WEBVTT - Ep. 877: Does Predator Management Actually Work? 0:00:08.920 --> 0:00:13.320 This is the me Eater podcast coming at you shirtless, severely, 0:00:13.480 --> 0:00:18.319 bug bitten, and in my case, underware listening podcast. You 0:00:18.360 --> 0:00:22.360 can't predict anything brought to you by first Light. When 0:00:22.360 --> 0:00:26.600 I'm hunting, I need gear that won't quit. First Light builds, 0:00:26.640 --> 0:00:30.160 no compromise, gear that keeps me in the field longer, 0:00:30.520 --> 0:00:34.200 no shortcuts, just gear that works. Check it out at 0:00:34.280 --> 0:00:37.559 first light dot com. That's f I R S T 0:00:38.280 --> 0:00:44.200 L I T E dot com. All right, Join today 0:00:44.200 --> 0:00:49.080 by Mike Bowdenchuck professional wildlife biologist forty five years professional 0:00:49.120 --> 0:00:51.959 experience and private, state and federal government services. 0:00:52.040 --> 0:00:53.440 Are you recently retired from. 0:00:53.280 --> 0:00:57.280 APHIS that's Animal Plant and Health Inspection Services. 0:00:57.280 --> 0:00:59.240 That's right, yep, you spent how many years doing that? 0:00:59.560 --> 0:01:02.120 I've been plenty four years with APHIS. I've spent about 0:01:02.120 --> 0:01:04.959 ten in the private sector and four with state government 0:01:05.000 --> 0:01:05.480 before that. 0:01:05.680 --> 0:01:11.080 Okay, and currently work as a private consulting wildlife biologist. 0:01:11.240 --> 0:01:13.560 That's correct. But your area of expertise, and this is 0:01:13.640 --> 0:01:16.560 how you were recommended to us by many people, not 0:01:16.600 --> 0:01:19.200 only wildlife capture, but you spent your whole career in 0:01:19.240 --> 0:01:25.480 predator prey interactions, predator management, invasive species management. 0:01:25.640 --> 0:01:29.039 You bet Aphis does a lot of predator control around 0:01:29.080 --> 0:01:32.600 the state, or predator predation control. Actually, we don't control 0:01:32.760 --> 0:01:34.720 numbers as much as we do the ones that are 0:01:34.720 --> 0:01:35.720 actually killing. 0:01:35.560 --> 0:01:35.920 Got it. 0:01:36.240 --> 0:01:39.039 But I've done that really my whole career. 0:01:39.040 --> 0:01:39.200 You know. 0:01:39.680 --> 0:01:41.760 But besides, when you were a DJ. 0:01:42.000 --> 0:01:43.560 Yeah, no, that was a long time. 0:01:44.080 --> 0:01:44.720 What was up with that? 0:01:45.000 --> 0:01:47.280 Yeah, I've been in high school. I had a job. 0:01:47.640 --> 0:01:50.200 I think they just needed somebody who could read and 0:01:50.280 --> 0:01:51.600 talk and I could do both. 0:01:51.680 --> 0:01:53.600 So so you were a DJ. I was a DJ 0:01:53.720 --> 0:01:55.240 for a couple of years. Yeah, what stage? 0:01:55.240 --> 0:01:57.200 You grew up in New Mexico. I lived in New 0:01:57.200 --> 0:01:59.920 Mexico for quite a while and moved around the country 0:02:00.040 --> 0:02:00.400 after that. 0:02:00.880 --> 0:02:02.880 So as a kid you cut your teeth like hunting 0:02:02.920 --> 0:02:03.360 and trapping. 0:02:04.000 --> 0:02:07.400 Yeah, I did actually growing up. I got to hunt 0:02:07.440 --> 0:02:09.120 deer back when we had a lot of deer right 0:02:09.160 --> 0:02:11.400 in New Mexico in the seventies. There was a lot 0:02:11.440 --> 0:02:14.000 of deer, and I got to do that. I started 0:02:14.000 --> 0:02:17.920 trapping when I was eleven years old. So what year 0:02:18.040 --> 0:02:21.800 to that had been, oh, man sixty eight? Oh okay, 0:02:22.040 --> 0:02:25.880 So yeah, so I've been trapping for a long time and. 0:02:25.840 --> 0:02:28.720 Then eventually got into that kind of work professional. 0:02:29.120 --> 0:02:31.480 Yeah, you know. When I went to college, that's all 0:02:31.560 --> 0:02:34.960 I wanted to do was become a wildlife biologist. I 0:02:34.960 --> 0:02:40.800 wanted to work outdoors, and that's when I graduated college. 0:02:41.240 --> 0:02:44.720 Jobs with game Department were on a hiring freeze, and 0:02:44.760 --> 0:02:47.280 a job came open with the predator control program in 0:02:47.280 --> 0:02:50.799 New Mexico, so I started with that outfit. After four years, 0:02:50.840 --> 0:02:53.480 I was getting hunters on the side and went into 0:02:53.520 --> 0:02:57.920 the private sector, guiding and managing ranches in Texas, doing 0:02:57.960 --> 0:03:01.200 some consulting work down in Old Mexico and Sonora. Well 0:03:01.240 --> 0:03:04.240 you did, yeah, yeah, there was a center for desert ecology. 0:03:04.320 --> 0:03:06.440 We built a zoo down there in Norma, seou and 0:03:06.560 --> 0:03:10.240 moved some animals into it, captured pronghorn in San Lui. 0:03:10.280 --> 0:03:10.640 Suppos. 0:03:10.840 --> 0:03:12.480 Did a lot of a lot of work in Mexico 0:03:12.520 --> 0:03:16.480 over the years. After my kids were born, I decided 0:03:16.480 --> 0:03:18.560 maybe I needed a job that had health insurance. 0:03:19.080 --> 0:03:19.480 Got it. 0:03:19.560 --> 0:03:22.880 I came back to government work in Mississippi as a 0:03:22.880 --> 0:03:27.200 beaver trapper, and then South Dakota. Spent thirteen years in Utah, 0:03:27.400 --> 0:03:31.920 mostly predator management. The deer herds crash there in nineteen 0:03:32.000 --> 0:03:34.800 ninety two ninety three, and I moved in there just 0:03:35.040 --> 0:03:37.760 after that, so we started doing preader control for domestic 0:03:37.760 --> 0:03:41.400 sheep protection, but also from you deer pronghorn protection, big 0:03:41.440 --> 0:03:45.120 horn sheep restoration. And that's how I got this deep 0:03:45.160 --> 0:03:47.440 into it, studying predator pray relationships. 0:03:47.520 --> 0:03:50.400 Yeah, let me hit you with one that wasn't expecting 0:03:50.400 --> 0:03:51.960 to ask you about. But I was just reading about 0:03:52.000 --> 0:03:54.960 it yesterday. Have you spent much time working in Alaska. 0:03:55.320 --> 0:03:58.320 I've been up to Alaska several times, but never never, 0:03:58.880 --> 0:03:59.800 you know, a month at a time. 0:04:00.160 --> 0:04:01.920 Yeah, a couple of weeks here and there. There's a 0:04:01.960 --> 0:04:04.800 little there's a it was a constantly a brew hanl 0:04:04.800 --> 0:04:08.440 ask about predator management around coyote stuff. But I was 0:04:08.440 --> 0:04:17.279 reading about some conflict and some lawsuits around doing bear 0:04:17.960 --> 0:04:21.960 control to try to re establish a caribou herd that 0:04:22.040 --> 0:04:24.839 had gone through like a ninety four percent decline. 0:04:25.160 --> 0:04:30.719 Yeah, that it is a worldwide phenomenon. I was an 0:04:30.760 --> 0:04:36.480 invited speaker at the first European Trapping Conference and they 0:04:36.480 --> 0:04:39.960 were saying, look, our endangered wolves are eating our endangered reindeer. 0:04:40.960 --> 0:04:44.400 But what do we do with that? Yeah, I'm working 0:04:44.400 --> 0:04:48.160 with a group in Alberta that's trying to to standardize 0:04:48.160 --> 0:04:51.719 predation management to protect wildlife. That that deal in Alaska 0:04:51.760 --> 0:04:56.599 is a long brewing controversy going way back. But but 0:04:57.320 --> 0:05:01.039 predators can put prey in a predator pit. When the 0:05:01.080 --> 0:05:05.320 prey drop off because environmental conditions and the predators don't 0:05:05.400 --> 0:05:08.599 drop off, that's when we have a predator pit. And 0:05:08.640 --> 0:05:12.360 that's what they're trying to reverse in Alaska. It's interesting 0:05:12.360 --> 0:05:15.200 because it gets to their constitution. They have to actually 0:05:15.640 --> 0:05:20.360 support subsistence use yep. And so it's a real tussle 0:05:20.440 --> 0:05:22.920 between our well, are you eating caribou or can you 0:05:22.920 --> 0:05:23.440 eat a bear? 0:05:23.800 --> 0:05:24.800 Yeah? I got it, got it. 0:05:24.839 --> 0:05:28.000 But in those areas they are doing bear removals and 0:05:28.040 --> 0:05:31.160 some wolf removals to protect caribou, especially at calving time. 0:05:31.240 --> 0:05:33.880 The bears are hard on them and it's working right. 0:05:33.920 --> 0:05:35.960 We've seen some of those caribou herds and will chatting 0:05:35.960 --> 0:05:37.480 the herds up about twenty five percent. 0:05:37.600 --> 0:05:38.599 Yeah, that's what I'm talking about. 0:05:38.640 --> 0:05:40.200 That was the one that had the ninety four percent 0:05:40.279 --> 0:05:42.080 decline and now it's kind of coming out of it. 0:05:42.320 --> 0:05:45.159 And there's a naturally cyclical element to it. But I 0:05:45.160 --> 0:05:47.800 think you can probably take you can probably take the bottoms, 0:05:47.839 --> 0:05:52.440 You can probably round the bottoms off with control or 0:05:53.240 --> 0:05:54.719 decrease the timeline. 0:05:54.760 --> 0:05:57.800 Right in Wyoming, prong horn will die in a severe winter. 0:05:58.160 --> 0:06:01.480 We know that that's the limiting factor winter. But do 0:06:01.520 --> 0:06:03.560 we want to wait seven years for them to come 0:06:03.600 --> 0:06:05.160 back up or do we want to bump them up 0:06:05.160 --> 0:06:07.240 in two or three years? You know that's when we're 0:06:07.279 --> 0:06:09.720 talking about predation management on wildlife. 0:06:09.839 --> 0:06:10.080 Yep. 0:06:10.279 --> 0:06:13.440 Yeah, what pratory have you spent the most time on 0:06:13.520 --> 0:06:13.760 in your. 0:06:13.720 --> 0:06:18.720 Career, without a doubt, coyotes, right. I actually trapped coyotes 0:06:18.760 --> 0:06:21.240 and hunting coyotes for fur when I was in college, 0:06:21.320 --> 0:06:24.360 back when college was cheap and fur was valuable, And 0:06:25.080 --> 0:06:27.720 I spent most of my time around coyote management and 0:06:27.760 --> 0:06:29.200 all the rest. But I spent a lot of time 0:06:29.400 --> 0:06:32.760 with mountain lions. I did postgraduate work on mountain lions 0:06:32.800 --> 0:06:36.160 in New Mexico and had hounds for thirty years, So 0:06:36.279 --> 0:06:38.200 I spent a lot of time online work. 0:06:38.720 --> 0:06:41.560 You know you had spent college, being when college was 0:06:41.800 --> 0:06:42.800 different amounts of money. 0:06:42.800 --> 0:06:44.440 I always tell people that this is no joke. 0:06:44.520 --> 0:06:47.120 Man. When I got out of high school, I didn't 0:06:47.120 --> 0:06:49.400 think I was going to go to college, got kicked 0:06:49.440 --> 0:06:52.520 around going to the army and then like decided to 0:06:52.520 --> 0:06:55.039 go to college. And I remember we had a community college. 0:06:55.080 --> 0:06:58.600 We called it thirteenth grade. And I went down there, 0:06:58.640 --> 0:07:01.080 and I'm not kidding you, man, I went down there 0:07:01.120 --> 0:07:04.800 and wrote a check for six hundred dollars at like 0:07:04.839 --> 0:07:08.719 a cashier window, Yeah, to pay my tuition. Yeah, my 0:07:08.800 --> 0:07:11.200 tuition was six hundred dollars for a semester, and I 0:07:11.280 --> 0:07:13.960 like scratched out a check. So now my wife's all 0:07:13.960 --> 0:07:16.160 talking about putting our kids going to college, knowing how 0:07:16.200 --> 0:07:18.480 expensive it is, I'm like six hundred bucks. 0:07:18.600 --> 0:07:24.840 Yeah, semester Ice little to Bob gat In in the 0:07:24.880 --> 0:07:29.000 spring of seventy nine for three hundred bucks and walked 0:07:29.040 --> 0:07:32.440 across campus with that check and paid off the last 0:07:32.480 --> 0:07:33.560 of that semester's mill. 0:07:33.840 --> 0:07:34.280 Is that right? 0:07:34.360 --> 0:07:38.120 Yeah? Yeah, yeah, that's that's Those days are gone now, 0:07:38.600 --> 0:07:38.800 you know. 0:07:39.440 --> 0:07:40.240 On Kyle's Man. 0:07:40.280 --> 0:07:43.080 Here's the conversation I have people all the time, is, uh, 0:07:45.640 --> 0:07:47.640 you get a guy, you know, you get a well 0:07:47.880 --> 0:07:53.640 meaning landowner, right, farm manager, ranch manager, the farmer himself, 0:07:53.680 --> 0:07:57.160 the ranch manager himself, the ranch himself, whatever, and they 0:07:57.200 --> 0:08:00.720 have the idea that like they want to help their 0:08:00.760 --> 0:08:04.000 dear population now, they want to increase their turkey population 0:08:04.160 --> 0:08:08.400 or whatever. And they'll occasionally in November shoot a kyo, 0:08:09.560 --> 0:08:11.480 right because they're thinking like, well, you know, trying to 0:08:11.480 --> 0:08:14.720 help out help Yeah. Or you'll say to a farmer 0:08:16.160 --> 0:08:17.480 you'd be like, oh, you know, I was out there, 0:08:17.520 --> 0:08:21.560 we saw a couple kyles. You didn't shoot them, you know, Yeah, 0:08:22.080 --> 0:08:24.360 and you have this conversation You're like like, listen, man, 0:08:24.360 --> 0:08:27.240 I don't have any problem with you doing whatever you 0:08:27.280 --> 0:08:33.440 want to do. But that doesn't matter, right, Like, like 0:08:34.559 --> 0:08:38.000 this kind of stuff is so it's it's like timing 0:08:38.160 --> 0:08:41.960 and intensity dependent. Absolutely. 0:08:42.040 --> 0:08:42.520 Yeah. 0:08:42.760 --> 0:08:45.920 Can you explain that a little bit from a professional 0:08:45.960 --> 0:08:52.720 perspective of when, like how do people kind of view 0:08:52.960 --> 0:08:55.960 predator control and what does predator control look like when 0:08:55.960 --> 0:08:57.800 it's done in a professional sense? Sure? 0:08:58.240 --> 0:09:00.679 And it's it is very very comp I don't want 0:09:00.679 --> 0:09:02.600 to go too far down a rabbit hole, but it's 0:09:02.640 --> 0:09:03.559 a deep rabbit hole. 0:09:03.679 --> 0:09:05.440 Sure, man, That's what I'm asking about it. 0:09:05.520 --> 0:09:09.360 Yeah. Predators impact prey in one of two different ways. 0:09:09.360 --> 0:09:11.200 The first one is how many get eaten? Right, And 0:09:11.200 --> 0:09:14.880 that's what we'd studied for fifty years. How many deer 0:09:14.920 --> 0:09:18.360 fons get eaten by coyotes and a deer adult deer 0:09:18.360 --> 0:09:26.560 get eaten by mountain lions and no kidding, decades of research. 0:09:26.679 --> 0:09:30.520 The very first wildlife monograph is about the Pants basin 0:09:30.559 --> 0:09:33.080 and they followed deer radio coolor deer followed them around 0:09:34.120 --> 0:09:38.640 Pants basin northwest Colorado. And if a deer starved to death, 0:09:38.640 --> 0:09:40.880 they said, see it's habitat related. And if a deer 0:09:40.920 --> 0:09:44.960 was killed by coyotes, they said, see it's predation. And 0:09:45.000 --> 0:09:48.920 that's what we understood about predator management or predator impacts 0:09:49.120 --> 0:09:52.560 for a long time. Really, when we started putting wolves 0:09:52.600 --> 0:09:56.480 in Yellowstone, we started looking at other predation impacts. And 0:09:57.360 --> 0:10:01.679 the secondary impacts of predation are behavioral changes in those 0:10:01.800 --> 0:10:08.959 prey animals to avoid predation. So wolves didn't eat elk 0:10:09.480 --> 0:10:12.320 on the Northern deer herd or northern elk herd near 0:10:12.360 --> 0:10:15.480 as much as they change their behavior. Instead of them 0:10:15.480 --> 0:10:17.800 being out in the meadows getting all the grass they want, 0:10:18.120 --> 0:10:21.600 they're now standing on the slopes where they can escape predation, 0:10:22.760 --> 0:10:25.120 but they're starving to death in what we used to 0:10:25.120 --> 0:10:28.679 consider a mild winter. I got it, and that secondary 0:10:28.720 --> 0:10:32.640 impact is probably as important as the primary impact. So 0:10:32.640 --> 0:10:34.679 shooting a coyote in the fall is that going to 0:10:34.720 --> 0:10:39.520 help anything. If secondary impacts are what's knocking those numbers down, 0:10:40.040 --> 0:10:42.400 then maybe very small. 0:10:42.440 --> 0:10:43.600 Maybe. 0:10:43.640 --> 0:10:47.400 If primary impacts are there, there's absolutely no impact whatsoever. 0:10:47.600 --> 0:10:52.600 Coyotes get replaced. They're very resilient as a specie, and 0:10:52.120 --> 0:10:56.679 they can come back from any kind of pressure up 0:10:56.720 --> 0:11:00.720 to sixty five percent removal. You'll have them back within 0:11:00.760 --> 0:11:01.240 a single. 0:11:03.040 --> 0:11:03.319 Remove. 0:11:03.480 --> 0:11:09.360 Yeah, they bounce back in a single breeding season. And 0:11:09.400 --> 0:11:14.000 this is this is neat research stuff. Researchers like aquatic 0:11:14.080 --> 0:11:17.400 systems to look at predation because they can't leave, right, 0:11:17.480 --> 0:11:18.400 fish can't. 0:11:18.120 --> 0:11:18.959 Get all that. 0:11:19.880 --> 0:11:21.960 And so there's been a couple of studies where they had, 0:11:22.920 --> 0:11:27.280 you know, a lake with algae eating fish out in it, 0:11:27.400 --> 0:11:29.840 and the fish were evenly distributed in the lake and 0:11:29.840 --> 0:11:32.640 they ate the algae and they kept algae blooms from occurring. 0:11:33.040 --> 0:11:36.680 And they put in just a couple of predatory fish 0:11:36.800 --> 0:11:39.760 in that system and all the algae eaters go to 0:11:39.840 --> 0:11:42.800 the thick cover and that open water now doesn't have 0:11:42.840 --> 0:11:45.920 any algae eaters in it, and you have an algae bloom. 0:11:46.520 --> 0:11:49.320 And think about that. If you understand food pyramids and 0:11:49.320 --> 0:11:52.600 all the rest, that's the basis of life being directed 0:11:52.720 --> 0:11:55.960 by a predator species because. 0:11:55.760 --> 0:11:57.040 They can't do what they want no more. 0:11:57.080 --> 0:11:59.720 They can't they can't safely go out there. They change 0:11:59.760 --> 0:12:04.040 their behavior and it changes the ecosystem. In another experiment, 0:12:04.080 --> 0:12:06.720 and I love this one, they had snails in an 0:12:06.760 --> 0:12:10.800 aquarium with with growing plants, and the snails will feed 0:12:10.880 --> 0:12:13.040 up and down the stock of that plant, including the 0:12:13.120 --> 0:12:15.120 terminal bud at the tip of the plant. They eat 0:12:15.160 --> 0:12:18.839 it and they keep the plants crop back. They didn't 0:12:18.840 --> 0:12:22.800 introduce snail predators, they introduced the smell of snail predators. 0:12:22.840 --> 0:12:25.800 They had its snail preadors another aquarium. They circulated that 0:12:25.880 --> 0:12:26.760 water over there. 0:12:27.200 --> 0:12:29.080 Really, what's the snail predator? 0:12:29.520 --> 0:12:32.440 Some other kind of fish that that'll crunch the shells? 0:12:32.960 --> 0:12:36.640 And those snails retreated to the basal clump of the 0:12:36.679 --> 0:12:39.280 plant and the plants grew up and choked out the systems. 0:12:39.360 --> 0:12:40.640 They can smell those sons of bitches. 0:12:40.720 --> 0:12:43.480 They can smell them. I may not be giving snails 0:12:43.600 --> 0:12:48.400 enough credit for their thought process, but that's an evolutionary thing, right, 0:12:48.480 --> 0:12:54.640 It's yeah, that was it. That's it. That's evolution, and 0:12:54.760 --> 0:12:57.480 that's the that's the ying and yang of predation, right. 0:12:57.520 --> 0:13:00.680 It shapes the predator, It shapes the prey. When things 0:13:00.720 --> 0:13:05.360 get out of whack, when people interfere, then then predators 0:13:05.360 --> 0:13:10.840 can have an impact on them. Yeah, another fascinating deal. 0:13:11.800 --> 0:13:14.640 When I was in Utah, they asked me to identify 0:13:14.760 --> 0:13:17.240 why predators have an impact and what would we do 0:13:17.360 --> 0:13:20.160 to manage that impact. And I started looking at mountain 0:13:20.160 --> 0:13:22.600 lions and deer, and mountains kill deer, that's their job, 0:13:22.679 --> 0:13:25.640 that's how they evolve. Deer evolved feeding mountain liones. That 0:13:25.920 --> 0:13:33.200 that's that's the relationship. But mountain lions kill bucks in 0:13:33.320 --> 0:13:36.760 greater percentage than the percentage in the population. If bucks 0:13:36.760 --> 0:13:39.240 are twenty percent of a population, it might be thirty 0:13:39.720 --> 0:13:42.960 thirty five percent of what lines killed. And the reasons 0:13:43.000 --> 0:13:47.240 for that are multiple. But bucks are solitary, Bucks move more, 0:13:47.320 --> 0:13:50.040 so they're easier to detect. They don't get in big groups, 0:13:50.040 --> 0:13:52.520 so it's you know, they don't have a defensive of 0:13:52.559 --> 0:13:53.400 a lot of eyes. 0:13:54.320 --> 0:13:58.240 Yeah, because you would think just on that point, I've 0:13:58.240 --> 0:14:02.160 seen that before, yeah, and heard that before. But you 0:14:02.200 --> 0:14:07.400 would think in some ways, the fact that it's for 0:14:07.520 --> 0:14:12.880 much of the year it's armed with antlers, you would 0:14:12.920 --> 0:14:14.800 think in some ways that would offset that, and maybe 0:14:14.800 --> 0:14:16.760 it does. Maybe it'd be maybe if they didn't have 0:14:16.920 --> 0:14:18.680 if they didn't have antlers, to be even worse. But 0:14:18.720 --> 0:14:21.040 you'd see in some ways, I feel like I'm surprised 0:14:21.080 --> 0:14:22.280 that doesn't turn a cat off. 0:14:23.520 --> 0:14:25.360 Yeah, it doesn't turn a cat off. 0:14:25.760 --> 0:14:25.840 There. 0:14:26.640 --> 0:14:29.800 They're they're pretty adept at grabbing them over the shoulders 0:14:29.840 --> 0:14:32.400 and biting where they need to bite. 0:14:32.160 --> 0:14:34.240 You know, Yeah, you'll need the antlers or no. 0:14:34.320 --> 0:14:38.040 I've seen them kill adult bull elk, you know, with antlers. 0:14:38.720 --> 0:14:43.119 So mountainin z O will killed dozen fawns and relative 0:14:43.240 --> 0:14:46.600 abundance to each other. And and so if there's a. 0:14:46.640 --> 0:14:48.320 Hundred songs, explain it again. 0:14:48.640 --> 0:14:52.320 They killed dozen fawns and relative abundance to each other. 0:14:52.560 --> 0:14:55.360 So you take the buck mortality out of it. If 0:14:55.360 --> 0:14:57.400 you got a hundred phones per one hundred doze, which 0:14:57.400 --> 0:15:01.000 we never have, half a wood a lion kill is 0:15:01.040 --> 0:15:04.680 going to be fons and half will be doze if 0:15:04.720 --> 0:15:05.520 you had that ratio. 0:15:05.680 --> 0:15:08.400 They don't care, right because you don't know what's in 0:15:08.440 --> 0:15:09.560 his head. But that's what you see. 0:15:09.800 --> 0:15:14.640 That's it's just availability. They're equally vulnerable to predation, so 0:15:14.680 --> 0:15:17.640 it's just whatever he comes on the closest one to 0:15:17.680 --> 0:15:23.200 the bushes, right. What we found out though, is that 0:15:23.320 --> 0:15:29.040 when predator or when fall numbers are down, lion mortality 0:15:29.160 --> 0:15:33.920 is additive to other mortality. They're killing breeding age dos 0:15:34.440 --> 0:15:36.160 and that can drive a population down. 0:15:36.200 --> 0:15:39.840 I explain that's me and I got confused there. 0:15:39.640 --> 0:15:42.400 The model that I had when numbers are down. 0:15:42.200 --> 0:15:45.360 When fall numbers are down, when you get below fifty 0:15:45.400 --> 0:15:48.560 falls per one hundred dos, what a lion's killing is 0:15:48.680 --> 0:15:52.560 two thirds of what the non bucks are adult breeding 0:15:52.600 --> 0:15:56.120 age doughs. Only one third is fonts. If they're killing fans, 0:15:56.120 --> 0:15:58.600 it doesn't have an impact on the population. You still 0:15:58.640 --> 0:16:00.920 got some that are coming up to be breeding. 0:16:00.640 --> 0:16:01.240 Age I got you. 0:16:01.360 --> 0:16:03.800 If they're killing breeding age dose, you can see the 0:16:03.840 --> 0:16:07.880 whole population depressed because they're the ones that are that 0:16:07.920 --> 0:16:10.000 are feeding in, you know, young into the system. 0:16:10.120 --> 0:16:11.920 Like that's when it's happened, like a sort of a 0:16:11.960 --> 0:16:13.560 greater population level impact. 0:16:13.680 --> 0:16:16.280 Right. When we modeled this in Utah, the official line 0:16:16.320 --> 0:16:19.760 population was about three thousand based on densities and all 0:16:19.800 --> 0:16:23.080 the rest. The official deer population was three hundred thousand, 0:16:23.440 --> 0:16:26.800 or about one line for every one hundred deer. When 0:16:26.920 --> 0:16:31.200 faun ratios got blowed fifty falls per hundred doze. The 0:16:31.240 --> 0:16:37.720 population was declining because lions were killing does yep. Lions 0:16:37.760 --> 0:16:40.720 aren't nearly as abundant on the landscape as coyotes. The 0:16:40.760 --> 0:16:44.280 reason the faun ratios were low was because coyotes were 0:16:44.280 --> 0:16:45.120 eating the falls. 0:16:45.600 --> 0:16:45.960 Got it. 0:16:46.080 --> 0:16:49.000 So when we started doing predator management in the book 0:16:49.000 --> 0:16:51.480 Cliffs in the Henry Mountains, we went in there after 0:16:51.560 --> 0:16:54.960 the coyotes. We tried to get our fahone numbers up 0:16:55.000 --> 0:16:58.080 above fifty falls per hundred doze. We get above sixty 0:16:58.160 --> 0:17:01.200 seventy falls per hundred doze. Lions can eat whatever they 0:17:01.240 --> 0:17:04.000 want to eat and you don't notice the difference. So 0:17:04.080 --> 0:17:07.399 you got two predators working on the same population, but 0:17:07.440 --> 0:17:13.280 they're hitting different levels of the population. One's eating fams 0:17:13.280 --> 0:17:15.959 and the others eating adult dose. It can depress that 0:17:16.040 --> 0:17:16.960 population as. 0:17:16.800 --> 0:17:17.280 Well, got it. 0:17:17.880 --> 0:17:21.960 So there's all kinds of factors. We identified eighteen factors 0:17:22.040 --> 0:17:26.160 that affect predation rates. Give me some more linear habitat. 0:17:27.400 --> 0:17:30.680 So a predator, a single predator walking a dike can 0:17:30.720 --> 0:17:33.399 find every duck nest if they have to nest on 0:17:33.400 --> 0:17:33.800 the edge of you. 0:17:34.240 --> 0:17:35.639 Yeah, I'm glad you brought this up, because this is 0:17:35.680 --> 0:17:38.920 in our notes. This linear I've never heard this concept before. 0:17:39.119 --> 0:17:43.240 Yeah, so what's more linear than a beach? A beach 0:17:43.320 --> 0:17:46.119 sea turtles have to lay their eggs on the beach, 0:17:47.200 --> 0:17:50.679 and one coyote, one sounder of feral hogs walking that 0:17:50.800 --> 0:17:54.199 beach can find one hundred percent of the nest. Got it, 0:17:54.440 --> 0:17:56.280 Which also gets to what do you do about it? 0:17:56.320 --> 0:17:59.920 You can remove ninety percent of the coyotes, but one 0:18:00.080 --> 0:18:04.200 still lose all the nests. If you've got linear habitat's 0:18:04.560 --> 0:18:09.080 you've got to be really really intensive other factors like 0:18:09.200 --> 0:18:12.520 herd size and age. Right when wolves were killing elk 0:18:12.520 --> 0:18:15.520 and Yellowstone, the average age of the elk that were 0:18:15.560 --> 0:18:19.159 being shot by hunters cowilk was about five, but the 0:18:19.200 --> 0:18:21.640 average age that the wolves were eaten was like eleven. 0:18:23.080 --> 0:18:25.520 That's because they're at the back of the herd. The 0:18:25.600 --> 0:18:27.840 eleven year olds are the slower ones at the back 0:18:27.880 --> 0:18:29.960 of the herds. That's the one the wolves you're sharketing 0:18:30.000 --> 0:18:33.640 and pulling down, so they're not equally distributing that predation 0:18:33.840 --> 0:18:38.119 effort over the whole population. You can lose some eleven 0:18:38.200 --> 0:18:40.560 year olds, you start losing your five year olds, and 0:18:40.600 --> 0:18:43.600 your population is going to decline because that's the reproductive 0:18:43.640 --> 0:18:45.280 capacity of that herd right there. 0:18:45.480 --> 0:18:46.480 Got it. 0:18:46.480 --> 0:18:51.800 It's a fascinating, fascinating picture. Some of the some of 0:18:51.840 --> 0:18:57.479 the factors involve the predators, coyotes. You probably know this, 0:18:57.600 --> 0:19:01.640 but coyotes don't breed till their second year, So right 0:19:01.800 --> 0:19:04.720 out on the landscape, you've got the current year's pups, 0:19:05.119 --> 0:19:09.880 which are like fifty percent of the population. Numerically, you've 0:19:09.920 --> 0:19:14.920 got the last year's coyotes, the yearlings that are out there, 0:19:15.200 --> 0:19:18.800 transient coyotes between territories trying to stay out of everybody's 0:19:18.840 --> 0:19:22.960 way and not get attacked by the territorial coyotes. Territorial 0:19:23.000 --> 0:19:26.160 coyotes are like twenty percent of the population. Two year 0:19:26.200 --> 0:19:31.959 olds and up. Those coyotes are the ones that are 0:19:32.000 --> 0:19:35.480 doing most of the killing of wildlife. Bose are the coyotes. Yeah, 0:19:35.520 --> 0:19:37.199 you got a four year old pair that's been on 0:19:37.280 --> 0:19:41.560 that territory for three breeding seasons. They're going to know 0:19:41.600 --> 0:19:43.640 where to go hunt. Phones they're the ones that are 0:19:43.960 --> 0:19:45.879 the most active at killing. 0:19:46.160 --> 0:19:47.520 Just think they got the most experience. 0:19:47.520 --> 0:19:50.919 They've got the most experience, and they're feeding pups. In 0:19:51.040 --> 0:19:54.720 experiments with livestock predation, when when you remove the pups 0:19:54.800 --> 0:19:58.760 from the equation, the adult coyotes quit killing within two days. 0:19:59.160 --> 0:20:01.120 Got it visioning. 0:20:00.720 --> 0:20:04.119 Those pups and you think about it. Kyote pups are 0:20:04.119 --> 0:20:07.840 born right around April fifteenth, deer phones hit the ground 0:20:07.600 --> 0:20:11.080 the end of June, first of July. Those pups got 0:20:11.080 --> 0:20:13.680 to eat meat. Those kyotes are killing as fast as 0:20:13.680 --> 0:20:15.640 they can to provision their pups. 0:20:15.920 --> 0:20:18.280 You know, I gotta just keep your train of thought. 0:20:18.359 --> 0:20:21.040 Yeah, But the idea of buddy of mine, who's a songwriter, 0:20:22.680 --> 0:20:24.439 he says, hey, I got a kyote question for you. 0:20:25.359 --> 0:20:26.680 And I don't know where he's got I don't even know. 0:20:26.720 --> 0:20:28.199 He didn't even I didn't even ask him that this 0:20:28.240 --> 0:20:31.399 has to do with something he's writing. But he happens 0:20:31.400 --> 0:20:33.360 to be a songwriter and he happens to have called 0:20:33.400 --> 0:20:35.280 me and asked me this question, but no explanation of 0:20:35.320 --> 0:20:38.000 why he was asking. But he said, do you ever 0:20:38.119 --> 0:20:42.159 see And I didn't have great answer for him. I 0:20:42.160 --> 0:20:44.520 could just tell him anecdotally, or I could just tell 0:20:44.520 --> 0:20:46.920 my opinion on it, theory on it. But he said, 0:20:46.920 --> 0:20:51.679 do you ever see a kyote take a step to 0:20:51.840 --> 0:20:56.679 dispatch something? Or do they just kind of like grab 0:20:56.720 --> 0:20:59.439 it and start eating it. She's talking about, why do 0:20:59.440 --> 0:21:01.480 you see him walking around with a rabbit that's still 0:21:01.480 --> 0:21:03.919 looking around. You know, I mean like some things. If 0:21:04.000 --> 0:21:06.240 you think of like a terrier, right when if a 0:21:06.320 --> 0:21:12.200