1 00:00:05,192 --> 00:00:07,080 - And today we have Maggie Batton, 2 00:00:07,080 --> 00:00:08,470 who is a graduate student 3 00:00:08,470 --> 00:00:10,520 with the Rubenstein School of Environment 4 00:00:10,520 --> 00:00:14,920 and Natural Resources within the University of Vermont. 5 00:00:14,920 --> 00:00:17,210 Maggie is also a biological science technician 6 00:00:17,210 --> 00:00:20,143 with USDA APHIS Wildlife Services Program. 7 00:00:21,090 --> 00:00:24,180 Maggie's talk today will be 8 00:00:24,180 --> 00:00:26,860 Evaluating ONRAB Vaccine Bait Fate 9 00:00:26,860 --> 00:00:28,320 and the Use of Bait Stations 10 00:00:28,320 --> 00:00:30,420 in the Oral Rabies Vaccination Program 11 00:00:30,420 --> 00:00:31,793 in Burlington, Vermont. 12 00:00:33,870 --> 00:00:35,230 - [Maggie] Thank you for being here. 13 00:00:35,230 --> 00:00:37,020 My name is Maggie Batton. 14 00:00:37,020 --> 00:00:38,810 My co-presenters are James Murdoch 15 00:00:38,810 --> 00:00:40,250 from the University of Vermont, 16 00:00:40,250 --> 00:00:42,210 Richard Chipman from the United States' 17 00:00:42,210 --> 00:00:43,320 Department of agriculture, 18 00:00:43,320 --> 00:00:45,460 National Rabies Management Program. 19 00:00:45,460 --> 00:00:48,950 Amy Gilbert from the USDA National Wildlife Research Center, 20 00:00:48,950 --> 00:00:53,330 Shiloh Johnson also from the USDA NWRC and Fred Pogmore 21 00:00:53,330 --> 00:00:55,660 from the USDA Wildlife Services. 22 00:00:55,660 --> 00:00:57,860 And today I'm going to be discussing my research, 23 00:00:57,860 --> 00:00:59,850 looking into improving the success 24 00:00:59,850 --> 00:01:02,460 of rabies vaccination strategies in wildlife 25 00:01:02,460 --> 00:01:04,623 across the greater Burlington area. 26 00:01:06,490 --> 00:01:08,690 So you've most likely heard of rabies before, 27 00:01:08,690 --> 00:01:09,780 but you might not be aware 28 00:01:09,780 --> 00:01:12,440 of how dangerous this virus actually is. 29 00:01:12,440 --> 00:01:14,400 It is a fatal zoonotic disease 30 00:01:14,400 --> 00:01:15,840 that's spread through the bite or scratch 31 00:01:15,840 --> 00:01:17,730 of an infected animal. 32 00:01:17,730 --> 00:01:19,810 So if somebody is exposed to this virus, 33 00:01:19,810 --> 00:01:22,460 it needs to be quickly treated because if it's not, 34 00:01:22,460 --> 00:01:25,320 then it is fatal in both wildlife and in people. 35 00:01:25,320 --> 00:01:27,920 So this makes it one of the world's most deadly diseases, 36 00:01:27,920 --> 00:01:30,950 and it's also a major global public health concern. 37 00:01:30,950 --> 00:01:31,783 And because of this, 38 00:01:31,783 --> 00:01:33,000 there is a global initiative 39 00:01:33,000 --> 00:01:36,520 to eliminate dog mediated rabies virus by 2030, 40 00:01:36,520 --> 00:01:37,900 because this is the most common source 41 00:01:37,900 --> 00:01:39,533 of exposure in the world. 42 00:01:41,230 --> 00:01:44,800 So we have eliminated the canine variant from North America, 43 00:01:44,800 --> 00:01:46,320 but we still have the highest diversity 44 00:01:46,320 --> 00:01:48,970 of carnivores rabies vector species. 45 00:01:48,970 --> 00:01:51,320 Different strains tend to dominate specific regions 46 00:01:51,320 --> 00:01:54,160 of the US, but within the Eastern United States, 47 00:01:54,160 --> 00:01:57,060 the raccoon variant is a major source of exposure. 48 00:01:57,060 --> 00:01:58,920 And although very few people actually die 49 00:01:58,920 --> 00:02:00,379 from rabies in the US, 50 00:02:00,379 --> 00:02:04,690 about 55,000 people are exposed to this virus every year, 51 00:02:04,690 --> 00:02:07,443 which requires an extremely expensive treatment. 52 00:02:09,000 --> 00:02:12,645 So the USDA National Rabies Management Program was created 53 00:02:12,645 --> 00:02:16,040 to manage rabies virus in the US and they've developed 54 00:02:16,040 --> 00:02:18,840 a plan to manage the raccoon variant in the Eastern US 55 00:02:18,840 --> 00:02:20,400 over the next 30 years. 56 00:02:20,400 --> 00:02:23,360 And they have two main goals for managing raccoon rabies. 57 00:02:23,360 --> 00:02:26,020 The first is to stop the spread of rabies virus outside 58 00:02:26,020 --> 00:02:28,550 of areas enzoonotic to the disease 59 00:02:28,550 --> 00:02:31,260 and to vaccinate a sufficient percentage 60 00:02:31,260 --> 00:02:32,930 of target populations, 61 00:02:32,930 --> 00:02:37,930 which is thought to be about 70% to create herd immunity. 62 00:02:38,690 --> 00:02:39,523 And they do this 63 00:02:39,523 --> 00:02:42,160 through a program known as Oral Rabies Vaccination, 64 00:02:42,160 --> 00:02:46,710 which involves distributing baits on a landscape 65 00:02:46,710 --> 00:02:49,110 that contain liquid vaccines that are covered 66 00:02:49,110 --> 00:02:50,210 in an attractant. 67 00:02:50,210 --> 00:02:52,680 So the idea is that the animal will bite into the bait, 68 00:02:52,680 --> 00:02:54,760 exposing themselves to the vaccine, 69 00:02:54,760 --> 00:02:56,830 which will in turn activate 70 00:02:56,830 --> 00:02:59,753 a rabies virus neutralizing antibody response. 71 00:03:01,840 --> 00:03:04,110 So these baits are currently being distributed 72 00:03:04,110 --> 00:03:05,700 over a range of landscapes. 73 00:03:05,700 --> 00:03:07,080 And there are a few different methods 74 00:03:07,080 --> 00:03:09,710 that are used to most effectively deploy these baits, 75 00:03:09,710 --> 00:03:12,780 which is dependent on the landscape that's being targeted. 76 00:03:12,780 --> 00:03:14,710 Fixed wing aircraft is the most efficient 77 00:03:14,710 --> 00:03:18,000 and commonly used method across the entire baiting zone, 78 00:03:18,000 --> 00:03:21,050 but it can't really be used in densely populated areas 79 00:03:21,050 --> 00:03:22,750 or where there are a lot of buildings. 80 00:03:22,750 --> 00:03:25,850 So in more suburban and urban areas, 81 00:03:25,850 --> 00:03:30,160 we have a few other baiting methods that are used. 82 00:03:30,160 --> 00:03:32,440 So helicopters are typically used 83 00:03:32,440 --> 00:03:34,180 in more suburban environments. 84 00:03:34,180 --> 00:03:37,640 Hand baiting is often used in urban areas 85 00:03:37,640 --> 00:03:39,990 and then bait stations are less frequently used, 86 00:03:39,990 --> 00:03:44,710 but can be used in both urban and suburban settings. 87 00:03:44,710 --> 00:03:46,400 So using these methods, 88 00:03:46,400 --> 00:03:50,330 the USDA is able to distribute over 9.5 million baits, 89 00:03:50,330 --> 00:03:52,300 and this is over 18 states. 90 00:03:52,300 --> 00:03:54,193 So this is a very large program. 91 00:03:56,330 --> 00:03:57,860 There are many considerations 92 00:03:57,860 --> 00:03:59,400 that need to be taken into account 93 00:03:59,400 --> 00:04:01,190 when planning for ORV. 94 00:04:01,190 --> 00:04:04,190 Bait density is a really critical factor because it needs 95 00:04:04,190 --> 00:04:06,960 to reflect the target species population densities 96 00:04:06,960 --> 00:04:09,220 in the areas that we're targeting. 97 00:04:09,220 --> 00:04:11,580 But there are several factors that limit the ability 98 00:04:11,580 --> 00:04:13,380 to bait at this density in real time, 99 00:04:13,380 --> 00:04:16,040 such as increased amounts of sidewalks and roads 100 00:04:16,040 --> 00:04:17,410 or open water. 101 00:04:17,410 --> 00:04:19,330 So the NRMP developed an algorithm 102 00:04:19,330 --> 00:04:22,650 to estimate how much of an area is actually unbaitable. 103 00:04:22,650 --> 00:04:25,110 And this is referred to as the off-time calculator, 104 00:04:25,110 --> 00:04:27,630 which adjusts the target number of baits on a landscape. 105 00:04:27,630 --> 00:04:30,300 So areas with high amounts of pavement usually end up 106 00:04:30,300 --> 00:04:31,453 with less baits. 107 00:04:33,670 --> 00:04:36,140 So ORV has been shown to be really successful 108 00:04:36,140 --> 00:04:37,710 in more rural settings, 109 00:04:37,710 --> 00:04:40,270 but the ORV percentage is much lower 110 00:04:40,270 --> 00:04:42,420 in many urban environments. 111 00:04:42,420 --> 00:04:44,870 And there's a lot of questions about why this is, 112 00:04:46,310 --> 00:04:49,850 but baiting in urban areas does come with many challenges. 113 00:04:49,850 --> 00:04:53,180 For one, there's an abundance of alternative food sources, 114 00:04:53,180 --> 00:04:55,770 there's a chance of non-target bait competitors. 115 00:04:55,770 --> 00:04:57,500 And there are some hand baiting limitations 116 00:04:57,500 --> 00:04:59,770 because it does mostly occur on roads, 117 00:04:59,770 --> 00:05:01,170 sidewalks, and trails, 118 00:05:01,170 --> 00:05:04,800 so we might not be accessing some of the suitable habitat. 119 00:05:04,800 --> 00:05:06,660 And although there have been studies looking 120 00:05:06,660 --> 00:05:08,350 into these potential factors, 121 00:05:08,350 --> 00:05:10,850 we haven't found the solution to this problem yet. 122 00:05:12,660 --> 00:05:16,150 So this map shows the current ORV operations in Vermont, 123 00:05:16,150 --> 00:05:19,530 anything that shaded orange or tan received baits mostly 124 00:05:19,530 --> 00:05:21,824 by aircraft in August of every year, 125 00:05:21,824 --> 00:05:24,570 but in our more urban areas, 126 00:05:24,570 --> 00:05:27,630 we do use hand baiting to distribute these baits. 127 00:05:27,630 --> 00:05:29,760 So this is a zoomed in view of our operations 128 00:05:29,760 --> 00:05:32,870 in the greater Burlington area and the gridded areas 129 00:05:32,870 --> 00:05:35,290 where we hand bait and the area shaded orange 130 00:05:35,290 --> 00:05:38,370 around that is where we've recently began to use helicopters 131 00:05:38,370 --> 00:05:40,570 to deploy these baits 132 00:05:40,570 --> 00:05:43,250 and between aerial helicopter and hand baiting, 133 00:05:43,250 --> 00:05:47,683 Vermont receives over 428,000 vaccine baits. 134 00:05:49,820 --> 00:05:53,830 So ORV has been successful in rural areas of Vermont, 135 00:05:53,830 --> 00:05:56,990 but seroprevalence is consistently low in Burlington 136 00:05:56,990 --> 00:05:58,450 for some unknown reasons. 137 00:05:58,450 --> 00:06:00,270 And this is reflected on this graph, 138 00:06:00,270 --> 00:06:02,390 which shows a percentage of target animals 139 00:06:02,390 --> 00:06:05,120 with a positive RVNA response after hand baiting 140 00:06:05,120 --> 00:06:07,263 in Burlington over several years. 141 00:06:08,210 --> 00:06:10,470 Hand baiting has been the only ground baiting strategy 142 00:06:10,470 --> 00:06:12,720 that's ever been used in Vermont. 143 00:06:12,720 --> 00:06:15,280 And we have tried refining this hand baiting strategy 144 00:06:15,280 --> 00:06:18,350 to get a more even bait distribution across the area, 145 00:06:18,350 --> 00:06:20,270 but it's still largely restricted to roads 146 00:06:20,270 --> 00:06:22,650 and we haven't seen a positive increase 147 00:06:22,650 --> 00:06:24,373 in our RVNA as of yet. 148 00:06:25,490 --> 00:06:27,330 Burlington has also been a study area 149 00:06:27,330 --> 00:06:30,290 for multiple rabies resource projects over the years 150 00:06:30,290 --> 00:06:32,690 to try and better understand the factors that are relating 151 00:06:32,690 --> 00:06:34,890 to these low seroconversion rates. 152 00:06:34,890 --> 00:06:36,300 But despite these studies, 153 00:06:36,300 --> 00:06:38,750 there's still a lot of questions left unanswered. 154 00:06:41,230 --> 00:06:43,160 So this is where my study comes in. 155 00:06:43,160 --> 00:06:44,820 I have two objectives and I'll walk you 156 00:06:44,820 --> 00:06:46,600 through each individually. 157 00:06:46,600 --> 00:06:50,390 So the goal of objective one is to improve the effectiveness 158 00:06:50,390 --> 00:06:53,210 of current ORV strategies in urban environments 159 00:06:53,210 --> 00:06:55,960 by understanding the factors that are driving bait survival 160 00:06:55,960 --> 00:06:57,580 on the landscape. 161 00:06:57,580 --> 00:06:59,560 And I'm going to be using camera traps 162 00:06:59,560 --> 00:07:02,520 to monitor ONRAB vaccine bait. 163 00:07:02,520 --> 00:07:05,880 And I'll be mimicking our current hand baiting strategy 164 00:07:05,880 --> 00:07:07,930 to try and identify what's actually happening 165 00:07:07,930 --> 00:07:10,950 to these baits once they're on the landscape. 166 00:07:10,950 --> 00:07:13,010 So my study area is separated 167 00:07:13,010 --> 00:07:16,250 into three categories being high, medium, 168 00:07:16,250 --> 00:07:18,150 and low human development, 169 00:07:18,150 --> 00:07:20,910 to be able to observe any differences in bait up take 170 00:07:20,910 --> 00:07:23,180 across these categories. 171 00:07:23,180 --> 00:07:25,780 So this is what the current bait distribution looks like 172 00:07:25,780 --> 00:07:28,288 in Burlington within the hand baiting zone. 173 00:07:28,288 --> 00:07:31,670 The area is split into one kilometer squared cells, 174 00:07:31,670 --> 00:07:34,970 and each of these cells receive a specific number of baits. 175 00:07:34,970 --> 00:07:36,050 So using this data, 176 00:07:36,050 --> 00:07:38,960 I was able to select a subset of 100 locations 177 00:07:38,960 --> 00:07:40,850 for my camera sites. 178 00:07:40,850 --> 00:07:44,720 So the cameras are split up between each development type. 179 00:07:44,720 --> 00:07:47,090 So we have 40 cameras in my high development areas, 180 00:07:47,090 --> 00:07:49,910 40 cameras in the medium development. 181 00:07:49,910 --> 00:07:51,200 And then I have 20 cameras 182 00:07:51,200 --> 00:07:53,463 in the low development helicopter zone. 183 00:07:56,300 --> 00:07:58,440 So the objectives are to monitor the fate 184 00:07:58,440 --> 00:08:01,490 of ONRAB vaccine bait using camera traps in real time, 185 00:08:01,490 --> 00:08:05,640 as a USDA is conducting hand baiting operations in the area. 186 00:08:05,640 --> 00:08:08,570 And then I will be summarizing the fates of the baits 187 00:08:08,570 --> 00:08:10,310 from the camera data that was collected 188 00:08:10,310 --> 00:08:12,460 and modeling the probability of bait uptake 189 00:08:12,460 --> 00:08:13,930 as a function of several site level 190 00:08:13,930 --> 00:08:15,803 and landscape level characteristics. 191 00:08:17,640 --> 00:08:19,860 So my cameras are out for a maximum of four weeks 192 00:08:19,860 --> 00:08:21,720 from the date of the bait was deployed. 193 00:08:21,720 --> 00:08:23,980 And I had weekly site checks to record the status 194 00:08:23,980 --> 00:08:27,680 of the bait as one of four options being present and whole, 195 00:08:27,680 --> 00:08:29,950 it could be partially chewed, but viable. 196 00:08:29,950 --> 00:08:31,820 It could be fully chewed and non-viable, 197 00:08:31,820 --> 00:08:34,200 or it could be taken from the camera site. 198 00:08:34,200 --> 00:08:35,840 And by the conclusion of the study, 199 00:08:35,840 --> 00:08:38,340 65% of the bait had been taken, 200 00:08:38,340 --> 00:08:42,120 25% were chewed and non-viable 201 00:08:42,120 --> 00:08:46,313 and 10% were still present and viable at the camera site. 202 00:08:48,720 --> 00:08:50,140 The camera data also revealed 203 00:08:50,140 --> 00:08:52,590 that only 14% of the baits had been taken 204 00:08:52,590 --> 00:08:54,320 from target animals. 205 00:08:54,320 --> 00:08:56,140 The top cause of bait loss in this study 206 00:08:56,140 --> 00:08:57,750 was actually lawnmowers, 207 00:08:57,750 --> 00:09:02,040 which took 28% of the baits across the entire study. 208 00:09:02,040 --> 00:09:05,840 Following the lawnmower were squirrels which took 14%. 209 00:09:05,840 --> 00:09:08,759 And then the raccoon, which was our main target animal, 210 00:09:08,759 --> 00:09:12,053 well, had only taken 8% of these vaccines. 211 00:09:15,130 --> 00:09:16,840 When looking at the cause of bait loss 212 00:09:16,840 --> 00:09:18,780 in the different levels of development, 213 00:09:18,780 --> 00:09:20,900 you can see that across all three categories, 214 00:09:20,900 --> 00:09:24,670 non-target species are the number one cause of bait loss. 215 00:09:24,670 --> 00:09:27,070 The low development area does have the highest rate 216 00:09:27,070 --> 00:09:28,820 of bait uptake by target animals, 217 00:09:28,820 --> 00:09:32,830 which was found to be at 30% of baits in that area. 218 00:09:32,830 --> 00:09:34,680 But the high development category was found 219 00:09:34,680 --> 00:09:36,380 to have the lowest rate of bait uptake 220 00:09:36,380 --> 00:09:40,290 by target species at just 7.5%. 221 00:09:40,290 --> 00:09:41,980 And then the average persistent time 222 00:09:41,980 --> 00:09:45,390 of baits also vary depending on the development type. 223 00:09:45,390 --> 00:09:47,570 So in the highly developed areas, 224 00:09:47,570 --> 00:09:49,880 the average number of days that these baits were left 225 00:09:49,880 --> 00:09:51,820 on the landscape was four, 226 00:09:51,820 --> 00:09:55,010 in moderately developed areas these baits average 227 00:09:55,010 --> 00:09:56,880 about seven days on the landscape, 228 00:09:56,880 --> 00:09:58,860 and then in the low development zone, 229 00:09:58,860 --> 00:10:03,123 they were on the landscape for an average of two days. 230 00:10:05,210 --> 00:10:08,270 So the camera data revealed a pretty clear pattern, 231 00:10:08,270 --> 00:10:10,450 but there may be other factors that are contributing 232 00:10:10,450 --> 00:10:11,960 to the cause of bait loss. 233 00:10:11,960 --> 00:10:14,100 So I'm going to be using a modeling approach 234 00:10:14,100 --> 00:10:17,000 to gain insight into how different site level 235 00:10:17,000 --> 00:10:19,590 and landscape level factors influences probability 236 00:10:19,590 --> 00:10:22,203 of bait uptake by target species over time. 237 00:10:24,500 --> 00:10:26,820 Some of the hypotheses we believe may be contributing 238 00:10:26,820 --> 00:10:30,220 to bait loss are the chance of bait counter, 239 00:10:30,220 --> 00:10:35,220 food abundance, bait competitors, and habitat configuration. 240 00:10:35,320 --> 00:10:37,530 And looking into these hypotheses may help 241 00:10:37,530 --> 00:10:40,770 to reveal the factors that most influence bait uptake. 242 00:10:40,770 --> 00:10:43,280 And then we can predict the rates of uptake across a range 243 00:10:43,280 --> 00:10:44,613 of values over time. 244 00:10:46,540 --> 00:10:48,940 Okay, so I'm gonna move on to objective two. 245 00:10:48,940 --> 00:10:51,960 And the goal of this objective is to assess 246 00:10:51,960 --> 00:10:53,930 the effectiveness of adding bait stations 247 00:10:53,930 --> 00:10:55,040 to hand baited areas 248 00:10:55,040 --> 00:10:57,300 at improving seroprevalence conversion rates 249 00:10:57,300 --> 00:10:59,000 in urban environments. 250 00:10:59,000 --> 00:11:01,420 Specifically, I'll be quantifying the effect 251 00:11:01,420 --> 00:11:03,480 of adding bait stations to hand baited areas 252 00:11:03,480 --> 00:11:07,150 by comparing the results with hand baited only areas. 253 00:11:07,150 --> 00:11:09,670 And the reason why we're using bait stations in conjunction 254 00:11:09,670 --> 00:11:13,290 with hand baiting is because they do provide many benefits. 255 00:11:13,290 --> 00:11:14,340 As I mentioned before, 256 00:11:14,340 --> 00:11:18,010 we've never used them in Burlington before, 257 00:11:18,010 --> 00:11:20,720 but you can be very strategic about their placement. 258 00:11:20,720 --> 00:11:24,050 So you can put them in areas with high wildlife corridors 259 00:11:24,050 --> 00:11:25,320 or near a resource. 260 00:11:25,320 --> 00:11:27,220 So for example, 261 00:11:27,220 --> 00:11:31,494 I put this bait station this summer next to a compost pile 262 00:11:31,494 --> 00:11:33,970 in somebody's backyard. 263 00:11:33,970 --> 00:11:36,010 They also give you a chance to distribute baits away 264 00:11:36,010 --> 00:11:38,200 from roads and more urban settings. 265 00:11:38,200 --> 00:11:40,610 And studies have also shown that there's a lower chance 266 00:11:40,610 --> 00:11:42,790 of non-target bait competitors taking baits 267 00:11:42,790 --> 00:11:44,053 from these stations. 268 00:11:46,230 --> 00:11:48,490 So my study has two treatments. 269 00:11:48,490 --> 00:11:50,450 The first being bait stations added 270 00:11:50,450 --> 00:11:51,330 to hand baited cells 271 00:11:51,330 --> 00:11:53,650 where off-time is greater than 50%. 272 00:11:53,650 --> 00:11:56,680 The second is bait stations added to hand baited cells 273 00:11:56,680 --> 00:11:59,390 where off-time is between 30 and 50%. 274 00:11:59,390 --> 00:12:00,650 And then I have a control group, 275 00:12:00,650 --> 00:12:02,990 which is my hand baited cells only. 276 00:12:02,990 --> 00:12:05,090 So I have a total of 16 treatment cells 277 00:12:05,090 --> 00:12:06,740 and four control cells. 278 00:12:06,740 --> 00:12:08,410 And two bait stations were added 279 00:12:08,410 --> 00:12:11,740 to each of the treatment cells to increase the bait density, 280 00:12:11,740 --> 00:12:14,810 to absolute density and to provide an additional opportunity 281 00:12:14,810 --> 00:12:17,160 for these target animals to encounter the bait. 282 00:12:19,310 --> 00:12:22,190 So unique molecular biomarkers have been placed 283 00:12:22,190 --> 00:12:24,770 in all of the bait in this study to identify 284 00:12:24,770 --> 00:12:26,970 where the animals ingested the bait. 285 00:12:26,970 --> 00:12:29,690 So all of the hand baiting baits have one biomarker 286 00:12:29,690 --> 00:12:32,930 and all of the bait station bait have another biomarker. 287 00:12:32,930 --> 00:12:35,530 And this biomarker is identifiable in the blood serum 288 00:12:35,530 --> 00:12:39,000 of the animals that ingest this bait. 289 00:12:39,000 --> 00:12:41,080 So the reason why we're using these biomarkers 290 00:12:41,080 --> 00:12:42,990 is because we conduct trapping sessions 291 00:12:42,990 --> 00:12:45,620 that occurred before and after ORV takes place 292 00:12:45,620 --> 00:12:47,250 in July and October. 293 00:12:47,250 --> 00:12:48,760 And during these trapping sessions, 294 00:12:48,760 --> 00:12:51,680 we collect biological data from target species 295 00:12:51,680 --> 00:12:54,160 and then the blood serum from all the captured animals 296 00:12:54,160 --> 00:12:56,870 will be analyzed for evidence of RVNA response, 297 00:12:56,870 --> 00:12:59,453 as well as evidence of that biomarker. 298 00:13:01,780 --> 00:13:04,210 So I did just include my first field season, 299 00:13:04,210 --> 00:13:05,650 where I distributed bait stations 300 00:13:05,650 --> 00:13:07,770 during the hand baiting operations. 301 00:13:07,770 --> 00:13:09,669 And by the conclusion of the baiting period, 302 00:13:09,669 --> 00:13:13,940 93% of the baits had been taken from the bait stations. 303 00:13:13,940 --> 00:13:16,910 In addition, we wrapped up our pre and post bait trapping, 304 00:13:16,910 --> 00:13:19,560 where we had a total of 1200 trap locations 305 00:13:19,560 --> 00:13:21,480 across the whole study area. 306 00:13:21,480 --> 00:13:23,240 And we had a record number of captures 307 00:13:23,240 --> 00:13:25,670 in both July and October. 308 00:13:25,670 --> 00:13:27,360 So in our July trapping session, 309 00:13:27,360 --> 00:13:28,360 we collected data 310 00:13:28,360 --> 00:13:32,720 from gray foxes, red foxes, raccoons, and skunks. 311 00:13:32,720 --> 00:13:34,390 And after 10 days of trapping, 312 00:13:34,390 --> 00:13:38,937 we had captured a total of 489 unique target animals. 313 00:13:38,937 --> 00:13:41,860 In October, we collected data from the same species 314 00:13:41,860 --> 00:13:43,610 and by the end of that trapping period, 315 00:13:43,610 --> 00:13:46,683 we had processed 470 animals. 316 00:13:49,700 --> 00:13:52,230 So the data that's collected from this study will be used 317 00:13:52,230 --> 00:13:55,020 to develop a model of a raccoon being vaccinated 318 00:13:55,020 --> 00:13:57,220 as of a function of variables that fall 319 00:13:57,220 --> 00:13:58,800 within one of two categories, 320 00:13:58,800 --> 00:14:00,550 being those related to baits 321 00:14:00,550 --> 00:14:02,900 and those related to land cover. 322 00:14:02,900 --> 00:14:05,600 So each animal that's captured in the post-baiting period 323 00:14:05,600 --> 00:14:07,890 will have one of these four outcomes, 324 00:14:07,890 --> 00:14:08,900 and then I'll be developing 325 00:14:08,900 --> 00:14:10,960 a multinomial logistic regression model 326 00:14:10,960 --> 00:14:13,960 that will predict the probability of each of these outcomes. 327 00:14:14,890 --> 00:14:16,810 So this study will identify 328 00:14:16,810 --> 00:14:19,000 the what and the why our seroconversion rates 329 00:14:19,000 --> 00:14:20,590 are lower in Burlington, 330 00:14:20,590 --> 00:14:23,170 and then hopefully be able to inform the NRMP 331 00:14:23,170 --> 00:14:26,130 on the best ORV strategy to maximize the probability 332 00:14:26,130 --> 00:14:28,340 of bait consumption by target species, 333 00:14:28,340 --> 00:14:31,120 and then be able to apply this to other urban areas. 334 00:14:31,120 --> 00:14:33,960 Understanding why the RVNA responses are much lower 335 00:14:33,960 --> 00:14:35,020 in urban settings, 336 00:14:35,020 --> 00:14:37,370 and then being able to successfully increase the rate 337 00:14:37,370 --> 00:14:40,010 of consumption in target species is crucial 338 00:14:40,010 --> 00:14:41,740 to managing raccoon rabies 339 00:14:41,740 --> 00:14:44,730 because about 13% of the area focused 340 00:14:44,730 --> 00:14:46,948 on targeting raccoon rabies elimination, 341 00:14:46,948 --> 00:14:49,573 are always within these urban environments. 342 00:14:51,230 --> 00:14:53,250 I also just wanna take a moment to acknowledge 343 00:14:53,250 --> 00:14:55,530 those who have participated in this study. 344 00:14:55,530 --> 00:14:57,418 This has been a huge collaborative project 345 00:14:57,418 --> 00:15:01,070 between the USDA National Rabies Management Program, 346 00:15:01,070 --> 00:15:03,860 USDA National Wildlife Research Center, 347 00:15:03,860 --> 00:15:07,250 the USDA Vermont Wildlife Services Operational Program, 348 00:15:07,250 --> 00:15:09,563 and the UVM Rubenstein School. 349 00:15:10,530 --> 00:15:12,283 Here's a list of my references. 350 00:15:13,660 --> 00:15:14,653 And thank you. 351 00:15:17,390 --> 00:15:19,113 - Wonderful, thank you Maggie. 352 00:15:21,960 --> 00:15:25,033 And we have a few minutes to take any questions. 353 00:15:57,760 --> 00:15:58,900 - [James] Maybe I'll jump in there. 354 00:15:58,900 --> 00:16:00,150 Hi, this is James Murdoch, 355 00:16:00,150 --> 00:16:00,983 Maggie, of course, 356 00:16:00,983 --> 00:16:04,190 we work closely together on this great presentation. 357 00:16:04,190 --> 00:16:06,100 Again, really appreciate it, 358 00:16:06,100 --> 00:16:09,030 I had some Zoom headaches joining the meeting. 359 00:16:09,030 --> 00:16:10,690 So I missed the first bit, 360 00:16:10,690 --> 00:16:12,450 but I am aware of the presentation. 361 00:16:12,450 --> 00:16:17,450 So with the bait stations, I mean, what would prevent 362 00:16:17,933 --> 00:16:21,120 a little raccoon from reaching into the bait station 363 00:16:21,120 --> 00:16:26,120 and basically consuming all of the baits that are in there. 364 00:16:26,860 --> 00:16:31,040 And is that also maybe bias or does that create any problems 365 00:16:31,040 --> 00:16:33,331 in terms of your analysis? 366 00:16:33,331 --> 00:16:35,863 If you get one greedy raccoon that eats them all. 367 00:16:37,630 --> 00:16:39,401 - Yeah, that's definitely a possibility 368 00:16:39,401 --> 00:16:42,870 and something that we've discussed, 369 00:16:42,870 --> 00:16:47,570 but we will hopefully be able to see that if we can catch 370 00:16:47,570 --> 00:16:51,880 the animals that did eat, take baits in the bait stations, 371 00:16:51,880 --> 00:16:53,573 we should be able to see, 372 00:16:55,350 --> 00:16:57,010 it has like a level of how much 373 00:16:57,010 --> 00:16:58,590 of the biomarker is in there I believe, 374 00:16:58,590 --> 00:17:01,650 so, hopefully we'll be able to identify 375 00:17:01,650 --> 00:17:03,900 if there's one raccoon that took a bunch, 376 00:17:03,900 --> 00:17:06,070 we won't be able to say how many they actually took, 377 00:17:06,070 --> 00:17:09,250 but it might give us some insight into. 378 00:17:09,250 --> 00:17:11,570 You know why we don't really see that so many baits 379 00:17:11,570 --> 00:17:15,710 were taken, but not actually seeing that similar results 380 00:17:15,710 --> 00:17:18,770 in the biomarker results. 381 00:17:18,770 --> 00:17:20,240 - Right. - Yeah. 382 00:17:20,240 --> 00:17:21,073 - [James] Interesting, well, 383 00:17:21,073 --> 00:17:22,590 let me ask you one follow up question, 384 00:17:22,590 --> 00:17:27,140 sorry, just getting back to the main problem here 385 00:17:27,140 --> 00:17:29,790 which is just low vaccination, 386 00:17:29,790 --> 00:17:32,913 low bait uptake in the urban environment of Burlington. 387 00:17:34,461 --> 00:17:37,480 Is that problem common to other urban areas 388 00:17:37,480 --> 00:17:41,180 that are sort of along this leading edge of rabies 389 00:17:41,180 --> 00:17:44,700 or is it something that's just particular to Burlington? 390 00:17:44,700 --> 00:17:48,060 - I know Burlington is kind of an anomaly 391 00:17:48,060 --> 00:17:53,060 and has a little bit more unique situation than other areas, 392 00:17:53,450 --> 00:17:58,030 but I know that, or I believe that some will be able 393 00:17:58,030 --> 00:18:00,360 to apply this to some of the other urban areas 394 00:18:00,360 --> 00:18:03,080 to try to increase their serology as well. 395 00:18:03,080 --> 00:18:07,203 But I do know that Burlington is particularly unique. 396 00:18:08,330 --> 00:18:09,923 - In so many ways. - Yeah. 397 00:18:09,923 --> 00:18:10,800 (both laughs) 398 00:18:10,800 --> 00:18:12,045 - [James] Thank you. 399 00:18:12,045 --> 00:18:13,890 - [Suzanne] Hi Maggie, this is Suzanne Gifford, 400 00:18:13,890 --> 00:18:15,430 ecologist and wildlife biologists 401 00:18:15,430 --> 00:18:18,560 with the Green Mountain and Finger Lakes National Forest. 402 00:18:18,560 --> 00:18:20,920 Two questions, one. 403 00:18:20,920 --> 00:18:23,280 Why are the higher elevation areas excluded 404 00:18:23,280 --> 00:18:27,670 from your orange bait area? 405 00:18:27,670 --> 00:18:30,280 Is it just because of species ranges 406 00:18:30,280 --> 00:18:32,090 and two, as time allows, 407 00:18:32,090 --> 00:18:34,200 will you please tell us on how you capture 408 00:18:34,200 --> 00:18:36,130 and draw blood from a skunk? 409 00:18:36,130 --> 00:18:37,400 - Sure. Yeah. 410 00:18:37,400 --> 00:18:40,700 So, the National Rabies Management Program 411 00:18:40,700 --> 00:18:43,760 is pretty strategic about where they put their baits 412 00:18:43,760 --> 00:18:47,970 when flying and the species that ranges, 413 00:18:47,970 --> 00:18:50,530 they do take into account the elevation 414 00:18:50,530 --> 00:18:53,770 and do eliminate the, 415 00:18:53,770 --> 00:18:55,190 like we don't bait over those 416 00:18:55,190 --> 00:18:57,300 because the species' home ranges 417 00:18:58,220 --> 00:19:02,010 and the second question about trapping. 418 00:19:02,010 --> 00:19:04,770 So basically we just put out a set number 419 00:19:04,770 --> 00:19:09,560 of the Tomahawk (indistinct) traps across the area 420 00:19:09,560 --> 00:19:11,720 that we're trying to capture these animals. 421 00:19:11,720 --> 00:19:15,340 And we do that like the afternoon before that we hope 422 00:19:15,340 --> 00:19:16,173 to capture them. 423 00:19:16,173 --> 00:19:17,006 And then you come back, 424 00:19:17,006 --> 00:19:18,833 check the trap in the morning, 425 00:19:19,720 --> 00:19:22,310 and then you would bring that animal to, 426 00:19:22,310 --> 00:19:24,170 if you caught an animal, 427 00:19:24,170 --> 00:19:27,820 you bring it to a central processing site to get processed. 428 00:19:27,820 --> 00:19:29,220 And then it's released just a few hours 429 00:19:29,220 --> 00:19:31,602 after that once it recovers. 430 00:19:31,602 --> 00:19:33,830 - [Suzanne] So specific describes, actually I'm asking, 431 00:19:33,830 --> 00:19:36,262 how do you avoid getting sprayed? 432 00:19:36,262 --> 00:19:37,095 (Maggie laughs) 433 00:19:37,095 --> 00:19:39,700 - There is a couple of different strategies. 434 00:19:39,700 --> 00:19:43,070 I personally think that making them aware 435 00:19:43,070 --> 00:19:45,980 of your presence and this sounds kind of weird and crazy, 436 00:19:45,980 --> 00:19:48,530 but like singing to them or like talking like you would 437 00:19:48,530 --> 00:19:51,170 to an animal, like a dog or something. 438 00:19:51,170 --> 00:19:52,220 And just letting them know 439 00:19:52,220 --> 00:19:54,530 that you're not a threat really helps, 440 00:19:54,530 --> 00:19:56,090 people still get post sprayed, 441 00:19:56,090 --> 00:19:59,200 but I knock on wood, 442 00:19:59,200 --> 00:20:01,750 I've never gotten sprayed and that seems to help me. 443 00:20:01,750 --> 00:20:04,160 And then we put a burlaps, like burlap over them 444 00:20:04,160 --> 00:20:05,780 because I think being covered 445 00:20:05,780 --> 00:20:07,970 in their darkness makes them feel safer. 446 00:20:07,970 --> 00:20:10,644 And then usually once you have them covered, 447 00:20:10,644 --> 00:20:13,870 there isn't really a threat of them spraying, 448 00:20:13,870 --> 00:20:15,943 but it happens frequently to people. 449 00:20:16,890 --> 00:20:17,890 - [Suzanne] Interesting, thank you. 450 00:20:17,890 --> 00:20:19,393 I appreciate that. - Yeah. 451 00:20:21,896 --> 00:20:23,546 - Hope we have one more question. 452 00:20:24,600 --> 00:20:28,360 The 28% whacked by lawn mowers, 453 00:20:28,360 --> 00:20:31,280 were many or most of those destroyed where they were placed 454 00:20:31,280 --> 00:20:33,493 and did they end up somewhere else somehow? 455 00:20:34,840 --> 00:20:38,210 - Yeah, most of those ones were thrown, 456 00:20:41,990 --> 00:20:43,850 like were actually were placed there 457 00:20:43,850 --> 00:20:45,670 so they didn't just get moved. 458 00:20:45,670 --> 00:20:47,850 And then people just weren't aware 459 00:20:47,850 --> 00:20:51,453 that there was a bait there. 460 00:20:52,570 --> 00:20:53,403 So cause you try 461 00:20:53,403 --> 00:20:55,980 to get a pretty even bait distribution across the area. 462 00:20:55,980 --> 00:20:57,130 So I think that might have something 463 00:20:57,130 --> 00:20:59,880 to do with why they're getting thrown on lawn sometimes, 464 00:20:59,880 --> 00:21:02,710 or sometimes you want to throw them 465 00:21:02,710 --> 00:21:04,890 in a tall grassy area and then you think 466 00:21:04,890 --> 00:21:07,060 that they're not gonna get, 467 00:21:07,060 --> 00:21:08,090 there's not going to be a lawnmower there 468 00:21:08,090 --> 00:21:09,570 and then somebody does end up coming 469 00:21:09,570 --> 00:21:12,870 to mow that area within the next couple of days. 470 00:21:12,870 --> 00:21:17,870 So, yeah, I don't know if that answers your question a bit. 471 00:21:20,970 --> 00:21:22,880 - Thank you for your presentation. 472 00:21:22,880 --> 00:21:24,283 - Thanks, thank you so much.