Forest biomass and rootâshoot allocation in northeast China Forest ...
Forest Ecology and Management 255 (2008) 4007–4020
Contents lists available at ScienceDirect
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
Contents lists available at ScienceDirect
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
