1 00:00:08,040 --> 00:00:10,310 - Welcome, everyone, to the second day 2 00:00:10,310 --> 00:00:12,530 of the FEMC conference. 3 00:00:12,530 --> 00:00:14,040 I know there were some challenges 4 00:00:14,040 --> 00:00:16,180 with getting into the Zoom events platform yesterday. 5 00:00:16,180 --> 00:00:18,240 So thanks to everyone for sticking with it. 6 00:00:18,240 --> 00:00:20,060 And I hope that you found, once in there, 7 00:00:20,060 --> 00:00:21,560 it's actually been a pretty easy way 8 00:00:21,560 --> 00:00:24,050 to navigate this online meeting. 9 00:00:24,050 --> 00:00:25,940 Yesterday, we started with a deep dive 10 00:00:25,940 --> 00:00:28,230 into the nature of stewardship groups and their supporters 11 00:00:28,230 --> 00:00:30,730 working at scales from the individual to the national, 12 00:00:30,730 --> 00:00:33,620 to do two really important functions, 13 00:00:33,620 --> 00:00:35,530 to meet the ongoing and basic needs 14 00:00:35,530 --> 00:00:37,630 of their communities and their environment. 15 00:00:37,630 --> 00:00:39,520 And they also pivot or span 16 00:00:39,520 --> 00:00:41,753 to respond to disruptions and change. 17 00:00:43,670 --> 00:00:47,270 But then they return to keep the original work going too. 18 00:00:47,270 --> 00:00:48,400 I see a lot of parallels 19 00:00:48,400 --> 00:00:50,750 between how we utilize both monitoring and research, 20 00:00:50,750 --> 00:00:52,920 and in the forest systems we all work in. 21 00:00:52,920 --> 00:00:54,050 And I imagine you do too. 22 00:00:54,050 --> 00:00:55,170 So while yesterday, 23 00:00:55,170 --> 00:00:58,180 we talked a lot about how communities and groups of humans 24 00:00:58,180 --> 00:01:00,330 respond to disruption and change, 25 00:01:00,330 --> 00:01:02,610 today we're gonna dive into communities of forest plants 26 00:01:02,610 --> 00:01:04,170 to think more broadly about disturbance 27 00:01:04,170 --> 00:01:05,330 and change in these systems 28 00:01:05,330 --> 00:01:08,330 of overlapping and complementary functions. 29 00:01:08,330 --> 00:01:09,520 To take us on this journey, 30 00:01:09,520 --> 00:01:12,260 it's my pleasure to welcome up to the zoomodium, 31 00:01:12,260 --> 00:01:14,490 Dr. Julia Burton. 32 00:01:14,490 --> 00:01:16,290 Julia, sorry, an associate professor 33 00:01:17,149 --> 00:01:20,047 at Michigan Technological University, 34 00:01:20,047 --> 00:01:21,870 and an adjunct associate professor 35 00:01:21,870 --> 00:01:25,270 at SUNY College of Environmental Science and forestry, 36 00:01:25,270 --> 00:01:28,250 and her team examines the role of silviculture 37 00:01:28,250 --> 00:01:30,380 in sustaining a broad range of values, 38 00:01:30,380 --> 00:01:32,820 functions and services in forests, 39 00:01:32,820 --> 00:01:36,130 such as carbon sequestration, wildlife habitat, 40 00:01:36,130 --> 00:01:38,290 biodiversity, and cultural values, 41 00:01:38,290 --> 00:01:40,030 as well as timber production. 42 00:01:40,030 --> 00:01:41,800 And they look at how to sustain these 43 00:01:41,800 --> 00:01:44,960 in the face of changes in climate and disturbance regimes. 44 00:01:44,960 --> 00:01:47,280 Her recent work looks at how plant functional traits 45 00:01:47,280 --> 00:01:50,760 can illuminate forest community responses to climate change 46 00:01:50,760 --> 00:01:52,180 and how these changes cascade 47 00:01:52,180 --> 00:01:54,223 to ecosystem functions and services. 48 00:01:55,120 --> 00:01:57,710 This research clearly has real-world implications 49 00:01:57,710 --> 00:01:59,970 for those of us who are working in agencies 50 00:01:59,970 --> 00:02:03,240 or with landowners, for conservation organizations 51 00:02:03,240 --> 00:02:04,810 who are tasked with managing forest 52 00:02:04,810 --> 00:02:06,290 for a range of ecosystem services 53 00:02:06,290 --> 00:02:08,780 in an increasingly uncertain world. 54 00:02:08,780 --> 00:02:10,560 And I'm thrilled to have her speak to us today 55 00:02:10,560 --> 00:02:12,680 on the topic of forest ecosystem change 56 00:02:12,680 --> 00:02:14,830 through the lens of plant functional traits, 57 00:02:14,830 --> 00:02:17,250 measurement, management, and monitoring. 58 00:02:17,250 --> 00:02:18,413 Julia, welcome. 59 00:02:22,520 --> 00:02:24,273 - Thank you so much. 60 00:02:24,273 --> 00:02:28,363 Let's see. I'm gonna get my screen shared here. 61 00:02:31,180 --> 00:02:32,013 How's that? 62 00:02:33,240 --> 00:02:35,167 - Looks great. - Great. 63 00:02:36,590 --> 00:02:37,970 Well, good morning, everyone. 64 00:02:37,970 --> 00:02:42,300 And thanks so much for being here. 65 00:02:42,300 --> 00:02:45,900 It's an honor for me to be here speaking with you today, 66 00:02:45,900 --> 00:02:49,150 and I really appreciate the invitation 67 00:02:50,660 --> 00:02:54,540 and the recommendation of Colin Beier, 68 00:02:54,540 --> 00:02:56,133 my colleague at SUNY ESF. 69 00:02:57,060 --> 00:03:01,700 I very much enjoyed the sessions yesterday, 70 00:03:01,700 --> 00:03:04,470 the plenary presentation from Erica 71 00:03:04,470 --> 00:03:07,670 and all the contributed presentations and the workshop. 72 00:03:07,670 --> 00:03:08,990 Everything has been really great. 73 00:03:08,990 --> 00:03:11,930 I haven't participated in this conference before, 74 00:03:11,930 --> 00:03:14,360 so I'm just really impressed 75 00:03:14,360 --> 00:03:17,023 by what you've all put together. 76 00:03:18,310 --> 00:03:21,920 And I've enjoyed seeing what everyone's up to 77 00:03:21,920 --> 00:03:23,907 and learning from your all. 78 00:03:24,820 --> 00:03:26,890 I'm also really excited to share 79 00:03:26,890 --> 00:03:28,150 a little about the work I do 80 00:03:28,150 --> 00:03:30,193 with my students and collaborators. 81 00:03:33,070 --> 00:03:35,490 And so for this talk, I thought I would focus 82 00:03:35,490 --> 00:03:38,840 on looking at the issues that we've been discussing 83 00:03:38,840 --> 00:03:41,483 through the lens of plant functional traits. 84 00:03:47,310 --> 00:03:52,310 Forests provide a very broad range of values and services, 85 00:03:53,770 --> 00:03:56,450 and we're increasingly managing forests 86 00:03:56,450 --> 00:04:00,610 for this broader range of values and services, 87 00:04:00,610 --> 00:04:05,370 from raw materials for the many wood products we rely on, 88 00:04:05,370 --> 00:04:10,230 to wildlife habitat and recreational and spiritual values. 89 00:04:10,230 --> 00:04:15,110 And Erica really touched on those values yesterday. 90 00:04:15,110 --> 00:04:18,573 And I think that this pandemic has made us, 91 00:04:19,470 --> 00:04:23,430 provided a heightened awareness of these values 92 00:04:23,430 --> 00:04:26,413 that we should learn from and leverage. 93 00:04:30,300 --> 00:04:34,320 And we're currently facing changes 94 00:04:34,320 --> 00:04:38,090 in climate and disturbance 95 00:04:38,090 --> 00:04:42,350 that work together to influence forest composition, 96 00:04:42,350 --> 00:04:43,940 density and structure, 97 00:04:43,940 --> 00:04:46,830 as well as those ecosystem services 98 00:04:47,750 --> 00:04:48,850 that we rely on. 99 00:04:48,850 --> 00:04:52,420 And these changes in climate lead to an increase 100 00:04:52,420 --> 00:04:56,570 in managing forests to offset greenhouse gas emissions 101 00:04:56,570 --> 00:04:58,263 and mitigate climate change. 102 00:05:02,580 --> 00:05:04,220 And at the same time, 103 00:05:04,220 --> 00:05:07,910 climate change threatens the ability of forests to do this. 104 00:05:07,910 --> 00:05:09,963 So of course, we're concerned. 105 00:05:13,309 --> 00:05:16,470 Silvicultural management can be used, 106 00:05:16,470 --> 00:05:20,380 can provide a powerful tool for managing change, 107 00:05:20,380 --> 00:05:24,320 so as to continue to provide these services that we value 108 00:05:24,320 --> 00:05:27,693 despite changes in species composition and structure. 109 00:05:29,240 --> 00:05:32,660 And to leverage this silvicultural toolbox, 110 00:05:32,660 --> 00:05:36,440 we really need to draw on a mechanistic understanding 111 00:05:36,440 --> 00:05:38,660 of how climate change influences 112 00:05:38,660 --> 00:05:41,000 plant communities and forests, 113 00:05:41,000 --> 00:05:43,000 and how community structure 114 00:05:43,840 --> 00:05:46,473 is related to ecosystem services. 115 00:05:52,740 --> 00:05:56,190 And so in order to do this, one way to do this 116 00:05:56,190 --> 00:06:00,030 is to look at forest communities and ecosystems 117 00:06:00,030 --> 00:06:03,103 through the lens of plant functional traits. 118 00:06:05,150 --> 00:06:07,710 And so we can define a plant trait 119 00:06:07,710 --> 00:06:10,740 as any characteristic of a plant, 120 00:06:10,740 --> 00:06:14,670 and a functional trait is a trait that influences 121 00:06:14,670 --> 00:06:18,433 or is related to the performance of that plant. 122 00:06:20,170 --> 00:06:23,083 And I apologize for the red green here, 123 00:06:24,290 --> 00:06:27,480 but these hump-shaped curves are 124 00:06:29,200 --> 00:06:31,240 the distribution of species abundance 125 00:06:31,240 --> 00:06:35,910 for some hypothetical species along a climate gradient, 126 00:06:35,910 --> 00:06:40,880 while the asymptotic curves in red 127 00:06:41,830 --> 00:06:45,950 show the relationship of trait variation 128 00:06:45,950 --> 00:06:48,560 along the gradients. 129 00:06:48,560 --> 00:06:52,160 So this is saying that the distribution of abundance 130 00:06:52,160 --> 00:06:54,970 is related to species traits 131 00:06:54,970 --> 00:06:58,780 and how they vary along environmental gradients. 132 00:06:58,780 --> 00:07:01,900 And so when we look at communities 133 00:07:01,900 --> 00:07:03,140 through the lens of traits, 134 00:07:03,140 --> 00:07:05,460 we can characterize them quantitatively. 135 00:07:05,460 --> 00:07:08,090 There's similarities and differences, 136 00:07:08,090 --> 00:07:11,480 with quantitative trait data, 137 00:07:11,480 --> 00:07:15,563 measured traits that reflect their performance. 138 00:07:17,350 --> 00:07:19,140 And this is really useful 139 00:07:19,140 --> 00:07:23,910 compared to looking at species individually 140 00:07:23,910 --> 00:07:27,410 for communities of multiple complex communities 141 00:07:27,410 --> 00:07:29,440 of multiple species. 142 00:07:29,440 --> 00:07:32,420 It compresses a matrix 143 00:07:32,420 --> 00:07:36,920 of 50 or more species into a single number 144 00:07:38,090 --> 00:07:39,643 that we can analyze. 145 00:07:43,920 --> 00:07:48,920 For example, traits of a lot of interest 146 00:07:49,100 --> 00:07:50,630 include those associated 147 00:07:50,630 --> 00:07:54,400 with the worldwide leaf economic spectrum. 148 00:07:54,400 --> 00:07:55,400 And this is 149 00:07:58,860 --> 00:08:01,910 a spectrum of correlated leaf traits 150 00:08:01,910 --> 00:08:03,330 that reflect a trade-off 151 00:08:03,330 --> 00:08:06,680 between fast returns 152 00:08:07,620 --> 00:08:10,893 on investments of nutrients in dry mass and leaves, 153 00:08:12,800 --> 00:08:14,853 and slow returns. 154 00:08:17,650 --> 00:08:20,067 And so that contrasts species 155 00:08:22,010 --> 00:08:25,860 with thick long-lived leaves 156 00:08:25,860 --> 00:08:29,810 that are very conservative in their use of resources 157 00:08:29,810 --> 00:08:34,560 from species with very thin, short-lived leaves 158 00:08:34,560 --> 00:08:36,083 that are more acquisitive. 159 00:08:37,670 --> 00:08:41,510 And these trade-offs can really affect plant performance 160 00:08:41,510 --> 00:08:44,523 under different climatic and management regimes. 161 00:08:45,590 --> 00:08:47,630 And this is just one example 162 00:08:47,630 --> 00:08:52,483 of a spectrum of traits. 163 00:08:54,520 --> 00:08:57,550 Additional spectra that have been proposed 164 00:08:57,550 --> 00:09:00,293 include a wood economic spectrum, 165 00:09:01,210 --> 00:09:03,773 a root economic spectrum. 166 00:09:04,690 --> 00:09:08,230 And then there are questions about, are all these traits 167 00:09:08,230 --> 00:09:11,720 just correlated together in a single axis 168 00:09:11,720 --> 00:09:13,750 of variation and performance 169 00:09:15,040 --> 00:09:16,940 called the plant economic spectrum, 170 00:09:16,940 --> 00:09:21,510 or do they represent different axes and dimensions 171 00:09:23,208 --> 00:09:27,687 of ecological strategies and differences among plants? 172 00:09:35,300 --> 00:09:40,300 These traits can be aggregated across the community 173 00:09:41,590 --> 00:09:44,573 by calculating community weighted mean traits. 174 00:09:47,170 --> 00:09:49,900 And these are calculated 175 00:09:49,900 --> 00:09:52,870 as the sum of the relative abundance 176 00:09:52,870 --> 00:09:55,240 of species in the community 177 00:09:55,240 --> 00:09:58,823 multiplied by their average trait value. 178 00:10:00,320 --> 00:10:01,880 And so there are a lot of questions 179 00:10:01,880 --> 00:10:05,490 about, well, can we really just use one trait value 180 00:10:05,490 --> 00:10:07,203 to represent a species? 181 00:10:15,110 --> 00:10:18,090 This approach is gaining popularity 182 00:10:20,130 --> 00:10:24,240 with the advent of global trait databases, 183 00:10:24,240 --> 00:10:26,920 where you're able to just download trait data 184 00:10:26,920 --> 00:10:30,090 for the species that you're studying in your community. 185 00:10:30,090 --> 00:10:31,797 And so it's raised a lot of questions 186 00:10:31,797 --> 00:10:34,520 about whether it's reasonable to do that, 187 00:10:34,520 --> 00:10:37,540 or if there's intraspecific variation 188 00:10:37,540 --> 00:10:39,510 that needs to be accounted for 189 00:10:39,510 --> 00:10:43,463 in these models of community weighted mean traits. 190 00:10:50,480 --> 00:10:54,710 So this approach of looking at forest communities 191 00:10:54,710 --> 00:10:58,200 through the lens of traits 192 00:10:59,890 --> 00:11:03,700 is a powerful approach that can provide a mechanistic basis 193 00:11:03,700 --> 00:11:06,493 for predicting the effects of climate change. 194 00:11:08,070 --> 00:11:10,560 That represents a compromise 195 00:11:10,560 --> 00:11:14,950 between really complex and detailed population models 196 00:11:14,950 --> 00:11:19,950 and simple spatial correlation or climate envelope models. 197 00:11:20,060 --> 00:11:22,770 And so this mechanistic basis is really important 198 00:11:22,770 --> 00:11:27,053 because it's what separates causation from correlation. 199 00:11:28,030 --> 00:11:33,030 These predictions can also be generalized across systems. 200 00:11:33,110 --> 00:11:36,113 So if we look at trends in the Northeast, 201 00:11:37,660 --> 00:11:42,200 although the composition of forests in Europe 202 00:11:42,200 --> 00:11:45,300 in similar climate conditions, 203 00:11:45,300 --> 00:11:47,780 the species composition differs, 204 00:11:47,780 --> 00:11:51,240 we can compare results in terms of traits, 205 00:11:51,240 --> 00:11:52,900 so it can be generalized. 206 00:11:52,900 --> 00:11:55,483 These results can be generalized to other systems. 207 00:11:57,120 --> 00:12:00,260 Furthermore, it provides a way to link 208 00:12:00,260 --> 00:12:01,890 community weighted mean traits 209 00:12:01,890 --> 00:12:05,460 to species composition and ecosystem services, 210 00:12:05,460 --> 00:12:08,123 so effects on ecosystem services. 211 00:12:11,300 --> 00:12:13,990 So it's a really powerful framework, 212 00:12:13,990 --> 00:12:16,100 and we can look at interactions 213 00:12:16,100 --> 00:12:18,860 between climate and disturbance 214 00:12:21,300 --> 00:12:26,010 or looking at how silvicultural management can be used 215 00:12:26,010 --> 00:12:29,520 to manipulate community weighted mean traits 216 00:12:29,520 --> 00:12:31,150 in the face of climate change 217 00:12:31,150 --> 00:12:34,293 in order to sustain ecosystem services. 218 00:12:41,330 --> 00:12:44,070 So looking at those questions of measurement, 219 00:12:44,070 --> 00:12:46,840 which traits do we measure, and at what intensity, 220 00:12:46,840 --> 00:12:50,350 this is a question that Matthew Hecking, 221 00:12:50,350 --> 00:12:54,240 a graduate student I advised at SUNY ESF, 222 00:12:54,240 --> 00:12:58,410 he recently graduated, was interested in. 223 00:12:58,410 --> 00:13:02,530 And Matthew is presenting a poster on this study, 224 00:13:02,530 --> 00:13:06,260 which was recently just accepted for publication. 225 00:13:06,260 --> 00:13:08,563 So congratulations, Matthew. 226 00:13:09,920 --> 00:13:12,110 So he's looking at the question 227 00:13:12,110 --> 00:13:14,713 of, you know, which traits do we measure? 228 00:13:16,630 --> 00:13:21,630 What are the axes or dimensions of variation among species 229 00:13:21,660 --> 00:13:25,340 in tree species in Northeastern forest? 230 00:13:25,340 --> 00:13:29,360 And at what intensity, so what are the levels 231 00:13:29,360 --> 00:13:33,500 of intraspecific variability, and how do they relate 232 00:13:33,500 --> 00:13:36,550 to environmental conditions and climate 233 00:13:36,550 --> 00:13:39,763 and light transmission in the understory? 234 00:13:41,360 --> 00:13:44,330 And so I encourage you 235 00:13:44,330 --> 00:13:47,370 to visit his poster today 236 00:13:47,370 --> 00:13:50,843 and learn more about what he found there. 237 00:13:51,870 --> 00:13:55,820 His second chapter or manuscript 238 00:13:55,820 --> 00:13:58,540 then applies these trait values to communities. 239 00:13:58,540 --> 00:14:01,180 And this is a collaboration 240 00:14:01,180 --> 00:14:04,010 with Martin Dovciak 241 00:14:04,010 --> 00:14:08,950 and his student, Justin Tourville at SUNY ESF, 242 00:14:08,950 --> 00:14:12,030 so applying these trait data that Matthew collected 243 00:14:12,030 --> 00:14:16,940 to the vegetation data in the Dovciak lab 244 00:14:16,940 --> 00:14:20,210 to assess community weighted mean traits 245 00:14:20,210 --> 00:14:22,790 in relation to elevation gradients 246 00:14:22,790 --> 00:14:26,160 associated with climate conditions. 247 00:14:26,160 --> 00:14:29,150 And so what Matthew is finding 248 00:14:29,150 --> 00:14:32,700 is that at the community level, 249 00:14:32,700 --> 00:14:37,700 specifically area increases with mean annual temperature. 250 00:14:38,230 --> 00:14:39,250 So this is one trait 251 00:14:39,250 --> 00:14:41,890 associated with the leaf economic spectrum. 252 00:14:41,890 --> 00:14:44,900 And on the other side of the leaf economic spectrum, 253 00:14:44,900 --> 00:14:47,880 leaf dry matter content decreases 254 00:14:47,880 --> 00:14:50,010 with mean annual temperature. 255 00:14:50,010 --> 00:14:53,830 So this is associated with elevation gradients. 256 00:14:53,830 --> 00:14:56,923 And so as you go up the mountain, 257 00:14:57,780 --> 00:15:02,350 leaves become thicker and more long-lived 258 00:15:02,350 --> 00:15:05,320 as you transition from that temperate deciduous forest 259 00:15:05,320 --> 00:15:08,593 to the high elevation coniferous forests. 260 00:15:09,610 --> 00:15:10,960 And this is looking 261 00:15:10,960 --> 00:15:15,960 at understory tree seedling communities in blue, 262 00:15:17,170 --> 00:15:21,050 and in gaps, tree seedling communities in gaps. 263 00:15:21,050 --> 00:15:25,900 And so Matthew is seeing that specific leaf area 264 00:15:25,900 --> 00:15:29,870 is also a little bit higher in the gaps, 265 00:15:29,870 --> 00:15:31,710 and this represents a shift 266 00:15:31,710 --> 00:15:35,783 in about 0.25 degrees mean annual temperature. 267 00:15:39,120 --> 00:15:42,040 And this is consistent with a lot of literature 268 00:15:42,040 --> 00:15:43,790 that's been coming out recently 269 00:15:43,790 --> 00:15:46,390 looking at the effects of gaps on the microclimate, 270 00:15:46,390 --> 00:15:50,270 and thus the thermophilization of understory communities 271 00:15:50,270 --> 00:15:53,630 or the transition to warmer adapted species 272 00:15:53,630 --> 00:15:54,763 in the understory. 273 00:15:55,760 --> 00:15:59,310 And I don't think this is particularly a bad thing. 274 00:15:59,310 --> 00:16:01,240 I guess what's concerning is 275 00:16:01,240 --> 00:16:04,840 that a 0.25 degrees Celsius isn't enough 276 00:16:04,840 --> 00:16:08,550 when we're considering warming 277 00:16:08,550 --> 00:16:10,903 of 1.5 degrees or higher. 278 00:16:20,220 --> 00:16:25,070 We can develop these statistically mechanistic models 279 00:16:25,070 --> 00:16:28,453 of community weighted mean trait response to climate, 280 00:16:29,630 --> 00:16:34,240 and use the parameters to project effects of climate change 281 00:16:34,240 --> 00:16:36,640 on community weighted mean traits 282 00:16:36,640 --> 00:16:39,583 and associated ecosystem services. 283 00:16:42,340 --> 00:16:45,630 I've been working on a project out of Western Oregon 284 00:16:45,630 --> 00:16:48,700 that does this for understory plant communities 285 00:16:48,700 --> 00:16:51,570 of coastal Douglas fir forests. 286 00:16:51,570 --> 00:16:56,570 And we're looking at using a density management experiment 287 00:16:57,320 --> 00:17:00,160 that's replicated across a climate gradient 288 00:17:01,213 --> 00:17:03,130 to parameterize these models 289 00:17:03,130 --> 00:17:05,260 and project the effects of climate change 290 00:17:05,260 --> 00:17:08,623 for different density management scenarios, 291 00:17:11,320 --> 00:17:15,960 and translate that into effect traits or ecosystem services, 292 00:17:15,960 --> 00:17:20,720 focusing on early-seral habitat and pollination services, 293 00:17:20,720 --> 00:17:24,670 as well as indigenous cultural ecosystem services. 294 00:17:24,670 --> 00:17:28,640 So we can design silvicultural prescriptions 295 00:17:28,640 --> 00:17:33,280 and management strategies that sustain these services 296 00:17:34,320 --> 00:17:35,763 in the face of change. 297 00:17:40,720 --> 00:17:42,490 And this is a bit of a sidebar, 298 00:17:42,490 --> 00:17:46,170 but I just want to share with you how excited I am 299 00:17:46,170 --> 00:17:49,530 about looking at these cultural services 300 00:17:49,530 --> 00:17:54,530 of indigenous cultural services of these understory species. 301 00:17:56,780 --> 00:18:01,040 And I'm drawing on this very recent publication 302 00:18:02,780 --> 00:18:06,300 coming out of California in the Klamath River Basin 303 00:18:07,660 --> 00:18:10,220 that provides a new conceptualization 304 00:18:10,220 --> 00:18:13,700 of indigenous cultural ecosystem services 305 00:18:13,700 --> 00:18:16,910 that doesn't just lump all cultural values 306 00:18:16,910 --> 00:18:19,840 into one corner, but recognizes 307 00:18:19,840 --> 00:18:24,250 that these indigenous cultural ecosystem services 308 00:18:24,250 --> 00:18:29,050 provide regulating, provisioning and supporting services 309 00:18:29,970 --> 00:18:32,940 that influence species identities, 310 00:18:32,940 --> 00:18:35,173 experience and capabilities. 311 00:18:37,690 --> 00:18:40,030 So this new conceptualization 312 00:18:40,030 --> 00:18:42,800 also integrates cultural practices, 313 00:18:42,800 --> 00:18:47,800 and so the influence of management on ecosystems. 314 00:18:48,470 --> 00:18:51,370 So this is a really complex figure. 315 00:18:51,370 --> 00:18:54,170 And I don't expect, I'm not trying to get everyone 316 00:18:54,170 --> 00:18:57,050 to digest it all right now, 317 00:18:57,050 --> 00:18:59,400 but I just wanted to kind of share it to you 318 00:18:59,400 --> 00:19:03,510 'cause it's something I came across recently 319 00:19:03,510 --> 00:19:04,853 that kind of blew my mind. 320 00:19:06,470 --> 00:19:09,020 And I'm excited about digging into that 321 00:19:09,020 --> 00:19:10,493 with this work in Oregon. 322 00:19:12,300 --> 00:19:15,580 Okay, so now we'll come back up that rabbit hole 323 00:19:19,852 --> 00:19:22,400 and get back to silviculture. 324 00:19:22,400 --> 00:19:26,437 We often focus on variability in forest structure 325 00:19:26,437 --> 00:19:31,437 and species diversity as a source of adaptive capacity. 326 00:19:31,760 --> 00:19:35,040 And so conventional silviculture 327 00:19:35,040 --> 00:19:38,660 represented here as managed uneven-aged stands 328 00:19:38,660 --> 00:19:41,290 and managed even-aged stands 329 00:19:41,290 --> 00:19:44,900 focuses on controlling developmental trajectories 330 00:19:44,900 --> 00:19:49,080 and maximizing yield by maintaining fully stocked stands 331 00:19:49,080 --> 00:19:53,340 and simplifying stand composition and structure. 332 00:19:53,340 --> 00:19:57,300 So this strategy really aims for a very narrow target 333 00:19:57,300 --> 00:20:00,010 in range of conditions 334 00:20:00,010 --> 00:20:01,400 that can maintain high rates 335 00:20:01,400 --> 00:20:04,280 of carbon storage and productivity, 336 00:20:04,280 --> 00:20:07,273 but it also reduces options for the future. 337 00:20:10,010 --> 00:20:13,030 This is concerning particularly as climate conditions 338 00:20:15,102 --> 00:20:16,780 and disturbance regimes change 339 00:20:16,780 --> 00:20:21,653 and make conditions unsuitable for target tree species. 340 00:20:23,410 --> 00:20:26,370 So this uncertainty we face suggests the need 341 00:20:26,370 --> 00:20:28,290 for more of a bet hedging approach 342 00:20:29,990 --> 00:20:32,060 that builds adaptive capacity 343 00:20:32,060 --> 00:20:34,100 by managing for a broader range 344 00:20:34,100 --> 00:20:38,010 and diversity of ecosystem characteristics 345 00:20:38,010 --> 00:20:40,890 thinking about species and structural conditions. 346 00:20:40,890 --> 00:20:43,200 So we can further extend this logic 347 00:20:44,870 --> 00:20:46,490 to traits, right? 348 00:20:46,490 --> 00:20:50,420 So we can incorporate traits into silvicultural planning 349 00:20:50,420 --> 00:20:52,453 for climate change and adaptation, 350 00:20:53,770 --> 00:20:56,540 climate change adaptation and mitigation 351 00:20:56,540 --> 00:21:01,120 by considering not only specific target trait values, 352 00:21:01,120 --> 00:21:04,463 but also variability in traits. 353 00:21:09,140 --> 00:21:11,980 And so to manage the adaptive capacity 354 00:21:11,980 --> 00:21:15,270 or adaptive complexity of ecosystems, 355 00:21:15,270 --> 00:21:18,470 we need to not only consider structural complexity, 356 00:21:18,470 --> 00:21:22,123 but also the functional complexity of the forest ecosystem. 357 00:21:23,280 --> 00:21:26,420 And so structural complexity often refers 358 00:21:26,420 --> 00:21:30,650 to variation in the density and age structure 359 00:21:32,048 --> 00:21:34,790 and spatial configurations of trees. 360 00:21:34,790 --> 00:21:37,560 But we also need to think about species 361 00:21:37,560 --> 00:21:41,093 and functional trait diversity and complexity. 362 00:21:46,470 --> 00:21:48,420 In Northern hardwood forests 363 00:21:48,420 --> 00:21:51,563 of the Northeast and Great Lakes region, 364 00:21:52,930 --> 00:21:56,890 species structural and functional diversity 365 00:21:58,386 --> 00:21:59,823 is often degraded, 366 00:22:02,030 --> 00:22:04,860 for example, by exploitative logging. 367 00:22:04,860 --> 00:22:06,720 Most Northern forests in the Northeast 368 00:22:06,720 --> 00:22:10,120 are considered understocked relative to their capacity 369 00:22:10,120 --> 00:22:12,950 as a result of a history of high-grade logging 370 00:22:12,950 --> 00:22:14,800 and other disturbances 371 00:22:14,800 --> 00:22:17,723 which are caused by introduced pests and pathogens. 372 00:22:18,620 --> 00:22:22,090 Furthermore, there's degradation in soil fertility 373 00:22:22,090 --> 00:22:25,870 associated with the legacy of agriculture and acid rain, 374 00:22:25,870 --> 00:22:28,420 that has reduced the capacity of trees 375 00:22:28,420 --> 00:22:30,483 to go and sequester carbon. 376 00:22:32,830 --> 00:22:36,470 Finally, we have issues with regeneration lags 377 00:22:36,470 --> 00:22:38,660 associated with high deer populations 378 00:22:38,660 --> 00:22:40,063 and Beech bark disease. 379 00:22:42,220 --> 00:22:44,600 And so here's a picture contrasting 380 00:22:44,600 --> 00:22:48,000 in an old growth Northern hardwood stand. 381 00:22:48,000 --> 00:22:49,610 This is from the lake states 382 00:22:49,610 --> 00:22:52,240 in the Sylvania Wilderness Area 383 00:22:52,240 --> 00:22:56,870 with a younger second growth stand in Wisconsin, 384 00:22:56,870 --> 00:22:59,470 showing the type of degradation 385 00:22:59,470 --> 00:23:01,853 in structural and functional diversity. 386 00:23:03,960 --> 00:23:07,230 So for climate change adaptation and mitigation, 387 00:23:07,230 --> 00:23:09,510 one strategy could be to restore 388 00:23:09,510 --> 00:23:13,983 and rehabilitate functional diversity. 389 00:23:21,210 --> 00:23:24,930 One of my master's students at SUNY ESF, Keenan Rivers, 390 00:23:24,930 --> 00:23:27,600 has been particularly interested 391 00:23:27,600 --> 00:23:30,170 in this Beech bark disease issue 392 00:23:30,170 --> 00:23:33,780 and the development of beech thickets in aftermath forests. 393 00:23:33,780 --> 00:23:38,370 And so he's looking at spatial patterns of beech thickets 394 00:23:38,370 --> 00:23:42,030 and thicket structure and development over time, 395 00:23:42,030 --> 00:23:44,550 and how that is influenced 396 00:23:44,550 --> 00:23:47,230 by various drivers at different scales, 397 00:23:47,230 --> 00:23:49,500 and feeds back to influence 398 00:23:49,500 --> 00:23:52,253 other aspects of the understory community. 399 00:23:55,200 --> 00:23:57,790 So he's working on coming up with a classification 400 00:23:57,790 --> 00:24:01,060 of different structures and stages 401 00:24:01,060 --> 00:24:03,760 of aftermath thicket development, 402 00:24:03,760 --> 00:24:06,050 and relating that to variation 403 00:24:06,050 --> 00:24:07,320 in the understory communities. 404 00:24:07,320 --> 00:24:09,490 And so far, he's finding 405 00:24:09,490 --> 00:24:13,890 that as the thicket becomes denser and more intense, 406 00:24:15,700 --> 00:24:18,490 the cover of forbs and graminoids decreases, 407 00:24:18,490 --> 00:24:22,160 while that of ferns, shrubs and trees increases. 408 00:24:22,160 --> 00:24:24,223 So there's some linkages there. 409 00:24:27,030 --> 00:24:29,280 So in addition to controlling beech 410 00:24:29,280 --> 00:24:31,520 and thinking about how to manage the effects of beech, 411 00:24:31,520 --> 00:24:33,263 we also need to focus on deer. 412 00:24:34,500 --> 00:24:37,470 This is a forest in the Catskills, 413 00:24:37,470 --> 00:24:39,330 a Northern hardwood forest in the Catskills 414 00:24:39,330 --> 00:24:42,593 at the Frost Valley YMCA. 415 00:24:43,560 --> 00:24:44,430 And you can see 416 00:24:46,569 --> 00:24:48,080 a shelterwood harvest 417 00:24:49,440 --> 00:24:51,940 where deer have not been excluded 418 00:24:51,940 --> 00:24:54,010 compared to where they have been excluded, 419 00:24:54,010 --> 00:24:55,970 so no regeneration 420 00:24:55,970 --> 00:24:59,870 where deer have been allowed to graze and browse 421 00:25:00,870 --> 00:25:04,773 compared to really dense regeneration in the exclosures. 422 00:25:06,640 --> 00:25:08,160 So here's another look at that 423 00:25:08,160 --> 00:25:13,160 from outside the exclosure here to inside. 424 00:25:13,300 --> 00:25:17,080 So the deer interacts with the understory community, 425 00:25:17,080 --> 00:25:22,080 creating this really dense vegetation 426 00:25:22,130 --> 00:25:27,130 that makes it very difficult for seedlings to establish. 427 00:25:31,800 --> 00:25:34,570 So when restoring adaptive capacity 428 00:25:34,570 --> 00:25:36,380 and managing for adaptive capacity, 429 00:25:36,380 --> 00:25:41,283 we really need to think about controlling deer and beech. 430 00:25:45,280 --> 00:25:48,920 So that one strategy is to kind of work with what we have. 431 00:25:48,920 --> 00:25:51,530 However, we know that climate change 432 00:25:51,530 --> 00:25:54,140 outpaces the ability of plants 433 00:25:54,140 --> 00:25:56,803 to grow and migrate to new conditions. 434 00:26:00,950 --> 00:26:05,150 So another strategy is to transition forests 435 00:26:05,150 --> 00:26:08,750 to species that are better adapted to future conditions, 436 00:26:08,750 --> 00:26:12,570 whether that's moving populations 437 00:26:12,570 --> 00:26:16,180 from warmer future climates, 438 00:26:16,180 --> 00:26:20,710 to expanding the ranges of species slightly, 439 00:26:20,710 --> 00:26:22,900 to larger migrations 440 00:26:24,130 --> 00:26:26,037 and moving species 441 00:26:29,010 --> 00:26:31,623 across natural migration boundaries. 442 00:26:40,320 --> 00:26:42,130 I've been working 443 00:26:42,130 --> 00:26:44,790 with colleagues at ESF 444 00:26:44,790 --> 00:26:47,780 and now Michigan Technological University 445 00:26:47,780 --> 00:26:51,080 to develop alternative silvicultural strategies 446 00:26:51,080 --> 00:26:54,083 for climate change adaptation and mitigation. 447 00:26:57,250 --> 00:27:00,160 And so we're gonna compare a no action 448 00:27:00,160 --> 00:27:02,190 and a business as usual scenario 449 00:27:02,190 --> 00:27:05,133 that don't focus on adaptive capacity, 450 00:27:06,570 --> 00:27:10,620 to this restoration and rehabilitation approach, 451 00:27:10,620 --> 00:27:14,363 which is similar to ecological ideas, 452 00:27:15,240 --> 00:27:18,013 underlying ecological silviculture, 453 00:27:19,761 --> 00:27:21,233 and the transition. 454 00:27:25,400 --> 00:27:29,140 The business as usual in Northern hardwoods 455 00:27:32,190 --> 00:27:34,690 we think is generally a shelterwood, 456 00:27:34,690 --> 00:27:36,523 so even-aged shelterwood. 457 00:27:37,858 --> 00:27:40,480 In Michigan and the UP, 458 00:27:40,480 --> 00:27:43,040 it's more of a single tree selection. 459 00:27:43,040 --> 00:27:46,513 And both of these strategies utilize natural regeneration. 460 00:27:47,380 --> 00:27:51,580 So to develop the restoration and rehabilitation option, 461 00:27:51,580 --> 00:27:56,580 we can build on that to add things like legacy retention, 462 00:27:56,580 --> 00:27:58,643 larger canopy openings, 463 00:28:00,390 --> 00:28:02,700 ensure that the residual target structure 464 00:28:02,700 --> 00:28:05,000 increases functional diversity, 465 00:28:05,000 --> 00:28:07,280 and use population enrichment planting 466 00:28:07,280 --> 00:28:09,303 to increase functional diversity. 467 00:28:11,040 --> 00:28:15,140 In contrast, the transition for Northern hardwoods 468 00:28:16,470 --> 00:28:17,970 is most likely going to be 469 00:28:17,970 --> 00:28:21,230 a variable density shelterwood with understory removal 470 00:28:21,230 --> 00:28:26,230 combined with underplanting of future-adapted species, 471 00:28:26,340 --> 00:28:30,860 oaks and hickories, mixing in some conifers like white pine 472 00:28:30,860 --> 00:28:33,763 for that functional diversity component. 473 00:28:40,980 --> 00:28:43,960 And so we expect some trade-offs 474 00:28:43,960 --> 00:28:47,820 between the ability for these different strategies 475 00:28:47,820 --> 00:28:51,880 to provide carbon storage 476 00:28:51,880 --> 00:28:53,350 and sequestration rates, 477 00:28:53,350 --> 00:28:54,240 which will be higher 478 00:28:54,240 --> 00:28:56,370 in the no action and business as usual, 479 00:28:56,370 --> 00:29:00,120 which retain higher levels of overstory stocking, 480 00:29:00,120 --> 00:29:02,530 compared to building adaptive capacity, 481 00:29:02,530 --> 00:29:07,530 where we reduce tree stocking to a greater degree 482 00:29:07,540 --> 00:29:10,490 to develop this adaptive capacity 483 00:29:10,490 --> 00:29:13,450 and the regeneration layer. 484 00:29:13,450 --> 00:29:16,280 And so there might be some trade-offs initially, 485 00:29:16,280 --> 00:29:20,470 but over time we would expect these adaptive capacities 486 00:29:20,470 --> 00:29:24,440 to store and sequester more carbon 487 00:29:24,440 --> 00:29:27,543 than the business as usual and no action. 488 00:29:32,470 --> 00:29:35,750 We've scoped out two sites 489 00:29:35,750 --> 00:29:37,910 to replicate the experiment in New York, 490 00:29:37,910 --> 00:29:41,810 and collected, established permanent plots 491 00:29:41,810 --> 00:29:43,710 and experimental units 492 00:29:43,710 --> 00:29:45,660 and collected a bunch of pre-treatment data. 493 00:29:45,660 --> 00:29:47,960 So one is in the Allegheny Plateau 494 00:29:49,120 --> 00:29:50,650 at Heiberg Memorial Forest, 495 00:29:50,650 --> 00:29:53,920 and another at Huntington Wildlife Forest 496 00:29:53,920 --> 00:29:55,303 in the Adirondacks. 497 00:29:58,190 --> 00:30:00,610 We're also going to replicate the experiment 498 00:30:00,610 --> 00:30:03,563 here in the Upper Peninsula at the Ford Forest. 499 00:30:06,470 --> 00:30:09,410 There are advantages to, it's exciting to be able 500 00:30:09,410 --> 00:30:10,960 to replicate this experiment 501 00:30:10,960 --> 00:30:14,343 across a gradient of environmental conditions, 502 00:30:16,120 --> 00:30:19,730 with the Adirondacks and the Upper Peninsula 503 00:30:19,730 --> 00:30:23,210 being similar, mean at more similar in climate conditions 504 00:30:23,210 --> 00:30:25,783 than the Allegheny Plateau. 505 00:30:29,640 --> 00:30:30,960 And there's also differences 506 00:30:30,960 --> 00:30:34,410 in how the climates are projected to change 507 00:30:34,410 --> 00:30:35,930 at these locations, 508 00:30:35,930 --> 00:30:40,020 with the New York replicates 509 00:30:40,020 --> 00:30:43,900 changing primarily in mean annual temperature, 510 00:30:43,900 --> 00:30:45,890 while the Upper Peninsula is expected 511 00:30:45,890 --> 00:30:48,450 to increase in mean annual temperature, 512 00:30:48,450 --> 00:30:51,123 but also decline in summer precipitation. 513 00:30:53,590 --> 00:30:56,860 So by replicating the experiment across this gradient, 514 00:30:56,860 --> 00:30:59,420 we can look at interesting interactions 515 00:30:59,420 --> 00:31:01,640 between silvicultural management 516 00:31:01,640 --> 00:31:05,003 and climate and climate change over time. 517 00:31:10,880 --> 00:31:15,230 Overall, we expect this study to result 518 00:31:15,230 --> 00:31:17,410 in the development of trait-based prescriptions 519 00:31:17,410 --> 00:31:20,023 for climate change adaptation and mitigation. 520 00:31:21,130 --> 00:31:25,040 We'll be able to quantify vegetation and ecosystem responses 521 00:31:25,040 --> 00:31:28,980 to these alternative silvicultural approaches, 522 00:31:28,980 --> 00:31:31,710 and compare treatment effects across sites 523 00:31:31,710 --> 00:31:36,410 that vary in climate and species composition. 524 00:31:36,410 --> 00:31:41,160 For example, contrasting our forests and Northern hardwoods 525 00:31:41,160 --> 00:31:44,230 in upper Michigan that have no beech 526 00:31:44,230 --> 00:31:46,580 with Northern hardwoods in New York, 527 00:31:46,580 --> 00:31:49,500 which have that significant beech component 528 00:31:49,500 --> 00:31:52,913 and beech thicket issue. 529 00:31:59,530 --> 00:32:03,940 And of course, we're gonna monitor 530 00:32:03,940 --> 00:32:05,480 and do adaptive management. 531 00:32:05,480 --> 00:32:08,580 This doesn't just stop at the implementation. 532 00:32:08,580 --> 00:32:13,580 There's a lot of uncertainty about how forests will respond, 533 00:32:13,642 --> 00:32:17,330 about climate change in general and changes in disturbance, 534 00:32:17,330 --> 00:32:20,070 and not only how that's going to change, 535 00:32:20,070 --> 00:32:23,183 but how that's going to affect the forests. 536 00:32:24,701 --> 00:32:26,190 And it's very complex. 537 00:32:26,190 --> 00:32:29,593 So we need to be humble and we need to assess, 538 00:32:31,190 --> 00:32:35,580 incorporate, close the loop and incorporate research, 539 00:32:35,580 --> 00:32:39,000 integrated research and monitoring and assessment, 540 00:32:39,000 --> 00:32:41,430 and measures of success, 541 00:32:41,430 --> 00:32:44,673 to adapt these strategies and develop them over time. 542 00:32:45,700 --> 00:32:49,883 So we'll be looking at both near and long-term effects. 543 00:32:54,930 --> 00:32:59,067 We'll also use the experiment 544 00:32:59,067 --> 00:33:03,350 as a platform to engage with various stakeholders, 545 00:33:03,350 --> 00:33:06,930 including land trust managers and teachers. 546 00:33:06,930 --> 00:33:10,790 This bullseye diagram is something I developed 547 00:33:10,790 --> 00:33:13,000 working with Shari Dann 548 00:33:14,530 --> 00:33:18,913 at SUNY ESF, as well as Stacy McNulty. 549 00:33:19,790 --> 00:33:22,570 So we're kind of looking at this bullseye approach 550 00:33:22,570 --> 00:33:25,980 to engaging with key stakeholders, 551 00:33:25,980 --> 00:33:29,423 but also lots of other groups like graduate students, 552 00:33:30,820 --> 00:33:34,010 through research, undergrads and graduates. 553 00:33:34,010 --> 00:33:38,350 With coursework, these sites are really accessible 554 00:33:39,220 --> 00:33:41,950 and can be used on field trips, 555 00:33:41,950 --> 00:33:44,930 engaging with forestry professionals and managers 556 00:33:47,190 --> 00:33:48,060 on field trips, 557 00:33:48,060 --> 00:33:51,700 but also to collaboratively develop 558 00:33:51,700 --> 00:33:53,860 and implement these prescriptions, 559 00:33:53,860 --> 00:33:58,210 as well as children and recreationists. 560 00:33:58,210 --> 00:34:00,790 So we have lots of opportunities 561 00:34:00,790 --> 00:34:04,563 to engage various groups with this experiment. 562 00:34:06,630 --> 00:34:08,470 Moreover, I wanna recognize 563 00:34:08,470 --> 00:34:11,000 that like we're in the nascent stages of this, 564 00:34:11,000 --> 00:34:13,860 and there are other similar experiments. 565 00:34:13,860 --> 00:34:18,710 The ASCC network was really influential in our design. 566 00:34:18,710 --> 00:34:22,150 And so we're really building off of that 567 00:34:22,150 --> 00:34:24,023 and tweaking it in various ways. 568 00:34:25,050 --> 00:34:27,890 But we're really excited about those studies 569 00:34:27,890 --> 00:34:30,560 and opportunities to collaborate 570 00:34:30,560 --> 00:34:33,190 across studies in the ASCC network. 571 00:34:33,190 --> 00:34:35,740 There's also the DREAM Project, 572 00:34:35,740 --> 00:34:37,850 which is another experiment 573 00:34:37,850 --> 00:34:40,580 looking at transition strategies, 574 00:34:40,580 --> 00:34:44,733 and in doing so, working with NIACS. 575 00:34:53,050 --> 00:34:57,150 We're living in a very disruptive time on multiple levels, 576 00:34:57,150 --> 00:34:59,283 but we can't turn away. 577 00:35:00,310 --> 00:35:02,040 We have to face these. 578 00:35:02,040 --> 00:35:05,010 We have to continue to face these challenges head on 579 00:35:05,010 --> 00:35:09,470 and use all of our tools to reimagine forest communities 580 00:35:09,470 --> 00:35:13,393 and make those changes acceptable. 581 00:35:15,620 --> 00:35:20,370 So this trait-based framework provides powerful tools, 582 00:35:20,370 --> 00:35:22,460 not only for projecting change, 583 00:35:22,460 --> 00:35:24,300 but also looking at ways 584 00:35:24,300 --> 00:35:27,510 of silviculturally managing that change 585 00:35:27,510 --> 00:35:31,373 in order to sustain the values and services of forests. 586 00:35:36,230 --> 00:35:40,160 So I'd like acknowledge all the collaborators 587 00:35:40,160 --> 00:35:41,690 that I work with on this, 588 00:35:41,690 --> 00:35:44,980 the many undergraduate and graduate students 589 00:35:44,980 --> 00:35:49,610 and other colleagues and partners at ESF, 590 00:35:49,610 --> 00:35:53,063 Michigan Tech and Oregon State University. 591 00:35:55,840 --> 00:35:59,290 I'd love to address any questions 592 00:35:59,290 --> 00:36:04,290 or discussion that anyone would like to have at this point. 593 00:36:08,640 --> 00:36:10,950 - Great. Thank you, Julia. 594 00:36:10,950 --> 00:36:15,950 Yep, everyone, you can post your questions in the Q&A box 595 00:36:16,359 --> 00:36:17,790 and we can get to them there. 596 00:36:17,790 --> 00:36:19,050 You can also put them in the chat, 597 00:36:19,050 --> 00:36:20,940 or feel free to use the raise hand function 598 00:36:20,940 --> 00:36:24,670 at the bottom of your Zoom screen and we can get to you. 599 00:36:24,670 --> 00:36:27,143 It looks like we have one coming in already. 600 00:36:27,990 --> 00:36:29,620 This is from Samantha Myers, 601 00:36:29,620 --> 00:36:32,150 says "hi, Julia, thanks for a great talk. 602 00:36:32,150 --> 00:36:33,880 I'm a grad student at UMass 603 00:36:33,880 --> 00:36:36,020 and have also been thinking about how to integrate 604 00:36:36,020 --> 00:36:37,960 individual level functional traits 605 00:36:37,960 --> 00:36:40,160 into forest dynamic frameworks 606 00:36:40,160 --> 00:36:43,060 to predict forest carbon storage and long-term resilience. 607 00:36:44,070 --> 00:36:45,610 You mentioned using CWM 608 00:36:45,610 --> 00:36:47,970 versus integrated intraspecific variability, 609 00:36:47,970 --> 00:36:49,420 and I was wondering what you've found 610 00:36:49,420 --> 00:36:50,880 using these two approaches. 611 00:36:50,880 --> 00:36:52,290 I would also be very interested 612 00:36:52,290 --> 00:36:53,880 if you'd be willing to share an example 613 00:36:53,880 --> 00:36:55,870 of the trait-based silvicultural prescriptions 614 00:36:55,870 --> 00:36:57,520 that you mentioned you're using." 615 00:36:59,700 --> 00:37:00,533 - Yeah. 616 00:37:02,430 --> 00:37:05,080 Yeah, so Matthew Hecking 617 00:37:05,920 --> 00:37:09,810 is the recent master's student who just finished, 618 00:37:09,810 --> 00:37:12,030 who's really been developing this 619 00:37:12,030 --> 00:37:15,010 for forests in the Northeast. 620 00:37:15,010 --> 00:37:17,720 And I also did a little work on this 621 00:37:17,720 --> 00:37:22,720 in understory communities of Oregon, on the Oregon project. 622 00:37:24,805 --> 00:37:28,520 So what we're generally finding is 623 00:37:28,520 --> 00:37:33,520 that intraspecific variation, 624 00:37:34,530 --> 00:37:36,890 levels of intraspecific variation 625 00:37:36,890 --> 00:37:40,690 vary across traits and are greater 626 00:37:40,690 --> 00:37:44,480 in more of the chemical-based traits, 627 00:37:44,480 --> 00:37:47,640 like nitrogen content, phosphorous content, 628 00:37:47,640 --> 00:37:49,920 than the more morphological traits 629 00:37:50,840 --> 00:37:53,263 associated with the leaf economic spectrum. 630 00:37:56,473 --> 00:37:57,960 So the sensitivity 631 00:38:00,400 --> 00:38:01,870 varies among the traits, 632 00:38:01,870 --> 00:38:05,920 and that intraspecific variation is associated 633 00:38:05,920 --> 00:38:10,320 with gradients in light availability 634 00:38:10,320 --> 00:38:11,290 for the most part. 635 00:38:11,290 --> 00:38:12,730 There's some influence of climate, 636 00:38:12,730 --> 00:38:17,490 but light is really more important for forests, 637 00:38:18,730 --> 00:38:20,093 for these forest species. 638 00:38:26,610 --> 00:38:29,233 Generally in the morphological traits, 639 00:38:30,378 --> 00:38:32,910 the taxonomic variation is more important, 640 00:38:32,910 --> 00:38:34,763 explains a lot of the variation. 641 00:38:35,700 --> 00:38:39,700 So the intraspecific variation is relatively low 642 00:38:39,700 --> 00:38:42,303 and not as much of a concern. 643 00:38:46,020 --> 00:38:47,700 And we've been exploring ways 644 00:38:47,700 --> 00:38:50,193 of, you know, we can't just go out, 645 00:38:51,930 --> 00:38:54,490 to incorporate intraspecific variability, 646 00:38:54,490 --> 00:38:56,900 you know, we can't just go out 647 00:38:56,900 --> 00:38:58,680 and measure every individual, right? 648 00:38:58,680 --> 00:38:59,513 Then we're back 649 00:38:59,513 --> 00:39:03,363 to the complex population demographic models. 650 00:39:05,300 --> 00:39:08,510 So looking at ways of modeling, 651 00:39:08,510 --> 00:39:12,760 Matthew has been looking at ways of modeling that variation 652 00:39:12,760 --> 00:39:17,200 and predicting how traits will vary 653 00:39:17,200 --> 00:39:20,350 within a species based on environmental conditions, 654 00:39:20,350 --> 00:39:22,600 and incorporating that 655 00:39:22,600 --> 00:39:27,083 into the community weighted trait metric. 656 00:39:30,023 --> 00:39:33,030 He does that, he explores that more in a second chapter. 657 00:39:33,030 --> 00:39:35,190 So it's a little bit more preliminary, 658 00:39:35,190 --> 00:39:39,750 but it's generally species turnover 659 00:39:39,750 --> 00:39:43,733 that is more important than this intraspecific variability. 660 00:39:45,750 --> 00:39:47,920 So I don't know. 661 00:39:47,920 --> 00:39:49,900 I think I'm coming around to think 662 00:39:52,490 --> 00:39:56,420 that, you know, there's important intraspecific variability 663 00:39:56,420 --> 00:39:57,693 across the gradients, 664 00:40:00,920 --> 00:40:03,280 but it's not the main story. 665 00:40:03,280 --> 00:40:06,313 And we can look at how to incorporate it, 666 00:40:07,630 --> 00:40:12,013 particularly how those traits change on average, 667 00:40:14,770 --> 00:40:18,880 but we don't wanna get too caught up in that 668 00:40:18,880 --> 00:40:23,880 and lose sight of the forest for the trees, so to speak. 669 00:40:24,130 --> 00:40:29,130 It doesn't seem to be the most important dynamic out there, 670 00:40:32,230 --> 00:40:33,710 but what is important 671 00:40:35,110 --> 00:40:38,850 is making sure that those traits are measured 672 00:40:38,850 --> 00:40:42,780 across species under similar environmental conditions. 673 00:40:42,780 --> 00:40:46,510 So instead of taking a simple average, 674 00:40:46,510 --> 00:40:49,420 we can use our models to estimate, 675 00:40:49,420 --> 00:40:50,950 if we're gonna use a single value, 676 00:40:50,950 --> 00:40:52,650 estimate that single value 677 00:40:52,650 --> 00:40:55,253 under a common set of environmental conditions. 678 00:40:56,410 --> 00:41:01,120 And that way we don't get that intraspecific variability 679 00:41:01,120 --> 00:41:03,060 having an unknown effect 680 00:41:03,060 --> 00:41:06,743 on our community weighted trait metrics. 681 00:41:10,710 --> 00:41:14,290 And so dealing with that may be important, 682 00:41:14,290 --> 00:41:15,840 relatively more or less important 683 00:41:15,840 --> 00:41:20,150 depending on the ecosystem function side 684 00:41:20,150 --> 00:41:24,140 of the response effect trait framework. 685 00:41:24,140 --> 00:41:27,280 So if you're looking at ecosystem functions 686 00:41:27,280 --> 00:41:29,370 like productivity and carbon storage, 687 00:41:29,370 --> 00:41:32,370 or using these community weighted traits 688 00:41:33,930 --> 00:41:36,863 to parameterize a process-based model, 689 00:41:40,090 --> 00:41:44,173 incorporating that variation may become more important. 690 00:41:48,500 --> 00:41:50,450 - Thanks. - Yeah. 691 00:41:50,450 --> 00:41:54,270 - Just on the question of some examples 692 00:41:54,270 --> 00:41:56,800 of the trait-based silvicultural prescriptions 693 00:41:56,800 --> 00:41:57,633 that you've mentioned, 694 00:41:57,633 --> 00:42:00,013 is that something you could speak to a little more? 695 00:42:02,220 --> 00:42:07,220 - Yeah, so I haven't like finalized those prescriptions yet, 696 00:42:09,500 --> 00:42:14,500 but some kind of general principles will be involving 697 00:42:18,130 --> 00:42:20,640 considering the functional traits 698 00:42:20,640 --> 00:42:22,863 of the species that you're planting, 699 00:42:31,220 --> 00:42:32,430 and selecting species 700 00:42:32,430 --> 00:42:35,890 that will increase functional diversity, 701 00:42:35,890 --> 00:42:39,833 as well as, excuse me, 702 00:42:54,670 --> 00:42:57,280 as well as make the community better adapted 703 00:42:57,280 --> 00:42:59,393 to future climate conditions, 704 00:43:01,506 --> 00:43:04,323 so developing a framework for that, 705 00:43:05,690 --> 00:43:10,690 as well as specific recommendations 706 00:43:11,140 --> 00:43:14,230 for what species to retain on site 707 00:43:15,239 --> 00:43:18,293 in the harvesting, in the silvicultural treatment. 708 00:43:19,740 --> 00:43:22,013 If it's a regeneration treatment, 709 00:43:24,200 --> 00:43:26,700 you know, making sure to retain conifers 710 00:43:26,700 --> 00:43:29,343 and a diversity of species and traits. 711 00:43:30,910 --> 00:43:33,400 So that's kind of the general guidance, 712 00:43:33,400 --> 00:43:37,470 and then kind of getting specific there, 713 00:43:37,470 --> 00:43:40,047 considering your specific site 714 00:43:40,047 --> 00:43:43,183 and forest and pre-treatment structure. 715 00:43:45,290 --> 00:43:46,990 - Great. Thank you. 716 00:43:46,990 --> 00:43:49,680 We've had a couple coming in on the chat as well. 717 00:43:49,680 --> 00:43:53,250 From Jerry Carlston, "Brilliant and innovative. 718 00:43:53,250 --> 00:43:56,070 How can we put these data-heavy decisive models 719 00:43:56,070 --> 00:43:59,240 into existing strata at the landscape ecosystem 720 00:43:59,240 --> 00:44:02,930 soil, hydrological, geological scale, et cetera?" 721 00:44:02,930 --> 00:44:05,250 Kind of how do you take this 722 00:44:05,250 --> 00:44:07,830 and then put it out into a specific site 723 00:44:07,830 --> 00:44:09,683 when you've got the findings? 724 00:44:14,897 --> 00:44:18,130 - How do we kind of scale up the results? 725 00:44:18,130 --> 00:44:19,680 - I think that's what it's getting at. 726 00:44:19,680 --> 00:44:22,320 And Jerry, feel free to correct me in the chat if I'm wrong. 727 00:44:22,320 --> 00:44:27,320 Yeah. These are very data-heavy, decisive models. 728 00:44:27,450 --> 00:44:30,370 How do you put them onto the landscapes 729 00:44:30,370 --> 00:44:32,143 that individuals are managing? 730 00:44:34,290 --> 00:44:36,940 So it's probably like a scale-up, scale-out question. 731 00:44:38,620 --> 00:44:40,773 - Yeah. I've been thinking about that. 732 00:44:44,660 --> 00:44:48,410 So yeah, like the more we can do this at a bigger scale 733 00:44:48,410 --> 00:44:51,740 and incorporate geospatial data 734 00:44:53,550 --> 00:44:57,140 to develop these models, 735 00:44:57,140 --> 00:45:01,150 the more we can extrapolate across the landscape. 736 00:45:01,150 --> 00:45:03,730 So one thing I've been thinking about 737 00:45:03,730 --> 00:45:08,560 with the Oregon project and the coastal Douglas fir 738 00:45:08,560 --> 00:45:11,537 is to kind of map it using FIA. 739 00:45:12,490 --> 00:45:13,750 If our predictors 740 00:45:15,560 --> 00:45:19,270 include climate and overstory conditions, 741 00:45:19,270 --> 00:45:22,770 you know, can we use FIA data, intercalated FIA data 742 00:45:22,770 --> 00:45:25,380 to map it at larger scales 743 00:45:25,380 --> 00:45:27,650 and, you know, make kind of assumptions 744 00:45:27,650 --> 00:45:31,410 for management scenarios 745 00:45:31,410 --> 00:45:33,090 that we could map 746 00:45:37,080 --> 00:45:39,123 and apply and scale up. 747 00:45:40,445 --> 00:45:43,173 And a similar thing could be said, 748 00:45:45,420 --> 00:45:46,940 could be done in the Northeast 749 00:45:50,193 --> 00:45:53,027 with the Northeastern forest vegetation data. 750 00:45:56,330 --> 00:45:58,400 Some of the issues come into play 751 00:45:58,400 --> 00:46:01,000 when you have variables that are important like light 752 00:46:01,000 --> 00:46:02,113 that aren't measured. 753 00:46:03,290 --> 00:46:06,120 So it'd be involved coming up with a way 754 00:46:06,120 --> 00:46:11,100 to maybe estimate light from those structural variables 755 00:46:11,100 --> 00:46:13,363 that are in the FIA database. 756 00:46:16,130 --> 00:46:17,453 There are also, 757 00:46:21,361 --> 00:46:23,440 there have been a lot of advances recently 758 00:46:23,440 --> 00:46:28,440 with mapping traits and estimating traits 759 00:46:28,520 --> 00:46:32,600 and physiological perimeters for process-based models 760 00:46:32,600 --> 00:46:36,433 using hyperspectral data, 761 00:46:41,270 --> 00:46:44,943 using remote sensing, using hyperspectral sensors. 762 00:46:46,460 --> 00:46:50,640 So that would be if those were put on satellites 763 00:46:50,640 --> 00:46:52,150 and more broadly available, 764 00:46:52,150 --> 00:46:55,283 that would be very useful for this kind of thing as well. 765 00:46:57,591 --> 00:47:00,530 Then you kind of lose the species composition information. 766 00:47:00,530 --> 00:47:02,850 And so you'd have to estimate species composition 767 00:47:02,850 --> 00:47:06,540 from the traits, which can be done, 768 00:47:06,540 --> 00:47:07,780 but that's how that would work. 769 00:47:07,780 --> 00:47:09,907 It'd be a little different. 770 00:47:11,017 --> 00:47:12,073 - Great. Thanks. 771 00:47:13,060 --> 00:47:16,667 Another one here in the chat, this one from Jeff Warren. 772 00:47:16,667 --> 00:47:19,140 "Thank you for a thought-provoking presentation. 773 00:47:19,140 --> 00:47:21,790 The trait approach seems to be appropriate 774 00:47:22,964 --> 00:47:25,370 for index development and potentially linked 775 00:47:25,370 --> 00:47:29,920 to bird, mammal and herp taxa habitat needs. 776 00:47:29,920 --> 00:47:31,220 Has this been considered?" 777 00:47:36,840 --> 00:47:39,090 - And linked to I'm sorry, what was it, herp? 778 00:47:40,440 --> 00:47:43,993 - Bird, mammal and herp taxa habitat needs. 779 00:47:45,840 --> 00:47:47,540 - Habitat names? 780 00:47:47,540 --> 00:47:49,123 - Needs. - Needs, okay, yeah. 781 00:47:52,170 --> 00:47:55,790 Yeah, so that's one aspect of the project in Oregon. 782 00:47:55,790 --> 00:47:58,110 I'm sorry, my speakers, I have a new computer 783 00:47:58,110 --> 00:47:59,770 and I need to get some new speakers. 784 00:47:59,770 --> 00:48:00,870 They're not very good. 785 00:48:02,420 --> 00:48:04,040 So we are considering 786 00:48:04,040 --> 00:48:08,560 kind of early seral habitat considerations in Oregon, 787 00:48:08,560 --> 00:48:11,200 where that's a really big issue. 788 00:48:11,200 --> 00:48:14,570 Its industry is a big landowner, 789 00:48:14,570 --> 00:48:17,500 and they kind of skip over that stage 790 00:48:17,500 --> 00:48:19,700 by creating dense plantations 791 00:48:19,700 --> 00:48:23,093 with combining that with aggressive vegetation control. 792 00:48:23,990 --> 00:48:27,540 So there's a lot of interest in managing 793 00:48:27,540 --> 00:48:30,650 for high quality early seral wildlife habitat 794 00:48:30,650 --> 00:48:31,893 on federal lands. 795 00:48:32,740 --> 00:48:36,040 And one of our goals with the Oregon project 796 00:48:36,040 --> 00:48:39,750 is to develop a trait-based method 797 00:48:39,750 --> 00:48:42,080 for understanding the linkages 798 00:48:42,080 --> 00:48:46,000 between response traits 799 00:48:46,000 --> 00:48:51,000 and effect traits that focus on the wildlife values 800 00:48:51,420 --> 00:48:52,593 of those plants. 801 00:48:53,640 --> 00:48:55,500 So we look at each individual species, 802 00:48:55,500 --> 00:48:58,210 and we can characterize its value 803 00:48:59,130 --> 00:49:03,423 as wildlife habitat, birds, mammals, 804 00:49:05,709 --> 00:49:07,823 herps, that's what we said, yeah, 805 00:49:09,140 --> 00:49:11,443 would be one approach to that. 806 00:49:12,638 --> 00:49:14,430 And that's exactly what we're doing 807 00:49:14,430 --> 00:49:15,900 with the cultural services too. 808 00:49:15,900 --> 00:49:18,180 We look at each individual plant 809 00:49:18,180 --> 00:49:21,913 and we classify it with respect to its values. 810 00:49:23,306 --> 00:49:27,653 And then we can use the response traits 811 00:49:28,700 --> 00:49:30,320 for the mechanistic linkage 812 00:49:30,320 --> 00:49:34,640 and the statistically mechanistic model, 813 00:49:34,640 --> 00:49:38,840 and then use this maximum entropy model 814 00:49:40,850 --> 00:49:44,920 to reconstruct species composition 815 00:49:44,920 --> 00:49:46,750 from that response trait. 816 00:49:46,750 --> 00:49:48,560 So it looks at your response trait, 817 00:49:48,560 --> 00:49:50,950 and it's like, what is the most likely distribution 818 00:49:50,950 --> 00:49:55,080 of species given their traits for that response trait. 819 00:49:55,080 --> 00:49:56,800 So then we have our species-based 820 00:49:56,800 --> 00:50:01,140 species abundance community, and we can take that 821 00:50:01,140 --> 00:50:04,830 and turn it into the effect trait, 822 00:50:04,830 --> 00:50:07,920 the function or the services that it provides 823 00:50:07,920 --> 00:50:10,070 and characterize the services. 824 00:50:10,070 --> 00:50:14,650 And so these linkages, I think that's a really cool process 825 00:50:14,650 --> 00:50:18,450 because these linkages between responses and effects 826 00:50:18,450 --> 00:50:19,650 are sometimes direct 827 00:50:19,650 --> 00:50:23,093 as in the case of parameterizing a process model, 828 00:50:24,940 --> 00:50:27,000 a physiological model 829 00:50:27,000 --> 00:50:30,228 or a radiative transfer model. 830 00:50:30,228 --> 00:50:33,770 Those models all use traits to parameterize. 831 00:50:33,770 --> 00:50:36,150 So we can use, and that's more of a direct linkage 832 00:50:36,150 --> 00:50:38,560 to the effect in the ecosystem services. 833 00:50:38,560 --> 00:50:41,090 But sometimes the services have nothing to do 834 00:50:41,090 --> 00:50:44,750 with how the plants respond to climate. 835 00:50:44,750 --> 00:50:47,630 So we can use this maximum entropy model 836 00:50:47,630 --> 00:50:49,572 to reconstruct the community 837 00:50:49,572 --> 00:50:52,470 and characterize the ecosystem service 838 00:50:52,470 --> 00:50:54,420 in a way that's completely independent. 839 00:50:55,930 --> 00:50:58,873 And that's what we're doing with the Oregon project. 840 00:51:01,010 --> 00:51:01,843 - That's interesting. 841 00:51:01,843 --> 00:51:05,500 It kind of raised a question that I have been pondering 842 00:51:05,500 --> 00:51:06,400 as you've been going through this 843 00:51:06,400 --> 00:51:08,863 'cause I think there are traits and functions, 844 00:51:09,780 --> 00:51:13,550 there are functions that are gonna to be somewhat tied 845 00:51:13,550 --> 00:51:15,830 to a particular species or ones that may be tied 846 00:51:15,830 --> 00:51:18,660 to a service that species provides 847 00:51:18,660 --> 00:51:20,820 that could be substituted with another species. 848 00:51:20,820 --> 00:51:25,630 Do you deal with needing to have two species, 849 00:51:25,630 --> 00:51:28,210 like the connection between different plant species 850 00:51:28,210 --> 00:51:30,350 in order to provide a single service, right? 851 00:51:30,350 --> 00:51:32,500 If you boil traits and functions 852 00:51:32,500 --> 00:51:35,020 down to just the species itself, 853 00:51:35,020 --> 00:51:37,010 do you ever end up with functions that only occur 854 00:51:37,010 --> 00:51:38,393 when species co-occur? 855 00:51:39,320 --> 00:51:40,370 Does that make sense? 856 00:51:45,340 --> 00:51:46,970 - Yeah, I mean, that's a good question 857 00:51:46,970 --> 00:51:49,313 and something I haven't considered before. 858 00:51:50,650 --> 00:51:52,427 Can you think of an example? 859 00:51:52,427 --> 00:51:54,210 - No, I wish I could off the top of my head, 860 00:51:54,210 --> 00:51:55,250 but I was trying to come up with one 861 00:51:55,250 --> 00:51:56,870 probably more around the cultural services 862 00:51:56,870 --> 00:52:00,400 where it may be that you're getting wildlife habitat, 863 00:52:00,400 --> 00:52:02,920 but it's not important just to have habitat 864 00:52:02,920 --> 00:52:05,230 for say bear or moose, 865 00:52:05,230 --> 00:52:07,863 but to also have a certain set, 866 00:52:10,780 --> 00:52:13,910 a certain tree species in combination with that. 867 00:52:13,910 --> 00:52:14,743 So I was trying to think 868 00:52:14,743 --> 00:52:16,300 more from the cultural services framework, 869 00:52:16,300 --> 00:52:18,233 but I don't have a specific example. 870 00:52:23,840 --> 00:52:26,510 - Yeah. I'll have to think about that. 871 00:52:26,510 --> 00:52:30,610 It seems like this is such a flexible framework 872 00:52:30,610 --> 00:52:32,700 that there's gotta be a way 873 00:52:34,680 --> 00:52:36,033 to incorporate that. 874 00:52:39,470 --> 00:52:42,480 - I don't see any others coming into the chat. 875 00:52:42,480 --> 00:52:43,990 Does anyone else have a question 876 00:52:43,990 --> 00:52:47,200 they want to raise their hand and ask on their mic 877 00:52:47,200 --> 00:52:48,870 or add to the chat? 878 00:52:48,870 --> 00:52:50,863 I've got a couple of hands popped up. 879 00:52:53,720 --> 00:52:55,490 Jerry, can you go ahead, please? 880 00:52:55,490 --> 00:52:57,460 You should be able to unmute yourself. 881 00:52:57,460 --> 00:52:59,270 - Can you hear me? - Yes. 882 00:52:59,270 --> 00:53:01,880 - [Jerry] Okay, yeah, brilliant. 883 00:53:01,880 --> 00:53:06,625 I love this, and what I'm really wondering about is 884 00:53:06,625 --> 00:53:09,410 because I'm in the Northeast, catastrophic losses. 885 00:53:09,410 --> 00:53:11,210 How do you see that being integrated? 886 00:53:11,210 --> 00:53:12,870 You know, I mean we've lost chestnut. 887 00:53:12,870 --> 00:53:15,960 We're probably gonna lose ash, hemlock 888 00:53:15,960 --> 00:53:20,960 and other severe species losses at the landscape level. 889 00:53:21,440 --> 00:53:26,440 I mean, the cultural and ecological impact of these things 890 00:53:26,727 --> 00:53:29,570 are huge and probably not gonna stop. 891 00:53:29,570 --> 00:53:31,420 I'd like to hear you comment on that. 892 00:53:33,570 --> 00:53:35,890 - Yeah. That's a great question. 893 00:53:35,890 --> 00:53:38,420 There, like gosh, I could approach answering that 894 00:53:38,420 --> 00:53:39,930 in a number of different ways. 895 00:53:39,930 --> 00:53:41,360 And I think I'll just start 896 00:53:41,360 --> 00:53:45,270 with talking about this other project 897 00:53:45,270 --> 00:53:48,650 that I've been working on out of ESF looking at 898 00:53:50,910 --> 00:53:55,870 restoring American Chestnut using the transgenic varieties 899 00:53:55,870 --> 00:53:58,220 that Bill Powell has developed. 900 00:53:58,220 --> 00:54:01,440 So ESF has this oak forest, 901 00:54:01,440 --> 00:54:04,420 and we did this variable density shelterwood, 902 00:54:04,420 --> 00:54:09,420 and underplanted these transgenic American chestnuts 903 00:54:10,180 --> 00:54:12,950 alongside a bunch of other species 904 00:54:12,950 --> 00:54:15,400 that chestnut would have grown with historically, 905 00:54:17,320 --> 00:54:20,623 including northern red oak, pignut hickory, 906 00:54:22,250 --> 00:54:25,810 shagbark hickory, bitternut hickory. 907 00:54:25,810 --> 00:54:29,470 And there's also a lot of existing natural regeneration 908 00:54:29,470 --> 00:54:32,353 of sugar maple and white ash, 909 00:54:33,350 --> 00:54:37,370 and excitingly for me as a silviculturist 910 00:54:37,370 --> 00:54:41,043 who knows that oak regeneration is kind of a magic trick 911 00:54:41,043 --> 00:54:42,790 and very challenging, 912 00:54:42,790 --> 00:54:44,640 the shelterwood cuts coincided 913 00:54:44,640 --> 00:54:47,160 with a mass year of acorn production in there, 914 00:54:47,160 --> 00:54:50,350 so there's lots of little baby red oaks everywhere. 915 00:54:50,350 --> 00:54:53,073 And I feel like I won already. 916 00:54:55,180 --> 00:54:58,270 But we know there's a lot of issues with competition 917 00:54:58,270 --> 00:54:59,360 that we still have to look at. 918 00:54:59,360 --> 00:55:02,210 But anyway, this is kind of I'm going down a rabbit hole. 919 00:55:05,220 --> 00:55:07,650 So Garrett Evans is a master's student 920 00:55:07,650 --> 00:55:11,530 who's looking at the physiological 921 00:55:11,530 --> 00:55:13,270 and growth responses 922 00:55:13,270 --> 00:55:16,520 of those American chestnuts and other species 923 00:55:16,520 --> 00:55:20,300 to this variation and residual stocking 924 00:55:20,300 --> 00:55:23,540 associated with our shelterwood treatments 925 00:55:24,920 --> 00:55:29,920 to look at how growth responses to light 926 00:55:30,950 --> 00:55:32,500 vary among these species 927 00:55:32,500 --> 00:55:34,620 and kind of better delineate 928 00:55:34,620 --> 00:55:39,557 that ecological niche of chestnuts. 929 00:55:43,660 --> 00:55:46,690 Dr. John Drake is a tree physiologist. 930 00:55:46,690 --> 00:55:49,753 He and I co-advise Garrett in this project. 931 00:55:53,020 --> 00:55:56,130 And so he's looking at how these growth responses differ 932 00:55:56,130 --> 00:55:59,050 and eventually we'll link that back to traits. 933 00:55:59,050 --> 00:56:02,570 And we have some hypotheses 934 00:56:02,570 --> 00:56:06,713 related to, you know chestnut being able to kind of outgrow, 935 00:56:08,120 --> 00:56:11,210 outgrow all of its associates 936 00:56:11,210 --> 00:56:14,193 in really high resource conditions, 937 00:56:15,550 --> 00:56:17,580 which allowed it to kind of dominate 938 00:56:17,580 --> 00:56:20,863 the oak hickory forest in the Northeast historically. 939 00:56:22,020 --> 00:56:24,560 And so we would expect like trait hierarchies 940 00:56:24,560 --> 00:56:25,940 and growth hierarchies 941 00:56:26,930 --> 00:56:29,163 that might shift across that light gradient. 942 00:56:34,730 --> 00:56:36,610 So that's one way we were looking at that, 943 00:56:36,610 --> 00:56:40,070 and this trait-based approach can also allow us 944 00:56:40,070 --> 00:56:45,070 to identify replacement species. 945 00:56:45,500 --> 00:56:47,840 So if we're losing ash 946 00:56:48,720 --> 00:56:50,470 you know, and Tony D'Amato 947 00:56:50,470 --> 00:56:52,613 has, of course done a lot of work with us, 948 00:56:54,730 --> 00:56:56,800 using a trait-based approach 949 00:56:56,800 --> 00:56:59,750 to look at which species are most similar in traits 950 00:56:59,750 --> 00:57:01,330 and what are some options 951 00:57:01,330 --> 00:57:05,400 for replacing those species, 952 00:57:05,400 --> 00:57:08,190 whether it's ash or hemlock, 953 00:57:08,190 --> 00:57:11,163 with other ecologically similar species. 954 00:57:12,800 --> 00:57:15,980 So, yeah, that's a whole, whole area 955 00:57:15,980 --> 00:57:20,980 that I'd love to do work on as well. 956 00:57:24,720 --> 00:57:26,300 - Great. Thank you for the question, Jerry. 957 00:57:26,300 --> 00:57:29,670 I saw another hand pop up. I'm not sure. 958 00:57:29,670 --> 00:57:30,540 I think it was Julia. 959 00:57:30,540 --> 00:57:32,743 I don't know if you wanna ask a question, 960 00:57:33,600 --> 00:57:34,500 give you a second. 961 00:57:43,180 --> 00:57:44,250 It happened again very briefly, 962 00:57:44,250 --> 00:57:45,600 but then it went back down. 963 00:57:46,980 --> 00:57:47,880 There you are, Julia. 964 00:57:47,880 --> 00:57:48,746 All right, go ahead. 965 00:57:48,746 --> 00:57:51,610 You should be able to unmute. 966 00:57:51,610 --> 00:57:54,280 - [Julia] Okay. Hi, thank you for this presentation. 967 00:57:54,280 --> 00:57:56,420 This has been super informative. 968 00:57:56,420 --> 00:57:57,610 I was kind of struggling 969 00:57:57,610 --> 00:58:00,800 with like the best way to ask this question, 970 00:58:00,800 --> 00:58:03,460 but are there, 971 00:58:03,460 --> 00:58:06,710 do you know of any landback initiatives 972 00:58:06,710 --> 00:58:10,090 that are occurring in your area, and have thoughts 973 00:58:10,090 --> 00:58:13,070 on how frameworks such as this 974 00:58:13,070 --> 00:58:15,360 could be adapted 975 00:58:15,360 --> 00:58:20,110 by community groups that are reclaiming, 976 00:58:20,110 --> 00:58:21,880 like indigenous community groups 977 00:58:21,880 --> 00:58:24,543 that are reclaiming their land and managing it? 978 00:58:28,480 --> 00:58:33,300 - I missed, what kind of initiatives did you say? 979 00:58:33,300 --> 00:58:34,520 - [Julia] Landback. 980 00:58:34,520 --> 00:58:36,980 - Oh, landback initiatives, yeah. 981 00:58:36,980 --> 00:58:40,740 Yeah, that's exactly where this Oregon works fits in 982 00:58:42,350 --> 00:58:47,350 is that some of the tribes were, 983 00:58:50,500 --> 00:58:54,530 they obtained some significant land 984 00:58:54,530 --> 00:58:56,123 from the BLM. 985 00:58:57,390 --> 00:58:59,906 And so there's reinvigorated interest 986 00:58:59,906 --> 00:59:02,830 in doing this research for them 987 00:59:02,830 --> 00:59:07,330 to look at managing 988 00:59:07,330 --> 00:59:10,220 indigenous cultural ecosystem services. 989 00:59:10,220 --> 00:59:13,010 And, you know, they obviously already know a lot 990 00:59:13,010 --> 00:59:16,790 about how to manage these plants 991 00:59:16,790 --> 00:59:20,063 and cultivate these services. 992 00:59:23,410 --> 00:59:26,300 So I'm basically trying to like contribute, 993 00:59:26,300 --> 00:59:27,780 you know, additional information 994 00:59:27,780 --> 00:59:30,590 about what can they expect under climate change 995 00:59:33,630 --> 00:59:34,760 on their land. 996 00:59:34,760 --> 00:59:39,760 And it's also important for considering treaty rights. 997 00:59:40,370 --> 00:59:43,940 So sometimes in the treaties, 998 00:59:43,940 --> 00:59:46,570 the tribes have the ability 999 00:59:46,570 --> 00:59:49,850 to, you know, forage 1000 00:59:50,920 --> 00:59:55,170 and utilize natural resources in their ceded territories 1001 00:59:56,810 --> 01:00:00,263 that they don't necessarily own today. 1002 01:00:02,003 --> 01:00:04,884 And so what does, 1003 01:00:04,884 --> 01:00:06,840 how does climate change 1004 01:00:06,840 --> 01:00:09,150 and the effect that that's gonna have on those services 1005 01:00:09,150 --> 01:00:12,567 affect their treaty rights and our ability 1006 01:00:12,567 --> 01:00:14,740 to uphold their treaty rights 1007 01:00:15,600 --> 01:00:17,480 were some of the original considerations 1008 01:00:17,480 --> 01:00:19,653 when we developed that project. 1009 01:00:22,990 --> 01:00:24,653 - Thanks for the question, Julia. 1010 01:00:26,640 --> 01:00:30,480 I'm curious how this framework, 1011 01:00:30,480 --> 01:00:33,610 or this lens intersects with invasive plants, 1012 01:00:33,610 --> 01:00:37,470 and potentially do you end up 1013 01:00:37,470 --> 01:00:39,630 with a different conception 1014 01:00:39,630 --> 01:00:44,120 of what is good or bad for an ecosystem 1015 01:00:44,120 --> 01:00:45,980 when you think about traits alone 1016 01:00:45,980 --> 01:00:47,953 and not the source of those traits? 1017 01:00:49,550 --> 01:00:50,480 - Yeah, for sure. 1018 01:00:50,480 --> 01:00:55,020 It kind of takes out those value-laden 1019 01:00:57,700 --> 01:01:01,740 classifications of invasive or introduced 1020 01:01:02,670 --> 01:01:06,433 to look at at the functions and the services. 1021 01:01:13,700 --> 01:01:15,950 Yeah, I think there are some instances 1022 01:01:15,950 --> 01:01:17,590 where these introduced species 1023 01:01:17,590 --> 01:01:20,240 kind of, you know, they have traits 1024 01:01:20,240 --> 01:01:21,680 that are significantly different 1025 01:01:21,680 --> 01:01:23,810 from the rest of the community, 1026 01:01:23,810 --> 01:01:27,173 and that's what allows them to be so successful. 1027 01:01:28,140 --> 01:01:29,410 So it provides insight 1028 01:01:29,410 --> 01:01:31,567 into, you know, what are the ecological differences, 1029 01:01:31,567 --> 01:01:35,030 and that can give you information on how to control them 1030 01:01:35,030 --> 01:01:36,853 if you wanna control them. 1031 01:01:38,050 --> 01:01:40,590 In some cases, you know, an introduced species 1032 01:01:40,590 --> 01:01:42,640 might not necessarily be invasive 1033 01:01:42,640 --> 01:01:45,570 and it might have, just like fits in. 1034 01:01:45,570 --> 01:01:49,183 And so it does cause you to question your values, 1035 01:01:52,890 --> 01:01:55,783 you know, how you receive these introduced species. 1036 01:01:57,270 --> 01:02:00,523 It challenges your notion that introduced species are bad. 1037 01:02:01,760 --> 01:02:03,470 And that is further highlighted 1038 01:02:03,470 --> 01:02:08,470 when you have European collaborators (laughs) 1039 01:02:08,690 --> 01:02:12,693 who seem to empathize with those introduced species. 1040 01:02:14,980 --> 01:02:19,003 - Thanks. Are there other questions folks have? 1041 01:02:20,550 --> 01:02:21,920 You can either raise your hand 1042 01:02:21,920 --> 01:02:23,693 or put something into the Q&A box. 1043 01:02:24,610 --> 01:02:26,463 We do have a couple more minutes. 1044 01:02:28,930 --> 01:02:30,280 - Me? 1045 01:02:30,280 --> 01:02:32,000 - We have, I just said we have a couple more minutes 1046 01:02:32,000 --> 01:02:35,313 in the session, so if there's any other questions. 1047 01:02:36,350 --> 01:02:37,670 I'll ask one more. 1048 01:02:37,670 --> 01:02:40,410 When I think about some of the work 1049 01:02:40,410 --> 01:02:43,440 that our staff are doing for the FEMC, 1050 01:02:43,440 --> 01:02:45,660 we talk a lot about 1051 01:02:48,170 --> 01:02:49,507 data collection through monitoring plots 1052 01:02:49,507 --> 01:02:51,940 and in forest, specifically forest inventory plots, 1053 01:02:51,940 --> 01:02:53,660 forest health monitoring plots, 1054 01:02:53,660 --> 01:02:55,520 and I think there's often a desire 1055 01:02:55,520 --> 01:02:59,140 to hang more data collection on these networks 1056 01:02:59,140 --> 01:03:01,693 that have long-term ongoing collection. 1057 01:03:03,790 --> 01:03:07,320 I'm curious if you see any, 1058 01:03:07,320 --> 01:03:08,440 like if you had an ask 1059 01:03:08,440 --> 01:03:10,890 for folks who are collecting inventory data 1060 01:03:10,890 --> 01:03:13,170 or collecting regular survey data, 1061 01:03:13,170 --> 01:03:16,100 is there additional types of collections we should be doing 1062 01:03:16,100 --> 01:03:18,980 to support better understanding of functional traits 1063 01:03:18,980 --> 01:03:22,913 and their kind of presence/absence on the landscape? 1064 01:03:24,820 --> 01:03:28,760 - Yeah, I think that would be a really great idea 1065 01:03:28,760 --> 01:03:33,760 to incorporate trait data collection with monitoring. 1066 01:03:33,790 --> 01:03:34,640 Like I said, 1067 01:03:37,700 --> 01:03:40,640 similar to the population models that say, 1068 01:03:40,640 --> 01:03:42,900 you know, just individuals vary so much 1069 01:03:42,900 --> 01:03:44,560 and we need to measure every tree 1070 01:03:44,560 --> 01:03:46,700 and measure fecundity and growth 1071 01:03:46,700 --> 01:03:49,070 and, you know, every aspect of every tree 1072 01:03:49,070 --> 01:03:51,050 over and over and over every year, 1073 01:03:51,050 --> 01:03:55,280 like to understand what's gonna happen, 1074 01:03:55,280 --> 01:03:57,910 that same kind of thing happens with the traits. 1075 01:03:57,910 --> 01:04:01,400 You know, if we think about individuals varying, 1076 01:04:01,400 --> 01:04:03,170 we need to measure every individual. 1077 01:04:03,170 --> 01:04:05,710 So yeah, Matthew and I have been working 1078 01:04:05,710 --> 01:04:09,410 on, you know, how do we kind of bridge that 1079 01:04:09,410 --> 01:04:11,290 and model that variation, 1080 01:04:11,290 --> 01:04:13,100 but there are a lot of unanswered questions. 1081 01:04:13,100 --> 01:04:16,840 So if we could actually, you know, measure traits 1082 01:04:16,840 --> 01:04:18,440 at permanent plots every year, 1083 01:04:18,440 --> 01:04:22,420 we could address a lot of the questions related to that 1084 01:04:22,420 --> 01:04:25,810 and develop either more complex scenarios 1085 01:04:25,810 --> 01:04:27,483 or put those concerns to bed. 1086 01:04:30,580 --> 01:04:33,680 But I think there are a lot of interesting questions 1087 01:04:33,680 --> 01:04:35,290 and people are really interested 1088 01:04:35,290 --> 01:04:39,840 in that individual level variation and intraspecific. 1089 01:04:39,840 --> 01:04:42,360 And yeah, we can talk about transitioning 1090 01:04:45,330 --> 01:04:47,550 in terms of climate change adaptation, 1091 01:04:47,550 --> 01:04:49,030 but there's a lot of potential 1092 01:04:49,030 --> 01:04:52,684 of our existing plants and native forests. 1093 01:04:52,684 --> 01:04:56,843 And I think we should explore that and give it a chance. 1094 01:04:58,240 --> 01:04:59,870 - Great, thanks. 1095 01:04:59,870 --> 01:05:01,830 Jerry, I see you have another question. 1096 01:05:01,830 --> 01:05:04,280 You should be able to unmute yourself. 1097 01:05:04,280 --> 01:05:07,420 - [Jerry] Yeah, listening to that talk. I just thought 1098 01:05:07,420 --> 01:05:12,420 given the evolution of more holistic ecosystem approaches 1099 01:05:12,460 --> 01:05:15,793 to traits and services, 1100 01:05:17,320 --> 01:05:21,230 I'd like to hear, understand that perhaps silviculture, 1101 01:05:21,230 --> 01:05:23,853 the term and the science itself is becoming passe. 1102 01:05:29,293 --> 01:05:32,420 - Are you asking if or are you positing that it is? 1103 01:05:32,420 --> 01:05:35,540 - [Jerry] I'm asking if Julia has a comment on that 1104 01:05:35,540 --> 01:05:39,123 'cause obviously she's silviculturally oriented, so yeah. 1105 01:05:40,960 --> 01:05:44,280 - So given this focus on traits 1106 01:05:46,120 --> 01:05:50,310 and these ecological silviculture, 1107 01:05:50,310 --> 01:05:55,150 and is silviculture as we know it conventionally, 1108 01:05:55,150 --> 01:05:57,620 is that passe, is that kind of what you're getting at? 1109 01:05:57,620 --> 01:05:59,710 - [Jerry] I mean like the silviculture 1110 01:05:59,710 --> 01:06:02,590 I learned 40 years ago, is it still meaningful? 1111 01:06:02,590 --> 01:06:04,513 - Yeah. Oh yeah. 1112 01:06:05,520 --> 01:06:08,813 Yeah, it certainly is and it's important. 1113 01:06:13,340 --> 01:06:15,430 I teach silviculture. 1114 01:06:15,430 --> 01:06:17,880 I've been teaching silviculture for years. 1115 01:06:17,880 --> 01:06:19,680 I don't remember how many right now. 1116 01:06:22,171 --> 01:06:24,990 And so there's kind of 1117 01:06:26,210 --> 01:06:27,760 this like textbook silviculture 1118 01:06:29,210 --> 01:06:33,200 you know, principles and practices that we still use. 1119 01:06:33,200 --> 01:06:34,710 And we're just integrating 1120 01:06:35,673 --> 01:06:39,540 a broader range of ecosystem objectives 1121 01:06:39,540 --> 01:06:41,650 that we have to apply these tools for. 1122 01:06:41,650 --> 01:06:44,140 So we have to think about how we're applying them. 1123 01:06:44,140 --> 01:06:45,977 You know, a shelterwood is still a shelterwood 1124 01:06:45,977 --> 01:06:47,720 and the principles don't change. 1125 01:06:47,720 --> 01:06:49,333 And it's really important. 1126 01:06:51,250 --> 01:06:55,460 There's a lot of, you know, ecologists 1127 01:06:55,460 --> 01:06:59,220 who don't, you know, 1128 01:06:59,220 --> 01:07:00,730 haven't had a silviculture class 1129 01:07:00,730 --> 01:07:05,500 and don't understand all the rich ecology, 1130 01:07:05,500 --> 01:07:07,210 applied ecology and thought 1131 01:07:07,210 --> 01:07:10,083 that goes into these silvicultural practices. 1132 01:07:11,130 --> 01:07:15,070 You know, thinning and extending biological rotations 1133 01:07:15,070 --> 01:07:17,000 is completely relevant 1134 01:07:17,000 --> 01:07:19,990 to climate change mitigation, 1135 01:07:19,990 --> 01:07:22,320 and is one of the main strategies. 1136 01:07:22,320 --> 01:07:24,620 And that's intensive silviculture. 1137 01:07:24,620 --> 01:07:27,310 Like that is, we have an app for this. 1138 01:07:27,310 --> 01:07:30,560 And so people with a silvicultural background 1139 01:07:30,560 --> 01:07:33,783 need to recognize the power and importance of these tools. 1140 01:07:39,100 --> 01:07:42,210 It's not necessarily that the tools themselves are changing 1141 01:07:42,210 --> 01:07:45,380 or the principles, but the circumstances 1142 01:07:45,380 --> 01:07:47,130 and the objectives are. 1143 01:07:47,130 --> 01:07:49,720 So we have to be flexible 1144 01:07:52,630 --> 01:07:54,870 and share the power of those tools 1145 01:07:54,870 --> 01:07:56,940 for satisfying a broader range of objectives 1146 01:07:56,940 --> 01:07:58,473 than just timber production. 1147 01:08:02,160 --> 01:08:04,410 - Thanks. I think that's well said. 1148 01:08:04,410 --> 01:08:07,710 We had a question come into the Q&A box from Nancy Patch 1149 01:08:07,710 --> 01:08:10,117 regarding introduced species. 1150 01:08:10,117 --> 01:08:13,310 "Is there any concern over transgenic trees in the wild? 1151 01:08:13,310 --> 01:08:15,150 Chestnut has a long lifespan. 1152 01:08:15,150 --> 01:08:16,770 Will the use of transgenic trees 1153 01:08:16,770 --> 01:08:19,590 for endangered species restoration open the door 1154 01:08:19,590 --> 01:08:23,100 for transgenic use in common trees or for market traits, 1155 01:08:23,100 --> 01:08:25,147 or is this already too late?" 1156 01:08:29,690 --> 01:08:30,540 - Can I see that? 1157 01:08:31,730 --> 01:08:32,691 - Yep, you should be able 1158 01:08:32,691 --> 01:08:33,524 - Do I have access to that? 1159 01:08:33,524 --> 01:08:35,674 - You should see the Q&A bar on the bottom. 1160 01:08:36,790 --> 01:08:38,840 - When there's lots of details like that. 1161 01:08:39,753 --> 01:08:41,433 - I think it makes sense. - There it is, okay. 1162 01:08:44,510 --> 01:08:49,223 Yeah, certainly that's a big consideration right now. 1163 01:08:50,610 --> 01:08:54,883 Will the use transgenic? 1164 01:08:57,970 --> 01:09:00,762 You know, there's a lot of resistance too. 1165 01:09:00,762 --> 01:09:03,860 So we're using transgenic trees in this experiment 1166 01:09:03,860 --> 01:09:06,503 under very regulated circumstances. 1167 01:09:07,630 --> 01:09:11,260 And honestly, that's not my area 1168 01:09:11,260 --> 01:09:13,400 and I don't have all the information 1169 01:09:13,400 --> 01:09:16,030 about what those regulations are. 1170 01:09:16,030 --> 01:09:18,030 But I think unless there's further approval, 1171 01:09:18,030 --> 01:09:21,970 like these chestnuts have to be killed 1172 01:09:21,970 --> 01:09:24,173 before they're able to reproduce. 1173 01:09:26,080 --> 01:09:31,080 And it's a very small scale that we've planted them on, 1174 01:09:31,090 --> 01:09:33,603 in a very controlled setting. 1175 01:09:34,710 --> 01:09:35,543 That said, 1176 01:09:37,890 --> 01:09:42,512 Bill Powell and others are strongly seeking approval 1177 01:09:42,512 --> 01:09:45,530 to use these transgenic trees 1178 01:09:45,530 --> 01:09:50,373 in the wild and restore them at a grand scale. 1179 01:09:55,077 --> 01:09:56,650 I think there is a lot of concern 1180 01:09:56,650 --> 01:09:59,980 about, you know, the slippery slope that might be there, 1181 01:09:59,980 --> 01:10:04,980 opening the door for transgenic use in other species. 1182 01:10:10,538 --> 01:10:12,100 But my impression is 1183 01:10:12,100 --> 01:10:16,270 that like this is a very regulated process 1184 01:10:16,270 --> 01:10:17,530 and it's not easy, 1185 01:10:17,530 --> 01:10:22,530 and there's a good case to be made that there's more good 1186 01:10:22,620 --> 01:10:25,630 than harm that can be done in this case 1187 01:10:26,480 --> 01:10:28,840 where, you know, we already have hybrid chestnuts 1188 01:10:28,840 --> 01:10:30,190 being planted everywhere. 1189 01:10:30,190 --> 01:10:32,603 You can go get hybrid chestnuts at the nursery. 1190 01:10:34,420 --> 01:10:39,420 Transgenic has more genes of our American Chestnut 1191 01:10:39,420 --> 01:10:41,773 than the transgenic does, 1192 01:10:43,502 --> 01:10:46,130 and is expected to perform better 1193 01:10:46,130 --> 01:10:48,863 in the face of Chestnut blight. 1194 01:10:51,050 --> 01:10:53,890 You know, the slippery slope is always concerning, 1195 01:10:53,890 --> 01:10:56,633 and I just think we need to avoid the slippery slope. 1196 01:10:59,560 --> 01:11:02,650 - Thanks Julia, certainly an active area of discussion, 1197 01:11:02,650 --> 01:11:05,160 not just in terms of silviculture and forest ecology, 1198 01:11:05,160 --> 01:11:08,090 but really kind of what our ethics are gonna be as a people 1199 01:11:08,090 --> 01:11:09,540 trying to address this issue. 1200 01:11:10,970 --> 01:11:12,240 We're almost at time. 1201 01:11:12,240 --> 01:11:14,800 I'm gonna just kind of pose a broad question 1202 01:11:14,800 --> 01:11:16,920 to take us out here. 1203 01:11:16,920 --> 01:11:19,660 You know, you are looking at the future 1204 01:11:19,660 --> 01:11:21,510 and you're trying to better understand 1205 01:11:21,510 --> 01:11:23,460 how we get to where we wanna be. 1206 01:11:23,460 --> 01:11:27,410 When you think of what gives you hope and excitement 1207 01:11:27,410 --> 01:11:30,100 and the feeling of a bright day ahead, 1208 01:11:30,100 --> 01:11:32,270 what is it that drives you? 1209 01:11:32,270 --> 01:11:33,103 - Oh, that's great. 1210 01:11:33,103 --> 01:11:36,100 And I kind of meant to leave this in more to my talk, 1211 01:11:36,100 --> 01:11:37,450 but you know how that goes. 1212 01:11:38,920 --> 01:11:40,830 I was gonna talk about all these students 1213 01:11:40,830 --> 01:11:44,830 that I worked with at ESF on these projects, 1214 01:11:44,830 --> 01:11:47,400 and how gritty they are. 1215 01:11:47,400 --> 01:11:50,090 During the pandemic, you know, a lot of this work 1216 01:11:50,090 --> 01:11:52,460 that we did together was in 2020 1217 01:11:52,460 --> 01:11:54,320 in the early stages of the pandemic. 1218 01:11:54,320 --> 01:11:57,210 And holy cow, we got a lot done 1219 01:11:57,210 --> 01:11:59,633 in the face of a lot of obstacles. 1220 01:12:01,811 --> 01:12:04,810 I think their passion and grit, 1221 01:12:08,660 --> 01:12:11,890 just being lovely people, 1222 01:12:11,890 --> 01:12:16,180 makes it, brightens my day and inspires me 1223 01:12:16,180 --> 01:12:18,360 to keep pushing forward 1224 01:12:18,360 --> 01:12:21,730 and, you know, face these challenges head on 1225 01:12:22,900 --> 01:12:26,673 and try to work with them to carve out a brighter future. 1226 01:12:28,370 --> 01:12:30,400 - That is so well said. Thank you. 1227 01:12:30,400 --> 01:12:32,680 Well, thank you, Julia. 1228 01:12:32,680 --> 01:12:34,250 That was really wonderful. 1229 01:12:34,250 --> 01:12:35,760 And thanks for engaging 1230 01:12:35,760 --> 01:12:38,593 in such a good discussion after as well. 1231 01:12:39,580 --> 01:12:42,153 So that concludes our plenary session.