林业科学  2016, Vol. 52 Issue (1): 136-142   PDF    
DOI: 10.11707/j.1001-7488.20160116
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文章信息

王海珍, 韩路, 徐雅丽, 牛建龙, 于军
Wang Haizhen, Han Lu, Xu Yali, Niu Jianlong, Yu Jun
灰胡杨叶片气孔导度特征及数值模拟
Characteristics of Stomatal Conductance of Populus pruinosa and the Quantitative Simulation
林业科学, 2016, 52(1): 136-142
Scientia Silvae Sinicae, 2016, 52(1): 136-142.
DOI: 10.11707/j.1001-7488.20160116

文章历史

收稿日期:2014-11-04
修回日期:2015-10-21

作者相关文章

王海珍
韩路
徐雅丽
牛建龙
于军

灰胡杨叶片气孔导度特征及数值模拟
王海珍1, 2, 韩路1, 2, 徐雅丽1, 牛建龙1, 于军1    
1. 塔里木大学植物科学学院 阿拉尔 843300;
2. 新疆生产建设兵团塔里木盆地生物资源保护利用重点实验室 阿拉尔 843300
摘要[目的] 构建适用于极端干旱荒漠区灰胡杨叶片气孔导度对环境因子响应的数学模型,为准确定量探讨灰胡杨叶片气孔调节和构建干旱荒漠区的水碳循环耦合机制模型奠定基础。[方法] 以塔里木河流域荒漠河岸林建群种灰胡杨为研究对象,利用LI-6400光合测定仪于2012年7-9月、2013年6-9月测定灰胡杨叶片气体交换参数与环境因子的日变化,利用逐步回归方法分析灰胡杨叶片气孔导度(Gs)对环境因子的响应,并应用国际上2类构建机制不同的代表性气孔导度模型对其气孔导度变化进行模拟及验证比较。[结果] 不同年份生长季各月灰胡杨叶片Gs的日变化均呈单峰曲线,峰值大小、出现时间与变幅不同,其中以9月峰值出现时间最早,日变幅最大,6月日变幅最小。Gs对环境因子变化敏感,与光合有效辐射(PAR)、大气CO2浓度(Ca)、大气湿度(RH)呈正相关,而与水汽压亏缺(VPD)、大气温度(Tair)呈负相关,尤其是PAR,VPD和Tair对全天与上午时段Gs的影响最显著,而下午时段Gs还受Ca,RH影响,表明不同时段灰胡杨Gs受不同的环境因子调控。利用国际上2类代表性Gs模型拟合并建立全天、上午与下午不同时段灰胡杨Gs的数学模型,Jarvis气孔导变模型对Gs变异程度的总体解释能力分别为69.1%,62.2%和63.3%,Leuning-Ball气孔导变模型对Gs变异程度的总体解释能力分别为53.5%,30.6%和44.5%。Jarvis气孔导变模型拟合全天、上午与下午时段Gs的效果均优于Leuning-Ball气孔导变模型,而Leuning-Ball气孔导变模型拟合下午时段Gs的效果优于上午时段,这与不同时段调控Gs的环境因子不同有关。依据野外实测数据对2类代表性Gs模型验证表明,Jarvis气孔导变模型比Leuning-Ball气孔导变模型更适合灰胡杨叶片Gs模拟,其可有效改善气孔导度环境响应行为的数值模拟效果。[结论] 生长季不同时段影响灰胡杨Gs的环境因子不同,Jarvis非线性气孔导度模型构建的不同时段灰胡杨气孔导度模型均优于Leuning-Ball线性气孔导度模型,其在极端干旱荒漠区具有更好的适用性。据此,构建适用于塔里木极端干旱荒漠区灰胡杨叶片气孔导度对环境因子的响应模型;Gs=PAR(0.001T2air+0.013Tair-0.090)/((260.443+PAR)(-0.219+VPD))。
关键词极端干旱区    灰胡杨    气孔导度    环境因子    模拟    
Characteristics of Stomatal Conductance of Populus pruinosa and the Quantitative Simulation
Wang Haizhen1, 2, Han Lu1, 2, Xu Yali1, Niu Jianlong1, Yu Jun1    
1. College of Plant Science, Tarim University Alar 843300;
2. Key Laboratory of Protection and Utilization of Biological Resource in Tarim Basin, Xinjiang Production&Construction Groups Alar 843300
Abstract: [Objective] The responses model of leaf stomatal conductance to environmental factors of Populus pruinosa in different periods constructed would be very helpful to elucidate stomatal regulation behavior of P. pruinosa, and to further simulate the dynamics of leaf photosynthesis and to develop a new water-carbon coupling cycle model in an extreme arid terrestrial ecosystem. P. pruinosa has been declining in recent years because of the increasingly worsening ecological environment, mainly caused by increased human water consumption. Up to now, the adjustment mechanisms of its stomatal conductance (Gs) are not clear. Our study is to elucidate current understanding of the mechanism that underlay the responses of stomatal conductance to variable environmental factors, and thereby to build up a model that expresses the relationship between stomatal conductance and environmental factors. This study would help us to further understand the photo-physiological characteristics of P. pruinosa and provide valuable information for protection of this vulnerable species. [Method] P. pruinosa, a constructive species of desert riparian forests in an extreme arid region in northwest China, was used as experimental material in this study. The leaf gas exchange parameters and environmental factors were measured with Li-6400 portable photosynthesis system during June to September in 2012 and 2013. The dynamic characteristics and the relationship between stomatal conductance and environmental factors were analyzed based on field observation data. Further, Jarvis and Leuning-Ball models were used to simulate the dynamic process of leaf stomatal conductance, and applicability of the two models in extreme arid region was compared. [Result] The diurnal courses of stomatal conductance of P. pruinosa were a single peak curve in growth season, there were obviously differences in peak values, time and amplitude in every months. Especially, peak time appeared the earliest and largest amplitude in September, and minimum amplitude of peak value in June. The leaf stomatal conductance was sensitive to photosynthesis active radiation, vapor pressure deficit and air temperature. The leaf stomatal conductance increased with photosynthesis active radiation, atmospheric CO2 concentration and air humidity, and decreased with increase of the vapor pressure deficit and air temperature. Statistical analysis showed that photosynthesis active radiation, vapor pressure deficit and air temperature significantly affected stomatal conductance of whole day and forenoon, while the stomatal conductance in afternoon was affected obviously by atmospheric CO2 concentration and air humidity. Stomatal conductance of P. pruinosa in different periods was regulated by the different environmental factors. The fitted models of stomatal conductance of P. pruinosa in different periods were simulated and constructed with two representative stomatal conductance models, Jarvis model could explain on average 69.1%, 62.2%, and 63.3% of variation and Leuning-Ball model could explain on average 53.5%, 30.6%, and 44.5% of variation in the observed stomatal conductance at whole day, forenoon and afternoon, respectively. The sensitivity and fitting effect of Jarvis model was better than that of Leunning-Ball model at different periods. The fitting effect of Leunning-Ball model in afternoon was better than that in forenoon, indicating that the environmental factors that affected stomatal conductance were different in different periods. The validations of Leuning-Ball linear and Jarvis non-linear models based on field data of leaf stomatal conductance indicated that Jarvis model was better estimation of stomatal conductance than Leuning-Ball model, and Jarvis model could improve the simulation effect of stomatal conductance. [Conclusion] The environmental factors of different periods affecting stomatal conductance of P. pruinosa were obviously different in growth seasons. The sensitivity and fitting effect of Jarvis non-linear stomatal conductance models were better than that of Leunning-Ball linear model at different periods, it had better applicability in the extremely arid-desert region. The relationship among leaf Gs and environmental factors in extremely arid Tarim basin could be expressed as:Gs=PAR(0.001Tair2+ 0.013 Tair -0.090)/((260.443+PAR)(-0.219+VPD)).
Key words: extreme arid region    Populus pruinosa    stomatal conductance    environmental factors    simulation    

气孔是植物发育的具有复杂调节功能的器官(王玉辉等,2000),可调控植物体与外界环境之间的水气交换(郑云普等,2015),并随着所处的生境不同而发生变化,不仅是影响植物光合气体交换过程的关键要素,也是调控土壤-植被-大气连续体之间物质和能量交换的关键环节(唐凤德等,2008)。气孔开闭在某种程度上反映了植物的新陈代谢状况,其对环境响应的敏感度已成为评价植物抗旱性能的重要指标(司建华等,2008)。气孔调节在植物适应全球气候变化和逆境生存中扮演着关键角色,探讨植物气孔运动的外界环境因素和内在生理活动的影响及气孔开闭对生境变化的响应机制,有助于了解植物光合作用的限制因素、水分利用效率以及拟合作物生产力。同时,气孔导度模拟与模型构建可为揭示植物与大气间物质循环、能量流动过程和植被对环境变化的生理生态适应机制提供理论依据,从而进一步深入阐明植物气体交换特性与气孔调控机制(鱼腾飞等,2012),对构建全球不同类型生态系统的水碳循环耦合模型、促进植被恢复与生态环境改善具有重要意义。

灰胡杨(Populus pruinosa)是极端干旱荒漠区特有的珍稀渐危木本植物,集中分布于我国新疆塔里木河流域的上游区域,与胡杨(P.euphratica)、柽柳(Tamarix ramosissima)等植物构成独特的荒漠河岸林景观,在防风固沙、防治荒漠化和维系绿洲生态安全、保障社会经济持续发展中发挥着重要作用。近60年以来,受全球气候变暖和南疆大规模毁林开荒、大水漫灌等人类活动的影响,塔里木河河水径流量持续减少,地下水埋深不断降低,荒漠生态系统格局与过程被改变,植被稀疏,长势衰败,土地沙化,因此,挽救塔里木河流域极端干旱荒漠区的灰胡杨林迫在眉捷。目前,国内外对胡杨的生理生态研究较多(Gries et al., 2003; Chen et al., 2011; Overdieck et al., 2013),主要集中于光合作用动态过程、影响因素(于军等,2008)、光响应参数拟合(伍维模等,2007)、蒸腾耗水特性(王海珍等,2010)及生理生态适应机制等方面(Bogeat-Triboulot et al., 2007)。尽管已有研究对灰胡杨的光合气体交换特性和生理生态适应机制有所阐述,但对灰胡杨气孔导度的数值模拟尚未见报道。鉴于此,本文以塔里木盆地灰胡杨为研究对象,根据2年野外气体交换参数的实测数据,探讨其气孔导度对干旱荒漠环境的生态响应方式,并以国际上2类典型的气孔模型模拟和建立不同时段灰胡杨叶片的气孔导度模型,以期为构建适合极端干旱荒漠区植物水碳循环耦合模型及揭示荒漠植物与大气间物质、能量的交换机制提供理论依据。

1 材料与方法 1.1 试验材料

在新疆叶尔羌河下游阿瓦提县丰收三场(80°25' E,39°40' N)大面积集中分布的天然灰胡杨林内选择地势平坦、长势良好的灰胡杨样方(20 m×20 m),测定每木胸径等指标。根据灰胡杨径级分布选出无病虫害、枝叶繁茂的5~8株作为标准木。

1.2 试验方法

于2012年7—9月、2013年6—9月中旬晴朗天气下,利用光合测定仪(LI-6400,USA)测定标准木树冠外围小枝上位置相同成熟叶的气孔导度(Gs)、净光合速率(Pn)和环境因子日动态,每株标准木测定3个叶片,从8:00开始每隔2 h测定1次相关气体交换参数,于20:00结束。

1.3 数据处理

采用Orign8.0软件作图,并利用Statistic统计软件进行参数估算。模型拟合效果依据决定系数(R2)和均方根误差(root mean sguare error,RESM)(钟楚等,2013)来判定。

2 结果与分析 2.1 灰胡杨叶片气孔导度动态

植物叶片气孔导度(Gs)对诸如光合有效辐射、温度、水汽压亏缺及环境中CO2浓度等环境因子反映十分敏感(Kim et al., 1991)。由图 1可知,不同年份生长季各月灰胡杨叶片Gs的日变化均呈单峰曲线,随着光合有效辐射(PAR)增加,Gs逐渐上升,于10:00—12:00达到1天中的最高峰,随后一直持续下降。2012,2013年中除9月Gs峰值均出现在上午10:00外,其余月份Gs峰值出现时间不同。而且不同月份Gs也不同,2012年日平均Gs依次为9月>7月>8月,2013年则为8月>9月>7月>6月; 9月Gs日变幅最大(37.01%),6月日变幅最小。可见,晴朗天气中午由于PAR、大气温度(Tair)和叶内外水汽压差(VPD)都达到了1天内的较高值,此时气孔适时关闭可避免植物体内水分大量散失和维持木质部生理活动。这表明在极端干旱荒漠区,灰胡杨气孔的自我调控是对极限逆境的一种自我调整和适应。

图1 灰胡杨叶片气孔导度动态 Fig.1 Dynamics of leaf stomatal conductance(Gs)of P.pruinosa
2.2 灰胡杨叶片气孔导度对环境因子的响应

1)光合有效辐射(PAR)H 由图 2A可知,灰胡杨Gs随PAR升高而增大,二者呈幂函数关系。PAR<100 μmol ·m-2s-1Gs上升较快,PAR>100 μmol ·m-2s-1时上升速度减慢,表明高PAR对灰胡杨Gs有制约作用。

2)空气温度(Tair)H 由图 2B可知,灰胡杨GsTair呈二次曲线关系。GsTair升高逐渐增大,在25 ℃左右达到最大,此后随Tair升高Gs逐渐减小,表明灰胡杨可通过降低Gs开张程度来避免高温伤害与树体水分损失,以适应极端干旱荒漠生境。

图2 灰胡杨叶片气孔导度对环境因子的响应 Fig.2 The response of leaf stomatal conductance(Gs)of P.pruinosa to environmental factors

3)相对湿度(RH)H 由图 2C可知,灰胡杨Gs与RH呈二次曲线关系。清晨RH较高,Gs随RH上升而增大,在RH为53%左右达到最大,此后随RH上升逐渐下降。维持较大气孔导度的RH在30%~55%之间,过高与过低均会降低Gs,这是由于RH受PAR和Tair的控制而引起Gs变化。

4)水汽压亏缺(VPD)H VPD是温度和空气湿度的综合反应(李炜等,2012)。由图 2D可知,灰胡杨随VPD的升高而增大,之后又随VPD升高逐渐降低。这是因为低VPD下灰胡杨蒸腾耗水相对较弱,水分供给基本能满足植物水分需求,未明显引起保卫细胞失水收缩; 而高VPD下因蒸腾失水过多,

水分供应不足引起保卫细胞大量失水,导致Gs逐渐关闭。过高VPD下,植物气孔的适时关闭是减少水分利用数量、提高水分利用效率和维持木质部生理活动的关键行为(李炜等,2012)。

5)大气CO2 浓度(Ca)H 由图 2E可知,灰胡杨GsCa的升高而增大,至400 μmol ·mol-1左右开始逐渐降低,二者呈二次曲线关系。

2.3 环境影响因子分析

根据2年野外测定的Gs,PAR,VPD,Tair,RH和大气CO2浓度(Ca)数据,采用逐步回归方法寻找影响灰胡杨叶片Gs的主要环境因子。由表 1可知,灰胡杨叶片气孔导度的回归方程表现出极显著的回归关系,2年中VPD,Tair,PAR出现频率较高,而RH,Ca仅出现3次。全天、上午和下午不同时段各环境因子对Gs的影响程度不同,全天Gs主要由VPD,Tair,PAR决定,上午主要由VPD,Tair决定,而下午则由VPD,Tair,RH决定。总体来看,整个生长季6—9月,PAR,VPD,Tair 3个环境因子对Gs的影响最为显著,而Ca,RH相对较弱。

表1 灰胡杨叶片气孔导度对环境因子的响应 Tab. 1 The effect of leaf stomatal conductance (Gs)of P. pruinosa on environmental factors
2.4 灰胡杨叶片气孔导度响应模型拟合

影响植物Gs的环境因子众多,作用不同。目前,国际上有2类构建机制不同的代表性Gs模型(Jarvis,1976; Ball et al., 1987; Leuning,1995)。

1)Jarvis气孔导度模型H 影响荒漠河岸林灰胡杨叶片Gs的主要环境因子为PAR,Tair和VPD(表 1),因此,众多学者通过科学研究建立了Gs与环境因子(PAR,VPD,Tair)之间的数量关系(Kim et al., 1991; Turner et al., 1984; Hofstra et al., 1969),并将其应用到Jarvis气孔导度模型(Jarvis,1976)中,形成了Gs对环境因子的响应数学模型:

${G_s} = PAR{{{\alpha _3}T_{air}^2 + {\alpha _4}{T_{air}} + {\alpha _5}} \over {\left( {{\alpha _1} + PAR} \right)\left( {{\alpha _2} + VPD} \right)}}$ (1)
式中α1,α2,α3,α4,α5均为常量。

将野外测定的相关数据(PAR,VPD,TairGs)代入方程(1),拟合参数(表 2)与构建模型如下:

全天:${G_s} = PAR{{0.001T_{air}^2 + 0.013{T_{air}} - 0.090} \over {\left( {260.443 + PAR} \right)\left( { - 0.219 + VPD} \right)}}$
上午:${G_s} = PAR{{0.001T_{air}^2 + 0.014{T_{air}} - 0.173} \over {\left( {246.369 + PAR} \right)\left( { + 0.256 + VPD} \right)}}$
下午:${G_s} = PAR{{0.006T_{air}^2 + 0.370{T_{air}} + 6.582} \over {\left( {134.766 + PAR} \right)\left( { - 0.949 + VPD} \right)}}$

表2 模型拟合参数 Tab. 2 Estimated parameters at Jarvis and Leuning-Ball models

该模型对全天、上午和下午时段灰胡杨叶片气孔导度变异程度的总体解释能力分别为69.1%,62.2%和63.3%,参数估算时模拟值与实测值的相关系数分别为0.829 6,0.799 8和0.550 7,达到极显著水平(P<0.01)。这说明非线性气孔导度模型能够较好说明荒漠河岸林灰胡杨叶片气孔导度与环境因子之间的动态关系。

2)Leuning-Ball气孔导度模型Ball等(1987)研究指出GsPn呈线性相关,提出了代表性的线性Gs模型:

${G_s} = m{{{P_n} \times RH} \over {{C_a}}} + b$。

Leuning(1995)指出Ball方程存在的缺陷,认为Gs对VPD的响应灵敏性强于对RH,因此改进Ball模型用CaΓ (CO2补偿点)代替Ca,改进后的Leuning-Ball气孔导度模型为:

${G_s} = m{{{P_n}} \over {\left( {{C_a} - \Gamma } \right)\left( {1 + VP{D_s}/VP{D_0}} \right)}} + {G_{s0}}$
式中:VPDs是叶面水汽压亏缺(Pa);VDP0是气孔导度对VPD敏感的经验常数;Gs0是在光补偿点处的Gs值。

基于王海珍等(2015)估算灰胡杨叶片在25 ℃时的Γ 值和2年野外测定的PAR,VPD,GsCaPn数据,代入Leuning-Ball 气孔导度模型,参考唐凤德等(2008)方法估算出相关参数(表2)。不同时段灰胡杨叶片Gs模型为:

全天:${G_s} = 161.125{{{P_n}} \over {\left( {{C_a} - \Gamma } \right)\left( {1 + VP{D_s}/0.066} \right)}} + 0.099$
上午:${G_s} = 246.355{{{P_n}} \over {\left( {{C_a} - \Gamma } \right)\left( {1 + VP{D_s}/0.035} \right)}} + 0.139$
下午:${G_s} = 85.694{{{P_n}} \over {\left( {{C_a} - \Gamma } \right)\left( {1 + VP{D_s}/0.073} \right)}} + 0.162$

该模型对全天、上午和下午时段灰胡杨叶片气孔导度变异程度的总体解释能力分别为53.5%,30.6%和44.5%,参数估算时模拟值与实测值的相关系数分别为0.730 7,0.797 7和0.672 1,达极显著水平(P<0.01)。从模型解释能力来看,Leuning-Ball气孔导度模型拟合下午时段灰胡杨叶片Gs的精准度明显高于上午时段,由于此模型中引入了RH和Ca,与逐步回归分析结果一致。

综合比较上述模型,不同时段Jarvis气孔导度模型的解释能力均优于Leuning-Ball气孔导度模型。H 2.5 2类代表性模型精度对比H W S 由图 3A可知,Jarvis气孔导度模型模拟未参与模型构建的灰胡杨Gs实测数据的模拟值与实测值的相关系数r(0.830 4)、直线斜率b(0.670 5)均大于Leuning-Ball气孔导度模型的r(0.730 7)和b(0.533 8)。同时,利用Jarvis和Leuning-Ball气孔导度模型对灰胡杨叶片Gs日动态进行模拟计算,Jarvis气孔导度模型的模拟值与实测值较接近且日变化规律更相似(图 3B),表明Jarvis非线性气孔导度模型优于Leuning-Ball线性气孔导度模型,这是由于气孔导度对环境因子的响应是一种非线性的(表 1)。从不同时段2类模型Gs拟合值的均方根误差(RMSE)(表 3)可看出,Jarvis气孔导度模型拟合全天、上午与下午时段的效果均优Leuning-Ball气孔导度模型,但下午时段Leuning-Ball气孔导度模型拟合效果优于上午时段,这与不同时段Gs拟合模型的R2表现一致。此外,GsPn间呈线性关系这一试验条件是Ball模型构建的前提(Ball et al., 1987)。本研究中灰胡杨叶片GsPn间呈极显著正相关,但二者呈二次曲线关系(图 4),这与Ball模型构建机制不一致,从而导致Ball模型拟合效果不如Jarvis模型。

图3 气孔导度模型与Leuning-Ball气孔导度模型的验证比较 Fig.3 Validation and comparison of Jarvis model and Leuning-Ball model

图4 净光合速率与气孔导度的关系 Fig.4 The relationship between net photosynthesis rate(Pn) and stomatal conductance(Gs)

表3 2类模型拟合效果评价(RMSE) Tab. 3 RMSEs evaluation of simulation effect by two models
3 结论

1)不同年份生长季各月灰胡杨叶片Gs日变化均呈单峰曲线,Gs随着环境因子的变化而变化,10:00—12:00达峰值。不同月份Gs峰值出现时间与大小存在明显差异,日平均Gs依次为8月>9月>7月>6月(2013年),这与塔里木河洪水发生时间、低水多变的生境有关。

2)荒漠河岸林灰胡杨叶片Gs与环境因子关系较密切,逐步回归分析表明,PAR,VPD,Tair3个环境因子对Gs的影响最为显著,其次为Ca与RH。上午、下午不同时段影响灰胡杨Gs的主要环境因子不同,上午Gs主要由VPD,Tair,PAR决定,下午还受Ca,RH的影响。

3)利用Jarvis模型和Leuning-Ball模型拟合上午、下午不同时段灰胡杨Gs动态,结果表明Jarvis模型拟合上午、下午时段灰胡杨Gs的效果相似; 但Leuning-Ball模型拟合下午时段灰胡杨Gs的效果优于上午,因为上午、下午不同时段影响Gs的环境因子不同及此模型中引入了RH或Ca

4)拟合效果表明,以Jarvis非线性模型为模板建立的不同时段灰胡杨气孔导度模型优于Leuning-Ball线性模型,这是因为灰胡杨GsPn间呈二次曲线关系,与Ball模型构建的前提条件不一致导致的。塔里木极端干旱荒漠区灰胡杨叶片气孔导度的最优响应模型为:Gs=PAR(0.001Tair2+ 0.013 Tair -0.090)/[(260.443+PAR)(-0.219+VPD)]。

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