SUPPLEMENTARY INFORMATION

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1 SUPPLEMENTARY INFORMATION DOI: /NCLIMATE1293 A drought-induced pervasive increase in tree mortality across Canada s boreal forests Changhui Peng 1,2,*, Zhihai Ma 1, Xiangdong Lei 3,1, Qiuan Zhu 2,1, Huai Chen 2,1, Weifeng Wang 1, Shirong Liu 3, Weizhong Li 2,1, Xiuqin Fang 1, and Xiaolu Zhou 1 Supplementary Notes Data selection and analysis For our analysis, we strictly selected forest plots from Canada s boreal and hemiboreal forest regions based on the following criteria: (1) All plots were in natural forest stands, which we defined as stands that developed naturally rather than after harvesting or other silvicultural treatments. (2) Because at least two different time intervals were required to compare changes in tree recruitment and mortality rates, all plots had at least three consecutive censuses. (3) We required complete tree recruitment and mortality records. In addition, every tree above a defined diameter at breast height (DBH, 1.3 m above the ground) was measured during the initial census. (4) The individual trees must have been clearly marked and repeatedly measured. (5) To detect potential long-term changes in tree recruitment and mortality rates, all plots had at least 10 years of observations between their first and last census. (6) To avoid changes in tree recruitment and mortality rates caused by disturbance, we chose only plots with no evidence of fire, flood, storm, or insect disturbance and no evidence of forest management such as thinning for at least four decades. (7) To avoid changes in tree recruitment and mortality rates associated with stand development and succession, the stand age was 80 years in all plots. This age was chosen based on the mature ages of the major species in Canada s boreal forest (S1, S2). The stand ages were obtained by counting tree rings in increment cores obtained from the largest trees or from available historical records. (8) To reduce random variation in plot-level demographic data, we only used plots with a large enough number of live trees ( 80, which is based on our calculation of averaged tree number over 38 minimum sample plots (plot size = 0.04 ha)) at their initial census. (9) To obtain climatic data for each plot, the spatial location of all plots was required. The plots collected by Provincial forest inventories and permanent sample plot (PSP) were originally established for diverse purposes, such as to investigate forest growth and yield, describe different stages of forest development, document the dynamics of certain forest types, explore forest dynamics along environmental gradients, or act as controls for silvicultural experiments. Table S1 summarizes the key characteristics of the 96 plots that met these criteria. Their locations are shown in Figure 1. The shapefiles defining Canada s boreal and hemiboreal NATURE CLIMATE CHANGE 1

2 regions were developed by Natural Resources Canada s (S19) and can be freely downloaded from the following Web page: (last accessed 29 March 2011). To find data that met these criteria, we selected and thoroughly reviewed data from permanent sample plots (PSPs) in Alberta (S3), Saskatchewan (S4), Manitoba (S5), Ontario (S6), and Quebec (S7). We did not consider PSP data from British Columbia and New Brunswick because most plots in these two provinces were not boreal forest types. Although there were many boreal PSPs in each province (i.e., 580 plots for Alberta, 2426 plots for Saskatchewan, 368 plots for Manitoba, more than 4000 plots for Ontario, and about plots for Quebec), most of the plots did not meet our criteria, either because they did not have three or more censuses or they were not located in undisturbed mature forest. In total, the plots contained living trees, for which observations were obtained. Please note that using these 96 representative plots to characterize and scale up tree mortality trends across Canadian boreal forests must be cautious. To obtain data for the climatic variables associated with the individual plots, we used the daily 10-km raster-gridded climate dataset for Canada from 1961 to 2003 (S8), which contains data for daily maximum temperature ( C; T max ), minimum temperature ( C; T min ), and precipitation (mm; PRE) for the Canadian landmass south of 60 N. The km grid data were interpolated from daily Environment Canada climate station observations using a thinplate-smoothing spline-surface-fitting method implemented by the ANUSPLIN V4.3 software (S9). The climatic data for plots with census years after 2003 were downloaded from the nearest meteorological station (S10). We used the annual climate moisture index (CMI) (S11) to indicate the annual climatic water deficit. Monthly CMI values were calculated as monthly PRE minus PET (S11), where PET is the potential evapotranspiration, which is estimated from T max, T min, and elevation. Annual CMI was calculated by summing the monthly CMI values from January through December. Positive CMI values indicate relatively moist conditions and negative CMI values indicate relatively dry conditions. In addition to the annual CMI, we also calculated the annual moisture index (AMI) (S12), which is defined as the ratio of the annual number of degree-days above 5 C to the mean annual precipitation. Large values of AMI indicate dry conditions due to high heat (thus, high evaporative demand) relative to the available moisture, whereas low values of AMI represent relatively wet conditions. Therefore, the larger the value of AMI, the greater the probability of drought. Both CMI and AMI were used to measure climatic water deficits in this study. The annual mean temperature and annual PRE were also calculated. To model the changes in 2

3 demographic parameters (mortality and recruitment rates) as a function of climatic parameters, we averaged the annual climatic variables across all years within each census interval for a given plot. Statistical Models We used the same statistical models of van Mantgem et al. (S13), which were simple, appropriate to the data, and capable of detecting directional changes in mortality and recruitment rates. Plot-specific trends in these rates were analyzed with generalized nonlinear models. When analyzing trends across multiple plots, we added a normal random effect based on plot identity to the linear function to account for differences among plots. Specifically, to estimate changes in annual mortality rates we modeled the rate as a logistic function exp(β 0 + β 1 t j + γ i ) / (1 + exp[β 0 + β 1 t j + γ i ]), where i represents the plot number, j represents the census number, t j represents the duration between consecutive censuses, and β 0, β 1, and γ i are regression parameters. We applied a statistical model to our data in which n ij was the number of trees alive at the previous census for the i-th plot and the j-th census, and m ij represents the corresponding mortality rate: m ij γ i ~ negative binomial distribution with mean n ij p ij and variance n ij p ij n p 1 ij ij + α (1) 1 α 2 β γ i ~ N(0, σ γ ) (2) c p j ij = 1- (1+ exp( 0 + β1t j + γ i )), where p ij represents the probability of mortality over the census interval, t j represents the year of the j-th census, and c represents the census interval in years. The random intercept parameter (γ i ) follows a normal distribution. The negative binomial distribution is an extension of the Poisson distribution with α > 0 representing overdispersion. We modeled annual recruitment rates (r ij ) as exp(β 0 + β 1 t j + γ i ) and applied a similar statistical model in which r ij is the number of recruits: r ij γ i ~ negative binomial distribution with mean n ij p ij and variance n ij p ij n p 1 ij ij + α (3) 1 α c j 2 p ij = (1+ exp( β 0 + β1t j + γ i )) 1, γ i ~ N(0, σ γ ) (4) where p ij represents the rate of recruitment over the census interval. We used the maximum-likelihood method to estimate model parameters that produced the most likely annual mortality and recruitment rates that, when compounded based on the length of the census intervals, best corresponded to the rates observed in the data. We tested models that 3

4 included parameters for random slopes among plots and additional polynomial terms, but found little support for these models. Estimated annual fractional changes in mortality and recruitment rates (a) were calculated as exp(β 1 )-1, which provides an exact value for the recruitment rates and, when mortality rates are small (as they were in this study), a close approximation of the fractional changes in mortality rates. Trends in mortality and recruitment as a function of the climatic parameters, census interval length, forest density, and basal area were estimated using linear mixed models (LMMs) following the methods of van Mantgem et al. (S13). Potential temporal autocorrelations were accounted for using a first-order autoregressive autocorrelation function. Supplementary Discussion Evaluation of possible methodological problems Mantel tests suggested no evidence of positive spatial autocorrelation among all plots considered together, or among the plots within the eastern and western regions, for (i) the average mortality rate (P 0.070) and (ii) the annual fractional change in the mortality rate (P 0.065) The lack of spatial autocorrelation was expected, since our plots primarily occurred in heterogeneous ecological regions, where close geographic proximity does not imply similarity in forest characteristics or in the climatic, edaphic, or topographic environments. To identify whether heterogeneity in plot characteristics might have affected the value of a (the modeled annual fractional change in mortality rates), we regressed the calculated values of a for individual plots as a function of plot size, average census interval length, forest density, and stand age, all of which varied widely among the plots. Our linear regressions showed that a was not significantly correlated with plot size, average census interval length, forest density, or stand age (Fig. S1). We therefore concluded that even though plot size, average census interval length, and forest density varied widely among the plots, this variation had no significant effect on the modeled annual fractional changes in mortality rates. However, the higher rate of increase at the largest replicated plot size (0.12 ha) was due to 2 larger plots located in Ontario while mortality rate of other 8 plots in Ontario were decreased. We used simple analyses and graphs as independent checks on the model results for the two regions. To do so, we compared the mean mortality rates during the first and last census intervals (Fig. S2). Consistent with the model results, the mean mortality rate was significantly 4

5 higher during the last census interval in the western region (P < ; paired permutation tests), but there was no significant increase for the eastern region (P < , paired permutation tests) Explanation of why endogenous processes are unlikely to be major contributors 1) Effects of the competition resulting from increasing forest density and basal area We examined the potential effect of forest density and basal area, which are two of the best-known causes of increasing tree mortality rates as a result of increased competition among trees, on tree mortality using LMMs (S13). We found no significant relationship between tree mortality and forest density (Fig. S1). Forest density declined significantly (P 0.001, LMM; Table S3), whereas no significant change (or a slight decline) in basal area occurred in both regions (Table S3) during the study period. Thus, the changes in forest structure are consistent with a slight decline in the potential for competition during the study period, which suggests that increasing tree mortality was not caused by an endogenous increase in competition due to changes in forest structure. This result is consistent with recent findings by van Mantgem et al. (S13) in the coniferous forests of the western United States. 2) Aging factors Since our results showed a parallel increase in the mortality rates of small and big trees (Figure 2, Table 1), the observed mortality increase does not appear to be attributable to aging. Recent research in western Canada (S14) and in Colorado (S15) has demonstrated that the level of drought-induced dieback and mortality was not significantly related to the age of the trembling aspen overstory. Van Mantgem et al. (S13) reported that the widespread increase of tree mortality rates in the western United States cannot be attributed only to aging of the old and large trees, which is consistent with the present results. It is assumed that the death and falling of old trees may cause the increase of morality rates of smaller trees because of the majestic forest effect (S16). If this is true in our case, the small died and standing trees may not have an increase of morality rate (S13). because such deaths would be independent of deaths in the oldest cohorts. However, the mortality rate of small trees that died standing has increased significantly and rapidly in recent decades, doubling in 24 years (P < , GNMM; Fig. 2, Table 1); thus, other mechanisms must be acting on the younger trees. Our results also show that mortality rates increased in all major species rather than being limited to those dominated by a particular life history trait (such as shade intolerance). This suggests that successional dynamics are unlikely to be the primary drivers of the increasing mortality rates. We also found no relationship between tree mortality and either plot size or census interval (Fig. S1), which is consistent with the results of van Mantgem et al. (S13) for temperate forests in the western United States. 5

6 Assessing possible exogenous causes of increasing mortality rates We hypothesize based on this evidence that endogenous processes are unlikely to be major contributors to the observed rapid and synchronous doubling of mortality rates in our heterogeneous sample of old forests at a national scale. Moreover, the available evidence from previous research suggests that there is no major role for additional possible exogenous cause such as air pollution (S13), because there has been no rigorous proof that regional pollution (e.g., acid deposition, other regionally dispersed pollutants) has reduced the average growth rates or productivity, or has changed the development, of any forest in North America (S17, S18). Climatic data modeled for all plots combined (Table S4) indicated significantly increasing average temperatures (β year = 0.044, S.E. = 0.003, P < , LMM) but with no significant trend in precipitation (β year = , S.E. = 0.283, P = 0.107, LMM). The combination of these two factors resulted in significant decreases in CMI (β year = , S.E. = 0.029, P < , LMM) and significant increases in AMI (β year = 3.810, S.E. = 0.477, P < , LMM). The average temperature, CMI, and AMI were significantly correlated with tree mortality rates (Table 2). 6

7 1 2. Plot ID* Table S1. Characteristics of the 96 forest plots that met the criteria for inclusion in our analysis. Region Longitude ( W) Latitude ( N) Plot size (ha) Elevation (m asl) Initial stand age (years) Initial no. Calendar year of census live trees Year1 Year2 Year3 Year4 Year5 Species composition AB5 west BS 0.78 LP 0.22 AB6 west JP 1.00 AB7 west LP 1.00 AB8 west LP 1.00 AB9 west LP 0.55 ES 0.34 AF 0.11 AB10 west LP 0.93 ES 0.03 AF 0.03 AB11 west PF 0.97 WP 0.03 AB12 west PF 0.91 AF 0.05 ES 0.03 AB13 west LP 0.58 ES 0.25 AF 0.16 AB14 west LP 0.92 AF 0.05 ES 0.02 AB15 west LP 1.00 AB16 west LP 1.00 AB17 west LP 0.90 WS 0.09 BS 0.01 AB18 west WS 0.47 TA 0.24 BF 0.12 LP 0.11 BP 0.06 AB19 west LP 0.68 TA 0.30 WS 0.01 BP 0.01 AB20 west TA 0.95 WS 0.05 AB21 west WS 0.59 BF 0.17 LP 0.10 WB 0.08 TA 0.06 AB22 west BS 0.97 LP 0.03 AB23 west AF 0.43 ES 0.39 LP 0.18 AB24 west ES 0.74 AF 0.15 LP 0.11 AB25 west LP 0.92 ES 0.07 AF 0.01 AB26 west LP 0.98 AF 0.01 ES 0.01 AB27 west LP 0.59 ES 0.29 AF 0.11 AB28 west AF 0.73 ES 0.25 LP 0.02 AB29 west TA 0.44 WS 0.42 BP 0.14 LP 0.01 AB30 west BS 0.98 WS 0.02 AB31 west LP 0.75 BS 0.25 SK1 west BS 0.85 WS 0.12 BP 0.03 SK2 west BS 0.89 BP 0.07 WS 0.04 SK3 west BS 0.67 WS 0.18 TA 0.13 BP

8 SK4 west BS 0.87 BP 0.11 WS 0.02 SK5 west BS 0.87 BP 0.07 JP 0.06 SK6 west BS 0.84 JP 0.15 WB 0.01 SK7 west BS 0.99 WS 0.01 SK9 west BS 0.99 WS 0.01 SK10 west BS 0.88 BF 0.07 WS 0.02 TA 0.01 WB 0.01 SK11 west WS 0.82 TA 0.14 BP 0.05 SK12 west WS 0.97 BP 0.03 SK13 west WS 0.78 TA 0.13 BS 0.07 BP 0.02 SK14 west WS 0.93 TA 0.05 BP 0.02 SK15 west TA 0.73 WS 0.26 BS 0.01 SK16 west WS 0.54 TA 0.45 BP 0.01 SK17 west WS 0.51 BP 0.19 BF 0.19 BS 0.05 WB 0.04 TA 0.01 SK18 west WS 0.72 BF 0.13 WB 0.07 BP 0.06 BS 0.02 SK19 west WS 0.61 BP 0.37 BS 0.03 SK20 west WS 0.63 TA 0.30 BP 0.07 SK21 west WS 0.67 TA 0.23 BP 0.10 SK22 west WS 0.65 TA 0.31 BS 0.05 SK23 west TA 0.58 BS 0.32 WS 0.11 SK24 west TA 0.60 WS 0.36 BS 0.04 SK25 west WS 0.63 TA 0.29 JP 0.07 BS 0.01 SK26 west TA 0.57 WS 0.42 BF 0.01 SK27 west TA 0.55 WS 0.45 SK28 west TA 0.68 WS 0.32 SK29 west WS 0.45 TA 0.43 BF 0.10 BS 0.02 SK30 west WS 0.65 TA 0.34 BP 0.01 SK31 west JP 0.89 BS 0.11 MB1 west BS 0.91 LP 0.09 MB2 west CE 0.95 WS 0.04 LP 0.01 MB3 west BS 0.93 JP 0.06 LP 0.01 MB4 west BS 0.71 WS 0.13 BP 0.05 TA 0.05 JP 0.04 WS 0.03 MB5 west JP 0.98 BS 0.01 BF 0.01 MB6 west BS 0.84 LP 0.11 BF 0.05 MB7 west BS 0.99 LP

9 MB8 west BS 1.00 MB9 west BS 0.99 LP 0.01 MB10 west TA 1.00 MB11 west BS 0.98 LP 0.02 MB12 west JP 0.57 BS 0.43 MB13 west BS 0.93 BP 0.06 WS 0.01 MB14 west BS 0.82 WS 0.14 TA 0.04 MB15 west JP 0.69 BS 0.30 WS 0.02 MB16 west BS 0.99 WB 0.01 ON1 east TA 0.72 JP 0.12 WS 0.08 BF 0.05 BS 0.04 ON2 east JP 0.58 BS 0.41 TA 0.01 ON3 east JP 0.70 BS 0.22 BF 0.05 WS 0.03 ON4 east TA 0.36 BP 0.33 WS 0.13 WB 0.11 BF 0.07 ON5 east TA 0.59 BP 0.41 ON6 east JP 0.61 BS 0.35 TA 0.02 WB 0.02 ON7 east BS 0.79 TA 0.17 BF 0.03 BP 0.01 ON8 east JP 0.90 BF 0.09 BS 0.01 ON9 east JP 0.85 WB 0.09 TA 0.06 BS 0.01 ON10 east JP 0.79 BS 0.10 BF 0.05 WB 0.04 TA 0.02 QC1 east BS 0.53 BF 0.41 WB 0.06 QC2 east BS 0.99 BF 0.01 QC3 east BS 0.98 BF 0.02 QC4 east BS 0.98 BF 0.02 QC5 east BS 0.91 WB 0.09 QC6 east BS 1.00 QC7 east BS 1.00 QC9 east BS 1.00 QC10 east BS 0.99 BF 0.01 QC11 east BS 0.99 BF 0.01 QC12 east BS 0.59 BF 0.41 QC13 east BS 0.75 BF 0.25 QC14 east BS 0.97 BF 0.03 QC15 east BF 0.66 WB 0.29 WS 0.03 BS 0.01 QC16 east BF 0.58 BS 0.42 QC17 east BS 0.69 BF

10 * AB = Alberta, SK = Saskatchewan, MB = Manitoba, ON = Ontario, and QC = Quebec. Species composition of the stand at the time of the initial census. Values represent the proportion of the basal areas of each species and may not add to 1 due to rounding. AF = alpine fir (Abies lasiocarpa), BF = balsam fir (Abies balsamifera), BP = balsam poplar (Populus balsamifera), BS = black spruce (Picea mariana), CE=cedars (Genus cedrus), ES = Engelmann spruce (Picea engelmanni), JP = jack pine (Pinus banksiana), LP = lodgepole pine (Pinus contorta), PF = limber pine (Pinus flexilis), TA = trembling aspen (Populus tremuloides), WB = white birch (Betula papyrifera), WP=eastern white pine (Pinus strobus), WS = white spruce (Picea glauca). 10

11 Table S2: Annual recruitment rate trends from generalized nonlinear mixed effect models and d is the estimated annual fractional change in recruitment rate. West and East represent Western and Eastern Canada, respectively. Models Data β d = exp( β ) 1 Std. Error P n Overall recruitment All trend Recruitment trends West (119 ~97 ) by longitude East (94 ~65 ) Recruitment trends < by latitude 51 ~ > Recruitment trends < 500 m by elevation 500~1200 m > 1200 m Table S3 Trends in forest density (number of trees per ha) from the linear mixed models. Data Independent Variable β Std. error P n All plots Year < West Year < East Year < Trends in basal area (m 2 ha -1 ) from the linear mixed models. Data Independent Variable β Std. error P n All plots Year West Year East Year Trends in census interval (years) from the linear mixed models. Data Independent Variable β Std. error P N All plots Year West Year East Year

12 Table S4 Climate trends as function of time (years) from the linear mixed-effect models. Data Dependent variable Independent Variable β Std. Error P N All plots West East Annual mean temperature Year < Annual mean precipitation Year Annual climate moisture index (CMI) Year < Annual moisture index (AMI) Year < Annual mean temperature Year < Annual mean precipitation Year Annual climate moisture Year < index (CMI) Annual moisture index (AMI) Year < Annual mean temperature Year < Annual mean precipitation Year < Annual climate moisture Year index (CMI) Annual moisture index (AMI) Year

13 0.3 A 0.3 B Annual frantional change in mortality rate C Plot area (ha) AverageCensusInterval vs FractionalChange y = x , P = y = x , P = Average census interval (year) D -0.2 y = x , P = y = x , P = Density (trees/ha) Stand age (year) Figure S1: Annual fractional change in tree mortality rate (a) in individual plots as a function of (A) plot area, (B) average census interval length, (C) forest density, and (D) stand age. 13

14 Mortality rate (%/year) West East First Census Interval Last Figure S2. Mean mortality rates (means ± 1 S.E.) during the first and last census intervals for all plots in each region. For western Canada, the mean mortality rate increased significantly (P < ; paired permutation test), whereas for eastern Canada, the mean mortality rate did not increase significantly (P < ; paired permutation tests). 14

15 4 3 East West (a) 2 Temperature C Year West East (b) Growing season CMI (cm) Year Figure S3. Comparisons of ( a) the average annual temperature (ºC) for western and eastern Canada, and ( b) average CMI (climate moisture index) for growing season (from May to September) for western and eastern Canada. 15

16 Literaure Cited in the Supplementary Information S1 Boudreault, C., Bergeron, Y.,Gauthier, S. and Drapeau, P. Bryophyte and lichen communities in mature to old-growth stands in eastern boreal forests of Canada. Canadian Journal of Forest Research 32,1080 (2002). S2 Harper, K. A., and Macdonald, S. E. Structure and composition of edges next to regenerating clearcuts in the mixed wood boreal forest. Journal of Vegetation Science 13: 535 (2002). S3. Albert permanent sample plot (PSP) field procedures manual.public Lands and Forests Division, Forest Management Branch 8 th FL, Street Edmonton, AB. T5K 2M4. Phone: (780) website: /ForestManagement /PermanentSamplePlots/ForestManagementBranchPSPManuals.aspx (last accessed November 22, 2010) (2005). S4. Gordon, E. F. Saskatchewan growth and yield survey field procedures manual. Department of Tourism and Renewable Resources Forestry Branch, Forest Inventory Section (1981). S5. Manitoba provincial forest inventory field instructions. Forestry Branch of Manitoba (1998). S6. Hayden J. et al., Ontario forest growth and yield program field manual. Ontario Ministry of Natural Resources and Ontario Forest Research Institute, P.O.Box 969, 1235 Queen Street East, Sault Ste. Marie, Ontario, P6A 5N5 (1995). Ontario Terrestrial Assessment Program (OnTAP). S7. Duchesne, L and Ouimet, R. Population dynamics of tree species in southern Quebec,Canada: Forest Ecology and Management 255: 3001 (2008). S8 Daily 10 Km Gridded Climate Dataset: [computer file]. Version 1.0, [Ottawa]: Agriculture and Agri-Food Canada. National Land and Water Information Service (2007). S9 Hutchinson, M.F. ANUSPLIN Version Centre for Resource and Environmental Studies, Australian National University (2004). S10 The downloaded climatic variables for plots with census years after plot year Climate station name Climate station locations Longitude Latitude AB LOST CREEK SOUTH AB ENTWISTLE MB PINE RIVER MB OSTENFELD MB PINEY MB RENNIE MB GREAT FALLS CLIMATE MB GREAT FALLS CLIMATE MB GREAT FALLS CLIMATE MB HODGSON MB HODGSON MB GRAND RAPIDS HYDRO MB GRAND RAPIDS HYDRO MB GRAND RAPIDS HYDRO

17 MB THE PAS A MB THE PAS A MB THE PAS A MB THE PAS A ON EAR FALLS (AUT) ON GERALDTON A ON NAGAGAMI (AUT) ON KAPUSKASING A ON KIRKLAND LAKE CS ON SIOUX LOOKOUT A ON THUNDER BAY A ON WHITEFISH LAKE ON UPSALA (AUT) ON TIMMINS VICTOR POWER A S11 Hogg, E.H. Temporal scaling of moisture and the forest grassland boundary in western Canada. Agri. For. Meteorol. 84:115 (1997). S12 Rehfeldt, G.E., Crookston, N.L., Warwell, M.V. & Evans, J.S. Empirical analyses of plant climate relationships for the western United States. International Journal of Plant Sciences 167, 1123 (2006). S13. Van Mantgem, P. et al. Widespread increase of tree mortality rates in the western United States. Science 323:521 (2009). S14. Hogg, E.H., Brandt, J.P. & Michaelian, M. Impacts of a regional drought on the productivity, dieback, and biomass of western Canadian aspen forests. Canadian Journal of Forest Research 38, 1373 (2008). S15. Worrall, J.J. et al. Rapid mortality of Populus tremuloides in southwestern Colorado, USA. Forest Ecology and Management 255, 686 (2008). S16. O. L. Phillips et al., Pattern and process in Amazon tree turnover, Philos. Trans. R. Soc. London Ser. B 359, 381 (2004) S17. Woodman, J.N. Pollution-induced injury in North American forests: facts and suspicions. Tree Physiology 3, 1 (1987). S18. Percya, K.E., & Ferrettib, M. Air pollution and forest health: toward new monitoring concepts. Environmental Pollution 130,113 (2004). S19. Brandt, J.P. The extent of the NorthAmerican boreal zone. Environmental Reviews 17, 101 (2009). 17