1 00:00:05,200 --> 00:00:08,640 Speaker 1: Welcome to this Country Life. I'm your host, Brent Reeves. 2 00:00:09,119 --> 00:00:12,680 Speaker 1: From coon hunting to trot lining and just general country living. 3 00:00:12,920 --> 00:00:14,680 Speaker 1: I want you to stay a while as I share 4 00:00:14,760 --> 00:00:19,200 Speaker 1: my experiences and life lessons. This Country Life is presented 5 00:00:19,239 --> 00:00:23,200 Speaker 1: by Case Knives on Meat Eaters Podcast Network, bringing you 6 00:00:23,280 --> 00:00:28,240 Speaker 1: the best outdoor podcast the airwaves had off. All right, friends, 7 00:00:28,600 --> 00:00:31,680 Speaker 1: grab a chair or drop that tail gate. I've got 8 00:00:31,680 --> 00:00:41,120 Speaker 1: some stories to share. Welcome to this country Life podcast. 9 00:00:41,159 --> 00:00:44,080 Speaker 1: This one's kind of different. You can see me on here, 10 00:00:44,280 --> 00:00:46,920 Speaker 1: and you can see my guest on here. Today we're 11 00:00:47,040 --> 00:00:49,680 Speaker 1: doing an audio and a video version of this. And 12 00:00:49,760 --> 00:00:53,920 Speaker 1: I am in at Kansas State University in Manhattan, Kansas, 13 00:00:54,240 --> 00:00:57,240 Speaker 1: and I'm talking with my friend, doctor Drew Ricketts. Drew 14 00:00:57,560 --> 00:01:00,880 Speaker 1: is with the Extension Wildlife Specialists or he is an 15 00:01:00,920 --> 00:01:04,039 Speaker 1: Extension Wildlife Specialist here and he works in the Hortor, 16 00:01:04,080 --> 00:01:09,440 Speaker 1: Culture and Natural Resources Department here and as a professor. Drew, 17 00:01:09,560 --> 00:01:10,479 Speaker 1: thank you for being here. 18 00:01:10,520 --> 00:01:13,240 Speaker 2: Oh absolutely, thanks for having me on your show. 19 00:01:13,280 --> 00:01:13,520 Speaker 1: Brent. 20 00:01:13,560 --> 00:01:15,120 Speaker 2: This is a pretty big honor for me. 21 00:01:15,280 --> 00:01:18,280 Speaker 1: Well, you know, we met I guess a month ago 22 00:01:18,840 --> 00:01:22,320 Speaker 1: and we were decoy dog hunting with my friend Jeff Ryder, 23 00:01:22,360 --> 00:01:25,280 Speaker 1: who you met on that hunt and that's that is 24 00:01:25,319 --> 00:01:28,919 Speaker 1: a video that will come out sometime in twenty twenty 25 00:01:28,959 --> 00:01:32,880 Speaker 1: six and Decoy Dogs give a brief history of that. 26 00:01:33,000 --> 00:01:35,640 Speaker 1: Folks that's familiar with it, or maybe I did an 27 00:01:35,680 --> 00:01:40,200 Speaker 1: episode on this a few several months back about decoy 28 00:01:40,280 --> 00:01:42,880 Speaker 1: dog hunting and hunting with Jeff. But I tried to 29 00:01:42,920 --> 00:01:47,319 Speaker 1: explain how it all works, which is the interaction between 30 00:01:47,440 --> 00:01:52,160 Speaker 1: two different canines is. It's pretty cool. But it's always 31 00:01:52,200 --> 00:01:58,840 Speaker 1: brought up a you know, references are it's brought up 32 00:01:58,960 --> 00:02:03,280 Speaker 1: thoughts about how couts or coutes, however you want to 33 00:02:03,280 --> 00:02:08,440 Speaker 1: pronounce it, affect the environment. And surprising to me a 34 00:02:08,520 --> 00:02:11,200 Speaker 1: kid that grew up my father was a longtime cout 35 00:02:11,440 --> 00:02:16,200 Speaker 1: hunter and he was just running coats with dogs, you know, 36 00:02:16,240 --> 00:02:18,160 Speaker 1: they chasing it. Listened to the dogs and the cout 37 00:02:18,200 --> 00:02:21,480 Speaker 1: gets away. You know. It was never usually the cout 38 00:02:21,800 --> 00:02:24,000 Speaker 1: never lost and was never killed or caught by the 39 00:02:24,040 --> 00:02:27,720 Speaker 1: dogs or whatever. It was just a sport where you 40 00:02:27,760 --> 00:02:30,760 Speaker 1: listened to the dogs running. Whoever's dog was in front 41 00:02:30,919 --> 00:02:32,880 Speaker 1: was the winner, you know, and that was the end 42 00:02:32,919 --> 00:02:37,480 Speaker 1: of it. So someone who from a small child up 43 00:02:37,560 --> 00:02:43,560 Speaker 1: until a large adult, now I had a wrong view 44 00:02:43,880 --> 00:02:47,880 Speaker 1: of how couts really affect the landscape. And when once 45 00:02:47,919 --> 00:02:50,600 Speaker 1: I got to talking to Jeff Ryder and seeing the 46 00:02:50,639 --> 00:02:53,639 Speaker 1: different things and seeing things for myself, and then especially 47 00:02:53,760 --> 00:02:57,080 Speaker 1: when you came in and started adding the facts to 48 00:02:57,200 --> 00:03:01,239 Speaker 1: what the figures just didn't add up, it's been very 49 00:03:01,280 --> 00:03:04,480 Speaker 1: intriguing for me. And we're gonna get into all of that, sure. 50 00:03:04,600 --> 00:03:06,359 Speaker 1: The first I want to give give me a little 51 00:03:06,480 --> 00:03:09,200 Speaker 1: a bio about yourself, Drew, and how you wound up 52 00:03:09,200 --> 00:03:10,440 Speaker 1: being here where you are today. 53 00:03:10,800 --> 00:03:14,400 Speaker 2: Well, I grew up in Southeast Kansas. I've lived in 54 00:03:14,480 --> 00:03:17,839 Speaker 2: Kansas my whole life. Grew up fishing, hunting, trapping. When 55 00:03:17,880 --> 00:03:22,880 Speaker 2: I was a toddler, dad was trapping for a living 56 00:03:22,960 --> 00:03:26,520 Speaker 2: and writing trapping books really, and so my daycare was 57 00:03:26,800 --> 00:03:31,200 Speaker 2: riding on his shoulders checking a trap line basically during 58 00:03:31,320 --> 00:03:33,800 Speaker 2: during that season of the year. And and you know, 59 00:03:33,919 --> 00:03:36,200 Speaker 2: lots of fishing when I was young, hunting as I 60 00:03:36,240 --> 00:03:40,080 Speaker 2: grew up, and I kind of quit trapping for a while, 61 00:03:40,160 --> 00:03:42,640 Speaker 2: got into coon hunting real big for a time when 62 00:03:42,640 --> 00:03:46,720 Speaker 2: I was in college, and then got out of college, 63 00:03:46,920 --> 00:03:50,080 Speaker 2: came back to doing a lot of trapping, started doing 64 00:03:50,080 --> 00:03:55,080 Speaker 2: a lot of fur trapping for coyotes, and during I 65 00:03:55,080 --> 00:03:57,680 Speaker 2: guess I got a degree at what at k State 66 00:03:57,760 --> 00:04:02,720 Speaker 2: and wildlife management in between in there, had a brief 67 00:04:02,760 --> 00:04:06,280 Speaker 2: stint in South Dakota studying some badgers and stuff like that, 68 00:04:06,400 --> 00:04:10,280 Speaker 2: putting radio colors on those kind of critters. Came back 69 00:04:10,320 --> 00:04:14,760 Speaker 2: to Kansas and started a habitat management business, doing a 70 00:04:14,800 --> 00:04:16,839 Speaker 2: lot of pasture clearing and that kind of stuff too, 71 00:04:17,760 --> 00:04:21,520 Speaker 2: And that's when I really got into coyota trapping. And 72 00:04:21,600 --> 00:04:24,680 Speaker 2: then I really I beat up my body pretty bad 73 00:04:24,760 --> 00:04:27,240 Speaker 2: doing that stuff for a living, and I got bored, 74 00:04:29,080 --> 00:04:31,040 Speaker 2: and so I just decided I wanted to come back 75 00:04:31,080 --> 00:04:34,640 Speaker 2: to school and pursue some kind of path that had 76 00:04:34,720 --> 00:04:36,680 Speaker 2: let me fool with animals a little bit more for 77 00:04:36,839 --> 00:04:41,479 Speaker 2: my job. Got a degree a PhD from Case State 78 00:04:41,560 --> 00:04:45,640 Speaker 2: in in biology with a focus in wildlife management, studying 79 00:04:45,760 --> 00:04:51,960 Speaker 2: small mammals. So I trapped like two thousand mice and 80 00:04:52,080 --> 00:04:55,240 Speaker 2: rats about five thousand times over four years. 81 00:04:55,279 --> 00:05:00,360 Speaker 1: And Jesus you mentioned now there's no difference in mice rats. 82 00:05:00,440 --> 00:05:02,960 Speaker 1: Let me go and correct you. There's only a big rats, 83 00:05:02,960 --> 00:05:06,279 Speaker 1: a little rags. Yeah, And I don't like none of yeah. Yeah. 84 00:05:06,320 --> 00:05:08,839 Speaker 2: But during that time I had to figure out how 85 00:05:08,880 --> 00:05:11,840 Speaker 2: to keep myself saying, because fooling was with mice is 86 00:05:12,000 --> 00:05:15,880 Speaker 2: my favorite thing either. Really, So I've got some funding 87 00:05:15,920 --> 00:05:19,000 Speaker 2: to put GPS callers on coyotes during that time and 88 00:05:19,080 --> 00:05:21,160 Speaker 2: kind of studied how they moved around, looked at what 89 00:05:21,240 --> 00:05:23,000 Speaker 2: killed them and that sort of thing, and that turned 90 00:05:23,000 --> 00:05:25,560 Speaker 2: into part of my PhD degree, and then after that 91 00:05:25,680 --> 00:05:27,200 Speaker 2: I just kind of rolled into this job. 92 00:05:27,360 --> 00:05:29,560 Speaker 1: So, yeah, let me ask you a question before we 93 00:05:29,600 --> 00:05:31,800 Speaker 1: get started on the on the on the stuff here. 94 00:05:32,200 --> 00:05:36,240 Speaker 1: Did you learn more about coats trapping them or did 95 00:05:36,279 --> 00:05:39,000 Speaker 1: you learn more about coats in school. 96 00:05:39,680 --> 00:05:43,640 Speaker 2: All of the above. Yeah, but the school part of 97 00:05:43,680 --> 00:05:46,480 Speaker 2: it isn't something that I learned in class. It's what 98 00:05:46,560 --> 00:05:49,680 Speaker 2: I learned from putting GPS callers on coyotes and tracking 99 00:05:49,720 --> 00:05:54,040 Speaker 2: them around, and then learning how to trap coyotes during 100 00:05:54,040 --> 00:05:57,599 Speaker 2: the summertime is a totally different ballgame than trapping coyotes 101 00:05:57,640 --> 00:05:59,480 Speaker 2: during during the fall when they're easy to get. 102 00:05:59,640 --> 00:06:02,280 Speaker 1: My brother there is a a as a trapper, as 103 00:06:02,320 --> 00:06:04,840 Speaker 1: a coat trapper, and a good one, and he's he's 104 00:06:04,880 --> 00:06:08,320 Speaker 1: catching a lot of them for these fox pens and 105 00:06:08,040 --> 00:06:13,000 Speaker 1: stuff like that, and for you know, to control them 106 00:06:13,040 --> 00:06:15,159 Speaker 1: my own places where there's too many coats. If the 107 00:06:15,279 --> 00:06:18,920 Speaker 1: landowners want to thind out and he'll tell you quick 108 00:06:19,160 --> 00:06:23,760 Speaker 1: there is there's two different seasons to trapping codes. So 109 00:06:23,760 --> 00:06:26,960 Speaker 1: it's and I probably phrased that question wrong. As far 110 00:06:27,040 --> 00:06:30,880 Speaker 1: as did you learn more, let me let me ask 111 00:06:30,920 --> 00:06:36,800 Speaker 1: you this. After you started with the radio callers, did 112 00:06:36,800 --> 00:06:40,400 Speaker 1: you learn things that relearned things you thought you knew 113 00:06:40,440 --> 00:06:44,720 Speaker 1: about how colds act or move across the landscape? 114 00:06:44,800 --> 00:06:44,880 Speaker 2: Oh? 115 00:06:45,000 --> 00:06:47,719 Speaker 1: Yeah, something, What was probably the most surprising thing that 116 00:06:47,800 --> 00:06:48,320 Speaker 1: you learned? 117 00:06:48,400 --> 00:06:51,920 Speaker 2: Well, you know, I had I had read about some 118 00:06:52,000 --> 00:06:55,440 Speaker 2: of the differences in the social structure of coyotes. And 119 00:06:57,320 --> 00:06:59,960 Speaker 2: to keep it simple, there's there's residents and there's train 120 00:07:00,480 --> 00:07:03,840 Speaker 2: and residents are going to be the pairs that are 121 00:07:03,880 --> 00:07:06,680 Speaker 2: breeding some of their offspring from last year that we 122 00:07:06,680 --> 00:07:09,160 Speaker 2: would call helpers that they allowed to stay around and 123 00:07:09,200 --> 00:07:11,480 Speaker 2: not disperse, and then the pups from this year if 124 00:07:11,520 --> 00:07:13,160 Speaker 2: it's during a time of the year when they would 125 00:07:13,160 --> 00:07:16,160 Speaker 2: have had pups already. And then the transients are these 126 00:07:16,360 --> 00:07:19,160 Speaker 2: nomadic coyotes that aren't really tied to a home range. 127 00:07:19,840 --> 00:07:23,560 Speaker 2: And when I got those GPS callers on the coyotes, 128 00:07:23,640 --> 00:07:27,280 Speaker 2: just realizing how big of an area those transients cover, 129 00:07:28,960 --> 00:07:33,040 Speaker 2: that was very interesting to me. Another thing that was 130 00:07:33,160 --> 00:07:37,520 Speaker 2: really surprising is how often and how many times, even 131 00:07:37,680 --> 00:07:41,320 Speaker 2: way into the future they are, not into the future, 132 00:07:41,360 --> 00:07:44,520 Speaker 2: but long time after a cow or a buffalo has died. 133 00:07:45,080 --> 00:07:47,520 Speaker 2: How many times they come back to that carcass, even 134 00:07:47,520 --> 00:07:50,920 Speaker 2: when there's nothing left to eat, that remains an important 135 00:07:50,960 --> 00:07:54,040 Speaker 2: part of their territory for six months or a year 136 00:07:54,120 --> 00:07:55,320 Speaker 2: after that food's gone. 137 00:07:55,680 --> 00:07:59,080 Speaker 1: Wow. Yeah, well, I'm going to mess around and learn 138 00:07:59,160 --> 00:08:07,600 Speaker 1: something today. Let's get started, man, from from your point 139 00:08:07,640 --> 00:08:12,320 Speaker 1: of view, when we got a list here stuff kyot 140 00:08:12,400 --> 00:08:16,680 Speaker 1: biology and our historical kyote distribution. You know, a lot 141 00:08:16,680 --> 00:08:20,320 Speaker 1: of people and me being included, have learned recently that 142 00:08:20,480 --> 00:08:24,560 Speaker 1: kyotes haven't always been where colotes are. And I mean 143 00:08:24,760 --> 00:08:26,960 Speaker 1: you see the stuff. You see the post on social media. 144 00:08:26,960 --> 00:08:31,920 Speaker 1: You see the news reports of somebody's cat getting snatched 145 00:08:31,920 --> 00:08:33,720 Speaker 1: in the middle of the chair by kydi, you know, 146 00:08:33,720 --> 00:08:36,480 Speaker 1: because they saw it on the security camera or whatever. 147 00:08:36,880 --> 00:08:40,600 Speaker 1: But that ain't always been the case. They haven't always 148 00:08:40,640 --> 00:08:43,920 Speaker 1: been there. How did they get to where coyotes are now? Sure? 149 00:08:44,080 --> 00:08:47,480 Speaker 2: So you know, there's been several different changes that have 150 00:08:47,840 --> 00:08:51,320 Speaker 2: allowed them to expand their range Historically. You got that 151 00:08:51,400 --> 00:08:53,679 Speaker 2: picture in front of you, and we can make that 152 00:08:53,760 --> 00:08:56,680 Speaker 2: available on the on the YouTube stream when you put 153 00:08:56,720 --> 00:08:59,520 Speaker 2: that up. But it's there's there's a big red area 154 00:08:59,640 --> 00:09:03,320 Speaker 2: and now that's where coyotes were. That's their historic distribution 155 00:09:03,480 --> 00:09:04,679 Speaker 2: prior to nineteen hundred. 156 00:09:04,760 --> 00:09:07,440 Speaker 1: And if you're listening to this and not seeing the 157 00:09:07,480 --> 00:09:11,920 Speaker 1: graft that's up, it's covering like two thirds of the 158 00:09:12,040 --> 00:09:15,600 Speaker 1: United States from westward, like up to I assume that's 159 00:09:15,760 --> 00:09:17,640 Speaker 1: like right along the Mississippi. 160 00:09:17,080 --> 00:09:20,440 Speaker 2: River, Mississippi and Ohio. Okay, so that's that boundary on 161 00:09:20,520 --> 00:09:21,840 Speaker 2: the eastern side. 162 00:09:21,720 --> 00:09:23,679 Speaker 1: And on all the way down to the Yucatan. 163 00:09:23,880 --> 00:09:27,520 Speaker 2: Yeah, yeah, yeah. And so if you're east of that 164 00:09:27,520 --> 00:09:30,840 Speaker 2: that boundary basically in Mississippi, I'm sure it's not a 165 00:09:31,120 --> 00:09:34,000 Speaker 2: solid boundary because kyotes could swim across the sure, right, 166 00:09:34,080 --> 00:09:37,280 Speaker 2: But that was basically the eastern extent of their range. 167 00:09:37,400 --> 00:09:42,040 Speaker 2: And since nineteen hundred they've expanded to occupy all that 168 00:09:42,160 --> 00:09:45,319 Speaker 2: area east of there. They've also gotten up into you know, 169 00:09:45,360 --> 00:09:48,920 Speaker 2: Nova Scotia. They're further into Alaska than they ever were 170 00:09:49,080 --> 00:09:52,800 Speaker 2: historically and further down into Central America. And so when 171 00:09:52,840 --> 00:09:57,960 Speaker 2: we think about coyotes having an impact on critters in places, 172 00:09:58,760 --> 00:10:01,440 Speaker 2: you know, a lot of the places where we hear 173 00:10:01,520 --> 00:10:05,000 Speaker 2: about coyotes having the most impact on species like deer 174 00:10:05,559 --> 00:10:08,360 Speaker 2: end up being places where there's only been coyotes for 175 00:10:09,520 --> 00:10:12,480 Speaker 2: maybe one hundred years, but in some instances it's thirty 176 00:10:12,559 --> 00:10:16,160 Speaker 2: years or sixty years, and so they're kind of a 177 00:10:16,200 --> 00:10:19,600 Speaker 2: new predator in some of those places now they've replaced 178 00:10:19,600 --> 00:10:22,480 Speaker 2: a predator. You know, the southeast would have been home 179 00:10:22,520 --> 00:10:26,000 Speaker 2: to the red red wolves, right, and so coyotes have 180 00:10:26,080 --> 00:10:29,400 Speaker 2: kind of expanded to occupy that niche that red wolves 181 00:10:29,440 --> 00:10:34,719 Speaker 2: previously occupied. When we're thinking about what led to them expanding, 182 00:10:35,240 --> 00:10:38,680 Speaker 2: the changes that people have made to the landscape, coyotes 183 00:10:38,720 --> 00:10:41,199 Speaker 2: aren't really good at making a living in just totally 184 00:10:41,240 --> 00:10:44,840 Speaker 2: forced dominated areas. So when we cleared the forest and 185 00:10:44,880 --> 00:10:47,720 Speaker 2: started farming, and those sorts of things that made it 186 00:10:47,760 --> 00:10:50,880 Speaker 2: easier for coyotes to make a living there, killing the 187 00:10:51,000 --> 00:10:55,079 Speaker 2: large predators that were there or extirpating them, removing them 188 00:10:55,080 --> 00:10:59,560 Speaker 2: from the landscape, gray wolves, red wolves, mountain lions, you know, 189 00:10:59,640 --> 00:11:04,720 Speaker 2: all the different species that sometimes kill coyotes and prey 190 00:11:04,760 --> 00:11:06,600 Speaker 2: on them or kill them because they don't like them. 191 00:11:06,880 --> 00:11:10,080 Speaker 2: You know, those all those things together are kind of 192 00:11:10,080 --> 00:11:15,040 Speaker 2: what's allowed them to expand. You mentioned the interactions in cities, 193 00:11:15,440 --> 00:11:17,480 Speaker 2: and that's kind of an interesting one because there's a 194 00:11:17,480 --> 00:11:22,880 Speaker 2: lot of evidence from from the southwestern US that coyotes 195 00:11:23,360 --> 00:11:27,760 Speaker 2: in the very large Native American cities that existed prior 196 00:11:27,760 --> 00:11:30,920 Speaker 2: to European settlement in North America. It looks like coyotes 197 00:11:30,960 --> 00:11:34,480 Speaker 2: were probably incorporated into those cities just like they are 198 00:11:34,520 --> 00:11:37,560 Speaker 2: into our modern cities really, and so it's just taking 199 00:11:37,640 --> 00:11:41,240 Speaker 2: them time to figure out how to deal with modern 200 00:11:41,320 --> 00:11:43,800 Speaker 2: humans a little bit better. And then the other thing 201 00:11:43,840 --> 00:11:46,679 Speaker 2: along with that too is you know, coyotes are small 202 00:11:46,800 --> 00:11:48,559 Speaker 2: enough that a lot of people are willing to tolerate 203 00:11:48,600 --> 00:11:51,480 Speaker 2: them in town and close to where they live. We 204 00:11:51,520 --> 00:11:53,400 Speaker 2: don't see them as a threat the same way that 205 00:11:53,440 --> 00:11:57,880 Speaker 2: we do gray wolves, you know, and so they're tolerated 206 00:11:58,120 --> 00:12:01,680 Speaker 2: more than the larger predators are, and that's why we 207 00:12:01,720 --> 00:12:04,600 Speaker 2: see more coyotes in cities than we would the larger predators. 208 00:12:05,440 --> 00:12:08,599 Speaker 1: Has that been you think the increase in sightings in 209 00:12:09,960 --> 00:12:14,680 Speaker 1: urban areas is because of technology now like cameras, security 210 00:12:14,679 --> 00:12:18,199 Speaker 1: cameras and stuff, or is the population growing or expanded 211 00:12:18,280 --> 00:12:19,480 Speaker 1: into urban areas more. 212 00:12:20,160 --> 00:12:23,360 Speaker 2: It's growing and expanding into urban areas more, for sure, 213 00:12:23,559 --> 00:12:26,719 Speaker 2: you know, going back into the seventies is when there 214 00:12:26,760 --> 00:12:30,000 Speaker 2: started to be reports of attacks on people in cities 215 00:12:30,040 --> 00:12:34,280 Speaker 2: in California. So it's just as coyotes have become more 216 00:12:34,320 --> 00:12:37,560 Speaker 2: and more abundant further east, they've started occupying some of 217 00:12:37,600 --> 00:12:39,960 Speaker 2: these cities that are further and further east and having 218 00:12:39,960 --> 00:12:41,280 Speaker 2: more interactions with people. 219 00:12:41,440 --> 00:12:45,800 Speaker 1: Okay, any fatal Has there been any fatal reports or 220 00:12:45,840 --> 00:12:48,840 Speaker 1: fatalities from a couple The. 221 00:12:50,559 --> 00:12:53,440 Speaker 2: Most recent one, I'm going to get the date wrong. 222 00:12:53,520 --> 00:12:55,960 Speaker 2: It was in the early two thousands, and it was 223 00:12:55,960 --> 00:12:59,280 Speaker 2: in Alaska as a woman who I believe was a 224 00:12:59,360 --> 00:13:01,840 Speaker 2: reporter and she was out for a jog and she 225 00:13:01,920 --> 00:13:03,920 Speaker 2: got attacked by a pack of coyotes. 226 00:13:03,559 --> 00:13:04,560 Speaker 1: And killed wow. 227 00:13:05,280 --> 00:13:11,520 Speaker 2: And some after that happened, they did some research on 228 00:13:11,600 --> 00:13:14,280 Speaker 2: that group of coyotes and figured out that there was 229 00:13:14,320 --> 00:13:18,920 Speaker 2: a really hard winner, or a series of hard winners, 230 00:13:19,040 --> 00:13:22,080 Speaker 2: and those coyotes had figured out how to prey on moose, 231 00:13:22,880 --> 00:13:26,319 Speaker 2: which is way way out of the normal prey range 232 00:13:26,320 --> 00:13:28,760 Speaker 2: for coyotes, right, But they figured out how to hunt 233 00:13:28,800 --> 00:13:31,240 Speaker 2: more like wolves. They figured out how to chase those 234 00:13:31,280 --> 00:13:36,040 Speaker 2: moose down in the snow, and so they became predators 235 00:13:36,040 --> 00:13:39,000 Speaker 2: of larger critters and it was that group of coyotes 236 00:13:39,080 --> 00:13:41,280 Speaker 2: that they believe attacked this woman. 237 00:13:41,360 --> 00:13:47,160 Speaker 1: So that behavior, yeah, yeah, we're still not helping with 238 00:13:47,160 --> 00:13:49,520 Speaker 1: a villain, no label that they've got. 239 00:13:49,480 --> 00:13:53,000 Speaker 2: No no, And that's okay. I mean, you know, all 240 00:13:53,080 --> 00:13:56,200 Speaker 2: critters have positive and negative values, sure right, I mean 241 00:13:57,080 --> 00:14:00,800 Speaker 2: recreational value, monetary value, and all those sorts of things. 242 00:14:00,840 --> 00:14:03,599 Speaker 2: But at the same time, we can have negative interactions 243 00:14:03,600 --> 00:14:05,840 Speaker 2: with critters that we love. You know what, white tailed 244 00:14:05,840 --> 00:14:08,520 Speaker 2: deer one of the most popular critters in the US, right, 245 00:14:08,600 --> 00:14:12,680 Speaker 2: but they're responsible for more dollars in property damage and 246 00:14:12,720 --> 00:14:15,000 Speaker 2: more of human fatalities than most of the critters that 247 00:14:15,040 --> 00:14:15,280 Speaker 2: we have. 248 00:14:15,520 --> 00:14:19,400 Speaker 1: Yeah, and to thin those down, I mean the object 249 00:14:19,400 --> 00:14:22,120 Speaker 1: when you say, you know, kill a coyote, save a deer, 250 00:14:23,240 --> 00:14:25,000 Speaker 1: I think, really how that goes? 251 00:14:25,160 --> 00:14:25,480 Speaker 2: Is it? 252 00:14:25,560 --> 00:14:25,920 Speaker 1: Well? 253 00:14:26,120 --> 00:14:28,840 Speaker 2: You know, it really depends on where you are how 254 00:14:28,920 --> 00:14:32,960 Speaker 2: much of an impact coyotes have on deer. So one 255 00:14:33,040 --> 00:14:36,440 Speaker 2: of the things that goes along with that range expansion 256 00:14:36,720 --> 00:14:40,920 Speaker 2: is as coyotes expanded into the east, there was some 257 00:14:41,040 --> 00:14:46,520 Speaker 2: hybridization that occurred with wolves and with domestic dogs and sot. 258 00:14:46,720 --> 00:14:50,080 Speaker 2: The next graph I brought to show you shows that 259 00:14:51,240 --> 00:14:54,840 Speaker 2: in dark gray, all the coyotes in that area in 260 00:14:54,960 --> 00:14:59,400 Speaker 2: dark gray are basically one hundred percent coyote, and all. 261 00:14:59,280 --> 00:15:03,720 Speaker 1: That pretty close to the historic range of them is. 262 00:15:03,800 --> 00:15:06,240 Speaker 2: That it's very close to the extoric range, but it 263 00:15:06,320 --> 00:15:09,160 Speaker 2: includes the areas to the north and west that coyotes 264 00:15:09,200 --> 00:15:14,240 Speaker 2: have expanded to beyond that historic range. In the eastern US, 265 00:15:14,760 --> 00:15:17,880 Speaker 2: then most of those coyotes have some dog or some 266 00:15:17,960 --> 00:15:22,680 Speaker 2: wolf in them, and they've documented that, you know, with 267 00:15:22,880 --> 00:15:27,040 Speaker 2: the longer legs, a more a little bit more of 268 00:15:27,080 --> 00:15:30,440 Speaker 2: a complex social structure where they might hunt in packs 269 00:15:30,440 --> 00:15:33,960 Speaker 2: more than Western coyotes do, and those sorts of things 270 00:15:33,960 --> 00:15:37,400 Speaker 2: that they're able to be better predators of larger animals. 271 00:15:37,600 --> 00:15:42,120 Speaker 1: So there's a site there's a physical difference in Western 272 00:15:42,120 --> 00:15:42,840 Speaker 1: and Eastern. 273 00:15:43,080 --> 00:15:45,280 Speaker 2: They tend to be a little larger, they tend to 274 00:15:45,520 --> 00:15:51,240 Speaker 2: have lankier legs, a skull structure that's not wolf like 275 00:15:51,360 --> 00:15:56,840 Speaker 2: but more more robust than a Western coyote, and that 276 00:15:57,240 --> 00:15:59,200 Speaker 2: translates into some of their behaviors too. 277 00:15:59,400 --> 00:16:02,520 Speaker 1: Yeah, well, I know that there's and forgive me if 278 00:16:02,560 --> 00:16:04,320 Speaker 1: I pronounced it's wrong, but I think it is not 279 00:16:04,760 --> 00:16:09,880 Speaker 1: called Bergman's rule or Bergsman's rule. Where animal or mammals 280 00:16:09,920 --> 00:16:12,880 Speaker 1: get larger, the further north, you go, yeah, in latitude, 281 00:16:13,160 --> 00:16:15,960 Speaker 1: So I didn't. I had no idea there would be 282 00:16:16,000 --> 00:16:19,520 Speaker 1: a difference going east and west instead of the north 283 00:16:19,560 --> 00:16:20,000 Speaker 1: and south. 284 00:16:20,120 --> 00:16:24,440 Speaker 2: There is, but it's more associated with their genetics than 285 00:16:24,480 --> 00:16:27,960 Speaker 2: it is with their physical environment. So that tendency for 286 00:16:28,080 --> 00:16:32,520 Speaker 2: critters to be smaller towards the equator and larger towards 287 00:16:32,520 --> 00:16:36,080 Speaker 2: the poles has to do with heat dissipation and heat retention, 288 00:16:37,320 --> 00:16:41,160 Speaker 2: whereas this other difference is because of that introgression of 289 00:16:41,200 --> 00:16:42,720 Speaker 2: wolf and dog DNA. 290 00:16:43,080 --> 00:16:45,880 Speaker 1: Well, tell me what's an old coyote? What's a lifespan 291 00:16:46,040 --> 00:16:46,560 Speaker 1: with Kyovida? 292 00:16:46,720 --> 00:16:50,520 Speaker 2: Well, a really old coyote, you know, in the wild, 293 00:16:50,640 --> 00:16:53,280 Speaker 2: is going to be like ten years the maximum known 294 00:16:53,320 --> 00:16:56,040 Speaker 2: age based on age teeth that I know of in 295 00:16:56,080 --> 00:16:58,240 Speaker 2: the literature. There may be something newer than this, but 296 00:16:58,440 --> 00:17:02,800 Speaker 2: fourteen years. Oh yeah, And in captivity they can live 297 00:17:02,840 --> 00:17:06,840 Speaker 2: to be over twenty. So basically, when we think about 298 00:17:06,840 --> 00:17:08,840 Speaker 2: their life span and that sort of thing, it's kind 299 00:17:08,840 --> 00:17:11,359 Speaker 2: of like a dog's yeah yeah, yeah. 300 00:17:11,080 --> 00:17:15,320 Speaker 1: Well, and you think about it too, at fourteen regardless 301 00:17:15,320 --> 00:17:20,080 Speaker 1: of how you correlate that to human years, because I 302 00:17:20,119 --> 00:17:22,040 Speaker 1: think seven has been kicked out. The one to hear 303 00:17:22,280 --> 00:17:25,880 Speaker 1: as of lately from K and nine are dogs to humans. 304 00:17:25,920 --> 00:17:28,560 Speaker 1: But I mean that dude's getting up and he's got 305 00:17:28,600 --> 00:17:30,399 Speaker 1: to make a living every day and find something to 306 00:17:30,440 --> 00:17:33,760 Speaker 1: eat right every day, and want in captivity somebody's bringing 307 00:17:33,800 --> 00:17:36,160 Speaker 1: him yeah, you know, a chicken leg or whatever, which 308 00:17:36,200 --> 00:17:37,879 Speaker 1: is kind of like the way I like to go 309 00:17:37,920 --> 00:17:40,280 Speaker 1: through life. It's I'm more successful. I wouldn't be as 310 00:17:40,280 --> 00:17:42,639 Speaker 1: big as I am if I had to rustle it 311 00:17:42,720 --> 00:17:47,719 Speaker 1: up every day. But there's so many things working against 312 00:17:47,720 --> 00:17:50,439 Speaker 1: these these these things out there just like it is 313 00:17:50,560 --> 00:17:53,760 Speaker 1: all wild animals, sure, but they have become quite a 314 00:17:53,800 --> 00:17:56,600 Speaker 1: depth that had adapted the difference surround us, which is 315 00:17:56,600 --> 00:17:59,240 Speaker 1: why you see them in urban areas and s and 316 00:17:59,359 --> 00:18:03,639 Speaker 1: making a living. But you've got a list here, stuff 317 00:18:03,640 --> 00:18:08,760 Speaker 1: to go through. You've got mortality listed up here. Yeah, 318 00:18:09,160 --> 00:18:12,160 Speaker 1: tell me what? Why? Why? This is why you've got 319 00:18:12,160 --> 00:18:14,080 Speaker 1: this listen to here? What's the points there that you 320 00:18:14,160 --> 00:18:14,880 Speaker 1: wanted to talk about? 321 00:18:14,920 --> 00:18:17,120 Speaker 2: Well, you know, I mean just thinking about how it's 322 00:18:17,160 --> 00:18:20,280 Speaker 2: different in different places. So one of the things that 323 00:18:21,119 --> 00:18:23,480 Speaker 2: we talked about with their expansion is the fact that 324 00:18:23,880 --> 00:18:26,480 Speaker 2: we released them from predation by bigger predators in a 325 00:18:26,520 --> 00:18:28,760 Speaker 2: lot of places, and that allowed them to expand. So 326 00:18:28,920 --> 00:18:31,760 Speaker 2: historically that would have been important in a lot of 327 00:18:31,800 --> 00:18:34,520 Speaker 2: the range. Now human cause mortality has taken the place 328 00:18:34,560 --> 00:18:39,960 Speaker 2: of that. So in many populations, human cause mortality is 329 00:18:40,000 --> 00:18:46,720 Speaker 2: the majority of mortality, and sometimes that's mostly harvest, sometimes 330 00:18:46,880 --> 00:18:51,040 Speaker 2: it's mostly vehicle collisions, and in a lot of places 331 00:18:51,080 --> 00:18:53,959 Speaker 2: it's probably going to be somewhere, you know, some combination 332 00:18:54,119 --> 00:18:56,480 Speaker 2: of that. But there's a recent study in Wisconsin that 333 00:18:56,600 --> 00:19:00,240 Speaker 2: found that that you know, human cause mortality would the 334 00:19:00,280 --> 00:19:04,040 Speaker 2: majority of their mortality, and harvest was like ninety some 335 00:19:04,280 --> 00:19:07,600 Speaker 2: percent of that, which was interesting to me because I 336 00:19:07,640 --> 00:19:10,720 Speaker 2: wouldn't think it would be that high. But then we've 337 00:19:10,720 --> 00:19:14,439 Speaker 2: got natural mortality, so they're they're susceptible to basically all 338 00:19:14,480 --> 00:19:18,400 Speaker 2: the diseases and parasites that we treat our dogs for. 339 00:19:19,320 --> 00:19:23,320 Speaker 2: So canine distemprovirus is a really important one, parvovirus would 340 00:19:23,320 --> 00:19:26,720 Speaker 2: be an important one, heartworm, hookworm, tapeworms, all those sorts 341 00:19:26,720 --> 00:19:29,720 Speaker 2: of yeah, all of that, yeah, yeah, and then mange 342 00:19:29,920 --> 00:19:32,040 Speaker 2: of course, oh yeah, I've seen that. You know where 343 00:19:32,080 --> 00:19:34,840 Speaker 2: that came from. No, if you if you do a 344 00:19:34,880 --> 00:19:37,760 Speaker 2: Google search or whatever internet deal you like to use, 345 00:19:38,359 --> 00:19:41,119 Speaker 2: you can find a court document from nineteen oh six, 346 00:19:42,320 --> 00:19:46,040 Speaker 2: where it might have been nineteen oh five. But anyway, 347 00:19:46,040 --> 00:19:48,320 Speaker 2: the state of Montana hired some biologists to go out 348 00:19:48,359 --> 00:19:50,280 Speaker 2: and catch coyotes and wolves and bring them into a 349 00:19:50,359 --> 00:19:53,200 Speaker 2: lab and infect them with scabies and turn them back 350 00:19:53,280 --> 00:19:56,679 Speaker 2: loose because they figured that would be cheaper, a cheaper 351 00:19:56,720 --> 00:20:00,800 Speaker 2: way to control coyotes and wolves then paying bounties would be. 352 00:20:01,080 --> 00:20:04,800 Speaker 2: And so that's how we got mange in our in 353 00:20:04,800 --> 00:20:06,240 Speaker 2: our wild cans. 354 00:20:06,720 --> 00:20:12,920 Speaker 1: Really. Yeah, well thanks a lot, Montown. I'm gonna blame 355 00:20:12,960 --> 00:20:16,600 Speaker 1: Garrett Long for that. I hope he's watching that is Uh, 356 00:20:16,680 --> 00:20:19,320 Speaker 1: that is so interesting. So and that brings up like 357 00:20:19,400 --> 00:20:24,080 Speaker 1: the bounty thing. Yeah, they've been vilified for so long. 358 00:20:24,520 --> 00:20:27,159 Speaker 1: You know, if you're paying a bounty on something just 359 00:20:27,200 --> 00:20:31,560 Speaker 1: to shoot it, sure, you know, was that was it 360 00:20:31,680 --> 00:20:35,200 Speaker 1: deserved when that when that came about? Or were they 361 00:20:35,600 --> 00:20:37,920 Speaker 1: or were they getting blamed for a lot of stuff 362 00:20:37,960 --> 00:20:39,600 Speaker 1: that they weren't actually doing. 363 00:20:39,840 --> 00:20:42,800 Speaker 2: Sure, it's I think it's probably all the above, right, 364 00:20:42,960 --> 00:20:48,600 Speaker 2: I Mean, the vast majority of coyotes don't fool with livestock. Uh, 365 00:20:48,640 --> 00:20:51,880 Speaker 2: And that's surprising to a lot of people. That's that's 366 00:20:51,960 --> 00:20:54,080 Speaker 2: basically an individual behavior. 367 00:20:55,640 --> 00:20:59,320 Speaker 1: It tends sorry I'm gonna get I'm gonna get in 368 00:20:59,320 --> 00:21:01,120 Speaker 1: a lot of trouble for doing that, right there. 369 00:21:02,920 --> 00:21:06,720 Speaker 2: Coyot coyotes praying on livestock. It tends to be dominated 370 00:21:06,760 --> 00:21:12,440 Speaker 2: by pair or family groups that have pups, uh, and 371 00:21:12,560 --> 00:21:16,679 Speaker 2: that also have territories that overlap mostly sheep and goats, 372 00:21:17,520 --> 00:21:22,720 Speaker 2: but but some some cattle as well. And when they 373 00:21:22,760 --> 00:21:26,879 Speaker 2: have those pups, and especially that period around whelping time 374 00:21:27,640 --> 00:21:31,439 Speaker 2: up until those pups can start getting out and hunting 375 00:21:31,440 --> 00:21:34,399 Speaker 2: a little bit, uh, that's when we see the most 376 00:21:34,520 --> 00:21:38,240 Speaker 2: large prey items being important to those coyotes. Most of 377 00:21:38,240 --> 00:21:43,400 Speaker 2: the time, coyotes are eating rabbits and voles and insects 378 00:21:43,480 --> 00:21:44,160 Speaker 2: and mice. 379 00:21:44,560 --> 00:21:47,600 Speaker 1: And now you tell me how you know that, because 380 00:21:47,600 --> 00:21:50,280 Speaker 1: somebody right now is looking at that radio that they're 381 00:21:50,320 --> 00:21:54,200 Speaker 1: listening to and saying doctor Drew has lost his month. Yeah, yeah, 382 00:21:54,400 --> 00:21:55,440 Speaker 1: tell me how you know that. 383 00:21:55,520 --> 00:21:59,000 Speaker 2: So, so here's here's the studies from Kansas we can 384 00:21:59,040 --> 00:22:04,520 Speaker 2: look at. Really, so I just I summarized one that 385 00:22:04,840 --> 00:22:08,720 Speaker 2: is from nineteen sixty eight, so it's really old. But 386 00:22:08,760 --> 00:22:11,320 Speaker 2: the cool thing about this study is it's based on 387 00:22:11,440 --> 00:22:15,000 Speaker 2: stomach contents, so it's back during the bounty period and 388 00:22:15,760 --> 00:22:18,719 Speaker 2: Professor by the last name of Guyer here at k 389 00:22:18,840 --> 00:22:22,480 Speaker 2: State had folks that were bringing in coyotes, and he 390 00:22:22,520 --> 00:22:25,400 Speaker 2: was looking at the reproductive tracks, looking at their stomach contents, 391 00:22:25,440 --> 00:22:26,520 Speaker 2: aging them, and doing all. 392 00:22:26,400 --> 00:22:28,320 Speaker 1: Kinds of at the universe. 393 00:22:28,320 --> 00:22:30,879 Speaker 2: Oh yeah, yeah, And he was a physiologist, so he 394 00:22:30,920 --> 00:22:33,520 Speaker 2: got into a lot of detail. But it's like twenty 395 00:22:33,520 --> 00:22:36,840 Speaker 2: three hundred coyotes, okay, so it's not a small sample size. 396 00:22:36,880 --> 00:22:39,639 Speaker 2: It's a really good sample size. Forty percent of the 397 00:22:39,680 --> 00:22:44,800 Speaker 2: stomachs had rabbits thirty one or sorry, this is actually 398 00:22:44,920 --> 00:22:49,480 Speaker 2: percentage of stomach contents. So forty percent rabbit, thirty one 399 00:22:49,520 --> 00:22:53,880 Speaker 2: percent roding, and twenty eight percent carrying. So eaton did 400 00:22:54,000 --> 00:22:58,160 Speaker 2: that stuff would regardless of But here's the deal. When 401 00:22:58,200 --> 00:23:01,160 Speaker 2: a coyotes got a belly full of meat, that's big meat. 402 00:23:02,240 --> 00:23:04,800 Speaker 2: We can't tell if it's from a calf that they 403 00:23:04,880 --> 00:23:07,119 Speaker 2: killed or a calf that they scavened. They found it, 404 00:23:07,600 --> 00:23:11,679 Speaker 2: and so that's all lumped into that bucket right there. 405 00:23:12,520 --> 00:23:16,040 Speaker 2: The thing about this one, though, is that we didn't 406 00:23:16,119 --> 00:23:19,640 Speaker 2: have all that many deer in Kansas when that study 407 00:23:19,720 --> 00:23:22,959 Speaker 2: was taking place. Our first modern deer season was nineteen 408 00:23:23,000 --> 00:23:24,840 Speaker 2: sixty five in the state of Kansas. 409 00:23:24,880 --> 00:23:26,560 Speaker 1: So that's just three years into that. 410 00:23:26,640 --> 00:23:29,600 Speaker 2: Yep, yep, and the study was actually done before then. 411 00:23:29,680 --> 00:23:31,840 Speaker 2: So if we look at some of the more modern studies, 412 00:23:31,880 --> 00:23:35,159 Speaker 2: these are based on scat contents. One of them a 413 00:23:35,200 --> 00:23:38,639 Speaker 2: little bit further west in Kansas, seventy six percent of 414 00:23:38,680 --> 00:23:43,880 Speaker 2: the scats had cotton rats, twenty nine percent voles, nineteen 415 00:23:43,920 --> 00:23:48,560 Speaker 2: percent cotton tails, four percent had deer in them, two 416 00:23:48,600 --> 00:23:52,639 Speaker 2: percent had cattle and this is hair, right, nineteen percent 417 00:23:52,680 --> 00:23:57,280 Speaker 2: had insects, nine percent had fruits, and so on. Another 418 00:23:57,320 --> 00:23:59,760 Speaker 2: one that was done by those same researchers about the 419 00:23:59,800 --> 00:24:03,560 Speaker 2: same time here really close to Manhattan, on a site 420 00:24:03,600 --> 00:24:07,640 Speaker 2: that had a lot higher deer densities. They found similar 421 00:24:07,680 --> 00:24:10,520 Speaker 2: results for the small mammals and critters like that, but 422 00:24:10,600 --> 00:24:15,680 Speaker 2: twenty percent of them had deer in them. So their 423 00:24:15,800 --> 00:24:19,919 Speaker 2: main prey is small mammals and insects and fruit and 424 00:24:19,920 --> 00:24:23,680 Speaker 2: stuff like that. But when deer are really abundant, they're 425 00:24:23,680 --> 00:24:25,800 Speaker 2: going to eat some deer. That doesn't mean that they 426 00:24:25,880 --> 00:24:27,960 Speaker 2: killed all the deer that they ate right. 427 00:24:27,880 --> 00:24:32,199 Speaker 1: Right exactly, Because I mean a mice mice or a 428 00:24:32,280 --> 00:24:34,240 Speaker 1: vole or a gopher or something's going to be a 429 00:24:34,280 --> 00:24:37,119 Speaker 1: whole lot easier to catch than a deer. Yeah, and 430 00:24:38,520 --> 00:24:43,800 Speaker 1: would you have a guess as to or is there 431 00:24:43,840 --> 00:24:46,959 Speaker 1: any data that supports how much of that is carrying 432 00:24:47,040 --> 00:24:51,639 Speaker 1: and how much is is our deer that they killed. 433 00:24:52,800 --> 00:24:55,440 Speaker 2: So the data that you could look at to think 434 00:24:55,480 --> 00:25:00,119 Speaker 2: about that is data from fond survival studies. So so 435 00:25:00,320 --> 00:25:03,200 Speaker 2: we did a study way out in western Kansas, put 436 00:25:03,240 --> 00:25:05,840 Speaker 2: GPS callers on a whole bunch of deer, both mule 437 00:25:05,880 --> 00:25:12,560 Speaker 2: deer and whitetails, and we on average, you know, fawn 438 00:25:12,640 --> 00:25:18,160 Speaker 2: survival in that study across three years was around thirty percent, okay, 439 00:25:19,600 --> 00:25:23,600 Speaker 2: of the of the fawns that were killed, thirty to 440 00:25:23,600 --> 00:25:26,920 Speaker 2: forty percent of those fawns were attributed to a loss 441 00:25:26,960 --> 00:25:30,680 Speaker 2: to a predator, and a majority of those predator losses 442 00:25:30,720 --> 00:25:36,919 Speaker 2: were coyotes. A fawn that dies out in the landscape 443 00:25:36,920 --> 00:25:39,840 Speaker 2: but wasn't killed by cote, a lot of those fawns 444 00:25:39,880 --> 00:25:41,920 Speaker 2: are still going to get scavenged by coyotes. 445 00:25:42,280 --> 00:25:42,639 Speaker 1: Okay. 446 00:25:42,720 --> 00:25:48,280 Speaker 2: So if you applied that same percentage to the scats 447 00:25:48,280 --> 00:25:51,520 Speaker 2: that had deer hair in them, and then about forty percent, 448 00:25:52,280 --> 00:25:54,600 Speaker 2: thirty to forty percent of those scats that had deer 449 00:25:54,640 --> 00:25:57,679 Speaker 2: hair in them were probably fawns that they killed. 450 00:25:59,440 --> 00:26:00,920 Speaker 1: Now, when you. 451 00:26:00,840 --> 00:26:05,560 Speaker 2: Look at other areas in Kansas, we haven't seen a 452 00:26:05,600 --> 00:26:09,120 Speaker 2: deer decline as coyotes have become more abundant through time 453 00:26:10,119 --> 00:26:13,280 Speaker 2: in the eastern US, the areas that they've expanded into. 454 00:26:13,960 --> 00:26:16,360 Speaker 2: Part of the reason that they're doing so much coyote 455 00:26:16,359 --> 00:26:20,960 Speaker 2: research related to coyote impact on deer is because as 456 00:26:21,040 --> 00:26:24,520 Speaker 2: coyotes increase, they did see a decline in deer numbers. 457 00:26:25,000 --> 00:26:27,800 Speaker 2: South Carolina, Georgia, some of those states are very good 458 00:26:27,800 --> 00:26:32,240 Speaker 2: examples of that. And if you look at this map, 459 00:26:32,840 --> 00:26:36,760 Speaker 2: which is this is these studies here are included in 460 00:26:36,800 --> 00:26:39,439 Speaker 2: these results, but they looked at a whole bunch of 461 00:26:39,480 --> 00:26:45,240 Speaker 2: different studies that looked at scat coyote scatt throughout the 462 00:26:45,359 --> 00:26:49,920 Speaker 2: US Okay, and the bars on these going from left 463 00:26:49,960 --> 00:26:57,920 Speaker 2: to right are small mammals, fruit, rabbits, and then ungulates, 464 00:26:57,960 --> 00:27:02,160 Speaker 2: which would be deer, caribou, anything we're going to think 465 00:27:02,160 --> 00:27:05,280 Speaker 2: about that's in the deer family, oh and also pronghorn. 466 00:27:06,400 --> 00:27:09,560 Speaker 2: So what you can see on this really clearly is 467 00:27:09,600 --> 00:27:14,200 Speaker 2: that in the Great Plains where coyotes are native, ungulates 468 00:27:14,240 --> 00:27:20,200 Speaker 2: are a small percentage of their diet. In the Southwest, 469 00:27:20,240 --> 00:27:24,399 Speaker 2: in those desert communities, ungulates are almost non existent in 470 00:27:24,440 --> 00:27:27,480 Speaker 2: their diet. Their diet is dominated by rabbits and small 471 00:27:27,520 --> 00:27:30,879 Speaker 2: mammals and a little bit of fruit. But as you 472 00:27:30,960 --> 00:27:36,159 Speaker 2: go further east, so in that dark green area in 473 00:27:36,200 --> 00:27:38,720 Speaker 2: the east, in the light green area in the northeast, 474 00:27:39,320 --> 00:27:42,320 Speaker 2: ungulates are a much larger percentage of the diet. And 475 00:27:42,400 --> 00:27:47,640 Speaker 2: ungulates actually dominate the diet in those studies in the southeast, okay. 476 00:27:47,760 --> 00:27:50,439 Speaker 1: Just because the sheer number of them being there. 477 00:27:50,840 --> 00:27:56,200 Speaker 2: Well, not just that, but other things too. Right, those 478 00:27:56,280 --> 00:27:59,879 Speaker 2: coyotes are the coyotes that have some wolf and doggedy 479 00:28:00,080 --> 00:28:02,240 Speaker 2: in a and them, so they're they're more apt to 480 00:28:02,320 --> 00:28:05,880 Speaker 2: be able to prey on those larger prey at thems 481 00:28:05,880 --> 00:28:08,679 Speaker 2: so and maybe it's something that they've had to figure 482 00:28:08,720 --> 00:28:10,320 Speaker 2: out as they moved in too. 483 00:28:11,560 --> 00:28:12,640 Speaker 1: But they. 484 00:28:14,000 --> 00:28:17,119 Speaker 2: I think that the broad answer to your question is 485 00:28:17,280 --> 00:28:21,200 Speaker 2: it really depends on where you are, So they there's 486 00:28:21,280 --> 00:28:26,760 Speaker 2: never a wherever you are with this species, it's always 487 00:28:26,760 --> 00:28:30,399 Speaker 2: this way in biology, and that's that's one of the 488 00:28:30,440 --> 00:28:34,879 Speaker 2: really big challenges that's associated with, you know, some of 489 00:28:34,920 --> 00:28:37,919 Speaker 2: these things like one of you we talked before about 490 00:28:37,920 --> 00:28:40,840 Speaker 2: that that meme from Facebook, let. 491 00:28:40,760 --> 00:28:44,680 Speaker 1: Me stuff you're out there, Yeah, because we I'm I'm 492 00:28:44,720 --> 00:28:46,560 Speaker 1: as bad as Clayton Newton. I'm gonna do a little 493 00:28:46,560 --> 00:28:49,760 Speaker 1: foreshadow because we're going to stop right here, and this 494 00:28:49,880 --> 00:28:52,760 Speaker 1: is going to be part one of my talk with 495 00:28:52,840 --> 00:28:55,360 Speaker 1: doctor Drew right here, and we're going to get into 496 00:28:55,400 --> 00:28:58,320 Speaker 1: the meat and the potatoes of what everybody is wanting 497 00:28:58,360 --> 00:29:03,120 Speaker 1: to know about, and that's the deer and cold relationship, 498 00:29:03,160 --> 00:29:05,800 Speaker 1: we'll call it, and whether or not we're doing the 499 00:29:05,880 --> 00:29:09,840 Speaker 1: Lord's work when we're killing colds to save our dear leases. 500 00:29:10,840 --> 00:29:14,720 Speaker 1: We'll be back next week for the next week. Thank you, doctor, 501 00:29:14,920 --> 00:29:17,160 Speaker 1: Thank you sir. Y' all be careful.