now 1184 00:59:45,440 --> 00:59:49,400 Speaker 1: we have bears. And I'm like, now you see bears. 1185 00:59:50,440 --> 00:59:53,400 Speaker 1: You didn't. You didn't understand how, you know, like we 1186 00:59:53,400 --> 00:59:56,400 Speaker 1: we think of these animals as sort of being out 1187 00:59:56,440 --> 00:59:58,920 Speaker 1: in the wild, you know, like a bear. It bears 1188 00:59:58,920 --> 01:00:01,000 Speaker 1: the prime example, right, Like, when the average person thinks 1189 01:00:01,000 --> 01:00:02,480 Speaker 1: of a bear, they think of it in the big woods, 1190 01:00:02,480 --> 01:00:03,680 Speaker 1: out in the wild, and you know, he's see it 1191 01:00:03,680 --> 01:00:05,920 Speaker 1: in this beautiful place or whatever. And they're living in 1192 01:00:05,920 --> 01:00:08,600 Speaker 1: your backyard, eating in your bird feeder, and you know, 1193 01:00:09,040 --> 01:00:11,920 Speaker 1: digging up like bees nests and stuff in on the 1194 01:00:11,960 --> 01:00:14,680 Speaker 1: soccer field. You know, like they they're living there right 1195 01:00:14,720 --> 01:00:17,640 Speaker 1: there with us, with these deer and they're just their 1196 01:00:17,640 --> 01:00:20,360 Speaker 1: populations are doing really well, and we kind of don't. 1197 01:00:20,640 --> 01:00:24,960 Speaker 1: We don't seem to really recognize how detrimental that can 1198 01:00:25,000 --> 01:00:27,280 Speaker 1: be until it catches up to our dear populations and 1199 01:00:27,320 --> 01:00:31,000 Speaker 1: by then you're in a really reactive state. It's rough. Yeah. 1200 01:00:31,120 --> 01:00:33,040 Speaker 1: And you know, the other thing is the dear populations 1201 01:00:33,040 --> 01:00:34,880 Speaker 1: have done the same thing that they were the first 1202 01:00:34,880 --> 01:00:37,120 Speaker 1: ones to move into the suburbs, you know, and in 1203 01:00:37,200 --> 01:00:39,680 Speaker 1: the urban suburban areas, and now these other things are 1204 01:00:39,680 --> 01:00:43,040 Speaker 1: following them in there because frankly, there's nothing for them 1205 01:00:43,040 --> 01:00:46,160 Speaker 1: to fear, you know, And that that's the whole concept 1206 01:00:46,200 --> 01:00:49,080 Speaker 1: in ecology called the ecology of fear, and fear is 1207 01:00:49,080 --> 01:00:52,520 Speaker 1: a very important driver on animals behavior, you know, fear 1208 01:00:52,520 --> 01:00:54,920 Speaker 1: of being predated on you know, you know, what does 1209 01:00:54,920 --> 01:00:57,360 Speaker 1: the kyot have to fear in the suburbs, Maybe the 1210 01:00:57,400 --> 01:00:59,560 Speaker 1: front end of a ford or something, but that's about it. 1211 01:00:59,640 --> 01:01:03,400 Speaker 1: You know, Uh, deer and suburbs don't have anything to fear. Now, 1212 01:01:03,480 --> 01:01:06,440 Speaker 1: bears are getting into the same situation. They're not hunted 1213 01:01:06,480 --> 01:01:09,160 Speaker 1: in these situations, so they have no fear of humans. 1214 01:01:09,680 --> 01:01:11,480 Speaker 1: And with having no fear of humans, are going to 1215 01:01:11,520 --> 01:01:14,800 Speaker 1: co exist with humans. Yeah, and it and I think 1216 01:01:14,800 --> 01:01:17,120 Speaker 1: that we really underestimate how good they are at not 1217 01:01:17,200 --> 01:01:21,400 Speaker 1: showing themselves to us, you know, they are they are 1218 01:01:21,520 --> 01:01:24,800 Speaker 1: masters of of staying hidden up, Carl. We are just 1219 01:01:24,840 --> 01:01:27,000 Speaker 1: about out of time. So I got one question I 1220 01:01:27,040 --> 01:01:31,240 Speaker 1: want to ask you. In all the research you've done, 1221 01:01:31,480 --> 01:01:33,640 Speaker 1: and it's been a ton, you've learned, a ton, You've 1222 01:01:33,680 --> 01:01:36,840 Speaker 1: you've contributed so much to this space, what's the question 1223 01:01:37,440 --> 01:01:39,440 Speaker 1: that you just that keeps you up at night that 1224 01:01:39,480 --> 01:01:41,400 Speaker 1: you want to learn about? Dear? Like, what's one thing 1225 01:01:41,880 --> 01:01:44,600 Speaker 1: that you're like, I wish if you could wave a 1226 01:01:44,640 --> 01:01:47,160 Speaker 1: magic one and you could study it and and probably 1227 01:01:47,200 --> 01:01:50,320 Speaker 1: come to an answer, what would it be? Well, I'll 1228 01:01:50,360 --> 01:01:52,640 Speaker 1: tell you what. It comes back to what we were 1229 01:01:52,680 --> 01:01:54,800 Speaker 1: talking about. If I could do like spot with a 1230 01:01:54,880 --> 01:01:57,880 Speaker 1: Bulkan mind meld, you know, get de side of your brain, 1231 01:01:58,480 --> 01:02:03,280 Speaker 1: you know, I'll think there there's just really getting a 1232 01:02:03,320 --> 01:02:06,920 Speaker 1: field for what their experience and when their experiences sensory 1233 01:02:06,960 --> 01:02:10,520 Speaker 1: inputs from there, either their eyes or their nose, you know, 1234 01:02:10,560 --> 01:02:12,240 Speaker 1: because we've done a lot of that stuff with the 1235 01:02:12,320 --> 01:02:13,920 Speaker 1: deer vision and stuff like that, and we've got a 1236 01:02:13,960 --> 01:02:17,720 Speaker 1: good feel for what they see. But you know, we 1237 01:02:17,760 --> 01:02:20,800 Speaker 1: can say it's say it's something, but we can't experience that, 1238 01:02:21,440 --> 01:02:23,160 Speaker 1: you know, So that'd be the neatest thing and be 1239 01:02:23,200 --> 01:02:26,000 Speaker 1: able to experience what they actually you know, experienced when 1240 01:02:26,000 --> 01:02:29,720 Speaker 1: they're visualizing something or you know, either whether a deer 1241 01:02:29,880 --> 01:02:31,800 Speaker 1: or a dog, Like you said, I've trained dog all 1242 01:02:31,800 --> 01:02:36,560 Speaker 1: my life as well scent dogs, and just the world 1243 01:02:36,600 --> 01:02:41,200 Speaker 1: that they live in in this world of aromas, world 1244 01:02:41,240 --> 01:02:45,959 Speaker 1: of chemicals, is just fascinating. We have absolutely no appreciation 1245 01:02:46,040 --> 01:02:49,800 Speaker 1: for it. I know. That's that drives me nuts too. 1246 01:02:50,160 --> 01:02:52,120 Speaker 1: I just wish. I mean when you think about if 1247 01:02:52,120 --> 01:02:54,320 Speaker 1: you just like if you're standing in a cattail slow 1248 01:02:54,360 --> 01:02:56,640 Speaker 1: and you just look around and you like all the 1249 01:02:56,680 --> 01:02:59,440 Speaker 1: details you see, like if you're pheasant hunting, you know, 1250 01:02:59,480 --> 01:03:02,320 Speaker 1: like just just visually what you can pick up, and 1251 01:03:02,320 --> 01:03:04,160 Speaker 1: then think about how if you could do that with 1252 01:03:04,200 --> 01:03:07,920 Speaker 1: your nose. You close your eyes and and perceive that 1253 01:03:08,000 --> 01:03:11,640 Speaker 1: much different stuff going on, but do it with your nose, 1254 01:03:11,680 --> 01:03:15,600 Speaker 1: and it's so crazy. Yeah. So you know you asked 1255 01:03:15,600 --> 01:03:17,040 Speaker 1: me earlier a question. I don't know if you got 1256 01:03:17,040 --> 01:03:19,240 Speaker 1: a second or not, but you know, which set would 1257 01:03:19,240 --> 01:03:23,000 Speaker 1: the deer most likely want not want to lose? You know, 1258 01:03:23,200 --> 01:03:26,400 Speaker 1: that idea about the sense of smells, if it's related 1259 01:03:26,440 --> 01:03:28,600 Speaker 1: to predation, that's that's one aspect. But you know, I 1260 01:03:28,640 --> 01:03:30,440 Speaker 1: think a deer without a sense of smell would have 1261 01:03:30,480 --> 01:03:33,040 Speaker 1: a hard time survival. I think a blind deer could 1262 01:03:33,040 --> 01:03:34,880 Speaker 1: survive better than a deal without a sense of smell 1263 01:03:35,160 --> 01:03:39,760 Speaker 1: because it's a communication or because of the predator and food. Oh, 1264 01:03:40,080 --> 01:03:42,360 Speaker 1: a blind deer can find something to eat, but a 1265 01:03:42,360 --> 01:03:47,000 Speaker 1: deer without a nose is going to have a hard time. Yeah, yeah, 1266 01:03:47,000 --> 01:03:49,560 Speaker 1: you don't think about that. Um, Carl, this has been 1267 01:03:49,600 --> 01:03:51,160 Speaker 1: so much fun. Man, I'm so glad I got to 1268 01:03:51,160 --> 01:03:53,040 Speaker 1: talk to you and have you on here. Um. I 1269 01:03:53,080 --> 01:03:55,880 Speaker 1: really appreciate it. It has been a quick hour. I'll 1270 01:03:55,880 --> 01:04:00,120 Speaker 1: tell you, well, we'll do we'll do it again more more. 1271 01:04:00,000 --> 01:04:04,160 Speaker 1: Welcome to awesome. Thank you so much, and that's it 1272 01:04:04,320 --> 01:04:06,400 Speaker 1: for this week, folks, be sure to tune in next 1273 01:04:06,400 --> 01:04:09,440 Speaker 1: week for some more white tailed goodness. This has been 1274 01:04:09,440 --> 01:04:11,840 Speaker 1: Wired to Hunt and I'm your guest host, Tony Peterson. 1275 01:04:12,040 --> 01:04:13,960 Speaker 1: As always, I just want to thank you so much 1276 01:04:14,000 --> 01:04:16,400 Speaker 1: for listening and so much for your support. And if 1277 01:04:16,400 --> 01:04:18,320 Speaker 1: you're in the market for a little bit more white 1278 01:04:18,320 --> 01:04:20,520 Speaker 1: tail content, you can head on over to the meat 1279 01:04:20,560 --> 01:04:24,400 Speaker 1: eater dot com slash wired again, that's the meat eater 1280 01:04:24,560 --> 01:04:28,040 Speaker 1: dot com slash wired and you'll see a pile of 1281 01:04:28,080 --> 01:04:31,720 Speaker 1: new articles by guys like Bo Martonic and Andy May 1282 01:04:31,800 --> 01:04:36,240 Speaker 1: and myself and Mark whole bunch of them, and if 1283 01:04:36,240 --> 01:04:39,080 Speaker 1: you want more, you can head over to our YouTube 1284 01:04:39,160 --> 01:04:41,800 Speaker 1: channel as well. If if those articles aren't enough, we 1285 01:04:41,840 --> 01:04:43,640 Speaker 1: have a Wired to Hunt YouTube channel and we dropped 1286 01:04:43,720 --> 01:04:46,000 Speaker 1: weekly how to videos there as well.