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林业科学 ›› 2021, Vol. 57 ›› Issue (1): 95-104.doi: 10.11707/j.1001-7488.20210110

• 论文与研究报告 • 上一篇    下一篇

气候敏感的青冈栎单木胸径生长模型

刘帅,李建军,卿东升,朱凯文,马振燕   

  1. 中南林业科技大学 长沙 410004
  • 收稿日期:2020-03-19 出版日期:2021-01-25 发布日期:2021-03-10
  • 通讯作者: 李建军
  • 基金资助:
    国家自然科学基金项目(31570627)

A Climate-Sensitive Individual-Tree DBH Growth Model for Cyclobalanopsis glauca

Shuai Liu,Jianjun Li,Dongsheng Qing,Kaiwen Zhu,Zhenyan Ma   

  1. Central South University of Forestry and Technology Changsha 410004
  • Received:2020-03-19 Online:2021-01-25 Published:2021-03-10
  • Contact: Jianjun Li

摘要:

目的: 构建气候敏感的青冈栎单木混合效应模型,探索气候对青冈栎胸径生长的长期影响,为未来气候变化下的青冈栎林经营决策提供依据。方法: 基于湖南省芦头林场青冈栎解析木数据,选择Mitscherlich生长方程作为基础模型,构建包含气候变量的再参数化模型和非线性混合效应模型,预测未来3种典型浓度路径(RCP2.6、RCP4.5和RCP8.5)下2011—2100年青冈栎单木胸径生长。结果: 1)非线性混合效应模型能够准确描述青冈栎单木胸径生长与气候变量之间的复杂关系,在拟合优度、误差水平等方面相比传统回归模型更具优势;2)加入气候变量的青冈栎生长模型能够响应气候变化对林木生长的影响,最冷月均温是影响青冈栎胸径生长最主要的气候变量,并负相关于胸径生长,其他气候变量在统计上不显著没有入选生长模型,对青冈栎生长的影响尚不明确;3)青冈栎胸径生长对不同时期不同气候场景的响应不同,高排放的RCP8.5对青冈栎胸径生长的不利影响更大,低排放的RCP2.6对青冈栎胸径生长的负面影响相对较小,这些影响随时间推移将更加强烈。预计至2100年,30年树龄的青冈栎胸径生长在RCP2.6、RCP4.5和RCP8.5场景下相比气候条件不变时将分别下降6.3%、15.6%和53.1%。结论: 本研究构建的青冈栎单木混合效应模型具有气候敏感、统计可靠和预测有效等优点,研究结果有助于林业工作者在经营实践中应对未来气候变化所带来的挑战。

关键词: 青冈栎, 胸径生长, 气候变量, 混合效应模型, 气候场景

Abstract:

Objective: Climate is considered as a potential driver of tree growth. Cyclobalanopsis glauca is an important timber species in southern China. However, we lack an understanding about the growth of this species and its response to climate. The purpose of this study was to explore the long-term effects of climate on the growth of C. glauca in order to provide a basis for the management decision of C. glauca forests under future climatic changes. Method: In this study, based on the data of C. glauca trees dissected in Lutou forest farm, Hunan Province, we constructed the re-parameterization model and the nonlinear mixed-effects(NLME) model with climate variables by using the Mitscherlich growth equation as the basic model, and the diameter at breast height(DBH) growth of C. glauca under the three representative concentration pathways(RCP2.6, RCP4.5 and RCP8.5) in the future was also predicted. Result: 1) The NLME growth model could accurately describe the complex nonlinear relationships between the DBH growth of C. glauca and climate variables, and exhibited more advantages than the traditional regression models in terms of fitting accuracy and error level. 2) The incorporation of climate variables enabled the growth model of C. glauca to respond to the impacts of climate changes on tree growth. The mean coldest month temperature was the most important climatic factor affecting the DBH growth of C. glauca, and was negatively correlated with tree DBH growth. Other climate factors were not included in the growth model because they were not statistically significant. So their effects on the growth of C. glauca were not clear. 3) The response of the DBH growth of C. glauca to different climate scenarios was different. High emission RCP8.5 had a greater negative impact on the DBH growth of C. glauca, while low emission RCP2.6 had a relatively small negative impact. These effects would become more pronounced over time. It was estimated that by 2100, the DBH growth of C. glauca at 30 ages would decrease by 6.3%, 15.6% and 53.1%, respectively, under the scenarios of RCP2.6, RCP4.5 and RCP8.5, compared with the current climate conditions. Conclusion: This study might be a beneficial exploration on the influences of climate changes on the growth of C. glauca, and the NLME DBH growth model for C. glauca proposed in our paper presented the advantages of climate-sensitivity, statistically reliability and predictive effectiveness, etc. These findings of the study would contribute to address the challenges of future climate changes in forest management.

Key words: Cyclobalanopsis glauca, DBH growth, climate variable, mixed-effects model, climate scenario

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