生长季五角枫茎干水分含量序列特征及其影响因素解译

doi:10.17521/cjpe.2023.0094

摘要/Abstract

摘要：

活立木茎干水分含量作为一种典型的生理水分参数, 一直是活立木生命状态最为有效的表征参数之一, 对维持整株树木水分平衡起着极其重要的作用, 受到研究者、林木抚育者的高度重视。但活立木茎干水分含量的获取方法一直受到操作烦琐、成本昂贵、实时性不强、准确率不高及有损活立木生命等因素的影响, 限制了对其深入的应用研究。该研究创新性地采用一种具有自主知识产权的茎干水分传感器, 实时、无损地获取活立木茎干水分含量, 并对茎干水分含量序列特征及其影响因素进行较为详细的研究。以常见的绿化树种五角枫(五角槭, Acer pictum subsp. mono)为研究对象, 采用基于驻波率原理的植物茎干水分传感器实现对五角枫茎干水分含量原位且无损监测, 同步监测相关气象因子, 分析生长季不同时期茎干水分含量序列特征及其与各气象因子的关系。结果显示: (1)茎干水分含量在春季(4月)和秋季中后期(9-10月)呈“昼升夜降”的趋势, 夏季至秋季中期(5-8月)呈“昼降夜升”的趋势; 阴天会抑制茎干水分含量的日极差与极大值, 雨天时, 茎干水分含量会明显上升; 干燥天气会使茎干水分含量呈下降趋势; (2)生长季萌芽期, 对茎干水分含量变化起直接作用的气象因子主要是空气温度和饱和水汽压差, 茎干水分含量与空气温度和土壤水分含量呈显著相关关系; 生长期, 影响茎干水分含量的主导因子为饱和水汽压差和土壤温度, 茎干水分含量与饱和水汽压差和空气温度呈显著相关关系; 落叶期, 对茎干水分含量变化起直接作用的因子主要为空气温度和饱和水汽压差, 且茎干水分含量与两因子的皮尔逊相关系数较高。采用逐步回归分析法构建3个时期茎干水分含量与各气象因子的回归方程模型, 不同时期进入回归模型中的气象因子均有所不同。综上所述, 该研究揭示了在不同气象因子协同作用下生长季茎干水分含量呈现出不同的序列特征及其影响因素, 可为理解植物茎干内部水分运移规律及其环境适应机制提供一定的借鉴作用。

关键词: 五角枫, 茎干水分含量, 气象因子, 生长季

Abstract:

Aims As a typical water-related physiological parameter, the stem water content of living standing trees has always been one of the most effective characterization parameters of the life state of living standing trees, which plays an important role in maintaining the water balance of the whole tree, and has been highly valued by researchers and forest tender. However, the method of obtaining stem water content sequence of living standing trees has been affected by factors such as cumbersome operation, expensive cost, poor real-time performance, low accuracy and damage to the life of living standing trees, which limits its in-depth research and application. We innovatively adopted a stem water content sensor with independent intellectual property rights to obtain the stem water content of standing trees in real time and non-destructively, and conducted a thorough investigation of the stem water content sequence and its influencing factors.

Methods A common virescence tree species Acer pictumsubsp. mono was chosen as the research object. The stem water content sensors based on the standing wave rate principle independently developed by our team were used to achieve in-situ and non-destructive monitoring of stem water content in the growing season. Meanwhile, meteorological parameters were simultaneously measured. The sequence characteristics of stem water content and its response to meteorological factors at different stages of growing season were analyzed.

Important findings (1) In spring (April) and mid-late autumn (from September to October), the stem water content presented the rhythm of “rising in the day and falling in the night”, while from summer to middle autumn (from May to August) presented a rhythm of “falling in the day and rising in the night”; the daily range and maximum of stem water content would be inhabited under the cloudy day, while rainy days led the stem water content surge to a large value, and dry days dropped the stem water content. (2) During the germination stage, the main meteorological factors that had direct effects on the variation of stem water content were air temperature and vapor pressure deficit. During the growth period, the main meteorological factors directly affecting stem water content were vapor pressure deficit and soil temperature. During the defoliation stage, the main meteorological factors that had direct effects on stem water content were air temperature and vapor pressure deficit, and the Pearson correlation coefficients between stem water content and these two factors were higher. A stepwise regression analysis method was used to model the relationship between stem water content and various meteorological factors in these three periods, the meteorological factors entering the regression model at different periods were different. In conclusion, this study revealed that stem water content exhibited different sequence characteristics and influencing factors under the synergistic effects of different meteorological factors, which could provide some insights into the internal water transport capacity and environmental adaptation mechanism of plant stems.

Key words: Acer pictum subsp. mono, stem water content, meteorological factor, growing season

许泽海, 赵燕东. 生长季五角枫茎干水分含量序列特征及其影响因素解译. 植物生态学报, 2024, 48(6): 794-808. DOI: 10.17521/cjpe.2023.0094

XU Ze-Hai, ZHAO Yan-Dong. Interpretation of sequence characteristics and influencing factors of stem water content for Acer pictum subsp. mono in its growing season. Chinese Journal of Plant Ecology, 2024, 48(6): 794-808. DOI: 10.17521/cjpe.2023.0094

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2023.0094

https://www.plant-ecology.com/CN/Y2024/V48/I6/794

图/表 10

参考文献 48

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月份 Month	峰值时刻 Peak time	峰值 Peak value (mV)	谷值时刻 Valley time	谷值 Valley value (mV)	日极差 Extreme difference range (mV)	平均气温 Mean air temperature (℃)	最高气温 Maximum air temperature (℃)	日出时刻 Sunrise time
4月 April	13:00	483	-	-	19	18.4	26.5	05:31
5月 May	07:50	538	18:50	493	45	20.7	31.4	05:02
6月 June	06:30	621	15:30	577	62	29.2	35.6	04:44
7月 July	07:10	758	16:10	673	49	31.8	37.3	05:15
8月 August	08:50	735	16:40	714	26	28.1	32.8	05:28
9月 September	10:00	603	-	-	24	21.9	31.6	06:04
10月 October	12:20	730	-	-	33	13.1	21.5	06:26

月份 Month	峰值时刻 Peak time	峰值 Peak value (mV)	谷值时刻 Valley time	谷值 Valley value (mV)	日极差 Extreme difference range (mV)	平均气温 Mean air temperature (℃)	最高气温 Maximum air temperature (℃)	日出时刻 Sunrise time
4月 April	13:00	483	-	-	19	18.4	26.5	05:31
5月 May	07:50	538	18:50	493	45	20.7	31.4	05:02
6月 June	06:30	621	15:30	577	62	29.2	35.6	04:44
7月 July	07:10	758	16:10	673	49	31.8	37.3	05:15
8月 August	08:50	735	16:40	714	26	28.1	32.8	05:28
9月 September	10:00	603	-	-	24	21.9	31.6	06:04
10月 October	12:20	730	-	-	33	13.1	21.5	06:26

因子 Factor	简单相关系数 Simple correlation coefficient	直接通径系数 Direct path coefficient	间接通径系数 Indirect path coefficient
因子 Factor	简单相关系数 Simple correlation coefficient	直接通径系数 Direct path coefficient	T_s	T	RH	VPD	PAR	SM	合计 Total
T_s	0.404^**	-0.139	-	-0.084 8	0.004 4	-0.061 4	0.021 1	-0.050 0	-0.170 7
T	0.669^**	1.534	0.936 0	-	-0.293 0	1.287 0	0.785 4	0.681 1	3.396 5
RH	0.407^**	0.092	-0.002 9	-0.017 6	-	-0.061 3	-0.043 1	0.014 0	0.110 9
VPD	0.247^**	-1.041	-0.460 1	-0.873 4	0.693 4	-	-0.674 6	-0.216 5	-1.531 2
PAR	0.192^**	0.027	-0.004 1	0.013 8	-0.012 6	0.017 5	-	0.002 6	0.017 2
SM	0.580^**	0.115	0.041 4	0.051 1	0.017 5	0.023 9	0.010 9	-	0.144 8

因子 Factor	简单相关系数 Simple correlation coefficient	直接通径系数 Direct path coefficient	间接通径系数 Indirect path coefficient
因子 Factor	简单相关系数 Simple correlation coefficient	直接通径系数 Direct path coefficient	T_s	T	RH	VPD	PAR	SM	合计 Total
T_s	0.404^**	-0.139	-	-0.084 8	0.004 4	-0.061 4	0.021 1	-0.050 0	-0.170 7
T	0.669^**	1.534	0.936 0	-	-0.293 0	1.287 0	0.785 4	0.681 1	3.396 5
RH	0.407^**	0.092	-0.002 9	-0.017 6	-	-0.061 3	-0.043 1	0.014 0	0.110 9
VPD	0.247^**	-1.041	-0.460 1	-0.873 4	0.693 4	-	-0.674 6	-0.216 5	-1.531 2
PAR	0.192^**	0.027	-0.004 1	0.013 8	-0.012 6	0.017 5	-	0.002 6	0.017 2
SM	0.580^**	0.115	0.041 4	0.051 1	0.017 5	0.023 9	0.010 9	-	0.144 8

因子 Factor	简单相关系数 Simple correlation coefficient	直接通径系数 Direct path coefficient	间接通径系数 Indirect path coefficient
因子 Factor	简单相关系数 Simple correlation coefficient	直接通径系数 Direct path coefficient	T_s	T	RH	VPD	PAR	SM	WS	合计 Total
T_s	-0.713^**	-0.702	-	-0.470 3	0.327 8	-0.398 7	0.072 3	0.558 8	-0.153 7	-0.136 1
T	-0.855^**	0.561	0.375 9	-	-0.509 9	0.548 6	0.305 7	-0.283 9	0.280 5	0.716 9
RH	0.788^**	-0.037	0.017 3	0.033 6	-	0.034 7	0.021 5	-0.008 9	0.021 4	0.119 6
VPD	-0.857^**	-1.108	-0.629 3	-1.083 6	1.038 2	-	-0.648 2	0.415 5	-0.582 8	-1.490 2
PAR	-0.304^**	-0.068	0.007 0	-0.037 1	0.039 4	-0.039 8	-	0.002 5	-0.023 4	-0.051 4
SM	0.412^**	-0.273	0.217 3	0.138 1	-0.066 0	0.102 4	0.010 1	-	0.009 8	0.411 7
WS	-0.441^**	0.007	0.001 5	0.003 5	-0.000 4	0.003 7	0.002 4	-0.000 2	-	0.010 5