Forest Ecology and Management 255 (2008) 4007–4020
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Forest Ecology and Management
journal homepage: www.elsevier.com/locate/foreco
Forest biomass and root–shoot allocation in northeast China
Xiangping Wang, Jingyun Fang *, Biao Zhu
Department of Ecology and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 4 November 2007
Received in revised form 19 March 2008
Accepted 26 March 2008
Temperate and boreal forests act as major sinks for atmospheric CO2. To assess the magnitude and
distribution of the sinks more precisely, an accurate estimation of forest biomass is required. However,
the determinants of large-scale biomass pattern (especially root biomass) are still poorly understood for
these forests in China. In this study, we used 515 field measurements of biomass across the northeast part
of China, to examine factors affecting large-scale biomass pattern and root–shoot biomass allocation. Our
results showed that, Picea & Abies forest and coniferous & broadleaf mixed forest had the highest mean
biomass (178–202 Mg/ha), while Pinus sylvestris forest the lowest (78 Mg/ha). The root:shoot (R/S)
biomass ratio ranged between 0.09 and 0.67 in northeast China, with an average of 0.27. Forest origin
(primary/secondary/planted forest) explained 31–37% of variation in biomass (total, shoot and root),
while climate explained only 8–15%, reflecting the strong effect of disturbance on forest biomass.
Compared with shoot biomass, root biomass was less limited by precipitation as a result of biomass
allocation change. R/S ratio was negatively related to water availability, shoot biomass, stand age, height
and volume, suggesting significant effects of climate and ontogeny on biomass allocation. Root–shoot
biomass relationships also differed significantly between natural and planted forests, and between
broadleaf and coniferous forests. Shoot biomass, climate and forest origin were the most important
predictors for root biomass, and together explained 83% of the variation. This model provided a better
way for estimating root biomass than the R/S ratio method, which predicted root biomass with a R2 of
0.71.
ß 2008 Elsevier B.V. All rights reserved.
Keywords:
Forest biomass
Climate
Forest origin
Root:shoot biomass ratio
Northeast China
1. Introduction
Temperate and boreal forests act as major sinks for atmospheric
CO2 (e.g. Myneni et al., 2001; Schimel et al., 2001; Goodale et al.,
2002), and have received increasing attention for the much greater
climatic warming in mid- and high-latitudes compared with lowlatitudes (Serreze et al., 2000; IPCC, 2007). A more precise mapping
of forest biomass at finer resolution is crucial for better estimation
of carbon sink (Houghton, 2005). However, considerable uncertainties still exist in the estimation of spatial distribution of
biomass (Goodale et al., 2002; Fang et al., 2006a), mainly due to
environmental heterogeneity and human activities (Brown, 2002;
Houghton, 2005). Understanding the determinants of large-scale
biomass pattern is not only important for improving the
estimation of carbon pools, but also crucial for predicting the
* Corresponding author at: Department of Ecology, College of Urban and
Environmental Sciences, Peking University, Beijing 100871, China.
Tel.: +86 10 62765578; fax: +86 10 62756560.
E-mail address: [email protected] (J. Fang).
0378-1127/$ – see front matter ß 2008 Elsevier B.V. All rights reserved.
doi:10.1016/j.foreco.2008.03.055
carbon budgets in response to climatic change, land use and forest
management (Banfield et al., 2002; Brown, 2002; Houghton, 2005).
Root biomass is a major source of uncertainties in large-scale
biomass estimation, and has become a research focus in recent
years (e.g. Cairns et al., 1997; Snowdon et al., 2000; Brown, 2002; Li
et al., 2003; Mokany et al., 2005). Mokany et al. (2005) have
indicated that root:shoot (below:above ground) biomass ratio (R/S
ratio) changed significantly with climatic and biotic factors
globally. By considering these effects on root–shoot allocation,
the estimations of large-scale root biomass were much higher (up
to 50%) than previous estimations with default R/S ratios (Cairns
et al., 1997; Mokany et al., 2005). This suggests that a better
understanding of biomass allocation, and the factors that regulate
it, will substantially improve the estimation of terrestrial carbon
stocks (Cairns et al., 1997; Mokany et al., 2005).
The northeast part of China (NE China hereafter; Fig. 1) is the
most important forest region in China, accounting for ca. 35% of
total country forest area and timber stocking (Wang, 2006), 50% of
the national timber production (Zhou, 1997) and 40% of total
country forest biomass (Fang et al., 2001). Located at the high
latitudes in China (39–53.58N), this region has experienced drastic
climatic warming over the past decades (e.g. Chen et al., 2005; Ren
4008
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
latitude East Asia monsoon, including warm temperate, temperate
and cool temperate zones latitudinally, and humid, semi-humid
and semiarid zones from east to west. The study area possesses all
the major forest types in northeast Asia, covering three forest
zones from south to north: warm temperate deciduous broadleaf
forest zone, temperate coniferous & broadleaf mixed forest zone,
and boreal forest zone (Zhou, 1997).
2.2. Data collection
Fig. 1. Study region and biomass data collected in this study. These data are
distributed well across the forest regions of the study area. Many plots overlap each
other and are thus not visible.
et al., 2005; Piao et al., 2006), and consequently significant
increases in forest biomass and productivity were observed (Fang
et al., 2001, 2003; Piao et al., 2005; Tan et al., 2007). These
significant changes suggest an urgent need for examining the
relationship between forest biomass and climate.
In the present study, using 515 field biomass measurements
from all major forest types across the study area, we attempt
to explore the following four questions. (1) What are the
major determinants for the regional biomass pattern of NE
China? (2) What is the difference between the climatic controls
of shoot and root biomass? (3) How does root–shoot biomass
allocation change with climatic and biotic factors? (4) Can
allometric model better estimate root biomass than the R/S ratio
method?
2. Methods
2.1. Study area
The northeast part of China is defined here to include
Heilongjiang, Jilin and Liaoning provinces, eastern Inner Mongolia
Autonomous Region, and northern Hebei province, covering an
area of ca. 1,600,000 km2 (Fig. 1). Geographically, this region is
characterized by plains separated by five major mountain ranges
(Changbai Mountains, Zhangguangcai Mountains, Xiaoxing’an
Mountains, Daxing’an Mountains and Yanshan Mountains), and
the majority of forests in NE China are distributed in these
mountain regions. The climate in NE China is controlled by the high
We collected tree biomass measurements for 515 field plots
across NE China (Fig. 1), including total, shoot (stem, branch and
leaf) and root biomass. In these plots, 85 plots were sampled by
ourselves from major forest types in Changbai, Zhangguangcai,
Xiaoxing’an and Daxing’an Mountains. For each plot, diameter at
breast height (DBH) and tree height were measured for stems
with DBH 3 cm. Shoot and root biomass for the plots were
estimated with DBH (and tree height) using allometric relationships. These site and species-specific allometric relationships
were developed by ourselves (Zhu, 2005) and other studies (Feng
and Yang, 1985; Chen and Zhu, 1989; Chen and Li, 1989; Wang,
2006).
Another 430 plots were complied from literatures, in which 161
plots were extracted from the database of Luo (1996) (see also Ni
et al., 2001) and others collected from 59 sources (Appendix A).
Tree biomass for the 430 plots was estimated with two methods as
follows. (1) The allometry method (85% of the plots), i.e. the same
method as we used. (2) The standard tree method, i.e. select some
‘‘standard trees’’ in several DBH classes for destructively sampling,
then the shoot and root biomass for each plot were estimated with
tree numbers within each DBH classes.
In the literatures, some authors used the ‘‘soil pit method’’ to
estimate root biomass (several soil pits were excavated in the plot
for root biomass measurement, and the plot biomass was
estimated based on the area of the plot and the soil pits). Most
studies using this method did not include root crown during root
sampling, which would cause remarkable underestimation of root
biomass (Mokany et al., 2005). Consequently, all root biomass data
using the method were omitted.
For each plot, we documented the following information
whenever available: (1) total, shoot and root biomass; (2)
geographic and climatic variables, including latitude, longitude,
altitude, annual mean temperature and precipitation; (3) forest
type, dominant tree species, forest origin (primary/secondary/
planted forest); (4) forest structure variables, including DBH, tree
height, tree density, stand age and volume.
As a result of data collection, our dataset documented 463 total
biomass data, 484 shoot biomass data and 432 root biomass and R/
S ratio data from 515 plots (Appendix A). These plots included all
the major forest types in NE China, and were grouped into seven
forest types as follows (Luo, 1996; Zhou, 1997): (1) coniferous &
broadleaf mixed forest (dominated by Pinus koraiensis and
Table 1
Geographic ranges for each of the forest types in our dataset (data)
Latitude (8N)
Coniferous & broadleaf mixed forest
Deciduous broadleaf forest
Larix forest
Picea & Abies forest
Pinus sylvestris forest
Pinus tabulaeformis forest
Populus & Betula forest
Longitude (8E)
Altitude (m)
Data
NE China
Data
NE China
Data
NE China
40.9–50.7
39.8–51.7
40.0–52.7
42.1–52.6
42.0–53.0
39.8–42.7
39.8–52.5
41–52
39–53
39–53
42–53
42–53
39–43
39–53
123.9–133.5
115.4–134.0
119.9–131.8
117.2–131.8
119.4–129.3
111.0–129.5
111.0–134.0
123–135
115–134
117–135
116–134
118–131
110–124
110–134
200–1085
177–1365
200–1440
280–1771
152–900
190–1800
150–1985
<1100
<1500
<1800
<1800
<1000
<1800
<2100
For comparison, distributions of these forest types in the study area (NE China) were also provided (Zhou, 1997).
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
4009
Fig. 2. Estimated climate in comparison with measured ones: (a) mean annual temperature, n = 46; (b) mean annual precipitation, n = 50. The diagonal is the 1:1 line.
Table 2
Total, shoot and root biomass (Mg/ha) for each forest type, and for primary, secondary and planted forests across the northeast part of China
Forest group
Total biomass
Shoot biomass
Mean
S.D.
Forest type
Coniferous & broadleaf mixed forest
Deciduous broadleaf forest
Larix forest
Picea & Abies forest
Pinus sylvestris forest
Pinus tabulaeformis forest
Populus & Betula forest
n
178.4 c
114.6ab
130.1 b
201.6 c
77.6 a
89.9 a
120.9 ab
113.3
51.2
63.6
96.9
42.8
58.7
55.9
Forest origin
Primary forest
Secondary forest
Planted forest
211.5c
121.1b
87.0a
93.0
46.1
56.9
Root biomass
Root:shoot ratio
Mean
S.D.
n
Mean
S.D.
n
79
55
83
37
31
82
96
143.2b
92.3a
99.6a
167.0b
65.5a
74.2a
93.8a
89.5
43.5
55.3
81.2
41.4
48.0
41.7
79
59
92
36
36
82
100
38.4 c
23.8 ab
26.3 ab
38.6 c
16.5 a
18.3 a
29.0b
28.5
9.2
12.0
20.6
7.7
11.8
14.1
77
55
83
34
22
77
84
115
160
188
166.4c
91.2b
71.1a
77.6
37.1
47.4
122
185
177
42.9 c
27.5 b
16.9 a
22.5
11.5
11.5
115
159
158
Mean
S.D.
n
0.25ab
0.28ab
0.28ab
0.23a
0.28ab
0.26ab
0.29b
0.11
0.09
0.12
0.06
0.08
0.05
0.08
77
55
83
34
22
77
84
0.26c
0.31b
0.24a
0.10
0.10
0.06
115
159
158
S.D., standard deviation; n, number of samples. Means that shared a same letter were not significantly different (P < 0.05, Games–Howell tests were used when variances
were heterogeneous, and Tukey’s tests used when variances were homogeneous).
forest type, the geographic ranges of our data were similar with its
distribution ranges in the study area.
As indicated in Fig. 1, the 515 plots are distributed well across
the forest region (see Tan et al., 2007) of NE China. They spanned a
latitudinal range of 40–538N, a longitudinal range of 111–1348E
and an altitudinal range of 150–1990 m (Table 1). Mean annual
broadleaf species); (2) deciduous broadleaf forest (Quercus and
Tilia spp., etc.); (3) Larix forest (Larix gmelini and Larix olgensis, etc.);
(4) Picea & Abies forest (Picea jezoensis, Abies nephrolepis and Picea
koraiensis, etc.); (5) Pinus sylvestris var. mongolica forest; (6) Pinus
tabulaeformis forest; (7) Populus & Betula forest (Populus davidiana,
Betula platyphylla and Betula ermanii). Table 1 shows that, for each
Table 3
Summary of general linear models for the effects of forest origin (FO: primary/secondary/planted forests), climate (growing season temperature and precipitation, GST and
GSP, respectively) and forest type (FT) on forest biomass and root:shoot ratio across northeast China
Terms
d.f.
FO
GST
GSP
FT
GST:FO
GSP:FO
GST:FT
GSP:FT
Res.
Total biomass
2
1
1
6
2
2
6
6
436
MS
562124.7
101217.1
273327.0
18860.4
60901.1
6223.4
9345.5
6794.7
2779.4
FO
GST
GSP
FT
GST:FO
GSP:FO
GST:FT
GSP:FT
Res.
Root biomass
2
1
1
6
2
2
6
6
405
22531.6
8605.5
2105.9
1503.6
3593.3
188.1
748.8
1055.3
150.2
P
%SS
d.f.
MS
P
%SS
0.000
0.000
0.000
0.000
0.000
0.108
0.003
0.025
36.8
3.3
8.9
3.7
4.0
0.4
1.8
1.3
39.7
Shoot biomass
2
1
1
6
2
2
6
6
457
347787.0
72207.8
227341.4
11947.3
32827.4
7302.6
10001.3
3054.2
1855.5
0.000
0.000
0.000
0.000
0.000
0.020
0.000
0.133
33.5
3.5
11.0
3.5
3.2
0.7
2.9
0.9
40.9
0.000
0.000
0.000
0.000
0.000
0.287
0.000
0.000
31.3
6.0
1.5
6.3
5.0
0.3
3.1
4.4
42.2
Root:shoot ratio
2
1
1
6
2
2
6
6
405
0.000
0.084
0.000
0.118
0.002
0.735
0.000
0.000
10.3
0.5
10.1
1.7
2.0
0.1
4.8
4.6
65.9
d.f., degree of freedom; MS, mean square; %SS, percentage of sum of squares explained; Res., residuals.
0.197
0.019
0.383
0.011
0.039
0.002
0.031
0.029
0.006
4010
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
temperature (MAT) for these plots ranged between 8 and 11 8C,
and mean annual precipitation (MAP) between 380 and 1050 mm.
According to data from 130 climate stations, MAT for the study
area ranged between 8 and 12 8C, while MAP ranged between 250
and 1300 mm. This suggests that our data covered most of the
climatic gradients in the study area. In summary, the large
geographic and climatic ranges of the dataset provided a good
opportunity for examining large-scale forest biomass pattern.
2.3. Estimation of climate
The majority of our biomass data were sampled from remote
mountainous areas, where the climate stations are unavailable.
Consequently, climatic variables for these records were estimated
using a well-established method (Fang et al., 1996; He et al., 2006;
Tang and Fang, 2006). Monthly mean temperature and precipitation were calculated with linear models using latitude, longitude
and altitude as predictors. The models were fitted with data from
130 well-distributed climate stations (for details of the method,
see Wang et al., 2006). We used records in our dataset which
documented climatic indices for the sampling sites to test the
accuracy of the models. Fig. 2 shows that, the R2 between the
estimated and measured values was 0.83 for MAT and 0.74 for
MAP; and the slope was 0.96 and 1.05, respectively. This suggests
that the estimates of the climatic variables can be used for our
large-scale study.
We calculated five climatic indices for each plot: MAT and MAP,
mean growing season temperature and precipitation (GST and GSP,
respectively), and the ratio of annual potential evapotranspiration
to mean annual precipitation (PET/MAP)—an index of the potential
availability of water to plants (Brown and Lugo, 1982). Growing
season is defined as months with mean temperature 5 8C (Kira,
1991).
2.4. Statistic analysis
The influences of environmental factors on forest biomass and
R/S ratio were analyzed with general linear models. The
Fig. 3. Changes in forest biomass with growing season temperature (GST) and precipitation (GSP). Regression lines are given for primary, secondary and planted forests and for
all data together, if the relationships are significant at P < 0.05. Total biomass shows similar pattern to shoot biomass and thus not presented.
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
4011
explanatory variables included climate (GST and GSP), forest origin
and forest type. We also replaced GST and GSP with MAT and MAP
to repeat the analysis, respectively. The results were similar and
thus not reported. Mean biomass and R/S ratio for the forest groups
were compared with multiple comparison tests. Games–Howell
tests were used when variances were heterogeneous (Levene’s
test), and Tukey’s tests were used when variances were homogeneous. Differences in regression lines among forest groups were
tested with analysis of covariance (Crawley, 2002). All statistic
analyses were conducted with the software R (R Development Core
Team, 2004).
3. Results
3.1. Biomass and R/S ratios of different forest types
Total, shoot and root biomass in NE China ranged between 11–
432, 2–369 and 2–106 Mg/ha, and averaged 130, 103 and 28 Mg/
ha, respectively. R/S ratio ranged between 0.09 and 0.67, with a
mean of 0.27.
Biomass differed significantly among forest groups (Table 2).
The mean total, shoot and root biomass for primary forests were
212, 166 and 43 Mg/ha, respectively, which were 75%, 82% and 56%
higher than that of secondary forests, and 143%, 134% and 154%
higher than that of planted forests. Picea & Abies forest and
coniferous & broadleaf mixed forest showed the highest mean
values for total, shoot and root biomass (178–202, 143–167 and
38–39 Mg/ha, respectively), while Pinus sylvestris forest and Pinus
tabulaeformis forest the lowest (78–90, 66–74 and 17–18 Mg/ha).
R/S ratios showed significant difference among secondary
(0.31), primary (0.26) and planted forests (0.24; Table 2). However,
most forest types were not statistically different in R/S ratios at
P < 0.05, except between Picea & Abies forest (0.23) and Populus &
Betula forest (0.29).
Fig. 4. Changes in root:shoot biomass ratio with potential water availability,
indicated by the ratio of annual potential evapotranspiration to precipitation (PET/
MAP). All regressions are significant at P < 0.05. The regression slopes were not
different among forest origins at P < 0.05, while the intercept of planted forest was
significantly lower (P < 0.01).
variance (Table 3). By contrast, climate (GST and GSP) explained
7.5–14.5% of variance in biomass, and forest type accounted for
only 3.5–6.3%. GSP was more powerful than GST in explaining total
(8.9% vs. 3.3%) and shoot (11.0% vs. 3.5%) biomass, but GST was
more important than GSP for root biomass (6.0% vs. 1.5%). The
interactions between climate and forest origin and forest type also
showed significant explanatory power (Table 3), suggesting an
importance of these interactions (Fig. 3).
3.2. Factors affecting forest biomass
3.3. Factors influencing root–shoot biomass relationship
Forest origin was the most powerful in explaining forest
biomass (total, shoot and root), and explained 31.3–36.8% of the
For R/S ratio, GSP and forest origin explained a similar
proportion of variance (ca. 10%), while forest type did not show
Fig. 5. Changes in root:shoot biomass ratios with (a) shoot biomass, (b) stand age, (c) maximum tree height, (d) mean diameter at breast height (DBH), (e) stand volume and (f)
tree density.
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
4012
Fig. 6. Relationship between root and shoot biomass for different forest groups. (a) The relationships are significantly different (P < 0.05) between primary, secondary and
planted forests. (b) The relationships are not different (P = 0.4) between broadleaf forest and coniferous & broadleaf mixed forest (CBM), but differed significantly between
other forest groups (P < 0.05).
significant effect (Table 3). Both root and shoot biomass increased
with increasing GSP; however, R/S ratio showed a decrease (Fig. 3).
This was because shoot biomass increased with GSP at a much
higher rate than root biomass (11 times). The R/S ratios for
primary, secondary and planted forests showed different relations
with GST. However, as indicated in Fig. 4, they were all positively
related to PET/MAP (i.e. negatively related to water availability).
This suggests that temperature affects R/S ratio indirectly,
through its effect on evapotranspiration and the water availability
to plants.
The R/S ratio showed weak but significant relations with biotic
factors at a large scale (Fig. 5). It was negatively related with shoot
biomass, stand age, maximum tree height, mean DBH and stand
volume, but positively related with stem density.
The root–shoot biomass relationship differed significantly
among forest origins (Fig. 6a), with the lowest regression slope
Table 4
Root:shoot biomass ratios of primary, secondary and planted forests for seven
major forest types in the northeast part of China
Forest origin
Primary forest
Secondary forest
Planted forest
Total mean
Forest type
Mean
0.32
b
S.D.
n
for planted forests. Broadleaf forests and coniferous & broadleaf
mixed forests did not show difference in slope, but they had
significantly higher slopes than coniferous forests (P < 0.05;
Fig. 6b).
3.4. Comparison of methods for estimating root biomass
There are two major ways to estimate root biomass from shoot
biomass, either using R/S ratios or root–shoot allometric relationships. Using the R/S ratios in Tables 2 and 4, we estimated root
biomass for our dataset, and found that the R/S ratios in Table 4
could generate better estimates (R2 of 0.71 between the estimated
and measured values) than ratios in Table 2 (R2 of 0.64). This
difference confirms the importance of forest development stage in
affecting R/S ratio.
Another way is to use allometric relationship parameterized
with bioclimatic variables (e.g. Cairns et al., 1997). For this
purpose, we started from a full model to explain root
biomass with shoot biomass, climate, forest origin, stand
age, forest type and life-form type (coniferous/broadleaf/
coniferous & broadleaf mixed forest). The final model was
selected based on Akaike Information Criterion (Johnson
and Omland, 2004). The result showed that climate and forest
origin were retained in the model, and explained 4.7% of
variance in addition to the 78.3% explained by shoot biomass
(Table 5).
Coniferous & broadleaf
mixed forest
Larix forest
Picea & Abies forest
Pinus sylvestris forest
Populus & Betula forest
0.13
36
a
0.25
0.24a
0.23a
0.20a
0.09
0.06
0.03
0.03
36
25
10
9
Deciduous broadleaf forest
Larix forest
Picea & Abies forest
Pinus tabulaeformis forest
Populus & Betula forest
0.29a
0.38b
0.22a
0.22a
0.31a
0.09
0.14
0.03
0.06
0.07
50
29
5
9
65
Table 5
General linear model for predicting root biomass (log transformed) with shoot
biomass, growing season temperature (GST), growing season precipitation (GSP)
and forest origin (primary/secondary/planted forest)
Coniferous & broadleaf
mixed forest
Deciduous broadleaf forest
Larix forest
Picea & Abies forest
Pinus sylvestris forest
Pinus tabulaeformis forest
Populus & Betula forest
0.20a
0.04
41
Variable
0.24
0.20a
0.22ab
0.32c
0.26b
0.23ab
0.04
0.05
0.04
0.09
0.05
0.07
5
18
4
12
68
10
(Intercept)
Log (shoot biomass)
GST
GSP
Primary
Secondary
0.27
0.09
432
ab
S.D., standard deviation; n, number of samples. For the same forest type, means
were significantly different among forest origins except for Picea & Abies forest.
Means that shared a same letter were not different within a forest origin (P < 0.05).
R2
F-value
Estimate
1.115
0.931
0.021
0.001
0.193
0.258
0.83
416.8
S.E.
t-Value
P
0.16
0.03
0.01
0.00
0.05
0.04
7.02
33.63
2.54
6.46
3.62
6.47
0.000
0.000
0.012
0.000
0.000
0.000
Adjusted R2
P
0.83
<0.0001
Percentage of variation explained: shoot biomass, 78.3%; GST, 0.1%; GSP, 2.9%;
forest origin, 1.7%. S.E., standard error.
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
4. Discussion
4.1. Factors affecting regional forest biomass pattern
Forest biomass varies across large scale as a result of moisture
and temperature gradients, and varies at fine scales as a result
of disturbances (Houghton, 2005). Many studies have analyzed
the forest biomass pattern in China and NE China (e.g. Fang et al.,
2001; Ni et al., 2001; Piao et al., 2005; Tan et al., 2007), however,
most of them have focused on estimating carbon pool (sink)
instead of examining biomass in relation to environmental
factors (but see Ni, 2003; Wang et al., 2006). The determinants of
large-scale biomass pattern have seldom been quantified for NE
China with field measured data. Our data showed that, ca. 60% of
the variance in biomass (total, shoot and root) was explained by
climate, forest origin, forest type and their interactions (Table 3).
Forest origin explained much higher proportion of variance (31–
37%) than climate (8–15%). By contrast, in a previous study we
showed that climate explained 44% of variation in total biomass
for primary forests in NE China (Wang et al., 2006). This
difference reflects the strong effect of human disturbance in
modifying the climate-induced pattern of forest biomass.
Recently, some authors suggest that the relationship between
abiotic factors and biomass can be used to improve the mapping
of forest biomass (Banfield et al., 2002; Castilho et al., 2006; Sales
et al., 2007). Our results support these efforts and suggest that
variables reflecting disturbance should be included (Banfield
et al., 2002).
Compared with aboveground biomass, our knowledge on root
biomass and its spatial distribution is far more limited (Cairns
et al., 1997; Mokany et al., 2005). Root biomass is reported to
change significantly with abiotic factors in some local scale
studies (see reviews in Vogt et al., 1996; Cairns et al., 1997).
However, it remains unclear whether root biomass is affected by
climate at the large scale. In a global analysis, Cairns et al. (1997)
found no relationship between root biomass and various abiotic
factors, e.g. latitude class, soil texture, temperature, precipitation
and temperature/precipitation. Our results revealed significant
climatic control of root biomass at a large scale (Fig. 3). We also
showed that, root biomass was much less limited by GSP
compared with shoot biomass (Table 3) as a result of allocation
change. The R/S ratio was negatively related with GSP (Fig. 3f),
suggesting that trees allocate a lower proportion of biomass to
root as water availability increases (e.g. Mokany et al., 2005).
Consequently, precipitation is less important in limiting root
biomass.
4.2. Factors influencing root–shoot biomass allocation
Many local scale studies reported that R/S ratio varies with
various abiotic (e.g. precipitation, soil moisture, soil texture and
fertility) and biotic factors (e.g. stand age and height, forest
type) (see reviews in Cairns et al., 1997; Li et al., 2003; Mokany
et al., 2005). However, evidence for climatic and ontogenetic
control of R/S ratio at large scale is scarce (e.g. Cairns et al.,
1997).
Recently, Mokany et al. (2005) showed that, R/S ratio was
negatively related to precipitation for forest and woodland across
the world. Our result, based on field measurements across NE
China, is consistent with their finding (Fig. 3f). Temperature is
generally supposed to influence R/S ratio indirectly, through its
effect on water availability to plants (Garkoti and Singh, 1995;
Oleksyn et al., 1998; Mokany et al., 2005). We also showed that,
though the R/S ratios for different forest origins differed in their
responses to GST gradient (Fig. 3e), they were all significantly
4013
limited by PET/MAP (Fig. 4). This confirms that temperature affects
R/S ratio through evapotranspiration and water availability. PET/
MAP is closely related to productivity (Brown and Lugo, 1982), thus
our result is consistent with the hypothesis that a lower proportion
of biomass is allocated to root at a more productive site. As
suggested by the optimal partitioning hypothesis, plants respond
to environmental gradients by adjusting their allocation pattern to
maximize plant growth rate (Friedlingstein et al., 1999; McConnaughay and Coleman, 1999).
R/S ratio decreased with increases in shoot biomass, stand age,
maximum tree height, mean DBH and stand volume, and increased
with higher tree density (Fig. 5). These trends are a natural
consequence of plant ontogeny, caused by the accumulation of
aboveground biomass in woody tissues as a stand develops
(McConnaughay and Coleman, 1999; Mokany et al., 2005). Our
study also found significant difference in root–shoot biomass
relationship among forest groups. Planted forest had a lower R/S
ratio than natural forests (Fig. 6a), which is also observed by other
studies (e.g. Mokany et al., 2005). This difference seems not to be
caused by the difference in climate, for planted forest had a lower
R/S ratio than natural forests at the same water availability level
(P < 0.01; Fig. 4). Thus, this difference may be associated with the
better site condition of planted forest compared with natural
forests (Mokany et al., 2005). Coniferous forest had a significantly
lower R/S ratio than broadleaf forest in NE China (Fig. 6b),
consistent with Jackson et al. (1996) who found higher R/S ratio for
temperate broadleaf forest than coniferous forest (0.23 vs. 0.18).
However, the result of Li et al. (2003) showed an opposite pattern
in Canada. These differences among regions may be why the two
global analyses (Cairns et al., 1997; Mokany et al., 2005) did not
find a significant difference in R/S ratio between coniferous and
broadleaf forests.
4.3. Methods for root biomass estimation
Because of the great difficulties in measuring root biomass for
forest, estimating root biomass from shoot biomass is the primary
way in large-scale carbon accounting (e.g. Cairns et al., 1997; IPCC,
2003). Though the R/S ratio method is now the most widely used
(Mokany et al., 2005), some authors have advocated the use of
allometric models parameterized with bioclimatic variables (e.g.
Cairns et al., 1997; Snowdon et al., 2000).
Using R/S ratios for different forest origins by each forest type
(Table 4), we got a better prediction for root biomass than using R/S
ratios of each forest types in Table 2 (R2 = 0.71 vs. 0.64). However,
we found that an allometric model including climate and forest
origin provided the best prediction, and the inclusion of climate
and forest origin improved the model R2 from 0.78 to 0.83
(Table 5). Our result is similar to Cairns et al. (1997), who found
that only stand age or latitudinal zone (out of various variables)
could be included into the allometric model (R2 increased from
0.83 to 0.84). These results confirm that, climate and forest
development stage are key factors modulating root–shoot biomass
relationship at a large scale.
Acknowledgements
We thank S.L. Piao, S.Q. Zhao, X.P. Wu, K. Tan and many others
for assistance in the field works. Thanks are also due to Dr. T.X. Luo
for providing his biomass dataset, and to the anonymous referees
for their valuable comments. This study was supported by the
National Natural Science Foundation of China (A3 Foresight
Program) and the China Postdoctoral Science Foundation
(20070410021).
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
4014
Appendix A. Forest biomass data collected in this study
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
FT
LAT (8N)
LON (8E)
ALT (m)
FO
GST (8C)
GSP (mm)
Biomass (Mg/ha)
Source
Total
Shoot
Root
R/S
LA
LA
DB
LA
DB
DB
PB
LA
LA
LA
PB
LA
LA
LA
LA
LA
LA
LA
PB
LA
LA
LA
PB
PA
PA
PA
PA
PA
PA
PA
PA
CBM
CBM
CBM
CBM
CBM
CBM
CBM
CBM
PB
PB
PB
PB
PB
PB
PA
PA
PA
PA
PA
PA
PA
CBM
CBM
PB
PB
PB
PA
PA
PA
PA
PA
PB
CBM
CBM
CBM
PB
LA
LA
LA
PB
PB
45.3
45.3
45.3
45.3
45.3
45.3
45.3
51.3
51.3
51.6
51.6
51.8
51.8
51.9
51.9
51.9
51.9
51.9
51.9
50.9
50.9
50.9
44.4
44.4
44.4
44.4
44.4
44.4
44.4
44.4
44.4
47.2
47.2
47.2
47.2
47.2
47.2
47.2
47.2
47.2
42.1
42.1
42.4
42.2
42.2
42.1
42.1
42.1
42.1
42.1
42.1
42.2
42.2
42.4
42.1
42.1
42.1
42.1
42.1
42.1
42.1
42.2
42.2
42.2
42.3
42.4
42.4
42.2
42.1
42.1
42.1
42.1
127.6
127.6
127.6
127.6
127.6
127.6
127.6
123.1
123.2
123.2
123.2
123.0
123.0
123.0
123.0
123.0
123.0
123.0
123.0
121.3
121.5
121.5
128.2
128.2
128.2
128.2
128.2
128.2
128.2
128.2
128.2
128.9
128.9
128.9
128.9
128.9
128.9
128.9
128.9
128.9
128.1
128.1
128.1
128.2
128.2
128.1
128.1
128.1
128.1
128.1
128.1
128.2
128.2
128.1
128.1
128.1
128.1
128.1
128.1
128.1
128.1
128.2
128.2
128.2
128.1
128.1
128.1
128.2
128.3
128.2
128.1
128.1
392
307
360
384
351
373
353
1069
1114
701
723
792
830
855
845
886
900
892
725
839
830
830
1524
1428
1327
1265
1185
1121
1080
1022
970
449
466
429
457
401
430
430
430
344
1920
1825
781
1150
1065
1545
1445
1771
1378
1619
1272
1202
1085
744
1985
1884
1910
1733
1674
1536
1370
1114
1054
1034
930
763
750
1125
1440
1436
1780
1945
PL
PL
SE
PL
SE
SE
PL
PR
PR
PR
SE
PR
PR
PR
PR
PR
PR
PR
SE
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
SE
PR
PR
PR
PR
PR
PR
PR
PR
SE
PR
PR
SE
SE
SE
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
PR
SE
PR
PR
PR
SE
PR
PR
PR
PR
PR
16.33
16.83
16.51
16.37
16.57
16.44
16.55
11.13
10.74
11.07
10.88
10.22
11.16
10.94
11.02
10.66
10.53
10.60
10.75
10.85
10.88
10.88
9.93
10.49
11.09
11.45
11.92
12.30
12.54
12.88
13.18
15.17
15.07
15.29
15.13
15.45
15.28
15.28
15.28
15.78
9.54
10.09
14.97
12.88
13.36
10.61
11.19
10.40
11.58
10.18
12.19
12.58
13.25
15.18
10.66
9.75
9.60
9.52
9.86
10.66
11.62
13.09
13.42
13.54
14.12
15.07
15.15
13.01
11.21
11.23
10.35
10.89
532.8
519.6
527.8
531.4
526.3
529.8
526.6
356.9
363.0
418.4
422.1
427.7
365.9
369.3
368.1
374.1
376.5
375.4
416.2
394.2
397.3
397.3
722.3
707.1
691.2
681.3
668.6
658.5
652.1
642.8
634.7
471.6
474.3
468.4
472.9
463.9
468.6
468.6
468.6
454.8
753.7
740.1
652.3
715.0
700.6
782.4
765.8
732.3
754.6
794.5
736.9
724.2
704.2
646.3
655.5
748.5
752.3
813.3
803.4
780.8
753.2
709.2
698.6
695.4
677.7
649.4
647.3
710.1
762.7
762.1
733.9
650.5
131.2
186.3
172.9
174.5
279.3
124.0
197.3
293.5
143.0
203.7
111.7
109.7
132.9
154.8
95.7
95.7
116.4
119.3
89.4
185.9
136.6
133.4
35.1
128.9
127.7
167.3
214.6
159.1
179.2
184.0
113.5
352.2
352.1
424.9
284.6
166.5
425.5
348.8
316.0
134.0
165.3
122.9
177.0
202.5
218.5
432.4
314.0
231.0
269.9
237.6
249.6
304.0
185.0
335.9
52.6
116.5
88.3
323.3
408.8
256.1
394.7
338.8
245.3
385.2
353.9
377.5
242.3
235.7
254.8
334.2
105.8
61.3
109.6
155.4
142.1
145.1
228.4
100.3
169.9
252.0
116.2
171.8
91.4
83.3
101.2
126.0
78.4
75.6
91.1
96.5
72.7
138.7
97.9
93.1
28.8
102.6
103.6
136.6
168.6
128.6
145.6
150.2
91.2
263.7
269.5
337.1
212.8
128.7
319.5
261.8
233.7
107.0
136.1
103.4
151.0
168.2
180.7
369.1
244.9
174.4
230.8
181.4
201.4
229.8
142.4
273.6
43.0
95.8
77.3
273.3
348.6
208.2
293.2
277.8
202.2
331.2
291.9
317.4
201.1
186.6
229.8
298.7
87.0
50.2
21.9
31.3
30.9
29.7
51.7
23.3
27.9
41.6
25.5
33.6
19.7
25.3
30.0
27.6
16.7
18.6
23.8
21.6
16.4
47.2
38.7
40.4
6.3
26.3
24.1
30.6
46.1
30.5
33.6
33.8
22.3
88.5
82.5
87.9
71.8
37.8
106.0
87.1
82.3
27.0
28.5
18.9
28.6
33.5
37.0
63.3
69.0
57.7
39.9
57.1
50.3
75.9
42.7
62.3
9.4
20.2
10.6
50.1
60.2
46.7
102.0
61.0
46.0
54.6
62.7
60.0
42.5
48.7
25.1
35.4
18.3
10.9
0.20
0.20
0.22
0.20
0.23
0.23
0.16
0.17
0.22
0.20
0.22
0.30
0.30
0.22
0.21
0.25
0.26
0.22
0.23
0.34
0.40
0.43
0.22
0.26
0.23
0.22
0.27
0.24
0.23
0.23
0.24
0.34
0.31
0.26
0.34
0.29
0.33
0.33
0.35
0.25
0.21
0.18
0.19
0.20
0.20
0.17
0.28
0.33
0.17
0.31
0.25
0.33
0.30
0.23
0.22
0.21
0.14
0.18
0.17
0.22
0.35
0.22
0.23
0.16
0.21
0.19
0.21
0.26
0.11
0.12
0.21
0.22
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This
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This
This
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This
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This
This
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This
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This
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X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
4015
Appendix A (Continued )
No.
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
FT
PB
CBM
CBM
DB
DB
DB
DB
DB
PB
PB
PB
PB
PB
PB
DB
DB
DB
DB
LA
LA
LA
LA
CBM
LA
PB
CBM
DB
CBM
CBM
DB
PA
PA
PA
PA
PA
PA
PA
CBM
CBM
CBM
CBM
CBM
CBM
CBM
CBM
CBM
CBM
PT
PB
PB
PB
PT
LA
LA
LA
LA
LA
PS
PB
LA
LA
LA
LA
LA
LA
LA
LA
LA
LA
LA
LA
LA
LA
LA
LAT (8N)
42.1
42.3
42.3
42.5
42.5
42.5
42.5
42.5
42.5
42.4
42.4
42.2
42.2
45.3
45.3
45.3
45.3
45.4
45.4
45.4
45.4
45.4
45.4
45.3
45.4
45.4
45.4
45.3
45.3
45.3
47.7
47.7
47.7
47.7
47.7
47.7
47.7
47.2
47.2
47.2
47.2
47.2
47.2
47.2
47.2
47.2
47.2
42.3
42.3
40.5
40.8
40.8
50.8
50.8
50.8
50.8
52.5
52.5
52.5
48.4
50.4
51.8
50.4
51.8
51.3
51.3
51.3
51.3
51.3
51.3
51.3
51.3
51.3
51.3
LON (8E)
128.1
128.1
128.1
128.3
128.3
128.3
128.3
128.3
128.3
128.1
128.1
128.2
128.2
127.5
127.5
127.5
127.5
127.5
127.5
127.5
127.5
127.5
127.5
127.6
127.3
127.3
129.4
127.5
127.5
129.3
127.8
127.8
127.8
127.8
127.8
127.8
127.8
128.9
128.9
128.9
128.9
128.9
128.9
128.9
128.9
128.9
128.9
119.0
119.0
111.4
111.0
111.0
121.5
121.5
121.5
121.5
124.5
124.5
124.5
121.4
121.7
122.0
121.7
122.0
121.5
121.5
121.5
121.5
121.5
121.5
121.5
121.5
121.5
121.5
ALT (m)
1745
900
890
640
650
680
730
765
560
550
545
880
880
350
350
350
350
300
300
300
300
300
400
350
350
350
525
350
350
430
349
280
320
320
290
400
450
400
400
400
400
400
400
400
400
400
400
843
843
1050
1800
1800
963
963
963
963
500
500
500
820
800
800
800
800
774
774
774
774
774
774
774
774
774
774
FO
PR
PR
PR
SE
SE
SE
SE
SE
SE
SE
SE
SE
SE
SE
SE
SE
SE
SE
PL
PL
PL
PL
PL
PL
SE
PL
SE
PL
PL
SE
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PR
PL
SE
SE
SE
SE
PL
PR
PR
SE
SE
PR
PR
SE
SE
SE
SE
SE
SE
SE
SE
SE
PR
PR
SE
SE
SE
PR
SE
GST (8C)
9.46
14.31
14.35
15.73
15.67
15.49
15.20
15.00
14.36
14.48
14.51
14.44
14.44
16.56
16.56
16.56
16.56
16.85
16.85
16.85
16.85
16.85
16.26
16.58
16.57
16.57
15.27
16.56
16.56
15.86
15.73
16.14
15.90
15.90
16.08
15.44
15.14
15.47
15.47
15.47
15.47
15.47
15.47
15.47
15.47
15.47
15.47
13.55
13.55
15.42
13.96
13.96
11.06
11.06
11.06
11.06
11.85
11.85
11.85
12.54
11.41
10.43
11.41
10.43
11.13
11.13
11.13
11.13
11.13
11.13
11.13
11.13
11.13
11.13
GSP (mm)
815.5
673.5
671.1
623.5
625.1
629.8
637.8
643.4
653.5
658.3
657.5
671.1
671.1
526.5
526.5
526.5
526.5
518.0
518.0
518.0
518.0
518.0
533.3
526.5
528.8
528.8
515.0
526.5
526.5
503.3
465.8
454.8
461.2
461.2
456.4
474.0
482.1
464.5
464.5
464.5
464.5
464.5
464.5
464.5
464.5
464.5
464.5
392.2
392.2
351.0
253.1
253.1
357.3
357.3
357.3
357.3
416.3
416.3
416.3
407.8
400.9
402.0
400.9
402.0
385.3
385.3
385.3
385.3
385.3
385.3
385.3
385.3
385.3
385.3
Biomass (Mg/ha)
Source
Total
Shoot
121.5
278.1
287.0
54.2
94.2
119.3
98.1
87.9
163.7
114.1
163.2
155.7
143.4
103.7
235.1
239.2
39.6
79.8
100.8
82.5
73.4
138.1
96.7
136.6
129.2
121.2
65.4
151.8
62.8
99.3
135.8
134.6
129.5
105.5
105.3
89.9
112.0
163.2
29.1
73.1
17.1
43.0
48.5
14.8
13.8
18.3
15.8
14.4
28.7
17.6
27.2
26.0
22.2
0.16
0.18
0.20
0.37
0.17
0.18
0.19
0.20
0.21
0.18
0.20
0.20
0.18
34.2
33.1
27.4
28.7
17.0
30.4
33.5
7.8
28.6
0.25
0.26
0.26
0.27
0.19
0.27
0.21
0.27
0.39
59.5
120.1
24.3
31.1
0.41
0.26
72.8
110.5
34.2
57.7
64.2
50.3
43.2
51.9
13.4
31.3
18.7
268.3
58.1
158.7
85.9
28.2
16.6
20.3
6.8
10.5
11.7
9.2
7.9
9.5
2.4
5.8
3.4
83.9
0.23
0.18
0.20
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.18
0.31
190.3
84.7
42.9
47.4
49.9
38.4
65.1
85.4
56.7
41.8
72.3
55.6
19.4
41.6
47.2
63.9
94.2
31.0
68.3
59.1
91.4
65.7
59.0
32.6
17.5
10.8
0.31
0.38
0.41
0.23
7.7
11.2
13.8
20.2
24.4
8.4
18.4
9.7
0.40
0.27
0.29
0.32
0.26
0.27
0.27
0.16
13.3
0.20
168.8
162.6
132.9
134.0
106.9
142.4
196.7
36.9
101.7
55.1
96.1
83.8
151.1
216.6
116.2
126.6
89.4
130.9
41.0
68.2
75.9
59.5
51.1
61.4
15.8
37.0
22.1
352.2
195.5
79.9
249.2
117.3
60.4
58.2
27.1
52.7
61.0
84.1
118.6
39.4
86.7
68.8
79.0
Root
R/S
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
This study
Chen et al. (1982)
Chen et al. (1982)
Chen et al. (1982)
Chen et al. (1982)
Ding and Sun (1989a)
Ding et al. (1990)
Ding et al. (1990)
Ding et al. (1990)
Ding et al. (1990)
Ding and Sun (1989b)
Liu et al. (1991)
Zhang and Zhou (1991)
Liu and Wang (2004)
Luo (1995)
Li et al. (1990)
Li et al. (1990)
Guo et al. (1995)
Mu et al. (1995b)
Mu et al. (1995a)
Mu et al. (1995a)
Mu et al. (1995a)
Mu et al. (1995b)
Mu et al. (1995b)
Mu et al. (1995b)
Bai (1982)
Bai (1982)
Bai (1982)
Bai (1982)
Bai (1982)
Bai (1982)
Bai (1982)
Bai (1982)
Zhan et al. (1990)
Cai et al. (1991)
Feng and Yang (1981)
Feng and Yang (1981)
Feng and Yang (1981)
Liu et al. (1996)
Liu et al. (1996)
Feng and Yang (1985)
Feng and Yang (1985)
Feng and Yang (1985)
Han (1994)
Xu et al. (1988)
Xu et al. (1988)
Xu et al. (1988)
Liu et al. (1994)
Liu et al. (1994)
Liu et al. (1994)
Liu et al. (1994)
Liu et al. (1994)
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
4016
Appendix A (Continued )
No.
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
FT
LA
LA
LA
LA
LA
LA
LA
LA
LA
LA
PB
DB
PT
PT
PT
PT
PT
DB
PT
PT
PT
DB
DB
DB
PT
PT
PT
PT
DB
DB
CBM
PA
PB
PB
CBM
CBM
CBM
DB
CBM
CBM
CBM
DB
DB
DB
CBM
CBM
CBM
CBM
CBM
CBM
PS
PS
PS
PS
PS
PS
PS
PS
PB
PB
PS
PS
PS
PS
PS
PS
PS
PS
PS
PB
PB
PB
PB
PB
LAT (8N)
51.3
51.3
51.3
51.3
52.7
52.7
52.7
52.7
52.7
52.7
40.0
40.0
40.0
39.8
39.8
39.8
40.3
40.3
40.3
40.3
40.3
40.5
40.5
40.5
39.8
39.8
39.8
39.8
39.8
40.5
42.6
42.1
42.1
42.6
42.6
42.6
42.6
42.6
42.6
42.6
42.6
44.3
44.3
44.3
41.8
41.8
41.8
41.8
41.8
41.8
42.8
42.8
42.8
42.8
42.8
42.6
42.5
42.5
44.0
44.0
48.0
48.0
48.0
46.3
48.0
48.0
48.0
48.0
48.0
48.0
48.0
48.0
48.0
48.0
LON (8E)
121.5
121.5
121.5
121.5
123.8
123.8
123.8
123.8
123.8
123.8
115.5
115.5
115.5
115.6
115.6
115.6
115.9
116.8
116.8
116.8
116.8
117.0
117.0
117.0
115.6
115.6
115.6
115.6
115.6
117.5
128.1
128.1
128.1
128.1
128.1
128.1
128.1
128.1
128.1
128.1
128.1
129.4
129.4
129.4
124.1
124.1
124.1
124.1
124.1
124.1
129.3
129.3
129.3
129.3
129.3
128.1
125.4
125.4
130.0
130.0
124.4
124.4
124.4
124.2
124.4
124.4
124.4
124.4
124.4
124.4
124.4
124.4
124.4
124.4
ALT (m)
774
774
774
774
520
520
520
520
520
520
1350
1150
1050
200
325
325
780
300
215
215
215
553
553
553
325
325
325
325
325
1250
800
1620
1860
800
800
800
800
800
650
650
650
520
540
690
200
200
280
300
300
280
650
650
650
650
650
650
400
400
575
575
170
170
170
152
170
170
170
170
170
170
170
170
170
170
FO
SE
SE
PR
PR
SE
PR
SE
PR
SE
PR
SE
SE
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
SE
PR
PR
PR
SE
PR
PR
PR
SE
PR
PR
PR
SE
SE
SE
PL
PL
PL
PL
PL
PL
SE
SE
SE
SE
SE
SE
SE
PL
SE
SE
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
GST (8C)
11.13
11.13
11.13
11.13
11.73
11.73
11.73
11.73
11.73
11.73
12.31
13.52
14.13
19.31
18.55
18.55
15.54
18.15
18.67
18.67
18.67
16.49
16.49
16.49
18.55
18.55
18.55
18.55
18.55
14.96
14.79
10.18
9.88
14.79
14.79
14.79
14.79
14.79
15.67
15.67
15.67
15.67
15.55
14.67
15.83
15.83
15.36
15.24
15.24
15.36
15.43
15.43
15.43
15.43
15.43
15.67
14.23
14.23
15.38
15.38
17.41
17.41
17.41
14.89
17.41
17.41
17.41
17.41
17.41
17.41
17.41
17.41
17.41
17.41
GSP (mm)
385.3
385.3
385.3
385.3
399.3
399.3
399.3
399.3
399.3
399.3
453.4
463.2
468.0
522.8
516.7
516.7
468.6
508.2
512.3
512.3
512.3
486.4
486.4
486.4
516.7
516.7
516.7
516.7
516.7
413.3
650.6
794.8
745.1
650.6
650.6
650.6
650.6
650.6
626.9
626.9
626.9
540.3
543.5
567.6
735.6
735.6
749.3
752.8
752.8
749.3
597.0
597.0
597.0
597.0
597.0
626.9
724.0
724.0
544.5
544.5
383.1
383.1
383.1
414.6
383.1
383.1
383.1
383.1
383.1
383.1
383.1
383.1
383.1
383.1
Biomass (Mg/ha)
Total
Shoot
154.3
168.3
182.6
157.8
47.2
62.8
68.0
73.6
64.8
189.8
99.8
75.4
108.0
32.6
29.1
42.7
68.1
53.6
53.5
60.4
46.3
25.5
26.9
20.1
69.9
74.5
54.1
64.5
52.8
127.7
112.4
131.2
149.8
133.5
37.0
50.2
54.0
59.4
51.6
158.0
82.2
58.2
85.2
27.3
24.7
36.0
55.5
43.7
43.7
47.0
37.0
21.2
21.3
16.6
52.5
54.0
42.4
48.6
40.2
95.3
269.0
236.6
93.0
82.8
296.7
232.2
170.9
164.7
237.7
228.6
226.2
105.0
102.2
147.8
83.9
68.8
54.1
36.0
37.0
56.4
53.4
74.1
88.8
101.8
127.0
123.1
42.3
91.9
60.2
22.8
69.6
26.6
154.1
369.5
287.2
206.9
197.3
290.6
255.2
273.9
128.3
124.6
183.7
103.4
83.5
68.0
43.1
43.4
65.2
106.2
80.6
30.1
18.8
92.5
27.3
53.9
66.3
146.2
75.0
71.5
65.0
46.8
44.8
Source
Root
R/S
41.9
37.1
34.2
24.3
10.2
12.6
14.0
14.2
13.2
31.8
15.8
18.4
19.2
5.3
4.4
6.6
12.7
10.0
9.9
13.5
9.3
4.3
5.6
3.6
17.4
20.5
11.7
15.9
12.7
32.3
0.37
0.28
0.23
0.18
0.28
0.25
0.26
0.24
0.26
0.20
0.19
0.32
0.23
0.19
0.18
0.18
0.23
0.23
0.23
0.29
0.25
0.20
0.26
0.21
0.33
0.38
0.28
0.33
0.31
0.34
72.8
55.0
36.1
32.6
52.9
26.5
47.7
23.4
22.3
35.9
19.5
14.7
14.0
7.1
6.4
8.8
0.25
0.24
0.21
0.20
0.22
0.12
0.21
0.22
0.22
0.24
0.23
0.21
0.26
0.20
0.17
0.16
14.3
20.4
7.3
0.16
0.34
0.32
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
Chen and Li (1989)
Wang et al. (2001)
Wang et al. (2001)
Wang et al. (2001)
Wang et al. (2001)
Wang et al. (2001)
Wang et al. (2001)
Fang et al. (2006b)
Fang et al. (2006b)
Fang et al. (2006b)
Chen et al. (1986)
Chen et al. (1984)
Chen et al. (1984)
Wu et al. (2006)
Bao et al. (1984)
Yao (1989)
Yao (1989)
Yao (1989)
Liu (2006)
Liu (2006)
Liu (2006)
Zhai (1982)
Zhai (1982)
Liu (1987)
Liu (1987)
Liu (1987)
Wang et al. (1998)
Li et al. (1981)
Li et al. (1981)
Li et al. (1981)
Li et al. (1981)
Xu et al. (1985)
Xu et al. (1985)
Xu et al. (1985)
Xu et al. (1985)
Chen and Guo (1986)
Chen and Guo (1986)
Chen and Guo (1986)
Xu et al. (2006)
Xu et al. (2006)
Xu et al. (2006)
Yu et al. (2005)
Yu et al. (2005)
Yu et al. (2005)
Yu et al. (2005)
Yu et al. (2005)
Yu et al. (2005)
Li et al. (1996)
Li et al. (1996)
Li et al. (1996)
Li et al. (1996)
Li et al. (1996)
Zhang (1992)
Zhang (1992)
Zou et al. (1995)
Mu et al. (2004)
Mu et al. (2004)
Yuan et al. (2000)
Yuan et al. (2000)
Yuan et al. (2000)
Zhang et al. (2006)
Zhang et al. (2006)
Zhang et al. (2006)
Zhang et al. (2006)
Zhang et al. (2006)
Zhang et al. (2006)
Zhang et al. (1997)
Zhang et al. (1997)
Zhang et al. (1997)
Zhang et al. (1997)
Zhang et al. (1997)
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
4017
Appendix A (Continued )
No.
FT
LAT (8N)
LON (8E)
ALT (m)
FO
GST (8C)
GSP (mm)
Biomass (Mg/ha)
Total
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
PB
PB
PB
PB
PB
PB
PB
PS
PS
PS
PB
PB
PB
PB
PB
PB
PB
PB
PB
PB
PB
PA
LA
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
PT
48.0
48.0
48.0
48.0
48.0
48.0
48.0
46.3
46.3
46.3
46.3
46.3
48.0
48.0
46.3
46.3
48.0
46.3
46.3
46.3
46.3
43.5
47.3
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.3
42.3
42.3
42.3
42.3
42.3
42.3
42.3
42.3
42.3
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
41.5
124.4
124.4
124.4
124.4
124.4
124.4
124.4
124.2
124.2
124.2
124.2
124.2
124.4
124.4
124.2
124.2
124.4
124.2
124.2
124.2
124.2
117.2
122.7
124.0
124.0
124.0
124.0
124.0
124.0
124.0
124.0
124.0
124.0
124.0
124.0
124.0
124.0
124.0
124.0
121.7
121.7
121.7
121.7
121.7
121.7
121.7
121.7
121.7
121.7
121.7
121.7
121.7
121.7
121.7
121.7
119.0
119.0
119.0
119.0
119.0
119.0
119.0
119.0
119.0
119.0
118.0
118.0
118.0
118.0
118.0
118.0
118.0
118.0
118.0
170
170
170
170
170
170
170
152
152
152
152
152
170
170
152
152
170
152
152
152
152
1300
1000
190
190
190
190
190
190
190
190
270
270
270
270
365
365
365
365
240
240
240
240
200
200
200
200
200
200
200
200
300
300
300
300
860
860
860
860
920
920
920
920
950
950
760
770
810
750
1030
865
1095
1135
1050
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PR
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
17.41
17.41
17.41
17.41
17.41
17.41
17.41
14.89
14.89
14.89
14.89
14.89
17.36
17.36
14.89
14.89
17.36
14.89
14.89
14.89
14.89
14.52
11.32
16.02
16.02
16.02
16.02
16.02
16.02
16.02
16.02
15.55
15.55
15.55
15.55
15.00
15.00
15.00
15.00
16.33
16.33
16.33
16.33
16.58
16.58
16.58
16.58
16.58
16.58
16.58
16.58
15.97
15.97
15.97
15.97
13.45
13.45
13.45
13.45
14.47
14.47
14.47
14.47
14.28
14.28
14.63
14.57
14.32
14.69
12.98
13.99
13.91
15.42
14.18
383.1
383.1
383.1
383.1
383.1
383.1
383.1
414.6
414.6
414.6
414.6
414.6
384.4
384.4
414.6
414.6
384.4
414.6
414.6
414.6
414.6
227.7
482.2
743.4
743.4
743.4
743.4
743.4
743.4
743.4
743.4
757.1
757.1
757.1
757.1
773.4
773.4
773.4
773.4
494.4
494.4
494.4
494.4
496.3
496.3
496.3
496.3
496.3
496.3
496.3
496.3
491.5
491.5
491.5
491.5
391.4
391.4
391.4
391.4
366.5
366.5
366.5
366.5
365.0
365.0
431.6
431.1
429.2
432.1
418.5
426.5
391.3
371.1
393.5
39.1
54.9
45.0
69.7
61.5
54.1
45.8
14.3
43.4
90.5
97.3
113.5
158.3
186.9
196.0
164.9
168.8
151.7
155.7
209.6
117.4
145.5
111.2
87.4
200.8
229.7
230.0
242.0
31.9
26.3
24.2
26.5
27.6
30.1
25.9
26.3
45.1
32.3
27.6
23.3
98.5
98.1
88.9
85.8
66.0
86.0
90.0
126.5
184.8
181.0
173.1
191.0
53.0
70.2
69.1
59.6
43.9
52.3
52.6
60.9
118.0
89.9
110.0
Shoot
57.8
50.9
44.2
37.5
11.0
1.5
13.9
58.4
54.3
12.0
30.5
29.8
102.3
78.2
72.5
43.3
40.0
26.4
69.8
94.2
126.3
149.5
156.4
132.4
137.9
123.3
126.9
170.6
94.9
117.9
90.0
70.5
165.5
189.3
189.4
199.2
24.3
19.7
18.7
19.9
21.1
23.2
19.9
20.3
35.3
25.3
21.2
17.7
86.1
85.4
77.3
74.9
50.8
66.1
69.2
97.5
141.0
138.1
132.1
145.8
41.0
54.3
54.4
47.0
34.7
41.2
41.4
48.0
92.9
70.8
86.5
Source
Root
R/S
11.9
10.6
9.9
8.2
0.21
0.21
0.22
0.22
2.3
12.9
0.19
0.42
12.3
0.16
27.4
19.3
32.0
37.5
39.5
32.5
30.9
28.4
28.8
39.0
22.5
27.6
21.2
16.9
35.4
40.4
40.6
42.8
7.6
6.6
5.5
6.6
6.4
6.9
6.0
5.9
9.9
7.0
6.4
5.7
12.4
12.6
11.6
10.9
15.2
19.9
20.8
28.9
43.8
42.9
41.0
45.3
12.0
15.9
14.7
12.6
9.3
11.1
11.1
12.9
25.1
19.1
23.5
0.39
0.21
0.25
0.25
0.25
0.25
0.22
0.23
0.23
0.23
0.24
0.23
0.24
0.24
0.21
0.21
0.21
0.21
0.31
0.34
0.29
0.33
0.30
0.30
0.30
0.29
0.28
0.28
0.30
0.32
0.14
0.15
0.15
0.15
0.30
0.30
0.30
0.30
0.31
0.31
0.31
0.31
0.29
0.29
0.27
0.27
0.27
0.27
0.27
0.27
0.27
0.27
0.27
Zhang et al. (1997)
Zhang et al. (1997)
Zhang et al. (1997)
Zhang et al. (1999)
Zhang et al. (1999)
Zhang et al. (1999)
Zhang et al. (1999)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Cao et al. (2004)
Chen and Chen (1980)
Han (1987)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
Ma (1988)
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
4018
Appendix A (Continued )
No.
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
FT
PT
PT
PT
PT
PT
PT
LA
LA
LA
LA
LA
LA
LA
LA
LA
PB
PB
PS
PT
PS
PT
PS
PS
PT
PS
PT
CBM
CBM
CBM
CBM
CBM
CBM
CBM
DB
CBM
CBM
CBM
CBM
CBM
CBM
CBM
CBM
CBM
CBM
CBM
PS
PS
PS
PS
PS
PS
PS
PT
PB
PS
PT
PB
PS
PT
PB
LAT (8N)
41.5
41.5
41.7
41.7
41.7
41.7
41.7
43.5
41.5
40.7
40.7
40.0
40.9
41.4
41.3
41.5
41.5
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
42.0
41.4
41.4
41.4
41.4
41.4
41.8
41.8
41.8
40.9
40.9
40.9
40.9
40.9
40.9
40.9
40.9
40.9
40.9
40.9
42.7
42.7
42.7
42.7
42.7
42.7
42.7
42.7
42.7
42.7
42.7
42.7
42.7
42.7
42.7
LON (8E)
118.0
118.0
117.2
117.2
117.2
117.2
125.0
125.5
123.3
124.7
124.7
124.2
123.9
125.1
120.3
119.4
119.4
121.7
121.7
121.7
121.7
121.7
119.4
119.4
121.7
121.7
125.1
125.1
125.1
125.1
125.1
125.3
125.3
125.3
123.9
123.9
123.9
123.9
123.9
123.9
123.9
123.9
123.9
123.9
123.9
122.4
122.4
122.4
122.4
122.4
122.4
122.4
122.4
122.4
122.4
122.4
122.4
122.4
122.4
122.4
ALT (m)
1040
950
1290
1260
1240
1270
350
400
200
400
400
350
350
650
650
500
500
240
240
240
240
240
500
500
240
240
500
500
500
500
500
700
700
700
350
350
350
350
350
350
350
350
350
350
350
227
227
227
227
227
227
227
227
227
227
227
227
227
227
227
FO
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
SE
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
PL
GST (8C)
14.24
13.47
14.75
14.93
15.06
14.87
14.91
13.77
16.08
15.10
15.10
15.74
15.38
13.27
14.52
15.69
15.69
16.33
16.33
16.33
16.33
16.33
15.56
15.56
16.33
16.33
14.14
14.14
14.14
14.14
14.14
16.00
16.00
16.00
15.38
15.38
15.38
15.38
15.38
15.38
15.38
15.38
15.38
15.38
15.38
15.98
15.98
15.98
15.98
15.98
15.98
15.98
15.98
15.98
15.98
15.98
15.98
15.98
15.98
15.98
GSP (mm)
394.0
422.4
332.1
333.6
334.7
333.1
746.8
692.6
762.8
790.7
790.7
811.8
792.2
803.4
491.1
469.9
469.9
494.4
494.4
494.4
494.4
494.4
435.7
435.7
494.4
494.4
777.7
777.7
777.7
777.7
777.7
707.5
707.5
707.5
792.2
792.2
792.2
792.2
792.2
792.2
792.2
792.2
792.2
792.2
792.2
459.8
459.8
459.8
459.8
459.8
459.8
459.8
459.8
459.8
459.8
459.8
459.8
459.8
459.8
459.8
Biomass (Mg/ha)
Source
Total
Shoot
78.3
52.7
61.6
16.7
0.27
71.0
70.8
53.9
43.1
66.4
60.2
42.8
83.9
78.5
53.8
155.7
161.8
79.0
39.0
15.3
32.3
22.8
26.0
22.9
45.4
52.1
25.2
14.4
16.2
50.6
59.2
75.5
55.4
69.8
104.9
69.9
67.6
83.2
67.7
68.3
73.9
161.6
145.0
159.3
195.9
190.4
188.8
160.2
30.6
62.4
23.2
48.4
11.2
8.8
8.3
9.5
6.0
14.7
17.3
9.4
20.8
21.9
16.7
9.9
3.4
15.0
5.7
9.5
6.3
20.1
21.1
8.5
5.1
4.5
10.1
12.3
15.9
8.3
7.2
15.8
15.8
16.4
14.6
10.4
9.0
14.9
49.1
40.8
49.3
40.9
39.0
35.7
29.6
9.1
16.1
7.1
13.0
3.0
1.9
0.12
0.16
0.14
0.18
0.22
0.17
0.13
0.14
0.21
0.25
0.22
0.46
0.25
0.37
0.27
0.44
0.41
0.34
0.35
0.28
0.20
0.21
0.21
0.15
0.10
0.15
0.23
0.24
0.18
0.15
0.13
0.20
0.30
0.28
0.31
0.21
0.20
0.19
0.18
0.30
0.26
0.30
0.27
0.27
0.22
74.7
69.8
48.8
98.6
95.8
63.2
176.5
183.7
95.6
49.0
18.7
47.3
28.5
35.6
29.2
65.5
73.2
33.8
19.5
20.7
60.6
71.5
91.4
63.7
77.0
120.7
85.7
84.0
97.8
78.1
77.4
88.8
210.7
185.8
208.5
236.8
229.4
224.5
189.8
39.6
78.5
30.3
61.4
14.1
10.7
84.4
64.1
88.0
64.0
42.3
32.4
56.9
33.9
33.0
Root
R/S
Ma (1988)
Guan and Dong (1986)
Nie et al. (1997)
Nie et al. (1997)
Nie et al. (1997)
Nie et al. (1997)
Wang et al. (1999)
Wang et al. (1999)
Wang et al. (1999)
Wang et al. (1999)
Wang et al. (1999)
Wang et al. (1999)
Wang et al. (1999)
Wang et al. (1999)
Wang et al. (1999)
Zhu et al. (1997)
Zhu et al. (1997)
Wang (1989)
Wang (1989)
Wang (1989)
Wang (1989)
Wang (1989)
Wang (1989)
Wang (1989)
Wang (1989)
Wang (1989)
Tan et al. (1990)
Tan et al. (1990)
Tan et al. (1990)
Tan et al. (1990)
Tan et al. (1990)
Guo et al. (1991)
Guo et al. (1991)
Guo et al. (1991)
Hu et al. (1999)
Hu et al. (1999)
Hu et al. (1999)
Hu et al. (1999)
Hu et al. (1999)
Hu et al. (1999)
Hu et al. (1999)
Hu et al. (1999)
Hu et al. (1999)
Hu et al. (1999)
Hu et al. (1999)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Jiao (1985)
Another 161 records are available from Luo (1996) and thus not presented. Note that when allometric relationships for total biomass were available, they were used to
estimate total biomass for a more precise estimation. Consequently there may be slight difference between total biomass and the sum of shoot and root biomass.
Abbreviations: FT, forest type; LAT, latitude; LON, longitude; ALT, altitude; FO, forest origin; GST, growing season temperature; GSP, growing season precipitation; R/S,
root:shoot biomass ratio; CBM, coniferous & broadleaf mixed forest; DB, deciduous broadleaf forest; LA, Larix forest; PA, Picea & Abies forest; PS, Pinus sylvestris forest; PT, Pinus
tabulaeformis forest; PB, Populus & Betula forest; PR, primary forest; SE, secondary forest; PL, planted forest.
X. Wang et al. / Forest Ecology and Management 255 (2008) 4007–4020
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