Abstract
Knowledge
of the
biomass
allometry and partitioning is essential for understanding shrub adaptive
strategies to extreme arid environments as well as for estimating
organic carbon storage. We studied biomass accumulation, allocation
patterns, and
allometric
models of Salsola passerina shrub in
Alxa
desert steppe, northwestern (NW) China. We measured above- and
below-ground biomass accumulation across different ages (0-50 years) by
destructive sampling. The biomass allocation patterns between
aboveground biomass (MA), leaves (ML),
branches (MB) and roots (MR) were
studied by fitting allometric functions for both pooled and age-classed
data. Allometric biomass models were developed by regressing on
single-input variable of basal diameter (D), crown area (C), height (H),
and age (A) alone or on the pairwise variables of above four parameters.
Biomass accumulation increased with age, aboveground components
represented 86–89% of the total biomass, root to shoot biomass ratios
increased with shrub age. Allometry patterns of S. passerina is
relatively constant, the growth rate of root was faster than that of
aboveground components. Allometric models with two-input variables were
obviously better than single variable models. C and D were the best
predictors for biomass ofS.
passerina shrub.
Keywords:Allocation;
Biomass partitioning; Allocation pattern; Allometric model;
Alxa
1 Introduction
Affected by climate change and human activities, Alxa steppe desert has
become one of the most severely desertified regions in China (Wan et al,
2018). As the dominant plant type, shrubs are of great significance in
fixing sand dunes and improving soil, and beneficial to the ecological
restoration of degraded desert ecosystems (Wang, Schaffer, Yang, &
Rodriguez-Iturbe, 2017). Salsola passerina , featured by strong
salinity, drought, and cold resistance, is one of the most widely
distributed species in Alxa steppe desert (37° to 42° N and 93° to 106°
E). Knowledge of the biomass allometry and partitioning of this shrub
has important significance for understanding the process of carbon
allocation among organs and estimation of organic carbon storage,
as well as shrub adaptive
strategies
to extreme arid environments (Jin et al., 2018; Peichl & Arain, 2007).
Previous studies on
biomass
partitioning of different plant species have been carried out throughout
most ecosystems, and a number of environmental and biological factors,
such as species, landform, soil texture, humidity, and nutrients, are
likely to affect biomass partitioning (Niklas & Enquist, 2002; Ong,
Gong, & Wong, 2004; Yang, Wang, Tan, & Gao, 2017). However, age, due
to the difficulty of sampling, is rarely studied in naturally growing
shrubs. It is well known that
biomass allocation of plant
components varies throughout the life cycle (Peichl & Arain, 2007).
Studies in even-aged plantation showed that biomass allocation changed
greatly with age
(Köhl,
Neupane, & Lotfiomran, 2017; Peichl & Arain, 2007). For many species,
the young plants have a greater proportion of roots and leaves than
older plants (Peichl & Arain, 2007; Tian et al, 2015). Thus, it is
necessary to understand the biomass partitioning characteristics for
different age to more accurately quantify biomass and carbon storage at
regional scale.
Optimal partitioning theory suggests that plants preferentially
partition more biomass to the tissue that acquires limited resources
(Gargaglione, Peri, & Rubio, 2010). This means that if light becomes
more limited, plants will partition more biomass to leaves and branches,
and if water or nutrients become limited, plants will partition more
biomass to roots (Mokany, Raison, & Prokushkin, 2006; Ryser & Eek,
2000). While the allometric theory suggests that the allocation of plant
biomass is restricted only by the size of the individual, and the
accumulation of biomass in different organs has an
allometric relationship which is
determined by a power function of the form\(\mathrm{Y}_{\mathrm{1}}\mathrm{\ }\mathrm{=}\mathrm{\ }\mathrm{\beta}\mathrm{Y}_{\mathrm{2}}^{\mathrm{\alpha}}\),
where Y1 and Y2 are interdependent
variables (e.g. above- and below-ground biomass), α and β are allometric
coefficient and allometric constant respectively. When α = 1 the
expression of the model becomes a linear equation describing the
isometric relation, and when α ≠ 1 the model expresses the allometric
relationship. Some studies have pointed out that the allometric theory
and optimal partitioning theory may be complementary to each other
instead of independent in explaining plant biomass allocation (Chen,
Zhao, He, & Fu, 2016).
In the past few decades, plenty of allometric biomass equations have
been reported for various species in many geographical and ecological
environmental. However, few researches were conducted on shrub species
in arid regions (Buras et al., 2012; Wang, Schaffer, Yang, &
Rodriguez-Iturbe, 2017), and most studies only focus on aboveground
biomass, especially in Alxa steppe desert. In addition, due to the
differences in sampling methods, definitions, and specific factors
influencing the biomass allometry, these existing biomass allocation
equations are difficult to trans-use (Jenkins, Birdsey, & Pan, 2001;
Lambert, Ung, & Raulier, 2005). Therefore, it is necessary to develop
biomass equations for typical plant species in specific areas.
The objectives of this research are: (i) to provide biomass partitioning
information of S. passerina over the entire lifetime, (ii) to
clarify the allometric patterns between different biomass components ofS. passerina , and (iii) to develop biomass equations for above-
and below-ground as well as total biomass of S. passerina in Alxa
desert steppe.
2 Methods
2.1 Study site
The study was conducted in the southwestern margin of the Alxa Plateau
(101° 34′ E, 38° 46′ N). The area is characterized as a transitional
desert steppe. Meteorological data during 1999 to 2018 were obtained
from the local Meteorological Authority. The mean annual precipitation
is 119.5 mm, 80% of which occur between May and September.
Precipitation is the only source of soil water. The groundwater level is
greater than 40 meters below ground. Pan evaporation is 2722 mm. The
mean annual air temperature is 9.4 °C. The average annual frost-free
period is 170 days. The soil type is grey-brown desert soil with 62%
sand, 20% silt, and 18% clay. The average organic matter content is
about 4.84 g kg-1. The native vegetation isReaumuria soongorica (Pall.) Maxim, Salsola passerina ,Kalidium foliatum , and Peganum harmala L . etc., withS. passerina and R. soongorica are the dominant shrubs.
2.2 Field measurement and biomass
sampling
Vegetation investigation was carried out in three square plots of 100
m2. In each plot, the total number of S.
passerina shrubs was counted, and the shrub density was 1070 ± 750
plants ha-1, basal diameter (D), height (H), and crown
area (C) of each S. passerina shrub were measured, and were 14.1
± 0.5 mm, 12.4 ± 0.5 cm and 389.3 ± 42.2 cm2respectively. In the adjacent area of the above mentioned three
vegetation investigation plots, a total of 143 individuals of S.
passerina were randomly selected and excavated in 2018 and 2019. Basal
diameter, height, and crown area of harvested shrubs were measured.
Basal diameter was the mean of two perpendicular diameter of stem base,
height was the vertical distance from the highest point of the canopy to
the surface ground, and crown area was calculated by taking the longest
and shortest diameters through the center of the fullest part of the
canopy. Cut shrubs up to the ground with pruning shears and all
above-ground components (branches and leaves) were placed in individual
paper bags for transport and drying. Roots of individual shrubs were
completely excavated with a shovel on a circular plot centered on stump
until no roots were found (approximate maximum rooting depth 0.4 m for
study area). Special personnel were assigned to collect broken roots
during excavation to minimize the loss of fine roots. In the laboratory,
branch-leaf components were allowed to air-dry for several days to
facilitate hand separation. The main stem of S. passerina was
classified as branch biomass. Roots were sorted into fine roots (≤2 mm
in diameter, with a main function to absorb water and nutrients from
soil) and coarse roots (>2 mm in diameter), and excavated
roots were cleaned manually with a brush. All component materials were
oven-dried at 65 °C to constant weight, allowed to cool for 4-6 h, and
weighed by an electronic balance (0.01 g) for biomass calculation.
Carbon content of different components was determined using the
dichromate oxidation method of Walkley and Blac (1934).
For
56 individuals harvested in 2019, HD photos of rings were taken for
analyzing age on computer, and their ages varied from 0 to 50 years,
dividing them into three age classes, 0-20, 21-30 and 31-50 years. Due
to the large sample size of 21-30 years, they were classed separately.
Basic characteristics of each group are shown in Table 1.
2.3 Biomass scaling
relations
Biomass partitioning patterns were studied by logarithmically
transformed allometric function with log10 transformed
data. The analyses for allometric scaling of aboveground biomass
(MA) vs. root biomass (MR), branch
biomass (MB) vs. MR, leaves biomass
(ML) vs. MR and ML vs.
MB. were conducted on classified and pooled biomass
data.
Data from 143 harvested shrubs were used to develop biomass equations.
Different biomass components were regressed on single shrub variable,
crown area (C), basal diameter (D), height (H) and age (A), and also on
the pairwise variables of above four parameters to obtain biomass
equations. Age-related equation only used biomass data harvested in
2019. The allometric equation of the form \(Y=c{\bullet X}^{a}\ \)was
used for plant biomass modeling, where Y is shrub biomass component
(e.g. leaf, branch, coarse root, fine root), X is a predictor (i.e.,
crown area, basal diameter, height and age), a and c are allometric
coefficients. The equation was logarithmically transformed into a linear
equivalent, \(ln(Y)=ln(c)+a\bullet ln(X)\). The equation with two
input variables was described by\(Y=c{\bullet X}_{1}^{a}{\bullet X}_{2}^{b}\), with logarithmically
transformed form\(ln(Y)=ln(c)+a\bullet ln(X_{1})+b\bullet ln(X_{2})\), where Y
is shrub biomass component, X1 and X2are predictors (i.e., crown area, basal diameter, height and age), a, b
and c are model parameters.
Models with one parameter were
assessed by coefficient of determination (R2), while
models with multiple input variables were assessed by adjusted
coefficient of determination (\(R_{\text{adj}}^{2}\)).The relative error
(RE) defined as the error of predicted biomass (BP)
relative to measured biomass (BM),\(RE=(B_{P}-B_{M})/B_{M}\) (Chave et al., 2005), the Akaike
information criterion (AIC) (Akaike, 1974), and R2 or\(R_{\text{adj}}^{2}\ \)were used to select the most suitable model with
the highest R2 or \(R_{\text{adj}}^{2}\), and lowest
RE and AIC values.
3 Result
3.1 Biomass partitioning
Dry biomass of S. passerina components classified by age (data
from harvested shrubs in 2019) is shown in Table 2. The average biomass
of each shrub component increased with age, most of the biomass was
pooled in aboveground components. Total shrub biomass increased from
43.9 g shrub-1in
0-20 years to 89.8 and 143.4 g shrub-1 in two older
classes, respectively (Table 2). The mean growth rate of total shrub
biomass was 2.6, 3.6, 3.6 g year-1 across the three
age classes.
Branches were the main above-ground biomass pool
containing 23.8, 53.4, and 89.0 g
shrub-1 and contributing 60.8, 66.9, and 71.5% of
above-ground biomass in age class 0-20, 21-30, and 31-50 years,
respectively. Foliage biomass increased from 15.3 g
shrub-1 to 26.4, and 35.5 g shrub-1across three age classes, respectively, while the proportion of leaf
biomass to above-ground biomass dropped from 39.2 to 28.5%.
The root biomass increased from 4.8 g shrub-1 in age
class 0-20 years to 10.0 and 18.9 g shrub-1in
two older classes, respectively. Coarse roots were the main underground
biomass pool containing 4.2, 8.5 and 16.6 g shrub-1and contributing 88.0, 84.8, and 87.7% of total underground biomass
across three age classes. Fine root biomass increased from 0.6 g
shrub-1 in age class 0-20 years to 1.5, and 2.3 g
shrub-1 in age class 21-30 and 31-50 years,
respectively, the proportion of fine root biomass to underground biomass
were 12.0, 15.2 and 12.3% across three age classes.
The relative proportions of shrub biomass components in different age
class are shown in Fig. 1. The relative proportion of branch biomass to
total shrub biomass rose from 54.1% in age class 0-20 years to 59.4 and
62.1% in age class 21-30 and 31-50 years, respectively. The relative
proportion of leaf to total shrub biomass dropped from 35.0 to 24.7%
across three age classes. The relative portion of above-ground biomass
dropped from 89.1% in the youngest age class to 86.8% in age class
31-50 years, respectively. The portions of coarse root biomass were 9.6,
9.5, and 11.6%, and corresponding portions of fine root biomass were
1.3, 1.7, and 1.6% across three age classes.
The
portion of belowground biomass increased from 10.9% to 13.2% with
shrub age.
The mean root to shoot biomass ratios were 0.12, 0.13, and 0.18 in three
age classes. The root to shoot for all pooled data (including data of
undetermined age) was 0.20. The above- and below-ground biomass of 143
harvested shrubs was analyzed by linear regression. Figure 2 shows a
fairly stable relationship with a regression slope corresponding to
0.20.
3.2 Allometric relations for biomass
partitioning
The linear relationship of biomass log10-transformed
data was used to represent biomass partition pattern among shrub
components. For pooled data,
R2varied from 0.42 to 0.73 (Fig. 3). The allometric scaling for above- and
underground biomass (MA vs. MR) was
0.757 with 95% CI 0.681 to 0.834. The allometric relationships of
branch biomass (MB) vs. root biomass
(MR), leaf biomass (ML) vs.
MR, and ML vs. MB were
0.851 (95% CI: 0.758 to 0.944), 0.562 (95% CI: 0.451 to 0.673), and
0.570 (95% CI: 0.464 to 0.677), respectively (Fig. 3).
For age-specific data (from harvested shrubs in 2019), the allometric
scaling for MA vs. MR was 0.88 (95% CI:
0.56 to 1.12), 0.90 (95% CI: 0.72 to 1.07), and 0.92
(95%
CI: 0.34 to 1.50) across three age classes, for MB vs.
MR was 1.00 (95% CI: 0.69 to 1.30), 0.92 (95% CI: 0.66
to 1.17), and 1.01 (95% CI: 0.36 to 1.66), for ML vs.
MR was 0.83 (95% CI: 0.31 to 1.35), 0.69 (95% CI: 0.49
to 0.89) and 0.53 (95% CI: 0.08 to 0.98), and for MLvs. MB was 0.71 (95% CI: 0.41 to 1.01), 0.59 (95% CI:
0.28 to 0.90), and 0.62 (95% CI: 0.26 to 0.98) (Fig. 4). The allometry
coefficient of three age classes has no significant difference.
3.3 Allometric biomass
models
Empirical allometric coefficients
for estimating biomass of different components based on crown area (C),
basal diameter (D), height (H), and age (A) are presented in
Table 3. R2 of total
biomass varied from 0.24 to 0.72 (Table 3). The shrub component biomass
relationships with C or D as only input variable were stronger than that
with H or A. C was significant predictor variable for all components
(P < 0.001) and estimated branch, leaf and aboveground
biomass with least RE (9, 10, and 6% for branch, leaf and aboveground
biomass), and also had a
relatively
small RE (7%) in estimating total biomass (Table 3). However, C
underestimated fine root biomass by 25%, coarse root biomass by 37%,
and belowground biomass by 17% (Table 3). D also was a significant
predictor variable for all biomass components (P <
0.001) and estimated branch and belowground components with least RE (9,
6, and 4% for branch, coarse root, and underground biomass,
respectively), the total biomass estimated by D had a minimum RE of 5%,
D underestimated leaf biomass by 14%, aboveground biomass by 7%, and
fine root biomass by 39% (Table 3). It seemed that C was better at
estimating aboveground biomass components, while D was better at
estimating belowground biomass components. H and A, by contrast, showed
a bad estimation for almost all components, and underestimated the
biomass of all components by over 10%. All predictors failed to
accurately estimate fine root biomass, the range of R2was 0.15 to 0.26, and the minimum RE for fine root was 30% (Table 3).
When including two input variables,
model fits were sharply improved. R2 of aboveground,
underground, and total biomass were 0.53 to 0.87, 0.41 to 0.82, and 0.54
to 0.87 (Table
4).
For aboveground biomass, fit of aboveground biomass with C-D as two
input variables reached the highest R2 (0.76), and the
corresponding RE was the least value (2%), and these values were
significantly better than other combinations of input variables (e.g.
C-H, C-A, and D-A). For belowground biomass, equation with D-H as input
variables was best, with highest R2 (0.82) and least
RE value (3%). R2 (0.81) and RE (4%) with C-D as
input variables was slightly worse than that with D-H in estimating
belowground biomass. Other combinations of input variables, by contrast,
were less good. Model with C-D as input variables had highest
R2 (0.87) and least RE value (2%) for estimating
total shrub biomass. AIC values supported the above results, the best
variable combination for biomass estimation of above-, under-ground, and
total biomass were C-D (AIC = -364.16), D-H (AIC = -284.00) or C-D (AIC
= -280.67), and C-D (AIC = -372.37),
respectively.
Thus, C-D was the best variables for
two-input variable equation in biomass estimation of S. passerinashrub.
The biomass equations with D and C as first and second variable,
aboveground
biomass=0.119×C0.621×D0.849,
belowground
biomass=0.042×C0.134×D1.834,
estimated biomass stored in S. passerina shrubs to be 48.4 ±
34.0, 12.6 ± 8.8, and 61.0 ± 42.8 kg ha-1 for above-
and below-ground as well as total biomass, respectively. Biomass
estimates were transformed to carbon
storage by carbon fractions 40.5
and 45.2% determined for above- and under-ground components by element
analysis. The carbon storage was about 19.6 ± 13.8 kg
ha-1 in aboveground biomass, and 5.7 ± 4.0 kg
ha-1 in root biomass. In total, S. passerinashrubs dominated desert steppe in Alxa Plateau were estimated to stock
carbon 25.3 ± 17.8 kg ha-1 in live S. passerinashrub biomass, on average.
Discussion
4.1 Biomass partitioning
Biomass partitioning among different shrub components varied with age in
our study. Considering that these samples were collected from the same
habitat, the difference of biomass partitioning may be mainly affected
by shrub age. The relative proportion of branch to total biomass
increased from 54 to 62% with reduction of relative proportion of leaf
from 35 to 25%. Consistent with our findings, Tian et al. (2015) and
Litton and Kauffman (2010) also confirmed this rule.
Some previous studies reported more resources were allocated to plant
root in early stage of growth, in order to improve nutrient absorption
capacity of young plant (Peichl & Arain, 2007; Weiner, 2004). In our
study, roots of young shrubs held a less proportion than old shrubs,
which may be caused by the drought conditions.
As the shrubs grow, the water demand
continues to increase, and water restriction causes plants to invest
more biomass into the root system to absorb more water to maintain
growth and reproduction, which has led to a continuous increase of root
during the life cycle. Consistent with our findings, Ryser and Eek
(2000) and Hartmann (2011) reported that plants partitioned more biomass
to their roots when water was
limiting
factors. In fact, other shrubs in our study area also showed similar
characteristics, sampling of Reaumuria soongorica found that the
ratio of root to total biomass increased from 21.8% in age class 0-10
years to 27.1% in in age class >40 years. Although the
collection of fine roots was incomplete, missing fine roots only
accounted for a small fraction of total root biomass (e.g., Peichl &
Arain, 2007), thus, it has no effect on calculations of total root
biomass.
The
root
to shoot ratios of S. passerina in our study ranged from 0.08 to
0.47, which met other arid land shrubs 0.07-1.55 in northern China
(Wang, Su, Yang, & Yang, 2013). The mean root to shoot ratio (0.20) of
all harvested S. passerina shrubs were lower than that reported
for S. passerina by Yang et al. (2013). The differences may be
caused by site-specific climatic and hydrogeologic conditions. Affected
by extreme arid environment of our study area, plants can only use a
small amount of precipitation and soil condensation water (Pan, Wang,
Zhang, & Hu, 2018). Additionally, thinner and poorer soil also limits
root development. While Tengger desert, by contrast, has more annual
precipitation (186 mm), higher groundwater level (4 below ground) and
more fertile soil. Consequently, a smaller root to shoot biomass ratio
in our study is reasonable. The root to shoot biomass ratios increased
with age in our study. Although not much, it indicates that biomass
partitioning of S. passerina change with shrub size. The ability
to alter biomass allocation according to environmental constraints and
individual size may contribute to the distribution of S.
passerina over a wide range of site conditions.
4.2 Allometric relations for biomass
partitioning
Regression analysis of log10-transformed biomass data
indicated that biomass partitioning of S. passerina follows
allometric rules. Consequently, the biomass allocation patterns can be
described by allometric relationship. Enquist and Niklas (2002)
suggested the log-transformed plant data satisfies the following
relationship, the allometric scaling of the leaves relative to stem
(include branches) or root was 0.75, that of stem to root was 1 (Enquist
& Niklas, 2002; Niklas & Enquist, 2002). Our results showed that
predictions were significantly different from observations for pooled
data. However, the data classified by age did not reject the hypothesis
suggested by Enquist and Niklas (2002). The confidence intervals of the
allometric scaling of classified data were very wide, which may be
caused by the large variation of biomass in each age class. The pooled
data avoided this problem because of the larger sample size, and their
allometric scaling seemed to stand for actual values. The allometric
relationships of S. passerina in the southeastern edge of the
Tengger desert by Yang et al. (2017) were also different from the
prediction by Enquist and Niklas (2002). This suggests that the
difference
of allometric relationship with prediction may be mainly caused by the
species. Age factor had no significant effect on all scaling exponents
in our study, which indicates that the allometry relationship ofS. passerinais
relatively constant.
In our study, the scaling exponents of aboveground biomass components
relative to roots were all less than 1, indicating that the growth rate
of root is faster than that of branches, leaves and the sum of them. In
fact, the relative proportion of root to total shrub biomass in 30-50
years is 1.2 times that in 0-20 years in our study. This also meets
the
optimal partitioning theory. Previous studies have suggested that plants
tend to increase root biomass under drought conditions (Bogeat-Triboulot
et al., 2007; Mokany, Raison, & Prokushkin,, 2006). Mokany et al.
(2006) reported relative proportion of root increase with decrease of
annual precipitation in woody plants. Bogeat-Triboulot et al. (2007)
found that the root to shoot ratio of Populus euphratica increase
in a drought treatment. The biomass allocation of S. passerinacan be well explained by optimal allocation theory and allometric
theory, indicating that the two theories complement each other (Chen,
Zhao, He, & Fu, 2016; Gargaglione, Peri, & Rubio, 2010).
4.3 Allometric biomass
models
We found that shrub biomass components showed a different correlation
with single input variable. Aboveground biomass components, by contrast,
had good correlation with crown area; this may be due to equal
distribution of branches and leaves in horizontal direction. In
agreement with our findings, Yang et al. (2017) reported that crown area
is a useful variable for estimating aboveground biomass components ofR. soongorica shrubs. Belowground biomass components had good
correlation with basal diameter, and this may be due to a closer
relationship that the base of main stem connected to the root. Kuyah et
al. (2013) reported that
basal
diameter fitted root biomass with a coefficient of determination as high
as 0.971 in farmed eucalyptus species. Research on Elaeagnus
mollis also confirmed the close correlation between root biomass and
basal diameter (Liu, Bi, & Zhao, 2009). In contrast, total biomass had
the highest correlation with base diameter (R2 = 0.72)
or canopy area (R2 = 0.72). In fact, many previous
studies have corroborated this result (Kuyah et al., 2013; Peichl &
Arain, 2007; Zeng, Liu, Feng, & Ma, 2010; Zhang, Cui, Shen, & Liu,
2016). Zhang et al. (2016) and Zeng et al. (2010) found that crown area
was a simple biomass predictor for desert shrubs. Peichl and Arain
(2007) and Kuyah et al. (2013) reported that there was a strong
correlation between diameter and total biomass of woody plant. Leaf and
fine root showed a bad correlation with all predictors. The best
correlation with leaf biomass was crown area with R2 =
0.57. This is reasonable in view of that foliage is a transient organ
and more susceptible by disturbances such as grazing, which affects the
allometric relationships (Rubilar et al., 2010). In agreement with our
results, Peichl and Arain (2007) and Kuyah et al. (2013) reported that
the relationship between foliage biomass and predictor was less
significant than for other biomass components. The determination
coefficients related to fine root biomass were all less than 0.25, which
indicates that fine root biomass is difficult to predict accurately.
This may be due to incomplete collection, rapid turnover, and
uncertainty in fine-root classification.
In
addition, the spatial heterogeneity of water caused by micro-topography
is also an important reason. In accordance with our findings, Perkins
and Owens (2003) and Kuyah et al. (2013) reported that accurate
estimation of fine root biomass was difficult. The relationships between
shrub biomass component and shrub height or age were less good in our
study. Consistent with our findings, Peichl and Arain (2007) reported
that age only improved biomass estimation of young white pine ,
while not biomass of all age class. Yang et al. (2017) found height was
not the best variable for biomass estimation of 12 desert shrub species
in northern China. Tong et al. (2018) reported that crown area was a
more effective variable than height for 3 common shrubs in Horqin sandy
land. In fact, heights of different sizes of S. passerina were
not much different, and the relationship between biomass and age was
rough (biomass classed by age showed a large variability). Although the
crown area and basal diameter were both the best variable for estimating
biomass of S. passerina , there were still some deviations. This
may be due to difficulties in quantifying crown area and basal diameter,
crown area can only be measured roughly based on the major and minor
axes of canopy, and the basal diameter tends to show more fluted cross
sections, this even becomes more apparent as increase of shrub
size, but there is no more suitable
single predictor.
The addition of age as second variable in crown area or basal diameter
based equations had a slight improvement for the equations of few
biomass components. In accordance with our results, Peichl and Arain
(2007) reported that plant age were inefficient variables because of
their marginal improvement for diameter-based equations. The addition of
height as second variable in basal diameter-based equations had
obviously improvement for model fit of aboveground biomass, but only
slight improvement for belowground and total shrub biomass. In
accordance with our results, Wagner and Ter-Mikaelian (1999) found that
height as a second variable improved the estimation of stem biomass,
rather than root biomass. Kuyah et al. (2013) reported that the
inclusion of height hardly improved the model fit in farmed eucalyptus
species. When the equation took crown area and basal diameter as two
input variables, the fits of above-, under-ground, and total biomass had
been significantly improved, and the RE of biomass estimation was the
smallest. This may be due to the complementation of the two variables. C
and D were the best predictors for above- and below-ground biomass,
respectively. Consistent with our findings, Kuyah et al. (2013) reported
that crown area improved the biomass
prediction model based on basal diameter.
Our biomass models were compared with models of the same species
developed for the southeastern edge of the Tengger desert (Yang, Wang,
Tan, & Gao, 2017). Allometric models reported by Yang et al. (2017)
caused RE of 16% and 126% for above- and below-ground biomass,
respectively. Differences of all pooled biomass estimate was 13%, which
can cause great errors in large-scale estimation of biomass and carbon
stocks in our study area. Thus, site-specific or similar regional models
should be applied in order to more accurately estimate biomass.
The default value of 0.5 defined by IPCC was often used to estimate
carbon storage from biomass. While the carbon contents vary greatly in
different species and organs. Fonseca et al. (2012) reported that the
use of default value may lead to an error of 10% in estimating carbon
storage. In our study, the carbon content of above- and below-ground
components were 40.5% and 45.2%, respectively, with a weighted average
of 41.5%. The actual carbon storage was 17.0% less than the default
value. The average carbon density of the S. passerina was 25.3 kg
ha-1 in Alxa steppe desert, which only accounts for a
small fraction of the carbon density (800 kg ha-1) of
desert steppe of China (Huang, Ji, Cao, & Li, 2006). However, S.
passerina , as one of the dominate species, is closely related to the
carbon exchange in the Alxa steppe desert and plays an important role in
maintaining the regional carbon pool.
5 Conclusions
Biomass partitioning and allometric relations of S. passerinashrub were studied in the Alxa steppe desert. Total biomass of
individual shrub increased with age, most of the biomass was pooled in
aboveground components. The relative proportion of branch to total
biomass increased as shrubs age, with reduction of relative proportion
of leaf. The proportion of root increased with age due to the drought
conditions.
Root
to shoot biomass ratios varied with environmental constraints and
individual size, which contributed to the distribution of S.
passerina over a wide range of site conditions. Biomass partitioning ofS. passerina followed allometric rules. Allometric relations were
relatively constant, and regardless of age. The root growth rate ofS. passerina was faster than that of aboveground components,
which met optimal partitioning theory. The allometric theory and optimal
partitioning theory complemented each other in explaining biomass
allocation of S. passerina . Allometric models with two-input
variables were obviously better than single variable models. C and D
were the best predictors for two-input variable models in estimating
biomass of S. passerina shrub.
Our
research contributes to accurately predict biomass and carbon storage ofS. passerina shrub, and is important for understanding shrub
adaptive strategies to extreme arid environments. The present study, as
well, is beneficial to the protection and sustainability of
eco-environment of the Alxa Plateau.
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Tables
TABLE 1 Basic characteristics of S. passerina stands in
the Alxa desert steppe (mean ± S.E.).