1 00:00:05,820 --> 00:00:06,653 Hi everyone. 2 00:00:06,653 --> 00:00:08,610 Thank you for coming to my talk. 3 00:00:08,610 --> 00:00:10,410 As Ben said, I'm Samantha Myers. 4 00:00:10,410 --> 00:00:13,710 I'm a graduate student in the Applied Forest Ecology Lab 5 00:00:13,710 --> 00:00:15,090 at UMass Amherst. 6 00:00:15,090 --> 00:00:17,040 And today I'm going to be talking about my research, 7 00:00:17,040 --> 00:00:19,680 looking at the effects of functional diversity 8 00:00:19,680 --> 00:00:22,860 and structural complexity on forest carbon dynamics 9 00:00:22,860 --> 00:00:25,503 in late-successional mixed hardwood forests. 10 00:00:27,720 --> 00:00:31,290 We all know that forests provide this powerful 11 00:00:31,290 --> 00:00:32,490 climate mitigation tool 12 00:00:32,490 --> 00:00:35,340 because they can sequester and store carbon. 13 00:00:35,340 --> 00:00:37,470 But forests in the Northeast are facing 14 00:00:37,470 --> 00:00:39,060 a variety of compounding threats, 15 00:00:39,060 --> 00:00:41,610 including invasive pests and pathogens 16 00:00:41,610 --> 00:00:44,430 and compounding extreme weather events 17 00:00:44,430 --> 00:00:47,040 that could fundamentally alter their ability 18 00:00:47,040 --> 00:00:50,283 to continue to sequester and store carbon in the long term. 19 00:00:51,420 --> 00:00:53,190 However, one way we can approach this is 20 00:00:53,190 --> 00:00:55,710 through adaptive forest management practices 21 00:00:55,710 --> 00:00:57,720 that can improve forest resilience 22 00:00:57,720 --> 00:00:59,670 and protect these carbon stores 23 00:00:59,670 --> 00:01:01,370 in these forests in the long term. 24 00:01:03,090 --> 00:01:06,090 And traditional benchmarks for adaptive forest management 25 00:01:06,090 --> 00:01:09,630 are typically to enhance the diversity of species 26 00:01:09,630 --> 00:01:13,950 in the overstory or enhance the structural complexity 27 00:01:13,950 --> 00:01:14,783 of the forest, 28 00:01:14,783 --> 00:01:18,360 including increasing the diversity of tree sizes, 29 00:01:18,360 --> 00:01:21,660 increasing the number of strata in the canopy, 30 00:01:21,660 --> 00:01:24,510 or creating gaps and enhancing the amounts 31 00:01:24,510 --> 00:01:26,700 of standing and downed deadwood. 32 00:01:26,700 --> 00:01:29,130 However, when we're thinking about management 33 00:01:29,130 --> 00:01:31,950 and specifically for forest carbon, 34 00:01:31,950 --> 00:01:34,330 we want to assess are these the best measures 35 00:01:35,400 --> 00:01:39,030 to maximize forest carbon benefits? 36 00:01:39,030 --> 00:01:41,340 And one element we can bring in here 37 00:01:41,340 --> 00:01:43,500 to assess as a potential benchmark 38 00:01:43,500 --> 00:01:46,293 is looking at functional trait diversity. 39 00:01:47,460 --> 00:01:49,680 Functional traits are these measurable traits 40 00:01:49,680 --> 00:01:53,400 that contribute to the potential growth or mortality risk 41 00:01:53,400 --> 00:01:56,790 of an individual and thereby impact the functioning 42 00:01:56,790 --> 00:01:59,760 of the ecosystem that that individual is in. 43 00:01:59,760 --> 00:02:01,770 And some examples of functional traits 44 00:02:01,770 --> 00:02:04,620 include stem traits like wood density, 45 00:02:04,620 --> 00:02:07,200 leaf traits like specific leaf area, 46 00:02:07,200 --> 00:02:10,983 and reproductive traits like seed mass. 47 00:02:12,540 --> 00:02:16,110 And specifically these traits are measured on individuals 48 00:02:16,110 --> 00:02:19,800 and you can look at the individual level of these traits 49 00:02:19,800 --> 00:02:22,890 in terms of the variability across individuals. 50 00:02:22,890 --> 00:02:27,420 Typically these traits are aggregated to the species level, 51 00:02:27,420 --> 00:02:30,210 looking at across all individuals of a species, 52 00:02:30,210 --> 00:02:33,570 what is the mean trait value of that species. 53 00:02:33,570 --> 00:02:37,740 And thereby the species level mean trait values 54 00:02:37,740 --> 00:02:39,930 can be scaled up to an ecosystem level 55 00:02:39,930 --> 00:02:43,710 based on the relative abundance of species in that ecosystem 56 00:02:43,710 --> 00:02:47,640 and quantified the total functional diversity 57 00:02:47,640 --> 00:02:48,633 in that system. 58 00:02:50,460 --> 00:02:52,350 And though functional diversity is linked 59 00:02:52,350 --> 00:02:55,560 to species diversity and forest structure, 60 00:02:55,560 --> 00:02:57,960 it's not commonly considered in the context 61 00:02:57,960 --> 00:02:59,913 of adaptive forest management. 62 00:03:03,510 --> 00:03:08,250 But functional diversity could be a key driver of forest, 63 00:03:08,250 --> 00:03:10,050 and when we're thinking about forest productivity 64 00:03:10,050 --> 00:03:11,880 in forest carbon stores. 65 00:03:11,880 --> 00:03:14,910 For example, functional diversity is known to relate 66 00:03:14,910 --> 00:03:18,360 to ecosystem productivity just on a baseline level. 67 00:03:18,360 --> 00:03:21,000 Looking at that, fundamental studies have looked 68 00:03:21,000 --> 00:03:23,430 at how increases in functional diversity 69 00:03:23,430 --> 00:03:26,220 translate to increases in plant biomass 70 00:03:26,220 --> 00:03:27,513 on the community level. 71 00:03:28,950 --> 00:03:31,533 Particularly in forests, a number of studies have found 72 00:03:31,533 --> 00:03:36,150 that functional traits have that drive forest productivity 73 00:03:36,150 --> 00:03:39,360 in both temperate and boreal forests. 74 00:03:39,360 --> 00:03:41,490 However, the few studies that have looked 75 00:03:41,490 --> 00:03:44,070 at functional diversity in forests 76 00:03:44,070 --> 00:03:48,660 tend to use these commonly considered species level 77 00:03:48,660 --> 00:03:51,873 mean trait values in their analyses. 78 00:03:54,600 --> 00:03:59,140 However, using these species level trait mean values 79 00:04:00,690 --> 00:04:04,110 doesn't account for the variability within a species. 80 00:04:04,110 --> 00:04:06,630 Multiple individuals within that species 81 00:04:06,630 --> 00:04:08,100 could have different trait values 82 00:04:08,100 --> 00:04:13,100 based on their environment, their size, their age. 83 00:04:13,680 --> 00:04:16,110 And these accounting for this variability 84 00:04:16,110 --> 00:04:18,720 is really important if we're trying to make connections 85 00:04:18,720 --> 00:04:22,530 between individual trait values and how they translate 86 00:04:22,530 --> 00:04:26,733 to that individual's growth or other demographic processes. 87 00:04:28,950 --> 00:04:31,470 In addition, a lot of these studies look at traits 88 00:04:31,470 --> 00:04:33,330 independently of one another, 89 00:04:33,330 --> 00:04:36,810 whereas we know ecologically that there is correlation 90 00:04:36,810 --> 00:04:40,410 among different kinds of traits due to these evolutionary 91 00:04:40,410 --> 00:04:45,030 physiological trade-offs that occur. 92 00:04:45,030 --> 00:04:49,290 And one example of this is if we think about 93 00:04:49,290 --> 00:04:50,790 there's an inverse relationship 94 00:04:50,790 --> 00:04:55,470 between leaf nitrogen content and the lifespan of that leaf. 95 00:04:55,470 --> 00:04:58,140 So it's important that we account for these correlations 96 00:04:58,140 --> 00:05:01,890 between traits when we're trying to think about 97 00:05:01,890 --> 00:05:05,493 the ecological meaning behind functional trait diversity. 98 00:05:07,800 --> 00:05:10,230 However, to account for both these things, 99 00:05:10,230 --> 00:05:14,970 we need data sets that have both local individual functional 100 00:05:14,970 --> 00:05:19,890 trait information and long-term demographic information. 101 00:05:19,890 --> 00:05:21,570 So the kinds of information you would get 102 00:05:21,570 --> 00:05:25,500 from forest inventories measured 103 00:05:25,500 --> 00:05:27,900 on the same individual trees. 104 00:05:27,900 --> 00:05:30,900 And there are sparse data sets with both these elements. 105 00:05:30,900 --> 00:05:34,890 We can think of one small example in the Northeast 106 00:05:34,890 --> 00:05:38,433 is the Harvard Forest NEON site has some of this data. 107 00:05:41,370 --> 00:05:44,820 But in our study, we are aiming to combine both 108 00:05:44,820 --> 00:05:48,570 Massachusetts CFI continuous forest inventory data 109 00:05:48,570 --> 00:05:52,440 with local individual functional trait observations 110 00:05:52,440 --> 00:05:55,200 at these CFI plots to be able to predict 111 00:05:55,200 --> 00:05:58,953 aboveground biomass in response to functional diversity. 112 00:06:01,050 --> 00:06:03,660 And specifically we look to quantify the effects 113 00:06:03,660 --> 00:06:06,417 of functional diversity, species diversity, 114 00:06:06,417 --> 00:06:08,820 and structural complexity as drivers 115 00:06:08,820 --> 00:06:11,490 of live aboveground biomass 116 00:06:11,490 --> 00:06:15,600 and in the context of late-successional forests. 117 00:06:15,600 --> 00:06:18,723 And why are we looking in late-successional forests? 118 00:06:20,250 --> 00:06:23,550 Late-successional forests tend to be model study systems 119 00:06:23,550 --> 00:06:26,520 when we're thinking about carbon storage. 120 00:06:26,520 --> 00:06:29,010 They tend to have high carbon stores 121 00:06:29,010 --> 00:06:31,113 and high structural complexity. 122 00:06:33,210 --> 00:06:35,220 But they're also assumed to have lower carbon 123 00:06:35,220 --> 00:06:36,990 sequestration rates. 124 00:06:36,990 --> 00:06:40,020 However, our understanding of carbon dynamics 125 00:06:40,020 --> 00:06:43,800 in northeastern hardwood forests and late-successional 126 00:06:43,800 --> 00:06:47,823 hardwood forests is not very well understood. 127 00:06:49,350 --> 00:06:52,710 And so what we can do is we come up with new information 128 00:06:52,710 --> 00:06:54,750 and assess the data that we collect 129 00:06:54,750 --> 00:06:59,160 against some more classical models of biomass development 130 00:06:59,160 --> 00:07:02,430 across successional stages in forests. 131 00:07:02,430 --> 00:07:05,220 For example, we can look at this Bormann and Likens model 132 00:07:05,220 --> 00:07:09,480 looking at hardwood biomass accumulation. 133 00:07:09,480 --> 00:07:12,390 I'm thinking about how late-successional forests 134 00:07:12,390 --> 00:07:16,920 are predicted to be in this steady state equilibrium phase 135 00:07:16,920 --> 00:07:20,220 when it comes to biomass accumulation 136 00:07:20,220 --> 00:07:22,410 that has this equilibrium of biomass 137 00:07:22,410 --> 00:07:24,630 with small scale fluctuations. 138 00:07:24,630 --> 00:07:27,690 So what we can do is we can collect data in the field 139 00:07:27,690 --> 00:07:32,690 and assess how it relates to classical models like this 140 00:07:33,660 --> 00:07:35,493 and update our understanding. 141 00:07:38,640 --> 00:07:41,970 So we went out and we utilized Massachusetts DCR 142 00:07:41,970 --> 00:07:45,600 continuous forest inventory data from seven state forest 143 00:07:45,600 --> 00:07:48,390 reserves that we identified plots in 144 00:07:48,390 --> 00:07:51,090 that were late-successional or old growth. 145 00:07:51,090 --> 00:07:53,463 And this is in western Massachusetts. 146 00:07:56,010 --> 00:08:00,150 And in 26 of these plots that are shown here on the map 147 00:08:00,150 --> 00:08:03,701 we sampled individuals in these plots 148 00:08:03,701 --> 00:08:06,990 for five different functional traits 149 00:08:06,990 --> 00:08:11,310 including specific leaf area, leaf nutrient content, 150 00:08:11,310 --> 00:08:13,680 and wood density, which are known to affect 151 00:08:13,680 --> 00:08:15,930 the photosynthetic capacity of these trees 152 00:08:15,930 --> 00:08:19,680 and affect these growth processes and carbon storage 153 00:08:19,680 --> 00:08:22,173 that we're most interested in. 154 00:08:24,870 --> 00:08:28,410 We then calculated total live aboveground biomass 155 00:08:28,410 --> 00:08:32,010 and structural complexity metrics at all the plots 156 00:08:32,010 --> 00:08:35,670 in these forests from inventory years starting in 2000 157 00:08:35,670 --> 00:08:36,543 to the present. 158 00:08:41,190 --> 00:08:43,500 And one of our obstacles with this data 159 00:08:43,500 --> 00:08:45,840 is that we wanted to figure out how to integrate 160 00:08:45,840 --> 00:08:48,720 individual level functional trait observations 161 00:08:48,720 --> 00:08:51,480 that we collected with stand-level structure 162 00:08:51,480 --> 00:08:56,430 and biomass calculations to predict aboveground biomass 163 00:08:56,430 --> 00:08:57,813 at that stand level. 164 00:08:59,880 --> 00:09:02,820 And to do this, our model uses information both 165 00:09:02,820 --> 00:09:06,033 from global trait databases where a lot of these studies 166 00:09:06,033 --> 00:09:09,070 that that use species-level trait mean values 167 00:09:09,990 --> 00:09:11,823 use these database mean values. 168 00:09:14,940 --> 00:09:17,160 We combine that with the information we collected 169 00:09:17,160 --> 00:09:19,830 in the field that includes the variability 170 00:09:19,830 --> 00:09:24,273 within within a species, the individual trait variability, 171 00:09:26,640 --> 00:09:30,870 to update our understanding of mean species-trait values. 172 00:09:30,870 --> 00:09:34,440 And I'll give you one example here of how we did that. 173 00:09:34,440 --> 00:09:37,050 So for example, we can see specific leaf area 174 00:09:37,050 --> 00:09:39,060 for four different species. 175 00:09:39,060 --> 00:09:43,500 And in blue we see the database mean value 176 00:09:43,500 --> 00:09:45,450 and standard deviation. 177 00:09:45,450 --> 00:09:48,840 And in orange we see the distribution of individual trait 178 00:09:48,840 --> 00:09:52,473 observations that we collected in our CFI plots. 179 00:09:53,310 --> 00:09:55,920 And with our model, we can take both of these pieces 180 00:09:55,920 --> 00:09:58,230 of information and update our understanding 181 00:09:58,230 --> 00:10:02,370 of the species-level mean trait value 182 00:10:02,370 --> 00:10:05,553 for each species and trait combination that we have. 183 00:10:09,300 --> 00:10:12,210 Our model also brings in this idea of the fact 184 00:10:12,210 --> 00:10:14,550 that traits are not independent of one another. 185 00:10:14,550 --> 00:10:17,640 So we are able to model dependence among these traits, 186 00:10:17,640 --> 00:10:21,420 accounting for these evolutionary trade-offs, 187 00:10:21,420 --> 00:10:23,853 physiological trade-offs between traits. 188 00:10:26,190 --> 00:10:29,100 And then we use these updated species-mean trait 189 00:10:29,100 --> 00:10:32,320 values to calculate functional diversity in each plot 190 00:10:33,510 --> 00:10:37,110 and use that looking at how we wanted to predict live 191 00:10:37,110 --> 00:10:40,950 aboveground biomass as a function of functional diversity, 192 00:10:40,950 --> 00:10:44,430 structural complexity, and also accounting for density 193 00:10:44,430 --> 00:10:46,650 and proportion soft wood. 194 00:10:46,650 --> 00:10:49,920 So we know that density obviously has a large impact 195 00:10:49,920 --> 00:10:53,460 on aboveground biomass forest density, 196 00:10:53,460 --> 00:10:57,270 and we also know that these are mixed hardwood forests, 197 00:10:57,270 --> 00:11:00,630 so it's important that we account for proportion soft wood, 198 00:11:00,630 --> 00:11:03,660 thinking about how soft wood species 199 00:11:03,660 --> 00:11:06,300 on the individual level have lower wood density 200 00:11:06,300 --> 00:11:07,713 and store less carbon. 201 00:11:10,440 --> 00:11:12,270 And we compared three different models. 202 00:11:12,270 --> 00:11:14,850 Two models looked at functional diversity, 203 00:11:14,850 --> 00:11:18,663 and one model included that local trait need update. 204 00:11:22,260 --> 00:11:25,410 We found that integrating local, individual functional trait 205 00:11:25,410 --> 00:11:27,450 information yielded the best predictions 206 00:11:27,450 --> 00:11:29,823 of live aboveground carbon in our plots. 207 00:11:32,220 --> 00:11:35,940 Also, as expected, we found strong effects of density 208 00:11:35,940 --> 00:11:39,420 proportion of soft wood species and diameter diversity 209 00:11:39,420 --> 00:11:41,790 on live aboveground biomass. 210 00:11:41,790 --> 00:11:46,380 And when we see this positive effect of diameter diversity, 211 00:11:46,380 --> 00:11:49,560 we think about how there are these large legacy trees 212 00:11:49,560 --> 00:11:53,550 in a lot of our plots that are storing the bulk of biomass 213 00:11:53,550 --> 00:11:56,010 and contributing to that positive relationship 214 00:11:56,010 --> 00:11:58,773 with diameter diversity. 215 00:12:01,110 --> 00:12:04,920 However, contrary to our initial beliefs, 216 00:12:04,920 --> 00:12:06,720 we found that functional diversity 217 00:12:06,720 --> 00:12:10,470 actually had a negative effect on aboveground biomass. 218 00:12:10,470 --> 00:12:12,660 And considering that we thought the relationship 219 00:12:12,660 --> 00:12:13,890 would be positive, 220 00:12:13,890 --> 00:12:16,413 we looked into why this might be the case. 221 00:12:18,480 --> 00:12:23,480 We found that in plots with the lowest functional diversity, 222 00:12:25,020 --> 00:12:26,880 we looked at the relative abundance 223 00:12:26,880 --> 00:12:30,270 of shade tolerance in each plot. 224 00:12:30,270 --> 00:12:33,930 We can see here each plot is represented by one vertical bar 225 00:12:33,930 --> 00:12:36,480 and we see that plots with the lowest functional diversity 226 00:12:36,480 --> 00:12:38,130 were dominated by species 227 00:12:38,130 --> 00:12:42,363 with mid to high shade tolerant hardwoods. 228 00:12:44,280 --> 00:12:46,800 On the other hand, in our plots with the highest functional 229 00:12:46,800 --> 00:12:48,780 diversity, we see a higher abundance 230 00:12:48,780 --> 00:12:51,930 of shade intolerant species and mid tolerant species 231 00:12:51,930 --> 00:12:56,930 as well as soft woods, which tell us that these plots 232 00:12:57,240 --> 00:13:01,830 potentially had a more recent small scale disturbance 233 00:13:01,830 --> 00:13:04,490 that effectively decreased the biomass stores 234 00:13:04,490 --> 00:13:07,470 in these plots and allowed for the facilitation 235 00:13:07,470 --> 00:13:11,163 of mid to of low shade tolerance species. 236 00:13:14,100 --> 00:13:17,520 We also found that there were slightly positive rates 237 00:13:17,520 --> 00:13:20,253 on average of biomass accrual in these plots. 238 00:13:21,780 --> 00:13:24,690 So we can look at the change in total aboveground biomass 239 00:13:24,690 --> 00:13:27,540 per year, and we see that on average 240 00:13:27,540 --> 00:13:30,570 these are slightly positive rates of biomass accrual. 241 00:13:30,570 --> 00:13:34,230 However, if we look at the scale of these, 242 00:13:34,230 --> 00:13:37,650 it's pretty small, indicating that these plots 243 00:13:37,650 --> 00:13:40,563 are generally around the biomass equilibrium. 244 00:13:43,440 --> 00:13:46,140 We can also look at this change in aboveground biomass 245 00:13:46,140 --> 00:13:48,150 compared to functional diversity. 246 00:13:48,150 --> 00:13:50,730 And although the relationship isn't very strong, 247 00:13:50,730 --> 00:13:52,800 we see a slight positive relationship 248 00:13:52,800 --> 00:13:54,630 between functional diversity 249 00:13:54,630 --> 00:13:57,300 and changes in aboveground biomass, 250 00:13:57,300 --> 00:14:01,350 indicating that these plots with higher functional diversity 251 00:14:01,350 --> 00:14:06,010 that have had a more recent small scale disturbance 252 00:14:06,960 --> 00:14:09,813 have slightly higher rates of biomass accrual. 253 00:14:11,670 --> 00:14:13,920 And we can take this information and compare it 254 00:14:13,920 --> 00:14:17,430 to our understanding of classical models, 255 00:14:17,430 --> 00:14:20,580 looking at how we believe that our plots are generally 256 00:14:20,580 --> 00:14:24,060 at this study state equilibrium phase 257 00:14:24,060 --> 00:14:27,990 in terms of having stable biomass over time. 258 00:14:27,990 --> 00:14:31,083 However, we see these variations, 259 00:14:33,270 --> 00:14:36,150 indicating the fluctuations in this graph, 260 00:14:36,150 --> 00:14:38,160 and we can kind of map those two. 261 00:14:38,160 --> 00:14:42,090 The fact that in our plots with lower functional diversity, 262 00:14:42,090 --> 00:14:45,450 we see higher carbon stores that are placed 263 00:14:45,450 --> 00:14:47,550 and can be kind of mapped to the peaks 264 00:14:47,550 --> 00:14:51,570 of this steady state fluctuations, 265 00:14:51,570 --> 00:14:54,120 whereas our low functional diversity plots 266 00:14:54,120 --> 00:14:57,390 that have slightly lower carbon stores can be found 267 00:14:57,390 --> 00:15:02,390 at these valleys of the steady state that have gone 268 00:15:03,330 --> 00:15:06,000 through those more recent small scale disturbances. 269 00:15:06,000 --> 00:15:09,670 However, given this, you see that the fluctuations 270 00:15:10,830 --> 00:15:13,790 don't change the overall biomass 271 00:15:13,790 --> 00:15:16,110 in these plots significantly, 272 00:15:16,110 --> 00:15:19,353 and it remains around equilibrium over time. 273 00:15:22,770 --> 00:15:25,440 So we found that forest exceptional dynamics 274 00:15:25,440 --> 00:15:28,590 shift the effects of functional diversity on productivity. 275 00:15:28,590 --> 00:15:32,910 While we've seen studies that have shown strong positive 276 00:15:32,910 --> 00:15:35,610 relationships between diversity and productivity 277 00:15:35,610 --> 00:15:38,460 in earlier to mid successional forests, 278 00:15:38,460 --> 00:15:41,280 we find that this relationship can decrease 279 00:15:41,280 --> 00:15:44,193 or even turn negative in later successional forests. 280 00:15:46,230 --> 00:15:49,680 We also see our plots as an example of this classical model 281 00:15:49,680 --> 00:15:53,520 of a dynamic steady state, where our biomass stores 282 00:15:53,520 --> 00:15:55,413 are pretty much at equilibrium, 283 00:15:56,610 --> 00:16:00,000 but we do see these small scale disturbances 284 00:16:00,000 --> 00:16:02,100 that create these small trade-offs 285 00:16:02,100 --> 00:16:05,070 between slight increases in functional diversity 286 00:16:05,070 --> 00:16:08,463 and simultaneous decreases in aboveground biomass. 287 00:16:10,890 --> 00:16:14,250 So what does this mean for forest carbon management? 288 00:16:14,250 --> 00:16:18,330 So this, our results show that we should be looking 289 00:16:18,330 --> 00:16:21,750 at forest carbon management from this landscape scale, 290 00:16:21,750 --> 00:16:24,870 thinking about emulating a shifting gap mosaic 291 00:16:24,870 --> 00:16:28,980 across the landscape with different stands 292 00:16:28,980 --> 00:16:32,730 across the landscape in different successional stages. 293 00:16:32,730 --> 00:16:35,430 Thinking about how in our later successional stands 294 00:16:35,430 --> 00:16:38,400 we can preserve them knowing that they have relatively 295 00:16:38,400 --> 00:16:40,860 stable high aboveground carbon stores 296 00:16:40,860 --> 00:16:43,080 or aboveground biomass. 297 00:16:43,080 --> 00:16:46,530 Whereas where small scale disturbances in these stands 298 00:16:46,530 --> 00:16:51,530 won't significantly change the amounts of additional biomass 299 00:16:52,590 --> 00:16:53,740 they're able to accrue. 300 00:16:55,530 --> 00:16:57,630 Whereas elsewhere in the landscape in earlier 301 00:16:57,630 --> 00:17:00,690 to mid successional stands, more active management 302 00:17:00,690 --> 00:17:03,810 can be used to take advantage of this positive diversity 303 00:17:03,810 --> 00:17:06,750 productivity relationship and the higher rates 304 00:17:06,750 --> 00:17:10,530 of biomass accrual, if we think about that model 305 00:17:10,530 --> 00:17:12,783 of biomass accumulation over time. 306 00:17:15,210 --> 00:17:17,910 And with that, I'd like to thank my advisor, Malcolm Itter, 307 00:17:17,910 --> 00:17:20,640 and my thesis committee as well as Bill Van Doren 308 00:17:20,640 --> 00:17:23,583 at Massachusetts DCR and my funders. 309 00:17:25,890 --> 00:17:27,553 And I'm over time, but... 310 00:17:27,553 --> 00:17:30,720 (audience applauding) 311 00:17:35,824 --> 00:17:36,657 [Chairperson] We've got about three minutes 312 00:17:36,657 --> 00:17:37,983 for questions, so- 313 00:17:39,630 --> 00:17:42,360 [Attendee] So I feel like I could come away from your talk 314 00:17:42,360 --> 00:17:45,840 with two very different perspectives. 315 00:17:45,840 --> 00:17:49,230 So I'm curious where you feel that your research fits. 316 00:17:49,230 --> 00:17:54,210 Do you see this, your research, as being evidence 317 00:17:54,210 --> 00:17:58,290 that managing for functional diversity and resilience 318 00:17:58,290 --> 00:18:02,070 is antithetical to carbon management 319 00:18:02,070 --> 00:18:04,683 or complimentary to carbon management? 320 00:18:07,500 --> 00:18:09,850 [Samantha] I think it depends on the context. 321 00:18:11,220 --> 00:18:16,220 I think that in later successional stands, 322 00:18:16,230 --> 00:18:18,423 the functional diversity, 323 00:18:20,010 --> 00:18:21,600 we didn't see that much variability 324 00:18:21,600 --> 00:18:23,400 in the functional diversity in these stands. 325 00:18:23,400 --> 00:18:28,400 So it might be more important to look in earlier 326 00:18:28,800 --> 00:18:31,140 to mid successional stands when thinking about diversity 327 00:18:31,140 --> 00:18:34,080 because later successional stands just tend to be, 328 00:18:34,080 --> 00:18:36,900 at least for canopy dominant species, 329 00:18:36,900 --> 00:18:40,170 tend to be dominated by more shade tolerant species. 330 00:18:40,170 --> 00:18:42,870 So that variability is less than if we're looking 331 00:18:42,870 --> 00:18:44,760 at early to mid successional stands when thinking 332 00:18:44,760 --> 00:18:45,860 about diversity there. 333 00:18:53,190 --> 00:18:54,390 [Attendee] I have a quick question. 334 00:18:54,390 --> 00:18:56,690 It's sort of like related to that one actually. 335 00:18:56,690 --> 00:19:00,480 In one of your slides that you gave a lot of statements 336 00:19:00,480 --> 00:19:04,170 about the state of flux of carbon in older stands 337 00:19:04,170 --> 00:19:06,450 or these successional stands, 338 00:19:06,450 --> 00:19:11,100 and it was all followed with some scientific references, 339 00:19:11,100 --> 00:19:14,910 except the statement is said, older late succession 340 00:19:14,910 --> 00:19:19,660 forests are assumed to have a slower sequestration rate 341 00:19:20,622 --> 00:19:22,680 than than younger forests did, 342 00:19:22,680 --> 00:19:25,188 but no scientific literature to cite that. 343 00:19:25,188 --> 00:19:26,420 Do you? Was that on purpose? 344 00:19:26,420 --> 00:19:27,400 I mean, do you have some scientific- 345 00:19:27,400 --> 00:19:29,557 [Samantha] Oh yeah, yeah, I'm sorry I didn't include that. 346 00:19:29,557 --> 00:19:31,297 Yeah, I'll consider that for next time. 347 00:19:31,297 --> 00:19:32,130 [Attendee] Yeah, that'd be awesome. 348 00:19:32,130 --> 00:19:32,970 Because you know, this is kind of like 349 00:19:32,970 --> 00:19:34,560 one of the big issues that we're dealing 350 00:19:34,560 --> 00:19:36,540 with on public lands anyway 351 00:19:36,540 --> 00:19:40,800 is the contention that to maximize carbon storage 352 00:19:40,800 --> 00:19:45,180 preserve the older mature forest stands 353 00:19:45,180 --> 00:19:46,470 and don't cut any trees. 354 00:19:46,470 --> 00:19:49,590 So that's the science that is being thrown out there 355 00:19:49,590 --> 00:19:50,610 from that perspective. 356 00:19:50,610 --> 00:19:52,950 And there's some literature out there that contends 357 00:19:52,950 --> 00:19:54,270 that that is the case. 358 00:19:54,270 --> 00:19:57,120 Whereas, you know, as a public land manager, 359 00:19:57,120 --> 00:20:00,660 we're still using the science that says that's not true. 360 00:20:00,660 --> 00:20:01,890 You know, it's the counter science. 361 00:20:01,890 --> 00:20:04,830 So I just wonder if you could comment on that. 362 00:20:04,830 --> 00:20:05,663 [Samantha] Yeah, definitely. 363 00:20:05,663 --> 00:20:07,710 I mean, like I said, a lot of the study 364 00:20:07,710 --> 00:20:09,780 was based on the fact that the carbon dynamics 365 00:20:09,780 --> 00:20:11,760 in these forests is not very well understood. 366 00:20:11,760 --> 00:20:15,990 So we need to keep assessing some of our classical thoughts 367 00:20:15,990 --> 00:20:19,860 about, you know, and over time consider also 368 00:20:19,860 --> 00:20:24,030 how global change is affecting the carbon sequestration 369 00:20:24,030 --> 00:20:25,200 as well. 370 00:20:25,200 --> 00:20:26,150 [Attendee] Great.