1 00:00:01,200 --> 00:00:04,480 Speaker 1: You're listening to the Sportsman's Nation podcast network, brought to 2 00:00:04,519 --> 00:00:09,120 Speaker 1: you by Outdoor Edge in their complete lineup of knives 3 00:00:09,200 --> 00:00:13,400 Speaker 1: and game processing kits. These guys right now are doing 4 00:00:13,440 --> 00:00:18,160 Speaker 1: an absolutely huge giveaway where you could win an elk hunt. 5 00:00:18,280 --> 00:00:21,079 Speaker 1: And not just any elk hunt. We're talking about a 6 00:00:21,200 --> 00:00:26,200 Speaker 1: seven or eight mile horseback ride into the back country. 7 00:00:26,200 --> 00:00:29,680 Speaker 1: We're talking a one on one and guided hunt. You're 8 00:00:29,680 --> 00:00:31,760 Speaker 1: gonna be sleeping in a wall tent, and you're going 9 00:00:31,840 --> 00:00:35,000 Speaker 1: to be doing that for five days with the founder 10 00:00:35,159 --> 00:00:40,880 Speaker 1: and CEO of Outdoor Edge, David Block. Now, if you've 11 00:00:40,880 --> 00:00:43,480 Speaker 1: never been on an elk count before, I'm telling your 12 00:00:43,600 --> 00:00:46,760 Speaker 1: right now, go sign up for this because if you 13 00:00:46,880 --> 00:00:51,320 Speaker 1: ever hear a elk bugle, whether it's at four hundred 14 00:00:51,400 --> 00:00:54,120 Speaker 1: yards or it's at forty yards, it is a life 15 00:00:54,240 --> 00:00:58,640 Speaker 1: changing experience. So here's how you enter. Go to outdoor 16 00:00:58,720 --> 00:01:01,000 Speaker 1: edge dot com. There's gonna be a big banner for 17 00:01:01,080 --> 00:01:03,720 Speaker 1: it somewhere on their homepage. All you have to do 18 00:01:04,000 --> 00:01:06,800 Speaker 1: is click on that, go fill out some information I 19 00:01:06,800 --> 00:01:09,520 Speaker 1: think your name, your email address, maybe some other stuff, 20 00:01:09,840 --> 00:01:12,000 Speaker 1: and that's all you have to do. That's how you 21 00:01:12,040 --> 00:01:16,960 Speaker 1: are entered. They're gonna be picking a winner. Oh always 22 00:01:17,040 --> 00:01:18,920 Speaker 1: from now so you have plenty of time to enter. 23 00:01:19,520 --> 00:01:24,320 Speaker 1: Go visit outdoor edge dot com. Sign up today, Here 24 00:01:24,319 --> 00:01:31,800 Speaker 1: we go. My name is Clay Nukeleman. I'm the host 25 00:01:31,840 --> 00:01:35,759 Speaker 1: of the Bear Hunting Magazine podcast. I'll also be your 26 00:01:35,800 --> 00:01:39,040 Speaker 1: host into the world of hunting the icon of the 27 00:01:39,120 --> 00:01:44,680 Speaker 1: North American Wilderness. Prepare. We'll talk about tactics, gear conservation, 28 00:01:45,200 --> 00:01:47,480 Speaker 1: but will also bring you into some of the wildest 29 00:01:47,520 --> 00:02:01,080 Speaker 1: country off the planet chasing fair. Always enjoy interviewing the professionals, 30 00:02:01,280 --> 00:02:05,640 Speaker 1: the people that have dedicated their lives to the subject 31 00:02:05,640 --> 00:02:10,959 Speaker 1: at hand. The subject today is bears, black bears in Oklahoma, 32 00:02:11,560 --> 00:02:15,160 Speaker 1: and Sarah Lita is a biologist and she's dedicated the 33 00:02:15,240 --> 00:02:19,520 Speaker 1: last twenty years of her life two black bears in Oklahoma, 34 00:02:19,960 --> 00:02:24,400 Speaker 1: and she is she she was the first person to 35 00:02:24,520 --> 00:02:28,639 Speaker 1: actually do research on bears in the early two thousands. 36 00:02:28,680 --> 00:02:33,480 Speaker 1: So Sarah is a great guest, extremely knowledgeable, and she 37 00:02:33,600 --> 00:02:35,800 Speaker 1: was hard to get on the podcast. It was easier 38 00:02:35,800 --> 00:02:38,480 Speaker 1: to get Ted Nugent and Steve Rinella. So you're gonna 39 00:02:38,600 --> 00:02:43,680 Speaker 1: enjoy Sarah Lita. One of my friends and a a great, 40 00:02:44,000 --> 00:02:50,320 Speaker 1: a great conservationist. Sarah Lita, the Western Bear Foundation is 41 00:02:50,320 --> 00:02:56,360 Speaker 1: a nonprofit hunting conservation organization standing up for the rights 42 00:02:57,480 --> 00:03:02,720 Speaker 1: of bears. Yep, you heard it. Airs conservationists and hunters 43 00:03:02,760 --> 00:03:06,160 Speaker 1: like us, like the Western Bear Foundation. We love bears. 44 00:03:06,480 --> 00:03:08,320 Speaker 1: We want them to be on the landscape. We want 45 00:03:08,360 --> 00:03:12,040 Speaker 1: them to thrive, we want them to be their numbers 46 00:03:12,080 --> 00:03:14,640 Speaker 1: to be in balance with a habitat so they can 47 00:03:14,680 --> 00:03:17,720 Speaker 1: be strong and healthy. There's some people that don't understand that. 48 00:03:17,760 --> 00:03:20,760 Speaker 1: They don't understand the North American blah blah wildlife conservation 49 00:03:20,800 --> 00:03:23,600 Speaker 1: and using hunting as a tool. Our buddies at the 50 00:03:23,639 --> 00:03:27,280 Speaker 1: Western Bear Foundation, they understand that, and they're standing up 51 00:03:27,320 --> 00:03:31,200 Speaker 1: for the rights of sportsmen and for bears out west. 52 00:03:31,240 --> 00:03:36,480 Speaker 1: Their nonprofit organization. You can join their organization and you'll 53 00:03:36,480 --> 00:03:40,280 Speaker 1: get some great perks, your voice will be in the 54 00:03:40,400 --> 00:03:44,880 Speaker 1: fight for bears. Check them out. So we just came 55 00:03:44,920 --> 00:03:47,640 Speaker 1: out with a really cool hat. It's a first Light 56 00:03:47,880 --> 00:03:52,440 Speaker 1: fusion cameo pattern actually first Light hat. It has a 57 00:03:52,480 --> 00:03:55,400 Speaker 1: first Light logo on the side, but we put our 58 00:03:55,640 --> 00:04:00,600 Speaker 1: our famous now famous bear grease pat on the front 59 00:04:00,600 --> 00:04:03,520 Speaker 1: of it. We're selling that hat. We sold so many 60 00:04:03,560 --> 00:04:06,800 Speaker 1: that we we sold out what we had in hand, 61 00:04:06,920 --> 00:04:10,440 Speaker 1: and so now we're taking back orders. Some of the 62 00:04:10,440 --> 00:04:13,360 Speaker 1: guys that ordered those hats are on back order. We 63 00:04:13,400 --> 00:04:16,360 Speaker 1: should have them relatively quick and we'll be sending them out. 64 00:04:16,480 --> 00:04:18,640 Speaker 1: But check out that hat. And we just came out 65 00:04:18,680 --> 00:04:21,880 Speaker 1: with a new mule riding bear hunter shirt. You're gonna 66 00:04:21,920 --> 00:04:24,920 Speaker 1: want to check that out at bear hyphen hunting dot com. 67 00:04:25,200 --> 00:04:30,160 Speaker 1: Check out all our merch and also Bear Hunting Magazine. Man, 68 00:04:30,200 --> 00:04:32,640 Speaker 1: we're the only print bear hunting magazine in the world. 69 00:04:32,960 --> 00:04:37,000 Speaker 1: We dedicate our lives. Our knuckles literally bleed on the 70 00:04:37,120 --> 00:04:43,640 Speaker 1: keyboard as we're building this magazine full of tactics, gear, conservation, adventure, hunting, 71 00:04:44,160 --> 00:04:47,000 Speaker 1: how to cook bear, what to do with bear hides, 72 00:04:47,520 --> 00:04:53,560 Speaker 1: and just the general spirit of goodwill and awesomeness for 73 00:04:54,200 --> 00:04:57,680 Speaker 1: black bear hunting across North America. Did you know that 74 00:04:58,600 --> 00:05:04,400 Speaker 1: black bears are the most numerous large carnivore in the world. 75 00:05:05,480 --> 00:05:08,600 Speaker 1: That's correct, You heard me right. Black bears are thriving. 76 00:05:08,839 --> 00:05:11,360 Speaker 1: Check out Bear Hunting Magazine. You can get a subscription 77 00:05:11,720 --> 00:05:14,120 Speaker 1: twenty five dollars a year. You spend that much money 78 00:05:14,440 --> 00:05:18,200 Speaker 1: at McDonald's. I mean, if you go to McDonald's, you'll 79 00:05:18,240 --> 00:05:22,440 Speaker 1: spend that much money at McDonald's. Get a subscription to 80 00:05:22,480 --> 00:05:32,839 Speaker 1: Bear Hunting Magazine bear ivan Hunting dot com. Sarah Lida 81 00:05:33,480 --> 00:05:36,960 Speaker 1: you you don't realize that, but you're a highly valued 82 00:05:37,000 --> 00:05:40,440 Speaker 1: guest on the Bear Hunting Magazine podcast. We've been We've 83 00:05:40,440 --> 00:05:42,160 Speaker 1: been trying to get her on here for a long time. 84 00:05:42,240 --> 00:05:48,000 Speaker 1: Story it's easier to get like Ted Nugent on this podcast. 85 00:05:48,040 --> 00:05:53,640 Speaker 1: And it's true. I'm hard to pin down. We're in 86 00:05:53,839 --> 00:05:58,520 Speaker 1: uh we're in southeast Oklahoma, overlooking an incredible view of 87 00:05:58,560 --> 00:06:04,760 Speaker 1: the washed all mountains. Uh for real beautiful and uh no, Sarah, thanks, 88 00:06:04,839 --> 00:06:06,719 Speaker 1: thank you for meeting with us. We try to do 89 00:06:06,760 --> 00:06:09,600 Speaker 1: this a couple other times and ran into some roadblocks. 90 00:06:09,640 --> 00:06:12,760 Speaker 1: But uh, I was just telling I was just telling 91 00:06:12,760 --> 00:06:17,160 Speaker 1: her that these biology podcasts always do good. People are 92 00:06:17,160 --> 00:06:20,200 Speaker 1: always interested. And I think you were worried that we 93 00:06:20,279 --> 00:06:22,040 Speaker 1: might be covering some of the same stuff that we 94 00:06:22,120 --> 00:06:25,719 Speaker 1: talked about with all these other biologists. But I don't care. 95 00:06:25,800 --> 00:06:27,880 Speaker 1: What if we talk about the same things all over again. 96 00:06:28,000 --> 00:06:30,120 Speaker 1: You may not have heard it. When I have any 97 00:06:30,200 --> 00:06:33,400 Speaker 1: kind of awkward silence and any conversation with anybody in 98 00:06:33,440 --> 00:06:36,479 Speaker 1: my life, if I'm at the grocery store, if you're 99 00:06:36,480 --> 00:06:39,760 Speaker 1: my best friend, if you're my wife. We talk about 100 00:06:39,880 --> 00:06:45,960 Speaker 1: the right. It's like, uh, you know how much about 101 00:06:45,960 --> 00:06:50,839 Speaker 1: delayed implantation and how cool bears are. That's so that's 102 00:06:51,000 --> 00:06:55,120 Speaker 1: my go to conversation. But now, Sarah, you are tell 103 00:06:55,160 --> 00:06:57,719 Speaker 1: me who you work for? Okay, So I work for 104 00:06:57,800 --> 00:07:02,719 Speaker 1: Oklahoma State University. I work through the Oklahoma Cooperative Fish 105 00:07:02,720 --> 00:07:06,120 Speaker 1: and Wildlife Research Unit, and all of the research that 106 00:07:06,160 --> 00:07:10,840 Speaker 1: we do is for the Oklahoma Department of Wildlife Conservation 107 00:07:11,520 --> 00:07:15,760 Speaker 1: in an effort to aid their management schemes. Now, where 108 00:07:15,880 --> 00:07:20,400 Speaker 1: is where? This is terrible? I did this to you 109 00:07:20,600 --> 00:07:27,560 Speaker 1: in I misstated the college that you work for very cautious. 110 00:07:29,480 --> 00:07:31,960 Speaker 1: It's so hard with state lines, Like I could tell 111 00:07:32,000 --> 00:07:35,800 Speaker 1: you every college in Arkansas drive ten minutes into Oklahoma, 112 00:07:35,880 --> 00:07:39,640 Speaker 1: and I'm like, on a different planet. You work for Oklahoma, 113 00:07:40,240 --> 00:07:46,200 Speaker 1: Oklahoma State University, State University, Cowboys. Where are they at city? 114 00:07:46,280 --> 00:07:48,760 Speaker 1: It's in still Water. The main campus is in Stillwater. 115 00:07:49,000 --> 00:07:52,400 Speaker 1: How big is How big is Oklahoma State University? I 116 00:07:52,480 --> 00:07:56,760 Speaker 1: believe that they have twenty two four thousand students or so. 117 00:07:57,160 --> 00:08:00,640 Speaker 1: It's a good side school. I didn't really still Water 118 00:08:00,760 --> 00:08:05,560 Speaker 1: was that big. It's it's growing, it's definitely growing. Quite frankly, 119 00:08:06,000 --> 00:08:08,880 Speaker 1: it's a wonderful town. I wish still Water though, was 120 00:08:08,960 --> 00:08:11,760 Speaker 1: about where Poto is, so that we could actually get 121 00:08:11,760 --> 00:08:15,720 Speaker 1: to the mountains more easily. But yeah, but it's it's 122 00:08:15,720 --> 00:08:17,760 Speaker 1: a great little town to live in, and it's growing, 123 00:08:18,200 --> 00:08:21,360 Speaker 1: growing really fast, and and UM it's the lane great 124 00:08:21,400 --> 00:08:26,600 Speaker 1: university in the state. So UM campuses is beautiful, very 125 00:08:26,680 --> 00:08:31,520 Speaker 1: much focused on outdoor beauty and agricultural sciences and and 126 00:08:31,600 --> 00:08:34,839 Speaker 1: that kind of thing. So that's who you that's who 127 00:08:34,840 --> 00:08:39,960 Speaker 1: you work for, and you are UM. You're working with 128 00:08:40,160 --> 00:08:45,480 Speaker 1: research students, graduate students on projects that are always related 129 00:08:45,520 --> 00:08:48,280 Speaker 1: to bear are not always related to bear right and 130 00:08:48,559 --> 00:08:54,200 Speaker 1: for the past well since I have been strictly bear related. UM. 131 00:08:54,240 --> 00:08:58,560 Speaker 1: I am employed through our grants for the bear research 132 00:08:58,800 --> 00:09:02,920 Speaker 1: and my job although it's adjusted over those years because 133 00:09:02,920 --> 00:09:06,320 Speaker 1: when I first started back in I was the only 134 00:09:06,360 --> 00:09:09,040 Speaker 1: one on the project and was actually doing the field 135 00:09:09,040 --> 00:09:14,679 Speaker 1: work and and reports and everything else. UM. Now as 136 00:09:14,720 --> 00:09:18,320 Speaker 1: we've moved forward as often, we started actually bringing on 137 00:09:18,400 --> 00:09:21,640 Speaker 1: graduate students and more technicians and that kind of thing. So, 138 00:09:21,640 --> 00:09:26,080 Speaker 1: so now you're overseeing these projects. I oversee all of 139 00:09:26,120 --> 00:09:30,400 Speaker 1: the field aspects of the bear research in Oklahoma, and 140 00:09:30,480 --> 00:09:32,600 Speaker 1: so we have. I should also mention, so we have 141 00:09:32,800 --> 00:09:36,200 Speaker 1: Dr Sux Fairbanks, who is a professor, a tenured professor 142 00:09:36,280 --> 00:09:39,520 Speaker 1: at os U, and she is the major advisor for 143 00:09:39,559 --> 00:09:41,600 Speaker 1: all of the graduate students that we have on the 144 00:09:41,600 --> 00:09:45,360 Speaker 1: Bear project, and she's technically our principal investigator, and so 145 00:09:45,960 --> 00:09:50,320 Speaker 1: she handles all thing research related in terms of writing 146 00:09:50,360 --> 00:09:54,760 Speaker 1: the papers and managing the students from an academic standpoint, 147 00:09:55,200 --> 00:09:58,000 Speaker 1: and then I oversee all of the research efforts from 148 00:09:58,000 --> 00:10:03,560 Speaker 1: a field standpoint. Um, maybe you know this answer. Does 149 00:10:03,679 --> 00:10:07,120 Speaker 1: your funding for your projects come from Pittman Robertson Fund 150 00:10:08,400 --> 00:10:13,520 Speaker 1: federal federal? Yes, yes, yeah, so we are and where 151 00:10:13,559 --> 00:10:15,440 Speaker 1: you know, all of the reports that we send in 152 00:10:15,520 --> 00:10:18,560 Speaker 1: they go to the odw BC, but then they also 153 00:10:18,679 --> 00:10:22,400 Speaker 1: get moved on to UM to the federals folks, so 154 00:10:22,559 --> 00:10:25,040 Speaker 1: that they can see what we're doing with that money. Um. 155 00:10:25,840 --> 00:10:28,160 Speaker 1: Can you just like a lot of people would be 156 00:10:28,240 --> 00:10:31,079 Speaker 1: familiar with Pittman Robertson, but like, can you describe that? 157 00:10:31,120 --> 00:10:32,480 Speaker 1: I mean, I could do it, but I would want 158 00:10:32,520 --> 00:10:34,240 Speaker 1: to hear you do it. I mean, it's it's a 159 00:10:34,320 --> 00:10:37,000 Speaker 1: it's a it's an excise tax, right, So it's a 160 00:10:37,240 --> 00:10:42,840 Speaker 1: the money comes from all the sales of anything sportsman related, 161 00:10:42,880 --> 00:10:47,800 Speaker 1: hunting and fishing related, and um, and so it's it's 162 00:10:47,840 --> 00:10:50,319 Speaker 1: our hunters, it's our conservationists that are out there that 163 00:10:50,360 --> 00:10:53,439 Speaker 1: are actually paying for the work that we're doing. So 164 00:10:54,120 --> 00:10:57,000 Speaker 1: there's that's the federal side of the match money. And 165 00:10:57,120 --> 00:11:00,240 Speaker 1: then of course the ODWO, BC and the Universe City 166 00:11:00,320 --> 00:11:03,839 Speaker 1: also have their side pitching in. But if if we 167 00:11:03,920 --> 00:11:07,760 Speaker 1: didn't have that federal money to fund this, we wouldn't 168 00:11:07,960 --> 00:11:12,319 Speaker 1: we wouldn't have That is like a big part and 169 00:11:12,559 --> 00:11:17,520 Speaker 1: it's become much more common knowledge inside the hunting community 170 00:11:17,600 --> 00:11:20,679 Speaker 1: in the last probably even five years. But just this 171 00:11:20,920 --> 00:11:24,079 Speaker 1: idea that the Pittman robertson money, which is an excise tax, 172 00:11:24,240 --> 00:11:29,040 Speaker 1: which means it's an additional it's an additional tax on guns, 173 00:11:29,360 --> 00:11:33,760 Speaker 1: ammunition and hunting related equipment. So sportsmen choose to you know, 174 00:11:33,880 --> 00:11:36,360 Speaker 1: by choose, we mean we just hadn't voted it out 175 00:11:36,400 --> 00:11:38,599 Speaker 1: and made it different. So we've made a choice that 176 00:11:39,320 --> 00:11:41,720 Speaker 1: this money is going to be given to the federal government. 177 00:11:42,200 --> 00:11:45,440 Speaker 1: In the federal government ear tags it for these specific 178 00:11:45,559 --> 00:11:49,280 Speaker 1: things all related to conservation and wildlife and one of 179 00:11:49,320 --> 00:11:52,480 Speaker 1: those things. So I've been doing some I've been doing 180 00:11:52,559 --> 00:11:54,839 Speaker 1: some study on it for something I'm writing. But you know, 181 00:11:55,280 --> 00:11:58,000 Speaker 1: there's these very many things that it could go to, 182 00:11:58,120 --> 00:12:00,280 Speaker 1: but one of them is research. And that's why I 183 00:12:00,400 --> 00:12:03,640 Speaker 1: wanted to see if this was funded by Pittman Robertson money. 184 00:12:04,080 --> 00:12:08,000 Speaker 1: I was actually last night, just last night, in my truck. 185 00:12:08,600 --> 00:12:12,400 Speaker 1: I had three people with me, none of them from 186 00:12:12,480 --> 00:12:17,400 Speaker 1: the United States. They were friends of my daughters from college. UM, 187 00:12:18,080 --> 00:12:22,800 Speaker 1: and UH have no context for North American hunting zero, 188 00:12:23,720 --> 00:12:26,120 Speaker 1: and so I took them coon hunting. It's coon season 189 00:12:26,120 --> 00:12:32,320 Speaker 1: in Arkansas and uh, that was my go to thing 190 00:12:32,559 --> 00:12:36,400 Speaker 1: too early on in the conversation because they asked because 191 00:12:36,440 --> 00:12:38,120 Speaker 1: they know I like to talk about this stuff, and 192 00:12:38,120 --> 00:12:41,920 Speaker 1: they're like, tell us why honeys is important is essentially 193 00:12:42,000 --> 00:12:45,000 Speaker 1: what they asked, almost word for word. Within five minutes, 194 00:12:45,040 --> 00:12:47,800 Speaker 1: I was talking about the Pittman Robertson. I was like, Hey, 195 00:12:47,880 --> 00:12:50,599 Speaker 1: we're funding this. This is a user paid system, and 196 00:12:50,840 --> 00:12:53,160 Speaker 1: I think it's important for people to know. I mean, 197 00:12:53,320 --> 00:12:56,520 Speaker 1: you know, it's not just like we like the coon hunt. 198 00:12:56,880 --> 00:12:59,520 Speaker 1: I mean, we do, but it's bigger than that. It's 199 00:12:59,559 --> 00:13:01,760 Speaker 1: there's more to it than that, you know. But we're 200 00:13:02,080 --> 00:13:04,920 Speaker 1: we're consumptive users, but we're also giving back. I mean, 201 00:13:05,000 --> 00:13:08,760 Speaker 1: that's that's the whole point, is that you know, people 202 00:13:08,880 --> 00:13:15,840 Speaker 1: hunt for various reasons, but it's if not of the time, 203 00:13:15,960 --> 00:13:18,640 Speaker 1: it's because we love the wildlife species that we're hunting 204 00:13:18,760 --> 00:13:21,360 Speaker 1: and that we're we're out viewing and watching, and so 205 00:13:21,520 --> 00:13:23,360 Speaker 1: we want to make sure that their managed properly so 206 00:13:23,480 --> 00:13:26,760 Speaker 1: we can continue that heritage. Yeah. I mean I grew 207 00:13:26,840 --> 00:13:29,199 Speaker 1: up on a hunting plantation in South Carolina that my 208 00:13:29,320 --> 00:13:32,120 Speaker 1: dad was the manager of. Um, he was a forester 209 00:13:32,240 --> 00:13:35,600 Speaker 1: and a wildlife biologist, and so I mean I grew 210 00:13:35,720 --> 00:13:37,760 Speaker 1: up in the woods, on the water and in the 211 00:13:37,840 --> 00:13:40,400 Speaker 1: skin and shed. You know, I would I had finished 212 00:13:40,440 --> 00:13:42,680 Speaker 1: my riding lessons, I get my homework done and wait 213 00:13:42,720 --> 00:13:44,280 Speaker 1: for the lights to roll in so I could go 214 00:13:44,400 --> 00:13:45,959 Speaker 1: sit at the skin and shed and see what came 215 00:13:46,000 --> 00:13:49,040 Speaker 1: in that night, you know. I mean, UM, it's it's 216 00:13:49,080 --> 00:13:53,000 Speaker 1: a way of life, and and we're extremely fortunate to 217 00:13:53,040 --> 00:13:56,120 Speaker 1: be able to participate in that and to know that 218 00:13:56,200 --> 00:13:59,839 Speaker 1: we're giving back. Yeah. So I want to get in 219 00:14:00,000 --> 00:14:03,360 Speaker 1: to your current research. I want to hear some maybe 220 00:14:03,440 --> 00:14:06,559 Speaker 1: some cool stories of just I mean, you've had a 221 00:14:06,640 --> 00:14:09,079 Speaker 1: lot of hands on experience with bears, but I want 222 00:14:09,120 --> 00:14:13,400 Speaker 1: to go back to uh, your you were one of 223 00:14:13,440 --> 00:14:16,719 Speaker 1: the first people doing research on these Oklahoma bears. I 224 00:14:16,840 --> 00:14:20,880 Speaker 1: am the first. Yeah, yeah, yeah, So just to give 225 00:14:20,880 --> 00:14:23,440 Speaker 1: a little bit of a context. And people have heard 226 00:14:23,520 --> 00:14:26,520 Speaker 1: me talk about, you know, the reintroduction in Arkansas quite 227 00:14:26,520 --> 00:14:28,400 Speaker 1: a bit, but you know, maybe some people wouldn't be familiar. 228 00:14:28,440 --> 00:14:31,920 Speaker 1: So I mean, essentially bears were here in in Oklahoma. 229 00:14:32,040 --> 00:14:34,040 Speaker 1: This is native range for them. They would have been 230 00:14:34,120 --> 00:14:37,920 Speaker 1: here almost statewide. I guess, I guess parts of the 231 00:14:38,160 --> 00:14:41,680 Speaker 1: Panhandle they wouldn't have been. But yeah, but well they 232 00:14:41,760 --> 00:14:43,520 Speaker 1: might have been out in the very western part of 233 00:14:43,560 --> 00:14:47,760 Speaker 1: the Panhandle because of that New Mexico and Colorado connection. Um, 234 00:14:47,880 --> 00:14:52,680 Speaker 1: but probably you know, safe from my thirty five west 235 00:14:53,520 --> 00:14:56,240 Speaker 1: they probably couldn't have really survived very well. So it 236 00:14:56,240 --> 00:14:58,680 Speaker 1: would have been mostly the forested areas of the state, 237 00:14:58,800 --> 00:15:01,400 Speaker 1: which would be like the east during third of Oklahoma, 238 00:15:01,560 --> 00:15:05,160 Speaker 1: probably about the eastern third. Yeah, And so just in 239 00:15:05,240 --> 00:15:09,400 Speaker 1: the last let's say fifty sixty years, bears have been 240 00:15:10,040 --> 00:15:13,320 Speaker 1: come back into Oklahoma, and so this population built and 241 00:15:13,360 --> 00:15:15,800 Speaker 1: then you showed up and we're the first one to 242 00:15:15,880 --> 00:15:19,920 Speaker 1: do actual research on these bears, right, So, um, I 243 00:15:20,080 --> 00:15:22,520 Speaker 1: know that Joe hemp Pill and Jeff Ford had been 244 00:15:22,560 --> 00:15:26,560 Speaker 1: working for years before we finally got the project started 245 00:15:26,640 --> 00:15:29,520 Speaker 1: that I got to work on UM. But in the 246 00:15:29,880 --> 00:15:32,680 Speaker 1: in the nineties and late nineties, they they started working 247 00:15:32,760 --> 00:15:35,320 Speaker 1: to try to get approval for a project because they 248 00:15:35,400 --> 00:15:37,800 Speaker 1: started having more and more sightings of bears in the 249 00:15:37,880 --> 00:15:43,120 Speaker 1: southeastern part of the state. And UM, as luck would 250 00:15:43,160 --> 00:15:46,760 Speaker 1: have it, UM, a friend of mine from University of Tennessee, 251 00:15:46,880 --> 00:15:50,240 Speaker 1: was out here getting his PhD. And they had a 252 00:15:50,280 --> 00:15:54,520 Speaker 1: student drop out of the project in early two thousand one, 253 00:15:55,520 --> 00:15:57,880 Speaker 1: and they were scrambling to find somebody who had bear experience. 254 00:15:57,920 --> 00:15:59,880 Speaker 1: And I had worked on a couple of UT projects 255 00:16:00,040 --> 00:16:04,760 Speaker 1: as an undergraduate at Auburn, and he was calling people 256 00:16:04,840 --> 00:16:06,840 Speaker 1: he knew that knew how to get out and trap, 257 00:16:06,960 --> 00:16:09,920 Speaker 1: and so I was fortunately one of those people he called. 258 00:16:10,320 --> 00:16:13,800 Speaker 1: In long story short, I drove to Oklahoma side Unseen 259 00:16:14,320 --> 00:16:18,080 Speaker 1: and started in the spring of two thousand one down 260 00:16:18,120 --> 00:16:21,840 Speaker 1: in the Wachitas doing that work. UM. So I moved 261 00:16:21,880 --> 00:16:25,000 Speaker 1: out I think it was in April, and I bought 262 00:16:25,000 --> 00:16:27,680 Speaker 1: all my supplies in still Water and came down here 263 00:16:27,760 --> 00:16:29,680 Speaker 1: and then had to start learning the study area and 264 00:16:29,840 --> 00:16:33,080 Speaker 1: figure out where we wanted to do it. That was 265 00:16:33,160 --> 00:16:36,840 Speaker 1: for my master's UM. So, with the help of the 266 00:16:36,920 --> 00:16:40,120 Speaker 1: od w C folks down here, Jeff was Jeff Ford 267 00:16:40,200 --> 00:16:47,000 Speaker 1: was definitely integral and that. Um but just yeah, yeah, yeah, 268 00:16:47,400 --> 00:16:50,960 Speaker 1: it's it's we've got quite a family here, fair people. 269 00:16:51,840 --> 00:16:54,200 Speaker 1: UM So, yeah, so I had to move down here, 270 00:16:54,440 --> 00:16:56,360 Speaker 1: I had to learn the area, we had to figure 271 00:16:56,400 --> 00:16:59,520 Speaker 1: out where we wanted our trap lines to go. Um 272 00:16:59,680 --> 00:17:01,600 Speaker 1: and all of that kind of based in what we 273 00:17:01,760 --> 00:17:05,080 Speaker 1: consider to be the core area of this watch tool population, 274 00:17:06,000 --> 00:17:09,960 Speaker 1: which is in the National Forest in flok County. UM So, 275 00:17:10,880 --> 00:17:16,600 Speaker 1: don't tell him that, Sarah. What's funny is in Oklahoma, 276 00:17:16,720 --> 00:17:20,040 Speaker 1: there's a we're talking about the topography and kind of 277 00:17:20,119 --> 00:17:24,760 Speaker 1: the layout of the state, like the Washtal Mountains infiltrate 278 00:17:25,359 --> 00:17:29,280 Speaker 1: into Oklahoma and just really four or five counties, I mean, 279 00:17:29,400 --> 00:17:33,480 Speaker 1: like mountainous stuff anyway, like probably the foothills the Washtalls 280 00:17:33,480 --> 00:17:36,080 Speaker 1: probably go maybe into more counties than that, but it's 281 00:17:36,119 --> 00:17:41,280 Speaker 1: just a pretty relatively small geographic area that would be 282 00:17:41,359 --> 00:17:43,959 Speaker 1: considered mountainous. And most people wouldn't think of mountains when 283 00:17:43,960 --> 00:17:45,960 Speaker 1: I think of Oklahoma. They certainly wouldn't think of what 284 00:17:46,080 --> 00:17:47,600 Speaker 1: we're looking at here. I mean this looks like the 285 00:17:47,840 --> 00:17:50,080 Speaker 1: Appalachians or something. I can tell you I did not. 286 00:17:50,280 --> 00:17:51,800 Speaker 1: I mean, I grew up in the low country of 287 00:17:51,840 --> 00:17:55,439 Speaker 1: South Carolina, and so I always thought Oklahoma was prairie. 288 00:17:55,840 --> 00:17:59,920 Speaker 1: You know, I'm with wolves, you know, like open prayer 289 00:18:00,359 --> 00:18:03,080 Speaker 1: and so um to know that you could come here 290 00:18:03,119 --> 00:18:05,400 Speaker 1: and find this kind of habitat. And the other neat 291 00:18:05,480 --> 00:18:07,760 Speaker 1: thing about these mountains is that they are east to 292 00:18:07,840 --> 00:18:11,480 Speaker 1: west running, and so that we we assume that that 293 00:18:11,680 --> 00:18:16,000 Speaker 1: also kind of aided in the expansion from Arkansas these 294 00:18:16,040 --> 00:18:19,280 Speaker 1: bears into Oklahoma. Obviously they can climb up and down mountains, 295 00:18:19,359 --> 00:18:21,120 Speaker 1: they don't have to have it running. But when you've 296 00:18:21,160 --> 00:18:24,240 Speaker 1: got ridges that run from east to west and the 297 00:18:24,359 --> 00:18:27,240 Speaker 1: drainages are also doing that, it makes for an easy, 298 00:18:27,680 --> 00:18:34,440 Speaker 1: easy travel cord or basically, yea, um, what were you? Uh? So, 299 00:18:34,560 --> 00:18:36,840 Speaker 1: what was your what were your objectives? Kobe? I just 300 00:18:36,920 --> 00:18:41,440 Speaker 1: almost nerded out on the washtalls right then I pulled 301 00:18:41,480 --> 00:18:45,399 Speaker 1: back just a little bit. You may notice that other 302 00:18:45,440 --> 00:18:49,000 Speaker 1: people may not. The washing tolls were some of these 303 00:18:49,000 --> 00:18:51,640 Speaker 1: mountains were once ten thousand feet and this is maybe 304 00:18:51,680 --> 00:18:54,239 Speaker 1: stuff you had heard. Some of these big mountains were 305 00:18:54,280 --> 00:18:58,399 Speaker 1: ten thousand feet. Washtalls were formed the East West running ridges. 306 00:18:58,560 --> 00:19:02,160 Speaker 1: Was when South America bumped into North America. It buckled 307 00:19:02,280 --> 00:19:07,960 Speaker 1: here and the Gulf of Mexico used to come up 308 00:19:08,640 --> 00:19:12,200 Speaker 1: to the Washingtal Mountains like you would have been. It 309 00:19:12,240 --> 00:19:16,200 Speaker 1: would have been a ten thousand foot highest peaks coastal 310 00:19:16,440 --> 00:19:19,719 Speaker 1: range of mountains. In the erosion of the Washtaal Mountains, 311 00:19:19,760 --> 00:19:21,000 Speaker 1: this is the way I understand it. Tell me if 312 00:19:21,040 --> 00:19:26,560 Speaker 1: you know something different. Those mountains eroded over a bazillion years. 313 00:19:27,280 --> 00:19:32,120 Speaker 1: And basically that erosion filled in what is now western Mississippi, 314 00:19:32,200 --> 00:19:36,240 Speaker 1: Louisiana in East Texas, and so they call Louisiana the 315 00:19:36,320 --> 00:19:40,960 Speaker 1: Washingtal Basin. So essentially, the the the erosion of the 316 00:19:41,000 --> 00:19:45,159 Speaker 1: Washtaal Mountains, which would have been this massive coastal range, 317 00:19:46,040 --> 00:19:50,840 Speaker 1: the erosion filled in the Gulf of Mexico. And so 318 00:19:50,960 --> 00:19:53,600 Speaker 1: when you look on there's certain and I'm I'm not 319 00:19:53,760 --> 00:19:59,000 Speaker 1: really a geologist, but there there was a time period when, yeah, 320 00:19:59,080 --> 00:20:01,280 Speaker 1: the Gulf of Mexico up to like right here, we 321 00:20:01,320 --> 00:20:03,879 Speaker 1: would be standing on mountains looking over the Gulf of Mexico. 322 00:20:04,000 --> 00:20:07,159 Speaker 1: So are you a ruined stone believer? I don't know 323 00:20:07,240 --> 00:20:10,000 Speaker 1: a ton about it. I don't. I don't either. All 324 00:20:10,040 --> 00:20:14,200 Speaker 1: I know is that they believe that the Vikings. Actually 325 00:20:14,320 --> 00:20:17,720 Speaker 1: we're in the Headner area at at one point back 326 00:20:17,760 --> 00:20:20,280 Speaker 1: when the ocean is supposed to be that close, and 327 00:20:20,520 --> 00:20:24,560 Speaker 1: they have what they believe we're Viking ruined stones. Um 328 00:20:24,840 --> 00:20:28,800 Speaker 1: at that little park there. Now, I have not been 329 00:20:28,880 --> 00:20:31,399 Speaker 1: there since grad school, so I can't I can't give 330 00:20:31,440 --> 00:20:35,320 Speaker 1: you all the complete details of it. But I've never 331 00:20:35,440 --> 00:20:37,439 Speaker 1: even stopped there. But when you said it, I mean 332 00:20:37,480 --> 00:20:40,040 Speaker 1: I remember, I mean Headner is known for the run stones. 333 00:20:40,160 --> 00:20:45,040 Speaker 1: We got stop there? What are we thinking? Yeah? No, 334 00:20:46,160 --> 00:20:49,840 Speaker 1: that fascinates me. But the east west running ridges also 335 00:20:50,080 --> 00:20:53,240 Speaker 1: make for unique habitat for bear because it's these different 336 00:20:54,040 --> 00:20:57,240 Speaker 1: different angles that sunlight's hit in the forest. You know, 337 00:20:57,320 --> 00:21:00,560 Speaker 1: the southern slopes are arid, the northern slopes or messic 338 00:21:00,680 --> 00:21:05,560 Speaker 1: I guess is you know, more water, more thick vegetation. 339 00:21:06,240 --> 00:21:10,600 Speaker 1: It's it's really an interesting um I tell people all 340 00:21:10,720 --> 00:21:13,359 Speaker 1: the time. So our our ridges might not be more 341 00:21:13,440 --> 00:21:16,119 Speaker 1: than three thousand feet, but this is some of the 342 00:21:16,200 --> 00:21:18,879 Speaker 1: most rugged country you could ever wish for if you 343 00:21:19,040 --> 00:21:21,560 Speaker 1: really get back in there, because what you're dealing with 344 00:21:21,720 --> 00:21:25,399 Speaker 1: really steep areas in most cases too. So it's not 345 00:21:25,640 --> 00:21:28,440 Speaker 1: just that you have these rolling hills that happen to 346 00:21:28,480 --> 00:21:32,200 Speaker 1: get up tall enough to be mountains. Um, it's they're 347 00:21:32,480 --> 00:21:35,119 Speaker 1: kind of steep peaks that you're dealing with. In particular, 348 00:21:35,200 --> 00:21:37,080 Speaker 1: you can really see it if you say, drive along 349 00:21:37,119 --> 00:21:40,760 Speaker 1: that Talamina drive and that's another good area to get 350 00:21:40,840 --> 00:21:44,440 Speaker 1: to see. Um, the differences in the habitats that you're 351 00:21:44,440 --> 00:21:47,760 Speaker 1: talking about two, And how how short and scrubby the 352 00:21:47,800 --> 00:21:49,720 Speaker 1: trees are when you get up on those really high 353 00:21:50,000 --> 00:21:52,639 Speaker 1: high peaks like that, because you're basically almost dealing with 354 00:21:52,720 --> 00:21:55,760 Speaker 1: a desert type situation because there's not a lot of 355 00:21:56,000 --> 00:21:59,800 Speaker 1: um top soil for those for those trees really take 356 00:22:00,040 --> 00:22:05,160 Speaker 1: root in. Um. You know, Oklahoma itself has is incredibly 357 00:22:05,240 --> 00:22:10,320 Speaker 1: diverse from the ecoregion standpoint. Um. But this this area 358 00:22:10,440 --> 00:22:14,440 Speaker 1: down here, UM, well it's I mean, it's a second 359 00:22:14,480 --> 00:22:17,440 Speaker 1: home to me. This is just I've never never lived 360 00:22:17,480 --> 00:22:20,520 Speaker 1: in mountains before I came here, but this is this 361 00:22:20,680 --> 00:22:22,680 Speaker 1: is a good place to be and it's been been 362 00:22:22,720 --> 00:22:27,680 Speaker 1: great for the bears obviously. So back to where I 363 00:22:27,760 --> 00:22:30,439 Speaker 1: was going before I got sidetracked by the wash dolls. Um, 364 00:22:31,800 --> 00:22:33,960 Speaker 1: what were you trying to do with your initial research? 365 00:22:34,000 --> 00:22:39,000 Speaker 1: So what was the objective so initially my first research 366 00:22:39,200 --> 00:22:43,320 Speaker 1: was was sort of basic in the grand scheme of 367 00:22:43,440 --> 00:22:47,440 Speaker 1: research these days, but the main goal was to come 368 00:22:47,520 --> 00:22:51,280 Speaker 1: up with a population estimate for the core area of 369 00:22:51,359 --> 00:22:55,119 Speaker 1: the population UM, and then also to move forward with 370 00:22:55,240 --> 00:22:58,080 Speaker 1: trying to see we we did caller back then they 371 00:22:58,119 --> 00:23:01,080 Speaker 1: were just VHF callers on female else to get a 372 00:23:01,160 --> 00:23:04,520 Speaker 1: feel for home ranges and habitat use. And then also 373 00:23:04,800 --> 00:23:07,359 Speaker 1: the important thing about those callers is it allowed us 374 00:23:07,440 --> 00:23:11,920 Speaker 1: to follow two dens and do the reproductive UM and 375 00:23:12,040 --> 00:23:14,920 Speaker 1: for get the reproductive information that we needed for the 376 00:23:15,000 --> 00:23:18,800 Speaker 1: population to see if it's in fact growing that kind 377 00:23:18,800 --> 00:23:21,440 Speaker 1: of thing. So did you going into it? Did you 378 00:23:21,600 --> 00:23:24,560 Speaker 1: have or did anybody have any idea how many bears 379 00:23:24,600 --> 00:23:28,440 Speaker 1: there were? I mean, there was no research, so nobody 380 00:23:28,600 --> 00:23:31,359 Speaker 1: but I could would did anybody guess right? I'm not 381 00:23:31,560 --> 00:23:34,560 Speaker 1: sure if anybody did. I would imagine Joe and Bill 382 00:23:34,640 --> 00:23:38,160 Speaker 1: and Jeff Ford probably had some ideas because it's how 383 00:23:38,240 --> 00:23:41,480 Speaker 1: long they had worked in this region and UM, and 384 00:23:41,640 --> 00:23:43,560 Speaker 1: they were the ones that would be dealing with any 385 00:23:43,720 --> 00:23:49,080 Speaker 1: conflict issues that we might have. UM. I don't recall 386 00:23:49,400 --> 00:23:52,880 Speaker 1: now I've slept you didn't you. I've slept a few 387 00:23:53,080 --> 00:23:56,200 Speaker 1: few days since then, But I don't recall coming into 388 00:23:56,320 --> 00:23:59,200 Speaker 1: it if anybody said to me, you know, we're kind 389 00:23:59,240 --> 00:24:03,200 Speaker 1: of thinking, this is the amount of listen biologists there there. 390 00:24:03,600 --> 00:24:07,520 Speaker 1: They don't like to guess, do they know if you 391 00:24:07,600 --> 00:24:09,840 Speaker 1: were if you were, if you were just a normal 392 00:24:09,960 --> 00:24:12,919 Speaker 1: person and not a biologist, and you had an objective 393 00:24:13,040 --> 00:24:15,520 Speaker 1: to try to understand how many bears there were. I mean, 394 00:24:15,600 --> 00:24:17,560 Speaker 1: me and Colby would be like taking bets, you know, 395 00:24:17,680 --> 00:24:20,320 Speaker 1: like I bet there's a thousand, I bet there's five thousands. 396 00:24:20,520 --> 00:24:24,800 Speaker 1: But Jasko biologists and they're just like unbiased. The research 397 00:24:24,880 --> 00:24:29,200 Speaker 1: will amongst ourselves. We might have our own theories, but 398 00:24:29,359 --> 00:24:32,200 Speaker 1: we definitely try to be very cautious about throwing that 399 00:24:32,320 --> 00:24:35,320 Speaker 1: out in the general public. That's there, sure, UM. But 400 00:24:35,600 --> 00:24:37,480 Speaker 1: you know, the other thing about it is I was 401 00:24:37,600 --> 00:24:42,920 Speaker 1: working in UM a pretty restricted area, and so from 402 00:24:43,000 --> 00:24:45,920 Speaker 1: my project we actually only estimated that they're about eighty 403 00:24:46,000 --> 00:24:49,960 Speaker 1: five bears in the area that I was working in. Now, 404 00:24:50,080 --> 00:24:52,760 Speaker 1: you weren't doing DNA hair snares, back then, were you. 405 00:24:53,040 --> 00:24:55,000 Speaker 1: It was I helped set that up right after I 406 00:24:55,119 --> 00:24:57,560 Speaker 1: finished my master's as student came in and did the 407 00:24:59,040 --> 00:25:02,080 Speaker 1: they have that tech knowledgy when you started, because now 408 00:25:02,200 --> 00:25:04,959 Speaker 1: that's like the norm. We Yeah, we collected hair from 409 00:25:05,000 --> 00:25:07,320 Speaker 1: all of our We collected hair samples off of all 410 00:25:07,359 --> 00:25:09,000 Speaker 1: of our captures and that kind of thing, the same 411 00:25:09,040 --> 00:25:12,399 Speaker 1: way we do now. Um. And so I think it 412 00:25:12,520 --> 00:25:16,639 Speaker 1: was definitely kind of becoming a more common practice. And 413 00:25:16,720 --> 00:25:18,560 Speaker 1: so how did y'all do it? How did you guys 414 00:25:19,000 --> 00:25:24,119 Speaker 1: back then like determined density? Um? So for our population estimate, 415 00:25:24,160 --> 00:25:26,520 Speaker 1: we use the Lincoln Peterson model and so it's from 416 00:25:26,560 --> 00:25:30,800 Speaker 1: a capture recapture study. So it was break it down 417 00:25:30,880 --> 00:25:32,760 Speaker 1: for me in a real simple way because I really 418 00:25:32,840 --> 00:25:36,800 Speaker 1: don't know what it so. Okay, so we had trap lines. 419 00:25:37,000 --> 00:25:39,639 Speaker 1: I have ran four different trap lines in different areas 420 00:25:39,720 --> 00:25:44,800 Speaker 1: than that they would have. Um, we used Aldrich snares. No, 421 00:25:45,040 --> 00:25:47,760 Speaker 1: this is so this is before the bucket snares. So 422 00:25:48,000 --> 00:25:50,600 Speaker 1: these are they are a foothold snare with the same 423 00:25:50,680 --> 00:25:54,240 Speaker 1: cable that the bucket snares use. Um. But there we 424 00:25:54,320 --> 00:25:57,000 Speaker 1: build them into the ground, so you're making that bear 425 00:25:57,080 --> 00:25:59,680 Speaker 1: step into a spot where you've got your pants set 426 00:26:00,400 --> 00:26:03,120 Speaker 1: and catch them that way, still attached to a tree, 427 00:26:03,320 --> 00:26:06,919 Speaker 1: still with springs built in to to protect them from 428 00:26:07,040 --> 00:26:11,120 Speaker 1: injuries and things like that. So, UM, what you're doing 429 00:26:11,200 --> 00:26:14,080 Speaker 1: when you're doing those is we're marking, marking and recapture. 430 00:26:14,119 --> 00:26:17,480 Speaker 1: That's what the Lincoln Peterson does. So you we capture individuals, 431 00:26:17,840 --> 00:26:20,919 Speaker 1: we mark them UM with ear tags. We actually use 432 00:26:21,040 --> 00:26:24,320 Speaker 1: several different methods. We do lip tattoos and ear tags 433 00:26:24,600 --> 00:26:28,800 Speaker 1: because oftentimes they'll rip those ear tags out UM and 434 00:26:28,880 --> 00:26:31,960 Speaker 1: then some bears would get callers of course, and so 435 00:26:32,359 --> 00:26:35,640 Speaker 1: you're keeping track of all of those individuals but also 436 00:26:35,800 --> 00:26:39,959 Speaker 1: recaptures and all of that information goes into After two 437 00:26:40,080 --> 00:26:43,240 Speaker 1: years of doing this work, you put it into this 438 00:26:43,640 --> 00:26:49,080 Speaker 1: UM into this equation that also takes into consideration, you know, 439 00:26:50,320 --> 00:26:52,600 Speaker 1: is it a closed system? Are you still having bears 440 00:26:52,680 --> 00:26:55,240 Speaker 1: moving in or out? Things like that. Lincoln Peterson is 441 00:26:55,320 --> 00:26:58,879 Speaker 1: probably like the most basic form of coming up with 442 00:26:59,000 --> 00:27:02,359 Speaker 1: a with a repulation estimate. These days, there are a 443 00:27:02,560 --> 00:27:06,280 Speaker 1: million different great ways to look into it. Where UM 444 00:27:06,840 --> 00:27:10,280 Speaker 1: like our our most recent students, um Erica Perez that 445 00:27:10,400 --> 00:27:14,600 Speaker 1: you got to meet UM, she actually did some modeling 446 00:27:14,760 --> 00:27:19,040 Speaker 1: for a current estimate down here where she actually incorporated 447 00:27:19,720 --> 00:27:24,240 Speaker 1: UM not only her capture recapture information, but also hunting 448 00:27:24,320 --> 00:27:29,119 Speaker 1: season harvests, also UM survival and things like that from 449 00:27:29,160 --> 00:27:31,920 Speaker 1: our callers that we put out so you can just 450 00:27:32,040 --> 00:27:35,560 Speaker 1: pile tons of great information in to then get a 451 00:27:35,680 --> 00:27:40,200 Speaker 1: better feel for what you're really dealing with. Down back then, um, 452 00:27:41,280 --> 00:27:44,040 Speaker 1: you were there was some correlation to how many times 453 00:27:44,080 --> 00:27:49,920 Speaker 1: you caught the same bear that if you cut the 454 00:27:50,000 --> 00:27:54,520 Speaker 1: same bear four different times and you're not catching a 455 00:27:54,600 --> 00:27:56,679 Speaker 1: whole bunch of new bunch of new bears, or if 456 00:27:56,720 --> 00:27:59,640 Speaker 1: you never caught the same bear twice, then that would 457 00:27:59,720 --> 00:28:03,080 Speaker 1: mean things that may be that there are more bears exactly. 458 00:28:03,240 --> 00:28:06,280 Speaker 1: So if if you're recapturing a bunch but not catching 459 00:28:06,320 --> 00:28:09,680 Speaker 1: a whole lot of new ones, um, then then that 460 00:28:09,760 --> 00:28:12,000 Speaker 1: would probably go into that and give you a feel 461 00:28:12,080 --> 00:28:15,320 Speaker 1: for well, maybe there aren't as many bears here, you know. 462 00:28:15,440 --> 00:28:18,119 Speaker 1: So we've kind of experienced that in certain portions up 463 00:28:18,160 --> 00:28:21,920 Speaker 1: in our ozark study area. It's a smaller population that's 464 00:28:22,000 --> 00:28:25,879 Speaker 1: just becoming established. And so once we trapped for a 465 00:28:25,960 --> 00:28:28,280 Speaker 1: few years, man, we were just seeing a lot of 466 00:28:28,359 --> 00:28:33,920 Speaker 1: our a lot of recaptures and or photos around to that. 467 00:28:34,000 --> 00:28:36,720 Speaker 1: So almost well twenty years ago, well the ozarks, for 468 00:28:36,800 --> 00:28:39,719 Speaker 1: the ozark population that we started that research in two 469 00:28:39,800 --> 00:28:43,960 Speaker 1: thousand eleven m but for here um and in fact 470 00:28:44,080 --> 00:28:47,080 Speaker 1: we believe we've actually started looking at the Ozark population 471 00:28:47,760 --> 00:28:51,240 Speaker 1: at an earlier place in their recolonization than we did 472 00:28:51,360 --> 00:28:54,680 Speaker 1: down here in the early two thousands. Um, you know, 473 00:28:54,800 --> 00:28:58,680 Speaker 1: I described a recollonizing population, and we were looking at 474 00:28:58,720 --> 00:29:02,240 Speaker 1: the expansion front right there in the core area down 475 00:29:02,400 --> 00:29:05,479 Speaker 1: here in two thousand, two thousand two, and they were 476 00:29:05,520 --> 00:29:09,160 Speaker 1: already here. They were already here. But when we started 477 00:29:09,200 --> 00:29:12,400 Speaker 1: in two thousand eleven up in that Ozark region, Um, 478 00:29:13,040 --> 00:29:16,440 Speaker 1: we're catching that even earlier. So you're starting to see 479 00:29:16,440 --> 00:29:21,000 Speaker 1: an increase, like you're, well, we would expect to start 480 00:29:21,040 --> 00:29:24,560 Speaker 1: seeing an increase from there, and and certainly we're getting 481 00:29:24,760 --> 00:29:27,280 Speaker 1: right now the up in the Ozark region. And I 482 00:29:27,360 --> 00:29:29,320 Speaker 1: know we're kind of bouncing back and forth here so 483 00:29:29,680 --> 00:29:35,640 Speaker 1: doing the typical biologist thing, but in that the full 484 00:29:35,720 --> 00:29:41,080 Speaker 1: scale thesis, that's right. So in the Ozarks, Um, right now, 485 00:29:41,640 --> 00:29:46,040 Speaker 1: that population is basically totally reliant on the influx from 486 00:29:46,160 --> 00:29:52,120 Speaker 1: the Arkansas side of things. Um, they're not these bears 487 00:29:52,240 --> 00:29:55,040 Speaker 1: here aren't going north and crossing my forty in the 488 00:29:55,160 --> 00:29:58,840 Speaker 1: Arkansas River, not that we've shown yet so far. So far, 489 00:29:58,960 --> 00:30:02,520 Speaker 1: the Ozark and the Watch sub populations are distinct populations, 490 00:30:02,600 --> 00:30:05,000 Speaker 1: and there's no flow back and forth. Now can bears 491 00:30:05,040 --> 00:30:08,880 Speaker 1: cross interstates and rivers, Absolutely, they don't really like to 492 00:30:09,360 --> 00:30:12,200 Speaker 1: in many ways. And so it seems as though in 493 00:30:12,400 --> 00:30:18,040 Speaker 1: both populations they're expanding westward as opposed to north and south. UM. 494 00:30:18,280 --> 00:30:22,400 Speaker 1: Now down here obviously because of there, there's so many 495 00:30:22,520 --> 00:30:24,280 Speaker 1: things that are so different about the Ozarks and the 496 00:30:24,320 --> 00:30:28,800 Speaker 1: watchitas here. Um, the Watchitas are wonderful, and that there's 497 00:30:29,000 --> 00:30:31,560 Speaker 1: so much public land, right. I mean, you've got all 498 00:30:31,600 --> 00:30:34,719 Speaker 1: of this National Forest that's what half a million acres 499 00:30:34,840 --> 00:30:38,160 Speaker 1: or something, and that also comes from from Arkansas over 500 00:30:38,320 --> 00:30:42,440 Speaker 1: and so the expanse of good, good habitat and not 501 00:30:42,600 --> 00:30:45,400 Speaker 1: only the National Forest, but then you've got also all 502 00:30:45,560 --> 00:30:48,640 Speaker 1: the um timber companies and things like that. So we've 503 00:30:48,720 --> 00:30:53,040 Speaker 1: got large blocks of forest down here. When you're looking 504 00:30:53,120 --> 00:30:58,440 Speaker 1: at the Ozarks and Oklahoma, UM, it's highly fragmented. So 505 00:30:58,600 --> 00:31:03,240 Speaker 1: we've got some really good habitat up there. There are yes. 506 00:31:03,400 --> 00:31:06,160 Speaker 1: So there's there's the Cooks and Wildlife Management Area that's 507 00:31:06,200 --> 00:31:11,240 Speaker 1: close to fifteen acres phenomenal bear habitat, but it's surrounded 508 00:31:11,280 --> 00:31:17,640 Speaker 1: by a bunch of privately owned well so in Sequoya, 509 00:31:17,760 --> 00:31:21,360 Speaker 1: Cherokee and eight are counties. Um, there are there definitely 510 00:31:21,480 --> 00:31:24,920 Speaker 1: some cattle grazing and whatnot, but that's those counties. There's 511 00:31:24,920 --> 00:31:28,520 Speaker 1: a lot more forested area and it's just broken up 512 00:31:28,560 --> 00:31:32,880 Speaker 1: because of all the private ownership UM, and so the 513 00:31:33,040 --> 00:31:36,080 Speaker 1: good habitat is fragmented more Now around that Cooks and 514 00:31:36,120 --> 00:31:39,520 Speaker 1: Wildlife Management area, there are some good blocks of property 515 00:31:39,560 --> 00:31:43,320 Speaker 1: owners that have large properties and with them butting up 516 00:31:43,360 --> 00:31:46,480 Speaker 1: to that wildlife management area. Man, that's that's provided a 517 00:31:46,560 --> 00:31:49,240 Speaker 1: really good good habitat for bears, and that's one of 518 00:31:49,320 --> 00:31:52,160 Speaker 1: the good areas for them. But when you pull out 519 00:31:52,200 --> 00:31:56,440 Speaker 1: and look at a larger scale, it's all fragmented and 520 00:31:56,520 --> 00:31:59,280 Speaker 1: it's so it's it's a challenge. UM. You know, we've 521 00:31:59,360 --> 00:32:02,160 Speaker 1: we've found and some of our research that the female 522 00:32:02,200 --> 00:32:06,360 Speaker 1: bears don't don't like that highly fragmented stuff and they 523 00:32:06,440 --> 00:32:09,360 Speaker 1: don't like to be they don't like to be close 524 00:32:09,440 --> 00:32:12,160 Speaker 1: to roads and highways, and they don't like to be 525 00:32:13,040 --> 00:32:18,080 Speaker 1: UM close to human disturbance. So when you're looking at UM, 526 00:32:18,240 --> 00:32:23,120 Speaker 1: a species that is wholly reliant on females to make 527 00:32:23,200 --> 00:32:26,640 Speaker 1: it stable and grow, UM, it's that that we're just 528 00:32:26,720 --> 00:32:30,880 Speaker 1: dealing with different not choosing that fragmented if they have 529 00:32:31,040 --> 00:32:33,280 Speaker 1: the choice they're going to want to be in those 530 00:32:33,440 --> 00:32:36,840 Speaker 1: large blocks, those large contiguous blocks, And so that's just 531 00:32:37,000 --> 00:32:39,760 Speaker 1: one of the management challenges that we have in the Ozarks, 532 00:32:40,320 --> 00:32:42,840 Speaker 1: and it's very different from what we're dealing with down 533 00:32:42,880 --> 00:32:45,040 Speaker 1: here in the watch it asks do you not notice 534 00:32:45,080 --> 00:32:46,880 Speaker 1: that as much with male bears, they just kind of 535 00:32:47,400 --> 00:32:50,200 Speaker 1: roam a little bit more freely. Yeah, they just cover 536 00:32:50,320 --> 00:32:53,840 Speaker 1: all kinds of territors, studying them, watching where they din. 537 00:32:53,960 --> 00:32:56,200 Speaker 1: That's you probably have more data on the females we've 538 00:32:56,280 --> 00:33:00,760 Speaker 1: got so so we are definitely um, you've got way 539 00:33:00,880 --> 00:33:05,120 Speaker 1: more coller data location data on females than we do 540 00:33:05,280 --> 00:33:08,400 Speaker 1: on males in the state. Now, we were initially collering 541 00:33:08,480 --> 00:33:10,440 Speaker 1: males as well up in the northeast, and actually we 542 00:33:10,560 --> 00:33:13,960 Speaker 1: still are um in the in the those Ark region. 543 00:33:14,360 --> 00:33:16,560 Speaker 1: In fact, we're doing some really cool stuff now where 544 00:33:16,600 --> 00:33:19,600 Speaker 1: we're actually callering yearling bears to track their dispersal from 545 00:33:19,640 --> 00:33:23,320 Speaker 1: the maternal home range. I've still got questions about your 546 00:33:23,400 --> 00:33:28,720 Speaker 1: original thesis, Sarah. It's just too much. Yeah. Gave her 547 00:33:28,760 --> 00:33:31,480 Speaker 1: an open door to it, she took it and ran. 548 00:33:32,440 --> 00:33:34,719 Speaker 1: So the original research well, let me let me let 549 00:33:34,760 --> 00:33:38,320 Speaker 1: me there's two questions I don't ask you, um, how 550 00:33:38,480 --> 00:33:41,000 Speaker 1: many bears did you find out that there were? So 551 00:33:41,600 --> 00:33:44,960 Speaker 1: in two summers, we actually captured fifty one individuals on 552 00:33:45,080 --> 00:33:50,440 Speaker 1: my original trap lines in two thousands, twenty years ago. 553 00:33:51,960 --> 00:33:54,040 Speaker 1: I haven't gotten any older, but you know it was. 554 00:33:55,320 --> 00:33:59,720 Speaker 1: Um so, so we captured fifty one individuals are population 555 00:34:00,000 --> 00:34:03,120 Speaker 1: sestimate for the study area? And that's that's a big 556 00:34:03,240 --> 00:34:05,440 Speaker 1: thing to to key in on, is the fact that 557 00:34:05,920 --> 00:34:10,799 Speaker 1: we're not saying the entire waits have only eighty five bears. 558 00:34:10,880 --> 00:34:13,480 Speaker 1: At that point, we were saying this study area that 559 00:34:13,640 --> 00:34:16,600 Speaker 1: encompassed the home ranges of the females that I was 560 00:34:16,719 --> 00:34:20,680 Speaker 1: callering and the trap lines that I worked on, UM 561 00:34:20,840 --> 00:34:23,880 Speaker 1: was eighty five or so bears. Now would that have 562 00:34:23,960 --> 00:34:26,839 Speaker 1: been like like one like you don't have to give 563 00:34:26,880 --> 00:34:29,080 Speaker 1: the specifics of was like what that would have been 564 00:34:29,120 --> 00:34:31,839 Speaker 1: about the size of one of these counties over here? 565 00:34:32,160 --> 00:34:35,120 Speaker 1: It seems like, if I'm remembering correctly, it seems like 566 00:34:35,640 --> 00:34:40,120 Speaker 1: the study area was defined is about fourteen hundred square 567 00:34:40,200 --> 00:34:43,600 Speaker 1: kilometers or something like that. Um, can you put that 568 00:34:43,640 --> 00:34:51,640 Speaker 1: in American? Well, so check out science made simple metric conversions. 569 00:34:56,080 --> 00:34:59,120 Speaker 1: So that's probably what like square miles wise, i'd be 570 00:34:59,280 --> 00:35:03,560 Speaker 1: what maybe six or seven hundred square miles. Yeah, I 571 00:35:03,680 --> 00:35:06,160 Speaker 1: think something probably like a like a like a county. 572 00:35:06,480 --> 00:35:10,280 Speaker 1: I mean, like I'm just thinking of average, and it wasn't. 573 00:35:10,320 --> 00:35:12,560 Speaker 1: I mean, as far as La floor County was concerned, 574 00:35:12,600 --> 00:35:15,840 Speaker 1: it was only a small portion of floor County. Bigger, 575 00:35:16,000 --> 00:35:18,719 Speaker 1: it was bigger than it's it's smaller than than the 576 00:35:18,800 --> 00:35:22,320 Speaker 1: floor county. My my study area was smaller than the 577 00:35:22,400 --> 00:35:24,400 Speaker 1: floor county for sure, because it was just in a 578 00:35:24,520 --> 00:35:27,799 Speaker 1: small portion of the national forest in the floor County. Okay, 579 00:35:28,080 --> 00:35:34,160 Speaker 1: we're going, so what's the most you hold it, yould 580 00:35:34,239 --> 00:35:40,280 Speaker 1: it your question. Wait, I'll hold it. Here's my question. 581 00:35:40,520 --> 00:35:43,360 Speaker 1: Hold what was it like catching your first bear? So 582 00:35:43,560 --> 00:35:47,560 Speaker 1: you you you had never you didn't know this country. 583 00:35:47,880 --> 00:35:51,160 Speaker 1: I presume you've never seen in Oklahoma bear before, and 584 00:35:51,280 --> 00:35:54,520 Speaker 1: you go out and you set these traps and you 585 00:35:55,040 --> 00:35:56,839 Speaker 1: catch a bear. I mean that had to have been 586 00:35:56,920 --> 00:36:00,279 Speaker 1: like an impacting thing to be like, holy smokes. This 587 00:36:00,520 --> 00:36:05,600 Speaker 1: is what I can tell you is that there is 588 00:36:05,719 --> 00:36:10,520 Speaker 1: no feeling that's better in this world than to have 589 00:36:10,719 --> 00:36:15,560 Speaker 1: the opportunity to to start a project like that from 590 00:36:15,600 --> 00:36:18,680 Speaker 1: the ground up, so to be the one responsible to 591 00:36:18,800 --> 00:36:22,799 Speaker 1: actually get in there do the footwork. Figure out where 592 00:36:22,840 --> 00:36:24,759 Speaker 1: you think the best places for your lines are going 593 00:36:24,800 --> 00:36:27,880 Speaker 1: to be. Set those lines, train people that come out 594 00:36:27,920 --> 00:36:32,040 Speaker 1: to work with you, set those lines, and then and 595 00:36:32,160 --> 00:36:36,440 Speaker 1: then begin capturing like we did. Um it's phenomenal and 596 00:36:36,719 --> 00:36:40,399 Speaker 1: it's still and then and not to keep jumping back 597 00:36:40,440 --> 00:36:42,719 Speaker 1: and forth. But so I did that work in two 598 00:36:42,760 --> 00:36:46,040 Speaker 1: thousand one and two thousand two, and then when we 599 00:36:46,280 --> 00:36:49,520 Speaker 1: got the new funding for the current projects started in 600 00:36:49,600 --> 00:36:52,480 Speaker 1: two thousand and fourteen, I was then able to take 601 00:36:52,680 --> 00:36:58,120 Speaker 1: Morgan Fander, a master's student, into those areas and we 602 00:36:58,280 --> 00:37:01,439 Speaker 1: started using those lines again and to see how things 603 00:37:01,480 --> 00:37:05,480 Speaker 1: have changed, and talk about full circle. I mean it 604 00:37:05,640 --> 00:37:08,040 Speaker 1: just I can get emotional about it and get weepy 605 00:37:08,239 --> 00:37:12,560 Speaker 1: anytime thinking about how how incredible it was too to 606 00:37:12,760 --> 00:37:15,960 Speaker 1: have been a part of that original kind of pioneer 607 00:37:16,560 --> 00:37:18,480 Speaker 1: and then to be able to share it with other 608 00:37:18,560 --> 00:37:20,920 Speaker 1: students and see how much farther they can take it 609 00:37:21,040 --> 00:37:23,000 Speaker 1: now than I ever could have back then. You know, 610 00:37:23,040 --> 00:37:27,040 Speaker 1: I mean, the technology and that we have now and 611 00:37:27,120 --> 00:37:30,400 Speaker 1: the intelligence of these students and the the things that 612 00:37:30,480 --> 00:37:33,520 Speaker 1: they bring to the project. It's just been it's been fun. 613 00:37:34,520 --> 00:37:36,320 Speaker 1: In a twenty year time period, probably a lot of 614 00:37:36,719 --> 00:37:39,400 Speaker 1: great stuff has happened. I mean in terms of of 615 00:37:40,680 --> 00:37:43,440 Speaker 1: ability to interpret data and research and stuff. I mean 616 00:37:43,480 --> 00:37:46,239 Speaker 1: pretty big leaps and different things. But you still didn't 617 00:37:46,239 --> 00:37:51,920 Speaker 1: answer my question, Sarah, Um, when you walked up on 618 00:37:52,040 --> 00:37:59,560 Speaker 1: the first bear, was that exciting? Remember the first one? Well, 619 00:37:59,800 --> 00:38:03,560 Speaker 1: so I will tell you. I don't remember now specifically 620 00:38:03,640 --> 00:38:09,000 Speaker 1: the first guy, but I do. Do you want me 621 00:38:09,080 --> 00:38:11,239 Speaker 1: to tell you about our girl that I think Jeff 622 00:38:11,320 --> 00:38:17,120 Speaker 1: actually mentioned in his podcast? So she was only that. 623 00:38:17,360 --> 00:38:19,440 Speaker 1: I think she was the third bear that we ever 624 00:38:19,520 --> 00:38:23,200 Speaker 1: caught on my project in two thousand one. And we 625 00:38:23,360 --> 00:38:25,960 Speaker 1: name our bears because I don't like tracking numbers. So 626 00:38:26,160 --> 00:38:30,960 Speaker 1: Bertha Bertha is our Um. No, I don't think she 627 00:38:31,000 --> 00:38:33,040 Speaker 1: was especially big. She was only a three about three 628 00:38:33,120 --> 00:38:38,360 Speaker 1: years old then. Always associate big with Bertha. Now Bertha 629 00:38:38,600 --> 00:38:43,400 Speaker 1: is big in terms of attitude and personality. So Bertha definitely, 630 00:38:43,560 --> 00:38:45,680 Speaker 1: and now she as an adult bear, she is a 631 00:38:45,719 --> 00:38:50,360 Speaker 1: good size bear. But um, so we caught Bertha. She 632 00:38:50,520 --> 00:38:52,560 Speaker 1: was I think only the third bear that I ever 633 00:38:52,640 --> 00:38:56,279 Speaker 1: caught down here starting that project. And Bertha showed back 634 00:38:56,320 --> 00:38:59,799 Speaker 1: up on our project when we started trapping down here. 635 00:39:00,760 --> 00:39:04,759 Speaker 1: And we still have Bertha Collard, She's still alive. She's 636 00:39:04,800 --> 00:39:07,200 Speaker 1: not had cubs the last two years, which makes this 637 00:39:07,400 --> 00:39:11,719 Speaker 1: very sad, but she's still an incredible condition. She think 638 00:39:11,800 --> 00:39:15,799 Speaker 1: she's years old. Wow, so you caught her that first 639 00:39:15,880 --> 00:39:20,879 Speaker 1: year and she's still alive and never when we started back, 640 00:39:22,760 --> 00:39:25,680 Speaker 1: of course, I had all the old numbers and data 641 00:39:25,960 --> 00:39:28,279 Speaker 1: that I passed. Actually, I don't even think I had 642 00:39:28,320 --> 00:39:31,160 Speaker 1: passed it off to the grad student then to Morgan, 643 00:39:31,680 --> 00:39:34,279 Speaker 1: because it just didn't even occur to me that we'd 644 00:39:34,320 --> 00:39:37,960 Speaker 1: start seeing these bears. And luckily the tattoos lasted long 645 00:39:38,080 --> 00:39:40,880 Speaker 1: enough that we ended up I think with six or 646 00:39:40,920 --> 00:39:44,400 Speaker 1: eight different bears that I marked in my study that 647 00:39:44,520 --> 00:39:47,799 Speaker 1: showed up again and our studies here. That's pretty neat. 648 00:39:48,360 --> 00:39:51,040 Speaker 1: That's very neat. But Bertha is if I was going 649 00:39:51,120 --> 00:39:53,400 Speaker 1: to have a favorite bear, she is definitely the favorite. 650 00:39:53,719 --> 00:39:57,600 Speaker 1: She stays that you guys know of in Oklahoma. I 651 00:39:57,680 --> 00:40:00,440 Speaker 1: think she's at the best we can tell she around 652 00:40:01,480 --> 00:40:05,080 Speaker 1: in Arkansas. The that's around the age of the most 653 00:40:05,360 --> 00:40:09,640 Speaker 1: the oldest documented bear. I've heard Randy Cross up in 654 00:40:09,719 --> 00:40:12,799 Speaker 1: Maine talk about thirty plus year old bears. I've seen 655 00:40:12,880 --> 00:40:16,520 Speaker 1: some of his stuff that he's written. Um anyway, but yeah, 656 00:40:16,560 --> 00:40:19,120 Speaker 1: that's that's old that's pretty cool. It's tricky. You've got 657 00:40:19,239 --> 00:40:22,040 Speaker 1: to have you know, what we found out the the 658 00:40:22,200 --> 00:40:27,200 Speaker 1: aging with the tooth of cementum manually is really good. Um. 659 00:40:27,440 --> 00:40:31,640 Speaker 1: But we've also found having marked her back in two 660 00:40:31,719 --> 00:40:35,000 Speaker 1: thousand one and as a younger bear you can it's 661 00:40:35,200 --> 00:40:40,040 Speaker 1: more trustworthy that processes. So we knew she was likely 662 00:40:40,160 --> 00:40:44,960 Speaker 1: three or four at that point. UM. So then when 663 00:40:45,040 --> 00:40:51,120 Speaker 1: they actually aged the second time around, it didn't peg 664 00:40:51,280 --> 00:40:54,680 Speaker 1: or as old as she really was. That's interesting. But 665 00:40:55,120 --> 00:40:58,920 Speaker 1: I don't know, I don't know. If we have she's 666 00:40:58,920 --> 00:41:01,759 Speaker 1: probably running out of teeth. The age, well, it's it's 667 00:41:01,880 --> 00:41:04,000 Speaker 1: you know, they wear them down. I mean they spend 668 00:41:04,320 --> 00:41:07,560 Speaker 1: half of their lives eating acorns and walnuts and things 669 00:41:07,640 --> 00:41:11,080 Speaker 1: like that, so it's going to wear them down. And um, 670 00:41:11,360 --> 00:41:13,360 Speaker 1: and the one that we use is such a tiny 671 00:41:13,520 --> 00:41:17,600 Speaker 1: little premolar. Could you use other? Um, that's just the 672 00:41:17,680 --> 00:41:20,399 Speaker 1: easiest one to get to that. Well, it's the it's 673 00:41:20,480 --> 00:41:25,279 Speaker 1: the one that they're not using. Its tiny. It's it's 674 00:41:25,280 --> 00:41:29,440 Speaker 1: a tiny one right behind that canine. And they're actually 675 00:41:29,480 --> 00:41:32,440 Speaker 1: I guess they're they technically have four because you can 676 00:41:32,520 --> 00:41:35,200 Speaker 1: technically take one from the bottom. The top is where 677 00:41:35,239 --> 00:41:38,040 Speaker 1: we usually pull from because that's usually the easiest to 678 00:41:38,200 --> 00:41:42,920 Speaker 1: pop loose. Um, it's not even it's not even a 679 00:41:43,000 --> 00:41:45,399 Speaker 1: quarter of an inch root at all once we get 680 00:41:45,440 --> 00:41:47,360 Speaker 1: it out of the out of the mouth. And so 681 00:41:47,520 --> 00:41:49,719 Speaker 1: that's the reason we use those, because it's not going 682 00:41:49,800 --> 00:41:56,200 Speaker 1: to inhibit their one. But that makes presumed that was 683 00:41:56,600 --> 00:42:00,200 Speaker 1: it's virtually vestigial. Basically, you know, it's virtually something that 684 00:42:00,239 --> 00:42:03,440 Speaker 1: they're not using anymore. It's still that's like appendix basically, 685 00:42:03,800 --> 00:42:07,080 Speaker 1: you know it's still there. Um, maybe it served a 686 00:42:07,160 --> 00:42:09,399 Speaker 1: purpose or still could help a little bit, but it's 687 00:42:09,400 --> 00:42:12,040 Speaker 1: so tiny that it's pretty much income you could annually 688 00:42:12,160 --> 00:42:15,360 Speaker 1: any tooth. I guess that's a good question, but I 689 00:42:15,440 --> 00:42:18,480 Speaker 1: believe so. I mean, they're they're all growing from the 690 00:42:18,520 --> 00:42:20,279 Speaker 1: time that they lose them as babies and get their 691 00:42:20,280 --> 00:42:24,239 Speaker 1: adult teeth, and they're gonna you know, Randy Cross told 692 00:42:24,280 --> 00:42:29,719 Speaker 1: me one time that, um, you're talking about tooth aging animals. 693 00:42:30,200 --> 00:42:34,640 Speaker 1: He said, he basically said, you can't put much stock 694 00:42:34,719 --> 00:42:38,000 Speaker 1: in toothwaar, just visible toothwar I wonder if you had 695 00:42:38,000 --> 00:42:41,000 Speaker 1: any correlation with this, like because he said we might. 696 00:42:41,080 --> 00:42:43,600 Speaker 1: He said, they might be working with a five year 697 00:42:43,640 --> 00:42:46,480 Speaker 1: old bear that has like rotten teeth and has broken 698 00:42:46,560 --> 00:42:49,320 Speaker 1: canines and busted up, and then they might have a 699 00:42:49,440 --> 00:42:54,040 Speaker 1: fifteen year old bear that has a fairly pristine set 700 00:42:54,080 --> 00:42:58,799 Speaker 1: of teeth, because you know, the generalization would be all 701 00:42:58,880 --> 00:43:01,640 Speaker 1: that bears old, it's teeth are wore down and broken. 702 00:43:01,719 --> 00:43:03,680 Speaker 1: I mean, probably most of the time that's true. But 703 00:43:03,800 --> 00:43:07,480 Speaker 1: he said there were notable exceptions that made him not 704 00:43:07,680 --> 00:43:10,000 Speaker 1: give much credit to just toothwaar. And I'm not talking 705 00:43:10,000 --> 00:43:13,440 Speaker 1: about annual cementum annually. I'm just talking about visible toothwaar. 706 00:43:14,200 --> 00:43:16,080 Speaker 1: And he thought it was genetics. He thought it was 707 00:43:16,160 --> 00:43:19,120 Speaker 1: just like humans, just like some people are prone to 708 00:43:19,719 --> 00:43:23,360 Speaker 1: more tooth decay than others. I just thought that was interesting. 709 00:43:23,520 --> 00:43:26,359 Speaker 1: I could definitely see that. I mean, I guess that's 710 00:43:26,400 --> 00:43:28,839 Speaker 1: with most wildlife species. It's like we've got this great 711 00:43:29,440 --> 00:43:32,960 Speaker 1: um formula for aging deer, but it depends on the 712 00:43:33,080 --> 00:43:36,439 Speaker 1: region that you're in and how many rocks they're chewing. Yeah, 713 00:43:36,520 --> 00:43:39,480 Speaker 1: what so, so it's the same idea with bears. We 714 00:43:39,600 --> 00:43:42,160 Speaker 1: always i mean, we're biologists, So we've got a data 715 00:43:42,239 --> 00:43:44,120 Speaker 1: sheet that's just chock full of stuff that we write 716 00:43:44,160 --> 00:43:47,120 Speaker 1: down and we make notes about what their toothwar looks like. 717 00:43:47,280 --> 00:43:51,880 Speaker 1: And if there's um, anything odd to report, UM. And 718 00:43:52,080 --> 00:43:56,040 Speaker 1: so I think I think it gives us some something 719 00:43:56,120 --> 00:43:58,720 Speaker 1: to go by, just if we're accustomed to seeing bears 720 00:43:58,760 --> 00:44:01,120 Speaker 1: in the wachtas and what their usually look like. It 721 00:44:01,239 --> 00:44:03,800 Speaker 1: has some bit of a spectrum for us that you know, 722 00:44:03,920 --> 00:44:06,719 Speaker 1: we could say, well, most likely it's within this range 723 00:44:06,840 --> 00:44:10,800 Speaker 1: or something. UM. But absolutely, I mean there's genetics, there's 724 00:44:11,080 --> 00:44:14,359 Speaker 1: what that particular bear eats a lot of did something 725 00:44:14,440 --> 00:44:19,520 Speaker 1: different than a bear thirty miles away, right, And like 726 00:44:19,640 --> 00:44:22,960 Speaker 1: I said, I'm sorry, Like I said, I mean, they're 727 00:44:22,960 --> 00:44:25,480 Speaker 1: when they're sitting there spending so much time crunching on 728 00:44:25,560 --> 00:44:28,600 Speaker 1: acorns and walnuts and things like that, you just just 729 00:44:28,800 --> 00:44:31,440 Speaker 1: never know. Do you remember how long it took you 730 00:44:31,520 --> 00:44:34,840 Speaker 1: to catch your first bear? Oh from when you started? 731 00:44:36,480 --> 00:44:40,000 Speaker 1: I do not remember specifically, but it seems as though 732 00:44:40,080 --> 00:44:43,360 Speaker 1: within the first week she's a good trapper. Because we 733 00:44:43,520 --> 00:44:48,040 Speaker 1: only um, I mean, we only run each line for 734 00:44:48,239 --> 00:44:50,680 Speaker 1: a couple of weeks apiece, and so at that time, 735 00:44:50,760 --> 00:44:53,920 Speaker 1: we would we would work for two weeks running those lines, 736 00:44:54,000 --> 00:44:56,279 Speaker 1: and then we'd take a couple of days off to 737 00:44:56,440 --> 00:44:58,840 Speaker 1: just do telemetry or whatever, and then we'd set the 738 00:44:58,920 --> 00:45:02,800 Speaker 1: next line. UM. Sounds like a fun research project to 739 00:45:02,840 --> 00:45:07,720 Speaker 1: start up. It really wasn't in such a beautiful place, 740 00:45:08,239 --> 00:45:10,040 Speaker 1: you know. Um, of course, I think it was that 741 00:45:10,200 --> 00:45:13,200 Speaker 1: very first line that also gave all of us a huge, 742 00:45:14,280 --> 00:45:18,799 Speaker 1: huge case of poison ivy because the absolutely perfect trap 743 00:45:18,920 --> 00:45:21,520 Speaker 1: tree that I found was covered, but I wasn't willing 744 00:45:21,600 --> 00:45:23,279 Speaker 1: to give it up, so we ripped out a bunch 745 00:45:23,320 --> 00:45:26,239 Speaker 1: of poison ivy and consequently all ended up in Fort 746 00:45:26,320 --> 00:45:32,040 Speaker 1: Smith at the doctor get shot that shot before because 747 00:45:32,120 --> 00:45:37,359 Speaker 1: we were so miserable. But we caught bears there. In fact, 748 00:45:37,520 --> 00:45:39,920 Speaker 1: the first bear we caught there we named Ivy because 749 00:45:42,280 --> 00:45:45,520 Speaker 1: what's the funniest name you've given in bear? Oh m, 750 00:45:46,560 --> 00:45:50,560 Speaker 1: it's interesting. That's a good question. I don't know. I 751 00:45:50,600 --> 00:45:53,239 Speaker 1: don't know if we have Oh I do know one. Um, 752 00:45:53,760 --> 00:45:56,759 Speaker 1: so I've heard you talk with somebody about how to 753 00:45:56,880 --> 00:46:00,640 Speaker 1: pronounce the watch how so many people bounce it? And 754 00:46:00,880 --> 00:46:02,960 Speaker 1: back then we used to laugh because people would say, 755 00:46:02,960 --> 00:46:06,480 Speaker 1: what cheetahs, So we called one of our bears wat cheetah. Oh, 756 00:46:06,840 --> 00:46:13,440 Speaker 1: that's just so. And then of course it's filled exactly 757 00:46:13,520 --> 00:46:20,480 Speaker 1: like you. That's funny. That's good, that's good. I like that. No, 758 00:46:20,840 --> 00:46:23,120 Speaker 1: I've got a bear name and story that involved Sarah, 759 00:46:23,760 --> 00:46:27,400 Speaker 1: and it ties right in. It's a beautiful segue into 760 00:46:28,080 --> 00:46:31,040 Speaker 1: den research. So the one time I went with you 761 00:46:31,120 --> 00:46:34,120 Speaker 1: guys on a then research project or a then a 762 00:46:34,239 --> 00:46:39,000 Speaker 1: then visit, I think it was twenty fifteen. I think 763 00:46:39,000 --> 00:46:41,200 Speaker 1: it was. I think it's five years ago. May it 764 00:46:41,320 --> 00:46:44,600 Speaker 1: may have been. It would have been twenty sixteen, I think, 765 00:46:44,600 --> 00:46:48,920 Speaker 1: because that's when Erica and Morgan were with us. So 766 00:46:49,280 --> 00:46:54,000 Speaker 1: four years ago. And uh so we were we were 767 00:46:54,080 --> 00:46:57,600 Speaker 1: going into a sal that you've done so many I 768 00:46:57,640 --> 00:47:00,520 Speaker 1: don't know if you if you even remember, but we 769 00:47:00,640 --> 00:47:03,040 Speaker 1: were going into a sala that didn't have cubs. We 770 00:47:03,480 --> 00:47:05,359 Speaker 1: didn't think, we didn't think. She thought she was too 771 00:47:05,400 --> 00:47:08,359 Speaker 1: young to have cubs. Yeah, And so we walk all 772 00:47:08,360 --> 00:47:09,919 Speaker 1: the way back in there and the whole time it's 773 00:47:09,960 --> 00:47:13,120 Speaker 1: like and the good the cool thing about it was 774 00:47:13,320 --> 00:47:15,400 Speaker 1: if it had cubs, I probably wouldn't have even been 775 00:47:15,440 --> 00:47:17,200 Speaker 1: on a trip. Part of the reason I got to 776 00:47:17,280 --> 00:47:20,160 Speaker 1: go was they were like, Hey, this isn't that important. 777 00:47:20,880 --> 00:47:24,120 Speaker 1: You're not that important, so you can come with us 778 00:47:24,160 --> 00:47:27,120 Speaker 1: on this one, and so you have to pay your dues. Yeah, 779 00:47:28,040 --> 00:47:30,920 Speaker 1: and so and I was. I was grateful just to 780 00:47:31,000 --> 00:47:34,560 Speaker 1: be there and so we went up and uh, I 781 00:47:34,600 --> 00:47:39,160 Speaker 1: mean it's a pretty cool process to like watch watch 782 00:47:39,239 --> 00:47:42,840 Speaker 1: you guys work in that scenario because you know pretty 783 00:47:42,920 --> 00:47:45,239 Speaker 1: much where the bears at and probably in this case 784 00:47:45,480 --> 00:47:47,560 Speaker 1: that I think Morgan knew exactly where it was at. 785 00:47:47,960 --> 00:47:50,520 Speaker 1: They had already gone in and located the din in 786 00:47:50,600 --> 00:47:52,640 Speaker 1: this rock cavity, and so you know, you have to 787 00:47:53,080 --> 00:47:55,840 Speaker 1: kind of sneak up, kind of be quiet, and but 788 00:47:56,120 --> 00:48:01,400 Speaker 1: these bears are unusually today eight, you know, just naturally 789 00:48:01,520 --> 00:48:05,120 Speaker 1: in the den, Like that's what's not people either think 790 00:48:05,160 --> 00:48:07,560 Speaker 1: they're dead asleep and you could just walk up to 791 00:48:07,680 --> 00:48:09,640 Speaker 1: them and poke them with a stick and they wouldn't 792 00:48:09,640 --> 00:48:12,800 Speaker 1: even wake up. Or when you tell them that, they're 793 00:48:13,600 --> 00:48:17,640 Speaker 1: like cognizant and like they're like looking at you out 794 00:48:17,680 --> 00:48:20,680 Speaker 1: of the den with their head up like you know, 795 00:48:21,120 --> 00:48:23,920 Speaker 1: you're like, well, why aren't they running off? Anyway, you 796 00:48:24,120 --> 00:48:26,319 Speaker 1: you sneak up to this den and kind of move 797 00:48:26,400 --> 00:48:30,879 Speaker 1: in quiet, and then I think Morgan darted the bear. 798 00:48:31,520 --> 00:48:33,560 Speaker 1: I think with an air gun. It may be in 799 00:48:33,600 --> 00:48:38,120 Speaker 1: a jabstick. We usually use a jabstick, but I think 800 00:48:38,200 --> 00:48:39,799 Speaker 1: I stayed back and let you guys go up there, 801 00:48:39,840 --> 00:48:42,080 Speaker 1: but usually use a jebstick. Yeah, she used the jet stick. 802 00:48:42,120 --> 00:48:44,360 Speaker 1: I remember putting it together now. So she has this 803 00:48:44,520 --> 00:48:47,680 Speaker 1: like spear with a syringe on the end of it. Poke, 804 00:48:48,600 --> 00:48:51,920 Speaker 1: poke this bear, and the bear just lets you do that, 805 00:48:52,680 --> 00:48:57,080 Speaker 1: and um, some but some of them. Yeah. But what 806 00:48:57,280 --> 00:48:59,320 Speaker 1: was cool about this one is that there were cubs. 807 00:48:59,800 --> 00:49:04,000 Speaker 1: So once we get up there, Morgan's like, there's cops 808 00:49:04,040 --> 00:49:06,400 Speaker 1: in there, and there wasn't supposed to be cubs. And 809 00:49:06,520 --> 00:49:10,040 Speaker 1: this was like a I guess a bear going into 810 00:49:10,120 --> 00:49:12,080 Speaker 1: its third was it like a two and a half 811 00:49:12,200 --> 00:49:13,680 Speaker 1: year old, but well, I would have been in the 812 00:49:14,000 --> 00:49:17,880 Speaker 1: would have been in the spring three already, yep, So 813 00:49:18,080 --> 00:49:20,360 Speaker 1: that's pretty young for a bear. So that meant that 814 00:49:20,480 --> 00:49:23,600 Speaker 1: she bread it to and then had her first litterate three. 815 00:49:23,920 --> 00:49:27,040 Speaker 1: And we've had that happen in the Ozarks in particular. 816 00:49:27,280 --> 00:49:31,000 Speaker 1: We've had that happened before. Um, but it's it's not 817 00:49:31,160 --> 00:49:34,120 Speaker 1: incredibly common. They usually say between three and five is 818 00:49:34,200 --> 00:49:37,839 Speaker 1: when they they first Yeah, I want to I want 819 00:49:37,840 --> 00:49:40,640 Speaker 1: to come back to that very point, but I gotta 820 00:49:40,719 --> 00:49:46,040 Speaker 1: finish the story. While Morgan and I were yeah, y'all 821 00:49:46,080 --> 00:49:50,000 Speaker 1: stayed back just a little bit, and Erica and Morgan up, 822 00:49:50,040 --> 00:49:51,759 Speaker 1: I think so that we only have a few people 823 00:49:51,840 --> 00:49:54,400 Speaker 1: that go in on the sedation team and rite it 824 00:49:54,520 --> 00:49:58,600 Speaker 1: like the peak moment of intensity, like we see the den. 825 00:49:58,760 --> 00:50:03,040 Speaker 1: The den's like right there. Morgan goes, there's a camera. 826 00:50:05,320 --> 00:50:12,000 Speaker 1: There was a like nineteen seventies style full frame thirty 827 00:50:12,080 --> 00:50:18,279 Speaker 1: five millimeter cannon camera laid on a rock as if 828 00:50:19,440 --> 00:50:24,280 Speaker 1: rumpel Stiltskin in nineteen seventy nine had laid that camera 829 00:50:24,360 --> 00:50:29,800 Speaker 1: there and it had been untouched for it was old. 830 00:50:30,520 --> 00:50:33,320 Speaker 1: This camera's just laying there. And she says, there's a 831 00:50:33,400 --> 00:50:35,719 Speaker 1: camera and we're in the middle of nowhere. Do you 832 00:50:35,760 --> 00:50:43,880 Speaker 1: remember that camera? Jeff Ford has it and and so 833 00:50:44,719 --> 00:50:48,040 Speaker 1: we're we gotta do the job. So we're like take note, like, okay, 834 00:50:48,080 --> 00:50:49,839 Speaker 1: there's a camera. We gotta come back and see what's 835 00:50:49,920 --> 00:50:51,239 Speaker 1: up with this. And when we go in and do 836 00:50:51,320 --> 00:50:54,520 Speaker 1: all the den work, and then I think after we 837 00:50:54,600 --> 00:50:58,160 Speaker 1: got her sedated, somebody walked over there and you know, 838 00:50:58,239 --> 00:51:00,160 Speaker 1: Morgan saw it, so it's you know, it was it 839 00:51:00,280 --> 00:51:03,480 Speaker 1: was it was hers, you know, and uh it we 840 00:51:04,320 --> 00:51:07,440 Speaker 1: It was crazy cool. And we named that cub cannon 841 00:51:08,080 --> 00:51:10,680 Speaker 1: because it's a cannon camera. And one of the cubs 842 00:51:10,800 --> 00:51:15,560 Speaker 1: was color phase. They were tiny and a color phase. 843 00:51:15,920 --> 00:51:18,520 Speaker 1: I remember one of them looked almost silver, and that's 844 00:51:18,520 --> 00:51:21,440 Speaker 1: the one we named Cannon. But anyway, it was just 845 00:51:21,760 --> 00:51:25,280 Speaker 1: I've never had that happening in in all my outdoor 846 00:51:25,400 --> 00:51:28,520 Speaker 1: adventures to find something quite that unique. And as I 847 00:51:29,400 --> 00:51:32,440 Speaker 1: followed up with Morgan later, and she wasn't able to 848 00:51:33,040 --> 00:51:37,360 Speaker 1: like the film was like unsalvageable, but she tried to 849 00:51:38,200 --> 00:51:40,919 Speaker 1: get it open. And but anyway, it's kind of neat. 850 00:51:41,760 --> 00:51:44,239 Speaker 1: But yeah, you never know what you're gonna find out 851 00:51:44,280 --> 00:51:47,480 Speaker 1: in these woods. You know, it's it's I'm always hauling 852 00:51:47,520 --> 00:51:50,319 Speaker 1: treasures in, but usually it's rocks and feathers and things 853 00:51:50,400 --> 00:51:53,920 Speaker 1: like that. But yeah, well I'd like to know the 854 00:51:53,960 --> 00:51:57,440 Speaker 1: story of the person that lost that. Um Okay, the 855 00:51:58,120 --> 00:52:02,480 Speaker 1: the reading age of these bears being two and three, Like, 856 00:52:03,000 --> 00:52:06,359 Speaker 1: what does it indicate when there's young females getting bread? 857 00:52:07,160 --> 00:52:11,120 Speaker 1: So what we suspect? Like I said, it's usually that 858 00:52:11,239 --> 00:52:13,440 Speaker 1: we've seen it the most up in our Ozark region, 859 00:52:13,480 --> 00:52:15,480 Speaker 1: although we have seen it down here in the Wachitas 860 00:52:15,520 --> 00:52:19,360 Speaker 1: a little bit. Um, But in that particular case, you 861 00:52:19,400 --> 00:52:23,919 Speaker 1: would expect that there are fewer females and so those 862 00:52:24,040 --> 00:52:28,040 Speaker 1: younger ones can actually become bread at an earlier age. 863 00:52:28,160 --> 00:52:32,080 Speaker 1: So that the males are actually covering younger females UM. 864 00:52:32,800 --> 00:52:35,000 Speaker 1: And and there's a lot that goes into it as 865 00:52:35,080 --> 00:52:38,400 Speaker 1: well in terms of interpreting. You know, a lot of 866 00:52:38,440 --> 00:52:41,800 Speaker 1: times those younger females have a harder time actually raising 867 00:52:41,840 --> 00:52:44,560 Speaker 1: off that first litter because there's got to be some 868 00:52:44,760 --> 00:52:48,399 Speaker 1: learning curve, right, I mean, um, if they're still only 869 00:52:49,800 --> 00:52:52,480 Speaker 1: only just now turning three years old and I've only 870 00:52:52,560 --> 00:52:55,680 Speaker 1: spent you know, six months or nine months on their own, 871 00:52:56,160 --> 00:52:59,600 Speaker 1: than them having to also teach cubs is a challenge 872 00:52:59,680 --> 00:53:01,840 Speaker 1: for them. Um. Some do a great job and some 873 00:53:02,239 --> 00:53:05,880 Speaker 1: some don't. UM. But but yeah, it's been an interesting 874 00:53:06,000 --> 00:53:09,120 Speaker 1: thing to see now down here and the Wachitas. I 875 00:53:09,239 --> 00:53:12,320 Speaker 1: believe that um most of the time, their first breeding 876 00:53:12,440 --> 00:53:15,759 Speaker 1: is around three and this is a more stable population 877 00:53:15,880 --> 00:53:19,839 Speaker 1: though correct there there there are more bears here there. 878 00:53:20,280 --> 00:53:22,759 Speaker 1: It's it's still growing, but it's still kind of a 879 00:53:22,800 --> 00:53:26,360 Speaker 1: more stable and they're more bears UM and more adult 880 00:53:26,400 --> 00:53:29,600 Speaker 1: females to choose from. Now, not that the male bears 881 00:53:29,640 --> 00:53:32,759 Speaker 1: are at all choosy. They're they're just covering as many 882 00:53:32,800 --> 00:53:37,000 Speaker 1: as they possibly can. UM. But there's just that that competition. 883 00:53:37,840 --> 00:53:43,120 Speaker 1: What's the what's the coolest like home range bear story 884 00:53:43,320 --> 00:53:47,200 Speaker 1: that you can recall. So you've done you've been around 885 00:53:47,320 --> 00:53:50,919 Speaker 1: some of these research projects with like bears. And I'm 886 00:53:50,960 --> 00:53:53,799 Speaker 1: sure you've heard about this bear that's come down from 887 00:53:54,320 --> 00:53:57,560 Speaker 1: like Wisconsin is now in eastern Arkansas. Have you heard 888 00:53:57,560 --> 00:54:00,680 Speaker 1: about that? Um? Briefly, I don't know a whole lot 889 00:54:00,680 --> 00:54:03,000 Speaker 1: about it, but that's all I know is what I 890 00:54:03,120 --> 00:54:05,640 Speaker 1: just told you. But they've been tracking this bear down 891 00:54:05,719 --> 00:54:08,880 Speaker 1: through the Midwest and he's ended up in like the 892 00:54:09,120 --> 00:54:14,120 Speaker 1: Lower White River drainage in Arkansas from Minnesota, not Minnesota, Wisconsin, 893 00:54:14,239 --> 00:54:18,640 Speaker 1: I think. But well, UM, So, so one thing that 894 00:54:18,680 --> 00:54:23,120 Speaker 1: I should mention about callers that UM is really important 895 00:54:23,280 --> 00:54:26,920 Speaker 1: to bring up about because our researches, we learned so 896 00:54:27,040 --> 00:54:29,400 Speaker 1: much more now these days, being able to have access 897 00:54:29,440 --> 00:54:32,200 Speaker 1: to the technology we have. So when I was in 898 00:54:32,280 --> 00:54:36,520 Speaker 1: graduate school, UM I referred to only having a VHF caller, 899 00:54:36,600 --> 00:54:39,080 Speaker 1: which means it only has a radio signal and you 900 00:54:39,239 --> 00:54:41,920 Speaker 1: have to get out there and manually track that caller 901 00:54:42,520 --> 00:54:45,360 Speaker 1: and triangulate. You have to have really good mapping skills 902 00:54:45,640 --> 00:54:48,719 Speaker 1: and you know, so you're out there having to do 903 00:54:48,880 --> 00:54:51,520 Speaker 1: the work to get any locations. So you can imagine 904 00:54:52,800 --> 00:54:55,040 Speaker 1: because of the time that it takes to actually get 905 00:54:55,239 --> 00:54:58,840 Speaker 1: enough readings on a certain bear and get a location 906 00:54:58,920 --> 00:55:01,440 Speaker 1: on it that you're your hands are tied to where 907 00:55:01,480 --> 00:55:04,560 Speaker 1: you probably got maybe I got three locations per bear 908 00:55:04,640 --> 00:55:07,799 Speaker 1: per week something like that when I had fifteen big 909 00:55:07,880 --> 00:55:11,160 Speaker 1: callers out or so um, and that's tracking six days 910 00:55:11,160 --> 00:55:13,800 Speaker 1: a week, you know. So Um, there's a lot of 911 00:55:13,880 --> 00:55:17,680 Speaker 1: effort that goes into getting just a handful of locations 912 00:55:17,719 --> 00:55:21,840 Speaker 1: and data. Now we're using satellite callers, and so we 913 00:55:22,000 --> 00:55:24,280 Speaker 1: can have those callers, I mean they can be programmed 914 00:55:24,360 --> 00:55:27,680 Speaker 1: to take locations every thirty minutes if we wanted to, 915 00:55:27,880 --> 00:55:31,040 Speaker 1: but most of ours take them. Um. Now on our 916 00:55:31,120 --> 00:55:33,960 Speaker 1: yearling bears, we're taking them every few hours. On our 917 00:55:34,000 --> 00:55:37,880 Speaker 1: adult bears were taking them every six or seven hours. Um. 918 00:55:37,960 --> 00:55:40,200 Speaker 1: So that you're getting locations at different times a day. 919 00:55:40,440 --> 00:55:44,560 Speaker 1: Now we aren't getting those locations every single time. It 920 00:55:44,680 --> 00:55:48,040 Speaker 1: makes an attempt, but you can see that if I'm 921 00:55:48,040 --> 00:55:50,520 Speaker 1: supposed to be getting three locations per day and only 922 00:55:50,560 --> 00:55:56,920 Speaker 1: get two, that's still there were exactly I mean, so 923 00:55:57,200 --> 00:55:59,879 Speaker 1: what we thought was happening back then based on VH 924 00:56:00,000 --> 00:56:03,480 Speaker 1: of work, Now we're really getting to see a lot 925 00:56:03,600 --> 00:56:08,000 Speaker 1: of movement that we wouldn't have gotten before. Um. So, 926 00:56:08,920 --> 00:56:11,960 Speaker 1: so we've had we've had one female in our ozark 927 00:56:12,080 --> 00:56:18,080 Speaker 1: region that primarily was in an area let's say eight 928 00:56:18,160 --> 00:56:20,680 Speaker 1: miles by eight miles that was her home range for 929 00:56:20,760 --> 00:56:25,720 Speaker 1: the most part, and that here working around in that area. 930 00:56:26,160 --> 00:56:31,960 Speaker 1: That's where she lived. But every fall for about a 931 00:56:32,040 --> 00:56:36,320 Speaker 1: week or two, she would make this jaunt into Arkansas, 932 00:56:36,680 --> 00:56:39,960 Speaker 1: which was probably where she was going. Was probably as 933 00:56:40,000 --> 00:56:44,640 Speaker 1: a crow flies thirty miles away or so, and she'd 934 00:56:44,640 --> 00:56:46,879 Speaker 1: go over there, and then she'd come back and then 935 00:56:47,640 --> 00:56:53,480 Speaker 1: back in Oklahoma again, and and so we never were able. 936 00:56:53,600 --> 00:56:55,520 Speaker 1: Actually we should pull that date up and maybe we 937 00:56:55,560 --> 00:56:58,080 Speaker 1: could get over there now that we've been we've been 938 00:56:58,160 --> 00:57:01,839 Speaker 1: working with Yeah, of course, it's it's been so long now. UM. 939 00:57:02,040 --> 00:57:04,560 Speaker 1: I mean you could say, well, maybe there's a feeder 940 00:57:04,680 --> 00:57:07,239 Speaker 1: over there that she's going to a wildlife feeder or something. 941 00:57:07,440 --> 00:57:10,200 Speaker 1: But she was surrounded by plenty of wildlife feeders in 942 00:57:10,360 --> 00:57:13,320 Speaker 1: her home range. Um, she was in an area that 943 00:57:13,440 --> 00:57:17,520 Speaker 1: had plenty of good oaks, you know, so, but there 944 00:57:17,640 --> 00:57:20,320 Speaker 1: was something over there that she would take this job 945 00:57:20,520 --> 00:57:23,480 Speaker 1: and then come back. Um. Do you think it could 946 00:57:23,480 --> 00:57:27,080 Speaker 1: have been like where she was raised or where she 947 00:57:27,200 --> 00:57:30,440 Speaker 1: came from it very well, a maternal home range or 948 00:57:30,560 --> 00:57:34,120 Speaker 1: something maybe you know, certainly that's close enough that that 949 00:57:34,480 --> 00:57:37,240 Speaker 1: you know, she could have potentially come from that area, 950 00:57:38,160 --> 00:57:41,440 Speaker 1: Um for sure. But why she wouldn't have set up 951 00:57:41,480 --> 00:57:46,160 Speaker 1: shop more in Arkansas than you know, because they don't 952 00:57:46,240 --> 00:57:49,800 Speaker 1: usually they don't typically disperse that far from the maternal 953 00:57:49,880 --> 00:57:52,360 Speaker 1: home ranges, the females don't. Would you say? It's pretty 954 00:57:52,480 --> 00:57:55,360 Speaker 1: uh standard? Like, well, I don't want to get ahead 955 00:57:55,360 --> 00:57:59,920 Speaker 1: of you on the other interesting stories, but like, give 956 00:58:00,000 --> 00:58:04,480 Speaker 1: me a generalization on bear home range here, Like, okay, 957 00:58:05,240 --> 00:58:08,880 Speaker 1: so they're they're pretty large here. Um, what we're probably 958 00:58:09,000 --> 00:58:14,320 Speaker 1: looking at is around and around thirty eight square miles 959 00:58:14,440 --> 00:58:17,800 Speaker 1: or so for females and a hundred and seventy or 960 00:58:17,880 --> 00:58:21,280 Speaker 1: so square miles for males. Were more bigger. I mean 961 00:58:21,360 --> 00:58:24,320 Speaker 1: we actually down here, I believe that there's an estimate 962 00:58:24,360 --> 00:58:27,040 Speaker 1: that was even close to like two fifty square miles. 963 00:58:27,280 --> 00:58:32,480 Speaker 1: Is that so? Laura Conley in Missouri told me something similar, 964 00:58:32,600 --> 00:58:36,520 Speaker 1: and that surprised me because and maybe twenty maybe ten 965 00:58:36,640 --> 00:58:40,560 Speaker 1: even ten years ago, some of the research that was 966 00:58:40,640 --> 00:58:44,400 Speaker 1: being at least in general terms, what I was hearing 967 00:58:45,200 --> 00:58:48,200 Speaker 1: in Arkansas, the Bear range wasn't that big, And I 968 00:58:48,240 --> 00:58:51,160 Speaker 1: don't know if that. You know, sometimes one person says 969 00:58:51,320 --> 00:58:55,280 Speaker 1: something as a generalization and then you hear that and 970 00:58:55,360 --> 00:58:57,440 Speaker 1: then you start repeating it and before you know it 971 00:58:58,120 --> 00:59:01,520 Speaker 1: it's science, when really it never is. So, but that's 972 00:59:01,560 --> 00:59:05,200 Speaker 1: really a big home range. There's a lot of variation. 973 00:59:05,320 --> 00:59:07,640 Speaker 1: I mean, that is definitely something to keep in mind, 974 00:59:07,840 --> 00:59:13,520 Speaker 1: is that um between individual bears and their area within 975 00:59:13,680 --> 00:59:17,840 Speaker 1: the population. Um. You know, their home range size is 976 00:59:17,920 --> 00:59:21,960 Speaker 1: wholly tied to the resources that they need to survive 977 00:59:22,200 --> 00:59:27,040 Speaker 1: on an annual basis. So that's well, it's just it 978 00:59:27,120 --> 00:59:28,920 Speaker 1: can be in a very small area. It could be 979 00:59:29,000 --> 00:59:32,240 Speaker 1: three square miles if they could find all of their 980 00:59:32,280 --> 00:59:36,200 Speaker 1: annual food food stuff within that three square miles, if 981 00:59:36,240 --> 00:59:38,760 Speaker 1: they can find their water, they've got good denning all 982 00:59:38,800 --> 00:59:41,880 Speaker 1: of that, it can be very small, um, but they 983 00:59:42,080 --> 00:59:44,320 Speaker 1: need to to be able to I mean, these are 984 00:59:44,480 --> 00:59:48,160 Speaker 1: very large animals in general that live off of nuts 985 00:59:48,240 --> 00:59:52,960 Speaker 1: and berries basically. So you can imagine how many, yeah, 986 00:59:53,000 --> 00:59:55,720 Speaker 1: how much they have to forage to get enough blackberries 987 00:59:55,760 --> 00:59:58,360 Speaker 1: for a day or or acorns for a day, that 988 00:59:58,560 --> 01:00:01,360 Speaker 1: kind of thing. So it's it's really a function of 989 01:00:02,080 --> 01:00:04,600 Speaker 1: of the habitat in how much area they have to 990 01:00:04,680 --> 01:00:08,240 Speaker 1: cover in general. Now, males, it also has to do 991 01:00:08,720 --> 01:00:12,960 Speaker 1: with them traveling to breed. The females aren't going to 992 01:00:13,440 --> 01:00:16,760 Speaker 1: take off and now their their summer home ranges are 993 01:00:16,920 --> 01:00:19,720 Speaker 1: larger than the fall. Usually the fall usually kind of 994 01:00:20,280 --> 01:00:23,680 Speaker 1: um gets a little more compact um. So the female 995 01:00:24,040 --> 01:00:26,200 Speaker 1: home ranges in the summer are bigger than the fall. 996 01:00:26,760 --> 01:00:29,600 Speaker 1: Males are obviously way bigger in the summer than they 997 01:00:29,600 --> 01:00:31,720 Speaker 1: are in the fall, because summer is the breeding season 998 01:00:31,800 --> 01:00:34,439 Speaker 1: and so they really put the pedal to the metal 999 01:00:34,520 --> 01:00:38,640 Speaker 1: and are out. That's a massive, massive area, you know. 1000 01:00:38,760 --> 01:00:42,080 Speaker 1: I wonder if sometimes I've thought about, like, uh, kind 1001 01:00:42,080 --> 01:00:44,320 Speaker 1: of like an anomaly like what you just described, like 1002 01:00:44,400 --> 01:00:46,800 Speaker 1: the south that just had this home range and she 1003 01:00:47,000 --> 01:00:50,280 Speaker 1: just went over and did something crazy and then would 1004 01:00:50,360 --> 01:00:56,360 Speaker 1: come back. I wonder if adaptation an evolution have some 1005 01:00:56,520 --> 01:01:04,400 Speaker 1: way rewarded kind of just something that seems erratic, but 1006 01:01:05,040 --> 01:01:09,200 Speaker 1: there was a biological advantage too, Like if you think 1007 01:01:09,200 --> 01:01:14,440 Speaker 1: about it like this, like maybe something catastrophic would happen 1008 01:01:14,520 --> 01:01:17,480 Speaker 1: in a certain area and the bear that wasn't there 1009 01:01:17,520 --> 01:01:20,920 Speaker 1: when it happened survived. Do you understand what I'm saying? 1010 01:01:21,320 --> 01:01:23,760 Speaker 1: So like, because you see these like we kind of 1011 01:01:23,840 --> 01:01:26,000 Speaker 1: want to take wildlife and make them into this like 1012 01:01:26,800 --> 01:01:29,440 Speaker 1: cardboard cut I do. Anyway, I know you're a biologist, 1013 01:01:29,480 --> 01:01:31,360 Speaker 1: so you don't, but I do. And so it's like 1014 01:01:31,520 --> 01:01:33,880 Speaker 1: a bear has he's in this home range, he's gonna 1015 01:01:33,920 --> 01:01:35,960 Speaker 1: stay here. But then you hear this stuff and and 1016 01:01:36,400 --> 01:01:39,160 Speaker 1: I'm you know, Jeff Ford told us a story about 1017 01:01:39,280 --> 01:01:43,680 Speaker 1: this bear that went like seventy miles. Yeah, we called 1018 01:01:43,760 --> 01:01:46,520 Speaker 1: him Rambler after that, and you know, and so my, 1019 01:01:47,760 --> 01:01:49,840 Speaker 1: you know, you think, well, a home range is a 1020 01:01:51,000 --> 01:01:54,160 Speaker 1: is a reflection of what that bear needs to survive 1021 01:01:54,440 --> 01:01:57,000 Speaker 1: and nothing more like that's what you That's what I think, 1022 01:01:57,360 --> 01:01:59,240 Speaker 1: or you know, that's what I wanted to like. So 1023 01:01:59,640 --> 01:02:05,000 Speaker 1: but Ambler, he bypassed a ton of great habitat, probably 1024 01:02:05,240 --> 01:02:07,640 Speaker 1: full of food source to go over there. For whatever 1025 01:02:07,800 --> 01:02:10,320 Speaker 1: this bear did. Your bear did the same thing. So like, 1026 01:02:10,400 --> 01:02:15,840 Speaker 1: I just wonder if there's like something rewarded inside of random, 1027 01:02:16,280 --> 01:02:20,240 Speaker 1: you know, eccentricity inside of a bear or any any 1028 01:02:20,440 --> 01:02:23,520 Speaker 1: any animal species. Well, and to us, it's it appears 1029 01:02:23,560 --> 01:02:27,280 Speaker 1: to be erratic, but there's there's a reason that they're 1030 01:02:27,320 --> 01:02:30,200 Speaker 1: making those moves. And it's just like, um, you know, 1031 01:02:30,280 --> 01:02:32,920 Speaker 1: we've We've worked to try to get it why they 1032 01:02:33,080 --> 01:02:37,680 Speaker 1: choose different den types, Right, Why when you've got say, crevices, 1033 01:02:37,760 --> 01:02:42,520 Speaker 1: and you've got hollow trees, and you've got good brush piles, 1034 01:02:42,640 --> 01:02:46,439 Speaker 1: why would they ever just go den in a ground nest? 1035 01:02:46,640 --> 01:02:49,000 Speaker 1: And why why would they do that? It seems ridiculous 1036 01:02:49,080 --> 01:02:52,320 Speaker 1: to us, But somehow it's working for them, and they've 1037 01:02:52,400 --> 01:02:56,040 Speaker 1: made those choices, and so we just have to do 1038 01:02:56,120 --> 01:02:58,120 Speaker 1: the best we can at looking at all the variables 1039 01:02:58,160 --> 01:03:00,720 Speaker 1: to try to flesh out why they're making these moves 1040 01:03:00,840 --> 01:03:03,720 Speaker 1: and these decisions that they're making. Um, and maybe it 1041 01:03:03,840 --> 01:03:05,800 Speaker 1: is random, you know, maybe maybe we try to put 1042 01:03:05,880 --> 01:03:08,520 Speaker 1: too much into it. But I like to think, I 1043 01:03:08,600 --> 01:03:11,480 Speaker 1: mean sort of supporting your your theory. I mean, I 1044 01:03:11,600 --> 01:03:14,520 Speaker 1: like to think that there's there's a reason for their movements. 1045 01:03:14,600 --> 01:03:16,720 Speaker 1: We just haven't seen it yet. We just haven't quite 1046 01:03:16,760 --> 01:03:19,520 Speaker 1: flushed it out. Yeah, it's such a mystery that that 1047 01:03:19,720 --> 01:03:22,520 Speaker 1: is the one thing that it's so cool about these animals, 1048 01:03:22,600 --> 01:03:24,680 Speaker 1: especially when we have this kind of research and data 1049 01:03:24,760 --> 01:03:26,880 Speaker 1: like we have now, like these GPS college as you 1050 01:03:26,960 --> 01:03:30,280 Speaker 1: get the unveiled, just a little bit of the mystery 1051 01:03:30,360 --> 01:03:32,240 Speaker 1: to see what they're doing. Right, do you have any 1052 01:03:32,240 --> 01:03:35,520 Speaker 1: other cool home range stories. I kind of interrupted you. Um, well, 1053 01:03:35,520 --> 01:03:40,840 Speaker 1: I was gonna mention Rambler and his movements. Um. Actually, uh, 1054 01:03:41,040 --> 01:03:44,760 Speaker 1: Courtney daughter, which are PhD student down on the Watchitas 1055 01:03:44,840 --> 01:03:46,800 Speaker 1: just sent me a map the other day of our 1056 01:03:46,840 --> 01:03:49,200 Speaker 1: girl birth though that I was just talking about. And 1057 01:03:49,440 --> 01:03:53,720 Speaker 1: so she is within our original core area here and 1058 01:03:54,040 --> 01:03:57,720 Speaker 1: um and has been has been really consistent over the years. 1059 01:03:57,760 --> 01:03:59,600 Speaker 1: I mean we literally were catching her like at the 1060 01:03:59,640 --> 01:04:02,000 Speaker 1: same app sites that we did from back in the 1061 01:04:02,040 --> 01:04:06,280 Speaker 1: early two thousand's and um, but she took a jaunt 1062 01:04:07,440 --> 01:04:11,360 Speaker 1: south that looks appears to be new this summer compared 1063 01:04:11,400 --> 01:04:14,440 Speaker 1: to what she's done before. And again I will say 1064 01:04:14,480 --> 01:04:16,800 Speaker 1: that she has not had cubs the last couple of years, 1065 01:04:17,280 --> 01:04:19,440 Speaker 1: and so maybe her movements might be a little bit 1066 01:04:19,520 --> 01:04:23,400 Speaker 1: different now that she's older and potentially not having cubs. Um, 1067 01:04:23,840 --> 01:04:26,840 Speaker 1: we're still holding out hope for this next year. Um, 1068 01:04:28,200 --> 01:04:30,640 Speaker 1: but she just took a jaunt south and I'm trying 1069 01:04:30,720 --> 01:04:33,960 Speaker 1: to think it's a good ten or twelve miles from 1070 01:04:34,040 --> 01:04:40,520 Speaker 1: kind of her normal area. UM. We speculated that maybe 1071 01:04:40,600 --> 01:04:42,840 Speaker 1: she was making her way down to the to the 1072 01:04:42,880 --> 01:04:44,880 Speaker 1: little cafe to get a burger and fries and a 1073 01:04:44,960 --> 01:04:47,120 Speaker 1: little shake or something since she didn't have to deal 1074 01:04:47,160 --> 01:04:56,919 Speaker 1: with kids. But yeah, um, den's is there? Is there? 1075 01:04:57,800 --> 01:05:03,840 Speaker 1: Where do they primarily den um? So, oh gosh, they 1076 01:05:03,960 --> 01:05:05,880 Speaker 1: use so many different types of Den's already kind of 1077 01:05:05,880 --> 01:05:08,440 Speaker 1: spatted off a few of them. It's it seems to 1078 01:05:08,480 --> 01:05:11,000 Speaker 1: be a little bit different between the Watchtows and the Ozarks, 1079 01:05:11,120 --> 01:05:15,160 Speaker 1: but that is only because the availability of crevice type 1080 01:05:15,240 --> 01:05:17,960 Speaker 1: or cave type dens and those arcs they're they're more 1081 01:05:18,080 --> 01:05:21,160 Speaker 1: available up in the Ozarks because of the geology in 1082 01:05:21,200 --> 01:05:25,160 Speaker 1: that area. So we get a lot you know, we 1083 01:05:25,240 --> 01:05:27,880 Speaker 1: definitely get more of them in those types of den's 1084 01:05:28,000 --> 01:05:31,760 Speaker 1: there um. But primarily we see a lot of ground excavations. 1085 01:05:32,000 --> 01:05:34,480 Speaker 1: So in the side of a hill, they literally will 1086 01:05:34,520 --> 01:05:37,240 Speaker 1: dig out a spot big enough for them to curl 1087 01:05:37,320 --> 01:05:39,960 Speaker 1: up in UM. Down here, we get a lot more 1088 01:05:40,160 --> 01:05:44,080 Speaker 1: in like the hollow bases of trees UM sometimes in 1089 01:05:44,240 --> 01:05:46,480 Speaker 1: elevated what I would call elevated tree ins where they 1090 01:05:46,480 --> 01:05:48,640 Speaker 1: actually have to go into a hole up twenty or 1091 01:05:48,680 --> 01:05:51,040 Speaker 1: thirty feet up and down. We've got some trees that 1092 01:05:51,280 --> 01:05:53,840 Speaker 1: big around here that have those kind do and and 1093 01:05:54,080 --> 01:05:56,680 Speaker 1: and it's kind of crazy because they actually don't have 1094 01:05:56,800 --> 01:05:58,560 Speaker 1: to be as big as you would envision them. I 1095 01:05:58,600 --> 01:06:01,560 Speaker 1: mean basically like mama beary. Then they're sitting here and 1096 01:06:01,680 --> 01:06:04,600 Speaker 1: that's about as much space as she's probably got. You 1097 01:06:04,680 --> 01:06:07,360 Speaker 1: can't hardly get to those bears though, for we have not. 1098 01:06:07,840 --> 01:06:10,320 Speaker 1: All we have done is actually we've done go pro 1099 01:06:10,840 --> 01:06:13,040 Speaker 1: We track them and then if we can, if we 1100 01:06:13,120 --> 01:06:15,200 Speaker 1: can climb the tree, if we can get up there, 1101 01:06:15,280 --> 01:06:17,240 Speaker 1: we try to shoot a go pro in there and 1102 01:06:17,360 --> 01:06:20,160 Speaker 1: see what see what we can go yeah that just 1103 01:06:20,320 --> 01:06:22,760 Speaker 1: to say okay, does she have cubs or not? If 1104 01:06:22,800 --> 01:06:26,040 Speaker 1: we can't climb it. Um. We also leave game cameras 1105 01:06:26,600 --> 01:06:28,880 Speaker 1: up there so that we can see try to see 1106 01:06:28,960 --> 01:06:31,560 Speaker 1: who comes out, just so we know if if she 1107 01:06:31,720 --> 01:06:34,480 Speaker 1: ends up having cubs or not. Usually those dens like 1108 01:06:34,600 --> 01:06:36,400 Speaker 1: that are going to be a female that's going to 1109 01:06:36,480 --> 01:06:42,040 Speaker 1: have cubs, not one that has yearlings. Space issue right, um, 1110 01:06:42,160 --> 01:06:45,240 Speaker 1: So lots of ground excavations. We've got the crevice dens 1111 01:06:45,360 --> 01:06:48,000 Speaker 1: up in the ozarks um what I call just kind 1112 01:06:48,040 --> 01:06:50,760 Speaker 1: of rocked in, so just spaces under the big boulders 1113 01:06:51,280 --> 01:06:55,560 Speaker 1: right now that would be here. It was just like 1114 01:06:55,680 --> 01:06:58,480 Speaker 1: a rock pile. You wouldn't have looked at it and thought, 1115 01:06:58,520 --> 01:07:01,120 Speaker 1: oh that's a bear den it was just of just 1116 01:07:01,280 --> 01:07:06,800 Speaker 1: a space probably twelve fourteen inches tall and you know, 1117 01:07:06,960 --> 01:07:10,840 Speaker 1: kind of obscure shaped, but maybe two or three ft wide, 1118 01:07:10,920 --> 01:07:13,200 Speaker 1: I mean, just enough for somebody to just kind of 1119 01:07:13,720 --> 01:07:16,880 Speaker 1: get half their body down in there. I was always 1120 01:07:16,920 --> 01:07:20,200 Speaker 1: really interesting. Still am the more the more bare dens 1121 01:07:20,280 --> 01:07:22,840 Speaker 1: that I've seen, the more interested I am in them. 1122 01:07:23,200 --> 01:07:26,600 Speaker 1: But people don't people most people don't understand what they 1123 01:07:27,000 --> 01:07:30,680 Speaker 1: look like. Probably if you're in the woods in the 1124 01:07:30,760 --> 01:07:33,920 Speaker 1: wintertime very much at all in a place that has bears, 1125 01:07:34,040 --> 01:07:36,920 Speaker 1: you've probably walked past bear dens and never never had 1126 01:07:36,960 --> 01:07:43,200 Speaker 1: a clue. It's it's unbelievable the spaces that they choose. Um. Actually, 1127 01:07:43,320 --> 01:07:47,560 Speaker 1: one of our coolest crevice dens that we have recorded 1128 01:07:47,680 --> 01:07:51,200 Speaker 1: so far was actually down here in the Wachitas, and 1129 01:07:52,320 --> 01:07:54,800 Speaker 1: she was so far I mean, it was a tight squeeze. 1130 01:07:54,880 --> 01:07:57,400 Speaker 1: You had to be comfortable with splunking to get into her. 1131 01:07:57,600 --> 01:08:00,240 Speaker 1: And we actually ended up because of the goal and 1132 01:08:00,320 --> 01:08:02,520 Speaker 1: where she was. We tried to dart her because she 1133 01:08:02,600 --> 01:08:04,880 Speaker 1: was so far back in there, but the angle the 1134 01:08:04,960 --> 01:08:08,040 Speaker 1: darts kept kind of glancing off of rock and whatnot. 1135 01:08:08,480 --> 01:08:11,400 Speaker 1: We ended up putting two jabsticks together, so like a 1136 01:08:11,520 --> 01:08:16,519 Speaker 1: sixteen foot long jabstick and feeding it. Like talk about teamwork. Um, 1137 01:08:16,920 --> 01:08:20,120 Speaker 1: it was phenomenal. We we made it work and we're 1138 01:08:20,120 --> 01:08:24,840 Speaker 1: able to sedate her and um and then get down 1139 01:08:24,920 --> 01:08:29,320 Speaker 1: in there. And those are what what There's not many 1140 01:08:29,520 --> 01:08:32,479 Speaker 1: caves like that here. Was it a cave? Would you 1141 01:08:32,560 --> 01:08:35,479 Speaker 1: call it a cave? I mean I would technically know 1142 01:08:35,840 --> 01:08:41,120 Speaker 1: it was. It was technically a cavers bluff it was 1143 01:08:41,240 --> 01:08:44,760 Speaker 1: in so it's in the side of a hill and um, 1144 01:08:46,080 --> 01:08:48,200 Speaker 1: and it's almost like a fissure, you know where I 1145 01:08:48,240 --> 01:08:50,920 Speaker 1: did like maybe maybe that's what was going on, is 1146 01:08:50,960 --> 01:08:54,040 Speaker 1: that you know, to get down into it, um, basically 1147 01:08:54,160 --> 01:08:56,840 Speaker 1: like you're squeezing yourself like you just have to slide 1148 01:08:56,880 --> 01:08:58,680 Speaker 1: down in and then it kind of opens up and 1149 01:08:58,760 --> 01:09:02,599 Speaker 1: moves around. Um. But she was down and like I said, 1150 01:09:02,680 --> 01:09:06,080 Speaker 1: it was our jab pole was sixteen feet one all 1151 01:09:06,200 --> 01:09:08,560 Speaker 1: was said and done, and we were hanging down into it. 1152 01:09:09,080 --> 01:09:12,720 Speaker 1: The our graduate student, will um Children's was actually kind 1153 01:09:12,720 --> 01:09:16,160 Speaker 1: of hanging laying down into the entrance using that jabstick. 1154 01:09:16,240 --> 01:09:20,160 Speaker 1: So UM, I mean she was down in there good ways. 1155 01:09:20,360 --> 01:09:22,479 Speaker 1: And we've had some of we've had some really neat 1156 01:09:22,560 --> 01:09:24,560 Speaker 1: ones up in the Ozarks that are similar where you 1157 01:09:24,880 --> 01:09:27,479 Speaker 1: you're like having to crawl into a tunnel to be 1158 01:09:27,560 --> 01:09:30,680 Speaker 1: able to even do the sedation and that kind of thing. Um. 1159 01:09:31,760 --> 01:09:34,519 Speaker 1: But typically it seems as though they're going to have 1160 01:09:34,680 --> 01:09:39,479 Speaker 1: smaller dens when they have cubs, um, and then when 1161 01:09:39,520 --> 01:09:41,920 Speaker 1: they're back in there with yearlings than they usually pick 1162 01:09:41,960 --> 01:09:45,760 Speaker 1: a little bit larger spaces. But that's I mean, that's 1163 01:09:45,800 --> 01:09:48,800 Speaker 1: not written in stone, nothing out here is. But that's 1164 01:09:48,840 --> 01:09:50,880 Speaker 1: usually Have you ever had any real close calls with 1165 01:09:51,000 --> 01:09:54,240 Speaker 1: bears like one like getting ugly with you? Um, some 1166 01:09:54,400 --> 01:09:56,400 Speaker 1: of the girls get pretty saucy with us in the 1167 01:09:56,800 --> 01:10:01,000 Speaker 1: in the den season, they take exception you know what 1168 01:10:01,160 --> 01:10:04,439 Speaker 1: you're saying about how calm they are, and that's i'd 1169 01:10:04,520 --> 01:10:09,800 Speaker 1: say percent of the time. They they look at you, 1170 01:10:10,080 --> 01:10:12,040 Speaker 1: they know you're there. They might huff a little bit, 1171 01:10:12,400 --> 01:10:14,599 Speaker 1: and then mostly they turn away from you and say, 1172 01:10:14,880 --> 01:10:16,680 Speaker 1: I'll just pretend like you're not there and maybe you'll 1173 01:10:16,720 --> 01:10:19,519 Speaker 1: go away. You know, they really just they don't want 1174 01:10:19,600 --> 01:10:21,439 Speaker 1: to mess with you. Yes, they can come out, they 1175 01:10:21,479 --> 01:10:23,760 Speaker 1: could get you if they wanted to, but mostly they 1176 01:10:23,840 --> 01:10:27,080 Speaker 1: just want you to go away. Um. But we definitely 1177 01:10:27,200 --> 01:10:29,679 Speaker 1: have certain ones that have been I mean they swat 1178 01:10:29,720 --> 01:10:32,559 Speaker 1: and bite at the jab stick and We primarily use 1179 01:10:33,000 --> 01:10:35,879 Speaker 1: the the aluminum jab hoole, and that can go anywhere 1180 01:10:35,960 --> 01:10:39,560 Speaker 1: from UM. I think they're like three foot sections or 1181 01:10:39,600 --> 01:10:41,400 Speaker 1: so two and a half or three foot sections, so 1182 01:10:41,479 --> 01:10:43,000 Speaker 1: we can get it up to about eight or nine 1183 01:10:43,040 --> 01:10:48,200 Speaker 1: ft if we need to. UM. We prefer that method to. 1184 01:10:48,439 --> 01:10:51,360 Speaker 1: We do have fancy dart projectors and things like that 1185 01:10:51,479 --> 01:10:53,679 Speaker 1: when we need to, but a lot of our areas 1186 01:10:53,720 --> 01:10:57,479 Speaker 1: it's so thick um getting into them that you have 1187 01:10:57,600 --> 01:10:59,960 Speaker 1: to get just about as close with the dart project 1188 01:11:00,360 --> 01:11:03,519 Speaker 1: as you do with your jabstick anyway, So we have 1189 01:11:03,680 --> 01:11:09,200 Speaker 1: so much more control over the placement of our you know, 1190 01:11:09,320 --> 01:11:11,720 Speaker 1: of our syringe and things like that with with the 1191 01:11:11,840 --> 01:11:14,840 Speaker 1: jabstick than we do I mean the there as I 1192 01:11:14,920 --> 01:11:16,920 Speaker 1: mentioned with the dart projector. I mean, you just have 1193 01:11:17,040 --> 01:11:19,559 Speaker 1: all kinds of weird things that happened depending on angles 1194 01:11:19,640 --> 01:11:22,759 Speaker 1: and things like that, and so even even an excellent 1195 01:11:23,080 --> 01:11:28,360 Speaker 1: marksman UM can still still mess up with that. So 1196 01:11:28,439 --> 01:11:31,280 Speaker 1: you've never been attacked by be I have never been 1197 01:11:31,320 --> 01:11:33,559 Speaker 1: attacked by a bear. I've had a bear that wanted 1198 01:11:33,600 --> 01:11:38,760 Speaker 1: to eat me. You're asking about memorable bears, and I 1199 01:11:38,840 --> 01:11:42,519 Speaker 1: think everybody that's worked with them has has a story 1200 01:11:42,640 --> 01:11:45,519 Speaker 1: probably about certain ones, and this is one that was down. 1201 01:11:45,720 --> 01:11:48,960 Speaker 1: It was all my second trap line um of the 1202 01:11:49,080 --> 01:11:54,680 Speaker 1: summer in grad school. And he was a probably a 1203 01:11:55,479 --> 01:11:58,840 Speaker 1: three year old male. And those guys tend to think 1204 01:11:58,920 --> 01:12:03,000 Speaker 1: that they're pretty tough stuff and they have something. They're 1205 01:12:03,040 --> 01:12:05,120 Speaker 1: the ones that think they have something to prove, you know, 1206 01:12:05,240 --> 01:12:07,760 Speaker 1: the big older males like our boys skip that's over 1207 01:12:07,840 --> 01:12:11,400 Speaker 1: five hundred pounds. He just sits there and looks actually like, 1208 01:12:11,960 --> 01:12:13,880 Speaker 1: I know, I'm the king of the forest. You know, 1209 01:12:14,080 --> 01:12:16,800 Speaker 1: it's no big deal. You don't scare me. But when 1210 01:12:16,840 --> 01:12:18,840 Speaker 1: you have those young males, they're the ones that kind 1211 01:12:18,880 --> 01:12:22,719 Speaker 1: of get pretty feisty sometimes. But he was, Thank goodness, 1212 01:12:22,800 --> 01:12:24,840 Speaker 1: we had a good a good catch on him because 1213 01:12:24,920 --> 01:12:28,599 Speaker 1: he was lunging back and forth between my technician and myself, 1214 01:12:28,720 --> 01:12:31,240 Speaker 1: like coming after us, and he was really range. He 1215 01:12:31,320 --> 01:12:34,120 Speaker 1: had long legs and so he had a really good 1216 01:12:34,400 --> 01:12:36,320 Speaker 1: good reach on him. And I was doing the job, 1217 01:12:36,439 --> 01:12:40,040 Speaker 1: and I mean he'd rush right at me and slap 1218 01:12:40,120 --> 01:12:42,200 Speaker 1: at the job stick and then he'd turn around and 1219 01:12:42,240 --> 01:12:44,439 Speaker 1: he'd do the same thing with my technician who had 1220 01:12:44,479 --> 01:12:46,559 Speaker 1: a stick, trying to keep his attention and that kind 1221 01:12:46,600 --> 01:12:50,000 Speaker 1: of thing. And so of I mentioned that we had 1222 01:12:50,080 --> 01:12:54,960 Speaker 1: fifty one bears individual bears captured those two years, and 1223 01:12:55,120 --> 01:12:57,320 Speaker 1: of those fifty one, he's the one that I would 1224 01:12:57,400 --> 01:13:00,519 Speaker 1: not wanted to bumped into in a berry patch. And 1225 01:13:00,640 --> 01:13:02,639 Speaker 1: I feel like I'd be like, you know, they all 1226 01:13:02,760 --> 01:13:05,400 Speaker 1: have a certain amount of fight in them when they're 1227 01:13:05,840 --> 01:13:10,200 Speaker 1: they're backed into a corner, certainly, but his behavior was 1228 01:13:10,280 --> 01:13:14,840 Speaker 1: totally different. And then we've got a female up in 1229 01:13:14,880 --> 01:13:19,599 Speaker 1: the Ozark region that um her name actually her name 1230 01:13:19,680 --> 01:13:25,439 Speaker 1: is Christy, but she is sorry, she is no angel. 1231 01:13:27,360 --> 01:13:29,880 Speaker 1: And and the cool thing about her was that we 1232 01:13:30,360 --> 01:13:34,160 Speaker 1: um the last time that we worked it in with her, UM, 1233 01:13:35,360 --> 01:13:38,200 Speaker 1: she had a male and a female cub, which is typical, 1234 01:13:38,800 --> 01:13:42,280 Speaker 1: and UM, the little female cub was just as saucy 1235 01:13:42,360 --> 01:13:45,560 Speaker 1: as Christie is. Usually, you know, they're just they just 1236 01:13:45,680 --> 01:13:47,719 Speaker 1: want to cart curl up and go to sleep or whatever. 1237 01:13:47,920 --> 01:13:50,320 Speaker 1: But that little girl was. It was a little bit 1238 01:13:50,439 --> 01:13:52,360 Speaker 1: later in March, and so she was older and a 1239 01:13:52,439 --> 01:13:55,160 Speaker 1: little bit bigger, and she already had the like pat 1240 01:13:55,280 --> 01:13:57,240 Speaker 1: the ground with her front feet and huff at you, 1241 01:13:58,200 --> 01:14:00,240 Speaker 1: and I mean she had all of the big bear 1242 01:14:00,560 --> 01:14:03,400 Speaker 1: stuff down and the little male just laid and slept 1243 01:14:03,439 --> 01:14:05,640 Speaker 1: in your arms. But she was not she was. She 1244 01:14:05,760 --> 01:14:09,519 Speaker 1: was biting us friendly trying to measure you know, we 1245 01:14:09,640 --> 01:14:13,080 Speaker 1: did we did, uh? I did a I did a 1246 01:14:13,160 --> 01:14:17,920 Speaker 1: story and I interviewed Randy Cross specifically about an idea 1247 01:14:18,040 --> 01:14:19,599 Speaker 1: that he had. And I don't know if you're familiar 1248 01:14:19,600 --> 01:14:23,439 Speaker 1: with Randy Cross, but he's a he's a the bear 1249 01:14:23,520 --> 01:14:27,320 Speaker 1: biology or was he's retired now, but reputable bear biologists 1250 01:14:27,320 --> 01:14:30,880 Speaker 1: the main he So we did a story on and this, 1251 01:14:31,120 --> 01:14:34,599 Speaker 1: and he contacted me. Well, now I didn't I contacted him, 1252 01:14:34,640 --> 01:14:37,320 Speaker 1: but he had this bear named Sarah that they had 1253 01:14:37,400 --> 01:14:42,360 Speaker 1: had for like uh twenty they had she lived to 1254 01:14:42,400 --> 01:14:46,360 Speaker 1: be No, Sara only lived to be fifteen. Long story short. 1255 01:14:46,439 --> 01:14:50,080 Speaker 1: The whole point of Sarah. She was that captured and 1256 01:14:50,240 --> 01:14:52,920 Speaker 1: lived her life back in like the eighties. She was 1257 01:14:53,000 --> 01:14:54,920 Speaker 1: one of the first bears they captured when they first 1258 01:14:54,920 --> 01:14:59,160 Speaker 1: started doing research in the eighties. And basically all her 1259 01:14:59,280 --> 01:15:04,560 Speaker 1: progeny to this day are exceptionally long lived as compared 1260 01:15:04,720 --> 01:15:08,879 Speaker 1: to other bears. So they have this massive research project 1261 01:15:08,960 --> 01:15:11,680 Speaker 1: that's now been going on for forty years. So they 1262 01:15:12,000 --> 01:15:17,400 Speaker 1: just have all this incredible data. His whole his what 1263 01:15:17,640 --> 01:15:23,840 Speaker 1: he believes is that um the nurture quote unquote like 1264 01:15:24,439 --> 01:15:28,559 Speaker 1: like what a female bear teaches her cubs about how 1265 01:15:28,720 --> 01:15:32,760 Speaker 1: to be a bear, not what's necessarily in aiding them. That, 1266 01:15:33,360 --> 01:15:39,160 Speaker 1: just as in their DNA, is really important because basically, 1267 01:15:40,000 --> 01:15:46,120 Speaker 1: generations later, Sarah's progeny are living like twice as long 1268 01:15:46,200 --> 01:15:49,600 Speaker 1: as the average bear in Maine. And he, you know, 1269 01:15:49,840 --> 01:15:51,560 Speaker 1: I'd have to read the story again to get the 1270 01:15:51,640 --> 01:15:57,640 Speaker 1: exact quote from Randy, but Sarah was she She was 1271 01:15:57,840 --> 01:16:00,160 Speaker 1: not killed over a bait, I don't think, because that's 1272 01:16:00,200 --> 01:16:02,840 Speaker 1: what they were trying to determine, as how are you know, 1273 01:16:02,880 --> 01:16:06,439 Speaker 1: how are these bears surviving are baited bear hunts? And 1274 01:16:06,560 --> 01:16:10,360 Speaker 1: basically he believed that she had a negative interaction at 1275 01:16:10,400 --> 01:16:13,759 Speaker 1: a bait site when she was young, and that spooked 1276 01:16:13,800 --> 01:16:17,719 Speaker 1: her so bad that she avoided bait sites her whole life, 1277 01:16:18,479 --> 01:16:21,040 Speaker 1: and she taught her young to do that, because that's 1278 01:16:21,080 --> 01:16:24,680 Speaker 1: what they're finding because like most of the mortality in 1279 01:16:24,840 --> 01:16:27,639 Speaker 1: Maine is from hunting over bait, which I mean, there's 1280 01:16:27,640 --> 01:16:29,880 Speaker 1: a management tool that's a positive thing, Like that's how 1281 01:16:30,040 --> 01:16:32,960 Speaker 1: the bears are being harvested, that's how they're managing their population. 1282 01:16:33,920 --> 01:16:38,000 Speaker 1: And uh, to this day, in Sarah's progeny are not 1283 01:16:38,200 --> 01:16:42,200 Speaker 1: getting killed over bait. That's really cool. Yeah, I was. 1284 01:16:42,439 --> 01:16:45,040 Speaker 1: And that's that's getting back to what I was saying about, 1285 01:16:45,120 --> 01:16:49,559 Speaker 1: you know, our our younger the younger females and um 1286 01:16:51,040 --> 01:16:53,960 Speaker 1: and and their ability to raise up and teach their 1287 01:16:54,040 --> 01:16:56,680 Speaker 1: cubs what they need to know to be bears. I mean, 1288 01:16:56,760 --> 01:17:00,680 Speaker 1: there's there's and you see, um And I don't think 1289 01:17:00,720 --> 01:17:03,519 Speaker 1: it's always age related, but certainly you would think that 1290 01:17:03,680 --> 01:17:05,439 Speaker 1: younger ones would have a little bit harder go of 1291 01:17:05,520 --> 01:17:09,880 Speaker 1: it than than the older females do. But um, but yeah, 1292 01:17:09,920 --> 01:17:11,720 Speaker 1: I mean I would agree. I mean, that's that's why 1293 01:17:11,840 --> 01:17:16,400 Speaker 1: it's so females and bear populations. Females are so important. 1294 01:17:17,320 --> 01:17:19,680 Speaker 1: We're a little biased. If you're a bar biologist, you're 1295 01:17:19,680 --> 01:17:21,920 Speaker 1: a little biased. You like, you like the girls, and 1296 01:17:22,040 --> 01:17:26,799 Speaker 1: that's because they are the soul factor that is allowing 1297 01:17:26,880 --> 01:17:29,800 Speaker 1: a population to be stable and or growing. So it's 1298 01:17:29,880 --> 01:17:33,280 Speaker 1: that those females and their recruitment of other females, right, 1299 01:17:34,120 --> 01:17:37,640 Speaker 1: and and so they're not only important in terms of 1300 01:17:37,760 --> 01:17:40,400 Speaker 1: having the cubs, but they're important and making sure that 1301 01:17:40,479 --> 01:17:45,640 Speaker 1: those cubs actually survived to become productive members of bare society. 1302 01:17:46,640 --> 01:17:52,920 Speaker 1: So well, that's pretty incredible, it really is. Um, Sara, 1303 01:17:53,040 --> 01:17:55,360 Speaker 1: is there anything that we've not talked about that you 1304 01:17:55,400 --> 01:18:00,280 Speaker 1: would like to Oh, well, I would like to. Yeah. 1305 01:18:00,400 --> 01:18:02,439 Speaker 1: I think the thing that that I'd like people to 1306 01:18:02,479 --> 01:18:04,880 Speaker 1: walk away from with this. I mean, we could talk 1307 01:18:04,920 --> 01:18:08,120 Speaker 1: bears all day long, and I do. UM. But as 1308 01:18:08,160 --> 01:18:12,080 Speaker 1: far as Oklahoma management is concerned, you know, we we 1309 01:18:12,240 --> 01:18:14,720 Speaker 1: touched on the fact that the ozark and the Watchtile 1310 01:18:14,800 --> 01:18:18,439 Speaker 1: populations are distinct and separate populations. And we touched on 1311 01:18:18,560 --> 01:18:22,519 Speaker 1: the fact that the um the landscape in those regions 1312 01:18:22,600 --> 01:18:26,160 Speaker 1: are so is so different. And that's just an important 1313 01:18:26,240 --> 01:18:28,400 Speaker 1: thing for people to think about, you know, when you're 1314 01:18:29,200 --> 01:18:31,920 Speaker 1: if you're a sportsman and you're interested in hunting, or 1315 01:18:32,080 --> 01:18:36,120 Speaker 1: you're hearing about UM the management plans here in Oklahoma 1316 01:18:36,200 --> 01:18:39,680 Speaker 1: for bears, just to consider that, Yeah, just because we 1317 01:18:39,800 --> 01:18:43,280 Speaker 1: have eleven or twelve hundred bears and the Watchitas doesn't 1318 01:18:43,360 --> 01:18:46,559 Speaker 1: mean that you can manage the ozark population the same way. 1319 01:18:46,720 --> 01:18:49,280 Speaker 1: So so the O d w C. And this is 1320 01:18:49,320 --> 01:18:51,400 Speaker 1: a big reason why they work so closely with us 1321 01:18:51,439 --> 01:18:55,519 Speaker 1: at os U UM. You know, they're working hard to 1322 01:18:55,960 --> 01:19:01,759 Speaker 1: manage these populations separately because of their different needs. You're saying, 1323 01:19:02,040 --> 01:19:07,439 Speaker 1: don't get upset if you can't, right, So we can't 1324 01:19:07,479 --> 01:19:10,679 Speaker 1: hunt there just yet because everything that we've shown from 1325 01:19:11,160 --> 01:19:14,439 Speaker 1: our den visits and our captures show that there are 1326 01:19:14,560 --> 01:19:18,000 Speaker 1: not enough females and there's not enough female recruitment in 1327 01:19:18,160 --> 01:19:21,680 Speaker 1: that population for it to be a stable population in 1328 01:19:21,760 --> 01:19:24,479 Speaker 1: and of itself. So if we weren't still getting bears 1329 01:19:24,520 --> 01:19:29,519 Speaker 1: coming over expanding from Arkansas, then we wouldn't be seeing 1330 01:19:29,640 --> 01:19:32,680 Speaker 1: much of a growth in that area, UM. And so 1331 01:19:33,280 --> 01:19:36,120 Speaker 1: so we've got to consider that, and we have every 1332 01:19:36,320 --> 01:19:40,280 Speaker 1: interest in UM. You know, the story down in the Wachitas, 1333 01:19:40,360 --> 01:19:44,720 Speaker 1: it's just such a conservation UM success story, right, you know, 1334 01:19:44,840 --> 01:19:47,280 Speaker 1: to be able to have this population that we can 1335 01:19:47,479 --> 01:19:51,160 Speaker 1: hunt now and give people the opportunity and it's still 1336 01:19:51,240 --> 01:19:56,639 Speaker 1: a growing population even with that that happening. That's it's 1337 01:19:56,720 --> 01:19:59,880 Speaker 1: just super exciting to know that we can give people 1338 01:20:00,000 --> 01:20:03,880 Speaker 1: the opportunity to hunt the species and still know that 1339 01:20:04,000 --> 01:20:07,760 Speaker 1: we're not negatively impacting that population, right, And that's what 1340 01:20:07,960 --> 01:20:09,680 Speaker 1: we would like to see in the northeast in the 1341 01:20:09,840 --> 01:20:12,719 Speaker 1: ozark region. UM. We just have to give it time. 1342 01:20:13,200 --> 01:20:15,240 Speaker 1: And but then the management is still going to have 1343 01:20:15,280 --> 01:20:16,840 Speaker 1: to be different up there, just because there are a 1344 01:20:16,920 --> 01:20:20,240 Speaker 1: lot more people and because of that fragmented habitat. So 1345 01:20:20,560 --> 01:20:22,920 Speaker 1: it will just be a totally different management scheme than 1346 01:20:22,960 --> 01:20:25,200 Speaker 1: what we're seeing down here in the Wachitas and and 1347 01:20:25,360 --> 01:20:29,760 Speaker 1: down here. We can bait barren private land now and 1348 01:20:29,960 --> 01:20:31,920 Speaker 1: like it used to be just four counties, but now 1349 01:20:32,000 --> 01:20:36,439 Speaker 1: they've expanded that I don't know, ten or twelve counties. Yeah, 1350 01:20:36,520 --> 01:20:40,240 Speaker 1: they went to highway markers instead of county lines just 1351 01:20:40,400 --> 01:20:43,680 Speaker 1: for the southeast region. Um makes more sense because you 1352 01:20:43,720 --> 01:20:45,840 Speaker 1: don't know when you cross a county line when you're 1353 01:20:45,840 --> 01:20:50,080 Speaker 1: in the woods necessarily, um. And so so yeah, I 1354 01:20:50,120 --> 01:20:53,960 Speaker 1: mean even with that expanded area. UM. Quite frankly, those 1355 01:20:54,200 --> 01:20:58,360 Speaker 1: those the expanded area that's now included in the hunting season. Um, 1356 01:20:59,439 --> 01:21:01,160 Speaker 1: you know those are going to be that productive. There's 1357 01:21:01,200 --> 01:21:03,280 Speaker 1: not that many bears out there just yet. I mean 1358 01:21:03,320 --> 01:21:06,160 Speaker 1: there will be at some point, um. And what will 1359 01:21:06,240 --> 01:21:08,880 Speaker 1: be out there are males most likely. And so for 1360 01:21:09,040 --> 01:21:12,880 Speaker 1: hunting purposes, that's fantastic. You know, you can you can have. 1361 01:21:13,040 --> 01:21:16,439 Speaker 1: The other population is growing west. It's moving west and 1362 01:21:16,560 --> 01:21:18,640 Speaker 1: moving and that's part of what we're looking at now, 1363 01:21:18,760 --> 01:21:21,240 Speaker 1: Courtney daughter, which is is going to be looking at. 1364 01:21:21,439 --> 01:21:23,880 Speaker 1: We've moved out of the core area for trapping, and 1365 01:21:23,960 --> 01:21:27,120 Speaker 1: now we're looking into some of those more expanded areas 1366 01:21:27,200 --> 01:21:29,479 Speaker 1: to see kind of how that density changes as we 1367 01:21:29,600 --> 01:21:33,439 Speaker 1: move west, um or the population size or density is 1368 01:21:33,520 --> 01:21:36,519 Speaker 1: moving and changing. And then and then where we kind 1369 01:21:36,520 --> 01:21:39,200 Speaker 1: of see that the females stop and it's just males, 1370 01:21:39,400 --> 01:21:41,400 Speaker 1: you know, trying to get a feel for what what 1371 01:21:41,520 --> 01:21:44,280 Speaker 1: we're looking at right now. Um, So they think there's 1372 01:21:44,280 --> 01:21:47,760 Speaker 1: only about bears in Oklahoma and in the watch a 1373 01:21:47,840 --> 01:21:52,400 Speaker 1: top population the most recent um from our research is 1374 01:21:52,400 --> 01:21:58,720 Speaker 1: about it's a relatively small area. But yeah, and I 1375 01:21:59,040 --> 01:22:01,280 Speaker 1: I know you were asking about density and some of 1376 01:22:01,360 --> 01:22:05,120 Speaker 1: the other podcasts you've done and that that is nobody 1377 01:22:05,200 --> 01:22:07,720 Speaker 1: ever gives me a good answer, Sarah, I got one 1378 01:22:07,840 --> 01:22:13,120 Speaker 1: for you. Okay. So from our our early studies and 1379 01:22:13,479 --> 01:22:19,160 Speaker 1: and like the work, um, it looks like there's about 1380 01:22:19,400 --> 01:22:22,479 Speaker 1: I think they reported about eleven point four bears per 1381 01:22:22,520 --> 01:22:26,400 Speaker 1: about thirty eight square miles. Now, so I heard you 1382 01:22:26,479 --> 01:22:28,519 Speaker 1: thrown out there is there one bear per one square 1383 01:22:28,560 --> 01:22:30,920 Speaker 1: mile kind of thing that shows that that's even less 1384 01:22:31,600 --> 01:22:35,000 Speaker 1: um and and that's really kind of for the core area. 1385 01:22:36,080 --> 01:22:39,200 Speaker 1: So that's where they're thick that's where they're the thickest. Right, 1386 01:22:41,320 --> 01:22:43,720 Speaker 1: But now we have to take that with a grain 1387 01:22:43,800 --> 01:22:46,280 Speaker 1: of salt. I mean, we're obviously we're having to say 1388 01:22:46,360 --> 01:22:49,160 Speaker 1: this is within our study area, so based on home 1389 01:22:49,320 --> 01:22:51,439 Speaker 1: ranges of the females and based on where our trap 1390 01:22:51,600 --> 01:22:54,200 Speaker 1: lines are. So it's within that area, and it's just 1391 01:22:54,439 --> 01:22:58,680 Speaker 1: with like one snapshot in time. Right. So so at 1392 01:22:58,680 --> 01:23:02,680 Speaker 1: any given time. What's so deceiving about that is you 1393 01:23:02,800 --> 01:23:04,920 Speaker 1: might go on that mountain right there and there may 1394 01:23:05,000 --> 01:23:10,080 Speaker 1: be three or four bears one time. I mean you 1395 01:23:10,160 --> 01:23:13,160 Speaker 1: might see them. If you've got one big clear cut 1396 01:23:13,280 --> 01:23:17,320 Speaker 1: and it's loaded down with blackberries or poke berries, depending 1397 01:23:17,360 --> 01:23:20,000 Speaker 1: on the time of year, then they're probably going to 1398 01:23:20,040 --> 01:23:21,679 Speaker 1: be more than one bear out in that big clear 1399 01:23:21,720 --> 01:23:24,960 Speaker 1: cut because that's where the food. Statistically, for the amount 1400 01:23:25,000 --> 01:23:31,040 Speaker 1: of land, not even one bear per square mont right. Yeah, Okay, 1401 01:23:31,360 --> 01:23:33,960 Speaker 1: you did a great job with us. Well you're the 1402 01:23:34,080 --> 01:23:40,439 Speaker 1: very first weller did a great job. I'm on the bus. 1403 01:23:40,560 --> 01:23:44,639 Speaker 1: You would have tried to complicate it, but they're they're 1404 01:23:44,680 --> 01:23:46,920 Speaker 1: the ones out there doing the hard work and giving 1405 01:23:47,000 --> 01:23:51,880 Speaker 1: us all this good information. So yeah, well that's pretty cool. 1406 01:23:52,040 --> 01:23:56,639 Speaker 1: That's really cool. Um, call me any any thoughts, any questions. Yeah, Oh, 1407 01:23:57,000 --> 01:23:59,240 Speaker 1: what's some of the other research projects that you guys 1408 01:23:59,320 --> 01:24:02,280 Speaker 1: have done or what or what's going on now? Or 1409 01:24:02,360 --> 01:24:05,960 Speaker 1: what's going on now? Uh, what was the research project 1410 01:24:06,080 --> 01:24:09,519 Speaker 1: that you know was the biggest surprise or you know, 1411 01:24:10,360 --> 01:24:12,560 Speaker 1: um the new thing that's happening right now. Well, I 1412 01:24:12,760 --> 01:24:15,840 Speaker 1: already mentioned that will Children's is starting to We just 1413 01:24:16,000 --> 01:24:19,720 Speaker 1: started callering um year links to truck for that and 1414 01:24:20,040 --> 01:24:23,320 Speaker 1: so we're we're new into that project and don't have 1415 01:24:23,400 --> 01:24:27,559 Speaker 1: any data on that yet. UM, not all put together yet. 1416 01:24:28,240 --> 01:24:31,200 Speaker 1: But that's that's very exciting. We're excited to see. UM, 1417 01:24:32,240 --> 01:24:35,599 Speaker 1: that information should tell us how they're going to expand 1418 01:24:35,840 --> 01:24:38,240 Speaker 1: into this new region in the Ozarks right since we're 1419 01:24:38,320 --> 01:24:41,680 Speaker 1: right at the beginning of that recolonization, it'll give us 1420 01:24:41,680 --> 01:24:43,640 Speaker 1: a better feel by tracking both the males and the 1421 01:24:43,680 --> 01:24:46,360 Speaker 1: females to see how they move into the the available 1422 01:24:46,400 --> 01:24:50,280 Speaker 1: habitat that's there. UM. So that's that's really exciting. Something 1423 01:24:50,360 --> 01:24:52,679 Speaker 1: that we're not very happy about but that we're looking 1424 01:24:52,760 --> 01:24:56,639 Speaker 1: into is the cases of sarcoptic mange in that Ozark region. 1425 01:24:57,280 --> 01:25:00,720 Speaker 1: It's happening in Arkansas. We've worked with Myron means Um 1426 01:25:00,920 --> 01:25:02,960 Speaker 1: as well. We kind of are trying to collaborate and 1427 01:25:03,000 --> 01:25:05,600 Speaker 1: everybody's scratching our heads trying to figure this out. But 1428 01:25:05,920 --> 01:25:09,680 Speaker 1: um so, part of the new research also UM is 1429 01:25:09,720 --> 01:25:13,280 Speaker 1: showing what we're doing is we're pulling blood samples and 1430 01:25:13,479 --> 01:25:16,080 Speaker 1: skin scrapings from every bear we have our hands on, 1431 01:25:16,240 --> 01:25:18,840 Speaker 1: both in the Ozarks and the Watchitas. We're not seeing 1432 01:25:18,880 --> 01:25:21,280 Speaker 1: it in the watch as yet, fingers crossed. It doesn't 1433 01:25:21,320 --> 01:25:25,680 Speaker 1: happen down here. Um. So we're pulling those samples and 1434 01:25:25,760 --> 01:25:28,880 Speaker 1: the hopes of maybe seeing what the underlying conditions are 1435 01:25:28,960 --> 01:25:32,200 Speaker 1: and why why certain bears in that region or get 1436 01:25:32,320 --> 01:25:36,439 Speaker 1: are susceptible. You know, we had a right in asking 1437 01:25:36,479 --> 01:25:39,880 Speaker 1: about you may not have seen it. Ask He asked 1438 01:25:39,920 --> 01:25:43,799 Speaker 1: me to ask somebody about mange and bears in Arkansas, 1439 01:25:43,920 --> 01:25:46,320 Speaker 1: and I forgot about it, but you just answered it. 1440 01:25:47,000 --> 01:25:50,040 Speaker 1: What did you call it? Sarcoptic? Sarcoptic mange and it 1441 01:25:51,880 --> 01:25:55,240 Speaker 1: like coyotes, like coyotes get that kind of thing. That's 1442 01:25:55,280 --> 01:25:57,320 Speaker 1: the ones that looked horrible, I mean, and it can 1443 01:25:57,400 --> 01:26:01,000 Speaker 1: kill them, it can. And we've actually, we believe that 1444 01:26:01,120 --> 01:26:07,240 Speaker 1: we've had um this is anecdotally, but we believe that 1445 01:26:07,360 --> 01:26:11,400 Speaker 1: we've lost a bear, two females to it because they 1446 01:26:11,479 --> 01:26:13,760 Speaker 1: had it so bad going into den season that they 1447 01:26:13,800 --> 01:26:16,639 Speaker 1: didn't it really takes its toll. They can't put back 1448 01:26:16,720 --> 01:26:18,760 Speaker 1: on the pounds like they need to, they lose all 1449 01:26:18,840 --> 01:26:22,040 Speaker 1: of their hair, they're itchy. They I mean, they don't 1450 01:26:22,080 --> 01:26:24,120 Speaker 1: end up staying in their dens We've had several that 1451 01:26:24,200 --> 01:26:27,719 Speaker 1: we've tracked they don't stay in their dens um because 1452 01:26:27,720 --> 01:26:32,479 Speaker 1: they're hungry one and because it's got to be uncomfortable. Um. Now, 1453 01:26:32,600 --> 01:26:34,960 Speaker 1: so it can kill them. But then there are also 1454 01:26:35,080 --> 01:26:41,280 Speaker 1: cases where they seem to have reconquered. Um. I'm sorry now, 1455 01:26:41,320 --> 01:26:43,240 Speaker 1: I was just gonna say that this is you know, 1456 01:26:43,360 --> 01:26:48,160 Speaker 1: there's there's a lot of research going into mange with 1457 01:26:48,280 --> 01:26:54,400 Speaker 1: bears right now. It is yeah, yeah, so Wisconsin, Pennsylvania. 1458 01:26:54,520 --> 01:26:58,960 Speaker 1: Pennsylvania is the closest to us until we got it here. Um, 1459 01:26:59,320 --> 01:27:02,840 Speaker 1: but it's happening all across the country. Um. There there's 1460 01:27:02,840 --> 01:27:05,759 Speaker 1: a lab and at University of Georgia that's that's actually 1461 01:27:06,080 --> 01:27:09,439 Speaker 1: kind of the center for all of the research for 1462 01:27:09,520 --> 01:27:12,200 Speaker 1: the mites and trying to figure this out. And they've 1463 01:27:12,240 --> 01:27:16,080 Speaker 1: even found that, you know, conventional wisdom was that the 1464 01:27:16,160 --> 01:27:18,160 Speaker 1: mic didn't last very long off of a host, like 1465 01:27:18,360 --> 01:27:20,360 Speaker 1: a couple of hours would be a long time. But 1466 01:27:20,640 --> 01:27:23,840 Speaker 1: what they found recently is that those mites in the 1467 01:27:24,040 --> 01:27:27,760 Speaker 1: right conditions can actually live up to two weeks off 1468 01:27:27,800 --> 01:27:31,400 Speaker 1: of a host. And and if that's the case, then 1469 01:27:31,720 --> 01:27:34,960 Speaker 1: I mean you can only imagine if we're congregating bears 1470 01:27:35,160 --> 01:27:39,840 Speaker 1: at wildlife feeders, um, you know, things like that, then 1471 01:27:40,280 --> 01:27:43,320 Speaker 1: then there's the potential for spread. UM. So, there are 1472 01:27:43,400 --> 01:27:46,160 Speaker 1: just a million questions that we have about this, and 1473 01:27:46,320 --> 01:27:49,960 Speaker 1: hopefully hopefully we can look into this even more. But 1474 01:27:50,080 --> 01:27:53,479 Speaker 1: at least right now, um will children so spearheaded, and 1475 01:27:53,600 --> 01:27:57,080 Speaker 1: we've we've started taking these samples and hopefully we can 1476 01:27:57,160 --> 01:28:00,679 Speaker 1: see if there are any underlying differences between the population 1477 01:28:01,120 --> 01:28:03,439 Speaker 1: or maybe it's just there there and it's just not 1478 01:28:03,560 --> 01:28:06,880 Speaker 1: here yet. I had a guy last fall I'd almost 1479 01:28:06,920 --> 01:28:10,560 Speaker 1: forgot about this, UM send me. Well, a friend of 1480 01:28:10,640 --> 01:28:12,599 Speaker 1: mine sent me a picture and it was his friend 1481 01:28:12,920 --> 01:28:17,080 Speaker 1: that had this picture of a hairless It was. It 1482 01:28:17,200 --> 01:28:20,040 Speaker 1: was a crazy sight. If you would have shown this 1483 01:28:20,120 --> 01:28:23,280 Speaker 1: picture to ten people and said what animal is this, 1484 01:28:23,920 --> 01:28:26,000 Speaker 1: you know, probably eight people would have got it wrong. 1485 01:28:26,600 --> 01:28:31,479 Speaker 1: But absolutely hairless bear emaciated. I mean you could see 1486 01:28:31,520 --> 01:28:35,639 Speaker 1: its ribs. And this guy was deer hunting feeding deer. 1487 01:28:36,200 --> 01:28:39,840 Speaker 1: Uh up in the Ozarks in Madison County and Uh, 1488 01:28:40,560 --> 01:28:44,200 Speaker 1: this bear was just like living on his cornpile first 1489 01:28:44,280 --> 01:28:47,479 Speaker 1: year and like wouldn't leave. I mean, the bear was 1490 01:28:47,760 --> 01:28:51,120 Speaker 1: about to die. And and he this guy messages to 1491 01:28:51,240 --> 01:28:53,200 Speaker 1: me and said, hey, send this to my iron means. 1492 01:28:53,760 --> 01:28:56,599 Speaker 1: And so I messaged it to my iron and uh, 1493 01:28:56,920 --> 01:28:59,800 Speaker 1: Myron asked for the guy's phone number, and and I 1494 01:29:00,000 --> 01:29:01,200 Speaker 1: don't know what they were going to try to do 1495 01:29:01,320 --> 01:29:03,760 Speaker 1: with it. If they were going to try to, I 1496 01:29:03,800 --> 01:29:05,840 Speaker 1: don't know what they did with I lost touch after that, 1497 01:29:06,000 --> 01:29:09,800 Speaker 1: but it was. There have been cases where they've they've 1498 01:29:09,840 --> 01:29:13,640 Speaker 1: had they've dispatched just just for humane reasons. I mean, 1499 01:29:13,720 --> 01:29:16,479 Speaker 1: when they get that bad, it's it's hard to see them, 1500 01:29:17,080 --> 01:29:22,040 Speaker 1: see them suffer like that. UM. There are UM, like 1501 01:29:22,800 --> 01:29:25,360 Speaker 1: I think it's in Wisconsin where they actually they kind 1502 01:29:25,360 --> 01:29:28,880 Speaker 1: of have a oh a scale for how bad the 1503 01:29:29,000 --> 01:29:34,400 Speaker 1: individual is and if it if it meets a certain requirement. 1504 01:29:34,439 --> 01:29:39,519 Speaker 1: They're actually treating with iver mactin and UM anecdotally they've 1505 01:29:39,600 --> 01:29:43,840 Speaker 1: had experience without actually working. We have treated a few bears. UM. 1506 01:29:44,720 --> 01:29:46,600 Speaker 1: We have treated a few bears that we've captured in 1507 01:29:46,680 --> 01:29:51,679 Speaker 1: our our study and it has worked. One bear did 1508 01:29:52,680 --> 01:29:55,960 Speaker 1: did get it again the next summer. UM. But one 1509 01:29:56,000 --> 01:30:00,960 Speaker 1: shot did the trick? Yeah, and so um you know so, 1510 01:30:01,320 --> 01:30:03,800 Speaker 1: so that's another thing that we're looking into now. We've 1511 01:30:03,840 --> 01:30:06,200 Speaker 1: started putting some collars on some of those main GI 1512 01:30:06,280 --> 01:30:10,400 Speaker 1: individuals without treatment to see can they recover from it 1513 01:30:11,160 --> 01:30:15,280 Speaker 1: without treatment or is treatment something that we need to 1514 01:30:15,360 --> 01:30:19,800 Speaker 1: look into from a long term management standpoint. Did there 1515 01:30:19,880 --> 01:30:21,960 Speaker 1: be any danger in eating the bear that had some 1516 01:30:22,439 --> 01:30:26,760 Speaker 1: bit of mange? I don't think so. Yeah, I mean, 1517 01:30:27,439 --> 01:30:30,120 Speaker 1: because it's all it's all in the skin. It's not 1518 01:30:30,439 --> 01:30:33,360 Speaker 1: inside them. It's just if they actually burrow and live 1519 01:30:33,439 --> 01:30:36,160 Speaker 1: in their skin. That's why they get that. It's real um, 1520 01:30:36,720 --> 01:30:40,200 Speaker 1: crusty and thick skin because those those mites are actually 1521 01:30:40,320 --> 01:30:43,120 Speaker 1: living inside the skin. So once you've skinned out that bear, 1522 01:30:43,240 --> 01:30:48,560 Speaker 1: everything underneath I would imagine should be just fine. Compromised 1523 01:30:48,680 --> 01:30:51,920 Speaker 1: in terms of health, I mean not in a z 1524 01:30:52,280 --> 01:30:57,439 Speaker 1: They're going to be thin, thinner or something for sure. Yeah, Yeah, 1525 01:30:57,520 --> 01:30:59,360 Speaker 1: that's interesting. I'm glad you brought that up, because that's 1526 01:30:59,360 --> 01:31:01,600 Speaker 1: somebody to ask us about that, and I didn't know 1527 01:31:01,840 --> 01:31:05,320 Speaker 1: I had seen it. I've seen I've seen a bear 1528 01:31:05,479 --> 01:31:08,800 Speaker 1: with mange up in the wilderness of Saskatchewan one time, 1529 01:31:08,840 --> 01:31:12,439 Speaker 1: but that's the only bear in the wild wild that 1530 01:31:12,479 --> 01:31:14,160 Speaker 1: I've ever seen that had it. So I mean, I 1531 01:31:14,240 --> 01:31:17,640 Speaker 1: guess it's common in a sense, but just becoming more 1532 01:31:17,760 --> 01:31:22,120 Speaker 1: common sounds like and in a word to the wise, Um, 1533 01:31:22,320 --> 01:31:26,840 Speaker 1: humans can get bear mange. I know from experience. We 1534 01:31:27,560 --> 01:31:30,479 Speaker 1: worked we worked um one of the females that we 1535 01:31:30,640 --> 01:31:33,719 Speaker 1: worked up, I actually ended up getting mites on my stomach. 1536 01:31:34,720 --> 01:31:36,960 Speaker 1: And um, the I mean, the good news, or at 1537 01:31:37,000 --> 01:31:39,880 Speaker 1: least what I read was that they technically they could 1538 01:31:39,920 --> 01:31:42,160 Speaker 1: bite me, but it would basically it's like a chicker bite. 1539 01:31:42,640 --> 01:31:45,120 Speaker 1: They're not living on me, you know, they couldn't complete 1540 01:31:45,160 --> 01:31:49,920 Speaker 1: a life cycle or anything. But well so I don't know. 1541 01:31:50,080 --> 01:31:52,320 Speaker 1: And again, this is all part of what they're learning 1542 01:31:52,360 --> 01:31:54,479 Speaker 1: about the mites, and and they used to think that 1543 01:31:54,560 --> 01:31:56,880 Speaker 1: it was species specific, and now I think they found 1544 01:31:56,920 --> 01:31:59,640 Speaker 1: that it's not necessarily So maybe coyotes can get the 1545 01:31:59,680 --> 01:32:03,000 Speaker 1: same that the bears do and that kind of thing. Um, 1546 01:32:03,800 --> 01:32:06,160 Speaker 1: those particular mites didn't get a chance to live on 1547 01:32:06,240 --> 01:32:08,559 Speaker 1: me because I might have dipped myself with dog dipp 1548 01:32:11,680 --> 01:32:14,160 Speaker 1: I didn't want to take don't want this reputation to 1549 01:32:14,280 --> 01:32:16,040 Speaker 1: follow you, But you're the one that brought it up. 1550 01:32:16,560 --> 01:32:18,759 Speaker 1: You are the only person I know that's had mains 1551 01:32:20,640 --> 01:32:24,040 Speaker 1: the job a badge of honor in our book. I 1552 01:32:24,120 --> 01:32:27,840 Speaker 1: have not shy about it, and I'd do it again 1553 01:32:27,920 --> 01:32:30,080 Speaker 1: if I had to. Yeah, you know that I actually 1554 01:32:30,200 --> 01:32:32,439 Speaker 1: was thinking that. I didn't say, I mean just like, yeah, 1555 01:32:32,520 --> 01:32:35,720 Speaker 1: can you get it? Yeah? So now we actually UM, 1556 01:32:36,080 --> 01:32:38,799 Speaker 1: so we carry in our kids, we've got taivex suits 1557 01:32:39,200 --> 01:32:42,760 Speaker 1: and UM and then to kill the mites on our gear. UM. 1558 01:32:42,880 --> 01:32:44,880 Speaker 1: We found that one of the things that the u 1559 01:32:44,960 --> 01:32:48,800 Speaker 1: J folks found was that if you freezed below a 1560 01:32:48,880 --> 01:32:51,160 Speaker 1: certain level for a certain amount of time, you'd kill 1561 01:32:51,280 --> 01:32:54,120 Speaker 1: all of them. And so every bit of our gear. 1562 01:32:54,200 --> 01:32:58,000 Speaker 1: If we actually handle any any Mangi bears or even 1563 01:32:58,120 --> 01:33:00,920 Speaker 1: suspect that we're dealing with one that have mane, then 1564 01:33:01,080 --> 01:33:03,360 Speaker 1: we have all these protocols so that we're not sharing 1565 01:33:03,479 --> 01:33:06,760 Speaker 1: with other bears. So they carry a couple of kits 1566 01:33:06,880 --> 01:33:08,560 Speaker 1: with them, just to be sure that in case you 1567 01:33:08,640 --> 01:33:10,680 Speaker 1: have more than one captured day, you're not having to 1568 01:33:10,840 --> 01:33:13,360 Speaker 1: use the same weight net on both bears. Things like that, 1569 01:33:14,080 --> 01:33:16,200 Speaker 1: because we certainly don't want to take any chances of 1570 01:33:16,320 --> 01:33:19,240 Speaker 1: us being the cause of the spread either. So it's 1571 01:33:19,280 --> 01:33:21,519 Speaker 1: it's changed the face of our research just a little 1572 01:33:21,520 --> 01:33:23,439 Speaker 1: bit not at not only in the fact that we're 1573 01:33:23,479 --> 01:33:25,760 Speaker 1: trying to look into this issue, but also how we 1574 01:33:25,880 --> 01:33:29,840 Speaker 1: function so that we don't contribute to the spread. Yeah, 1575 01:33:30,840 --> 01:33:34,639 Speaker 1: as long as our bears don't start getting COVID, well, 1576 01:33:34,880 --> 01:33:37,200 Speaker 1: I will tell you this that we are. We're already 1577 01:33:37,320 --> 01:33:41,160 Speaker 1: making making plans and discussions at this point. You know, 1578 01:33:41,280 --> 01:33:45,479 Speaker 1: we generally during our den work we usually have um 1579 01:33:45,600 --> 01:33:48,639 Speaker 1: groups of folks that go out with US landowners different 1580 01:33:48,680 --> 01:33:52,880 Speaker 1: things like that. And because we don't know if the 1581 01:33:53,000 --> 01:33:56,040 Speaker 1: bears can get it from us or not, we're already 1582 01:33:56,160 --> 01:33:59,519 Speaker 1: starting to think about how to manage our den seas 1583 01:33:59,520 --> 01:34:02,000 Speaker 1: in this next year. There's a lot of people had 1584 01:34:02,080 --> 01:34:05,240 Speaker 1: a lot of these are kind of so like it's 1585 01:34:05,280 --> 01:34:08,439 Speaker 1: an educational situation. So we have a lot of people 1586 01:34:08,720 --> 01:34:11,680 Speaker 1: and and we obviously already know that we are not 1587 01:34:11,840 --> 01:34:14,160 Speaker 1: going to be able to or willing to handle it 1588 01:34:14,280 --> 01:34:16,720 Speaker 1: the same way that we've done in the past. Um 1589 01:34:17,040 --> 01:34:19,920 Speaker 1: even even as even as cautious as we always are 1590 01:34:20,240 --> 01:34:23,439 Speaker 1: that we're having as little impact as possible on the 1591 01:34:23,520 --> 01:34:27,879 Speaker 1: bears to begin with, UM with, with COVID, were definitely 1592 01:34:27,960 --> 01:34:31,400 Speaker 1: concerned and have started started some plans to to see 1593 01:34:31,439 --> 01:34:34,960 Speaker 1: how we're going to manage that since next year, and 1594 01:34:35,040 --> 01:34:37,400 Speaker 1: in fact, it we even I mean even over the summer. 1595 01:34:37,520 --> 01:34:40,840 Speaker 1: We had to live and work under different conditions because 1596 01:34:40,880 --> 01:34:46,440 Speaker 1: of the university regulations, and we we took extra precautions 1597 01:34:46,479 --> 01:34:48,280 Speaker 1: even with the bears we were dealing with if we 1598 01:34:48,400 --> 01:34:50,799 Speaker 1: had any scares or anything that if we thought somebody 1599 01:34:50,880 --> 01:34:53,599 Speaker 1: had potentially been exposed, and we tried to keep them 1600 01:34:54,280 --> 01:34:56,600 Speaker 1: you know, with masks and gloves and things too. So 1601 01:34:56,840 --> 01:35:00,200 Speaker 1: it's it goes beyond just the human world. It's kind 1602 01:35:00,200 --> 01:35:02,040 Speaker 1: of weird to think that we're trying to protect these 1603 01:35:02,080 --> 01:35:05,320 Speaker 1: animals from COVID. I heard a story of a guy 1604 01:35:05,720 --> 01:35:11,719 Speaker 1: that I know that had a bat like like nesting 1605 01:35:11,880 --> 01:35:16,200 Speaker 1: in his like porch, and he knew the wildlife guy 1606 01:35:16,280 --> 01:35:18,240 Speaker 1: and I'm not even gonna name the state, and he 1607 01:35:18,400 --> 01:35:22,400 Speaker 1: texted the picture to his wildlife buddy about this bat 1608 01:35:22,600 --> 01:35:24,000 Speaker 1: and what he should do. This is right in the 1609 01:35:24,080 --> 01:35:27,760 Speaker 1: heat of COVID. And the guy was like, just make 1610 01:35:27,800 --> 01:35:30,400 Speaker 1: sure you don't I mean, essentially, he said, don't give 1611 01:35:30,560 --> 01:35:36,400 Speaker 1: the bat your COVID. Really, he said, he was like, 1612 01:35:36,680 --> 01:35:39,120 Speaker 1: don't get he wasn't worried about the bat having COVID 1613 01:35:39,439 --> 01:35:41,840 Speaker 1: give it to him. He was like if you have COVID, 1614 01:35:41,920 --> 01:35:44,080 Speaker 1: we don't want the bat to get COVID, and then 1615 01:35:45,240 --> 01:35:49,720 Speaker 1: that is totally red. Well, Sarah, truly a pleasure talking 1616 01:35:49,760 --> 01:35:52,200 Speaker 1: with it, for real. I respect what you're doing and 1617 01:35:52,560 --> 01:35:57,520 Speaker 1: just appreciate, appreciate your knowledge and expertise over here in Oklahoma. 1618 01:35:57,760 --> 01:36:00,479 Speaker 1: And I mean no doubt you've handled more bears than 1619 01:36:00,520 --> 01:36:04,960 Speaker 1: anybody over here. I would say from my I don't 1620 01:36:05,040 --> 01:36:08,840 Speaker 1: have an Oklahoma bear handling meter, but if I did, 1621 01:36:08,960 --> 01:36:11,759 Speaker 1: I think I think you would probably have. Jeff Jeff 1622 01:36:11,800 --> 01:36:15,960 Speaker 1: Ford might rival me on he might, but yeah, you're right, 1623 01:36:16,040 --> 01:36:19,320 Speaker 1: you're right. But I've been definitely extremely fortunate too to 1624 01:36:19,479 --> 01:36:21,960 Speaker 1: get to do what I've done here. Yeah, for sure, 1625 01:36:22,080 --> 01:36:24,000 Speaker 1: And I appreciate you having me. This is I could 1626 01:36:24,040 --> 01:36:26,680 Speaker 1: talk bears all day long, so well, and every time 1627 01:36:26,720 --> 01:36:29,719 Speaker 1: I do a podcast with somebody that's really an expert, 1628 01:36:29,840 --> 01:36:32,439 Speaker 1: always as soon as we shut this thing off, I'm 1629 01:36:32,479 --> 01:36:35,200 Speaker 1: gonna go bad. Damn. Why didn't I ask her about that? 1630 01:36:36,160 --> 01:36:38,439 Speaker 1: So now we'll maybe we'll find a reason to do 1631 01:36:38,520 --> 01:36:41,200 Speaker 1: this again. So follow up. Yeah, yeah, so I really 1632 01:36:41,200 --> 01:36:44,679 Speaker 1: appreciate it. But keep the wild place as wild because 1633 01:36:44,680 --> 01:36:45,559 Speaker 1: that's where the bears live.