不同去趋势方法对基于Dendrometer数据的茎干水分动态分析的影响——以白扦为例

doi:10.17521/cjpe.2021.0025

摘要/Abstract

摘要：

树木茎干半径变化记录仪(Dendrometer)监测的高精度数据不仅包括木质部的年内径向生长过程, 还包含由茎干水分的消耗和补充引起的可逆变化。然而, 不同的年内生长去趋势方法获得的茎干水分波动之间的差异性仍缺乏对比研究。基于芦芽山北坡针叶林下限白扦(Picea meyeri) 2015年生长季的茎干半径变化和环境因子的实时监测数据, 使用Gompertz生长模型(GPZ)、线性生长模型(LG)、零生长模型(ZG)、日值法(D)和茎干循环法(SC)模拟并去除茎干年内的生长趋势, 然后提取5种不同类型树木水分缺乏引起的茎干收缩(TWD_GPZ、TWD_LG、TWD_ZG、TWD_D和TWD_SC)以表征茎干水分亏缺, 并进一步对比分析了不同茎干水分亏缺序列对环境中水分状况的响应特征。研究发现: (1)不同去趋势方法计算的茎干水分亏缺的趋势和幅度有所差异, 可聚类为3组: TWD_LG和TWD_ZG、TWD_GPZ以及TWD_D和TWD_SC。同组或聚类距离接近的序列在生长季内每个月份都展现出显著的相关性。然而, TWD_LG、TWD_ZG和TWD_GPZ与TWD_D和TWD_SC在8月份相关性较弱。(2) TWD_D和TWD_SC与空气饱和水汽压差(VPD)的正相关关系比TWD_GPZ、TWD_LG和TWD_ZG更加稳定, 且具有更大的相关系数。5种茎干水分亏缺序列和土壤含水量(SWC)的关系在生长季内变化很大。(3)不同去趋势方法的茎干水分亏缺都随着水分胁迫程度(VPD/SWC)升高而显著增长。当胁迫程度较低时, TWD_SC对VPD/SWC的变化最为敏感(R² = 0.39, p < 0.001), 但是与TWD_ZG差别不大(R ² = 0.37, p < 0.001); 当胁迫程度较高时, TWD_ZG对VPD/SWC的敏感性最高(R ² = 0.59, p < 0.001)。综合对比来看, 零生长模型是比较适合研究区白扦生长季内茎干水分波动的去趋势方法, 其可为干旱胁迫条件下预测研究区树木的茎干水分动态及特征提供科学依据。

关键词: 茎干半径变化记录仪, 去生长趋势, 茎干水分亏缺, 滑动相关, 干旱

Abstract:

Aims The high-precision data measured by the Dendrometer includes not only the stem radial growth process caused by the enlargement of xylem cells but also the reversible changes caused by the consumption and replenishment of the stem water. Our objectives were to assess the difference in stem water relations of Picea meyeri obtained by different de-trending methods and their responses to water conditions in soil and air.
Methods Hourly stem radius variations in P. meyeri and corresponding environmental factors were monitored in the lower limit of the coniferous forest on the northern slope of Luya Mountain, northern Shanxi Province, China. Gompertz growth model (GPZ), linear growth model (LG), and zero growth model (ZG), daily approach (D), and stem cycle approach (SC) were used to fit the stem growth trend in the growing season of 2015. Then, the growth trend and extract five different types of tree water deficit-induced stem shrinkage (TWD_GPZ, TWD_LG, TWD_ZG, TWD_D, and TWD_SC) were removed to characterize the dynamic of stem water relations. Moving window correlation (31 days) and ordinary least square regression were further employed to analyze the responses of different types of stem water relations to soil and air moisture conditions.
Important findings The results showed that: 1) stem water relations derived from different de-trending methods had contrasting trends and amplitude, which could be clustered into three groups: TWD_LG and TWD_ZG, TWD_GPZ, and TWD_D and TWD_SC. Each month, significant correlations between stem water relations in the same group or a lower clustering distance showed, however, TWD_LG, TWD_ZG, and TWD_GPZ had weaker correlations with TWD and TWD_SC in August. 2) TWD_D and TWD_SC had a closer and more stable positive relationship with vapor pressure deficit (VPD) than TWD_GPZ, TWD_LG, and TWD_ZG did. The responses of all types of stem water relations to soil water content (SWC) varied greatly during the growing season. 3) All stem water relations significantly increased as the water stress (VPD/SWC) intensified (p < 0.05). When the stress was low, TWD_SC was most sensitive to the changes of VPD/SWC (R² = 0.39, p < 0.001), whereas it was not much different from TWD_ZG (R² = 0.37, p < 0.001); when the stress was high, TWD_ZG showed the greatest sensitivity to VPD/SWC (R² = 0.59, p < 0.001). Our results suggested that the zero-growth model was more suitable to detrend the stem radius variations during the growing season, and provided a crucial reference for predicting the stem water status and dynamics, especially under drought stress.

Key words: Dendrometer, growth detrend, stem shrinkage, moving window correlation, drought

薛峰, 江源, 董满宇, 王明昌, 丁新原, 杨显基, 崔明皓, 康慕谊. 不同去趋势方法对基于Dendrometer数据的茎干水分动态分析的影响——以白扦为例. 植物生态学报, 2021, 45(8): 880-890. DOI: 10.17521/cjpe.2021.0025

XUE Feng, JIANG Yuan, DONG Man-Yu, WANG Ming-Chang, DING Xin-Yuan, YANG Xian-Ji, CUI Ming-Hao, KANG Mu-Yi. Influence of different de-trending methods on stem water relations of Picea meyeri derived from Dendrometer measurements. Chinese Journal of Plant Ecology, 2021, 45(8): 880-890. DOI: 10.17521/cjpe.2021.0025

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2021.0025

https://www.plant-ecology.com/CN/Y2021/V45/I8/880

图/表 10

图1 气象站记录的山西芦芽山地区2015年月平均气温和月降水量变化与多年平均值(1957-2016年)的对比。

Fig. 1 Comparison of the mean monthly air temperatures and the monthly precipitation for the year 2015 and the 60 a average (1957-2016) of Luya Mountain, Shanxi Province, China.

表1 山西芦芽山白扦监测样树的基本信息

Table 1 Characteristics of four Picea meyeri sample trees of Luya Mountain, Shanxi Province, China

编号 Tree number	树高 Tree height (m)	胸径 Dimeter at breast height (cm)	冠幅 Crown dimeter (m)
1	8.0	14.7	2.0
2	9.0	16.9	2.7
3	12.0	20.1	3.5
4	9.0	18.8	2.5

图2 山西芦芽山2015年生长季白扦茎干半径变化和不同方法模拟的生长过程。

Fig. 2 Hourly time series of stem radial variations (SRV) and growth process calculated by different detrending approaches for Picea meyeri of Luya Mountain, Shanxi Province, China in the growing season of 2015. DOY, day of the year.

表2 山西芦芽山白扦不同去趋势的茎干水分亏缺(TWD)的特征参数

Table 2 Main parameters of tree water deficit-induced stem shrinkage (TWD) calculated by different detrending methods of Picea meyeri on Luya Mountain, Shanxi Province, China

	TWD_GPZ	TWD_LG	TWD_ZG	TWD_D	TWD_SC
平均值 Mean (μm)	100.2	328.2	262.0	126.8	144.6
标准差 SD (μm)	108.0	155.3	159.9	82.4	90.3
最大值 Maximum (μm)	388.8	685.9	630.1	367.8	398.2
最大值出现时间 Date of maximum occurrence (DOY)	241	241	241	133	133

图3 不同去趋势方法计算的山西芦芽山白扦茎干水分亏缺(TWD)。D, 日值法; GPZ, Gompertz生长模型; LG, 线性生长模型; SC, 茎干循环法; ZG, 零生长模型。虚线为平均值。

Fig. 3 Tree water deficit-induced stem shrinkage (TWD) of Picea meyeri on Luya Mountain, Shanxi Province, China, calculated by different detrending approaches. D, daily approach; GPZ, Gompertz model; LG, linear growth model; SC, stem cycle approach; ZG, zero growth model. Dashed lines indicate the average. DOY, day of the year.

图4 山西芦芽山白扦不同去趋势茎干水分亏缺(TWD)序列的聚类分析。D, 日值法; GPZ, Gompertz生长模型; LG, 线性生长模型; SC, 茎干循环法; ZG, 零生长模型。

Fig. 4 Cluster analysis of tree water deficit-induced stem shrinkage (TWD) of Picea meyeri on Luya Mountain, Shanxi Province, China, with different detrending methods. D, daily approach; GPZ, Gompertz model; LG, linear growth model; SC, stem cycle approach; ZG, zero growth model.

图5 山西芦芽山白扦不同去趋势茎干水分亏缺(TWD)在不同月份的相关系数矩阵。D, 日值法; GPZ, Gompertz生长模型; LG, 线性生长模型; SC, 茎干循环法; ZG, 零生长模型。*, p < 0.05; **, p < 0.01; ***, p < 0.001。

Fig. 5 Correlation matrix of tree water deficit-induced stem shrinkage (TWD) relations of Picea meyeri on Luya Mountain, Shanxi Province, China, developed from different detrending approaches in different months. D, daily approach; GPZ, Gompertz model; LG, linear growth model; SC, stem cycle approach; ZG, zero growth model. *, p < 0.05; **, p < 0.01; ***, p < 0.001.

图6 山西芦芽山白扦不同去趋势茎干水分亏缺(TWD)与饱和水汽压差和土壤含水量的滑动相关(31天)。D, 日值法; GPZ, Gompertz生长模型; LG, 线性生长模型; SC, 茎干循环法; ZG, 零生长模型。虚线表示p < 0.05.

Fig. 6 Moving window correlation (31 days) between tree water deficit-induced stem shrinkage (TWD) of Picea meyeri on Luya Mountain, Shanxi Province, China, and vapor pressure (VPD) and soil water content (SWC). D, daily approach; GPZ, Gompertz model; LG, linear growth model; SC, stem cycle approach; ZG, zero growth model. DOY, day of the year. Dotted lines indicate p < 0.05.

图7 山西芦芽山样点环境中水分胁迫因子的季节变化。SWC, 土壤含水量; VPD, 饱和水汽压差。虚线为平均值。

Fig. 7 Seasonal variations of water stress factor at monitoring site on Luya Mountain, Shanxi Province, China. SWC, soil water content; VPD, vapor pressure deficit. DOY, day of the year. The dashed line represents the average.

图8 山西芦芽山白扦不同去趋势茎干水分亏缺(TWD)对环境中水分胁迫因子(VPD/SWC)的敏感性。D, 日值法; GPZ, Gompertz生长模型; LG, 线性生长模型; SC, 茎干循环法; ZG, 零生长模型。SWC, 土壤含水量; VPD, 饱和水汽压差。虚线为VPD/SWC的平均值(2.7 kPa·m3·m-3)。

Fig. 8 Sensitivity of tree water dificit-induced stem shrinkage (TWD) relations of Picea meyeri on Luya Mountain, Shanxi Province, China, with different de-trending methods to water stress factors. D, daily approach; GPZ, Gompertz model; LG, linear growth model; SC, stem cycle approach; ZG, zero growth model. SWC, soil water content; VPD, vapor pressure deficit. The dashed line represents the average of VPD/SWC = 2.7 kPa·m3·m-3.

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