气候敏感的青冈栎单木胸径生长模型

doi:10.11707/j.1001-7488.20210110

摘要/Abstract

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

中图分类号:

S750

刘帅,李建军,卿东升,朱凯文,马振燕. 气候敏感的青冈栎单木胸径生长模型[J]. 林业科学, 2021, 57(1): 95-104.

Shuai Liu,Jianjun Li,Dongsheng Qing,Kaiwen Zhu,Zhenyan Ma. A Climate-Sensitive Individual-Tree DBH Growth Model for Cyclobalanopsis glauca[J]. Scientia Silvae Sinicae, 2021, 57(1): 95-104.

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数据Data	变量Variables	均值Mean	最大值Max.	最小值Min.	标准差SD
生长数据 Growth data	树龄Age/a	41.39	54.00	33.00	7.98
	胸径DBH/cm	15.57	29.10	8.60	5.54
	树高Tree height/m	12.36	16.00	7.80	2.23
	材积Volume/m³	0.147	0.447	0.035	0.117
气候数据 Climatic data	年平均温度MAT/℃	16.65	17.10	16.35	0.24
	最热月均温MWMT/℃	26.81	27.40	26.15	0.36
	最冷月均温MCMT/℃	4.48	6.60	3.05	0.72
	MWMT和MCMT温差TD/℃	21.14	22.50	20.25	0.99
	年平均降水MAP/mm	1 457.22	1 592.00	1 276.20	112.71
	干燥指数AHM	10.92	11.86	10.20	0.53

生长方程Growth equations	AIC	BIC	LogLik	RSS	MRE	RMSE
Mitscherlich	1 072.084	1 082.526	-533.042 2	1 193.661 3	0.263 8	2.234 8
Gomperz	1 083.415	1 097.338	-537.707 7	1 240.984 0	0.253 0	2.278 7
Korf	1 074.142	1 088.064	-533.070 8	1 193.945 8	0.267 8	2.235 1
Richards	—	—	—	—	—	—
Logistic	1 095.954	1 109.877	-543.977 0	1 307.541 2	0.262 5	2.339 0

编号 ID	混合效应参数 Mixed-effects parameter	AIC	BIC	LogLik	RSS	MRE	RMSE
N1	λ₀₀+γ₀₀	606.936 1	627.820 0	-297.468 1	109.447 1	0.160 0	0.676 7
N2	λ₀₁+γ₀₁	620.950 2	641.834 0	-304.475 1	116.773 9	0.161 2	0.699 0
N3	λ₁₀+γ₁₀	—	—	—	—	—	—
N4	λ₁₁+γ₁₁	—	—	—	—	—	—
N5	λ₀₀+γ₀₀, λ₀₁+γ₀₁	—	—	—	—	—	—
N6	λ₀₀+γ₀₀, λ₁₀+γ₁₀	—	—	—	—	—	—
N7	λ₀₀+γ₀₀, λ₁₁+γ₁₁	608.264 0	636.109 1	-296.132 0	108.175 2	0.159 4	0.672 8
D8	λ₀₁+γ₀₁, λ₁₀+γ₁₀	—	—	—	—	—	—
D9	λ₀₁+γ₀₁, λ₁₁+γ₁₁	—	—	—	—	—	—
N10	λ₁₀+γ₁₀, λ₁₁+γ₁₁	—	—	—	—	—	—
N11	λ₀₀+γ₀₀, λ₀₁+γ₀₁, λ₁₀+γ₁₀	582.217 8	620.504 9	-280.108 9	57.733 2	0.139 1	0.491 5
N12	λ₀₀+γ₀₀, λ₀₁+γ₀₁, λ₁₁+γ₁₁	614.266 1	652.553 1	-296.133 0	108.172 8	0.159 4	0.672 8
N13	λ₀₀+γ₀₀, λ₁₀+γ₁₀, λ₁₁+γ₁₁	614.275 6	652.562 7	-296.137 8	108.162 9	0.159 4	0.672 7
N14	λ₀₁+γ₀₁, λ₁₀+γ₁₀, λ₁₁+γ₁₁	—	—	—	—	—	—
N15	λ₀₀+γ₀₀, λ₀₁+γ₀₁, λ₁₀+γ₁₀, λ₁₁+γ₁₁	—	—	—	—	—	—

组成部分 Component		参数 Parameter	估计值 Estimation
固定效应 Fixed-effects	截距 Intercepts	λ₀₀	-8.249 7
	截距 Intercepts	λ₁₀	-0.016 5
	协变量 Covariates	λ₀₁	-6.881 6
	协变量 Covariates	λ₁₁	0.001 1
随机效应 Random-effects	方差 Variances	σ_γ₀₀²	1.050 1e^-5
		σ_γ₀₁²	5.346 7e^-1
		σ_γ₁₀²	7.619 6e^-3
	协方差 Covariances	σ_{γ₀₀×γ₀₁}	5.389 5e^-5
		σ_{γ₀₀×γ₁₀}	-4.640 0e^-8
		σ_{γ₀₁×γ₁₀}	-3.202 3e^-2

评价指标 Indicators	基础模型 Basic model	再参数化模型 Re-parameterization model	混合效应模型 NLME model
AIC	1 072.084 0	1 070.342 0	582.217 8
BIC	1 082.526 0	1 087.745 0	620.504 9
LogLik	-533.042 2	-530.171 1	-280.108 9
RSS	1 193.661 3	1 165.440 9	57.733 2
MRE	0.263 8	0.263 0	0.139 1
RMSE	2.234 8	2.208 2	0.491 5