气候敏感的马尾松生物量相容性方程系统研建

doi:10.11707/j.1001-7488.20190508

Abstract

Abstract: [Objective] Masson pine (Pinus massoniana) is an important species which is widely distributed in the subtropical zone in China. According to the national climate change assessment report, the temperature in China has raised by 0.5-0.8℃. The mean ground temperature in China might be raised by 3.9-6.0℃ to the end 2100. The method for detecting the response of biomass of Masson pine to climate change are the foundation for estimating forest biomass accurately. However, the effects of climate change on each component (wood, bark, branch and foliage) biomass in traditional systems of biomass equations without being considered. Therefore, in this study, the prediction accuracy of biomass was improved by developing a system of climate-sensitive biomass compatible equations.[Method] In this study, the method on developing systems of climate-sensitive biomass compatible equations using nonlinear simultaneous equations were proposed in detail based on the biomass data from 150 individual trees of Masson pine. The two-predictor equations with independent variables including diameter at breast height and total tree height were selected as base models for each components to develop the system of climate-sensitive biomass compatible equations. The one-fold cross validation was applied for model evaluation.[Result] Compared with the traditional biomass models without including climate factors, the prediction accuracy of the system of climate sensitive biomass compatible equations was substantially increased. In addition, the differences of biomass among different subtropical zones were also explained by the proposed system of climate sensitive biomass compatible equations, which ensured the sum of wood, bark, branch and foliage biomass equal to the total biomass.[Conclusion] The system of climate sensitive biomass compatible equations proposed in this study could be used as an effective tool to analyze the effects of climate change on each component biomass.

Key words: Masson pine, biomass, climate change, additive, nonlinear simultaneous equations

CLC Number:

S757

Li Yacang, Feng Zhongke. Developing a System Climate Sensitive Biomass Compatible Equations for Masson Pine[J]. Scientia Silvae Sinicae, 2019, 55(5): 65-73.

References

丁贵杰, 周志春, 王章荣. 2006. 马尾松纸浆用材林培育与利用. 北京:中国林业出版社
(Ding G J, Zhou Z C, Wang Z R. 2006. Cultivation and utilization of Masson pine pulpwood plantation. Beijing:China Forestry Publishing House.[in Chinese])
封晓辉,程瑞梅,肖文发,等. 2011. 北亚热带生长期温度对马尾松径向生长的影响. 生态学杂志, 30(4):650-655.
(Feng X H, Cheng R M, Xiao W F, et al. 2011. Effects of air temperature in growth season on Masson pine (Pinus massoniana) radial growth in north subtropical region of China. Chinese Journal of Ecology, 30(4):650-655.[in Chinese])
符利勇, 曾伟生, 唐守正. 2011a. 利用混合模型分析地域对国内马尾松生物量的影响. 生态学报, 31(19):5797-5808.
(Fu L Y, Zeng W S, Tang S Z. 2011a. Analysis the effect of region impacting on the biomass of domestic Masson pine using mixed model. Acta Ecologica Sinica, 31(19):5797-5808.[in Chinese])
符利勇, 李永慈, 李春明, 等. 2011b. 基于两水平非线性混合模型对杉木林优势高生长量研究. 林业科学研究, 24(6):720-726.
(Fu L Y, Li Y C, Li C M, et al. 2011b. Study of the dominant height for Chinese fir plantation using two-level nonlinear mixed effects model. Forest Research, 24(6):720-726.[in Chinese])
符利勇, 雷渊才, 曾伟生. 2014. 几种相容性生物量模型及估计方法的比较. 林业科学, 50(6):42-54.
(Fu L Y, Lei Y C, Zeng W S. 2014. Comparison of several compatible biomass models and estimation approaches. Scientia Silvae Sinicae, 50(6):42-54.[in Chinese])
国家气候变化评估报告编辑委员会. 2007. 国家气候变化评估报告. 北京:科学出版社
(Editorial Committee of National Climate Change Assessment Report. 2007. National climate change assessment report. Beijing:Science Press.[in Chinese])
唐守正, 李勇, 符利勇. 2015. 生物数学模型的统计学基础.2版. 北京:科学出版社.
(Tang S Z, Li Y, Fu L Y. 2015. Statistical foundation for biomathematical models.2^nd ed. Beijing:Higher Education Press.[in Chinese])
唐守正, 张会儒, 胥辉. 2000. 相容性生物量模型的建立及其估计方法研究. 林业科学, 36(zk):19-27.
(Tang S Z, Zhang H R, Xu H. 2000. Study on establish and estimate method of compatible biomass model. Scientia Silvae Sinicae, 36(zk):19-27.[in Chinese])
唐守正, 郎奎建, 李海奎. 2008. 统计和生物数学模型计算(ForStat教程). 北京:科学出版社.
(Tang S Z, Lang K J, Li H K. 2008. Statistics and computation of biomathematical models (ForStat course). Beijing:Science Press.[in Chinese])
曾伟生, 唐守正. 2010a. 利用混合模型方法建立全国和区域相容性立木生物量方程. 中南林业调查规划,29(4):1-6.
(Zeng W S, Tang S Z. 2010a. Using mixed-effects modeling method to establish compatible national and regional single-tree biomass equations. Central South Forest Inventory and Planning, 29(4):1-6.[in Chinese])
曾伟生, 唐守正. 2010b. 利用度量误差模型方法建立相容性立木生物量方程系统. 林业科学研究, 23(6):797-802.
(Zeng W S, Tang S Z. 2010b. Using measurement error modeling method to establish compatible single-tree biomass equations system. Forest Research, 23(6):797-802.[in Chinese])
Bi H, Turner J, Lambert M J. 2004. Additive biomass equations for native eucalypt forest trees of temperate Australia. Trees, 18(4):467-479.
Dong L H, Zhang L J, Li F R. 2015. A three-step proportional weighting system of nonlinear biomass equations. Forest Science, 61(1):35-45.
Fu L Y, Lei X D, Hu Z D, et al. 2017a. Integrating regional climate change into allometric equations for estimating tree aboveground biomass of Masson pine in China. Annals of Forest Science, 74:42. doi:10.1007/s13595-017-0636-z.
Fu L Y, Sharma R P, Hao K J, et al. 2017b. A generalized interregional nonlinear mixed-effects crown width model for Prince Rupprecht larch in northern China. Forest Ecology and Management, 389:364-373.
Fu L Y, Zeng W S, Tang S Z, et al. 2012. Using linear mixed model and dummy variable model approaches to construct compatible single-tree biomass equations at different scales-a case study for Masson pine in southern China. Journal of Forest Science, 58(3):101-115.
Fu L Y, Zeng W S, Zhang H R, et al. 2014. Generic linear mixed-effects individual-tree biomass models for Pinus massoniana Lamb. in Southern China. Southern Forests, 76(1):47-56.
Fu L Y, Lei Y C, Wang G X, et al. 2016. Comparison of seemingly unrelated regressions with error-invariable models for developing a system of nonlinear additive biomass equations. Trees, 30(3):839-857.
Hijmans R J, Cameron S E, Parra J L, et al. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology, doi:10.1002/joc.1276.
IPCC. 2007. Climate change 2007:the physical science basis.UK:Cambridge University Press.
Parresol B R. 1999. Assessing tree and stand biomass:a review with examples and, critical comparisons. Forest Science, 45(4):573-593.
Parresol B R. 2001. Additivity of nonlinear biomass equations. Canadian Journal of Forest Research, 31(5):865-878.
R Core Team. 2012. R:a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
Waring R H,Running S W. 1998. Forest ecosystems:analysis at multiple scales. 2^nd ed. Academic Press, San Diego, Calif.
Zeng W S, Tang S Z. 2012. Modeling compatible single-tree biomass equations of Masson pine (Pinus massoniana) in southern China. Journal of Forestry Research, 23(4):593-598.
Zeng W S, Zhang H R, Tang S Z. 2011. Using the dummy variable model approach to construct compatible single-tree biomass equations at different scales-a case study for Masson pine (Pinus massoniana) in Southern China. Canadian Journal of Forest Research, 41(7):1547-1554.
Zeng W S, Tang S Z, Xiao Q. 2014. Calorific values and ash contents of different parts of Masson pine trees in southern China. Journal of Forestry Research, 25(4):779-786.
Zeng W S. 2015. Using nonlinear mixed model and dummy variable model approaches to develop origin-based individual tree biomass equations. Trees, 29(1):275-283.
Zeng W S, Duo H R, Lei X D, et al. 2017. Individual tree biomass equations and growth models sensitive to climate variables for Larix spp. in China. European Journal of Forest Research, doi:10.1007/s10342-017-1024-9.
Zou W T, Zeng W S, Zhang L J, et al. 2015. Modeling crown biomass for four pine species in China. Forests, 6(2):433-449.

[1]	Liu Chunyang, Shi Tian, Shi Guo, Yang Linfei, Fan Xuefeng, Gao Shuangcheng, Zhang Gaina. Effects of Different Transplanting Periods on Growth of Tree Peony ‘Fengdan’ Seedlings and the Comprehensive Evaluation [J]. Scientia Silvae Sinicae, 2019, 55(8): 54-62.
[2]	Xue Chunquan, Xu Qihu, Lin Liping, He Xiao, Cao Lei, Li Haikui. Biomass Dynamic Predicting for Schima superba in Guangdong Based on Allometric and Theoretical Growth Equation [J]. Scientia Silvae Sinicae, 2019, 55(7): 86-94.
[3]	Ding Lingzhi, Man Xiuling, Xiao Ruihan, Cai Tijiu. Dynamics of Soil Microbial Biomass Carbon and Nitrogen in the Soil of Rhizosphere during Growing Season in the Cold Temperate Forests [J]. Scientia Silvae Sinicae, 2019, 55(7): 178-186.
[4]	Chang Jianguo. Longitudinal Distribution of Annual Stem Cross-Section Area Increment of Pinus tabulaeformis and Its Relationships with Annual Volume Increment and Leaf Biomass [J]. Scientia Silvae Sinicae, 2019, 55(6): 55-64.
[5]	Wang Yilin, Wang Weifeng, Zhang Yunxiang, Chang Shujun, Guo Jinping. Responses of Leaf Morphological Structure and Physiological Characteristics of Populus euramericana cv. ‘BYu’ to Drought Stress [J]. Scientia Silvae Sinicae, 2019, 55(4): 42-50.
[6]	Wang Xiaowei, Ren Xueyan, Liang Yingmei. MaxEnt-Based Prediction of Potential Geographic Distribution and Habitat Suitability Analysis for Dothistroma pini in China [J]. Scientia Silvae Sinicae, 2019, 55(4): 160-170.
[7]	Lü Zhengang, Li Wenbo, Huang Xuanrui, Zhang Zhidong. Predicting Suitable Distribution Area of Three Dominant Tree Species under Climate Change Scenarios in Hebei Province [J]. Scientia Silvae Sinicae, 2019, 55(3): 13-21.
[8]	Zeng Weisheng, He Dongbei, Pu Ying, Xiao Qianhui. Individual Tree Biomass and Volume Equation System with Region and Origin in Variables for Pinus massoniana in China [J]. Scientia Silvae Sinicae, 2019, 55(2): 75-86.
[9]	Xue Chunquan, Xu Qihu, Lin Liping, He Xiao, Luo Yong, Zhao Han, Cao Lei, Lei Yuancai. Biomass Models with Breast Height Diameter and Age for Main Nativetree Species in Guangdong Province [J]. Scientia Silvae Sinicae, 2019, 55(2): 97-108.
[10]	Zhang Yuanchen, Su Shanyu, Liu Ping, Zhang Yuxiao, Zhang Kunpeng, Wang Jingshun. Survey of Natural Enemies in Gall of Trichagalma acutissimae (Hymenoptera: Cynipidae) and Biological of Its Predominant Parasitoids [J]. Scientia Silvae Sinicae, 2019, 55(2): 137-142.
[11]	Wang Bo, Li Qin, Zhu Wei, Chen Wenhai, Zhu Hongliang, Shen Quan, Zhu Anming, Zhao Jiancheng. Effects of Mulching Management on Nutrient Contents, Enzyme Activities, and Microbial Biomass in the Soils of Moso Bamboo Forests [J]. Scientia Silvae Sinicae, 2019, 55(1): 110-118.
[12]	Chong Peifang, Zhan Jin, Jia Xiangyang, Li Yi. Influences of Elevated CO₂ and Precipitation Regimes on Photosynthesis and Growth of Desert Shrub Reaumuria soongarica [J]. Scientia Silvae Sinicae, 2018, 54(9): 27-37.
[13]	Han Zongtao, Jiang Hong, Wang Wei, Li Zengyuan, Chen Erxue, Yan Min, Tian Xin. Forest Above-Ground Biomass Estimation Using KNN-FIFS Method Based on Multi-Source Remote Sensing Data [J]. Scientia Silvae Sinicae, 2018, 54(9): 70-79.
[14]	Li Weicheng, Sheng Haiyan, Jiang Yueping, Wen Xing. Soil CO₂ Flux and Its Influence Factors of Different Bamboo Plantations in the Dike-Pond Ecosystem [J]. Scientia Silvae Sinicae, 2018, 54(8): 13-22.
[15]	Wang Jingru, Wang Minghao, Zhang Xiaowei, Sun Shan, Zhao Changming. The Ecological Divergence and Projection of Future Potential Distribution of Homoploid Hybrid Species Picea purpurea [J]. Scientia Silvae Sinicae, 2018, 54(6): 63-72.

Developing a System Climate Sensitive Biomass Compatible Equations for Masson Pine

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments