Coarse woody debris (CWD) is an important component of temperate forest ecosystems, and is relevant to biomass, habitats for plants, animals, and microorganisms,the nutrient cycle, and micro-geomorphology(Sollins 1982, Harmon et al. 1986). In particular, CWD performs an important function as a long-term carbon stock in the terrestrial carbon cycle because of its slow decomposition and long residence time (Harmon et al.1986, Ganjegunte et al. 2004, Zhou et al. 2007, Garrett et al. 2008). Therefore, the National Greenhouse Gas Inventory Report for the Intergovernmental Panel on Climate Change (IPCC) requires a carbon stock of dead wood,which is one of the forest carbon stocks contained in forest ecosystems (Intergovernmental Panel on Climate Change 2006). Additionally, CWD dynamics reflect regeneration and succession processes in forests (Sturtevant et al. 1997, Motta et al. 2006).
CWD studies have generally focused on managed forests in which harvesting, clear cutting, or thinning have been practiced (Stone et al. 1998, Pedlar et al. 2002, Densmore et al. 2004, Janisch et al. 2005). However, only limited study has been conducted thus far into mass dynamics in natural forests (Carmona et al. 2002, Wilcke et al. 2005, Sefidi and Marvie Mohadjer 2010). Additionally, little data is currently available on CWD in Korea, although some previous studies have been conducted on this subject (Jang and Youn 2003, Kim et al. 2006, Noh et al. 2010). Thus, the study of CWD mass and its dynamics on temperate natural forests in Korea is clearly important.
For the measurement of CWD dynamics, chronosequencing and long-term survey methods are generally used (Harmon and Sexton 1996, Tobin et al. 2007). The chronosequence method could be readily applied to trace the long-term CWD dynamics when the age of CWD and several sites of comparable conditions are available (Sturtevant et al. 1997, Idol et al. 2001, Carmona et al. 2002, Tobin et al. 2007, Sefidi and Marvie Mohadjer 2010). However, the chronosequence method is difficult to apply in natural forests because CWD in natural forests is not composed of homogenous age, history, and species. Moreover, the environmental conditions for each forest are hardly comparable. Thus, long-term surveys conducted in Long-Term Ecological Research (LTER) sites are clearly necessary for CWD mass dynamics studies in natural forests.
In this study, CWD mass dynamics in three temperate natural forests dominated by Quercus , Abies holophylla, and Pinus densiflora were studied at the Korea National Long-Term Ecological Research (KNLTER) site located in Mt. Jumbong, Korea. The primary objectives of this study were as follows: 1) to collect and provide data on long-term CWD mass dynamics, 2) to analyze the inventory of CWD mass with forest dynamics, and 3) to estimate the input rate of CWD mass and decay rate constants in temperate natural forests at Mt. Jumbong, Korea.
This study was conducted on the KNLTER site of Mt. Jumbong (38°0'-38°5' E, 128°25'-128°30' N,) located in
the Mt. Sorak Biosphere Reserve, designated by United Nations Educational, Scientific and Cultural Organization (UNESCO) in 1982 (http://www.unesco.org). At Mt. Jumbong, undisturbed and mature natural forests have developed well, allowing a variety of forest ecological studies including vegetation (Cho 1999, Lee and Cho 2000), regeneration (Kim and Kim 1995, Suh and Lee 1998), succession (Kim and Kim 1995, Lee et al. 2000, Jin and Kim 2005, 2006), and soil respiration (Kang et al. 2003) to be conducted. This site was included as one of the KNLTER sites since 2003 under the auspices of the KNLTER research project (http://www.knlter.net). The 30-year (1970-2000) mean temperature and annual precipitation of Inje, where the closest meteorological station from Mt. Jumbong is located, were 9.9°C (monthly mean temperature ranging from -5.2°C to 23.1°C) and 1,114 mm, respectively (http://www.kma.go.kr).
To compare the differences in CWD dynamics among forests of Mt. Jumbong, three forests were selected (Table 1). The first was a naturally regenerated deciduous forest dominated by Q. with Carpinus cordata, Tilia amurensis, and Acer pseudo-sieboldiana located on a ridge at an altitude of 1,105 m a.s.l. The age of the Q. trees was 80 years for the smaller trees whose diameter at breath height (DBH) was smaller than 30 cm, and 240 years for trees with DBH in excess of 40 cm (Kim and Kim 1995, Cho 1999). This forest is made up of mature deciduous forest in mountainous regions (Lee and Cho 2000). The second was a mixed forest dominated by A. holophylla with Q. and C. cordata located in a valley at an altitude of 1,091 m a.s.l. The age of the A. holophylla was 80 years. The third forest evaluated was a coniferous forest dominated by 60-year-old P. densiflora with Q. and C. condata located at the bottom slope, at an altitude of 613 m a.s.l.
In each forest, plots of different number and size were designed based on the forest structure and CWD distribution. In the Q. forest, three 20 m × 20 m plots with areas of 0.12 ha were installed in November 2003.
Two 20 m × 20 m plots and one 10 m × 10 m plot with an area of 0.09 ha for the A. holophylla forest and four 10 m × 10 m plots with an area of 0.04 ha for the P. densiflora forest were installed in May 2006. Within each plot, the CWD inventory was assessed via a fixed area plot sampling method (Harmon and Sexton 1996) twice per year in the early summer and early winter, except for the winter seasons of 2006 and 2009, due to heavy snowfall. Pieces of CWD with a base diameter greater than 5 cm were tagged and their species, types (log, snag, and stump), decay classes (I-V), diameters (base, middle, and top), and lengths were recorded in the field. Decay class was determined at five different levels according to a CWD decay classification system (Kim 2003, Kim et al. 2006), which was modified from the original criteria provided by Sollins (1982) and Sollins et al. (1987). For conversions of the CWD volume to CWD mass, cross-sections with a thickness of around 10 cm of all tagged pieces of CWD were sampled, wrapped in plastic bags, and stored at -15°C until their wood densities were analyzed.
CWD mass was calculated from its initial volume and the wood density of cross-sections sampled every 6 months. The initial volume of logs and stumps was calculated via Newton’s formula (Harmon and Sexton 1996) which requires length and diameter measurements at three positions (base, middle, and top):
V = L(Ab + 4Am + At)/6
in which V is volume, L is length, and Ab, Am and At are the areas of the base, middle, and top of the logs and stumps, respectively. The initial volume of snags was estimated using the formula developed by Whitmore (1984):
V = BA × H × 0.5
in which V is volume, BA is the basal area, and H is the height of snags.
The volume and dry weight of cross-sections were measured in order to calculate the wood densities. Diameters of each sample were measured at three points (base, middle, and top) with digital calipers and then their volumes were calculated. The samples were dried at 75°C to a constant mass and weighed. The wood density was calculated as the dry mass divided by the volume of each sample
The CWD mass obtained using the initial volume of CWD and wood density of each sample was classified into forest type, season, species, CWD type, and decay class. CWD mass input was calculated from the sum of newly tagged and recorded CWD mass at each survey. The mass loss rate of CWD was estimated from the difference in CWD mass between each survey interval. The decay rate constant was estimated based on a single exponential model:
k = ?ln(Xt/X0)/t
where k is the decay rate constant, Xt is the mass after t years, X0 is the initial mass, and t is the time (y). In addition, the disappearance of CWD from the sampling of all parts of the sample due to repeated samplings could pose a problem; however, this was not considered a relevant issue because of the small size and proportion.
All descriptive statistics and natural logarithmic regression analysis to examine the relationship between decay class and wood density of CWD were conducted using SAS ver. 9.2 (SAS Institute Inc. 2009).
CWD mass (Mg/ha) ranged from 17.7 to 24.0 for the Q. forest in 2003-2010, from 12.6 to 15.1 for the A. holophylla forest in 2006-2010, and from 3.4 to 6.7 for P. densiflora forest in 2006-2010 (Fig. 1). The mean CWD mass (Mg/ha) of Q. forest, A. holophylla forest, and P. densiflora forest during these periods were 20.6, 12.2, and 5.0, respectively. The coefficients of variation (CV) were 43.9%, 80.1%, and 68.3% for the Q. forest, A. holophylla forest, and P. densiflora forest, respectively.
The input rates of CWD mass (Mg ha-1 y-1) for the Q. forest, A. holophylla forest, and P. densiflora forest were 1.20, 0.44, and 0.00, respectively. CWD mass input (Mg/ha) in each period ranged from 0.00 to 4.02 for theQ.forest, from 0.00 to 1.15 for A. holophylla forest, and from 0.00 to 0.02 for P. densiflora forest, respectively (Table 2). The mass loss rates of CWD (Mg ha-1
y-1) were 1.07 for the Q. forest, 0.66 for the A. holophylla forest, and 0.33 for the P. densiflora forest. The decay rate constants (1/y) of the Q. forest, A. holophylla forest, and P. densiflora forest were estimated to be 0.058, 0.106, and 0.086, respectively.
Table 3 describes the contribution of CWD by species for each forest. Q. comprised the largest proportion (84.5%) of the total CWD mass for Q. forest, followed by C. cordata (5.4%), T. amurensis (5.0%), Acer spp. (1.8%), other species (0.5%), and unidentified species (1.7%). Acer spp. contributed a relatively small
proportion (1.8%) of the total CWD mass, considering that they occupied a large proportion (20.3%) of the total number of CWD pieces. In the case of the <C. cordata, a large proportion of the total CWD mass was contributed not only by A. holophylla (43.0%), but also by Q. (20.2%), other species (15.9%), and unidentified species (13.9%). A large number of CWD pieces was comprised by Q. (35.9%) and Acer spp. (33.5%) and A. holophylla contributed a small number of CWD pieces (11.0%) for the A. holophylla forest. P. densiflora contributed all CWD mass for the P. densiflora forest and no CWD was found for other species.
CWD mass by CWD type differed greatly among the three forests (Table 4). The proportion of CWD mass for logs was much higher than the other CWD types in the Q. forest (84.4% for logs), but was smaller than the proportion of snags in the A. holophylla forest (25.0% for logs and 50.0% for snags) or was similar to the proportion of snags in P. densiflora forest (41.6% for logs and 43.2% for snags). Although snags contributed a small number of CWD pieces in the A. holophylla forest (16.8%) and P. densiflora forest (15.0%), they comprised a large proportion of total CWD mass for both forests.
The distribution of CWD by decay classes for three forests in each period is shown in Table 5. In general, class II and class III comprised the majority of CWD mass in the Q. forest (29.5% and 51.6%, respectively) and in the A. holophylla forest (34.9% and 45.0%, respectively) while class III and class IV comprised the majority of CWD mass in the P. densiflora forest (58.6% and 34.7%, respectively). The proportion of CWD mass classified into class Ⅴ was nearly zero in all three of the forests.
CWD wood density by decay class was calculated for each species. CWD wood density (g/cm3) was 0.49 ± 0.01, 0.37 ± 0.01, 0.31 ± 0.01, 0.20 ± 0.01, and 0.17 ± 0.00 for class Ⅰ, Ⅱ, Ⅲ, Ⅳ, and Ⅴ of Q., 0.38 ± 0.05, 0.40 ± 0.02, 0.23 ± 0.01, and 0.16 ± 0.03 for class Ⅰ, Ⅱ, Ⅲ, and Ⅳ of A. holophylla, and 0.25 ± 0.00, 0.23 ± 0.02, 0.18 ± 0.01, and 0.15 ± 0.01 for class Ⅰ, Ⅱ, Ⅲ, and Ⅳ of P. densiflora, respectively. Natural logarithmic regression between decay class and wood density was established for three species (P < 0.0001) and the r-square of each regression was 0.45, 0.30, and 0.11 for Q. , A. holophylla, and P. densiflora, respectively (Fig. 2).
The CWD mass for Q. mongolica forest in Mt. Jumbong ranges within that for mature temperate oak forests. This is similar to the range of CWD mass in temperate oak forests (9-24 Mg/ha) as reviewed by Harmon et al. (1986) and a mature Quercus spp. forest (15.9-20.1 Mg/ha) in the Gwangneung Experimental Forest (Kim et al. 2004); however, it was higher than in younger forests such as the 30-year-old Quercus serrata stand (1.5-1.9 Mg/ha) and 40-year-old Quercus variabilis stand (7.0-7.5 Mg/ha) in Yangpyeong as previously reported by Kim et al. (2004).
Distribution of CWD number and mass by species would be similar to the distribution of living tree number and basal area in Q. mongolica forest. CWD distribution is reflective of living tree structure (Harmon et al. 1986). Kim and Kim (1995) reported that Q. mongolica and A. pseudosieboldianum represented approximately 75% of the total basal area and 33% of the total density in this forest. This could explain the large proportion of CWD mass by Q. mongolica and of the CWD number by Acer spp. in the Q. mongolica forest (Table 3). However, the large contribution of CWD input by Q. mongolica with A. pseudosieboldianum and T. amurensis in this research differs from that demonstrated in a mortality report conducted in this forest by Cho (1999), who asserted that small trees of A. pseudosieboldianum and T. amurensis contributed most to the mortality of trees in the Q. mongolica forest from 1995 to 1999. These differences may be attributable to the differences in the study periods of the two studies (from 2003 to 2010 in this study).
The majority of the CWD in Q. mongolica forest was classified into log. This is similar to a previous study conducted by Kim and Kim (1995) in which it was reported that a large proportion of the canopy gap in Q. mongolica forest was contributed by stem breakage (40%) and branch snap-off (32%), generating logs. Additionally, the Q. mongolica forest is located on a ridge where strong winds are frequent throughout the year (Lee and Cho 2000), and this may be one reason why the proportion of characlogs in Q. mongolica forest is larger than that in the A. holophylla forest located in the valley and the P. densiflora forest located on the bottom slope. Generally, wind-related mortality, which promotes input of logs from fallen stems and broken branches, is probably a salient issue in mature forests (Harmon et al. 1986). Previous studies have reported that Q. mongolica is one of the major climax species for cool temperate forests in Korea (Song and Jang 1997, Byun et al. 1998) and Mt. Jumbong (Lee et al. 2000, Jin and Kim 2005). Thus, the large amount of logs observed in the Q. mongolica forest might be attributable to specific climate events such as rainfall and windstorms on ridge topography, rather than competition among other species.
The proportion of CWD for A. holophylla (43.0%) in the A. holophylla forest at Mt. Jumbong is similar to that observed on Mt. Sorak (Jang and Youn 2003) which is close to Mt. Jumbong. However, CWD mass for the A. holophylla forest (12.2 Mg/ha) was lower than that for A. holophylla forest (17.3 Mg/ha) on Mt. Sorak which is converted from the CWD volume measured by Jang and Youn (2003) using the mean CWD wood density (0.29 g/cm3) obtained from this study. The proportion of snags and stumps for the A. holophylla forest was higher than that measured in the Q. mongolica forest. This may be attributable to the competition among species or individual trees (Harmon et al. 1986, Sturtevant et al. 1997). A large number of CWD was determined into Q. mongolica, which is expected to decrease in future (Lee et al. 2000) and Acer spp., which are major understory species suppressed by overstory trees.
CWD mass for the P. densiflora forest (5.0 Mg/ha) is similar to that for 50-year-old P. densiflora stand (6.4 Mg/ha) in Gwangneung, Korea (Lee et al. 2009), but lower than that for other forests in Mt. Jumbong (20.6 Mg/ha for Q. mongolica forest and 12.2 Mg/ha for A. holophylla forest in the present study) or Pinus contorta forests (29-121 Mg/ha) in Wyoming, USA (Tinker and Knight 2000). This may represent the middle stage of the succession sequence of P. densiflora forest as the lowest position in the general “U-shaped” temporal pattern of CWD mass. The “U-shaped” pattern explains the decline in the initially high CWD level after disturbance and higher inputs as the mature stand senescence through time sequence (Sturtevant et al. 1997, Tarasov and Birdsey 2001, Carmona et al. 2002). Moreover, the homogenous CWD composition by P. densiflora and the absence of CWD by other species may correspond to the succession studies in this region (Song and Jang 1997, Lee et al. 2000), which concluded that P. densiflora forest is generally altered to Q. mongolica or Q. variabilis forest. Thus, we anticipate that P. densiflora would tend to decline with the increasing presence of CWD; additionally, other understory trees such as Q. mongolica and C. condata, which are shade-tolerant (Cho 1999), may potentially be dominant without mortality in this forest for the foreseeable future.
We collected 20 decay rate constants for coniferous forests from 6 countries and 7 decay rate constants for deciduous forests from temperate ecosystems in 4 countries. Annual precipitation and mean temperature ranged from 660 mm to 2,500 mm and from 3.9°C to 14.8°C, respectively (Table 6). Overall, the decay rate constants (1/y) ranged from 0.015 to 0.157 for coniferous forests and from 0.018 to 0.109 for deciduous forests in temperate ecosystems, regardless of CWD type. We noted no significant differences in decay rate constants between coniferous forests and deciduous forests (P = 0.45). The value for A. holophylla forest (0.106) and P. densiflora forest (0.086) from this study was higher than the mean value of coniferous forests (0.068). On the other hand, the value for Q. mongolica (0.058) reported herein is similar to the mean value of deciduous forests (0.055) and the value for Quercus spp. (0.069) from the USA (Schowalter et al. 1998), where the climate conditions are similar to those on Mt. Jumbong.
In this study, the decay rate for the Q. mongolica forest was lower than that for the A. holophylla and P. densiflora forests; one possible explanation for this may involve the differences in initial wood density (g/cm3), which were 0.44 and 0.78 for P. densiflora and Q. mongolica, respectively (Korea Forest Research Institute 2007). Generally speaking, the decomposition process is affected by microclimate and woody substrate quality (Harmon et al. 1986, Ganjegunte et al. 2004, Zhou et al. 2007). Wood density, one indicator of substrate quality, is correlated negatively with humidity, because lower void volumes in wood with high wood density inhibit the infiltration of moisture into wood structures, and attenuate decomposition processes (Harmon et al. 1986, Yin 1999, Zhou et al. 2007). Thus, deciduous species tend to evidence lower decay rates than coniferous species because of the hardness and wood density of deciduous species.
Decay classification systems are representative of mean wood density for each class with external charac-
teristics of CWD such as bark cover, fungal cover, or color (Harmon et al. 1986). In this study, 45%, 30%, and 11% of the variation in CWD wood density was explained by decay classes in the Q. mongolica, A. holophylla, and P. densiflora forests, respectively (Fig. 2). The limited representativeness of the decay class to wood density in coniferous forests is attributable to the lack of a CWD decay classification system for coniferous species. The CWD decay classification system (Kim 2003, Kim et al. 2006) applied in this research was developed originally for Quercus spp. forest and may not be suitable for coniferous forests because CWD from each forest could have different external characteristics due to species, microclimate, and biota on CWD. The decay classification system could be regarded as specific to a site (Harmon and Sexton 1996). Thus, improvement and validation are necessary for reliable decay classifications for coniferous forests. For example, Naesset (1999) developed three classification systems for Picea abies CWD and evaluated the relationship between these classification system and relative wood density.
CV for A. holophylla forest and P. densiflora forest was larger than that for Q. mongolica forest. This may be due to plot size, which is a crucial consideration for CWD inventory (Harmon and Sexton 1996). Harmon and Sexton (1996) recommended that the cumulative areas of plots should be at least 0.1 ha to represent a normally stocked stand in CWD research. The cumulative area of plots for A. holophylla forest (0.09 ha) and P. densiflora forest (0.04 ha) were lower than the value of their recommendation. Although the plot size was determined with consideration of forest structures and CWD distribution in this study, further studies will be required, involving the installation of plots with cumulative areas greater than 0.1 ha to achieve better representativeness.
The chief objective of this study was to build an inventory of CWD mass and analyze its long-term dynamics in cool-temperate forests located at the Mt. Jumbong site in Korea. CWD mass and its dynamics are affected by microclimate factors including temperature, humidity, O2 and CO2 concentrations inside of CWD, and woody substrate quality including diameter, components, species, and organisms (Harmon et al. 1986, Ganjegunte et al. 2004, Zhou et al. 2007). Topography also affects the distribution of CWD loads across the landscape (Rubino and McCarthy 2003). However, it was difficult to analyze the effects of microclimate or topography in this study, because microclimate and topography data were insufficient and distinguishing the effects of dominant species, topography, and microclimate is difficult in natural forests where these factors are generally related and interact with one another. Thus, further studies will require data of long-term microclimate and CWD mass dynamics in a variety of forest types that can represent a variety of environmental factors.
We developed decay rate constants for each forest regardless of CWD type, decay class, and species in this study. However, decay rate constants could be developed with regard to other factors such as CWD size (Tarasov and Birdsey 2001, Beets et al. 2008), decay class (Tobin et al. 2007) and CWD type (Ganjegunte et al. 2004, Garrett et al. 2008). This study did not install enough CWD replicates to reflect all these factors, principally because it was tightly focused on CWD mass dynamics in natural forests, not developing heterogenous CWD decay rate constants. However, further research into the development of CWD decay rate constants should take into consideration these factors, which affect CWD decomposition. In particular, the development of CWD decay rate constants by decay class may prove essential, as further research into CWD mass inventory and its dynamics is necessary for the development of a CWD mass dynamics model in forests with continuous long-term monitoring. Stage-based CWD decomposition models according to decay classes by different transition rates (Kruys et al. 2002, Ranius et al. 2003, Montes and Canellas 2006) could be applied in the construction of CWD mass dynamics models in the future using the CWD inventory compiled in this study.