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1 Supplementary Material 2 3 Tree census protocols 4 For the 13,981 trees in our database, we encountered four types of tree-accounting 5 problems: moved or missing plot centers, “dead” trees returning to life, lost trees (tags that could 6 not be found), and missed trees in the original survey (trees measured for the first time in the 7 resurvey but too large to be considered new recruits). 8 Out of a total of 371 plots, we experienced three moved stakes (one of which was 9 obviously chewed up by an animal) and one entirely missing stake. In these cases, we relocated 10 the plot center by projecting the old boundaries from the position of the tagged trees. In only one 11 of the four cases was there any uncertainty about the specific location of the plot center. To 12 prevent this problem in the future, two witness trees were chosen in each plot. The bearing and 13 distance was recorded from the witness trees to the plot center. 14 Eight trees were recorded as dead in the original survey (8/1613 or 0.5%) but upon 15 resurvey were found to be alive (i.e. at least one live leaf). Tree status was changed to alive in 16 1995/6. Given the possibility that recently defoliated trees may re-leaf and survive, this source of 17 error cannot be completely eliminated given our criteria for determining the vitality of trees. 18 To mitigate the problem of lost stems, we ranked all tagged trees in the original survey 19 based on the certainty with which we knew their fate: 20 0: we found the tree with the tag 1 21 1: probable; we found the tree in the right place and it was approximately the correct size 22 and species 23 2: no trace; we never found a probable tree after 15 person-minutes of dedicated 24 searching (3 people searching for 5 minutes each or 1 person searching for 15 minutes) 25 26 Of the 13,981 trees in the original inventory, 97% still had a tag (find rank = 0) leaving 27 455 “lost” trees. Of these trees, 421 lacked tags but fit the description of a tree in the inventory 28 (find rank = 1). The remaining 34 trees could not be found after searching (find rank = 2) and 29 were assumed to be dead and downed (tree status = 6). Most of these 34 trees were relatively 30 small < 15 cm dbh) and located in dense forests and/or steep terrain. 31 “Missed” trees were live untagged trees that should have been large enough to be counted 32 in the original census but had not been inventoried. We considered the maximum possible 33 growth rate to be a 4 cm absolute diameter increment over 10 years – a maximum based on the 34 long-term growth records in HBEF (2007). However we did not consider every untagged tree in 35 the plot 14 cm dbh a missed tree. We defined a toroidal buffer zone that included the area 36 between the perimeter of the plot (12.62 m radius) and the perimeter of an “inner” plot 12.22 m 37 radius. We used this buffer zone to differentiate between trees that were truly missed in the 38 original survey and trees that may have been intentionally excluded because there were 39 considered outside of the plot. Our edge buffer assumes a 3% linear error in radial measurements 40 (6% error in area determination). Only trees 14 cm found outside the buffer zone (from the 41 center until 12.4 m from the center) were tagged and noted as missing in the original survey. For 42 example, a 15 cm tree at 12.1 m from the center would be tagged but a 15 cm tree 12.5 m from 43 the center would be ignored. By this protocol, 79 live untagged trees, corresponding to 0.06% of 2 44 all measured stems, were too large to be counted as recruits, meaning they must have been 45 missed in the original census. We created species-specific growth regressions to back-calculate 46 the dbh of the missed trees in 1995/6. This protocol for handling missed trees avoids a potential 47 accounting bias common to long-term forest plot studies. During the second inventory, we did 48 not check for trees that were included in the original survey but may be outside of the plot 49 boundary. It was simply too time consuming. Thus without an edge buffer zone, we would only 50 include missing trees but have no means to determine if existing tagged trees should be excluded. 51 We followed a strict protocol in measurement of tree diameters. We used a height pole to 52 determine breast height (1.37 m) and always recorded dbh from the uphill side of the tree. 53 Exceptional cases such as clumped, split, or wounded trees were dealt with by the following 54 rules: If more than one stem originated from the same root system, each stem 10-cm was 55 measured separately and given a unique tag. Split trees were counted as two trees if split below 56 breast height, but only as one tree if split above. Trees wounded at breast height were either 57 measured directly above or below the wound, depending on which would give the most 58 representative measure of the tree’s volume. Data recorders had access to the original data and 59 would check for “unusual” changes in diameter growth, i.e. > 3 cm increment or negative 60 growth. The data collector would be asked to check the measurement (without being told the 61 original measurement) and then asked to report any abnormalities (e.g., peeling bark, bulge in 62 trunk). The second measurement was used, regardless if the discrepancy persisted. Any 63 abnormalities in the stem that may influence dbh were recorded. This approach was designed to 64 minimize measurement error without introducing bias. 3 65 Biomass estimates 66 67 Stem volume was computed using species-specific allometric equations calculated for 68 trees harvested within Hubbard Brook Valley (Siccama et al. 1994 ; Whittaker et al. 1974). For 69 the three most important species in the Valley, separate equations were derived for trees sampled 70 across the elevation gradient. Bole parabolic volume was chosen as the best indicator of biomass. 71 Less important species that were not specifically sampled, were assigned equations for 72 morphologically similar species. 73 The general form of the equation is: 74 75 [S1] 76 77 where height is in centimeters, dbh is in centimeters, and parabolic volume is in cm3. Tree height 78 was estimated from a set of species- and elevation-specific allometric equations that predict 79 height as a function of species, dbh and elevation. The elevation bands are comprised of low (< 80 630 m), medium (630-710 m), and high (> 710 m). A complete table of coefficients is attached 81 (Table S1). The equation form for balsam fir height is linear: 82 83 [S2] 84 85 where a and b are estimated parameters. The equation form for all other species heights is 86 asymptotic: 87 4 88 [S3] 89 90 where x0 is the intercept and a and b are estimated parameters. 91 Once the parabolic volume of each tree was calculated, we applied the biomass equations 92 (Table S2) taken from Whittaker et al. (1974) and Siccama et al. (1994). The general form is: 93 94 [S4] 95 96 where PV is the parabolic volume in cm3. A biomass estimate is calculated for each part of a 97 tree: lightwood, darkwood, wood weight, bark weight, branch, dead branch, current twigs and 98 leaves, and root system. Darkwood and lightwood are calculated and converted to proportions, 99 which are then applied to the wood mass estimate and are called stem weight. It is necessary to 100 separate current twig and leaf equations since the twigs stay on the trees and the leaves fall off 101 (Whittaker et al. 1974). Additionally, old needle weight was estimated for conifers (Siccama et 102 al. 1994). The sum of the parts (stem, bark, branch, dead branch, current twigs and leaves, old 103 needle, root system) was then used to calculate total biomass. Standing dead and snags were 104 discounted with rotting factors for different biomass components. 105 We calculated total live biomass of the overall forest to be able to directly compare to 106 other studies, but for the population level analyses, we only report aboveground biomass, since 107 belowground biomass tracks aboveground (based on the equations) and aboveground biomass 108 allometry has been validated (Arthur et al. 2001; Fahey et al. 2005; Siccama et al. 1994). In the 109 validation study, an independent estimate of aboveground biomass in HBEF was compared with 110 allometric estimates. The allometric and measured estimates of aboveground biomass did not 5 111 differ significantly (Arthur et al. 2001). In addition, most of the official reported biomass 112 estimates from Hubbard Brook only include aboveground biomass. 113 Uncertainty analysis 114 115 To detect statistically relevant changes in forest biomass and tree demography, we 116 analyzed the uncertainty associated with key metrics. Non-overlap of 95% confidence intervals 117 (or their Bayesian equivalent, credibility intervals) was used as the standard of significant 118 change. 119 Fahey et al. (2005) described the error propagation techniques and accuracy assessment 120 for aboveground tree biomass estimates at Hubbard Brook. Briefly, they applied Monte Carlo 121 simulation (Fahey and Knapp 2007) to quantify the propagation of errors in plot-level estimates 122 of tree biomass. Sources of uncertainty include measurement error, sampling error, and 123 prediction error associated with two allometric equations (dbh-to-height, volume-to-mass). On 124 average the relative root mean square error (i.e., precision) of plot-level estimates was ± 34%. In 125 a stand-level comparison between allometric estimates of biomass (with error propagation) and 126 whole-tree harvest estimates (biomass weighed), the two approaches were within 3% (Fahey et 127 al. 2005). Note that this validation only applies to the aboveground biomass. Thus we only use 128 aboveground biomass for statistical inferences. In all cases, we ran 1000 Monte Carlo 129 simulations where the plot-level estimates varied by 34%. We report the mean value and the 2.5th 130 and 97.5th percentile. 131 We used a hierarchical Bayesian approach to calculate the uncertainty in the mortality 132 estimates. We followed the rationale described in Condit et al. (2006) but modified the 6 133 probability distributions to fit our data. At the species level, mortality was distributed as a 134 binomial probability – the tree is either dead or alive. The binomial uncertainty is based on the 135 observed mortality rate and the number of individuals. At the community level, we observed an 136 exponential distribution of annual mortalities for the 19 species in our sample. Only one 137 parameter needs to fit for the exponential distribution – the decay constant. The likelihood for the 138 entire community is the product of the species level binomial probability and the community 139 level exponential probability. Bayes theorem is used to define this joint probability distribution. 140 As described by Condit et al. (2006), the probability of observations for a single species is 141 integrated over every possible decay constant discounting each by the community-wide 142 probability. 143 We used a similar approach to quantify the uncertainty in recruitment and relative 144 diameter growth. For recruitment, we first had to develop a species level probability distribution. 145 For each species, there were 371 observations (i.e. each plot) with values = 0. All species, even 146 the common ones, had distributions with many zeroes. Moreover, the observed value did not 147 account for the chance that a recruit was missed during the interval .Therefore we simulated a 148 normal distribution for each species by randomly sampling the observed distribution with 149 replacement 1000 times. For each run we also estimated the chance that a recruit was missed 150 during the interval by using the observed rate (4.7%) from Siccama et al. (2007). The 151 community-level distribution of recruitment (recruitment rates for the 11 species with recruits) 152 most closely approximated a uniform distribution – all outcomes are equally likely. Thus it 153 provides no constraint on the species-level parameters. But for the sake of consistency with our 154 demographic analyses, we followed through with the hierarchical analysis. For relative growth, 7 155 both the within-species and between-species distributions followed a log-normal distribution – 156 the same distributions used in Condit et al. (2006) to calculate uncertainty in relative growth. 157 The Markov Chain Monte Carlo technique with the Gibbs sampler was used to solve 158 numerically the integration and to fit the parameters. The analysis was implemented in the R 159 statistical package using (modified) scripts provided by Condit et al. (2006). We ran the Gibbs 160 sampler for 6,000 steps and stored the last 5,000. The sampler routinely converged in < 100 161 steps. Best estimates of mortality and recruitment were the means of the stored chains. 162 Credibility intervals (Clark 2005) were taken as the 2.5th and 97.5th percentiles of the chains. 8 References Arthur, M.A., Hamburg, S.P., and Siccama, T.G. 2001. Validating allometric estimates of aboveground living biomass and nutrient contents of a northern hardwood forest. Can. J. For. Res. 31(1): 11-17. Clark, J.S. 2005. Ideas and Perspectives: Why environmental scientists are becoming Bayesians. Ecol. Lett. 8(1): 2-14. Condit, R., Ashton, P., Bunyavejchewin, S., Dattaraja, H., Davies, S., Esufali, S., Ewango, C., Foster, R., Gunatilleke, I., and Gunatilleke, C. 2006. The importance of demographic niches to tree diversity. Science 313(5783): 98. Fahey, T.J., and Knapp, A.K. 2007. Principles and standards for measuring primary production. Oxford University Press. Fahey, T.J., Siccama, T.G., Driscoll, C.T., Likens, G.E., Campbell, J., Johnson, C.E., Battles, J.J., Aber, J.D., Cole, J.J., Fisk, M.C., Groffman, P.M., Hamburg, S.P., Holmes, R.T., Schwarz, P.A., and Yanai, R.D. 2005. The biogeochemistry of carbon at Hubbard Brook. Biogeochemistry 75(1): 109-176. Siccama, T.G., Fahey, T.J., Johnson, C.E., Sherry, T.W., Denny, E.G., Girdler, E.B., Likens, G.E., and Schwarz, P.A. 2007. Population and biomass dynamics of trees in a northern hardwood forest at Hubbard Brook. Can. J. For. Res. 37(4): 737-749. Siccama, T.G., Hamburg, S.P., Arthur, M.A., Yanai, R.D., Bormann, F.H., and Likens, G.E. 1994. Corrections to allometric equations and plant tissue chemistry for Hubbard Brook Experimental Forest. Ecology 75(1): 246-248. 9 Whittaker, R.H., Bormann, F.H., Likens, G.E., and Siccama, T.G. 1974. The Hubbard Brook ecosystem study: Forest biomass and production. Ecol. Monogr. 44(2): 233-254. 10 Table S1. Coefficients for height equations by species and elevation in HBEF. The coefficients for sugar maple also apply to bigtooth aspen, black cherry, grey birch, shadbush and quaking aspen. Elevation codes refer to: H, high: > 710 m; M, mid: 630-710 m; L, low: < 630 m. The equation form for balsam fir is: height in meters = a + b∙dbh. For all other species, the equation form is: height -b∙ dbh in meters = x0 + a ∙ (1 - e ). Species xo a b Elevation Sugar maple 1.37 21.5794 0.061 H 1.37 23.855 0.0585 M 1.37 25.5443 0.0599 L Beech 1.37 19.1258 0.0627 H 1.37 21.676 0.0628 M 1.37 25.6262 0.0562 L Yellow birch 1.37 19.5743 0.0572 H 1.37 20.8744 0.0739 M 1.37 22.9096 0.0716 L White ash 1.37 29.534 0.1121 H 1.37 29.534 0.1121 M 1.37 29.534 0.1121 L Mountain maple 1.37 29.9598 0.0396 H 1.37 29.9598 0.0396 M 1.37 29.9598 0.0396 L Striped maple 1.37 29.9598 0.0396 H 1.37 29.9598 0.0396 M 1.37 29.9598 0.0396 L Choke cherry 1.37 19.5743 0.0572 H 1.37 19.5743 0.0572 M 1.37 19.5743 0.0572 L Pin cherry 1.37 19.5743 0.0572 H 1.37 19.5743 0.0572 M 1.37 19.5743 0.0572 L Balsam fir 0 2.0274 0.5215 H 0 2.0274 0.5215 M 0 2.0274 0.5215 L Red spruce 1.37 25.8714 0.0361 H 1.37 26.8832 0.0429 M 1.37 25.937 0.0533 L Paper birch 1.37 16.3946 0.0798 H 1.37 25.3076 0.0605 M 1.37 25.3076 0.0605 L Mountain ash 1.37 29.9598 0.0396 H 1.37 29.9598 0.0396 M 1.37 29.9598 0.0396 L Red maple 1.37 23.6571 0.076 H 1.37 23.6571 0.076 M 1.37 23.6571 0.076 L Hemlock 1.37 25.8714 0.0361 H 1.37 25.8714 0.0361 M 1.37 25.8714 0.0361 L 11 Table S2. Coefficients for parabolic volume equations by species and elevation in HBEF. The coefficients (coeff.) for sugar maple also apply to bigtooth aspen, black cherry, grey birch, shadbush and quaking aspen. Column headings: LIGHT= lightwood, DARK= darkwood, WW= wood weight, WB= bark weight, BRDW= branch weight, TLDW= dead branch weight, CTLGR= current twig and leaf weight, RSDW= root system weight, MASS= total mass. High-elevation (>710 m) Coeff. Species LIGHT DARK WW WB BRDW TLDW CTLGR RSDW MASS a Sugar maple 0.16964 -3.1448 0.1073 -0.8589 -2.3983 -3.5027 -0.755 -0.25366 0.08781 b 0.91478 1.2391 0.9317 0.9501 1.3276 1.3619 0.8341 0.912735 0.993031 a Beech 0.7826 -3.6197 0.39268 -0.51047 -0.66119 -1.9559 -0.1251 0.11505 0.57189 b 0.77499 1.3576 0.8721 0.8202 0.9922 1.0563 0.7044 0.84058 0.89184 a Yellow birch 0.086262 -6.4342 -0.2658 -1.124 -1.4132 -4.1681 -0.1649 -0.77214 -0.2195 b 0.90697 1.9188 0.9938 0.986 1.1616 1.478 0.7272 1.0103 1.0486 a White ash 0.7826 -3.6197 0.39268 -0.51047 -0.66119 -1.9559 -0.1251 0.11505 0.57189 b 0.77499 1.3576 0.8721 0.8202 0.9922 1.0563 0.7044 0.84058 0.89184 a Mountain maple 0.14647 -7.9527 0.1465 -0.3176 -1.4382 -1.5962 -0.2574 0.7017 0.64369 b 0.88014 1.3293 0.8801 0.7958 1.1896 1.0489 0.6757 0.6767 0.84777 a Striped maple 0.14647 -7.9527 0.1465 -0.3176 -1.4382 -1.5962 -0.2574 0.7017 0.64369 b 0.88014 1.3293 0.8801 0.7958 1.1896 1.0489 0.6757 0.6767 0.84777 a Pin cherry 0.086262 -6.4342 -0.2658 -1.124 -1.4132 -4.1681 -0.1649 -0.77214 -0.2195 b 0.90697 1.9188 0.9938 0.986 1.1616 1.478 0.7272 1.0103 1.0486 a Choke cherry 0.086262 -6.4342 -0.2658 -1.124 -1.4132 -4.1681 -0.1649 -0.77214 -0.2195 b 0.90697 1.9188 0.9938 0.986 1.1616 1.478 0.7272 1.0103 1.0486 a Balsam fir 0.53664 -3.8215 -0.0847 -0.8024 -0.6338 -0.1339 -0.1375 0.436 0.9711 b 0.80794 1.2345 0.9314 0.901 0.9188 0.7636 0.5941 0.7806 0.7926 a Red spruce 0.53664 -3.8215 0.5048 0.1198 -0.6338 -0.1339 -0.1375 0.436 0.9711 b 0.80794 1.2345 0.8138 0.7259 0.9188 0.7636 0.5941 0.7806 0.7926 a White birch 0.086262 -6.4342 -0.2658 -1.124 -1.4132 -4.1681 -0.1649 -0.77214 -0.2195 b 0.90697 1.9188 0.9938 0.986 1.1616 1.478 0.7272 1.0103 1.0486 a Mountain ash 0.14647 -7.9527 0.1465 -0.3176 -1.4382 -1.5962 -0.2574 0.7017 0.64369 b 0.88014 1.3293 0.8801 0.7958 1.1896 1.0489 0.6757 0.6767 0.84777 a Red maple 0.16964 -3.1448 0.1073 -0.8589 -2.3983 -3.5027 -0.755 -0.25366 0.08781 b 0.91478 1.2391 0.9317 0.9501 1.3276 1.3619 0.8341 0.912735 0.993031 a Hemlock 0.53664 -3.8215 0.5048 0.1198 -0.6338 -0.1339 -0.1375 0.436 0.9711 b 0.80794 1.2345 0.8138 0.7259 0.9188 0.7636 0.5941 0.7806 0.7926 12 Mid-elevation (630-710 m) Coeff. LIGHT DARK WW WB BRDW TLDW CTLGR RSDW MASS a Sugar maple 0.014922 -2.9012 0.003485 -0.94106 -2.6141 -2.3924 -0.93109 -0.1772 0.062109 b 0.94275 1.1023 0.94668 0.94726 1.3203 1.0571 0.84681 0.88769 0.98263 a Beech 0.32927 -3.0607 0.18266 -0.54617 -0.35331 -2.6934 0.42891 0.47863 0.61439 b 0.8760 1.2381 0.9147 0.83149 0.93443 1.2192 0.58175 0.7797 0.88721 a Yellow birch 0.093657 -5.1973 -0.0113 -0.69069 -1.4015 -2.3924 -0.41346 -0.86498 -0.07977 b 0.90745 1.6498 0.93533 0.88984 1.1498 1.0308 0.73652 1.019 1.0122 a White ash 0.32927 -3.0607 0.18266 -0.54617 -0.35331 -2.6934 0.42891 0.47863 0.61439 b 0.876 1.2381 0.9147 0.83149 0.93443 1.2192 0.58175 0.7797 0.88721 a Mountain maple 0.14647 -7.9527 0.1465 -0.3176 -1.4382 -1.5962 -0.2574 0.7017 0.64369 b 0.88014 1.3293 0.8801 0.7958 1.1896 1.0489 0.6757 0.6767 0.84777 a Striped maple 0.14647 -7.9527 0.1465 -0.3176 -1.4382 -1.5962 -0.2574 0.7017 0.64369 b 0.88014 1.3293 0.8801 0.7958 1.1896 1.0489 0.6757 0.6767 0.84777 a Pin cherry 0.093657 -5.1973 -0.2658 -1.124 -1.4132 -4.1681 -0.1649 -0.77214 -0.2195 b 0.90745 1.6498 0.9938 0.986 1.1616 1.478 0.7272 1.0103 1.0486 a Choke cherry 0.093657 -5.1973 -0.2658 -1.124 -1.4132 -4.1681 -0.1649 -0.77214 -0.2195 b 0.90745 1.6498 0.9938 0.986 1.1616 1.478 0.7272 1.0103 1.0486 a Balsam fir 0.53664 -3.8215 -0.0847 -0.8024 -0.6338 -0.1339 -0.1375 0.436 0.9711 b 0.80794 1.2345 0.9314 0.901 0.9188 0.7636 0.5941 0.7806 0.7926 a Red spruce 0.53664 -3.8215 0.5048 0.1198 -0.6338 -0.1339 -0.1375 0.436 0.9711 b 0.80794 1.2345 0.8138 0.7259 0.9188 0.7636 0.5941 0.7806 0.7926 a White birch 0.093657 -5.1973 -0.0113 -0.69069 -1.4015 -2.3924 -0.41346 -0.86498 -0.07977 b 0.90745 1.6498 0.93533 0.88984 1.1498 1.0308 0.73652 1.019 1.0122 a Mountain ash 0.14647 -7.9527 0.1465 -0.3176 -1.4382 -1.5962 -0.2574 0.7017 0.64369 b 0.88014 1.3293 0.8801 0.7958 1.1896 1.0489 0.6757 0.6767 0.84777 a Red maple 0.014922 -2.9012 0.003485 -0.94106 -2.6141 -2.3924 -0.93109 -0.1772 0.062109 b 0.94275 1.1023 0.94668 0.94726 1.3203 1.0571 0.84681 0.88769 0.98263 a Hemlock 0.53664 -3.8215 0.5048 0.1198 -0.6338 -0.1339 -0.1375 0.436 0.9711 b 0.80794 1.2345 0.8138 0.7259 0.9188 0.7636 0.5941 0.7806 0.7926 13 Low-elevation (<630 m) Coeff. LIGHT DARK WW WB BRDW TLDW CTLGR RSDW MASS a Sugar maple 0.05954 -3.2323 0.010929 -0.50204 -1.3983 -1.5819 -0.2618 -0.06452 3 0.2574 b 0.92549 1.1482 0.93825 0.87625 1.0759 0.7842 0.70421 0.85243 0.93604 a Beech 0.12799 -3.6915 -0.01576 -0.89476 -0.76433 -2.2116 0.015976 –0.081778 0.23996 b 0.9143 1.2857 0.9517 0.89931 1.02813 1.0802 0.66166 0.86955 0.95696 a Yellow birch 0.26066 -3.6229 0.209363 -0.20868 -1.09461 -2.6546 -0.59274 -0.03825 0.38366 b 0.88149 1.3459 0.89722 0.81439 1.0526 1.0996 0.77028 0.85765 0.92187 a White ash 0.12799 -3.6915 -0.01576 -0.89476 -0.76433 -2.2116 0.015976 –0.081778 0.23996 b 0.9143 1.2857 0.9517 0.89931 1.02813 1.0802 0.66166 0.86955 0.95696 a Mountain maple 0.14647 -7.9527 0.1465 -0.3176 -1.4382 -1.5962 -0.2574 0.7017 0.64369 b 0.88014 1.3293 0.8801 0.7958 1.1896 1.0489 0.6757 0.6767 0.84777 a Striped maple 0.14647 -7.9527 0.1465 -0.3176 -1.4382 -1.5962 -0.2574 0.7017 0.64369 b 0.88014 1.3293 0.8801 0.7958 1.1896 1.0489 0.6757 0.6767 0.84777 a Pin cherry 0.26066 -3.6229 -0.2658 -1.124 -1.4132 -4.1681 -0.1649 -0.77214 -0.2195 b 0.88149 1.3459 0.9938 0.986 1.1616 1.478 0.7272 1.0103 1.0486 a Choke cherry 0.26066 -3.6229 -0.2658 -1.124 -1.4132 -4.1681 -0.1649 -0.77214 -0.2195 b 0.88149 1.3459 0.9938 0.986 1.1616 1.478 0.7272 1.0103 1.0486 a Balsam fir 0.53664 -3.8215 -0.0847 -0.8024 -0.6338 -0.1339 -0.1375 0.436 0.9711 b 0.80794 1.2345 0.9314 0.901 0.9188 0.7636 0.5941 0.7806 0.7926 a Red spruce 0.53664 -3.8215 0.5048 0.1198 -0.6338 -0.1339 -0.1375 0.436 0.9711 b 0.80794 1.2345 0.8138 0.7259 0.9188 0.7636 0.5941 0.7806 0.7926 a White birch 0.26066 -3.6229 0.209363 -0.20868 -1.09461 -2.6546 -0.59274 -0.03825 0.38366 b 0.88149 1.3459 0.89722 0.81439 1.0526 1.0996 0.77028 0.85765 0.92187 a Mountain ash 0.14647 -7.9527 0.1465 -0.3176 -1.4382 -1.5962 -0.2574 0.7017 0.64369 b 0.88014 1.3293 0.8801 0.7958 1.1896 1.0489 0.6757 0.6767 0.84777 a Red maple 0.05954 -3.2323 0.010929 -0.50204 -1.3983 -1.5819 -0.2618 -0.06452 3 0.2574 b 0.92549 1.1482 0.93825 0.87625 1.0759 0.7842 0.70421 0.85243 0.93604 a Hemlock 0.53664 -3.8215 0.5048 0.1198 -0.6338 -0.1339 -0.1375 0.436 0.9711 b 0.80794 1.2345 0.8138 0.7259 0.9188 0.7636 0.5941 0.7806 0.7926 14 15

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