Journal of
Plant Ecology
Volume 7, Number 4,
Pages 356–363
august 2014
doi:10.1093/jpe/rtt041
Advance Access publication
27 August 2013
available online at
www.jpe.oxfordjournals.org
Effect of wood density and
water permeability on wood
decomposition rates of 32 Bornean
rainforest trees
Sanae Mori1, Akira Itoh1,*, Satoshi Nanami1, Sylvester Tan2,3,
Lucy Chong3 and Takuo Yamakura1
1
Laboratory of Plant Ecology, Graduate School of Science, Osaka City University, Sugimoto 3-3-138, Sumiyoshi-ku, Osaka
558–8585, Japan
2
Center for Tropical Forest Science, Smithsonian Tropical Research Institute and the Arnold Arboretum, Harvard University,
22 Divinity Avenue, Cambridge, MA 02138, USA
3
Applied Forest Science and Industry Development, Sarawak Forestry Corporation, Jalan Tapang, Kota Sentosa, Kuching
93250, Sarawak, Malaysia
*Correspondence address. Laboratory of Plant Ecology, Graduate School of Science, Osaka City University,
Sugimoto 3-3-138, Sumiyoshi-ku, Osaka 558–8585, Japan. Tel: +81-6-6605-3165; Fax: +81-6-6605-3167;
E-mail: [email protected]
Abstract
Aims
A better understanding of wood litter decomposition is essential for
predicting responses of forest ecosystems to global climate change.
Recent studies suggest that chemical properties of wood litters, rather
than physical ones such as wood density, are more important for interspecific differences in wood decomposition rates. However, empirical
data are still limited, especially for tropical trees. In addition, decomposition rate of wood litter often varies with time, which makes interspecific comparison difficult. We studied the wood decomposition of
32 rainforest trees to elucidate (i) the degree of interspecific variation
in wood decomposition rate of a given size and configuration and (ii)
if initial wood density and water permeability are consistent predictors
of the overall decomposition rate and its pattern over time.
Methods
A common garden decomposition experiment was conducted in a
tropical rainforest in Malaysian Borneo for 32 native tree species.
Small wood sticks were set on the forest floor and the weight loss
was monitored monthly for 2.7 years.
Important Findings
We found large variation in the wood decomposition rate (a 49-fold
range), suggesting that we need to consider this variation when calculating community-level carbon dynamics of tropical rain forests.
The physical traits of wood, i.e. wood density and water permeability, were related to wood decomposition rate and its pattern over
time. Decomposition half-time related positively and negatively to initial
wood density and water permeability, respectively. The time-dependentrate model fitted better for 18 species (56% of the study species) that had
higher water permeabilities than the others, suggesting that micelle
porosity in wood relates to temporal changes in decomposition rate.
Keywords: Sarawak, carbon dynamics, fine wood litter, wood
density, tropical rain forest
Received: 8 July 2012, Revised: 16 July 2013, Accepted: 19 July
2013
Introduction
Decomposition of woody litter is an important component
of the forest carbon cycle and a better understanding of its
controlling factors is essential for predicting responses of forest
ecosystems to global climate change (Cornwell et al. 2009;
Weedon et al. 2009). Recent studies suggest that interspecific
variation in decomposition rates is large for woody debris,
implying a need to integrate the variation in models to
estimate the forest carbon cycle (Cornwell et al. 2008,
2009; Weedon et al. 2009). However, determinant factors of
interspecific variation in wood decomposition rates are not
well understood. Some recent studies (e.g., Cornwell et al.
2009; van Geffen et al. 2010; Weedon et al. 2009) suggest that
wood chemical properties (i.e. N, P, and lignin concentrations,
and C/N ratio) are more important than physical ones (e.g.
© The Author 2013. Published by Oxford University Press on behalf of the Institute of Botany, Chinese Academy of Sciences and the Botanical Society of China.
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Mori et al. | Wood decomposition of Bornean trees357
wood density, conduit area, etc.). On the other hand, other
studies reported that high-density wood is decomposed more
slowly than low-density wood in tropical trees (Chambers
et al. 2000; Chave et al. 2009; Takahashi and Kishima 1973).
There are many studies on species traits and decomposition
rates of leaf litters, which provide a general agreement that
chemical properties of leaf litters relate to variation in decomposition rate (e.g. Cornelissen 1996; Cornwell et al. 2008;
Hobbie 2000; Zhang et al. 2008). In contrast, empirical data
are still limited for wood litter, particularly in tropical forests
(Weedon et al. 2009). As far as we know, there are no such
studies in Asian rain forests. Thus, we need to accumulate
more data to understand the general relation between wood
traits and decomposition rates (Weedon et al. 2009). Studies
in tropical forests are especially important because within-site
variation in wood traits is larger in tropical than in temperate
forests (Chave et al. 2006).
A technical constraint in interspecific comparison of wood
decomposition rate is the fact that decomposition rates of
woody debris are not always constant but rather they often
vary with time (Brischke and Rapp 2008; Freschet et al. 2012;
Harmon et al. 1986, 2000; Laiho and Prescott 2004; Yoneda
1975a, 1975b; Yoneda et al. 1977, 1990). If decomposition rate
varies with time, we must take care during the interspecific
and inter-site comparison of decomposition rates because species’ ranks in decomposition rates may vary with the length
of study period. It is, therefore, important to understand the
factors relating to the pattern of decomposition over time.
In this study, we measured wood decomposition rates of 32
tree species with a common garden experiment in a Bornean
rain forest. We addressed the following questions in this
study: (i) What is the extent of interspecific variation in wood
decomposition rates of a given size and configuration within
a tropical rain forest? (ii) Are initial wood density and water
permeability consistent predictors of the overall decomposition rate and its pattern over time?
Materials and Methods
Study site
This study was conducted in a Shorea splendida
(Dipterocarpaceae) plantation at Semenggoh Nature Reserve
(latitude ca. 1°23′N, longitude ca. 110°19′E), which is located
~19 km south of Kuching city, Sarawak, East Malaysia. The
plantation was established by Sarawak Forest Department
during the period 1927–40 in a riverine pan of the Semengoh
river, which has alluvial soil (Tan et al. 1987). The average
diameter and basal area in 1974 were 34.9 cm and 26.0 m2 ha−1,
respectively, and the average diameter growth rate was
0.72 cm per year (Tan et al. 1987). The soil of the least disturbed
natural forest in Semenggoh Nature Reserve was Ultisols, the
acidic forest soils [pH(H2O): 4.2–4.6], derived from argillaceous rocks (Hirobe et al. 2004). The mean annual temperature and rainfall during the experimental period were 26.4°C
and 4581.0 mm, respectively, at the Kuching airport, which
was located ~10 km north of the nature reserve (Malaysian
Meteorological Service, unpublished results).
Wood stick decomposition experiment
Sample wood sticks (1 × 1 × 15 cm) were made from 32 species
of air-dried timber wood (Table 1) purchased commercially
from local timber companies. All the species are common in
primary forests in the study region. Most of them are large
trees of lowland rain forest, but some are restricted to peat
swamp forest (i.e. Hopea pentanervia, Sandoricum beccarianum,
Parastemon urphyllum, Gonystylus bancanus). As we used only
commercially available species, which are often selected
for their wood durability, the species set we used could be
over-represented by slowly decomposing species comparing
to a natural forest tree community. Therefore, interspecific
variation in decomposition rate could be underestimated in
this study. However, results on the relation between wood
traits and decomposition rate may be less affected by species
selection if the selected species had a considerable range in
wood traits. Our species set included species with a wide
range of wood density, i.e. 0.294–0.945 g cm−3 (Table 1).
We used a fine-blade table saw for cutting the timber
wood, but the blade was not extremely fine and this might
have caused some damage on the surface of the wood samples to promote microbial access. This may have increased the
decomposition rates of each species comparing to naturally
fallen wood debris of the same species.
The wood sticks of each species were taken from several
(ca. 3–5) pieces of wood. As we did not check whether all the
wood pieces of the same species were from one or more individual trees, we could not exclude the possibility that we used
only one individual tree for some species and that the traits
measured in this study were not within-species average but
those of the individual tree. However, the overall pattern of
this study is likely to be robust for this problem because traits
relating to wood decomposition are generally smaller within
species than between species (Chave et al. 2006; Freschet et al.
2012; van Geffen et al. 2010).
Each sample stick was weighed and labeled with an aluminum numbering tag for identification. To convert the initial
air-dried weight to an oven-dried weight, 10 sample sticks of
the same species were dried in a ventilated air oven at 70°C
for a week. The remaining 36 sample sticks of a given species were laid on the forest floor of the study site. In order
to minimize environmental heterogeneity, all the samples
were set within a 2 × 3 m2 flat area. Wood sticks of the same
species were set along a line in an arbitrary direction under
the litter layer. Distance between the nearest wood sticks of a
same species was ≈1 cm and the lines of different species were
10–50 cm apart. As the distance between wood sticks of a
same species was only 1 cm, it was possible that the neighboring samples were connected by fungal networks via the soils.
It should also be noted that environmental heterogeneity at
very small spatial scale, if present, could have affected the
results as we placed wood sticks of a same species together.
Parastemon urophyllum
Parinari oblongifolia
Sterculia bicolor
Gonystylus bancanus
Gonystylus forbesii
Gonystylus sp.
Rosaceae
Rosaceae
Stercuriaceae
Thymelaeaceae
Thymelaeaceae
Thymelaeaceae
36
35
36
36
36
36
36
36
33
0.611
0.600
0.569
0.328
0.627
0.943
0.655
0.488
0.512
0.060
0.058
2.16
1.99
2.38
1.31
0.100
0.094
0.16
0.07
0.15
0.31
3.98
1.17
2.94
0.20
0.54
0.46
0.17
0.50
0.66
0.03
0.83
0.82
1.71
1.22
0.08
0.20
0.26
0.10
0.46
0.71
0.22
1.86
0.15
1.30
β (year−1)
0.021
0.018
0.025
0.027
0.029
0.026
0.032
0.026
0.018
0.013
0.023
0.015
0.023
0.007
0.021
0.018
0.026
0.030
0.012
0.013
0.016
0.014
0.022
0.020
0.024
0.050
0.025
0.028
U (g cm−3 h−1)
Logistic model
0.95
0.88
0.92
0.58
0.00
0.00
0.00
0.09
0.95
0.69
0.84
0.00
0.90
0.84
0.00
0.90
0.74
0.00
0.78
0.90
0.95
0.83
0.00
0.37
0.19
0.00
0.68
0.89
0.28
0.85
0.00
0.87
ID
1.4
1.1
1.1
0.9
4.4
10.2
4.5
2.4
0.8
1.2
0.7
3.6
4.4
4.3
4.0
4.7
2.4
27.7
2.1
3.0
1.7
1.6
8.8
4.9
3.1
7.1
3.0
3.2
3.9
1.1
4.7
1.7
T0.5 (year)
−96.8
−88.4
−72.6
−39.6
−92.1
−116.6
−142.3
−59.9
−40.3
−84.2
−68.0
−135.6
−127.9
−112.1
−100.8
−139.4
−36.7
−145.5
−102.9
−118.7
−73.7
−63.2
−174.1
−166.1
−72.7
−138.2
−95.2
−96.4
−108.2
−71.5
−85.0
−57.8
AIC
0.54
0.73
0.73
0.83
0.16
0.07
0.15
0.29
1.07
0.61
1.23
0.20
0.09
0.11
0.17
0.08
0.27
0.03
0.32
0.17
0.38
0.45
0.08
0.13
0.22
0.10
0.20
0.15
0.17
0.74
0.15
0.41
K (year−1)
1.3
0.9
1.0
0.8
4.4
10.2
4.5
2.4
0.6
1.1
0.6
3.6
7.5
6.4
4.0
8.6
2.6
27.7
2.2
4.2
1.8
1.5
8.8
5.3
3.2
7.1
3.5
4.6
4.1
0.9
4.7
1.7
T0.5 (year)
Exponential model
−30.2
−48.1
−32.0
−38.3
−94.1
−118.6
−144.3
−61.9
−26.8
−70.8
−49.3
−137.6
−121.0
−110.3
−102.8
−130.4
−37.1
−147.5
−88.2
−99.4
−33.9
−50.1
−176.1
−167.2
−74.7
−140.2
−94.2
−89.0
−110.0
−43.7
−87.0
−42.6
AIC
Figures in bold italics are those used in the best-fit model.
n = number of wood sticks recollected in the common garden experiment; ρ0 = initial wood density; U = index of water permeability; β, ID, k = coefficients of decomposition models;
T0.5 = decomposition half-life; AIC = Akaike Information Criterion.
Syzygium sp.
Myrtaceae
0.674
36
Artocarpus nitidus
Myristica lowiana
Moraceae
Sandoricum beccarianum
Meliaceae
Myristicaceae
0.294
36
Sindora leiocarpa
0.843
0.817
0.806
0.855
0.816
0.582
0.836
Leguminosae
36
36
36
32
36
36
36
0.454
0.920
Parkia speciosa
Eusideroxylon zwageri
Lauraciae
36
36
Leguminosae
Cratoxylum arborescens
Hypericaceae
0.412
Koompassia excelsa
Castanopsis foxworthyi
Fagaceae
36
0.450
0.747
Leguminosae
Shorea sp. 2
Dipterocarpaceae
36
36
Dialium palatysepalum
Shorea sp. 1
Dipterocarpaceae
0.945
Dialium kunstleri
Shorea superba
Dipterocarpaceae
36
0.556
0.915
Leguminosae
Shorea falciferoides
Dipterocarpaceae
36
36
Leguminosae
Hopea vesquei
Dipterocarpaceae
0.576
Copaifera palustris
Hopea pentanervia
Dipterocarpaceae
36
0.956
0.437
Dialium indum
Dryobalanops aromatica
Dipterocarpaceae
36
36
Leguminosae
Dillenia reticulata
Dilleniaceae
0.394
0.702
0.517
ρ0 (g cm−3)
Leguminosae
Dacryodes sp.
Burseraceae
36
35
Gluta macrocarpa
Dyera sp.
Anacardiaceae
Apocynaceae
Campnosperma auriculatum 36
Anacardiaceae
n
Species
Family
Table 1: parameters of wood decomposition of 32 Bornean rainforest tree species
358
Journal of Plant Ecology
Mori et al. | Wood decomposition of Bornean trees359
One stick per species was collected monthly from May
2001 to February 2004 (32 times in total). On the last collection date, we collected all the remaining sticks: five for
most species except four species, for which 1–3 sticks were
lost during the experiment. All collected sample sticks were
washed, dried in a ventilated air oven at 70°C for a week and
weighed.
Physical trait measurement
Initial wood density (ρ0) was measured for the wood sticks
that were used for air-to-oven-dry weight ratio. We calculated
ρ0 of each wood stick as the oven-dried weight divided by the
volume (15 cm3). The average of ρ0 of the 10 samples was
used as the initial wood density of each species.
Water permeability was measured for the initial wood samples of each species, according to the method of Hayashi and
Nishimoto (1965). Four wood sticks of each species were kept
in distilled water in a thermostatic water bath at 30°C for 4 h
to absorb the water. The permeability (U) [g cm−3 h−1] of each
wood sample was calculated as
U=
w 4 h − w0
,
v ×t
(1)
where w0, w4h, v and t are the initial weight of a wood sample,
the weight of the wood sample after the water absorption, the
volume of a wood stick (15 cm3) and the duration of experiment (4 h), respectively. The average of U of the four samples was used as the permeability index of each species. Wood
sticks used for the physical trait measurement were not used
for the decomposition experiment.
Statistical analysis
We fitted two decomposition models, exponential and logistic
models, to the results of the decomposition experiment. The
exponential model assumes a constant decomposition rate
and is formulated by the following equation:
w
= e−kt ,
w0
(2)
where t is time [year] after laying sample sticks on the forest
floor, w is the oven-dry weight of a wood stick [g] at time t, w0
is the initial oven-dry weight of the wood stick [g] at t = 0 and
k [per year] is the coefficient of the decomposition rate. This
model was developed by Jenny et al. (1949) and discussed in
detail by Olson (1963).
The logistic model (Yoneda 1975a) assumes that decomposition rate increases with time and is written in the form
w
1
, (3)
=
w0
ID + (1 − ID) e β t
where ID is an index of durability and β [per year] is the coefficient of decomposition rate when t is very large (Yoneda
1975a, 1975b). If ID = 0, Equation (3) is identical to Equation
(2) (k = β). If ID > 0, the decomposition rate increases with t
up to β. The larger the value of ID the slower the increase in
decomposition rate.
The coefficients of the models (k, β and ID) were estimated
for each species using the non-linear least squares method
with the function ‘nls’ on R version 2.10.1 (R Development
Core Team 2009). Initial values of k, β and ID were 0.004,
0.1, and 0.8, respectively. We used ‘port’ algorithm with the
lower bounds of zero for k, β and ID. We confirmed that all
the estimations had converged. The data of the last collection
date (n = 2–5) were averaged for each species. Data were
deleted from the analysis if w > 1.1w0, which may be a result
of our label handling errors (n = 6). We additionally deleted
61 data that were identified as outliers (25 upper and 36
lower ends of the data) by the function ‘lowess’ (Cleveland
1979, 1981) (online Supplementary Figure S1.). We used
0.5 for the span parameter and excluded the data from the
analyses as outliers if the values deviated more than 20%
from smoothing values. In order to select the best-fit model,
we compared the Akaike Information Criterion of the two
models. For comparison of species for which different models
provided the best fit, we calculated the decomposition halflife (T0.5; years needed to reach 50% mass loss) based on the
estimated model parameters. The removal of the outliers
produced small changes in the overall results and conclusion
except that removing outliers changed the best-fit model for
seven species. The fitting of the model was apparently worse
in four of the seven species without the removal of the outliers
(online Supplementary Figure S2).
An important difference in sampling design between the
current and many previous decomposition experiments was
the balance between frequency and number of replications
of sample collection. In this study, we collected samples frequently, i.e. monthly (32 times during the 2.7-year experiment) but only one sample was collected for each species at
each time. In many previous studies, samples were collected
less frequently but multiple samples were collected at each
collection time. Our sampling design (high frequency but no
replication at each sampling time) may have had low statistical accuracy. Therefore, we conducted computer simulations
to analyze the effect of sampling frequency and replications
on estimation of decomposition rates, and confirmed that
our sampling design had enough statistic power to estimate decomposition rate and its pattern over time (online
Supplementary Figures S3 and S4).
Relationships between the physical wood traits (ρ0 and U)
and the decomposition half-life (T0.5) were analyzed by the
Pearson’s correlation coefficient and the multiple regression
analysis. All variables were log-transformed in the multiple
regression analysis due to their right-skewed distributions.
Results
Interspecific variation in decomposition
Decomposition processes were highly variable among different species (Fig. 1, Table 1). After 2.7 years, Sindora leiocarpa
360
Journal of Plant Ecology
Figure 1: changes in relative weights of wood sticks during a 2.7-year common garden experiment. Solid and dotted lines are values predicted
by the logistic and exponential decay models, respectively. Species are arranged in order of increasing half-life (T0.5) from left to right and from
top to bottom.
had lost almost all of its initial dry weight (T0.5 = 0.7 years,
Table 1), whereas Eusideroxylon zwageri had lost only 5.7%
(T0.5 = 27.7 years). For 18 out of 32 study species, the best-fit
model was the logistic model, indicating that the decomposition rates increased with time (Fig. 2, Table 1). For the other
14 species, the exponential model was selected, indicating a
constant decomposition rate. Species whose best-fit model
was the logistic model has a significantly shorter decomposition half-life (T0.5) than those whose best-fit model was the
exponential model, with means of 2.1 (standard abbreviation
[SD] = 1.9) and 6.6 (SD = 6.5) years, respectively (Wilcoxon
test, P < 0.01).
Wood traits and decomposition
The mean water permeability of woods were significantly
larger for species whose best-fit model was the logistic
model than those that fitted well to the exponential model
(Wilcoxon test, P = 0.019, Fig. 2A). In contrast, wood density was lower for the logistic-model species although the difference was statistically marginal (Wilcoxon test, P = 0.059,
Fig. 2B).
T0.5 was significantly correlated positively to initial wood
density (Spearman’s rank correlation, rs = 0.674, P < 0.001)
and negatively to water permeability (rs = −0.843, P < 0.001)
(Fig. 3). Although initial wood density and water permeability
Figure 2: water permeability (A) and initial wood density (B) for
woods of two species groups having different decomposition characters. EXP spp are species (n = 14) for which decomposition followed
the exponential decay model, which has a constant decomposition
rate. LOG spp are those (n = 18) that follow the logistic decay model,
for which mortality rate increases with time. See text for details of
the models.
were significantly negatively correlated (rs = −0.630, P < 0.001),
the multiple regression analysis showed significant effects in
both traits (P = 0.014 and P < 0.001 for wood density and
water permeability; R2 = 0.729), indicating that both wood
density and water permeability were significant predictors of
the overall decomposition rate.
Mori et al. | Wood decomposition of Bornean trees361
Figure 3: relationship between decomposition half-life (T0.5), initial
wood density (A) and water permeability (B). Open and closed circles are species whose decomposition rates fitted to the logistic and
exponential decay models, respectively. Spearman’s rank correlation
coefficients (rs) are also shown. Note that both x and y axes are in log
scale.
Discussion
Limitation of interpretation
An important limitation of interpretation of the present study
is that it is difficult to estimate actual decay rates of woody
debris in natural forests from decay rates of the small-sized
wood samples we used. It is known that wood decomposition
rates vary greatly with difference in size of the wood sample (van Geffen et al. 2010). Because our wood samples were
small (1 × 1 × 15 cm), the surface/volume ratio was relatively
large and thus the decay rates and half-life times may be overand underestimations, respectively, for decay of larger wood
debris, such as tree stems.
It is also noted that presence of bark and its properties
have strong effect on decomposition rate. As we used artificial wood sticks without bark, variation in bark properties
of natural woody debris may well interfere with the species
ranking in decomposition rate and pattern in natural woody
debris with bark.
The effect of termites is another important factor to be
considered because termites are one of the major factors in
wood decomposition in many tropical forests (e.g. Abe 1980;
Cornwell et al. 2009; Wood and Sands 1978). We observed
visible evidence of termite attack only in a small proportion
of the wood samples (3.0% of 1151) in this study. We should,
thus, consider the decomposition rates of this study as those
mostly without termite effects. The low termite attack could be
by chance because we recorded more frequent termite attack
in a larger field experiment using the same wood sticks in a
tropical forest in Sarawak (Mori et al. unpublished results). We
set 5200 wood sticks of Dryobalanops aromatica at 1300 points
in a 52-ha area and we found evidence of termite attack in
49.5% of the samples. Termites could influence the species
ranking in decomposition rate if the between-species variation
in termite resistance is large. Further experiments controlling
the termite attack (e.g. Arango et al. 2006; Takamura 2001)
are needed to evaluate species’ specific termite resistance.
Finally, the wood traits we measured were not necessarily the (only) causal factors determining wood decomposition
rates. Because we did not analyze chemical wood properties,
it is possible that some chemical wood traits that relate to
the physical properties could be a reason for the correlation
observed in this study.
Despite these limitations, we think this study still allows
for between-species comparisons and is helpful for studies of
wood decomposition. As we used wood samples of same size,
which have been decomposed under similar environmental
conditions with little termite attack, the variation observed
must be due to differences in the structural and/or chemical
traits of the wood inside the bark.
Large interspecific variation in wood
decomposition rates
We found a very large within-site interspecific variation in
wood decomposition rates (Fig. 1, Table 1). Based on k values
of the exponential model, our result showed a 49.1-fold range
for all species and a 35.5-fold range for the middle 90% of
species. These values are much larger than the variations in
leaf decomposition rates. Cornwell et al. (2008) reported that
the average range of leaf decomposition rates within a site was
18.4-fold for all species and 10.5-fold for the middle 90% of
species for 14 sites over the world. Hirobe et al. (2004) found
a 6.2-fold range in leaf decomposition rates (k = 0.38–2.36) of
15 tree species in the same forest that we studied. Our result
strongly suggests a need to consider species composition
of wood debris in estimating wood decomposition rates
in tropical forests, as has been done for leaf decomposition
(Austin and Vitousek, 2000; Cornwell et al. 2008; Swift et al.
1979; Takeda et al. 1984; Vivanco and Austin 2006).
Time dependency of wood decomposition rates
The majority of species (18 of 32) had a time-dependent
decomposition rate. For these species, decomposition rates
were initially small and then increased as decomposition proceeded. Similar patterns of wood decomposition have been
reported for many temperate trees (e.g. Brischke and Rapp,
2008; Freschet et al. 2012; Harmon et al. 1986, 2000; Laiho
and Prescott 2004; Yoneda 1975a). Although the mechanisms
of the increase in decomposition rate in woody litter are not
yet clear, this study suggests that water permeability may
relate to the difference in decay models because the logistic
model fits better than the exponential model for species with
higher water permeability.
The water permeability may relate to the physical structure
of wood material. Yamamoto and Hong (1994) reported that
wood of Gonystylus species had one of the largest water permeabilities among 24 tropical species. They also found that
Gonystylus had a high proportion of micelle porosity, or fiber
gap, in the wood, suggesting that water permeability could be
used as an index of the proportion of micelle porosity. Micelle
porosity is a collection of very tiny gaps that are difficult for
decomposers to access from the outer surface of the wood.
362
As decomposition proceeds, the micelle pores may connect
to each other, resulting in a gradual increase in the decomposition surface inside the wood, which would enhance the
surface-to-volume ratio and hence also the decomposition
rate (Harmon et al. 1986). In contrast, for the wood with
only larger pores, such as vessels, decomposers may enter
the inside of the wood relatively easily from the early phase
of decomposition. This may result in a relatively constant
decomposition rate, expressed by the exponential model.
However, the above-mentioned mechanism determining
the temporal pattern is still very speculative. There may be
many other possible (non-exclusive) factors relating to the
temporal pattern, such as loss of initial heartwood defenses
over time (Harmon et al. 1986), gradual leaching of constitutive compounds (Spears and Lajtha 2004) and enhanced
activity of microbial decomposers as decomposition progresses (Cornwell et al. 2009). We need more detailed studies
on the effects of chemical and physical wood traits, as well as
the activity of decomposers to elucidate the temporal pattern
of wood decomposition and its mechanisms.
Wood density and decomposition rate
We observed a significant positive correlation between initial wood density and decomposition half-life, indicating
that higher density woods were decomposed more slowly.
Our results support the previous view that higher density
woods decompose more slowly (e.g. Chave et al. 2009) but
are in contrast with the recent studies showing no correlation
between wood density and decomposition rate (van Geffen
et al. 2010; Weedon et al. 2009). There may be several possible
explanations for this discrepancy.
First, as briefly mentioned above, decomposition of woods
with and without bark may differ considerably. We used artificial wood materials without bark, whereas Weedon et al.
(2009) and van Geffen et al. (2010) used natural woody
debris with bark. If variation in species’ bark properties affects
decomposition considerably, the effect of wood density may
be masked and the species rank in decomposition rate may
differ in wood materials with and without bark.
Second, the wider range in wood density used in our study
made the correlation statistically significant. Weedon et al.
(2009) is based mainly on species from temperate forests with
a limited number of tropical tree species, very few of which
had high wood density (only two species had wood densities
>0.8 g cm−3). The range of wood density was 0.22–0.69 g cm−3
in van Geffen et al. (2010), which lacked heavy wood species
in comparison to our study species (0.29–0.98 g cm−3).
According to Chave et al. (2009), the range of density of
tropical trees was as wide as 0.08–1.39 g cm−3. Many tropical
forests may include species with heavier wood than those
studied by van Geffen et al. (2010). For example, we could
not find significant correlations between wood density
and decomposition half-life when we classified the species
into two groups with smaller and larger wood density than
0.69 g cm−3 (n = 19 and 13, rs = 0.239 and −0.187, and P = 0.32
Journal of Plant Ecology
and 0.54 for the smaller and larger groups, respectively). The
relationship between wood density and decomposition rate in
tropical forest is likely to be driven by the presence of dense
wood species.
Conclusions
This study showed a very large interspecific variation in wood
decomposition rates of Bornean rainforest tree species. This
strongly suggests a need to incorporate species-specific decomposition rates when calculating ecosystem-level carbon dynamics for tropical forests. Our results suggest that a significant
relation between wood density and decomposition rate among
Bornean trees is likely to be driven by dense wood species. This
needs to be tested in other tropical forests with different species
sets. The novel finding of this study is that the water permeability of wood consistently explains interspecific variation not
only in the overall rate of wood decomposition but also in its
temporal pattern. Further experimental studies are required to
elucidate the mechanisms behind this relationship.
Supplementary material
Supplementary Figures S1–S4 are available at Journal of Plant
Ecology online.
Funding
Grants-in-Aid for Scientific Research from Japan Society for
the Promotion of Science (20405011).
Acknowledgements
We are grateful to the Forest Department of Sarawak, the Sarawak
Forestry Corporation, and the Management Office of the Semenggoh
Nature Reserve for permitting and helping us to survey in Sarawak,
Malaysia. We wish to thank Dr T. Yoneda for his advice in the decomposition experiments. We thank to anonymous reviewers for their
comments that improved the paper.
Conflict of interest statement. None declared.
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