1 00:00:09,000 --> 00:00:12,799 Speaker 1: This is the me Eater podcast coming at you shirtless, 2 00:00:12,880 --> 00:00:17,680 Speaker 1: severely bug bitten, and in my case, underwear listening podcast. 3 00:00:18,320 --> 00:00:28,600 Speaker 1: You can't predict anything, all right, Steve. I want you 4 00:00:28,640 --> 00:00:30,720 Speaker 1: to do um two things, but you gotta do it 5 00:00:30,720 --> 00:00:35,800 Speaker 1: in the in the order, in this order. Explain like 6 00:00:36,200 --> 00:00:39,159 Speaker 1: what is the neutria and where are they from and 7 00:00:39,200 --> 00:00:42,160 Speaker 1: all that, and then explain who you are and where 8 00:00:42,159 --> 00:00:45,080 Speaker 1: you're from and all that. All right? Yeah? Sure. A 9 00:00:45,159 --> 00:00:49,920 Speaker 1: neutria is a big semi aquatic rodent that's native to 10 00:00:49,920 --> 00:00:53,239 Speaker 1: South America. At one time, it was highly prized for 11 00:00:53,280 --> 00:00:57,600 Speaker 1: its fur, and it was introduced throughout the world and uh, 12 00:00:57,800 --> 00:01:02,200 Speaker 1: fur farms and things like that to create a economic 13 00:01:02,280 --> 00:01:07,360 Speaker 1: resource for rural folks. And similar to mink ranches and 14 00:01:07,400 --> 00:01:10,120 Speaker 1: that sort of thing. It's intermediate in size between a 15 00:01:10,160 --> 00:01:13,680 Speaker 1: muskrat and a beaver. Uh. For those familiar with our 16 00:01:13,800 --> 00:01:17,880 Speaker 1: North American semi aquatic how many pounds? The average is 17 00:01:17,880 --> 00:01:21,280 Speaker 1: probably between fifteen and seventeen pounds for an adult man. 18 00:01:21,520 --> 00:01:24,280 Speaker 1: You know, they're big, Yeah, and uh, you know, I 19 00:01:24,319 --> 00:01:26,600 Speaker 1: guess if I were to equate it too, yeah, compared 20 00:01:26,640 --> 00:01:31,080 Speaker 1: to something probably a raccoon, Yeah, raccoon bigger than a 21 00:01:31,120 --> 00:01:36,040 Speaker 1: woodchuck similar size to a raccoon. I think the biggest 22 00:01:36,080 --> 00:01:39,520 Speaker 1: female that we caught was about twenty one pounds. And 23 00:01:39,640 --> 00:01:43,000 Speaker 1: you know, when they're gravit and full of little ones, 24 00:01:43,560 --> 00:01:47,039 Speaker 1: they can tip the scales higher than the males do. 25 00:01:48,440 --> 00:01:53,120 Speaker 1: So what's the word you used, gravit? Pregnant? Gravit? Am 26 00:01:53,120 --> 00:02:00,280 Speaker 1: I getting all academic here? Pregnant is grab think, so 27 00:02:01,440 --> 00:02:07,640 Speaker 1: get out my thesaurus app check check. Um. So they're 28 00:02:07,680 --> 00:02:11,040 Speaker 1: really kind of an interesting animal too, though. Uh A 29 00:02:11,040 --> 00:02:13,200 Speaker 1: lot of people don't know this, but they get fascinated 30 00:02:13,200 --> 00:02:15,440 Speaker 1: when they hear that the nipples on a female nutrient 31 00:02:15,520 --> 00:02:18,280 Speaker 1: are located along their back, so the young can actually 32 00:02:18,320 --> 00:02:21,760 Speaker 1: suckle when they're swimming in the water either. Yeah, it's 33 00:02:21,760 --> 00:02:27,799 Speaker 1: pretty fascinating. Um. You know, I have like a I've 34 00:02:27,880 --> 00:02:30,240 Speaker 1: never laid eyes on a nutrient. You know, I hadn't 35 00:02:30,280 --> 00:02:33,320 Speaker 1: either before I took this job that that I took 36 00:02:33,360 --> 00:02:35,919 Speaker 1: to try to help eliminate them from the Chesapeake Bay. 37 00:02:35,960 --> 00:02:43,000 Speaker 1: But they're a pretty pretty amazing animal, very adaptable. They're herbivores, 38 00:02:43,040 --> 00:02:47,720 Speaker 1: as most rodents are. They dig up the roots and tubers, 39 00:02:47,800 --> 00:02:51,480 Speaker 1: the underground parts of plants in the wetland ecosystems here, 40 00:02:52,400 --> 00:02:57,320 Speaker 1: and in doing so, they expose it to tremendous erosion 41 00:02:57,360 --> 00:03:00,400 Speaker 1: with the tidal influx of water and and what. So 42 00:03:01,240 --> 00:03:05,240 Speaker 1: that's how muskrat feeds too, right, Yeah, I mean, yeah, 43 00:03:05,320 --> 00:03:07,760 Speaker 1: muskrats will eat you know. One of the things that 44 00:03:07,800 --> 00:03:11,200 Speaker 1: they're most known for is they'll eat cat tail roots 45 00:03:11,240 --> 00:03:13,440 Speaker 1: and parts of cattails. But all I mean, they'll eat 46 00:03:13,480 --> 00:03:17,560 Speaker 1: clams rarely, but mostly they eat aquatic vegetation and they 47 00:03:18,480 --> 00:03:21,800 Speaker 1: when they're Yeah, the excavating from banks is what causes 48 00:03:22,280 --> 00:03:26,880 Speaker 1: folks in trouble. With folks is their denning activities more 49 00:03:26,960 --> 00:03:33,480 Speaker 1: than their feeding activities. Is they dig bank dens and 50 00:03:33,520 --> 00:03:38,680 Speaker 1: make a under usually an underwater entrance, and then they'll 51 00:03:38,720 --> 00:03:41,520 Speaker 1: burrow up into a bank and then make a big 52 00:03:41,560 --> 00:03:43,800 Speaker 1: hollow in the bank. And then you know, some d 53 00:03:43,880 --> 00:03:45,200 Speaker 1: to be like mowing his lawn and all of a 54 00:03:45,200 --> 00:03:50,040 Speaker 1: sudden falls into a muskrat bank. Then that's what gets 55 00:03:50,120 --> 00:03:54,800 Speaker 1: muskrats in trouble. Get free haircuts that I used to 56 00:03:54,840 --> 00:03:57,680 Speaker 1: get free haircuts from some ladies that had a muskrat 57 00:03:57,720 --> 00:04:00,680 Speaker 1: problem in their pond. That they're little haircuttery in Plattsburgh, 58 00:04:00,680 --> 00:04:03,880 Speaker 1: New trapp mustgrats for haircuts. I've trapped muskrats. That was 59 00:04:03,920 --> 00:04:06,360 Speaker 1: the muskrat man to them, and I got my haircut 60 00:04:06,400 --> 00:04:10,480 Speaker 1: for free. So good. It's a bartery economy. So you 61 00:04:10,560 --> 00:04:12,280 Speaker 1: all right, we'll get into this whole trap of things. 62 00:04:12,360 --> 00:04:14,680 Speaker 1: So but now that now that you brought the subject, 63 00:04:14,720 --> 00:04:17,200 Speaker 1: we're going to explore the fact you were trapped before 64 00:04:17,240 --> 00:04:22,000 Speaker 1: you became a government trapper. I yes, I was. No, 65 00:04:22,120 --> 00:04:23,400 Speaker 1: I guess now is the time you can tell us 66 00:04:23,400 --> 00:04:26,440 Speaker 1: about who you are. I was wanted a little teaser 67 00:04:26,480 --> 00:04:31,200 Speaker 1: about those back nipples. Oh yes, So I'm Steve ken Drott. 68 00:04:31,440 --> 00:04:33,640 Speaker 1: I'm a wildlife biologist. I work for the U S 69 00:04:33,680 --> 00:04:38,239 Speaker 1: Department of Agricultures Wildlife Services Program, so small agency, about 70 00:04:38,240 --> 00:04:42,400 Speaker 1: two thousand people nationwide. That's it's UH within the Animal 71 00:04:42,440 --> 00:04:46,240 Speaker 1: and Plant Health Inspection Service for UH in the US 72 00:04:46,279 --> 00:04:50,360 Speaker 1: Department of Agriculture, and our primary mission is to provide 73 00:04:50,360 --> 00:04:55,159 Speaker 1: federal leadership in the there resolving human wildlife conflicts. And 74 00:04:55,880 --> 00:05:01,200 Speaker 1: our primary mission areas are to protect agriculture, human health 75 00:05:01,240 --> 00:05:05,920 Speaker 1: and safety, property, and increasingly natural resources, which is how 76 00:05:05,960 --> 00:05:10,640 Speaker 1: we're entwined with this whole nutrient eradication project to try 77 00:05:10,720 --> 00:05:16,080 Speaker 1: to save the Chesapeake Bay marsh Lands. So I've been 78 00:05:16,120 --> 00:05:19,520 Speaker 1: with that uh agency for about eighteen years now. I 79 00:05:19,560 --> 00:05:24,000 Speaker 1: started out as a wildlife biologist at an airport, UH 80 00:05:24,200 --> 00:05:27,200 Speaker 1: actually a military base in Virginia, and I also worked 81 00:05:27,200 --> 00:05:30,000 Speaker 1: at two of the Washington d C. Airports. But in 82 00:05:30,000 --> 00:05:33,279 Speaker 1: two thousand two I took this job in in Cambridge, Maryland. 83 00:05:33,400 --> 00:05:38,919 Speaker 1: Hold on question, what's what's an airport needed biologist for? Well, so, uh, 84 00:05:39,200 --> 00:05:43,240 Speaker 1: birds and planes both occupy the same airspace and uh 85 00:05:43,440 --> 00:05:47,120 Speaker 1: oftentimes too great detriment to both the birds and the planes. 86 00:05:47,240 --> 00:05:51,200 Speaker 1: So we actually have a very robust program throughout the country. 87 00:05:51,520 --> 00:05:54,440 Speaker 1: Most of our major airports, we've got wildlife biologist station 88 00:05:54,520 --> 00:06:00,320 Speaker 1: there working to provide guidance to the airport about how 89 00:06:00,400 --> 00:06:05,000 Speaker 1: to minimize uh wildlife habitat and attractants on the airfield 90 00:06:05,000 --> 00:06:09,440 Speaker 1: to keep them away from the runways and taxi ways. UH. Deer, coyotes, 91 00:06:09,480 --> 00:06:12,120 Speaker 1: other mammals are also problems. They get on the runways 92 00:06:12,120 --> 00:06:15,000 Speaker 1: and get hit by planes during takeoff and landing and 93 00:06:15,000 --> 00:06:17,920 Speaker 1: and so on and so forth. So it's uh a 94 00:06:18,200 --> 00:06:23,320 Speaker 1: pretty big human health and safety component of our program. Yeah, yeah, 95 00:06:23,360 --> 00:06:26,160 Speaker 1: because that could be like a lot of deaths all 96 00:06:26,240 --> 00:06:28,680 Speaker 1: at once. Absolutely. Yeah. Well, Yeah, the dude that had 97 00:06:28,800 --> 00:06:31,920 Speaker 1: the dude that put the plane down in the Hudson. Yeah, 98 00:06:31,960 --> 00:06:36,080 Speaker 1: several geese. Yep, miracle Alanna Hudson was the result of 99 00:06:37,000 --> 00:06:42,120 Speaker 1: a bird strike with Canada geese. There's also a tragic 100 00:06:42,480 --> 00:06:45,680 Speaker 1: air force accident back in Gosh, I don't even remember 101 00:06:45,680 --> 00:06:47,880 Speaker 1: when that was now, it's been so long since I've 102 00:06:47,920 --> 00:06:50,640 Speaker 1: worked on the airport stuff. But in elmendor Fell Air 103 00:06:50,680 --> 00:06:55,960 Speaker 1: Force Base. Uh, a plane went down from that and 104 00:06:56,080 --> 00:07:02,440 Speaker 1: killed quite a few service folks. So or Burkeley. Yeah, 105 00:07:02,880 --> 00:07:06,240 Speaker 1: so it's you know, you know, not to scare the 106 00:07:06,240 --> 00:07:08,080 Speaker 1: flying public. And now you guys will be on a 107 00:07:08,120 --> 00:07:10,640 Speaker 1: plane soon. Not a lot to worry about. It's a 108 00:07:11,360 --> 00:07:13,680 Speaker 1: it's a low risk but a high consequence kind of 109 00:07:14,360 --> 00:07:17,760 Speaker 1: kind of thing. So, uh, it's something in our the 110 00:07:17,760 --> 00:07:21,560 Speaker 1: airports take very seriously. The FAA takes very seriously. And uh, 111 00:07:21,760 --> 00:07:25,840 Speaker 1: as a result, we've got a pretty robust program nationwide. 112 00:07:26,200 --> 00:07:30,160 Speaker 1: Most biologists employed by your agency. Then, Yeah, and so 113 00:07:30,320 --> 00:07:32,240 Speaker 1: our agency is kind of unique in that it's not 114 00:07:32,480 --> 00:07:35,880 Speaker 1: it doesn't get a huge slug of federal appropriations or 115 00:07:36,000 --> 00:07:38,600 Speaker 1: tax dollars to do the work we do. We work 116 00:07:38,680 --> 00:07:46,560 Speaker 1: through cooperative agreements with other federal agencies, uh, municipalities, airports, 117 00:07:46,560 --> 00:07:50,440 Speaker 1: that sort of thing, private individuals. Uh. So it's very 118 00:07:50,480 --> 00:07:54,200 Speaker 1: much a cost sharing type program where the recipients of 119 00:07:54,360 --> 00:07:56,960 Speaker 1: the services that we provide are paying for at least 120 00:07:56,960 --> 00:07:59,920 Speaker 1: some portion, if not all, of the service that we provide. 121 00:08:01,280 --> 00:08:04,000 Speaker 1: So we're not a regulatory agency like some of the 122 00:08:04,000 --> 00:08:07,080 Speaker 1: other uh federal agencies of Fish and Wildlife Service that 123 00:08:07,520 --> 00:08:10,880 Speaker 1: enforced regulations about endangered species and all that kind of thing. 124 00:08:11,520 --> 00:08:18,440 Speaker 1: We're very much problem solving service orientation UM. And so yeah, 125 00:08:18,520 --> 00:08:21,080 Speaker 1: it's uh, it's been a really interesting agency to work for. 126 00:08:21,160 --> 00:08:23,600 Speaker 1: And this project in the Chesapeake Bay with the Nutria 127 00:08:23,800 --> 00:08:26,000 Speaker 1: is funded by the Fish and Wildlife Service, the United 128 00:08:26,040 --> 00:08:28,160 Speaker 1: States Fish and Wildlife Service, which is in the Department 129 00:08:28,160 --> 00:08:32,880 Speaker 1: of Interior. Um, there are the folks that oversee the 130 00:08:33,559 --> 00:08:37,760 Speaker 1: National Wildlife Refuge System. So so to jump into the 131 00:08:37,800 --> 00:08:39,920 Speaker 1: new Tria, like just to lay the groundwork on the 132 00:08:39,960 --> 00:08:45,559 Speaker 1: Nutria situation Chestpeake Bay, which is a pretty fascinating story. 133 00:08:46,280 --> 00:08:47,920 Speaker 1: But how did it begin? Like when did they come 134 00:08:47,920 --> 00:08:50,080 Speaker 1: in and why did they come in? So they were 135 00:08:50,559 --> 00:08:53,840 Speaker 1: a side note, can they live? They can they adapt 136 00:08:53,840 --> 00:08:57,400 Speaker 1: to northern climates or they like to have they or 137 00:08:57,480 --> 00:09:00,640 Speaker 1: do they need warmer weather and not severe winters. They 138 00:09:00,679 --> 00:09:05,280 Speaker 1: are limited in their northern distribution by by winter weather. However, 139 00:09:06,360 --> 00:09:09,200 Speaker 1: here in Maryland is about the northernmost distribution on the 140 00:09:09,240 --> 00:09:13,160 Speaker 1: east coast. But Neutria also they've been established in seventeen 141 00:09:13,200 --> 00:09:16,640 Speaker 1: or eighteen different states in the US, introduced in a 142 00:09:16,720 --> 00:09:19,680 Speaker 1: number more, but they haven't become established. A lot of 143 00:09:19,679 --> 00:09:23,040 Speaker 1: people don't know that. Uh, your home city of Seattle 144 00:09:23,120 --> 00:09:28,160 Speaker 1: and Portland, UM are home to the neutria as well. 145 00:09:29,960 --> 00:09:33,040 Speaker 1: UM all over in the coastal wetlands and a lot 146 00:09:33,080 --> 00:09:36,400 Speaker 1: of the lot of the parks in uh Portland, Oregon, 147 00:09:37,240 --> 00:09:40,559 Speaker 1: people feed carrots and stuff like that. It's kind of crazy. 148 00:09:40,559 --> 00:09:43,800 Speaker 1: So you don't have to go fired. I've never seen one, 149 00:09:43,840 --> 00:09:46,120 Speaker 1: as I'd always lived in the northern tier states. Yeah. 150 00:09:46,240 --> 00:09:49,680 Speaker 1: Now they're limited in the to the coastal distribution because 151 00:09:49,720 --> 00:09:56,920 Speaker 1: the temporary climate. They Um, So where was I introduced? 152 00:09:57,600 --> 00:09:59,719 Speaker 1: How and why did they come into how? And why 153 00:09:59,760 --> 00:10:02,439 Speaker 1: did they come into the chess Chesspeake Bay like there 154 00:10:02,520 --> 00:10:04,880 Speaker 1: for what reason? So the Nutria were brought to the 155 00:10:04,920 --> 00:10:09,440 Speaker 1: chess Peak region in the nineteen forty three or thereabouts. UM. 156 00:10:09,480 --> 00:10:15,640 Speaker 1: There was two entities that had nutrient brought in. The 157 00:10:15,679 --> 00:10:19,520 Speaker 1: Blackwater actually had the refuge itself had a fur bear 158 00:10:19,600 --> 00:10:22,920 Speaker 1: research station at one time, and they housed nutrient. They 159 00:10:22,920 --> 00:10:25,880 Speaker 1: were doing uh nutrient research as well as muskrat and 160 00:10:25,920 --> 00:10:28,280 Speaker 1: that sort of thing. But there are also some private 161 00:10:28,480 --> 00:10:32,720 Speaker 1: uh entrepreneurs that were bringing nutria in to farm them 162 00:10:32,800 --> 00:10:39,360 Speaker 1: and for some reason UH there that never really took 163 00:10:39,360 --> 00:10:42,360 Speaker 1: off economically, and eventually the farms either went out of 164 00:10:42,400 --> 00:10:45,200 Speaker 1: business and they released their animals, or the conditions just 165 00:10:45,320 --> 00:10:50,800 Speaker 1: became dilapidated and they escaped and whatnot. With the dropping 166 00:10:50,920 --> 00:10:54,680 Speaker 1: fur prices, no, that was quite a bit I think 167 00:10:54,720 --> 00:11:00,400 Speaker 1: before the dropping fur prices, and so nutrient didn't really 168 00:11:00,440 --> 00:11:04,480 Speaker 1: become of an ecological impact in the Chestspeake Bay region 169 00:11:04,559 --> 00:11:08,679 Speaker 1: until probably the late nineteen sixties nineteen seventies. And that's 170 00:11:08,720 --> 00:11:13,440 Speaker 1: pretty typical for a lot of introduced invasive species, is 171 00:11:13,480 --> 00:11:17,199 Speaker 1: that they'll they'll exist at fairly low levels for an 172 00:11:17,240 --> 00:11:22,199 Speaker 1: extended period of time and then begin to grow exponentially, 173 00:11:22,600 --> 00:11:25,560 Speaker 1: and once they hit that sort of critical mass and numbers, 174 00:11:26,080 --> 00:11:29,439 Speaker 1: then that population can just like explode and go through 175 00:11:29,480 --> 00:11:33,040 Speaker 1: the roof. And so that's what happened throughout the nineteen 176 00:11:33,760 --> 00:11:37,920 Speaker 1: seventies and eighties, and they estimated at one point that 177 00:11:37,960 --> 00:11:46,120 Speaker 1: blackwater was probably homed over fifty thousand nutrient and UM. 178 00:11:46,160 --> 00:11:51,560 Speaker 1: That corresponded with a very significant decline in UH emergent 179 00:11:51,640 --> 00:11:55,800 Speaker 1: marsh lands at Chesspeake UH at the Chestpeake marsh Lands 180 00:11:56,320 --> 00:12:00,960 Speaker 1: National Wildlife Complex or Refuge complex. But that black water 181 00:12:01,080 --> 00:12:05,360 Speaker 1: unit of at one point was about thirteen thousand or 182 00:12:05,400 --> 00:12:08,200 Speaker 1: so acres of wetlands and they lost about five thousand 183 00:12:08,200 --> 00:12:11,360 Speaker 1: acres of that over the course of well between nineteen 184 00:12:11,440 --> 00:12:16,520 Speaker 1: thirty eight and UM nineteen ninety nine or so, almost 185 00:12:16,520 --> 00:12:19,560 Speaker 1: a half. Yeah, it's very significant. You look at the 186 00:12:19,559 --> 00:12:24,600 Speaker 1: aerial photographs of the core of the heart of black 187 00:12:24,640 --> 00:12:27,800 Speaker 1: Water Refuge and you can see that it's just been 188 00:12:27,880 --> 00:12:33,640 Speaker 1: converted almost entirely to open water. So huge ecological impact. 189 00:12:34,440 --> 00:12:38,480 Speaker 1: And you said Emergent marshes, Yeah, Emergent marshes are those 190 00:12:38,679 --> 00:12:41,840 Speaker 1: uh wetlands where the plants actually emerge out of the water, 191 00:12:41,920 --> 00:12:44,920 Speaker 1: so you look out and you see vegetation basically as 192 00:12:44,920 --> 00:12:47,760 Speaker 1: opposed to like a lily pad type environment or that 193 00:12:47,840 --> 00:12:52,160 Speaker 1: sort of thing. UM. So the typical marshes that we 194 00:12:52,200 --> 00:12:54,480 Speaker 1: have here in the coastal regions of the Chesapeake bay 195 00:12:54,520 --> 00:13:02,120 Speaker 1: are characterized by uh three square bulrush um cat tails 196 00:13:02,240 --> 00:13:05,439 Speaker 1: in some of the more fresher headwaters of the this 197 00:13:05,800 --> 00:13:08,360 Speaker 1: sort of drainage system, and the closer you get to 198 00:13:08,400 --> 00:13:11,480 Speaker 1: the bay and higher selinities, you'll get more needle rush, 199 00:13:11,520 --> 00:13:17,360 Speaker 1: salt hay um, those different types of plant communities. So 200 00:13:18,480 --> 00:13:21,600 Speaker 1: the one that's really critical, and then probably from a 201 00:13:21,640 --> 00:13:25,000 Speaker 1: wildlife perspective, one of the most valuable habitat types is 202 00:13:25,040 --> 00:13:27,880 Speaker 1: the three square marsh. It produces a lot of seeds 203 00:13:27,920 --> 00:13:30,960 Speaker 1: that are fed on by migrating birds and that sort 204 00:13:30,960 --> 00:13:34,680 Speaker 1: of thing and play showed yesterday all the swamp. So 205 00:13:34,720 --> 00:13:36,720 Speaker 1: if you roll it between your fingers, it's got it's 206 00:13:37,040 --> 00:13:45,280 Speaker 1: very triangular in cross section, so um it's it tends 207 00:13:45,320 --> 00:13:48,840 Speaker 1: to grow in very organic based soils and it basically 208 00:13:48,880 --> 00:13:51,840 Speaker 1: accumulates is you know, pete over years and years and 209 00:13:51,920 --> 00:13:57,160 Speaker 1: years of rotting vegetation sort of builds up this peat layer, 210 00:13:57,360 --> 00:13:59,839 Speaker 1: so it doesn't have a real solid foundation, and they're 211 00:14:00,000 --> 00:14:03,120 Speaker 1: where it's very vulnerable to erosion when nutria come in 212 00:14:03,160 --> 00:14:07,760 Speaker 1: and start carving up that route map. So well, muskrat 213 00:14:07,960 --> 00:14:12,480 Speaker 1: do feed on the same types of plants. Three squares 214 00:14:12,559 --> 00:14:15,120 Speaker 1: very important for muskrat as well. They feed a little 215 00:14:15,160 --> 00:14:20,960 Speaker 1: bit differently. Um nutria tend to dig up the tubers 216 00:14:21,240 --> 00:14:24,920 Speaker 1: of the roots of the plants um and do it 217 00:14:25,040 --> 00:14:29,080 Speaker 1: in a very concentrated area. And even more importantly, what 218 00:14:29,120 --> 00:14:31,960 Speaker 1: they'll do is they'll dig swim channels kind of like 219 00:14:31,960 --> 00:14:36,560 Speaker 1: a beaver does, to get from the tidal waterways into 220 00:14:36,600 --> 00:14:39,400 Speaker 1: their feeding areas. What they're essentially doing is creating a 221 00:14:39,400 --> 00:14:41,160 Speaker 1: little stream bed for the tide to get in and 222 00:14:41,360 --> 00:14:44,000 Speaker 1: out so it penetrates further into their marsh. And then 223 00:14:44,000 --> 00:14:46,880 Speaker 1: when it comes out, all that material that they've dug 224 00:14:46,960 --> 00:14:50,720 Speaker 1: up is gathered and swept out to see more or 225 00:14:50,840 --> 00:14:53,600 Speaker 1: less or at least out to the bay. Um. Oh, 226 00:14:53,680 --> 00:14:55,440 Speaker 1: that's it. In Yeah, because I was hard, I was 227 00:14:55,440 --> 00:14:58,960 Speaker 1: having hard to have picture how what they were doing 228 00:14:59,040 --> 00:15:01,600 Speaker 1: was causing someone trouble. That's an interesting perspective on it, 229 00:15:02,240 --> 00:15:05,320 Speaker 1: not perspective on it, but like an interesting The way 230 00:15:05,360 --> 00:15:08,800 Speaker 1: to explain what's going on is those canals, like the 231 00:15:08,840 --> 00:15:10,920 Speaker 1: same canals beavers dig. I never thought that as being 232 00:15:10,960 --> 00:15:13,040 Speaker 1: a way for water just to snake its way further 233 00:15:13,160 --> 00:15:16,720 Speaker 1: up into stuff, so you get higher salinity water penetrating 234 00:15:16,760 --> 00:15:19,960 Speaker 1: further into what are typically brackish or even fresh water marks, 235 00:15:19,960 --> 00:15:22,320 Speaker 1: because beavers will dig those things. You'll you'll see him 236 00:15:22,400 --> 00:15:26,520 Speaker 1: hud yards long sometimes to to go access willow right, 237 00:15:27,920 --> 00:15:33,960 Speaker 1: So that contributes to this erosion problem. What actually happens 238 00:15:34,040 --> 00:15:37,360 Speaker 1: is that organic muck that this route mat is sort 239 00:15:37,360 --> 00:15:41,000 Speaker 1: of floating on uh gets eroded from underneath, and the 240 00:15:41,000 --> 00:15:43,880 Speaker 1: marsh begins to sink. And despite the fact that these 241 00:15:43,880 --> 00:15:47,800 Speaker 1: are wetland plants, they're very susceptible too and intolerant of 242 00:15:48,640 --> 00:15:52,800 Speaker 1: changes in the hydrology of the system. So all they 243 00:15:52,800 --> 00:15:54,840 Speaker 1: need is sink is just a few fractions of an 244 00:15:54,880 --> 00:15:58,440 Speaker 1: inch necessarily, and those plants can no longer survive. It 245 00:15:58,480 --> 00:16:00,840 Speaker 1: turns into a different community, and in many cases it 246 00:16:01,120 --> 00:16:03,600 Speaker 1: sinks so low that no plants can survive, and it 247 00:16:03,680 --> 00:16:08,000 Speaker 1: just turns into this kind of open water wasteland that 248 00:16:08,280 --> 00:16:11,320 Speaker 1: really doesn't produce much good habitat. You know, it's not 249 00:16:11,440 --> 00:16:14,080 Speaker 1: deep enough all the time for a fish community to 250 00:16:14,160 --> 00:16:16,600 Speaker 1: be really supported by it, and most of the fish 251 00:16:16,640 --> 00:16:19,600 Speaker 1: that we tend to see using those those open water 252 00:16:19,640 --> 00:16:23,280 Speaker 1: areas are also invasive species, carved and things like that. 253 00:16:23,360 --> 00:16:28,720 Speaker 1: So but you know, it's not solely the nutrients fault 254 00:16:28,840 --> 00:16:31,840 Speaker 1: that we've seen such wetland loss in the Chesapeake Bay 255 00:16:31,880 --> 00:16:35,960 Speaker 1: region because there are a number of threats to this ecosystem, 256 00:16:36,000 --> 00:16:41,800 Speaker 1: and that includes sea level rise, UM, land subsidence, through 257 00:16:41,880 --> 00:16:46,720 Speaker 1: the withdrawal of underground aquifers for human consumption, congregation, and 258 00:16:46,720 --> 00:16:51,640 Speaker 1: all that sort of thing um and so there's all 259 00:16:51,640 --> 00:16:56,320 Speaker 1: these sort of multiple factors impacting the marshes, which can 260 00:16:56,360 --> 00:16:59,960 Speaker 1: be fairly resilient until you put nutrient into the equation. 261 00:17:00,200 --> 00:17:04,040 Speaker 1: And there's sort of the catalyst that that little uh 262 00:17:04,119 --> 00:17:08,000 Speaker 1: that you know, your grandma's sweater that she needed for 263 00:17:08,040 --> 00:17:09,760 Speaker 1: you when you're a kid, and you get a thread 264 00:17:09,760 --> 00:17:11,200 Speaker 1: pulled on it, and all of a sudden, the whole 265 00:17:11,200 --> 00:17:13,200 Speaker 1: thing on ravels. The new tria is the one pulling 266 00:17:13,240 --> 00:17:16,720 Speaker 1: that thread, and that's all of the other things sort 267 00:17:16,720 --> 00:17:21,000 Speaker 1: of compound when nutria introduced to the equation. So they 268 00:17:21,040 --> 00:17:23,720 Speaker 1: did some really neat research back in the nineteen nineties 269 00:17:23,720 --> 00:17:27,000 Speaker 1: trying to figure out the role that neutria played. And 270 00:17:27,040 --> 00:17:29,000 Speaker 1: what they did was they went throughout the black Water 271 00:17:29,080 --> 00:17:32,000 Speaker 1: system and they put in a bunch of fences, basically 272 00:17:32,400 --> 00:17:36,600 Speaker 1: UM thirty exclusion fences that they buried into the ground 273 00:17:37,320 --> 00:17:39,920 Speaker 1: so that the nutria couldn't swim under it or dig 274 00:17:40,000 --> 00:17:43,080 Speaker 1: under it, and then they made sure there were no 275 00:17:43,160 --> 00:17:49,040 Speaker 1: nutria within them, and they just monitored the vegetation around them, 276 00:17:49,119 --> 00:17:53,119 Speaker 1: and they very quickly could see a distinct difference. Uh, 277 00:17:53,840 --> 00:17:57,440 Speaker 1: they put them in areas where there were compromised by nutria, 278 00:17:57,960 --> 00:18:01,400 Speaker 1: and outside the fences continue need to degrade and convert 279 00:18:01,440 --> 00:18:04,760 Speaker 1: to this muddy open water, and inside the fence the 280 00:18:04,800 --> 00:18:09,359 Speaker 1: plants came back. So it was a good indication that 281 00:18:09,440 --> 00:18:12,800 Speaker 1: if we could eliminate nutrient from the equation, that the 282 00:18:12,800 --> 00:18:18,320 Speaker 1: marsh could possibly restore itself. No real quick here do 283 00:18:19,200 --> 00:18:21,480 Speaker 1: they banked in or where do they sleep at night? 284 00:18:21,920 --> 00:18:25,800 Speaker 1: That's a great question. Um, they don't typically bank then. Um. 285 00:18:25,840 --> 00:18:28,679 Speaker 1: In fact, many of the areas that they live in 286 00:18:28,720 --> 00:18:31,920 Speaker 1: out in the open marsh don't have banks, so they 287 00:18:31,960 --> 00:18:36,960 Speaker 1: just live on they don't And that's one of the 288 00:18:37,000 --> 00:18:40,159 Speaker 1: reasons we think that they're limited in their northern distribution 289 00:18:40,280 --> 00:18:44,240 Speaker 1: because they don't have that thermal refuge from cold weather. 290 00:18:45,080 --> 00:18:48,600 Speaker 1: So what we would see layout they'll build like a 291 00:18:48,640 --> 00:18:53,960 Speaker 1: little nest, just a platform of vegetation, like a muskrat feedbed. Yeah, exactly, 292 00:18:54,680 --> 00:18:59,600 Speaker 1: And I've seen those built almost on stilts. When we 293 00:18:59,720 --> 00:19:01,880 Speaker 1: have high water events. We had a hurricane in two 294 00:19:01,960 --> 00:19:05,280 Speaker 1: thousand and two, I think Hurricane Isabella brought a six 295 00:19:05,320 --> 00:19:08,400 Speaker 1: foot storm surge over the whole marsh, and when the 296 00:19:08,400 --> 00:19:10,960 Speaker 1: waters were seated, we found nutria beds that were made 297 00:19:11,000 --> 00:19:14,280 Speaker 1: out of frag Mighty's the invasive reed that you've been 298 00:19:14,320 --> 00:19:18,720 Speaker 1: seeing lately. Um four ft off the surface of the marsh. 299 00:19:18,760 --> 00:19:20,960 Speaker 1: All folded over and made a nice little platform so 300 00:19:21,000 --> 00:19:23,199 Speaker 1: they could get up out of the water. But that 301 00:19:23,240 --> 00:19:25,480 Speaker 1: doesn't provide thermal protection. So what we see in the 302 00:19:25,480 --> 00:19:28,679 Speaker 1: wintertime with these critters is that they're very susceptible the 303 00:19:28,720 --> 00:19:31,879 Speaker 1: cold weather. They'll get frost bite on their tails and 304 00:19:31,960 --> 00:19:35,200 Speaker 1: over over the course of several years of a big 305 00:19:35,400 --> 00:19:38,680 Speaker 1: adult nutria might only have a stub six inch tailor 306 00:19:38,920 --> 00:19:41,960 Speaker 1: or less even um because it just gets frost bit. 307 00:19:43,040 --> 00:19:47,439 Speaker 1: Females under physiological stress from the cold weather will actually 308 00:19:47,440 --> 00:19:54,159 Speaker 1: abort their females or their fetuses, and so that's another 309 00:19:54,359 --> 00:19:57,720 Speaker 1: element of nutrient. Why there's such a difficult species to 310 00:19:57,880 --> 00:20:03,760 Speaker 1: deal with is because they reproduce extremely rapidly. They Nutria 311 00:20:03,880 --> 00:20:07,040 Speaker 1: come into heat and are ready to breed within four 312 00:20:07,119 --> 00:20:10,040 Speaker 1: or forty eight hours of giving birth, and they become 313 00:20:10,119 --> 00:20:12,320 Speaker 1: sexually mature at about six months of age. So once 314 00:20:12,359 --> 00:20:15,640 Speaker 1: a neutria hits six months of age, she's virtually pregnant 315 00:20:15,640 --> 00:20:18,880 Speaker 1: for the rest of her life. And in the wintertime. 316 00:20:18,960 --> 00:20:23,000 Speaker 1: If they clarify that when you first said that, I 317 00:20:23,000 --> 00:20:27,119 Speaker 1: thought you meant um that the young were. You're saying 318 00:20:27,359 --> 00:20:30,560 Speaker 1: a female will have a litter, and then that female 319 00:20:30,880 --> 00:20:34,520 Speaker 1: within within a couple of days is ready to breed again. 320 00:20:34,600 --> 00:20:37,480 Speaker 1: Absolutely yeah. And if her in her offspring can breed 321 00:20:37,520 --> 00:20:42,199 Speaker 1: win howld at about six months of age, so you 322 00:20:42,320 --> 00:20:48,760 Speaker 1: get it's about a four month gestation period, three to 323 00:20:48,840 --> 00:20:52,479 Speaker 1: four months, and so they can produce like three litters 324 00:20:52,520 --> 00:20:56,200 Speaker 1: a year. You're producing generations in a year. Yeah, oh yeah. 325 00:20:56,320 --> 00:20:59,359 Speaker 1: And in these northern climates where we do see these 326 00:20:59,720 --> 00:21:03,280 Speaker 1: period of stress on the animals where the females will 327 00:21:03,280 --> 00:21:07,720 Speaker 1: abort their litters if they're pregnant, they'll come into the 328 00:21:07,720 --> 00:21:11,320 Speaker 1: heat immediately after that. So it forces this sort of 329 00:21:11,359 --> 00:21:15,000 Speaker 1: cyclic and seasonality to the to the breeding pattern in 330 00:21:15,000 --> 00:21:17,680 Speaker 1: these northern climates where we tend to see big pulses 331 00:21:17,720 --> 00:21:23,680 Speaker 1: of reproduction. Uh, litters born in like May October in January, 332 00:21:23,840 --> 00:21:28,240 Speaker 1: and then usually the January litter doesn't really survive because 333 00:21:28,320 --> 00:21:31,040 Speaker 1: there's these young are born at at a time of 334 00:21:31,119 --> 00:21:35,840 Speaker 1: year where it just isn't conducive to survival. What so, 335 00:21:35,920 --> 00:21:38,560 Speaker 1: what year was it? Remind me again what year they 336 00:21:39,119 --> 00:21:43,439 Speaker 1: first may have gotten introduced here from early nineties, and 337 00:21:43,600 --> 00:21:46,159 Speaker 1: what year was it when the explosion, Like you know, 338 00:21:46,160 --> 00:21:48,760 Speaker 1: when you you're describing how they're just kind of putter 339 00:21:48,880 --> 00:21:50,439 Speaker 1: long and all of a sudden their numbers get to 340 00:21:50,440 --> 00:21:52,080 Speaker 1: a point where you can have this like all of 341 00:21:52,160 --> 00:21:56,240 Speaker 1: a sudden, it's like exponential, right, you know, was the 342 00:21:56,320 --> 00:21:59,399 Speaker 1: late nineteen sixties when they really started to notice, you know, 343 00:21:59,480 --> 00:22:03,000 Speaker 1: nutrient and abundance, and then through the seventies and eighties 344 00:22:03,040 --> 00:22:07,160 Speaker 1: that marsh lost really seemed to accelerate to the point 345 00:22:07,160 --> 00:22:09,359 Speaker 1: where you could people were seeing it in their own lifetime. 346 00:22:09,400 --> 00:22:12,480 Speaker 1: They were seeing the marshall. Yeah, definitely. I had employees 347 00:22:12,520 --> 00:22:14,960 Speaker 1: on the project that were born and raised on the 348 00:22:15,000 --> 00:22:19,040 Speaker 1: marshes of black Water, and they said when they were kids, 349 00:22:19,640 --> 00:22:23,159 Speaker 1: they could have walked in tennis shoes from the Wildlife 350 00:22:23,240 --> 00:22:26,320 Speaker 1: Drive on the black Water National Wildlife Refuge across to 351 00:22:26,400 --> 00:22:29,720 Speaker 1: the property where they grew up. It's about a two 352 00:22:29,840 --> 00:22:33,840 Speaker 1: or three mile distance probably, and the only place they 353 00:22:33,840 --> 00:22:36,439 Speaker 1: would have gotten wet was where the Blackwater River coursed 354 00:22:36,440 --> 00:22:41,520 Speaker 1: through that marsh. There's a high marsh, solid good, good 355 00:22:41,560 --> 00:22:45,920 Speaker 1: ground and uh, that's a big lake. Now okay, yah, 356 00:22:47,119 --> 00:22:52,520 Speaker 1: what year was it then that? Or let me ask 357 00:22:52,560 --> 00:22:56,479 Speaker 1: this to how okay, what year was it when someone said, like, 358 00:22:57,080 --> 00:22:59,240 Speaker 1: is there something we should be trying to do about this? 359 00:22:59,760 --> 00:23:03,439 Speaker 1: And how many neutria werether at that moment? Yeah? Can 360 00:23:03,480 --> 00:23:06,000 Speaker 1: you work in just like a general public perception into 361 00:23:06,040 --> 00:23:08,359 Speaker 1: that answer to like, what was the public thinking and 362 00:23:08,520 --> 00:23:11,040 Speaker 1: about it back then? Well? You know what, at first, 363 00:23:11,080 --> 00:23:13,600 Speaker 1: I think that it wasn't perceived as such a problem 364 00:23:13,600 --> 00:23:17,160 Speaker 1: because there was a fur market for for neutria, and 365 00:23:17,800 --> 00:23:20,520 Speaker 1: people would go out in the wintertime and they would 366 00:23:21,480 --> 00:23:23,680 Speaker 1: hunt and trap them and you know, get some money 367 00:23:23,720 --> 00:23:31,919 Speaker 1: for their for their pelts. Um excuse me, but really 368 00:23:32,080 --> 00:23:37,240 Speaker 1: from a wildlife perspective and from a local economy perspective, 369 00:23:37,880 --> 00:23:40,320 Speaker 1: the muskrat is the king here and when you lose 370 00:23:40,359 --> 00:23:44,359 Speaker 1: three square marks, you lose muskrat. And I think people 371 00:23:44,400 --> 00:23:48,400 Speaker 1: started to realize that that, at least from the trapping community, 372 00:23:48,920 --> 00:23:54,000 Speaker 1: that muskrat are much more desirable than neutria are. And 373 00:23:54,040 --> 00:23:56,680 Speaker 1: so there was support even from you know, that element 374 00:23:56,800 --> 00:23:59,280 Speaker 1: that we needed to do something about it. The big 375 00:23:59,320 --> 00:24:01,720 Speaker 1: thing was the law some marshes at black Water in 376 00:24:01,720 --> 00:24:06,639 Speaker 1: the surrounding Fishing Bay Wildlife Management Area. So it was 377 00:24:06,720 --> 00:24:11,720 Speaker 1: in the nineteen nineties that the various natural resource agencies 378 00:24:11,760 --> 00:24:16,760 Speaker 1: that have a role in managing and conserving UH these 379 00:24:16,880 --> 00:24:21,520 Speaker 1: important Chesapeake Bay resources got together and started thinking about, 380 00:24:22,000 --> 00:24:24,919 Speaker 1: you know, what we could do to try to stem 381 00:24:24,960 --> 00:24:28,280 Speaker 1: the loss and maybe even foster the recovery of the marsh. This, 382 00:24:28,440 --> 00:24:33,840 Speaker 1: this whole project is not about killing nutrients, about restoring wetlands, UM, 383 00:24:33,920 --> 00:24:38,720 Speaker 1: and that's the purpose of it. So it took some 384 00:24:38,840 --> 00:24:41,200 Speaker 1: time to get all the people on board and to 385 00:24:41,280 --> 00:24:44,320 Speaker 1: get congressional support for because this is a big initiative. 386 00:24:44,359 --> 00:24:48,800 Speaker 1: You know, we're talking uh, you know, a quarter million 387 00:24:48,840 --> 00:24:53,120 Speaker 1: acres of wetlands that we eventually have have treated across 388 00:24:53,200 --> 00:24:57,760 Speaker 1: this uh, this landscape, so no small task. UM. They 389 00:24:57,880 --> 00:25:02,760 Speaker 1: estimated population estimates UH somewhere in the vicinity of fifty 390 00:25:02,800 --> 00:25:07,120 Speaker 1: thousand nutrient Blackwater National Wildlife Refuge, and they had some 391 00:25:07,680 --> 00:25:12,040 Speaker 1: fairly good numbers to make estimates off through their trapping programs. 392 00:25:12,080 --> 00:25:16,280 Speaker 1: Because black Water and Fishing Bay are both available to 393 00:25:17,080 --> 00:25:22,040 Speaker 1: UH for trappers to to bid on. They control the 394 00:25:22,040 --> 00:25:24,480 Speaker 1: the amount of trapping. Trappers can come in and bid 395 00:25:24,480 --> 00:25:26,960 Speaker 1: on a lot like like like a chunk of ground 396 00:25:27,000 --> 00:25:32,920 Speaker 1: within the refuge just to work for their own trap line, right. Um, 397 00:25:32,960 --> 00:25:34,840 Speaker 1: And so they had harvest records and at one point 398 00:25:34,920 --> 00:25:38,600 Speaker 1: they were paying uh not a bounty per se, but 399 00:25:38,720 --> 00:25:43,120 Speaker 1: trappers that that did least the trapping units could turn 400 00:25:43,200 --> 00:25:47,320 Speaker 1: in nutrient tails for a dollar fifty credit towards the 401 00:25:47,359 --> 00:25:50,280 Speaker 1: total price of whatever they paid. So if a trapper paid, 402 00:25:50,320 --> 00:25:53,959 Speaker 1: you know, fift undred bucks for a trapping unit and 403 00:25:54,000 --> 00:25:58,639 Speaker 1: they caught what a hundred nutrient turn the tails and 404 00:25:58,640 --> 00:26:00,560 Speaker 1: they could get a hundred and fifty dollars per tail 405 00:26:00,640 --> 00:26:02,959 Speaker 1: and cover the cost of their least they couldn't they 406 00:26:02,960 --> 00:26:07,680 Speaker 1: couldn't catch you know, five nutrient getting extra money. But 407 00:26:07,800 --> 00:26:10,000 Speaker 1: that same guy would probably be that that same guy 408 00:26:10,000 --> 00:26:13,600 Speaker 1: would probably stacking up muskrats in the hundreds. Yeah, yeah, 409 00:26:13,840 --> 00:26:19,040 Speaker 1: for sure. These whitelands support quite high densities and muskrats. 410 00:26:19,440 --> 00:26:22,240 Speaker 1: And these are high quality muskrats around here. Yeah, they're 411 00:26:22,359 --> 00:26:25,320 Speaker 1: the chest Peake Bay region is renowned for the quality 412 00:26:25,320 --> 00:26:28,240 Speaker 1: of the muskrat pelts. Yeah, I heard a guy, you're not, 413 00:26:28,359 --> 00:26:33,800 Speaker 1: I mean, I were like in the early eight like 414 00:26:33,840 --> 00:26:37,919 Speaker 1: the late seventies early eighties muskrats, you know, I mean 415 00:26:37,960 --> 00:26:40,360 Speaker 1: got it's like, you know, extra large muskrats. You gotta 416 00:26:40,359 --> 00:26:43,040 Speaker 1: imagine like the economic difference now and then like what 417 00:26:43,080 --> 00:26:46,520 Speaker 1: it meant. But like seven or eight dollar muskrats in 418 00:26:46,560 --> 00:26:49,919 Speaker 1: the late seventies early eighties, adjusted for inflation, is like 419 00:26:49,960 --> 00:26:53,879 Speaker 1: a valuable animal for something that like an enterprising trapp 420 00:26:53,960 --> 00:26:56,440 Speaker 1: or I mean, there was guys who would quite handily 421 00:26:56,520 --> 00:27:02,680 Speaker 1: put um trap flesh and stretch upwards of a thousand 422 00:27:02,720 --> 00:27:04,959 Speaker 1: or even more muskrats in a year. You can make 423 00:27:05,000 --> 00:27:08,720 Speaker 1: a living trapping back then. Trapping muskrats. Now you're hard 424 00:27:08,720 --> 00:27:10,480 Speaker 1: to press to make a living trap muskrats. You're hard 425 00:27:10,520 --> 00:27:13,560 Speaker 1: press of paper, your expenses trap of muskrats. But at 426 00:27:13,560 --> 00:27:16,680 Speaker 1: the time, it's just like an incredible thing. I caught 427 00:27:16,680 --> 00:27:18,800 Speaker 1: the tail end of that, you know, I was coming 428 00:27:18,840 --> 00:27:21,400 Speaker 1: into trap and just as the you know, I set 429 00:27:21,440 --> 00:27:24,960 Speaker 1: my first muskrat trap and guys are already talking about 430 00:27:24,960 --> 00:27:27,000 Speaker 1: the good old days. But it was still pretty good. 431 00:27:27,119 --> 00:27:29,960 Speaker 1: It's not as good well, you know, in the market 432 00:27:30,240 --> 00:27:33,920 Speaker 1: continues to fluctuate. We we've never seen prices like back then. 433 00:27:35,560 --> 00:27:38,160 Speaker 1: But you know, in the time that I've been here. 434 00:27:39,080 --> 00:27:42,880 Speaker 1: Muskrat pelts extra largess have have gone up to close 435 00:27:42,920 --> 00:27:46,280 Speaker 1: to eight dollars and you know, five dollars with some regularity, 436 00:27:46,320 --> 00:27:49,560 Speaker 1: and they you know, Peakin Valley over time. But there's 437 00:27:49,600 --> 00:27:52,640 Speaker 1: also a market for the meats here as well. So 438 00:27:53,119 --> 00:27:55,320 Speaker 1: by the time that surprised me to hear, yeah, by 439 00:27:55,320 --> 00:27:56,760 Speaker 1: the time you sell the pelt and then you might 440 00:27:56,800 --> 00:27:59,159 Speaker 1: get through four or five bucks for the meats as well. 441 00:27:59,800 --> 00:28:02,280 Speaker 1: Uh uh, you're actually looking at ten or twelve dollars 442 00:28:02,320 --> 00:28:05,320 Speaker 1: per muskrat. And there are people here that that make 443 00:28:05,400 --> 00:28:08,919 Speaker 1: a significant part of their living off of muskrat. You know, 444 00:28:08,920 --> 00:28:11,160 Speaker 1: it's a seasonal work. A lot of farmers will trapped 445 00:28:11,240 --> 00:28:13,159 Speaker 1: during the winter when they're not tend in their fields 446 00:28:13,160 --> 00:28:17,439 Speaker 1: and whatnot. A lot of waterman well trapped during that 447 00:28:17,480 --> 00:28:19,159 Speaker 1: part of the year. So it's an important part of 448 00:28:19,160 --> 00:28:21,359 Speaker 1: the local economy. And and you know a lot of 449 00:28:21,400 --> 00:28:25,640 Speaker 1: people think that, you know, trapping is a anachronistic type thing, 450 00:28:25,680 --> 00:28:27,639 Speaker 1: it's a dead art, and it's we don't need it 451 00:28:27,680 --> 00:28:30,280 Speaker 1: in this modern society. But there's still folks here that 452 00:28:30,280 --> 00:28:34,440 Speaker 1: that really rely on income for trapping. You know, Dorchester 453 00:28:34,520 --> 00:28:38,520 Speaker 1: County is not a wealthy County. It's uh. The last 454 00:28:38,520 --> 00:28:41,640 Speaker 1: time I looked, which was some years ago, that the 455 00:28:41,680 --> 00:28:44,719 Speaker 1: media income was about two thous dollars a year. So 456 00:28:45,160 --> 00:28:48,960 Speaker 1: someone's catching three four five thousand dollars worth of muskrat 457 00:28:48,960 --> 00:28:52,120 Speaker 1: in a year. That's a significant chunk of their annual income. 458 00:28:52,640 --> 00:28:54,960 Speaker 1: So it's important to people. It's not just a you know, 459 00:28:55,000 --> 00:28:56,920 Speaker 1: a hobby or a pastime that people like to do. 460 00:28:57,000 --> 00:29:00,440 Speaker 1: It's it's really important. And this is a point that 461 00:29:00,520 --> 00:29:05,720 Speaker 1: I think is forgotten that those skills in the community, 462 00:29:06,440 --> 00:29:09,200 Speaker 1: that understanding of the natural world that we live in. 463 00:29:09,560 --> 00:29:12,360 Speaker 1: I think no one understands better than a trapper. You know, 464 00:29:12,560 --> 00:29:15,200 Speaker 1: having a no one animal well enough and it's habitat 465 00:29:15,240 --> 00:29:17,000 Speaker 1: well enough to be able to go out and catch 466 00:29:17,040 --> 00:29:22,400 Speaker 1: it requires a certain amount of knowledge and know how, 467 00:29:22,720 --> 00:29:27,040 Speaker 1: understanding and respect for the environment. And without those they're 468 00:29:27,040 --> 00:29:30,080 Speaker 1: doing that ninety days in a role. Yeah, absolutely, a dedication. 469 00:29:30,240 --> 00:29:35,760 Speaker 1: It's uh, it's hard work and having those skills, uh, 470 00:29:35,920 --> 00:29:39,800 Speaker 1: I think are incredibly important to conservation today. And you 471 00:29:39,840 --> 00:29:41,959 Speaker 1: can see it beyond what we did here, you know, 472 00:29:42,160 --> 00:29:47,640 Speaker 1: are the nutrient eradication campaign that we mounted in conjunction 473 00:29:47,720 --> 00:29:49,440 Speaker 1: with the Maryland d n R and the US Fish 474 00:29:49,440 --> 00:29:53,920 Speaker 1: and Wildlife Service was essentially a systematic hunting and trapping 475 00:29:53,960 --> 00:29:59,880 Speaker 1: program that we used paid employees to implement. So the 476 00:30:00,000 --> 00:30:05,200 Speaker 1: problem was identified and funding was secured, and someone floated 477 00:30:05,240 --> 00:30:10,840 Speaker 1: the idea of let's try to go kill all fifty 478 00:30:11,160 --> 00:30:16,680 Speaker 1: thousand neutria, every last one of them dead. Yep, that 479 00:30:16,720 --> 00:30:19,480 Speaker 1: was the goal. And what was the person if someone 480 00:30:19,520 --> 00:30:23,560 Speaker 1: told me that, if if you asked me, if you 481 00:30:23,640 --> 00:30:25,440 Speaker 1: asked me, like before I found out about this, if 482 00:30:25,480 --> 00:30:28,200 Speaker 1: you'd ask me like, do you think in a not 483 00:30:28,360 --> 00:30:31,160 Speaker 1: in an island, not in an island environment, but like 484 00:30:31,200 --> 00:30:34,160 Speaker 1: in an open environment like this, do you think you 485 00:30:34,240 --> 00:30:40,560 Speaker 1: could um mechanically remove an established population of a semi 486 00:30:40,560 --> 00:30:45,600 Speaker 1: aquatic rodent? I would tend to say no, you couldn't. 487 00:30:46,200 --> 00:30:50,600 Speaker 1: But people did it successfully accidentally with beavers in the 488 00:30:51,840 --> 00:30:56,440 Speaker 1: on horseback, So so that was still would be daunting 489 00:30:56,600 --> 00:30:57,880 Speaker 1: and would still be like, yeah, I don't know if 490 00:30:57,880 --> 00:31:00,640 Speaker 1: you could really do it or not. And so wisely, 491 00:31:00,680 --> 00:31:03,560 Speaker 1: the project began as a pilot study because we weren't 492 00:31:03,560 --> 00:31:06,000 Speaker 1: sure if it was feasible or not, and certainly there 493 00:31:06,000 --> 00:31:09,320 Speaker 1: were a lot of people, probably most people who thought 494 00:31:09,320 --> 00:31:11,840 Speaker 1: it probably wasn't. But we were able to convince enough 495 00:31:11,840 --> 00:31:14,680 Speaker 1: folks in the right places, uh, that it was a 496 00:31:14,880 --> 00:31:18,960 Speaker 1: worthy endeavor, and we got the funding to to do it. 497 00:31:19,080 --> 00:31:23,160 Speaker 1: It started with about a two year research project back 498 00:31:23,200 --> 00:31:27,240 Speaker 1: in two thousand and At that time the project was 499 00:31:27,280 --> 00:31:31,720 Speaker 1: being managed through the University of Maryland Eastern Shore, where 500 00:31:31,760 --> 00:31:34,120 Speaker 1: they had a cooperative Fish and Wildlife Research Unit. It 501 00:31:34,200 --> 00:31:39,560 Speaker 1: was part of the US Geological Surveys Research Branch, and 502 00:31:39,640 --> 00:31:43,160 Speaker 1: it's so it started looking at things like trying to 503 00:31:43,200 --> 00:31:47,320 Speaker 1: answer questions like, if you is there a density dependent 504 00:31:47,400 --> 00:31:53,800 Speaker 1: response of nutria reproduction in response to intensive trapping pressure? 505 00:31:53,880 --> 00:31:58,000 Speaker 1: So in other words, if you trap nutria, are they 506 00:31:58,000 --> 00:32:01,320 Speaker 1: gonna just make more nutria faster? And so that was 507 00:32:01,360 --> 00:32:05,320 Speaker 1: one of the areas of research. Um which there's alway 508 00:32:05,360 --> 00:32:08,600 Speaker 1: the problem people find with trying to get rid of coyotes. Yeah, 509 00:32:08,680 --> 00:32:14,400 Speaker 1: there are examples of species that do respond by increasing 510 00:32:14,400 --> 00:32:18,320 Speaker 1: their reproductive rates, whether it's on a per individual basis 511 00:32:18,360 --> 00:32:21,320 Speaker 1: by an increase in litter size, or if it's through 512 00:32:21,960 --> 00:32:26,440 Speaker 1: an increasing proportion of the population breeding, because you're destructing 513 00:32:26,480 --> 00:32:31,760 Speaker 1: the social dynamics of that species. Um. And some do 514 00:32:31,800 --> 00:32:34,800 Speaker 1: it by increasing dispersal too, right. When you get that, yeah, 515 00:32:34,800 --> 00:32:37,240 Speaker 1: well that's that sort of ties into the disrupting their 516 00:32:37,280 --> 00:32:40,040 Speaker 1: social thing, you know, especially with coyotes. You know, if 517 00:32:40,080 --> 00:32:43,360 Speaker 1: you have a saturated population of coyotes, they tend to 518 00:32:43,400 --> 00:32:46,480 Speaker 1: suppress breeding in the younger animals and they form more 519 00:32:46,720 --> 00:32:50,320 Speaker 1: family groups. And if you get a lot of mortality there, 520 00:32:50,360 --> 00:32:53,600 Speaker 1: then the family unit breaks up and the young disperse 521 00:32:53,720 --> 00:32:58,840 Speaker 1: and then you've got freedom to breed. So um. But 522 00:32:58,920 --> 00:33:02,440 Speaker 1: with Nutria, the other thing they're looking at was trying 523 00:33:02,480 --> 00:33:05,760 Speaker 1: to determine if there are any sort of parasite loads, 524 00:33:05,840 --> 00:33:08,600 Speaker 1: that sort of things, sort of some basic ecological research 525 00:33:08,640 --> 00:33:12,320 Speaker 1: on how this species exists in the chest Peak region, 526 00:33:12,720 --> 00:33:17,960 Speaker 1: but also trying to estimate populations. And they found it 527 00:33:18,120 --> 00:33:22,080 Speaker 1: challenging uh to catch enough animals to test some of 528 00:33:22,120 --> 00:33:26,520 Speaker 1: their hypotheses with these things, because any market capture study 529 00:33:27,480 --> 00:33:31,280 Speaker 1: requires large sample sizes to come up with a reliable 530 00:33:31,440 --> 00:33:36,600 Speaker 1: estimate of what that population is. And it got to 531 00:33:36,640 --> 00:33:40,720 Speaker 1: a point where I think the folks that were providing 532 00:33:40,720 --> 00:33:46,320 Speaker 1: the funding wanted to see us move into the eradication phase. 533 00:33:46,640 --> 00:33:48,200 Speaker 1: You know, let's see if we can get this done, 534 00:33:49,320 --> 00:33:52,080 Speaker 1: get to the kid right exactly. So in two thousand two, 535 00:33:52,160 --> 00:33:57,160 Speaker 1: Wildlife Services was asked to get involved as the implementing 536 00:33:57,200 --> 00:33:59,800 Speaker 1: agency to to sort of carry out this plan. And 537 00:33:59,840 --> 00:34:03,200 Speaker 1: it was very much an adaptive management type plan. And 538 00:34:03,280 --> 00:34:07,120 Speaker 1: we started at Blackwater. That's where you know the problem originated. 539 00:34:07,440 --> 00:34:11,840 Speaker 1: And uh, we actually had three main study areas that 540 00:34:11,920 --> 00:34:15,520 Speaker 1: were used during the research phase that we started out on, 541 00:34:15,760 --> 00:34:18,520 Speaker 1: and one was at Blackwater National Wildlife Refuge, the other 542 00:34:18,600 --> 00:34:21,520 Speaker 1: was on Fishing Bay Wildlife Management Area, and the third 543 00:34:21,600 --> 00:34:28,920 Speaker 1: was on a private conservation property that was adjacent to 544 00:34:29,040 --> 00:34:35,359 Speaker 1: the Fishing Bay and Blackwater complexes. So that that first 545 00:34:35,400 --> 00:34:38,000 Speaker 1: summer that we got engaged, you know, we worked on 546 00:34:38,120 --> 00:34:40,960 Speaker 1: trying to trap out the study areas that they had 547 00:34:41,080 --> 00:34:44,240 Speaker 1: started doing all this research on. And and those weren't 548 00:34:44,239 --> 00:34:46,879 Speaker 1: like enclosed areas though, no, no, not at and they 549 00:34:46,880 --> 00:34:50,320 Speaker 1: were iceolated. They were tied into the other population correct, correct, 550 00:34:50,320 --> 00:34:53,800 Speaker 1: So they were as I remember, they were about six 551 00:34:55,320 --> 00:34:59,120 Speaker 1: acres uh sort of plots within the black Water, Fishing 552 00:34:59,120 --> 00:35:02,239 Speaker 1: Bay and Tudor Farm system and that's where they had 553 00:35:02,280 --> 00:35:06,160 Speaker 1: done the bulk of the market recapture studies and and 554 00:35:06,239 --> 00:35:08,400 Speaker 1: that sort of thing. So we tried to go in 555 00:35:08,560 --> 00:35:13,600 Speaker 1: and work out our trapping techniques to sort of clear 556 00:35:13,640 --> 00:35:16,960 Speaker 1: out those areas and try to give some closure to 557 00:35:17,000 --> 00:35:19,200 Speaker 1: the mark recapture stuff so that we could get all 558 00:35:19,200 --> 00:35:23,040 Speaker 1: those tagged animals and account for them. It would this 559 00:35:23,080 --> 00:35:25,960 Speaker 1: be a good time to explain how the like, I 560 00:35:25,960 --> 00:35:28,400 Speaker 1: have no idea when you just say you're trapping neutral, Like, 561 00:35:28,560 --> 00:35:31,759 Speaker 1: what did that look like? Yeah? Sure, so we use 562 00:35:31,840 --> 00:35:36,880 Speaker 1: some pretty conventional for trapping techniques. Uh. The core of 563 00:35:36,920 --> 00:35:40,439 Speaker 1: our trapping toolbox was the two twenty counter bear, which 564 00:35:40,440 --> 00:35:45,240 Speaker 1: is an instant kill uh body gripping trap. It's commonly 565 00:35:46,560 --> 00:35:51,719 Speaker 1: pretty near, yes, submerge, it's fast, Yes it is. Um. 566 00:35:51,880 --> 00:35:57,480 Speaker 1: So you know we bought thousands of those things too. Yeah, 567 00:35:57,600 --> 00:36:02,759 Speaker 1: we had fifteen fold wilife specialists running like Victors or 568 00:36:02,840 --> 00:36:10,040 Speaker 1: Northwoods or just whoever. Yeah, I think Sleepy Creek. Actually, 569 00:36:10,080 --> 00:36:13,560 Speaker 1: I think we we had quite a few traps from them. 570 00:36:13,680 --> 00:36:16,800 Speaker 1: Um yeah, body grip and traps coming like serious sizes 571 00:36:16,840 --> 00:36:20,320 Speaker 1: so like there's like the one hundred series sizes which 572 00:36:20,320 --> 00:36:23,879 Speaker 1: are making muskrat. Two hundred series sizes are generally used 573 00:36:23,920 --> 00:36:31,040 Speaker 1: for raccoon, otter neutria fishers, guys who use one tens 574 00:36:31,120 --> 00:36:34,239 Speaker 1: on dry land, one tens for Martin Did I say 575 00:36:34,280 --> 00:36:36,840 Speaker 1: MANK I think I did. Then the three hundred series 576 00:36:37,080 --> 00:36:41,320 Speaker 1: are usually just beaver, but guys used three thirties also 577 00:36:41,360 --> 00:36:45,279 Speaker 1: for wolverines, and some guys liked, uh, some guys like 578 00:36:45,320 --> 00:36:49,000 Speaker 1: three thirties for otters because otters are harder defense into 579 00:36:49,080 --> 00:36:50,880 Speaker 1: a two twenty, but they don't work as good because 580 00:36:50,880 --> 00:36:53,280 Speaker 1: sometimes the otter can pop out of one of the jaws. 581 00:36:53,360 --> 00:36:57,640 Speaker 1: You know, some people like two twenties on those. So 582 00:36:57,680 --> 00:36:59,880 Speaker 1: they make any bigger is that the biggest? Never heard 583 00:37:00,000 --> 00:37:03,759 Speaker 1: anying bigger than three thirty series count of beer? They 584 00:37:03,800 --> 00:37:06,440 Speaker 1: actually do make a couple uh what do they call them? 585 00:37:06,480 --> 00:37:08,520 Speaker 1: Six sixties or something like that, and it's like two 586 00:37:08,640 --> 00:37:12,359 Speaker 1: three thirties welded together, so it's still got the ten 587 00:37:12,400 --> 00:37:15,520 Speaker 1: inch jaw spread from top to bottom, but they're twice 588 00:37:15,600 --> 00:37:17,600 Speaker 1: is wide. Yeah, that's good to throw in the measurements 589 00:37:17,600 --> 00:37:20,719 Speaker 1: because the two twenties or eight right, seven inches I 590 00:37:20,760 --> 00:37:23,560 Speaker 1: believe seven in Okay. Yeah, when you set this thing, 591 00:37:23,640 --> 00:37:26,880 Speaker 1: this looks like a little wirior box right right then. 592 00:37:27,280 --> 00:37:28,800 Speaker 1: But it's got springs out in the three thirty you 593 00:37:28,800 --> 00:37:31,399 Speaker 1: can break your arm, man your arm. Yeah, it's they're 594 00:37:31,440 --> 00:37:35,520 Speaker 1: not pleasant to have your appendage cut in. So you 595 00:37:35,520 --> 00:37:39,560 Speaker 1: guys went out and bought a mountain in the two twenties. 596 00:37:39,640 --> 00:37:41,480 Speaker 1: We bought a mount into two twenties and quite a 597 00:37:41,560 --> 00:37:44,360 Speaker 1: few foothold traps as well. And what what were you 598 00:37:44,440 --> 00:37:47,759 Speaker 1: doing up with those? What size traps? Uh, A lot 599 00:37:47,800 --> 00:37:50,239 Speaker 1: of one and a half coil spring traps, but we 600 00:37:50,400 --> 00:37:53,280 Speaker 1: also use some bigger So Neutria have a much bigger 601 00:37:53,400 --> 00:37:55,680 Speaker 1: hind foot than they do front foot, very much like 602 00:37:55,760 --> 00:38:00,600 Speaker 1: a beaver. And so the uh, the one and a 603 00:38:00,640 --> 00:38:04,239 Speaker 1: half coil spring trap, which has about a four and 604 00:38:04,239 --> 00:38:07,279 Speaker 1: a half inch jospread I think nutrius foot can span 605 00:38:07,360 --> 00:38:10,120 Speaker 1: the entire trap. So unless you catch them by the 606 00:38:10,160 --> 00:38:12,359 Speaker 1: front foot, you run the risk that that they might 607 00:38:12,440 --> 00:38:15,000 Speaker 1: just spring a trap and not get caught. So we 608 00:38:15,080 --> 00:38:18,439 Speaker 1: did have some larger traps that we used, number twos 609 00:38:18,480 --> 00:38:21,320 Speaker 1: and threes. I think that we sometimes set if you 610 00:38:21,360 --> 00:38:24,239 Speaker 1: were set for the hind leg. Yeah, so just just 611 00:38:24,360 --> 00:38:26,120 Speaker 1: for context for people, like a one and a half 612 00:38:26,200 --> 00:38:30,759 Speaker 1: double coil spring is a foothold trap, and that's the 613 00:38:30,800 --> 00:38:33,800 Speaker 1: size generally used. That's like v go to trap for 614 00:38:34,160 --> 00:38:42,680 Speaker 1: raccoon fox. Yeah, right, that that size critter. So and 615 00:38:42,760 --> 00:38:46,080 Speaker 1: it's important to have uh different tools and your trapping 616 00:38:46,120 --> 00:38:50,719 Speaker 1: toolbox because sometimes animals become trap shy. You know, if 617 00:38:50,760 --> 00:38:54,520 Speaker 1: they see a certain uh type of trap in there. 618 00:38:55,280 --> 00:38:58,799 Speaker 1: You know, some animals are less tolerant of new novelties 619 00:38:58,960 --> 00:39:01,400 Speaker 1: in their environment. They'll sort of steer clear of them. 620 00:39:01,480 --> 00:39:06,239 Speaker 1: So foothold trap is easier to camouflage and sort of disguise. Um. 621 00:39:07,040 --> 00:39:09,200 Speaker 1: So we tended to catch the bulk of the animals 622 00:39:09,239 --> 00:39:11,239 Speaker 1: that we captured in the two twenty conno bear. But 623 00:39:11,280 --> 00:39:14,400 Speaker 1: when we had animals that were clearly you know, avoiding 624 00:39:15,040 --> 00:39:19,480 Speaker 1: the body grouping traps, we would set the foothold traps. 625 00:39:19,560 --> 00:39:23,399 Speaker 1: And were you setting those two twenties in the channels? Yeah, 626 00:39:23,560 --> 00:39:26,560 Speaker 1: so we find those swing channels and paths through the 627 00:39:26,680 --> 00:39:29,160 Speaker 1: marsh and just neck them down and stay in there. 628 00:39:29,239 --> 00:39:35,120 Speaker 1: The perfect width, the size channel, perfect perfect width for 629 00:39:35,239 --> 00:39:38,040 Speaker 1: the two twenty. It wasn't it wasn't quite rocket science 630 00:39:38,080 --> 00:39:40,400 Speaker 1: to catch him. But would you guys use those uh 631 00:39:40,680 --> 00:39:43,000 Speaker 1: those basically harness rigs to hold the two twenties so 632 00:39:43,040 --> 00:39:44,520 Speaker 1: you can just stab it into the ground. No, we 633 00:39:44,560 --> 00:39:48,560 Speaker 1: actually used bamboo poles mostly, So we wired the trap 634 00:39:48,640 --> 00:39:52,719 Speaker 1: to a bamboo pole and then uh stuck it through 635 00:39:53,239 --> 00:39:56,280 Speaker 1: the spring in the corner of the trap to provide 636 00:39:56,360 --> 00:39:59,800 Speaker 1: some stabilization and also sort of a visual sort of 637 00:40:00,600 --> 00:40:03,560 Speaker 1: guide to send the animal through. You're calling all this 638 00:40:03,680 --> 00:40:07,719 Speaker 1: gear around what the canoe? A lot of it, well, yeah, 639 00:40:08,920 --> 00:40:14,319 Speaker 1: mostly john boats actually, Um, there are the main waterways 640 00:40:14,400 --> 00:40:16,239 Speaker 1: you can navigate through the marsh, but a lot of 641 00:40:16,320 --> 00:40:19,719 Speaker 1: it was over the shoulder, just carrying stuff through the 642 00:40:19,760 --> 00:40:22,560 Speaker 1: equipment through the marsh. The bamboo poles were kind of 643 00:40:22,600 --> 00:40:27,640 Speaker 1: handy because you could you could uh spring the trap 644 00:40:27,760 --> 00:40:29,879 Speaker 1: on the end of the bamboo pole and then gather 645 00:40:30,840 --> 00:40:33,440 Speaker 1: you know, ten or a dozen polls up with traps 646 00:40:33,480 --> 00:40:35,319 Speaker 1: on and throw him over your shoulder and carry him 647 00:40:35,320 --> 00:40:39,239 Speaker 1: across the marsh. So and it's stuff like that kind 648 00:40:39,280 --> 00:40:40,920 Speaker 1: of circle back to where I was going With the 649 00:40:40,960 --> 00:40:44,200 Speaker 1: importance of maintaining these trapping skills in the community. We 650 00:40:44,280 --> 00:40:48,960 Speaker 1: were very reliant on local knowledge and local trappers on 651 00:40:49,040 --> 00:40:52,120 Speaker 1: this project to understand how to work in this marsh 652 00:40:52,160 --> 00:40:56,440 Speaker 1: effectively and the techniques that that that work best. Because 653 00:40:57,080 --> 00:41:00,839 Speaker 1: while trapping isn't rocket science, it's got a steep learning 654 00:41:00,920 --> 00:41:04,400 Speaker 1: curve for a beginner and uh, you know, it can 655 00:41:04,440 --> 00:41:08,399 Speaker 1: take people a long time to sort of figure things out, uh, 656 00:41:08,640 --> 00:41:12,800 Speaker 1: and an experienced trapper can be very effective at selecting 657 00:41:13,160 --> 00:41:16,319 Speaker 1: the species that they're trying to target and avoiding those 658 00:41:16,360 --> 00:41:18,960 Speaker 1: other species that not but a novice trapper often is 659 00:41:19,360 --> 00:41:23,200 Speaker 1: is uh not as selective. So having that expertise in 660 00:41:23,239 --> 00:41:26,719 Speaker 1: the community, and this translates to much much beyond uh 661 00:41:27,440 --> 00:41:33,040 Speaker 1: you know, nutrient eradication uh. For trappers were incredibly important 662 00:41:33,040 --> 00:41:36,680 Speaker 1: to the restoration of a lot of uh endangered species 663 00:41:36,960 --> 00:41:39,279 Speaker 1: in the United States, wolves being probably one of the 664 00:41:39,360 --> 00:41:44,600 Speaker 1: most prime examples of that. You know, the last remaining 665 00:41:44,800 --> 00:41:47,440 Speaker 1: Mexican wolves were caught by a trapper named Roy McBride 666 00:41:47,480 --> 00:41:53,640 Speaker 1: and pulled into to captivity for a selective breeding program 667 00:41:53,719 --> 00:41:57,240 Speaker 1: to build those populations up and restore them to the wild. 668 00:41:57,360 --> 00:42:01,600 Speaker 1: The Yellowstone Wolf recovery effort was many of those wolves 669 00:42:01,640 --> 00:42:05,960 Speaker 1: were captured and traps set by trappers. Soever some years 670 00:42:06,000 --> 00:42:08,560 Speaker 1: ago some American fur trappers going down to help some 671 00:42:08,680 --> 00:42:13,719 Speaker 1: governments in South America catch jaguars. Yep, yeah, they're uh, 672 00:42:14,280 --> 00:42:19,080 Speaker 1: you know, a good trapper is a very valuable persons 673 00:42:19,080 --> 00:42:23,279 Speaker 1: of the wildlife conservation community. Um. So were you a 674 00:42:23,360 --> 00:42:26,440 Speaker 1: trapper before you got involved with he was trading muscratch 675 00:42:26,480 --> 00:42:33,040 Speaker 1: for haircuts? Yep, Okay, yeah, I was uh certainly not 676 00:42:33,320 --> 00:42:37,360 Speaker 1: a trapper in the sense that the folks here that 677 00:42:37,560 --> 00:42:39,160 Speaker 1: you know, make a living off of But how I 678 00:42:39,239 --> 00:42:41,799 Speaker 1: got started in it, and actually I had I grew 679 00:42:41,920 --> 00:42:44,680 Speaker 1: up with somewhat of a negative opinion of trapping because 680 00:42:44,800 --> 00:42:47,320 Speaker 1: my my grandfather was an avid bird hunter and always 681 00:42:47,400 --> 00:42:51,719 Speaker 1: had bird dogs. Oh yeah, it was gays and he'd 682 00:42:51,760 --> 00:42:53,960 Speaker 1: had a couple of dogs get caught in foothold trap 683 00:42:54,080 --> 00:42:59,160 Speaker 1: there with the Humane Society trapping, So yeah, he he 684 00:42:59,280 --> 00:43:03,680 Speaker 1: had sort of instilled in me this uh lack of appreciation, 685 00:43:03,760 --> 00:43:07,440 Speaker 1: shall I say, for for trapping in general. But you know, 686 00:43:07,520 --> 00:43:10,600 Speaker 1: I went to school for wildlife management, and I came 687 00:43:10,640 --> 00:43:14,640 Speaker 1: out of that, Uh, I went into graduate school for 688 00:43:15,480 --> 00:43:20,319 Speaker 1: uh wildlife ecology, and I was really dying to study carnivores. 689 00:43:20,360 --> 00:43:23,600 Speaker 1: And I managed to land a position, a graduate research 690 00:43:23,680 --> 00:43:27,480 Speaker 1: position at the State University of New York College of 691 00:43:27,560 --> 00:43:31,200 Speaker 1: Environmental Science and Forest We're doing coyote research, and you know, 692 00:43:31,360 --> 00:43:33,000 Speaker 1: for the first time I had to figure out how 693 00:43:33,040 --> 00:43:38,000 Speaker 1: to catch an animal with this sort of traditional for 694 00:43:38,160 --> 00:43:42,120 Speaker 1: harvesting tools. I've done you know, trapping with cage traps 695 00:43:42,200 --> 00:43:45,359 Speaker 1: for pine martin and other critters like that, but I'd 696 00:43:45,400 --> 00:43:50,680 Speaker 1: never for research. Yeah, but I've never set you know, 697 00:43:50,960 --> 00:43:53,640 Speaker 1: foothold traps, and so I had to learn how to 698 00:43:53,719 --> 00:43:57,160 Speaker 1: do it to catch study animals for my project. And 699 00:43:57,239 --> 00:43:59,720 Speaker 1: when you're making, you know, a stipend of eight hundred 700 00:43:59,760 --> 00:44:03,719 Speaker 1: bucks a month, it doesn't quite make ends meet and 701 00:44:04,960 --> 00:44:07,400 Speaker 1: learning a trap. And I'm also driving through all of 702 00:44:07,480 --> 00:44:10,720 Speaker 1: this probably muskrat country, and I'm seeing farmers with problems 703 00:44:10,760 --> 00:44:13,440 Speaker 1: with beavers and all that sort of stuff. And so 704 00:44:13,560 --> 00:44:15,920 Speaker 1: I just started learning. And I was very fortunate to 705 00:44:16,000 --> 00:44:20,360 Speaker 1: have a renowned fur buyer and trapper in the community 706 00:44:20,440 --> 00:44:23,960 Speaker 1: that I was living in in upstate New York, Paul 707 00:44:24,000 --> 00:44:31,719 Speaker 1: Grimshaw Grimshaw, and uh, he didn't exactly take me under 708 00:44:31,800 --> 00:44:35,680 Speaker 1: his wing. He's kind of a ordinary old guy, but 709 00:44:35,880 --> 00:44:40,960 Speaker 1: once you sort of connected with him, he'd start sharing knowledge. 710 00:44:41,000 --> 00:44:42,960 Speaker 1: And so I learned a tremendous amount from him. I 711 00:44:43,160 --> 00:44:46,120 Speaker 1: just go and watch and his wife's skin and stretch 712 00:44:46,239 --> 00:44:51,440 Speaker 1: muskrat pelts, and just learned, you know, the kind of 713 00:44:51,480 --> 00:44:54,279 Speaker 1: tricks of the trade for catching some of these other furbears. 714 00:44:54,360 --> 00:44:56,840 Speaker 1: And and I was able to supplement my income, you know, 715 00:44:57,000 --> 00:45:01,080 Speaker 1: not tremendous. I wasn't out there buying cars or anything 716 00:45:01,160 --> 00:45:03,279 Speaker 1: with it. But when you make a hundred bucks a month, 717 00:45:03,560 --> 00:45:07,879 Speaker 1: an extra month's salary, and in the course of a year. 718 00:45:08,360 --> 00:45:10,600 Speaker 1: It really helped out quite a bit. So I was 719 00:45:10,640 --> 00:45:12,719 Speaker 1: telling you honest the other day about I used to sell. 720 00:45:13,440 --> 00:45:16,800 Speaker 1: I didn't flesh and stretch my own raccoons. He's like, 721 00:45:16,960 --> 00:45:19,040 Speaker 1: no one likes doing that, you know. And I would 722 00:45:19,040 --> 00:45:21,799 Speaker 1: sell him to a fur buyer who would who would 723 00:45:21,920 --> 00:45:26,719 Speaker 1: uh got named Abe sell salicina Um. He was a 724 00:45:26,760 --> 00:45:30,600 Speaker 1: tomato enthusiast who grew tomatoes and bought fur. And you 725 00:45:30,600 --> 00:45:32,880 Speaker 1: would go into his selling raccoons and he'd be in 726 00:45:32,920 --> 00:45:35,480 Speaker 1: there and he'd heat his he'd heat his barn with 727 00:45:35,640 --> 00:45:38,560 Speaker 1: raccoon fat, no kidding, he'd open that door and take 728 00:45:38,560 --> 00:45:45,440 Speaker 1: a job with that raccoon in there like black smoke. Well, 729 00:45:45,480 --> 00:45:47,440 Speaker 1: you know it was it was neat to be in 730 00:45:47,600 --> 00:45:51,200 Speaker 1: such proximity to it, to Paul Grimshaw because you know, 731 00:45:51,360 --> 00:45:54,920 Speaker 1: he was an expert in putting up furs and whatnot. 732 00:45:55,000 --> 00:45:58,080 Speaker 1: So you know, I put up my own fur from 733 00:45:58,719 --> 00:46:01,000 Speaker 1: when I was doing that, and I enjoyed that part 734 00:46:01,000 --> 00:46:02,480 Speaker 1: of It's kind of like put you in your own meat. 735 00:46:02,600 --> 00:46:05,560 Speaker 1: You know, you're kind of taking that process from start 736 00:46:05,640 --> 00:46:07,440 Speaker 1: to finish, and you know, you get more money for 737 00:46:08,200 --> 00:46:12,200 Speaker 1: for when it's when it's put up properly get less 738 00:46:12,239 --> 00:46:14,399 Speaker 1: when it's put up poorly. But um, I think they'd 739 00:46:14,480 --> 00:46:17,279 Speaker 1: rather buy green fur than than poorly put up for 740 00:46:17,480 --> 00:46:22,839 Speaker 1: But that's like that like lingo is uh green fur 741 00:46:22,960 --> 00:46:25,719 Speaker 1: would be skinned but not flash and stretched if you 742 00:46:25,760 --> 00:46:28,719 Speaker 1: sold a muskrat like guys would also sell muskrats in 743 00:46:28,800 --> 00:46:33,600 Speaker 1: the round, which would mean just pull up at the end, 744 00:46:33,719 --> 00:46:35,560 Speaker 1: run your line and drive up and sell the guy 745 00:46:35,719 --> 00:46:37,480 Speaker 1: muskrats in the round. You might be selling them for 746 00:46:37,560 --> 00:46:40,719 Speaker 1: half what you'd get if you sold them stretched exactly. 747 00:46:42,200 --> 00:46:46,080 Speaker 1: So that was a real uh learning experience for me, 748 00:46:46,239 --> 00:46:48,320 Speaker 1: And the thing I liked about it is that it 749 00:46:49,080 --> 00:46:52,719 Speaker 1: really makes you step back and take time to learn 750 00:46:52,719 --> 00:46:55,080 Speaker 1: about animals you normally wouldn't even think about. You know 751 00:46:55,160 --> 00:46:58,600 Speaker 1: who thinks about muskrats unless you're trying to catch them. 752 00:46:58,760 --> 00:47:01,440 Speaker 1: And then so just I learned a whole lot about 753 00:47:01,560 --> 00:47:05,160 Speaker 1: the ecology of the region and and the behavior of 754 00:47:05,200 --> 00:47:08,120 Speaker 1: the animals, and the importance of different habitat types than 755 00:47:08,239 --> 00:47:11,440 Speaker 1: what lives there through this process of learning how to 756 00:47:11,520 --> 00:47:14,000 Speaker 1: trap and uh so when you call. But when you 757 00:47:14,080 --> 00:47:16,800 Speaker 1: caught wind of the you had to go apply for 758 00:47:16,880 --> 00:47:18,879 Speaker 1: the new Tria job or was it just like fall 759 00:47:18,960 --> 00:47:21,800 Speaker 1: into your lap No, I had to apply for it. So, 760 00:47:22,600 --> 00:47:24,720 Speaker 1: you know, when I was working in the Virginia Wildlife 761 00:47:24,719 --> 00:47:29,720 Speaker 1: Services program, every state has Wildlife Services has a program 762 00:47:29,840 --> 00:47:33,080 Speaker 1: in every state just about and so I was working 763 00:47:33,280 --> 00:47:39,399 Speaker 1: through the Virginia Division of Wildlife Services and we would 764 00:47:39,440 --> 00:47:42,840 Speaker 1: have these annual conferences where all the different states surrounding 765 00:47:42,880 --> 00:47:45,640 Speaker 1: would get together and intermingle. And for a couple of 766 00:47:45,760 --> 00:47:48,480 Speaker 1: years there I was hearing them talk about this Nutria 767 00:47:48,520 --> 00:47:52,600 Speaker 1: eradication study that they were undertaking in Maryland. And at 768 00:47:52,640 --> 00:47:56,440 Speaker 1: the time, Maryland Wildlife Services wasn't getting involved. We had 769 00:47:56,480 --> 00:47:59,120 Speaker 1: a state director at the time that was close to 770 00:47:59,239 --> 00:48:02,279 Speaker 1: retirement and I think it wasn't really all that keen 771 00:48:02,400 --> 00:48:06,440 Speaker 1: to take on this sort of massive project. Um. But 772 00:48:06,560 --> 00:48:11,600 Speaker 1: he retired and a new guy came in and jumped 773 00:48:11,640 --> 00:48:14,680 Speaker 1: on the opportunity to work with a fish and wildlife 774 00:48:14,719 --> 00:48:18,320 Speaker 1: service and UH put this vacancy announcement out there, and 775 00:48:19,680 --> 00:48:22,160 Speaker 1: Jesus sounded interesting to me. I had no idea what 776 00:48:22,239 --> 00:48:24,080 Speaker 1: a new trio was. I had to look it up 777 00:48:25,040 --> 00:48:27,759 Speaker 1: and I put my name in the hat. And I 778 00:48:27,840 --> 00:48:32,080 Speaker 1: don't know how I got picked, but I did experience. Yeah, 779 00:48:32,120 --> 00:48:37,520 Speaker 1: I guess so, um, but I feel very fortunate to 780 00:48:37,680 --> 00:48:40,200 Speaker 1: have been selected for that position because it's the kind 781 00:48:40,200 --> 00:48:43,680 Speaker 1: of thing that that doesn't happen too often in your 782 00:48:43,719 --> 00:48:45,880 Speaker 1: career where you get to work on a project that 783 00:48:46,040 --> 00:48:51,560 Speaker 1: just has sort of profound impacts on conservation and ecology 784 00:48:51,600 --> 00:48:54,680 Speaker 1: and results that you can see on the ground. And 785 00:48:54,840 --> 00:48:59,319 Speaker 1: so the position was and it was everything you liked. Man. Yeah. 786 00:48:59,400 --> 00:49:02,040 Speaker 1: I was able to take all of the skills that 787 00:49:02,160 --> 00:49:05,320 Speaker 1: I developed through my personal passions hunting and trapping and 788 00:49:05,440 --> 00:49:08,480 Speaker 1: that sort of thing and sort of devote them to 789 00:49:08,760 --> 00:49:13,120 Speaker 1: solving a conservation problem. And it really was rewarding to 790 00:49:13,200 --> 00:49:17,520 Speaker 1: be able to do that. And also, you know, I 791 00:49:17,600 --> 00:49:20,640 Speaker 1: have to give full credit to the guys that and 792 00:49:20,800 --> 00:49:23,440 Speaker 1: Gales that really got this job done because I was 793 00:49:23,560 --> 00:49:26,160 Speaker 1: not the trapper out there. I did trap some, but 794 00:49:27,600 --> 00:49:31,040 Speaker 1: I was a project manager, so I was supervising the team. 795 00:49:31,520 --> 00:49:36,400 Speaker 1: Uh uh, We're developing the strategies to you know, how 796 00:49:36,440 --> 00:49:40,759 Speaker 1: to work and devote our resources across the landscape and 797 00:49:41,000 --> 00:49:42,400 Speaker 1: you know, when is it time to move from this 798 00:49:42,520 --> 00:49:45,319 Speaker 1: area to that area? That sort of thing. Making sure 799 00:49:45,400 --> 00:49:48,280 Speaker 1: that guy's had all the equipment that they needed, the boats, 800 00:49:48,320 --> 00:49:52,160 Speaker 1: the motors, the waiters, the traps, all that stuff. Um. 801 00:49:53,880 --> 00:49:57,600 Speaker 1: So that was sort of my role, and uh, you know, 802 00:49:57,719 --> 00:50:02,160 Speaker 1: the opportunity to supervisor was link to me. I didn't 803 00:50:02,920 --> 00:50:04,840 Speaker 1: think so when I first got into wildlife that I 804 00:50:05,000 --> 00:50:09,440 Speaker 1: really want to be managing people, but you know, realizing 805 00:50:09,480 --> 00:50:14,040 Speaker 1: how much more you can accomplished by harnessing the energy 806 00:50:14,120 --> 00:50:19,520 Speaker 1: of others. Ah, it was a neat opportunity. So I'm 807 00:50:19,560 --> 00:50:21,160 Speaker 1: getting backed up on questions. They had a couple of 808 00:50:21,200 --> 00:50:25,239 Speaker 1: quickies out of context, out of order. Were you hiring 809 00:50:25,280 --> 00:50:28,879 Speaker 1: trapper trappers or you hiring or did that not? That's 810 00:50:28,880 --> 00:50:30,040 Speaker 1: not how it worked, Like, you didn't come in and 811 00:50:30,120 --> 00:50:34,200 Speaker 1: hire existing for trappers, so do the physical trapping. So 812 00:50:34,960 --> 00:50:38,080 Speaker 1: the research phase of the project had hired some local 813 00:50:38,160 --> 00:50:40,719 Speaker 1: trappers as part of the team. And when we came 814 00:50:40,760 --> 00:50:45,560 Speaker 1: to develop methodologies, yeah, well to implement the research study 815 00:50:45,640 --> 00:50:49,440 Speaker 1: that they designed. So that was primarily using cage traps, 816 00:50:49,760 --> 00:50:54,640 Speaker 1: um and some foothold traps. Uh. But they did reach 817 00:50:54,680 --> 00:50:57,120 Speaker 1: out to the local community and got a handful of 818 00:50:57,520 --> 00:51:00,400 Speaker 1: folks that were uh you know, born and raised in 819 00:51:00,440 --> 00:51:03,880 Speaker 1: the chest Peak Bay, knew how to trap and how 820 00:51:03,960 --> 00:51:06,440 Speaker 1: to handle animals and all that stuff, and so we 821 00:51:06,560 --> 00:51:10,720 Speaker 1: inherited when the Wildlife Services program took over they wanted 822 00:51:10,760 --> 00:51:13,040 Speaker 1: to provide a home for those you know, employment for 823 00:51:13,120 --> 00:51:15,799 Speaker 1: those folks that have dedicated themselves to the research phase 824 00:51:15,840 --> 00:51:19,240 Speaker 1: of the project, and so we were able to hire 825 00:51:19,320 --> 00:51:22,520 Speaker 1: them when we came on board. Not all of them 826 00:51:22,680 --> 00:51:26,080 Speaker 1: chose to come with us, so I had some vacancies 827 00:51:26,160 --> 00:51:28,759 Speaker 1: to fill, and you know, we put out vacancy announcements 828 00:51:29,719 --> 00:51:33,400 Speaker 1: and uh we tried to recruit locally, and we also 829 00:51:33,680 --> 00:51:37,640 Speaker 1: tried to recruit uh young wildlife professionals that we're just 830 00:51:37,760 --> 00:51:40,000 Speaker 1: getting out of college and starting their careers. And I 831 00:51:40,040 --> 00:51:42,920 Speaker 1: always strove to have a sort of a balance of 832 00:51:43,000 --> 00:51:46,800 Speaker 1: the two because those uh, those greenhorns don't know what 833 00:51:46,880 --> 00:51:49,439 Speaker 1: they're doing and they need someone to kind of show 834 00:51:49,480 --> 00:51:51,640 Speaker 1: them the ropes and whatnot. But they bring a lot 835 00:51:51,680 --> 00:51:54,640 Speaker 1: of energy and passion to their their work as well, um, 836 00:51:54,840 --> 00:52:00,319 Speaker 1: and it helps them in their career steppe. And then 837 00:52:00,360 --> 00:52:03,960 Speaker 1: our our sort of uh I always call them our 838 00:52:04,040 --> 00:52:08,440 Speaker 1: veteran employees. They weren't military veterans necessarily, but but the 839 00:52:08,480 --> 00:52:10,480 Speaker 1: folks that have been with a project from the beginning, 840 00:52:10,520 --> 00:52:13,480 Speaker 1: and our folks that were that were taught on the 841 00:52:13,560 --> 00:52:16,280 Speaker 1: Chess Peak, they went to the School of the Chesapeake, 842 00:52:16,360 --> 00:52:19,120 Speaker 1: you know, to learn their trade, and so those folks 843 00:52:19,200 --> 00:52:22,919 Speaker 1: would provide kind of the stability and the long term 844 00:52:23,320 --> 00:52:27,479 Speaker 1: institutional knowledge that we needed to keep the project going 845 00:52:27,600 --> 00:52:32,279 Speaker 1: because this is a long term project. So okay, so 846 00:52:33,320 --> 00:52:34,880 Speaker 1: the second quickie and then I'm gonna get into a 847 00:52:34,920 --> 00:52:39,320 Speaker 1: longer question. Um, what what do you guys do with 848 00:52:39,360 --> 00:52:42,040 Speaker 1: all the nutrita that you trap? You probably can't utilize 849 00:52:42,040 --> 00:52:44,200 Speaker 1: them because it's part of the government project, correct, So, 850 00:52:44,480 --> 00:52:46,640 Speaker 1: and there was no market for them or anything like that. 851 00:52:46,840 --> 00:52:50,400 Speaker 1: There's no use for the meats or anything. So at 852 00:52:50,440 --> 00:52:54,560 Speaker 1: first we were instructed to remove all of the carcasses 853 00:52:54,680 --> 00:52:58,799 Speaker 1: from the field and we're gonna compost them or bury 854 00:52:58,920 --> 00:53:08,440 Speaker 1: them or or do something. Pardon me, but uh, we 855 00:53:08,600 --> 00:53:11,800 Speaker 1: quickly found and at this time that that sort of 856 00:53:12,160 --> 00:53:15,719 Speaker 1: rule was put in place, we had expected that there 857 00:53:15,800 --> 00:53:18,359 Speaker 1: would be tens of thousands of nutrient that we were 858 00:53:18,400 --> 00:53:21,840 Speaker 1: going to be catching. And when reality said in we 859 00:53:21,920 --> 00:53:24,640 Speaker 1: started doing the trapping, had been coome become clear that 860 00:53:24,719 --> 00:53:27,320 Speaker 1: the population had begun to decline and we weren't catching 861 00:53:27,400 --> 00:53:30,600 Speaker 1: nearly the numbers that we expected. Began to decline because 862 00:53:30,640 --> 00:53:35,600 Speaker 1: of the trapping. No, not necessarily. I think the population 863 00:53:35,680 --> 00:53:38,680 Speaker 1: on its own had started sort of dwindling. And part 864 00:53:38,760 --> 00:53:41,640 Speaker 1: of the probably reason is probably that they had eaten 865 00:53:41,719 --> 00:53:43,719 Speaker 1: themselves out of house and home in many of these areas, 866 00:53:43,760 --> 00:53:47,160 Speaker 1: so there's habitat that was no longer there. They were 867 00:53:48,080 --> 00:53:53,440 Speaker 1: estimated to reach densities of gosh I should have boned 868 00:53:53,520 --> 00:53:59,480 Speaker 1: up on some of my my memory here um eight 869 00:53:59,560 --> 00:54:03,640 Speaker 1: to nine six to nine per square acre or per acre. 870 00:54:04,400 --> 00:54:09,520 Speaker 1: So at those densities, uh, which are tremendous densities. You know, 871 00:54:09,640 --> 00:54:12,440 Speaker 1: you could have seen thirty five to fifty thod neutrient blackwater, 872 00:54:12,640 --> 00:54:15,640 Speaker 1: but wipe five thousand acres habitat off the map and 873 00:54:16,080 --> 00:54:21,480 Speaker 1: got significantly fewer. So we were bringing the carcasses back, 874 00:54:21,560 --> 00:54:23,320 Speaker 1: but there weren't that many of them, and there's a 875 00:54:23,360 --> 00:54:26,200 Speaker 1: big concern for removing them from the marsh was the 876 00:54:26,880 --> 00:54:30,600 Speaker 1: potential for avian and avian batuli is um with all 877 00:54:30,640 --> 00:54:35,399 Speaker 1: these rotting carcasses out there potentially being fed on by birds. Yeah, 878 00:54:35,600 --> 00:54:42,920 Speaker 1: just this massive carrying that that would potentially create problems 879 00:54:43,000 --> 00:54:49,719 Speaker 1: for other wildlife. But it was very time consuming. They 880 00:54:49,760 --> 00:54:52,360 Speaker 1: remove the animals from the field. You know, at fifteen 881 00:54:52,360 --> 00:54:54,520 Speaker 1: pounds a piece, he gets four or five of them, 882 00:54:54,560 --> 00:54:57,880 Speaker 1: you've got sixty pounds of carcasses that you're hauling around, 883 00:54:58,600 --> 00:55:01,680 Speaker 1: So it was impeding our progress, and it didn't seem 884 00:55:01,760 --> 00:55:05,799 Speaker 1: like there were numbers that would contribute to the kind 885 00:55:05,840 --> 00:55:08,000 Speaker 1: of concerns that we initially had going into it. So 886 00:55:08,160 --> 00:55:11,319 Speaker 1: we opted at that point to leave the carcasses out 887 00:55:11,360 --> 00:55:15,920 Speaker 1: in the field, and very quickly the scavengers would would 888 00:55:16,280 --> 00:55:19,839 Speaker 1: convert them back to you know, ashes to ashes, dust 889 00:55:19,880 --> 00:55:22,719 Speaker 1: to dust, so it didn't turn out to be so 890 00:55:22,880 --> 00:55:24,719 Speaker 1: much of an issue to have to remove them from 891 00:55:24,760 --> 00:55:27,480 Speaker 1: the field, and that freed us up to you know, 892 00:55:27,640 --> 00:55:30,279 Speaker 1: better to spend our time moving traps and carrying more 893 00:55:30,360 --> 00:55:36,880 Speaker 1: traps out than carrying the exactly. So the longer question, no, 894 00:55:36,960 --> 00:55:41,719 Speaker 1: I got that quickie. Uh, what was the public perception 895 00:55:42,840 --> 00:55:47,200 Speaker 1: of the idea that we're gonna kill all the nutriments 896 00:55:47,239 --> 00:55:50,560 Speaker 1: get rid of them wholesale? People like good idea or 897 00:55:50,719 --> 00:55:53,399 Speaker 1: people like that's a horrible idea, not whether you can 898 00:55:53,640 --> 00:55:57,560 Speaker 1: not whether it's plausible, but whether it's like advisable. Right, So, 899 00:55:57,640 --> 00:56:02,680 Speaker 1: anytime you implement some sort of lethal management of wildlife populations, 900 00:56:02,719 --> 00:56:08,320 Speaker 1: there's often a outcry of public sentiment that opposes that. 901 00:56:09,320 --> 00:56:13,560 Speaker 1: But because of the ecological impact that these critters had 902 00:56:13,880 --> 00:56:19,600 Speaker 1: and it was obvious to anyone familiar with this ecosystem. Uh, 903 00:56:20,040 --> 00:56:23,239 Speaker 1: the ecological justification was so compelling that there was really 904 00:56:23,400 --> 00:56:26,879 Speaker 1: very little backlash. Add to that that you know, it's 905 00:56:26,920 --> 00:56:29,880 Speaker 1: basically a twenty pound rat doesn't generate a whole lot 906 00:56:29,960 --> 00:56:34,800 Speaker 1: of sympathy, But that wasn't more. Yeah that that that's 907 00:56:35,400 --> 00:56:38,000 Speaker 1: good information, And I like that you said it, but 908 00:56:38,080 --> 00:56:41,600 Speaker 1: I meant I wasn't so much interested in those guys. 909 00:56:42,120 --> 00:56:45,200 Speaker 1: I was interested in to people who actually liked the 910 00:56:45,320 --> 00:56:48,759 Speaker 1: neutral But you no do both versions. I hadn't even 911 00:56:48,800 --> 00:56:51,680 Speaker 1: intested you're you're talking about like the the animal welfare, 912 00:56:52,560 --> 00:56:55,080 Speaker 1: because I'm talking about the guys who were out there 913 00:56:55,560 --> 00:57:02,160 Speaker 1: perhaps hunting trapp and eating selling. So again back to 914 00:57:02,520 --> 00:57:07,440 Speaker 1: muskrat is the king um Nutria had a pretty detrimental 915 00:57:07,480 --> 00:57:14,120 Speaker 1: impact on muskrat populations, both and competition displacement competition, so 916 00:57:14,480 --> 00:57:20,080 Speaker 1: Neutria UH would in the winter months in particular, dig 917 00:57:20,160 --> 00:57:24,760 Speaker 1: into muskrat houses to try to seek refuge from UH 918 00:57:25,120 --> 00:57:28,240 Speaker 1: cold weather or sit on top of them and basking 919 00:57:28,320 --> 00:57:31,720 Speaker 1: the sun and whatnot, and they would basically destroy the 920 00:57:32,120 --> 00:57:36,640 Speaker 1: muskrat house for muskrats. So, you know, we had a 921 00:57:36,760 --> 00:57:41,120 Speaker 1: less valuable critter having a negative impact on a really 922 00:57:41,200 --> 00:57:44,880 Speaker 1: important and so there was no opposition. So there was 923 00:57:44,920 --> 00:57:48,200 Speaker 1: really no one who had any kind of reasonable widespread 924 00:57:49,600 --> 00:57:52,960 Speaker 1: that you should We had, you know, one landowner that 925 00:57:53,040 --> 00:57:54,880 Speaker 1: I worked with, and that's something I should mention too, 926 00:57:55,000 --> 00:57:57,760 Speaker 1: is that this project would have been inconceivable without the 927 00:57:57,800 --> 00:58:00,400 Speaker 1: support of private landowners because more than for the new 928 00:58:00,440 --> 00:58:02,720 Speaker 1: tria that we eventually removed came from private land. So 929 00:58:03,440 --> 00:58:08,320 Speaker 1: having their support was critical. Uh, as a federal agency, 930 00:58:09,160 --> 00:58:12,280 Speaker 1: a non regulatory wildlife services can't just go on private 931 00:58:12,320 --> 00:58:14,960 Speaker 1: property and conduct our work. We have to have their 932 00:58:14,960 --> 00:58:16,960 Speaker 1: permission and blessing, and they have to prove all the 933 00:58:17,040 --> 00:58:20,360 Speaker 1: methods that we use. So I had one farmer that 934 00:58:20,560 --> 00:58:23,960 Speaker 1: we encountered who refused us access to his property because 935 00:58:24,480 --> 00:58:28,000 Speaker 1: according to what he said was he liked to eat 936 00:58:28,080 --> 00:58:31,520 Speaker 1: nutrient and I like to have him a little refrigerator 937 00:58:31,560 --> 00:58:33,440 Speaker 1: out in the back forty where he could go and 938 00:58:34,280 --> 00:58:39,480 Speaker 1: pluck some neutria for dinner on occasion. Um. Fortunately, it 939 00:58:39,600 --> 00:58:41,480 Speaker 1: wasn't a big area and we were able to sort 940 00:58:41,520 --> 00:58:44,440 Speaker 1: of trap around it and kind of pull the animals 941 00:58:44,520 --> 00:58:47,000 Speaker 1: that were on his property off, so it didn't impede 942 00:58:47,040 --> 00:58:50,840 Speaker 1: our progress in the long term. But what mostly come 943 00:58:50,880 --> 00:58:53,640 Speaker 1: down to, I think is there's a fairly significant anti 944 00:58:53,880 --> 00:58:57,920 Speaker 1: government sentiment in the community just about anywhere you go, 945 00:58:58,040 --> 00:59:03,240 Speaker 1: but it's particularly noticeable here in Dorchester County. You know, 946 00:59:03,320 --> 00:59:05,960 Speaker 1: there's a lot of government regulations with you know, wetlands 947 00:59:06,040 --> 00:59:11,960 Speaker 1: and and harvested animals, fish, commercial fisheries and crabs and 948 00:59:12,040 --> 00:59:14,120 Speaker 1: all that sort of thing. So there's just this sort 949 00:59:14,160 --> 00:59:22,840 Speaker 1: of general resentment to government intrusion and peoples right, So 950 00:59:24,760 --> 00:59:28,960 Speaker 1: there wasn't that opposition, um and then from the animal 951 00:59:29,040 --> 00:59:32,520 Speaker 1: welfare side there also wasn't opposition, which is interesting because 952 00:59:32,560 --> 00:59:36,480 Speaker 1: at the same time that we were undertaking this eradication campaign, 953 00:59:36,960 --> 00:59:40,760 Speaker 1: the Maryland Department Natural Resources was trying to deal with 954 00:59:41,080 --> 00:59:47,800 Speaker 1: a rapidly expanding mute swan population had reached, another non 955 00:59:47,880 --> 00:59:52,120 Speaker 1: native species that does an aquatic ecosystems very much what 956 00:59:52,360 --> 00:59:57,240 Speaker 1: neutria do to these uh wetland ecosystems. So they feed 957 00:59:57,320 --> 01:00:01,320 Speaker 1: on the mute swans feed on the subaquatic uh submerged 958 01:00:01,600 --> 01:00:06,560 Speaker 1: aquatic vegetation that's really critical habitat for all sorts of fish, crabs, 959 01:00:06,680 --> 01:00:08,800 Speaker 1: and you know, it provides a nursery for all these 960 01:00:08,880 --> 01:00:12,600 Speaker 1: juvenile species and they'll go in imagine we're talking about 961 01:00:12,640 --> 01:00:18,120 Speaker 1: imagine the most beautiful, picturesque swan that you would just 962 01:00:18,240 --> 01:00:22,120 Speaker 1: want to protect and cuddle exactly, like take pictures of 963 01:00:22,240 --> 01:00:25,520 Speaker 1: and cuddle with. Yep. And they had been a population 964 01:00:25,680 --> 01:00:30,919 Speaker 1: master of the marsh. They had reached populations as close 965 01:00:31,000 --> 01:00:35,840 Speaker 1: to and explain like their explain their impact on ducks. 966 01:00:36,280 --> 01:00:40,680 Speaker 1: So they're very aggressive birds, and they will chase native 967 01:00:40,720 --> 01:00:44,400 Speaker 1: waterfall and shorebirds and displace them off of uh, the 968 01:00:44,480 --> 01:00:47,000 Speaker 1: nesting grounds and feeding grounds and stuff. So they're they're 969 01:00:47,000 --> 01:00:50,200 Speaker 1: pretty uh yeah, they'll play nice, they don't play He's like, 970 01:00:50,280 --> 01:00:52,080 Speaker 1: you know, I'm gonna nest here, and no one's gonna 971 01:00:52,120 --> 01:00:56,040 Speaker 1: nest within a hundred yards of my nest exactly. And 972 01:00:56,200 --> 01:01:00,120 Speaker 1: so in response to that initiative that the Department at 973 01:01:00,160 --> 01:01:04,800 Speaker 1: your Resources what's taking on, uh, there were animal welfare 974 01:01:04,800 --> 01:01:10,360 Speaker 1: groups erecting uh billboards along Route fifty save our swans, 975 01:01:10,440 --> 01:01:13,600 Speaker 1: and messages to the governor Andrew the ducks saved the 976 01:01:13,640 --> 01:01:19,040 Speaker 1: swans manly. So to their credit, the DNR persisted and 977 01:01:19,120 --> 01:01:21,680 Speaker 1: they have whittled that population down last I heard too, 978 01:01:22,040 --> 01:01:25,680 Speaker 1: probably less than fifty. So that you know, we got 979 01:01:26,000 --> 01:01:30,080 Speaker 1: to two big ecological problems that have been you know, 980 01:01:30,160 --> 01:01:32,360 Speaker 1: dealt with in this region over the past, and a 981 01:01:32,400 --> 01:01:35,240 Speaker 1: lot of that anti sentiment went into swans and not 982 01:01:35,360 --> 01:01:39,400 Speaker 1: into roads. Yeah, it was perhaps a bit of a distraction, 983 01:01:39,520 --> 01:01:43,280 Speaker 1: but you know, I I think that there's a an element. 984 01:01:44,720 --> 01:01:48,160 Speaker 1: A lot of these groups are reliant on donations for 985 01:01:49,160 --> 01:01:54,080 Speaker 1: uh their solvency, and people are much more willing to 986 01:01:54,120 --> 01:01:56,520 Speaker 1: open their wallet for a big, beautiful white bird than 987 01:01:57,560 --> 01:02:00,200 Speaker 1: brown twenty pound rat. But is it a weird because 988 01:02:00,240 --> 01:02:02,920 Speaker 1: you're kind of defending I mean, it's not weird at 989 01:02:02,920 --> 01:02:06,600 Speaker 1: all because people are people, and you look at things 990 01:02:06,640 --> 01:02:11,120 Speaker 1: and you you know, we tend to like and admire 991 01:02:11,560 --> 01:02:15,320 Speaker 1: animals that strike us as beautiful. But if you're if 992 01:02:15,360 --> 01:02:18,160 Speaker 1: you're based on the idea that you're just defending sentient life, 993 01:02:18,920 --> 01:02:21,959 Speaker 1: like you don't want to see damage come to sentient life. 994 01:02:22,560 --> 01:02:25,840 Speaker 1: It shouldn't matter if it's a swan or a giant rodent. 995 01:02:26,480 --> 01:02:28,920 Speaker 1: It's like it's a sentient being. Yeah, a man as 996 01:02:28,920 --> 01:02:30,840 Speaker 1: a pig, as a dog, as a boy. But people 997 01:02:31,240 --> 01:02:36,400 Speaker 1: uh really tend to um get very interested in defending 998 01:02:36,520 --> 01:02:41,120 Speaker 1: things that are that are you know, instagram worthy creatures 999 01:02:41,880 --> 01:02:45,600 Speaker 1: more sold than the ugly ones. Well in the cynic 1000 01:02:45,680 --> 01:02:48,720 Speaker 1: in me says, it's you know, it's driven more by 1001 01:02:49,400 --> 01:02:53,400 Speaker 1: money than it is by by commitment to the cause, 1002 01:02:53,480 --> 01:02:56,360 Speaker 1: because that's where you get funding. Who's gonna who's gonna 1003 01:02:56,800 --> 01:03:00,920 Speaker 1: pull out their wallet to save nutrient? Yeah? Um, So 1004 01:03:01,040 --> 01:03:03,400 Speaker 1: we were fortunate not to have to battle that sort 1005 01:03:03,480 --> 01:03:10,440 Speaker 1: of of opposition to the program. And much to our surprise, 1006 01:03:10,520 --> 01:03:12,960 Speaker 1: when we started reaching out to private landowners, when we 1007 01:03:13,200 --> 01:03:17,120 Speaker 1: kind of expected, given the government sentiments anti government sentiments, 1008 01:03:17,160 --> 01:03:23,360 Speaker 1: that we would have a hard time, people were opening 1009 01:03:23,400 --> 01:03:25,720 Speaker 1: their doors to us. Uh. You know, I can't tell 1010 01:03:25,720 --> 01:03:28,000 Speaker 1: you how many kitchens I sat in with my laptop, 1011 01:03:28,080 --> 01:03:31,280 Speaker 1: computer and a little slide show talking to farmers and 1012 01:03:31,320 --> 01:03:34,160 Speaker 1: other landowners about what we were attempting to do and 1013 01:03:34,240 --> 01:03:39,200 Speaker 1: why it was important for their assistance and whatnot. And hundreds, 1014 01:03:39,280 --> 01:03:42,640 Speaker 1: hundreds of landowners gave us access to tens of thousands 1015 01:03:42,680 --> 01:03:45,960 Speaker 1: of private acres of private property so that we could 1016 01:03:46,000 --> 01:03:48,760 Speaker 1: be successful in this, And a lot of them did 1017 01:03:48,800 --> 01:03:52,480 Speaker 1: it sort of begrudgingly. You know, they didn't think we'd 1018 01:03:52,520 --> 01:03:55,280 Speaker 1: be successful. Uh, maybe you could catch them all. No, 1019 01:03:55,880 --> 01:03:58,520 Speaker 1: hell no, you'll never get the last one. I don't 1020 01:03:58,560 --> 01:04:00,720 Speaker 1: even know why you guys are wasting all this government 1021 01:04:00,760 --> 01:04:04,720 Speaker 1: money on there. But as one farmer I remember distinctly said, 1022 01:04:04,760 --> 01:04:06,280 Speaker 1: but I'm not going to be the stick in the 1023 01:04:06,400 --> 01:04:11,640 Speaker 1: mud to keep you guys from from trying, so, you know, 1024 01:04:11,840 --> 01:04:13,960 Speaker 1: and we had a couple of key landowners that came 1025 01:04:14,040 --> 01:04:17,040 Speaker 1: on board early that sort of helped us set the stage. 1026 01:04:17,120 --> 01:04:20,680 Speaker 1: And you know, well, if if so and so let 1027 01:04:20,760 --> 01:04:23,880 Speaker 1: you on his property, I guess I'll let you on mine. 1028 01:04:24,680 --> 01:04:29,120 Speaker 1: And I think one of the most the biggest compliments 1029 01:04:29,120 --> 01:04:32,000 Speaker 1: I ever got working on this project over the twelve 1030 01:04:32,120 --> 01:04:34,720 Speaker 1: years that I was on it was from that farmer 1031 01:04:34,800 --> 01:04:36,320 Speaker 1: that said he didn't want to be the stick in 1032 01:04:36,360 --> 01:04:39,080 Speaker 1: the mud to stand in the way of trying to 1033 01:04:39,200 --> 01:04:42,080 Speaker 1: do this. But he had told me that there's no 1034 01:04:42,160 --> 01:04:43,640 Speaker 1: way in hell we were ever going to get the 1035 01:04:43,760 --> 01:04:46,880 Speaker 1: last ones or probably even make a dent in the population. 1036 01:04:47,720 --> 01:04:49,800 Speaker 1: And a couple of years after we had sort of 1037 01:04:49,880 --> 01:04:53,960 Speaker 1: swept through his area, um, I ran into him at 1038 01:04:54,000 --> 01:04:55,520 Speaker 1: a gas station. He came up to me and said, 1039 01:04:55,560 --> 01:04:58,200 Speaker 1: you know, I never thought i'd say this, but I've 1040 01:04:58,240 --> 01:05:01,000 Speaker 1: gotta I gotta eat some crow here. He said, I 1041 01:05:01,160 --> 01:05:04,240 Speaker 1: never thought you guys could have done what you did 1042 01:05:04,320 --> 01:05:07,480 Speaker 1: eliminating those New Tria so I've been out and he 1043 01:05:07,600 --> 01:05:09,200 Speaker 1: was a bird hunter and he would take his dogs 1044 01:05:09,240 --> 01:05:11,520 Speaker 1: and he used to take his labs out and and 1045 01:05:11,800 --> 01:05:14,280 Speaker 1: uh hunt new Trea in the marshes behind his house. 1046 01:05:14,360 --> 01:05:16,920 Speaker 1: And he said, I haven't seen a new tree in 1047 01:05:17,000 --> 01:05:19,200 Speaker 1: two years. And I don't know where they all went. 1048 01:05:19,400 --> 01:05:22,760 Speaker 1: But how you guys did it? But you know, it's 1049 01:05:22,880 --> 01:05:26,360 Speaker 1: it's an amazing thing that you've accomplished here. So and 1050 01:05:26,640 --> 01:05:28,520 Speaker 1: you know, it wasn't like we just went out and 1051 01:05:28,600 --> 01:05:30,800 Speaker 1: trapped one time and removed. It was a constant effort 1052 01:05:30,840 --> 01:05:33,200 Speaker 1: of going back and sweeping through and looking and making 1053 01:05:33,240 --> 01:05:36,000 Speaker 1: sure that we didn't leave anything behind. We'll back up 1054 01:05:36,040 --> 01:05:38,200 Speaker 1: to how back up to the first when he first 1055 01:05:38,400 --> 01:05:42,560 Speaker 1: isolated a test area and went for it. How long, 1056 01:05:43,120 --> 01:05:46,000 Speaker 1: like like roughly, how big was the test area? And 1057 01:05:46,080 --> 01:05:49,439 Speaker 1: how long did it take to wipe out the test area? 1058 01:05:49,520 --> 01:05:52,840 Speaker 1: And then how did you monitor it to see that 1059 01:05:52,920 --> 01:05:56,560 Speaker 1: you've got them all? So the test area began with 1060 01:05:56,800 --> 01:06:02,360 Speaker 1: these original six acre parcels or study sites, three of each. 1061 01:06:03,480 --> 01:06:08,640 Speaker 1: I think they're a total of I can't even remember now. Uh, 1062 01:06:10,200 --> 01:06:15,000 Speaker 1: there were three study sites on each property, and so 1063 01:06:15,400 --> 01:06:17,880 Speaker 1: we went in and we we just intensively trapped them 1064 01:06:17,920 --> 01:06:22,640 Speaker 1: off We created a grid across the entire UH study site, 1065 01:06:22,680 --> 01:06:25,440 Speaker 1: and then we deployed our trappers in sort of rose 1066 01:06:25,520 --> 01:06:27,560 Speaker 1: and columns, like a checkerboard type thing, and then we 1067 01:06:28,200 --> 01:06:32,360 Speaker 1: trapped across and saturated the whole area with traps. But 1068 01:06:33,160 --> 01:06:36,920 Speaker 1: these were basically islands in a sea of occupied habitat, 1069 01:06:37,080 --> 01:06:40,840 Speaker 1: so we very quickly could tell that that just immigration, 1070 01:06:41,480 --> 01:06:44,880 Speaker 1: we were creating a big population sink, but that if 1071 01:06:44,920 --> 01:06:46,480 Speaker 1: we were really going to do this and test the 1072 01:06:46,560 --> 01:06:49,840 Speaker 1: feasibility of eradication on a landscape scale, we couldn't do 1073 01:06:49,880 --> 01:06:52,360 Speaker 1: it on these little six hundred acre plots. So we 1074 01:06:52,520 --> 01:06:55,720 Speaker 1: finished those out just to provide closure to that research project, 1075 01:06:56,200 --> 01:07:00,280 Speaker 1: and then we sort of reimagined the whole landscape, and 1076 01:07:00,440 --> 01:07:04,200 Speaker 1: starting at the western edge of Blackwater National Wildlife Refuge, 1077 01:07:04,760 --> 01:07:09,760 Speaker 1: we created a huge grid over that entire landscape. Forty 1078 01:07:09,840 --> 01:07:14,720 Speaker 1: acres about four or yards per side was the size 1079 01:07:14,760 --> 01:07:19,680 Speaker 1: of these trapping units, and we would stack our trappers 1080 01:07:19,800 --> 01:07:23,000 Speaker 1: up in in these rows and they would work east 1081 01:07:23,040 --> 01:07:26,280 Speaker 1: to west or west to east across the marsh, sequentially 1082 01:07:26,320 --> 01:07:31,840 Speaker 1: trapping each UH, trapping each grid square. So we basically 1083 01:07:31,920 --> 01:07:34,640 Speaker 1: had a swath of intensive trapping activity that kind of 1084 01:07:34,720 --> 01:07:37,800 Speaker 1: moved across the landscape. We call it rolling thunder because 1085 01:07:37,840 --> 01:07:41,880 Speaker 1: it sounded cool, right, So how many how many traps 1086 01:07:41,960 --> 01:07:44,720 Speaker 1: was rolling thunder running per night? And how many how 1087 01:07:44,760 --> 01:07:47,400 Speaker 1: many new trew we were stacking up per day? So 1088 01:07:48,120 --> 01:07:51,600 Speaker 1: we had we sat pretty heavily at the beginning. Um, 1089 01:07:53,000 --> 01:07:54,320 Speaker 1: there are a lot of new tree to catch. We 1090 01:07:54,400 --> 01:07:57,560 Speaker 1: wanted to remove them as quickly as possible, and so 1091 01:07:57,960 --> 01:08:03,240 Speaker 1: we uh, we would have hundreds each employee. We could 1092 01:08:03,280 --> 01:08:05,919 Speaker 1: have a couple of hundred traps out at any given time. 1093 01:08:06,760 --> 01:08:09,000 Speaker 1: So with fifteen employees working on it, you know, we 1094 01:08:09,240 --> 01:08:11,360 Speaker 1: we had a couple of thousand traps out at any 1095 01:08:11,520 --> 01:08:14,960 Speaker 1: any given point of time. And so what we do 1096 01:08:15,240 --> 01:08:18,240 Speaker 1: is we set the first row or column of of 1097 01:08:18,479 --> 01:08:22,360 Speaker 1: trapping grids up and then once that was saturated and 1098 01:08:22,520 --> 01:08:25,920 Speaker 1: things started to catch, you know, you'll see a really 1099 01:08:26,960 --> 01:08:29,479 Speaker 1: uh you'll catch a lot of animals at first, and 1100 01:08:29,560 --> 01:08:33,080 Speaker 1: then it'll start to dwindle off and tail off. So 1101 01:08:34,600 --> 01:08:37,640 Speaker 1: when we started to see that dwindling in the first row, 1102 01:08:37,720 --> 01:08:40,080 Speaker 1: we'd start taking some of those traps and rolling them 1103 01:08:40,120 --> 01:08:42,400 Speaker 1: into the second row and getting that one set up. 1104 01:08:43,000 --> 01:08:47,960 Speaker 1: So we just kind of leap frogged ourselves along and 1105 01:08:48,200 --> 01:08:50,640 Speaker 1: it worked quite well. And what we did looking at 1106 01:08:50,680 --> 01:08:54,360 Speaker 1: the numbers is that and this was one of the 1107 01:08:54,439 --> 01:08:56,599 Speaker 1: things that we tried to do at the very beginning 1108 01:08:56,600 --> 01:09:00,880 Speaker 1: of the research phase was this mark recapture population estimate. Uh. 1109 01:09:01,040 --> 01:09:04,120 Speaker 1: We found it was actually much more accurate to just 1110 01:09:04,240 --> 01:09:07,040 Speaker 1: go out and remove all the animals and one fell 1111 01:09:07,120 --> 01:09:09,559 Speaker 1: swoop and count them all. Um. And what it turns 1112 01:09:09,600 --> 01:09:12,960 Speaker 1: out is that we could trap out a particular grid 1113 01:09:14,080 --> 01:09:17,880 Speaker 1: in about three weeks time, and you know, so we 1114 01:09:17,960 --> 01:09:20,720 Speaker 1: would catch looking at the numbers over time, we'd catch 1115 01:09:20,760 --> 01:09:24,160 Speaker 1: about of all the animals that occupied that plot in 1116 01:09:24,240 --> 01:09:26,600 Speaker 1: the first week of trapping. By the end of the 1117 01:09:26,680 --> 01:09:31,080 Speaker 1: second week we tried captured. By the end of the 1118 01:09:31,160 --> 01:09:35,200 Speaker 1: third week, we've trapped about and then we would continue 1119 01:09:35,240 --> 01:09:38,479 Speaker 1: to catch onesies and twosies for that remaining five percent. 1120 01:09:38,720 --> 01:09:41,120 Speaker 1: Over the it might take an additional four weeks to 1121 01:09:41,640 --> 01:09:44,160 Speaker 1: get all those. So if you rolled into a new 1122 01:09:44,240 --> 01:09:47,559 Speaker 1: area it was real hot, and you did a main 1123 01:09:47,680 --> 01:09:49,599 Speaker 1: set and you got a few hundred traps in the water, 1124 01:09:50,200 --> 01:09:54,200 Speaker 1: what might the first catch be, like what percent trap 1125 01:09:54,479 --> 01:09:58,799 Speaker 1: of traps would be full? It depended on the trapper. 1126 01:09:58,880 --> 01:10:01,600 Speaker 1: There's a lot of variability there. Um. You know, some 1127 01:10:01,680 --> 01:10:04,479 Speaker 1: of the more experienced trappers would set fewer and more targeted, 1128 01:10:04,560 --> 01:10:07,280 Speaker 1: and others would set more broadly and trying to catch 1129 01:10:07,320 --> 01:10:10,040 Speaker 1: them out once. And I honestly, I can't tell you. 1130 01:10:10,280 --> 01:10:14,280 Speaker 1: I remember anybody that had a double digit day was 1131 01:10:14,680 --> 01:10:17,080 Speaker 1: feeling pretty good about their efforts. You know. You know 1132 01:10:17,120 --> 01:10:19,160 Speaker 1: what's the thing that I think of him when I 1133 01:10:19,200 --> 01:10:23,760 Speaker 1: used to trap muskrats is that when you went into 1134 01:10:24,040 --> 01:10:30,400 Speaker 1: an area, you couldn't if you were being like forward thinking, 1135 01:10:31,479 --> 01:10:34,000 Speaker 1: you wouldn't run more than two or you'd never run 1136 01:10:34,080 --> 01:10:36,479 Speaker 1: more than two or three nights because if you went 1137 01:10:36,560 --> 01:10:38,160 Speaker 1: into a marsh and and ice trapped a lot of 1138 01:10:38,240 --> 01:10:41,880 Speaker 1: isolated Okay, so if you went into a marsh and 1139 01:10:43,040 --> 01:10:45,160 Speaker 1: set up it's like I got a good number of 1140 01:10:45,200 --> 01:10:50,400 Speaker 1: muskrats the first night, you might so the first night's catch, 1141 01:10:50,520 --> 01:10:52,799 Speaker 1: so you sat during the day, let them sit overnight, 1142 01:10:52,920 --> 01:10:55,840 Speaker 1: check them the next day. That night you might run 1143 01:10:56,000 --> 01:11:01,720 Speaker 1: six full traps. I mean, if you knew what you're 1144 01:11:01,760 --> 01:11:06,080 Speaker 1: doing right the next night, you're gonna go back. And 1145 01:11:06,120 --> 01:11:09,960 Speaker 1: that's gonna drop down to if you if you were 1146 01:11:10,000 --> 01:11:14,679 Speaker 1: like a smart guy thinking about next year's season, you'd 1147 01:11:14,760 --> 01:11:17,800 Speaker 1: pull at that point. I could imagine and I know 1148 01:11:17,880 --> 01:11:20,240 Speaker 1: you never did this because it just wasn't it wasn't practical, 1149 01:11:20,280 --> 01:11:22,320 Speaker 1: and there's no motivation to do it. But I could 1150 01:11:22,360 --> 01:11:25,920 Speaker 1: imagine just now here listening to you, just imagine like, yeah, 1151 01:11:25,920 --> 01:11:28,920 Speaker 1: if you stayed in that marsh for two weeks, three 1152 01:11:29,040 --> 01:11:33,439 Speaker 1: weeks and kept running all those sets, you could absolutely yeah. 1153 01:11:33,840 --> 01:11:36,360 Speaker 1: But but there you're talking about isolated spots. It's hard. 1154 01:11:36,439 --> 01:11:38,040 Speaker 1: I mean, they obviously got there in the first place, 1155 01:11:38,080 --> 01:11:39,920 Speaker 1: but it's a you know, it's not it's not to 1156 01:11:40,000 --> 01:11:42,519 Speaker 1: matter them just swimming over to your area. They have 1157 01:11:42,600 --> 01:11:44,840 Speaker 1: to do it across land spring. You know, they'll migrate 1158 01:11:44,880 --> 01:11:47,240 Speaker 1: in the spring. But yeah, man, thinking about it now, 1159 01:11:47,280 --> 01:11:49,560 Speaker 1: I could totally picture you could just like but just 1160 01:11:50,120 --> 01:11:53,920 Speaker 1: for three weeks, nothing nothing, nothing. Oh here's another one, 1161 01:11:54,000 --> 01:11:56,680 Speaker 1: nothing nothing, there's another one. You'd eventually just probably get him. 1162 01:11:57,520 --> 01:12:01,000 Speaker 1: And so that is the most critical opponent. You know, 1163 01:12:01,040 --> 01:12:03,760 Speaker 1: there are a number of factors that you have to meet, 1164 01:12:03,960 --> 01:12:07,160 Speaker 1: criteria have to meet for eradication of any species to 1165 01:12:07,280 --> 01:12:12,600 Speaker 1: be feasible. So you know, in an eradication campaign like this, 1166 01:12:12,720 --> 01:12:16,040 Speaker 1: a trapping based eradication campaign, that's sort of like all 1167 01:12:16,080 --> 01:12:18,200 Speaker 1: the other kind of management things you hear about that 1168 01:12:18,760 --> 01:12:22,240 Speaker 1: eight percent of your energy is spent on this or whatnot. 1169 01:12:22,280 --> 01:12:27,160 Speaker 1: And what what we found out is that that like 1170 01:12:28,160 --> 01:12:32,680 Speaker 1: eight of our energy was spent capturing the last few 1171 01:12:32,800 --> 01:12:36,560 Speaker 1: animals in a population. They get trap shy on you, 1172 01:12:36,920 --> 01:12:39,560 Speaker 1: they get trap shy, and it's just, uh, you know, 1173 01:12:40,120 --> 01:12:42,960 Speaker 1: it gets down to fewer animals leave less signs, So 1174 01:12:43,080 --> 01:12:46,040 Speaker 1: where exactly are they? Um? And then that's a lot 1175 01:12:46,080 --> 01:12:48,960 Speaker 1: of activity on the marsh, so their their behavioral change 1176 01:12:49,000 --> 01:12:50,960 Speaker 1: will change the way they use the marsh, and they'll 1177 01:12:51,040 --> 01:12:53,800 Speaker 1: move and different times of the year there, you know, 1178 01:12:53,880 --> 01:12:56,160 Speaker 1: in the summertime they can go anywhere they want because 1179 01:12:56,160 --> 01:12:59,360 Speaker 1: there's an abundance of food everywhere. So you know, you'd 1180 01:12:59,360 --> 01:13:01,599 Speaker 1: find a little pocket and you'd set traps and then 1181 01:13:01,640 --> 01:13:03,720 Speaker 1: you did not catch anything and find that they've moved 1182 01:13:03,760 --> 01:13:06,040 Speaker 1: two hundred yards and so you gotta go find them. 1183 01:13:06,080 --> 01:13:08,519 Speaker 1: And are you guys toting around twenty two as well 1184 01:13:08,560 --> 01:13:11,639 Speaker 1: into shooting when you see them. Yeah, in the winter 1185 01:13:11,720 --> 01:13:15,600 Speaker 1: months in particular, we would uh do systematic hunting. So 1186 01:13:15,800 --> 01:13:19,400 Speaker 1: when the marsh froze, yeah, I don't know what that means, 1187 01:13:19,520 --> 01:13:23,000 Speaker 1: but when the uh, when the marsh would freeze in 1188 01:13:23,000 --> 01:13:25,080 Speaker 1: the wintertime, if we were so lucky, to get that 1189 01:13:25,200 --> 01:13:28,200 Speaker 1: and a little dusting of snow we could catch and 1190 01:13:28,720 --> 01:13:30,719 Speaker 1: we could shoot a lot of nutrient a short amount 1191 01:13:30,760 --> 01:13:33,000 Speaker 1: of time just getting out on that marsh and hunt them, 1192 01:13:33,720 --> 01:13:37,760 Speaker 1: you know. So that the whole concept of all of 1193 01:13:37,840 --> 01:13:42,240 Speaker 1: this energy being consumed using you know, catching the last 1194 01:13:42,360 --> 01:13:47,400 Speaker 1: few animals really forced us to think about uh kind 1195 01:13:47,439 --> 01:13:52,400 Speaker 1: of different strategies to find those remaining animals. And one 1196 01:13:52,439 --> 01:13:55,840 Speaker 1: of the things that you mentioned earlier asked if there 1197 01:13:55,880 --> 01:13:58,400 Speaker 1: was any opposition, and there actually was a little bit. 1198 01:13:58,880 --> 01:14:02,000 Speaker 1: Um there were some folks that thought that we should 1199 01:14:02,040 --> 01:14:04,800 Speaker 1: just offer a bounty and that the local trappers take 1200 01:14:04,840 --> 01:14:06,920 Speaker 1: care of the problem if you off for a bounty. Yeah, 1201 01:14:06,960 --> 01:14:08,760 Speaker 1: but they're only gonna they're not gonna chase after the 1202 01:14:08,840 --> 01:14:12,920 Speaker 1: last exactly. And so that was why we elected to 1203 01:14:14,400 --> 01:14:17,479 Speaker 1: we're essentially paying people to check empty traps, you know, 1204 01:14:17,960 --> 01:14:20,439 Speaker 1: the stuff they caught at the beginning. Well, that's that's 1205 01:14:20,479 --> 01:14:23,120 Speaker 1: the easy part. The hard part is catching the last few, 1206 01:14:23,280 --> 01:14:27,240 Speaker 1: and that's where we need to to keep that effort going. 1207 01:14:28,560 --> 01:14:33,720 Speaker 1: And so the the trick became how to find more 1208 01:14:33,800 --> 01:14:36,800 Speaker 1: efficiently those last remaining animals, And we tried a number 1209 01:14:36,840 --> 01:14:39,600 Speaker 1: of different things to do that. One of those was 1210 01:14:40,600 --> 01:14:44,880 Speaker 1: utilizing neutria themselves to find other neutria. So I had 1211 01:14:44,920 --> 01:14:47,240 Speaker 1: gone to some conferences and met with some folks that 1212 01:14:47,280 --> 01:14:52,479 Speaker 1: work internationally on invasive species eradication campaigns and whatnot. And 1213 01:14:52,560 --> 01:14:55,800 Speaker 1: in the gallopagus, they'd used Judas goats. So they had 1214 01:14:55,960 --> 01:14:59,439 Speaker 1: these goats that they captured and put radio collars on them, 1215 01:14:59,520 --> 01:15:02,200 Speaker 1: our gp S colors, and they let them go their 1216 01:15:02,479 --> 01:15:05,559 Speaker 1: social and gregarious creature. So they seek out other goats. 1217 01:15:05,640 --> 01:15:07,679 Speaker 1: And they would go up in a helicopter and find 1218 01:15:07,840 --> 01:15:11,639 Speaker 1: these Judas goats that they put out there, and then 1219 01:15:11,920 --> 01:15:16,639 Speaker 1: uh take out all of the other animals that all 1220 01:15:16,760 --> 01:15:18,919 Speaker 1: the new all the new friends that he'd made exactly. 1221 01:15:19,960 --> 01:15:24,840 Speaker 1: And so, knowing that nutria or social uh and sought 1222 01:15:24,880 --> 01:15:27,200 Speaker 1: out other nutria, I thought, oh, I wonder if this 1223 01:15:27,280 --> 01:15:31,040 Speaker 1: could work for us. So well, I'm pardon my ignorant ignorance. 1224 01:15:31,160 --> 01:15:36,960 Speaker 1: Where does the Judas um? What's like some kind of 1225 01:15:37,000 --> 01:15:40,800 Speaker 1: pagan household? You guessed it. That's how you do grow up, 1226 01:15:40,840 --> 01:15:48,360 Speaker 1: some kind of pagan household. So Judas betrayed Christ. Yeah, 1227 01:15:49,040 --> 01:15:53,320 Speaker 1: in the in the in the Last Supper painting. He's 1228 01:15:53,320 --> 01:15:54,880 Speaker 1: the only one that won't look at He's the only 1229 01:15:54,920 --> 01:15:59,880 Speaker 1: one not looking at Christ. Yeah, betrayed to the room. 1230 01:16:02,000 --> 01:16:05,880 Speaker 1: I'm glad he's here as the historian to answer that question. 1231 01:16:06,120 --> 01:16:08,519 Speaker 1: I know it was biblical, but I don't like to 1232 01:16:08,560 --> 01:16:10,840 Speaker 1: go too deep into that biblical stuff. So thank you 1233 01:16:11,120 --> 01:16:14,840 Speaker 1: well you have providing that insight. But but yeah, so 1234 01:16:15,320 --> 01:16:22,280 Speaker 1: we can't you here fell about name of Moses. Yeah, 1235 01:16:24,760 --> 01:16:29,880 Speaker 1: I didn't know how deep we had dive here. So 1236 01:16:30,080 --> 01:16:33,320 Speaker 1: we we actually got a special grant to look at 1237 01:16:34,120 --> 01:16:38,479 Speaker 1: whether or not this concept would be feasible because we 1238 01:16:38,520 --> 01:16:41,000 Speaker 1: didn't want to sort of detract from ongoing efforts to 1239 01:16:41,080 --> 01:16:44,840 Speaker 1: trap and remove by diverting funds. So we got some 1240 01:16:45,280 --> 01:16:48,719 Speaker 1: additional grant funding to support this effort from the National 1241 01:16:48,800 --> 01:16:53,240 Speaker 1: Fish and Wildlife Foundation and we captured a bunch of neutria. 1242 01:16:53,360 --> 01:16:57,000 Speaker 1: We had them surgically sterilized because we didn't want to 1243 01:16:57,040 --> 01:17:01,479 Speaker 1: be releasing animals that could then you know, escape us 1244 01:17:01,640 --> 01:17:04,320 Speaker 1: and begin breeding out there, and as quickly as they breed, 1245 01:17:04,360 --> 01:17:06,439 Speaker 1: we knew that could be a problem as as well 1246 01:17:06,520 --> 01:17:09,559 Speaker 1: as a perception issue. You know, here we are spending 1247 01:17:09,600 --> 01:17:14,920 Speaker 1: millions of dollars to so so they were all surgically sterilized. 1248 01:17:14,960 --> 01:17:19,879 Speaker 1: The males were vasectimized and the females had a tubal ligation. 1249 01:17:20,360 --> 01:17:25,200 Speaker 1: Had I had the former yeah, um, And then we 1250 01:17:25,280 --> 01:17:28,880 Speaker 1: put radio collars and in some instances they also had 1251 01:17:28,920 --> 01:17:31,439 Speaker 1: a GPS collar on them that stored the data that 1252 01:17:31,560 --> 01:17:34,680 Speaker 1: it collected on board. So at the time the technology 1253 01:17:34,840 --> 01:17:39,720 Speaker 1: was still pretty limiting that that we couldn't uh you know, 1254 01:17:39,840 --> 01:17:42,960 Speaker 1: these devices take a lot of power, and power means 1255 01:17:43,080 --> 01:17:46,559 Speaker 1: big batteries, and putting a big package on a smaller 1256 01:17:46,840 --> 01:17:50,519 Speaker 1: animal was especially one that's got the body confirmation of 1257 01:17:50,560 --> 01:17:55,960 Speaker 1: a nutrient, it's challenging. So we had these little devices 1258 01:17:56,040 --> 01:17:58,280 Speaker 1: custom made by a company in New Zealand that that 1259 01:17:58,960 --> 01:18:01,760 Speaker 1: made them recharge bowl and they would collect data for 1260 01:18:01,840 --> 01:18:04,840 Speaker 1: about a month and then it would store it all 1261 01:18:04,880 --> 01:18:07,080 Speaker 1: on board and we'd go back out and catch those 1262 01:18:07,120 --> 01:18:11,760 Speaker 1: animals and retrieve that GPS unit downloaded, and we could 1263 01:18:11,760 --> 01:18:14,599 Speaker 1: see everywhere that that neutria had been. Every ninety minutes 1264 01:18:14,640 --> 01:18:16,600 Speaker 1: it would collect the location. We could see where it 1265 01:18:16,680 --> 01:18:20,120 Speaker 1: had been over the past month. And he's an unfamiliar 1266 01:18:20,160 --> 01:18:22,360 Speaker 1: train when you turn them loose, correct we So the 1267 01:18:22,560 --> 01:18:26,439 Speaker 1: the intent here was to determine if we left nutria 1268 01:18:26,520 --> 01:18:28,880 Speaker 1: behind in areas we already tripped so we would release 1269 01:18:28,960 --> 01:18:31,200 Speaker 1: them into areas that we thought were devoid of nutria. 1270 01:18:32,000 --> 01:18:34,640 Speaker 1: Well o and behold, some of these animals started, uh, 1271 01:18:35,040 --> 01:18:38,240 Speaker 1: moving pretty widely across the landscape. They also, the color 1272 01:18:38,320 --> 01:18:40,920 Speaker 1: also had a standard radio transmitter, and so we could 1273 01:18:40,920 --> 01:18:42,519 Speaker 1: actually go out and track it on a day to 1274 01:18:42,600 --> 01:18:44,639 Speaker 1: day basis, so we would at least know the general 1275 01:18:44,760 --> 01:18:49,000 Speaker 1: vicinity they were in. And uh, we noticed, you know, 1276 01:18:49,120 --> 01:18:53,560 Speaker 1: after some wide movements across the landscape miles. Yeah, in 1277 01:18:53,680 --> 01:18:59,880 Speaker 1: some cases, uh, once they sort of sort of can 1278 01:19:00,000 --> 01:19:03,880 Speaker 1: elaborated into a smaller area, I thought, I wonder if 1279 01:19:03,920 --> 01:19:07,000 Speaker 1: there could be other nutrient there. You know, it's spending 1280 01:19:07,000 --> 01:19:08,800 Speaker 1: a lot of time in this one area. So we'd 1281 01:19:08,840 --> 01:19:10,920 Speaker 1: go out and we'd set our cage traps to try 1282 01:19:10,960 --> 01:19:14,720 Speaker 1: to catch these animals back and lo and behold, we 1283 01:19:14,840 --> 01:19:17,720 Speaker 1: caught a few animals that were not tagged. I was like, 1284 01:19:18,520 --> 01:19:20,880 Speaker 1: this could work in areas he thought you had trapped 1285 01:19:20,880 --> 01:19:24,920 Speaker 1: out right, So we knew there was a likelihood there 1286 01:19:24,960 --> 01:19:27,559 Speaker 1: are probably some animals. But you know, we had done 1287 01:19:27,560 --> 01:19:32,000 Speaker 1: the initial knockdown, uh, and we'd gone back through and 1288 01:19:32,240 --> 01:19:35,600 Speaker 1: it sort of mopped up. Those are kind of terminologies 1289 01:19:35,640 --> 01:19:38,519 Speaker 1: we used to describe the different phases of the eradication campaign, 1290 01:19:39,280 --> 01:19:42,840 Speaker 1: and so it wasn't a complete surprise to us, but 1291 01:19:42,960 --> 01:19:46,080 Speaker 1: it sure was handy to know where they were um 1292 01:19:46,439 --> 01:19:50,479 Speaker 1: from these critters. But the problem that we had tracking 1293 01:19:50,560 --> 01:19:53,400 Speaker 1: them on a daily basis with their radio collars is 1294 01:19:53,439 --> 01:19:55,960 Speaker 1: that because they moved so far, and because it's a 1295 01:19:56,400 --> 01:19:59,840 Speaker 1: thick vegetation environment and they're often in the water, so 1296 01:20:00,080 --> 01:20:03,280 Speaker 1: the signal from that device doesn't travel that far. You 1297 01:20:03,360 --> 01:20:05,920 Speaker 1: had to be pretty close to even to take the signal. 1298 01:20:06,760 --> 01:20:08,840 Speaker 1: So in cases where they might have moved two or 1299 01:20:08,880 --> 01:20:11,800 Speaker 1: three miles overnight, we would spend all day, you know, 1300 01:20:11,920 --> 01:20:16,400 Speaker 1: trying to find them. And so it turned out that 1301 01:20:16,600 --> 01:20:20,639 Speaker 1: while the technique worked to expose the existence of other 1302 01:20:20,720 --> 01:20:25,120 Speaker 1: nutrient in the environment, from an operational standpoint, it wasn't 1303 01:20:25,120 --> 01:20:28,240 Speaker 1: really practical. I mean the human resources that it took 1304 01:20:28,280 --> 01:20:30,519 Speaker 1: to just keep up with these animals. So what would 1305 01:20:30,560 --> 01:20:33,080 Speaker 1: have really been valuable was to have had a GPS 1306 01:20:33,160 --> 01:20:37,320 Speaker 1: collar that could, through either cell phone technology or satellite technology, 1307 01:20:37,920 --> 01:20:41,400 Speaker 1: relay that information to us remotely. And then so that 1308 01:20:41,520 --> 01:20:45,240 Speaker 1: exists for a lot of different species that it can 1309 01:20:45,360 --> 01:20:51,120 Speaker 1: carry that additional battery. I want to ask you questions 1310 01:20:51,200 --> 01:20:54,880 Speaker 1: not related to Nutria is related to tracking devices. So 1311 01:20:55,960 --> 01:21:01,120 Speaker 1: let's say, let's just say that, uh, you know that 1312 01:21:01,240 --> 01:21:07,320 Speaker 1: in your state there is a coloring program going on 1313 01:21:07,520 --> 01:21:13,240 Speaker 1: with elk, and you know there are some elk wearing collars. Um, 1314 01:21:15,800 --> 01:21:18,840 Speaker 1: what prevents a person who just likes to tinker with 1315 01:21:18,960 --> 01:21:23,880 Speaker 1: kind of stuff? What presents prevents a person from finding uh, 1316 01:21:25,000 --> 01:21:26,920 Speaker 1: from building up his own kid to go track that 1317 01:21:27,080 --> 01:21:30,479 Speaker 1: same to go track those same animals just to find 1318 01:21:30,479 --> 01:21:32,439 Speaker 1: out where elk hereds are so you can go on them. 1319 01:21:33,400 --> 01:21:35,759 Speaker 1: So a couple of things make it difficult. It's certainly 1320 01:21:35,800 --> 01:21:40,000 Speaker 1: not impossible, and that's been an issue in places, I'm sure. Um, 1321 01:21:40,720 --> 01:21:45,360 Speaker 1: But the FCC designates certain bandwidths for government research, and 1322 01:21:46,439 --> 01:21:48,240 Speaker 1: you know you can get these kinds of devices for 1323 01:21:48,320 --> 01:21:50,519 Speaker 1: hunting dogs and that sort of thing, So they sort 1324 01:21:50,520 --> 01:21:53,640 Speaker 1: of segregate the use categories of the different bandwidths, so 1325 01:21:53,720 --> 01:21:57,400 Speaker 1: that make it hurts or frequencies that that these things 1326 01:21:57,520 --> 01:22:03,639 Speaker 1: admit on. And so the the systems that are used 1327 01:22:03,720 --> 01:22:08,240 Speaker 1: for wildlife tracking, they're not generally available to the public. Um. 1328 01:22:08,720 --> 01:22:13,000 Speaker 1: And there it's fairly expensive too. So UM, it's it 1329 01:22:13,080 --> 01:22:18,360 Speaker 1: doesn't happen too often, but it's certainly can happen and 1330 01:22:18,439 --> 01:22:21,280 Speaker 1: there's no um so there would be like a there 1331 01:22:21,280 --> 01:22:23,360 Speaker 1: would be a component of law would come into it 1332 01:22:23,400 --> 01:22:25,640 Speaker 1: that you're using frequencies you're not supposed to use or 1333 01:22:25,720 --> 01:22:29,080 Speaker 1: is that just you know, I don't know. I don't 1334 01:22:29,080 --> 01:22:31,160 Speaker 1: even I've never had anybody say to me that they 1335 01:22:31,160 --> 01:22:33,479 Speaker 1: were trying to do this. This just always puzzled me 1336 01:22:34,560 --> 01:22:37,280 Speaker 1: that right that you always more if you would be 1337 01:22:37,360 --> 01:22:40,599 Speaker 1: like breaking a law to go out with a receiver 1338 01:22:41,479 --> 01:22:43,960 Speaker 1: of some sort and be like and also sort of 1339 01:22:44,040 --> 01:22:47,040 Speaker 1: tracking collared animals the same way that the researchers tracking 1340 01:22:47,080 --> 01:22:50,639 Speaker 1: them well, And so we had actually an interesting thing there. 1341 01:22:50,920 --> 01:22:54,280 Speaker 1: I'm not sure about the legality of it, but researchers 1342 01:22:54,320 --> 01:22:58,800 Speaker 1: in general, uh keep it really tightly in on color frequencies. 1343 01:22:59,000 --> 01:23:02,479 Speaker 1: You know, it's not for me aastion that they share readily, 1344 01:23:02,640 --> 01:23:05,600 Speaker 1: so they keep that pretty close to the vest so 1345 01:23:05,720 --> 01:23:07,599 Speaker 1: that you don't have problems like that. But we had 1346 01:23:07,760 --> 01:23:09,800 Speaker 1: we talked to the reason I first started thinking about this. 1347 01:23:09,880 --> 01:23:13,320 Speaker 1: Remember your friend up in Fairbanks who had all those 1348 01:23:13,360 --> 01:23:16,160 Speaker 1: moves collared. And one day I made a joke being like, 1349 01:23:16,200 --> 01:23:17,800 Speaker 1: I bet there's a lot of dudes and Fairbanks that 1350 01:23:17,960 --> 01:23:21,920 Speaker 1: like to track those moves with you because you just 1351 01:23:22,040 --> 01:23:25,080 Speaker 1: had ones that were out in honorable areas, you know, 1352 01:23:26,360 --> 01:23:29,639 Speaker 1: so sorry, go ahead. Well, so the way the government, 1353 01:23:29,720 --> 01:23:32,639 Speaker 1: the FCC divvys up these bandwidths, so like the federal 1354 01:23:32,680 --> 01:23:37,360 Speaker 1: government gets you know, one six, four dot whatever, and 1355 01:23:38,240 --> 01:23:44,080 Speaker 1: private or academic institutions get a totally different bandwidth. So 1356 01:23:44,479 --> 01:23:48,240 Speaker 1: in theory, you shouldn't have all of this sort of overlapping. Uh. 1357 01:23:48,439 --> 01:23:51,160 Speaker 1: You know, when when I call up a telemetry company 1358 01:23:51,200 --> 01:23:53,800 Speaker 1: and say order a little bunch of collars, I don't 1359 01:23:53,800 --> 01:23:57,720 Speaker 1: get to pick and choose my my frequencies. But I 1360 01:23:57,800 --> 01:24:00,519 Speaker 1: can be relatively assured that someone else who's doing research 1361 01:24:00,560 --> 01:24:03,240 Speaker 1: in the same area through a university is not going 1362 01:24:03,280 --> 01:24:05,679 Speaker 1: to be anywhere close to where I am because they're 1363 01:24:05,720 --> 01:24:09,519 Speaker 1: on a totally different bandwidth. But it turns out that 1364 01:24:10,280 --> 01:24:15,560 Speaker 1: you can get these errant signals. They're basically harmonics. I 1365 01:24:15,600 --> 01:24:17,840 Speaker 1: don't know exactly how it works. But there was another 1366 01:24:17,920 --> 01:24:20,160 Speaker 1: study going on in the area where we were doing 1367 01:24:20,720 --> 01:24:25,160 Speaker 1: Judas project that was looking at seka deer and so 1368 01:24:25,280 --> 01:24:27,000 Speaker 1: they had whole bunch of collars on seekret here and 1369 01:24:27,040 --> 01:24:32,360 Speaker 1: we had a whole bunch of collars on on neutria, 1370 01:24:32,720 --> 01:24:35,320 Speaker 1: and we were working in the same general area, and 1371 01:24:35,439 --> 01:24:37,400 Speaker 1: so we were out there looking. One day we got 1372 01:24:37,439 --> 01:24:39,479 Speaker 1: a nice strong signal on one of our nutria and 1373 01:24:40,320 --> 01:24:42,800 Speaker 1: we're going through and it's an area that we trapped out. 1374 01:24:43,080 --> 01:24:47,120 Speaker 1: We had a beautiful frozen marsh, snow all over the place, 1375 01:24:47,200 --> 01:24:49,759 Speaker 1: and we're tracking this thing um and we kept bumping 1376 01:24:49,800 --> 01:24:54,120 Speaker 1: the secret here and were like, no tracks in the 1377 01:24:54,160 --> 01:24:55,720 Speaker 1: snow from neutria, and we were like, what the heck 1378 01:24:55,840 --> 01:24:58,400 Speaker 1: is going on here? And so we started doing a 1379 01:24:58,439 --> 01:25:02,240 Speaker 1: little more digging, and finally I called the graduate student 1380 01:25:02,280 --> 01:25:05,360 Speaker 1: that was doing that project up. I said, do you 1381 01:25:05,400 --> 01:25:07,240 Speaker 1: buy any chance to have a secret deer down in 1382 01:25:07,520 --> 01:25:11,720 Speaker 1: this area? And he said that he did, And I 1383 01:25:11,760 --> 01:25:13,639 Speaker 1: asked him what the frequency was, and it was way 1384 01:25:13,720 --> 01:25:15,920 Speaker 1: off and it shouldn't even have been detectable on our 1385 01:25:17,280 --> 01:25:21,120 Speaker 1: radio system, but as it turns out, it was. We 1386 01:25:21,240 --> 01:25:25,240 Speaker 1: were getting these weird harmonics that would even though it 1387 01:25:25,320 --> 01:25:28,479 Speaker 1: was the wrong system, it still came in on our radio. 1388 01:25:29,320 --> 01:25:31,040 Speaker 1: Oh man. We had a whole bunch of data we 1389 01:25:31,120 --> 01:25:33,040 Speaker 1: had to throw out because it just we couldn't be 1390 01:25:33,160 --> 01:25:37,280 Speaker 1: sure that what we were tracking was was neutrient versus 1391 01:25:37,400 --> 01:25:39,640 Speaker 1: secret deer. So it was a little bit of a 1392 01:25:39,880 --> 01:25:45,040 Speaker 1: learning curve there, but it was really neat to be 1393 01:25:45,120 --> 01:25:47,880 Speaker 1: able to see how these animals used the landscape. Um, 1394 01:25:48,600 --> 01:25:51,600 Speaker 1: different animals released in different areas would move across the 1395 01:25:51,680 --> 01:25:57,280 Speaker 1: Blackwater Refuge system and it was amazing how similarly they 1396 01:25:58,000 --> 01:26:00,360 Speaker 1: used the landscape. You know, there are certain points almost 1397 01:26:00,439 --> 01:26:02,920 Speaker 1: every animal that we released, no matter where we released them, 1398 01:26:03,320 --> 01:26:05,840 Speaker 1: would pass by. So it gave us some insights on 1399 01:26:06,640 --> 01:26:09,759 Speaker 1: how we might utilize those points as either trapping sites 1400 01:26:09,840 --> 01:26:14,160 Speaker 1: or detection sites. Um. That's yeah. So you're saying the 1401 01:26:14,240 --> 01:26:16,679 Speaker 1: way that that the that the animals would be somehow 1402 01:26:17,760 --> 01:26:22,120 Speaker 1: funneled by the topography or landscape and whatever they're looking 1403 01:26:22,200 --> 01:26:26,960 Speaker 1: for would would bring them by the Yep. What would 1404 01:26:26,960 --> 01:26:30,839 Speaker 1: those features be points of points of land or channels, 1405 01:26:31,080 --> 01:26:36,400 Speaker 1: influence of too, tributaries, um, any sort of point that 1406 01:26:36,720 --> 01:26:40,640 Speaker 1: sticks out of So yeah, it was you'd give you 1407 01:26:40,640 --> 01:26:42,679 Speaker 1: a good idea where to look in the future. Exactly. 1408 01:26:43,000 --> 01:26:44,840 Speaker 1: So did it did it start to have as the 1409 01:26:44,920 --> 01:26:49,120 Speaker 1: project went along and you started to sort of get 1410 01:26:49,160 --> 01:26:56,280 Speaker 1: the sense like like holy shit, we maybe are going 1411 01:26:56,320 --> 01:26:59,799 Speaker 1: to catch them all? Did it feel like a hard stop? 1412 01:27:00,000 --> 01:27:02,600 Speaker 1: It was? It just like the son of like gradual 1413 01:27:03,520 --> 01:27:06,679 Speaker 1: wine down. Well, we had a lot of real estate 1414 01:27:06,760 --> 01:27:10,200 Speaker 1: to cover, so you know, when it was winding down 1415 01:27:10,240 --> 01:27:12,680 Speaker 1: in one area, we would move to another area, so 1416 01:27:13,520 --> 01:27:15,799 Speaker 1: you know, we'd get these sort of peaks and valleys 1417 01:27:15,840 --> 01:27:19,000 Speaker 1: in our capture eates. And that helped keep the staff 1418 01:27:19,520 --> 01:27:22,479 Speaker 1: sort of motivated because you know, even though their job 1419 01:27:22,479 --> 01:27:26,680 Speaker 1: as eradication, most trappers evaluate their success by how many 1420 01:27:26,720 --> 01:27:29,280 Speaker 1: critics I catch, And when you're trying to catch something 1421 01:27:29,360 --> 01:27:31,679 Speaker 1: that's not there, it's a pretty frustrating and real busting. 1422 01:27:31,720 --> 01:27:33,240 Speaker 1: No I would be if I had that job, I'll 1423 01:27:33,280 --> 01:27:35,200 Speaker 1: be really excited every time we moved into new area, 1424 01:27:35,680 --> 01:27:40,120 Speaker 1: and then then the and the guys were But eventually 1425 01:27:40,640 --> 01:27:43,479 Speaker 1: we hit all the known populations and it became this 1426 01:27:44,080 --> 01:27:47,720 Speaker 1: this is just drudgery of of kind of looking and 1427 01:27:47,800 --> 01:27:51,439 Speaker 1: looking and looking. And so when you're relying on an 1428 01:27:51,479 --> 01:27:55,040 Speaker 1: observer based system to find an animal, it's like looking 1429 01:27:55,080 --> 01:27:58,000 Speaker 1: for Bigfoot. You know, you might never see him, but 1430 01:27:58,120 --> 01:28:01,599 Speaker 1: you can't prove he doesn't exists. Right, that's the problem 1431 01:28:01,640 --> 01:28:04,120 Speaker 1: we had with big Foot. Yeah, exactly. So no one 1432 01:28:04,120 --> 01:28:08,240 Speaker 1: will ever believe you because you can't prove something doesn't exist. 1433 01:28:09,200 --> 01:28:11,360 Speaker 1: So one of the problems with an observer based system 1434 01:28:11,439 --> 01:28:13,920 Speaker 1: is that people get fatigued, they get bored, they're looking 1435 01:28:14,000 --> 01:28:16,240 Speaker 1: for something that needle in the haystack, they don't find it. 1436 01:28:16,400 --> 01:28:18,960 Speaker 1: They get distracted that maybe their text goes off and 1437 01:28:19,000 --> 01:28:22,400 Speaker 1: they happen to look at their phone while they drive 1438 01:28:22,520 --> 01:28:25,400 Speaker 1: by a floating nutrient turn in the water and they 1439 01:28:25,479 --> 01:28:29,000 Speaker 1: miss it. Right, So we wanted to develop some other 1440 01:28:29,200 --> 01:28:32,719 Speaker 1: detection techniques that wouldn't be so reliant on a human observer. 1441 01:28:33,120 --> 01:28:36,000 Speaker 1: The human observer is incredibly important. You can't get this 1442 01:28:36,080 --> 01:28:38,600 Speaker 1: work done without people. But you've got to come up 1443 01:28:38,640 --> 01:28:42,000 Speaker 1: with techniques that that sort of compensate for the weaknesses 1444 01:28:42,120 --> 01:28:45,960 Speaker 1: in different systems of detection. And so one of the 1445 01:28:46,080 --> 01:28:48,439 Speaker 1: things we had worked on that actually evolved from a 1446 01:28:48,520 --> 01:28:52,160 Speaker 1: trapping technique was the guys would would use what they 1447 01:28:52,200 --> 01:28:55,120 Speaker 1: called false beds. So they'd make a fake nutria bed 1448 01:28:55,200 --> 01:28:57,920 Speaker 1: along a waterway, set a foothold trap or a counter 1449 01:28:58,000 --> 01:28:59,960 Speaker 1: bear on it, and then the Nutria would come along 1450 01:29:00,000 --> 01:29:03,320 Speaker 1: and see that, and they're like, oh, hey, there's any trip. Yeah, yeah, 1451 01:29:03,400 --> 01:29:07,160 Speaker 1: just quick tech question, when you guys would set up 1452 01:29:07,240 --> 01:29:10,240 Speaker 1: a when you guys would set up a bed set 1453 01:29:10,320 --> 01:29:12,800 Speaker 1: like that, would you guys run a one way drown 1454 01:29:12,920 --> 01:29:16,160 Speaker 1: or locked down to a steak. Yeah, exactly. So we 1455 01:29:16,240 --> 01:29:18,439 Speaker 1: were required to check any trap that held an animal 1456 01:29:18,520 --> 01:29:21,200 Speaker 1: alive every twenty four hours, but we had an exemption 1457 01:29:21,280 --> 01:29:23,880 Speaker 1: that allowed us to go as long as seventies six 1458 01:29:24,000 --> 01:29:26,880 Speaker 1: I think, or maybe even ninety six hours if it 1459 01:29:27,000 --> 01:29:29,920 Speaker 1: was a killing trap. And so we did rely on 1460 01:29:30,080 --> 01:29:33,200 Speaker 1: the submersion sets to make sure that the animals were dead. 1461 01:29:33,240 --> 01:29:35,200 Speaker 1: And yeah, so we're talking about there is Imagine that 1462 01:29:35,280 --> 01:29:37,639 Speaker 1: you you got a trap set up at the surface 1463 01:29:37,680 --> 01:29:39,240 Speaker 1: of the water on a little better you could set 1464 01:29:39,320 --> 01:29:43,519 Speaker 1: set up just for illustrations sake. Imagine set on the Yeah, 1465 01:29:43,560 --> 01:29:47,840 Speaker 1: trap set on the bank of a river. That trap 1466 01:29:48,000 --> 01:29:50,680 Speaker 1: chain like the little tether of that trap has a 1467 01:29:50,720 --> 01:29:53,599 Speaker 1: thing called it one way slide on it. And then 1468 01:29:53,640 --> 01:29:56,320 Speaker 1: you drive a steak into the river bank right next 1469 01:29:56,360 --> 01:29:59,400 Speaker 1: to the trap and run a wire from that steak 1470 01:29:59,520 --> 01:30:03,000 Speaker 1: to another their steak that's driven down into the bottom 1471 01:30:03,080 --> 01:30:05,519 Speaker 1: of the river out in the deep water. And when 1472 01:30:05,560 --> 01:30:09,320 Speaker 1: the animal got hooked in that trap, especially aquatic road 1473 01:30:09,520 --> 01:30:11,120 Speaker 1: is instinctive, We're just gonna jump in the water and 1474 01:30:11,200 --> 01:30:14,200 Speaker 1: dive down to get away. So he runs that one 1475 01:30:14,280 --> 01:30:18,800 Speaker 1: way sliding lock down that wire, but it won't come 1476 01:30:18,800 --> 01:30:24,160 Speaker 1: back up right so that was an effective and important 1477 01:30:24,320 --> 01:30:27,200 Speaker 1: tool for us, and these these false beds became more 1478 01:30:27,240 --> 01:30:31,840 Speaker 1: and more important as a detection tool. So when we 1479 01:30:31,920 --> 01:30:35,920 Speaker 1: were going back through and and mopping up these areas, 1480 01:30:36,760 --> 01:30:38,760 Speaker 1: we didn't want to set traps if we didn't have 1481 01:30:39,200 --> 01:30:41,640 Speaker 1: good reason to believe there were new tria there. So 1482 01:30:41,760 --> 01:30:43,680 Speaker 1: we would just make these false beds and check them. 1483 01:30:43,760 --> 01:30:45,479 Speaker 1: But the problem is when the tide would come up 1484 01:30:45,520 --> 01:30:47,439 Speaker 1: and wash them away or the grass would grow up 1485 01:30:47,439 --> 01:30:51,000 Speaker 1: through them. So uh, one of the guys thought, well, hey, 1486 01:30:51,080 --> 01:30:53,000 Speaker 1: what if we put down a piece of plywood to 1487 01:30:53,120 --> 01:30:55,519 Speaker 1: keep the new grass froom growing up? And then well, 1488 01:30:55,600 --> 01:30:57,600 Speaker 1: that solved that problem, but it's still washed away on 1489 01:30:57,640 --> 01:31:00,080 Speaker 1: a high tide. So said, well what if we what 1490 01:31:00,120 --> 01:31:01,599 Speaker 1: if we put it on a little piece of styre 1491 01:31:01,680 --> 01:31:04,160 Speaker 1: foam and then so we we did that, and then 1492 01:31:04,200 --> 01:31:06,280 Speaker 1: we built a little rim around it to keep the 1493 01:31:06,320 --> 01:31:08,439 Speaker 1: wind and water from blowing stuff off the top, and 1494 01:31:08,560 --> 01:31:12,240 Speaker 1: we so he's this thing evolved into this detection platform 1495 01:31:12,479 --> 01:31:15,759 Speaker 1: that we would put these things out by the hundreds 1496 01:31:16,720 --> 01:31:19,840 Speaker 1: and check them for scat. And we wanted to get 1497 01:31:19,880 --> 01:31:23,000 Speaker 1: a sense of how nutrient interacted with these devices, so 1498 01:31:23,080 --> 01:31:26,040 Speaker 1: we put some remote trail cameras on them, and we 1499 01:31:26,120 --> 01:31:29,160 Speaker 1: got a couple interesting things, and they were staked with 1500 01:31:29,200 --> 01:31:31,280 Speaker 1: a fiberglass pul so if the water came up, they 1501 01:31:31,320 --> 01:31:33,519 Speaker 1: would actually float and they would so they would work 1502 01:31:33,520 --> 01:31:36,160 Speaker 1: all the time, whether the tide was higher down. And 1503 01:31:37,520 --> 01:31:39,800 Speaker 1: we got one video where to nutrient got on a 1504 01:31:39,880 --> 01:31:42,640 Speaker 1: single platform and they were twenty four inches square, so 1505 01:31:42,760 --> 01:31:44,960 Speaker 1: one it was already up there, and the second one 1506 01:31:45,040 --> 01:31:46,680 Speaker 1: got up and it was just too much weight and 1507 01:31:46,720 --> 01:31:49,960 Speaker 1: the whole thing kind of tipped sideways. Water swept over 1508 01:31:50,040 --> 01:31:53,400 Speaker 1: the whole thing, and then the first nutria got off 1509 01:31:53,479 --> 01:31:55,599 Speaker 1: and the second nutria got on and all the water 1510 01:31:55,760 --> 01:31:58,400 Speaker 1: and everything just kind of swept out the opening. We 1511 01:31:58,479 --> 01:32:00,880 Speaker 1: had one one side. It had an opening with a 1512 01:32:00,920 --> 01:32:03,040 Speaker 1: little brace on it that we could set a conterby 1513 01:32:03,120 --> 01:32:06,040 Speaker 1: trip out, so if we detected something, we could instantly 1514 01:32:06,280 --> 01:32:09,880 Speaker 1: turn it into a removal device. And so that got 1515 01:32:10,000 --> 01:32:12,720 Speaker 1: us thinking of like, man, we could be losing all 1516 01:32:12,760 --> 01:32:15,680 Speaker 1: these opportunities to detect sign if we're relying solely on 1517 01:32:15,840 --> 01:32:20,800 Speaker 1: the presence of scat to to do that. So one 1518 01:32:20,800 --> 01:32:22,519 Speaker 1: of the guys went back to the drawing board and 1519 01:32:23,120 --> 01:32:27,960 Speaker 1: came up with a really clever um use for snare 1520 01:32:28,040 --> 01:32:30,840 Speaker 1: cable or aircraft cable, and he took about a three 1521 01:32:30,920 --> 01:32:33,439 Speaker 1: or four inch piece of it and he frayed the 1522 01:32:33,560 --> 01:32:35,760 Speaker 1: ends of it, made a little tool to make it 1523 01:32:35,840 --> 01:32:37,920 Speaker 1: easy to do, and built it sort of bent all 1524 01:32:37,960 --> 01:32:41,080 Speaker 1: the little strands backwards, so it formed this like multi pronged, 1525 01:32:41,160 --> 01:32:44,280 Speaker 1: tiny little grappling hook. And then he built a little 1526 01:32:44,360 --> 01:32:47,200 Speaker 1: support wire like a snare support wire out of stainless 1527 01:32:47,200 --> 01:32:51,160 Speaker 1: steel welding rod and attached that to the platform and 1528 01:32:51,240 --> 01:32:54,080 Speaker 1: then made this little figure eight loop system on the 1529 01:32:54,280 --> 01:32:57,640 Speaker 1: end of the welding wire catch hair to put that 1530 01:32:57,960 --> 01:33:01,080 Speaker 1: snare in, and it would catch hair when the neutria 1531 01:33:01,360 --> 01:33:06,320 Speaker 1: brushed against it. So we we actually implemented that and 1532 01:33:06,360 --> 01:33:09,320 Speaker 1: then we used the cameras to determine what We actually 1533 01:33:09,360 --> 01:33:13,240 Speaker 1: did a little study looking at the detectability of neutria 1534 01:33:13,320 --> 01:33:16,840 Speaker 1: on these devices by the camera, by the presence of 1535 01:33:16,920 --> 01:33:19,840 Speaker 1: scat and by the presence of this hair snare, and 1536 01:33:19,960 --> 01:33:25,600 Speaker 1: the hair snare was like remarkably effective. It detected of 1537 01:33:25,680 --> 01:33:29,880 Speaker 1: the visits to the platform, whereas the was you don't 1538 01:33:29,920 --> 01:33:31,600 Speaker 1: know if he had reliable you don't know if he 1539 01:33:31,640 --> 01:33:34,880 Speaker 1: had to go or not exactly. So you know, all 1540 01:33:34,920 --> 01:33:37,519 Speaker 1: of these techniques sort of evolved out of necessity. And 1541 01:33:38,240 --> 01:33:40,640 Speaker 1: you know, this had never been done before using the 1542 01:33:40,720 --> 01:33:44,200 Speaker 1: tools that we had available to us anywhere in the world. 1543 01:33:44,320 --> 01:33:47,960 Speaker 1: There had been one successful eradication of neutria in England, 1544 01:33:48,520 --> 01:33:52,679 Speaker 1: but it was all done using bated cage traps on rafts, 1545 01:33:53,560 --> 01:33:56,040 Speaker 1: and so you know, we had to sort of forged 1546 01:33:56,080 --> 01:33:58,960 Speaker 1: our our own path on this and uh so all 1547 01:33:59,040 --> 01:34:04,880 Speaker 1: of this creativity stemming from our local trappers, uh was 1548 01:34:04,960 --> 01:34:08,240 Speaker 1: critical and sort of evolving our tools as the needs 1549 01:34:08,320 --> 01:34:12,599 Speaker 1: of the program changed as we approached eradication. So how 1550 01:34:12,640 --> 01:34:15,880 Speaker 1: many years into it or how many years did it 1551 01:34:15,960 --> 01:34:20,920 Speaker 1: take to get there? So we began trapping out the 1552 01:34:21,080 --> 01:34:23,839 Speaker 1: Coral black Water in two thousand and two. We trapped 1553 01:34:23,840 --> 01:34:32,280 Speaker 1: out the last known infested watershed in twenty oh gosh, 1554 01:34:33,840 --> 01:34:35,720 Speaker 1: I think the White Comico River was the last one 1555 01:34:35,760 --> 01:34:41,000 Speaker 1: we trapped out, and so I left the project in 1556 01:34:41,960 --> 01:34:46,320 Speaker 1: to take a promotion. And so the folks that have 1557 01:34:46,479 --> 01:34:50,120 Speaker 1: continued on in my absence have continued to look and 1558 01:34:50,320 --> 01:34:53,720 Speaker 1: they did clean out a few animals in that White 1559 01:34:53,760 --> 01:34:57,120 Speaker 1: Comical watershed the following year. But it has now been 1560 01:34:57,680 --> 01:35:00,240 Speaker 1: uh two and a half years since we've to the 1561 01:35:00,320 --> 01:35:04,160 Speaker 1: nutria anywhere in this ecosystem that we've trapped. Now, we've 1562 01:35:04,200 --> 01:35:07,960 Speaker 1: had some struggles. Uh, you know, funding. Uh, one of 1563 01:35:08,040 --> 01:35:11,920 Speaker 1: the criterias for eradication. Institutional support has to continue throughout 1564 01:35:11,960 --> 01:35:13,920 Speaker 1: the length of the project. So you've got to have 1565 01:35:14,120 --> 01:35:17,080 Speaker 1: that commitment to provide the resources to get the job done. 1566 01:35:17,960 --> 01:35:21,720 Speaker 1: But beencounters like to look at, you know, results, and 1567 01:35:21,760 --> 01:35:23,880 Speaker 1: the easiest result for them to measure is how many 1568 01:35:23,920 --> 01:35:26,400 Speaker 1: neutria we're catching. When you're not catching neutria, it's a 1569 01:35:26,439 --> 01:35:28,880 Speaker 1: lot easier to start sort of pulling back some of 1570 01:35:28,960 --> 01:35:31,920 Speaker 1: that funding. So we've had some issues there. The staff 1571 01:35:32,040 --> 01:35:35,760 Speaker 1: is about a third of what it was at the 1572 01:35:36,200 --> 01:35:40,120 Speaker 1: peak of our efforts, but we've tried to combat that 1573 01:35:40,240 --> 01:35:43,680 Speaker 1: by also increasing our efficiency. One of the things that 1574 01:35:43,840 --> 01:35:47,800 Speaker 1: I had started before I left the project was this 1575 01:35:48,320 --> 01:35:51,920 Speaker 1: concept of using scat sniffing dogs to to help us 1576 01:35:52,200 --> 01:35:56,519 Speaker 1: find neutria and more importantly in helping us find nutria, 1577 01:35:56,520 --> 01:35:59,839 Speaker 1: because we had used dogs throughout the program. To eliminate 1578 01:36:00,000 --> 01:36:05,040 Speaker 1: Autria is a hunting technique, but when you're trying to 1579 01:36:05,160 --> 01:36:08,920 Speaker 1: prove they call it proof of freedom in the the 1580 01:36:09,360 --> 01:36:12,680 Speaker 1: invasive species Proof that an area is free of an 1581 01:36:12,760 --> 01:36:22,040 Speaker 1: invasive species UM is to layer multiple detection technologies and 1582 01:36:22,200 --> 01:36:25,639 Speaker 1: techniques on top of each other to give you enhanced 1583 01:36:25,760 --> 01:36:29,280 Speaker 1: confidence that that there's nothing there. You know, you can 1584 01:36:29,320 --> 01:36:33,920 Speaker 1: never prove that they're gone, but by building a strong 1585 01:36:34,080 --> 01:36:37,240 Speaker 1: case of circumstantial evidence, you can reach a conclusion that's 1586 01:36:37,800 --> 01:36:41,280 Speaker 1: that the nutrient have been eradicated. Like human observation, no 1587 01:36:41,360 --> 01:36:43,840 Speaker 1: one's seen any They're not showing up in your fur 1588 01:36:44,000 --> 01:36:47,960 Speaker 1: catchers on your floats. Dogs aren't finding their droppings exactly, 1589 01:36:48,760 --> 01:36:51,800 Speaker 1: And so the dogs true value here is not in 1590 01:36:52,240 --> 01:36:55,720 Speaker 1: really finding nutria, although it would be important if they did. 1591 01:36:56,080 --> 01:36:59,360 Speaker 1: Their true values and is enhancing our confidence that they 1592 01:36:59,400 --> 01:37:03,280 Speaker 1: are in fact on um because their sense of smell 1593 01:37:03,400 --> 01:37:06,519 Speaker 1: is remarkable and a human observer can just is going 1594 01:37:06,560 --> 01:37:08,640 Speaker 1: off visual cues and you know, you walk through this 1595 01:37:08,800 --> 01:37:10,880 Speaker 1: marsh and you you know, you were in it today 1596 01:37:11,120 --> 01:37:14,479 Speaker 1: hunting for secret here, and imagine would be a good 1597 01:37:15,680 --> 01:37:18,439 Speaker 1: we were moved to probably seventy or eighty neutria from 1598 01:37:18,479 --> 01:37:21,360 Speaker 1: that general area right in front of where you were hunting. 1599 01:37:22,280 --> 01:37:26,800 Speaker 1: That that was all prime neutria habitant. Yep, that's the 1600 01:37:26,840 --> 01:37:30,320 Speaker 1: kind of stuff they liked. Yeah, So imagine having to 1601 01:37:30,439 --> 01:37:33,240 Speaker 1: cover like every inch of that marsh between where you 1602 01:37:33,320 --> 01:37:35,640 Speaker 1: were in that far wood line, and we had to 1603 01:37:35,680 --> 01:37:38,200 Speaker 1: scour that. And when you're down to maybe there's only 1604 01:37:38,240 --> 01:37:40,519 Speaker 1: one or two neutria left in there, what are the 1605 01:37:40,560 --> 01:37:43,040 Speaker 1: odds that that two, three, or even four people just 1606 01:37:43,200 --> 01:37:45,840 Speaker 1: walking back and forth they're actually gonna find that one 1607 01:37:45,880 --> 01:37:48,599 Speaker 1: little piece of scat or whatnot. So well, Steve had 1608 01:37:48,640 --> 01:37:51,360 Speaker 1: a dead deer d fifty yards from and you know, 1609 01:37:51,600 --> 01:37:55,679 Speaker 1: without GPS technology, hard to find that. Yeah, it's hard. 1610 01:37:55,720 --> 01:37:57,360 Speaker 1: I had to walk around here a little bit even, 1611 01:37:57,479 --> 01:38:00,840 Speaker 1: I like I shot away point from tree stand to 1612 01:38:00,840 --> 01:38:04,360 Speaker 1: where our thought he was, you know, just like bearing 1613 01:38:04,520 --> 01:38:07,880 Speaker 1: and in a distance. I walked over there and and 1614 01:38:08,439 --> 01:38:10,759 Speaker 1: I was like, I don't know, he's gotta being here somewhere, 1615 01:38:10,800 --> 01:38:14,800 Speaker 1: and just yeah, you can miss a lot. So right now, 1616 01:38:17,840 --> 01:38:21,640 Speaker 1: if I like, do you think that, if you know 1617 01:38:22,880 --> 01:38:25,000 Speaker 1: within twenty mile radius where we're sitting right now, you 1618 01:38:25,000 --> 01:38:27,880 Speaker 1: don't think there's a single living neutrient or do you 1619 01:38:27,920 --> 01:38:29,960 Speaker 1: think there's got to be one that you've you've missed? 1620 01:38:30,479 --> 01:38:33,040 Speaker 1: You know that wouldn't be one, right one. There's no 1621 01:38:33,160 --> 01:38:36,439 Speaker 1: worry to one boy and one girl exactly. And so 1622 01:38:37,840 --> 01:38:42,920 Speaker 1: what we know about their ability to reproduce and their 1623 01:38:42,960 --> 01:38:46,840 Speaker 1: detectability when they reached some sort of critical mass. Uh, 1624 01:38:48,680 --> 01:38:51,120 Speaker 1: we would reasonably expect to be able to find them 1625 01:38:51,240 --> 01:38:54,320 Speaker 1: if over the course of two years that you know, 1626 01:38:54,400 --> 01:38:57,719 Speaker 1: we've been looking that if they had sort of rebuilt 1627 01:38:58,040 --> 01:39:01,280 Speaker 1: a small population, we probably have detected it. I'm pretty 1628 01:39:01,320 --> 01:39:04,120 Speaker 1: confident of that. If you talk about a small population, 1629 01:39:04,160 --> 01:39:08,559 Speaker 1: you have to get in there and just like rapid response, 1630 01:39:09,640 --> 01:39:13,120 Speaker 1: get it out as as quickly as possible. So I'm 1631 01:39:13,160 --> 01:39:15,760 Speaker 1: actually I had a lot of confidence in the crew 1632 01:39:15,840 --> 01:39:18,080 Speaker 1: that we put together, in the folks that have remained 1633 01:39:18,120 --> 01:39:23,800 Speaker 1: on the project are really committed and talented, and I 1634 01:39:23,920 --> 01:39:27,040 Speaker 1: think that the fact that they're not finding anything is 1635 01:39:27,120 --> 01:39:29,479 Speaker 1: an indication that there's nothing to find. So how are 1636 01:39:29,560 --> 01:39:31,160 Speaker 1: those people that are still working at how are they 1637 01:39:31,240 --> 01:39:36,679 Speaker 1: coping with the job anymore? Well, so, you know, about 1638 01:39:36,720 --> 01:39:40,040 Speaker 1: five of them now are detected dog handlers, and and honestly, 1639 01:39:40,800 --> 01:39:46,040 Speaker 1: part of my rationale for for getting that tool off 1640 01:39:46,080 --> 01:39:48,840 Speaker 1: the ground is a bit of a morayal boost. People 1641 01:39:48,920 --> 01:39:53,240 Speaker 1: love to work with dogs, and so even if on 1642 01:39:53,360 --> 01:39:56,320 Speaker 1: a daily basis they're not getting their own personal satisfaction 1643 01:39:56,400 --> 01:39:59,599 Speaker 1: by finding a neutria, they're getting some satisfaction of working 1644 01:39:59,680 --> 01:40:01,720 Speaker 1: with a dog and training the dog and making sure 1645 01:40:01,760 --> 01:40:04,840 Speaker 1: the dog is is still up on on the top 1646 01:40:04,880 --> 01:40:09,439 Speaker 1: of things. UM. But even then it's it's a challenge, 1647 01:40:09,600 --> 01:40:12,760 Speaker 1: you know. The the the guys that have been with 1648 01:40:12,840 --> 01:40:16,479 Speaker 1: a project from the very beginning talk fondly of the 1649 01:40:16,520 --> 01:40:20,320 Speaker 1: good old days, you know, and they miss it. Be careful. 1650 01:40:20,479 --> 01:40:23,600 Speaker 1: One of them might just keep their jobs to retirement. 1651 01:40:25,640 --> 01:40:27,320 Speaker 1: I wish I could tell the story we heard the 1652 01:40:27,400 --> 01:40:31,600 Speaker 1: other day, not about neutria, but question already I have 1653 01:40:31,760 --> 01:40:34,280 Speaker 1: it is, UM, because I got one more main question? 1654 01:40:35,200 --> 01:40:38,840 Speaker 1: Then can you mind away from one more main one? UM? 1655 01:40:40,600 --> 01:40:45,040 Speaker 1: Have so other places that deal with neutria? Okay, looking 1656 01:40:45,040 --> 01:40:47,560 Speaker 1: at like you know, they still deal with in Louisiana 1657 01:40:47,640 --> 01:40:52,880 Speaker 1: to like have our other places that are dealing with infestations? 1658 01:40:53,960 --> 01:40:56,640 Speaker 1: Are they looking to what you guys did. Are you 1659 01:40:57,280 --> 01:41:00,360 Speaker 1: in exporting the technologies or is it just like so 1660 01:41:00,680 --> 01:41:04,200 Speaker 1: regionally specific. No, Actually, that's a great question. And one 1661 01:41:04,240 --> 01:41:07,280 Speaker 1: of the one of the criteria that was built into 1662 01:41:07,320 --> 01:41:10,200 Speaker 1: the funding legislation that supported this program from beginning was 1663 01:41:10,320 --> 01:41:13,400 Speaker 1: that that one of our missions was to help to 1664 01:41:13,560 --> 01:41:16,760 Speaker 1: educate everyone else is dealing with nutria with tools and 1665 01:41:16,840 --> 01:41:21,320 Speaker 1: techniques that can be helpful elsewhere. UM. So we've we 1666 01:41:21,400 --> 01:41:23,680 Speaker 1: put a lot of effort into outreach and working with 1667 01:41:23,800 --> 01:41:26,720 Speaker 1: other UH folks that are dealing with nutria. We still 1668 01:41:26,760 --> 01:41:29,479 Speaker 1: get a lot of calls from from people dealing with nutria. 1669 01:41:29,520 --> 01:41:33,160 Speaker 1: There's been a new outbreak or invasion in California where 1670 01:41:33,200 --> 01:41:36,439 Speaker 1: they had previously there's been a small population that had 1671 01:41:36,479 --> 01:41:38,759 Speaker 1: been eliminated, so they didn't think they had a problem, 1672 01:41:38,800 --> 01:41:41,760 Speaker 1: but I think stuff is now moving down from from 1673 01:41:41,880 --> 01:41:47,840 Speaker 1: the Northern States there and UH just this past summer, 1674 01:41:47,920 --> 01:41:52,720 Speaker 1: I was invited to visit UH Holland where they have 1675 01:41:53,120 --> 01:41:56,760 Speaker 1: a problem with Nutria invading from Germany, coming across the 1676 01:41:56,840 --> 01:42:00,719 Speaker 1: border and in infiltrating their canal system which is critical 1677 01:42:00,840 --> 01:42:05,519 Speaker 1: to life as they know it in Holland. So you know, 1678 01:42:05,600 --> 01:42:08,559 Speaker 1: we've we've shared this technology, these techniques with other folks 1679 01:42:08,600 --> 01:42:11,000 Speaker 1: about the world. But I started to talk earlier and 1680 01:42:11,000 --> 01:42:13,040 Speaker 1: I'm not sure I ever finished the thought on the 1681 01:42:13,120 --> 01:42:16,000 Speaker 1: sort of the biological criteria have to be able to 1682 01:42:16,080 --> 01:42:19,840 Speaker 1: meet UH in order for oh, yeah, I got yours 1683 01:42:20,800 --> 01:42:22,880 Speaker 1: be feasible, and one of those is you have to 1684 01:42:22,920 --> 01:42:24,840 Speaker 1: be able to put every animal at risk. And that's 1685 01:42:24,840 --> 01:42:29,040 Speaker 1: why getting the private landowners on board was so important. 1686 01:42:29,920 --> 01:42:37,599 Speaker 1: UM and oh gosh, all these thoughts running through my head. 1687 01:42:37,640 --> 01:42:39,920 Speaker 1: I've forgotten all the things that I used to be 1688 01:42:39,960 --> 01:42:43,720 Speaker 1: able to rattle off in all my slide presentations. But yeah, 1689 01:42:43,880 --> 01:42:49,040 Speaker 1: it's been a little while. Um, so you have to 1690 01:42:49,120 --> 01:42:53,720 Speaker 1: have techniques that are effective. So we you know, work 1691 01:42:53,800 --> 01:42:59,559 Speaker 1: on all these different, uh, different techniques, techniques that are 1692 01:42:59,600 --> 01:43:03,400 Speaker 1: socially acceptable. So you know, they're toxic ins that could 1693 01:43:03,479 --> 01:43:07,200 Speaker 1: be applied to eliminate nutria, but they're probably also going 1694 01:43:07,240 --> 01:43:10,800 Speaker 1: to eliminate a whole lot of other species. So so, uh, 1695 01:43:11,360 --> 01:43:15,040 Speaker 1: that's an important feature. But when you start looking at 1696 01:43:15,120 --> 01:43:18,160 Speaker 1: those criteria and applying them to other areas, we were 1697 01:43:18,280 --> 01:43:23,240 Speaker 1: very fortunate in um Maryland. The del Marva Peninsula, which 1698 01:43:23,320 --> 01:43:26,720 Speaker 1: is that land spit between the Chespeake Bay and the 1699 01:43:26,840 --> 01:43:29,720 Speaker 1: Atlantic Ocean that's comprised of the state of Delaware and 1700 01:43:29,800 --> 01:43:33,439 Speaker 1: the eastern shore of the Chespeake portions of Maryland and 1701 01:43:33,520 --> 01:43:37,280 Speaker 1: Virginia is essentially an island, and the nutria that we 1702 01:43:37,360 --> 01:43:41,320 Speaker 1: had here were introduced. They didn't expand from somewhere else. 1703 01:43:41,479 --> 01:43:44,599 Speaker 1: It was an expanding population and they're limited in their 1704 01:43:44,640 --> 01:43:49,439 Speaker 1: northern distribution by winter weather. So essentially we had an 1705 01:43:49,479 --> 01:43:51,560 Speaker 1: island a big island, but it you know, it was 1706 01:43:51,600 --> 01:43:55,080 Speaker 1: an island that we could sort of get around and eliminate. 1707 01:43:56,000 --> 01:43:58,519 Speaker 1: So if you look at places like Louisiana, which have 1708 01:43:58,800 --> 01:44:02,240 Speaker 1: an almost identical ecological problem to what we have here, 1709 01:44:02,280 --> 01:44:04,479 Speaker 1: they have the same sort of coastal marshes, the same 1710 01:44:04,960 --> 01:44:08,320 Speaker 1: suite of plant species that are impacted, and the same 1711 01:44:08,760 --> 01:44:14,639 Speaker 1: effects of nutria, but it's orders of magnitudes greater than 1712 01:44:15,200 --> 01:44:17,560 Speaker 1: both in size and in numbers of nutria than we 1713 01:44:17,680 --> 01:44:20,679 Speaker 1: have here. They don't have a severe winners, so they've 1714 01:44:20,720 --> 01:44:24,960 Speaker 1: got more reproduction taking place, and they estimate in Louisiana, 1715 01:44:25,680 --> 01:44:28,160 Speaker 1: in the ten years the first ten years after nutria 1716 01:44:28,240 --> 01:44:32,240 Speaker 1: were introduced, as few as probably twenty animals had attained 1717 01:44:32,439 --> 01:44:37,800 Speaker 1: populations of twenty million UM. And they just they're everywhere, 1718 01:44:37,880 --> 01:44:41,240 Speaker 1: and they're in adjacent states. So there's there's just, uh, 1719 01:44:43,680 --> 01:44:45,800 Speaker 1: there's no way to get around the problem, you know. 1720 01:44:46,000 --> 01:44:51,000 Speaker 1: So that one uh, one of the other criteria is 1721 01:44:51,120 --> 01:44:54,400 Speaker 1: the risk of reinvasion needs to be near zero. So 1722 01:44:54,640 --> 01:44:56,800 Speaker 1: on the del Marva Peninsula we had that very low 1723 01:44:56,960 --> 01:44:59,920 Speaker 1: risk of reinvasion unless someone choose to bring one in Louisia, 1724 01:45:00,000 --> 01:45:03,960 Speaker 1: an unfortunately, uh, surrounded by a sea of Nutria, so 1725 01:45:04,439 --> 01:45:07,120 Speaker 1: they'll just had a constant influx. So they took a 1726 01:45:07,200 --> 01:45:12,160 Speaker 1: much different approach um and rather than hiring trappers to 1727 01:45:12,920 --> 01:45:16,000 Speaker 1: you know, trap down to a near zero population level, 1728 01:45:16,640 --> 01:45:20,519 Speaker 1: they looked at the history of Nutria trapping activities in 1729 01:45:20,640 --> 01:45:24,200 Speaker 1: relation to the firm market, and they established a bounty 1730 01:45:24,280 --> 01:45:30,439 Speaker 1: system based on historical PELP prices UH to encourage and 1731 01:45:30,520 --> 01:45:36,360 Speaker 1: incentivize trappers to UH pursue Nutria in the hopes that 1732 01:45:36,479 --> 01:45:41,280 Speaker 1: they could depress the population enough that they wouldn't see 1733 01:45:41,280 --> 01:45:43,959 Speaker 1: the amount of damage that they did. So they monitored 1734 01:45:44,000 --> 01:45:46,720 Speaker 1: that by conducting annual vegetation surveys and they take a 1735 01:45:46,760 --> 01:45:50,600 Speaker 1: helicopter fly these They had like miles of transax or 1736 01:45:50,640 --> 01:45:53,439 Speaker 1: something like that, and every time they reached a neutria, 1737 01:45:54,320 --> 01:45:57,000 Speaker 1: it's called to eat out. When they sort of destroy 1738 01:45:57,360 --> 01:46:00,320 Speaker 1: an area of marsh, that fly a circle around it, 1739 01:46:00,360 --> 01:46:03,320 Speaker 1: and they do that every year and measure the size 1740 01:46:03,320 --> 01:46:06,200 Speaker 1: of those circles and and if they were contracting, then 1741 01:46:06,240 --> 01:46:08,400 Speaker 1: they were sort of moving in the right direction. If 1742 01:46:08,439 --> 01:46:11,240 Speaker 1: they were getting bigger, they weren't taking enough neutria. So 1743 01:46:12,880 --> 01:46:17,280 Speaker 1: to put it in context with Maryland, over the life 1744 01:46:17,320 --> 01:46:20,080 Speaker 1: of the project, and you remember earlier I said, you know, 1745 01:46:20,360 --> 01:46:23,920 Speaker 1: as many as fifty thousand new tria on Blackwater Refuge alone. 1746 01:46:25,120 --> 01:46:28,120 Speaker 1: We've removed about fourteen thousand neutria over the lifespan of 1747 01:46:28,200 --> 01:46:33,240 Speaker 1: this project, far fewer than we anticipated at the beginning. UH. 1748 01:46:33,439 --> 01:46:38,040 Speaker 1: And if you compare that to Louisiana there uh incentive 1749 01:46:38,080 --> 01:46:41,920 Speaker 1: program they remove. Their goal is to remove about four 1750 01:46:42,000 --> 01:46:45,880 Speaker 1: hundred thousand new tria a year, and that seems to 1751 01:46:45,920 --> 01:46:48,559 Speaker 1: be enough the target that keeps that marsh damage at 1752 01:46:48,600 --> 01:46:53,040 Speaker 1: a somewhat acceptable level. So other places that have expressed 1753 01:46:53,080 --> 01:46:58,960 Speaker 1: an interest. UH. We've had visits from folks in South Korea, China, Israel. UM. 1754 01:46:59,479 --> 01:47:01,639 Speaker 1: We were invit did to participate in a big workshop 1755 01:47:01,920 --> 01:47:06,920 Speaker 1: in uh the Pacific Northwest if you probably ten years 1756 01:47:06,960 --> 01:47:12,040 Speaker 1: ago now um uh. But then it even crosses species. 1757 01:47:12,120 --> 01:47:14,600 Speaker 1: We were actually asked to come and consult on a 1758 01:47:14,680 --> 01:47:20,320 Speaker 1: beaver infestation problem in Gyodo, Fuego and Extreme South America, 1759 01:47:20,360 --> 01:47:22,920 Speaker 1: where they were introduced ironically about the same time that 1760 01:47:23,000 --> 01:47:27,479 Speaker 1: new trou were introduced here. We did a large ye 1761 01:47:27,880 --> 01:47:32,280 Speaker 1: you see cappy Barra yep, cav bar last winter man 1762 01:47:32,439 --> 01:47:36,560 Speaker 1: neutra actually pretty closely related to cappy bara. UM. But 1763 01:47:36,680 --> 01:47:42,560 Speaker 1: the Argentinian military brought North American beaver into establish of 1764 01:47:42,800 --> 01:47:46,280 Speaker 1: fur for military clothing. So they released them and they 1765 01:47:46,320 --> 01:47:48,639 Speaker 1: thought they'd have trappers go out, and well, it turns 1766 01:47:48,640 --> 01:47:51,400 Speaker 1: out they didn't have a trapping community. People didn't know 1767 01:47:51,479 --> 01:47:55,120 Speaker 1: what to do with them, and they expanded and proliferated, 1768 01:47:55,200 --> 01:47:58,559 Speaker 1: and now they're should have tried to introduce trappers too. 1769 01:48:00,200 --> 01:48:02,519 Speaker 1: Well that's cut a few cut a male and a 1770 01:48:02,560 --> 01:48:07,719 Speaker 1: female trapper loose over our Our agency has been approached 1771 01:48:07,760 --> 01:48:12,720 Speaker 1: about conducting some training activities down there to help kind 1772 01:48:12,720 --> 01:48:16,439 Speaker 1: of educate folks on how to effectively trap beaver and whatnot. 1773 01:48:16,960 --> 01:48:20,200 Speaker 1: But part of that is, you know, the goals eradication 1774 01:48:20,360 --> 01:48:23,360 Speaker 1: and and trapping for fur and trapping for eradication are 1775 01:48:23,400 --> 01:48:29,280 Speaker 1: two different for reasons we explored here tonight. Yeah. It's 1776 01:48:29,280 --> 01:48:32,519 Speaker 1: a good story, man, Yeah, it uh, it was a 1777 01:48:32,520 --> 01:48:34,920 Speaker 1: pretty exciting chapter in my career. And yeah, the story 1778 01:48:34,960 --> 01:48:36,840 Speaker 1: starts to make its own grave. You know, it's like 1779 01:48:36,960 --> 01:48:40,800 Speaker 1: just got like a lot to it. Man. Yeah, yann, 1780 01:48:40,960 --> 01:48:44,719 Speaker 1: what was your what was your conservation through eradication? Steve 1781 01:48:44,800 --> 01:48:47,360 Speaker 1: first told me. Now I was like, man, that's a ringer. Um. 1782 01:48:47,920 --> 01:48:49,599 Speaker 1: Now you pretty much answered it. But I was gonna 1783 01:48:49,600 --> 01:48:53,759 Speaker 1: ask like where the closest next population is to the south, 1784 01:48:54,960 --> 01:48:57,439 Speaker 1: and then you know if it could become this way. 1785 01:48:57,479 --> 01:48:59,600 Speaker 1: But it sounds like you have that the barrier of 1786 01:48:59,760 --> 01:49:02,639 Speaker 1: the uh Chests Speake Bay that they're not going to swim. 1787 01:49:03,000 --> 01:49:07,560 Speaker 1: They are in relatively close proximity. In Virginia Beach is 1788 01:49:07,600 --> 01:49:10,000 Speaker 1: probably the closest place that we know about. Virginia Beach 1789 01:49:10,040 --> 01:49:13,960 Speaker 1: has something. Yeah, that whole stretch that's southeast southeastern Virginia 1790 01:49:14,080 --> 01:49:17,519 Speaker 1: and northeastern North Carolina, that whole complex down on the 1791 01:49:18,160 --> 01:49:22,200 Speaker 1: Eblemarle Sound and Alligator River National Wildlife Refuge, Madam Mesquite 1792 01:49:22,240 --> 01:49:25,880 Speaker 1: National Wildlife Refuge, and then right up into the intensively 1793 01:49:26,040 --> 01:49:32,479 Speaker 1: urbanized areas in Virginia Beach, all the the drainage systems 1794 01:49:32,520 --> 01:49:35,439 Speaker 1: and whatnot. There's a new Tria on the Naval Air 1795 01:49:35,520 --> 01:49:40,400 Speaker 1: Force bases down there. It's got alligators. Man. Well, in Louisiana, 1796 01:49:40,479 --> 01:49:43,360 Speaker 1: it is the h A lot of the the new 1797 01:49:43,439 --> 01:49:46,840 Speaker 1: tree trappers sell the carcasses to delligator farmers. So they 1798 01:49:46,880 --> 01:49:48,960 Speaker 1: get five bucks for the tail that they turn in 1799 01:49:49,120 --> 01:49:50,840 Speaker 1: and then they get a buck or two for the 1800 01:49:50,920 --> 01:49:57,519 Speaker 1: carcass that they provide. So so, but you know the 1801 01:49:57,640 --> 01:50:00,800 Speaker 1: Chestpeake Bay. The mouth of Chestpeake Bay is about fourteen miles. Why, 1802 01:50:00,920 --> 01:50:03,880 Speaker 1: that would be a pretty significant dispersal effort to get 1803 01:50:04,120 --> 01:50:08,960 Speaker 1: a nutrie to swim across that. Uh So, the narrowest 1804 01:50:09,000 --> 01:50:12,400 Speaker 1: point of the bay outside of the mouth of are 1805 01:50:12,520 --> 01:50:15,479 Speaker 1: the head of the bay where the Susquehanna River feeds 1806 01:50:15,520 --> 01:50:18,120 Speaker 1: into it is actually right here in Dorchester County. It's 1807 01:50:18,120 --> 01:50:21,960 Speaker 1: about four and a half miles across uh to Calvert County. 1808 01:50:22,160 --> 01:50:25,800 Speaker 1: And in the nineteen nineties they found a small population 1809 01:50:25,920 --> 01:50:30,759 Speaker 1: in a tributary of the Potomac River and the Maryland 1810 01:50:30,800 --> 01:50:33,800 Speaker 1: Department Natural Resources trapped about fifty animals out there and 1811 01:50:33,880 --> 01:50:37,759 Speaker 1: they've they've never seen a resurgence of that population, although 1812 01:50:37,760 --> 01:50:41,680 Speaker 1: occasionally we get reports. You guys need to move on 1813 01:50:42,080 --> 01:50:47,080 Speaker 1: the Norway rat eliminate rats. You know what interesting tidbit is. 1814 01:50:47,280 --> 01:50:51,760 Speaker 1: Uh My brother was telling me the anchorage is the 1815 01:50:52,120 --> 01:50:57,439 Speaker 1: world's largest port city with no rats. Interesting. And when 1816 01:50:57,479 --> 01:51:00,160 Speaker 1: they get a boat that comes in, if they if 1817 01:51:00,200 --> 01:51:02,559 Speaker 1: they inspect it, if they find rats on that boat 1818 01:51:02,600 --> 01:51:06,479 Speaker 1: does not touch shore. Yeah, well, you've been to New Zealand. 1819 01:51:06,520 --> 01:51:12,519 Speaker 1: Their biosecurity uh procedures are pretty remarkable. Fly in they 1820 01:51:12,880 --> 01:51:16,599 Speaker 1: put out literature about making sure your hiking boots don't 1821 01:51:16,640 --> 01:51:19,439 Speaker 1: have weed seeds and you're camping equipment. Yeah, did you 1822 01:51:19,479 --> 01:51:21,479 Speaker 1: get did you did you get messed with for having 1823 01:51:21,560 --> 01:51:23,599 Speaker 1: muddy boots coming in New Zealand. I don't know if 1824 01:51:23,600 --> 01:51:24,920 Speaker 1: we got. I can't remember now if we get it 1825 01:51:25,000 --> 01:51:27,080 Speaker 1: was a long time ago we got messed with, or 1826 01:51:27,439 --> 01:51:30,920 Speaker 1: if it was just protocol. But they basically took all 1827 01:51:31,479 --> 01:51:35,800 Speaker 1: my fishing gear, not not the flies and reels and 1828 01:51:35,880 --> 01:51:39,600 Speaker 1: polls themselves, but the you know, clothing type gear and 1829 01:51:39,680 --> 01:51:42,040 Speaker 1: all of our camping gear, and they took it into 1830 01:51:42,080 --> 01:51:44,160 Speaker 1: a room and I think you can actually watch it, 1831 01:51:44,240 --> 01:51:46,720 Speaker 1: and they basically fumigated it and then they put in 1832 01:51:46,800 --> 01:51:50,320 Speaker 1: a plastic bag to here you go have fun. Yeah. Yeah, 1833 01:51:50,360 --> 01:51:53,040 Speaker 1: they take their invasive species stuff pretty seriously, and they're 1834 01:51:53,400 --> 01:51:58,599 Speaker 1: not that they don't have a thousand invasive and they're 1835 01:51:58,640 --> 01:52:03,599 Speaker 1: working aggressively to try to eliminate them as much as possible. So, yeah, 1836 01:52:03,640 --> 01:52:05,840 Speaker 1: we saw traps all over the place for what they 1837 01:52:05,960 --> 01:52:12,519 Speaker 1: call stout, which is uh um Stevie. You any final 1838 01:52:12,640 --> 01:52:14,880 Speaker 1: things that fall so far out of context that you 1839 01:52:14,920 --> 01:52:18,479 Speaker 1: didn't get a chance to bring them up? Oh man, 1840 01:52:19,360 --> 01:52:22,240 Speaker 1: so much I was initially wondering if I could possibly 1841 01:52:22,400 --> 01:52:29,280 Speaker 1: talk about this for two hours, and but yeah, you know, 1842 01:52:29,360 --> 01:52:31,080 Speaker 1: I think that the one thing I'd like to just 1843 01:52:31,280 --> 01:52:36,160 Speaker 1: reiterate as a concluding thought is is a plea for 1844 01:52:36,360 --> 01:52:42,639 Speaker 1: people to you know, recognize the importance of traditionally urban 1845 01:52:43,040 --> 01:52:46,880 Speaker 1: or rural values and activities and the contributions they make 1846 01:52:46,960 --> 01:52:50,920 Speaker 1: to modern day conservation. You know, this project would have 1847 01:52:51,000 --> 01:52:54,479 Speaker 1: been extremely difficult without the local knowledge we were able 1848 01:52:54,520 --> 01:53:00,400 Speaker 1: to tap into through the trapping community. And uh, you know, 1849 01:53:00,479 --> 01:53:03,439 Speaker 1: as I mentioned earlier, that crosses species, and you know, 1850 01:53:03,600 --> 01:53:06,080 Speaker 1: it's a segment of our society that's much maligned and 1851 01:53:06,400 --> 01:53:09,840 Speaker 1: in their routinely efforts to eliminate trapping and the the 1852 01:53:10,000 --> 01:53:15,000 Speaker 1: kinds of traditional wildlife management tools that that that we've used, 1853 01:53:15,200 --> 01:53:18,920 Speaker 1: and you know, they still have an incredibly important place 1854 01:53:19,080 --> 01:53:22,439 Speaker 1: in a modern society. And and uh, I guess that's 1855 01:53:22,479 --> 01:53:28,360 Speaker 1: something even I think, Uh, non trapping sports people uh 1856 01:53:28,680 --> 01:53:31,519 Speaker 1: often don't think about trapping that much and don't have 1857 01:53:31,640 --> 01:53:37,200 Speaker 1: the support very ill there's a very uh ill advised 1858 01:53:37,240 --> 01:53:41,559 Speaker 1: group folks um that tried to This is my concluding 1859 01:53:41,600 --> 01:53:47,240 Speaker 1: thought that the last year, during the last election cycle, 1860 01:53:48,200 --> 01:53:52,479 Speaker 1: I tried to get through an initiative in Montana to 1861 01:53:52,640 --> 01:53:57,280 Speaker 1: ban trapping on public land. And you know, I thought 1862 01:53:57,360 --> 01:53:59,920 Speaker 1: of you know, I had in my in my pocket, 1863 01:54:00,320 --> 01:54:02,120 Speaker 1: like a dozen reasons why I thought that was a 1864 01:54:02,200 --> 01:54:05,160 Speaker 1: bad idea. One of the things in my pocket that 1865 01:54:05,360 --> 01:54:07,120 Speaker 1: one of the things that my pocket did not include 1866 01:54:07,240 --> 01:54:11,160 Speaker 1: was the one the director of the State Wildlife Agency 1867 01:54:12,520 --> 01:54:18,320 Speaker 1: UM came out and said, why uh would we be 1868 01:54:18,400 --> 01:54:24,400 Speaker 1: putting ourselves into a situation to pay government people to 1869 01:54:24,560 --> 01:54:27,240 Speaker 1: do something that you have other people paying us to 1870 01:54:27,320 --> 01:54:30,960 Speaker 1: go do, Speaking of beaver removal, He's like, we do 1871 01:54:31,080 --> 01:54:35,400 Speaker 1: not have the budget right to take care of all 1872 01:54:35,800 --> 01:54:40,040 Speaker 1: of the conflicts agricultural road other all of just the 1873 01:54:40,160 --> 01:54:45,240 Speaker 1: beaver conflicts alone. Yep, you got a whole squad of 1874 01:54:45,280 --> 01:54:48,840 Speaker 1: people out there who you know, are running like little 1875 01:54:48,880 --> 01:54:51,080 Speaker 1: small businesses traveling beavers, and you want to take that 1876 01:54:51,160 --> 01:54:53,840 Speaker 1: away from someone's gonna those beavers gonna cause problems and 1877 01:54:53,880 --> 01:54:57,320 Speaker 1: then we're gonna have government guys doing it. M You know, 1878 01:54:57,480 --> 01:55:03,560 Speaker 1: it was that that matter was soundly defeated. Yeah. I 1879 01:55:03,600 --> 01:55:08,800 Speaker 1: actually had a second concluding thought. Concluding concluding, want to 1880 01:55:09,320 --> 01:55:12,440 Speaker 1: just make sure I emphasize the importance of sort of 1881 01:55:12,520 --> 01:55:18,200 Speaker 1: partnerships in tackling monumental conservation issues like this, and without 1882 01:55:18,320 --> 01:55:21,000 Speaker 1: the joint efforts of the U. S. Fish and Wildlife Service, 1883 01:55:21,080 --> 01:55:24,680 Speaker 1: the Maryland Department and Natural Resources, Tutor Farms, and the 1884 01:55:24,880 --> 01:55:27,960 Speaker 1: hundreds of private landowners that supported, as well as a 1885 01:55:28,120 --> 01:55:33,440 Speaker 1: ton of of several dozen of non governmental organizations like 1886 01:55:33,480 --> 01:55:36,840 Speaker 1: the Maryland Trappers Association and the Salesbury Zoo and other 1887 01:55:36,960 --> 01:55:39,680 Speaker 1: groups that were that sort of rally around the environment 1888 01:55:39,760 --> 01:55:42,160 Speaker 1: that that really helped to generate the support, to keep 1889 01:55:42,200 --> 01:55:44,320 Speaker 1: the funding in place, and and all that sort of thing. 1890 01:55:44,600 --> 01:55:50,440 Speaker 1: Those partnerships are just critical for um the successive programs 1891 01:55:50,520 --> 01:55:54,840 Speaker 1: like this right, so be a good partner. That's my 1892 01:55:54,960 --> 01:55:58,400 Speaker 1: final concluder. Thank you for listening. Thank you