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庐山日本柳杉径向生长对气候响应的边际效应

花匠小妙招
2025-02-04 02:41

摘要:

目的深入认知树轮−气候要素之间的关系，揭示树木径向生长的主要限制性因素的相对贡献率及边际效应，以提升树轮在树轮生态学、树轮气候学研究中的应用价值。

方法基于庐山地区日本柳杉树轮宽度资料和庐山气象站气象资料，采用树轮气候学方法研制树轮宽度年表，基于相关分析方法初步识别树木径向生长的主要限制性因素，进而利用增强回归树分析方法揭示了庐山日本柳杉径向生长的主要影响因素相对贡献及边际效应。

结果正向影响庐山日本柳杉径向生长的因素按重要程度从大到小依次为当年1—3月平均最低气温（20.66%）、上年2—11月相对湿度（15.4%）、当年2—3月平均水汽压（9.47%），负向影响庐山日本柳杉径向生长的因素按重要程度从大到小依次为上年11月日照时数（20.81%）、上年5月最大日降水量（20.54%）、当年7月平均气温（13.11%）；树轮−气候之间的关系在阈值范围之内具有较好的线性关系，阈值范围外则不具有线性关系。

结论庐山日本柳杉径向生长受多种气候要素的综合影响，任一要素的影响均不是简单的线性关系，均存在明显的阈值效应。在分析树木径向生长对气候要素响应及进行树轮气候重建时，对边际效应问题应予以重视，以增强树轮−气候间关系的可信度及气候重建的可靠性。

Abstract:

Objective This paper aims to further understand the relationship between tree radial growth and climatic factors, reveal the relative importance as well as marginal effect of main driving climatic factors of tree radial growth, so as to enhance the application value of tree ring in the research of dendroecology and dendroclimatology.

Method Based on the tree ring width data of Cryptomeria japonica in Lushan Mountain area and the meteorological data of Lushan Mountain meteorological station, the tree ring width chronology was developed by the dendroclimatology method. The main driving climatic factors of tree radial growth were initially identified based on the correlation analysis method, and then the relative importance and marginal effect of the main influencing factors on Cryptomeria japonica radial growth were revealed by utilizing boosted regression tree (BRT) method.

Result The research results showed that the climatic factors that positively affected the radial growth of Cryptomeria japonica in Lushan Mountain, in descending order of importance, were the average minimum temperature from January to March of the current year (20.66%), the relative humidity from February to November of the previous year (15.4%), and the average vapor pressure from February to March of the current year (9.47%); the climatic factors that negatively affected the radial growth of Cryptomeria japonica in Lushan Mountain, in descending order of importance, were the sunshine hours in November of the previous year (20.81%), the maximum daily precipitation in the May of the previous year (20.54%) and the average temperature in July of the current year (13.11%). The relationship between tree ring and climate had a good linear relationship within the threshold range, and there was no linear relationship outside the threshold range.

Conclusion The radial growth of Cryptomeria japonica in Lushan Mountain is affected by many climatic factors, and the influence of each factor has obvious marginal effect. It is important to pay close attention to the problem of marginal effects when performing tree ring based climate reconstruction, which will enhance the reliability of tree ring climate relationship and climate reconstruction.

图 1 采样点位置图

Figure 1. Location of sampling sites

图 2 年表及样本量

Figure 2. Chronologies and sample size

图 3 差值年表与气候要素相关关系

TMINC1-C3为当年1—3月平均最低气温；TEMC7为当年7月平均气温；SUNP11为上年11月日照时数；WVPC2-C3为当年2—3月平均水汽压；RHP2-P11为上年2—11月相对湿度；PREP5为上年5月最大日降水量。下同。TMINC1-C3, average minimum temperature from January to March of the current year; TEMC7, average temperature in July of the current year; SUNP11, sunshine hours in November of the previous year; WVPC2-C3, average water vapor pressure from February to March of the current year; RHP2-P11, relative humidity from February to November of the previous year; PREP5, maximum daily precipitation in May of the previous year. The same below.

Figure 3. Relationship between difference chronology and climate factors

图 4 各气候变量相对重要性

Figure 4. Relative importance of climate variables

图 5 边际效应结果

Figure 5. Results of marginal effect

表 1 年表统计特征及公共区间分析结果

Table 1 Descriptive statistics of tree ring width chronology and the results of common interval analysis

年表统计量
Chronological statistics (1932—2018)公共区间统计量
Common interval statistics (1950—2016)平均敏感度
Mean
sensitivity标准差
Standard
deviation一阶自相关系数
First-order
autocorrelation
coefficient平均相关系数
Mean
correlation
coefficient树内相关系数
Within-tree
correlation
coefficient树间相关系数
Between-trees
correlation
coefficient
信噪比
Signal-to-noise
ratio样本总体代表性
Total
representativeness
of sample第1主成分
解释方差量
Variance explained
by the first
principal component 0.1330.1750.4740.3630.5570.35924.460.96139.3% [1]

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