膨压丧失点和木质部栓塞抗性:预测植物和生态系统对干旱反应的关键水力特征
The Leaf Turgor Loss Point and Xylem Embolism Resistance: Key Hydraulic Traits for Predicting Plant and Ecosystem Responses to Drought
摘要: 随着全球变暖,干旱事件的频率与强度持续增加,对生态系统的影响日益加剧。叶片膨压丧失点(πₜₗₚ)与木质部栓塞抗性(P50)是评估植物干旱耐受性的两个关键水力性状。近年研究表明,πₜₗₚ与物种分布、水力安全边界、叶经济谱及生态系统对气候变化的响应密切相关,而P50是预测森林衰退与树木死亡的核心指标之一。本文系统综述了πₜₗₚP50的生物学意义、决定因素、可塑性及其在预测物种与生态系统干旱响应中的潜力,旨在为全球变化背景下的植物生态学与森林管理提供理论依据。
Abstract: With the global warming, the frequency and intensity of drought events continue to increase, exerting growing impacts on ecosystems. The leaf turgor loss point (πₜₗₚ) and xylem embolism resistance (P50) are key hydraulic traits for assessing plant drought tolerance. Recent studies have shown that πₜₗₚ is closely related to species distribution, hydraulic safety margins, the leaf economics spectrum, and ecosystem responses to climate change, while P50 serves as a core indicator for predicting forest decline and tree mortality. This paper provides a systematic review of the biological significance, determinants, plasticity, and predictive potential of πₜₗₚ and P50 for species- and ecosystem-level drought responses. It aims to offer a theoretical foundation for plant ecology and forest management in the context of global change.
文章引用:王璐. 膨压丧失点和木质部栓塞抗性:预测植物和生态系统对干旱反应的关键水力特征[J]. 林业世界, 2026, 15(2): 400-405. https://doi.org/10.12677/wjf.2026.152049

1. 引言

植物水力学(plant hydraulics)研究植物如何通过木质部将水分从土壤运输至地上部分,并维持气孔导度、蒸腾作用与细胞膨压[1]。这一过程不仅决定了植物的光合效率和生长潜力,还在干旱、病害及火灾风险的调控中发挥核心作用[1]。近年来,随着气候变化导致的干旱频发和极端高温,全球农林生态系统正面临前所未有的挑战[1]。作物减产、森林大面积死亡、病虫害频发及火灾加剧等现象均与植物水力学功能失衡密切相关[1]。水力功能失效被普遍认为是干旱导致植物死亡的主要机制[1]-[3]。其中,P50是评估木质部抗空穴化能力的经典指标[4],在不同物种和品种间具有较高的保守性[1] [5] [6]。随着干旱加剧,植物的光合作用、气孔导度和细胞活性会依次下降,最终可能触发死亡、加剧病虫害扩散或提升火灾风险[1] [2]

2. 叶片膨压丧失点(πₜₗₚ)与木质部栓塞抗性(P50)的概念与意义

2.1. πₜₗₚ的定义与生态意义

叶片膨压丧失点水势(Leaf turgor loss point, πₜₗₚ; MPa)是指叶片细胞失去膨压、叶片开始萎蔫时的水势(如表1)。其测定通常采用压力-容积曲线(Pressure-volume curves,简称P-V曲线) [7]或蒸汽压力渗透仪[8]πₜₗₚ反映了植物在脱水过程中维持细胞膨压的能力,是全球尺度上预测植物干旱耐受性的重要指标[9];研究表明,πₜₗₚ在预测植物耐旱性方面通常优于比叶质量(LMA)和其他P-V曲线参数(如表1) [9]。植物可通过渗透调节(降低饱和渗透势πₒ)、弹性调节(改变弹性模量ε)或质外体水分配(调节质外体含水量af)来调整πₜₗₚ,以适应水分胁迫[9]

全球整合分析表明,πₜₗₚ与生境水分有效性显著相关,即干旱地区的物种通常具有更负的πₜₗₚ (即耐受性更强),而湿润地区(如热带雨林、红树林)的物种πₜₗₚ则相对较高[10]。这一规律印证了πₜₗₚ在预测植物干旱响应方面的能力[9]。针对中国9个主要木本生物群落的研究也发现,πₜₗₚ与干旱指数呈显著正相关,进一步证实了其作为生物群落尺度旱耐指示性状的潜力[10]。此外,πₜₗₚ对常绿和落叶树种的分布预测能力存在差异。在巴拿马的研究中,常绿树种的πₜₗₚ能显著预测其在局域和区域尺度上对干旱生境的偏好,解释率可达28%~32%;而落叶树种的πₜₗₚ则与其生境偏好无关[11]。这可能是因为落叶树种通过落叶策略来规避干旱胁迫,导致πₜₗₚ对落叶物种的生理意义在生长季减弱。

Table 1. The leaf functional traits in this study

1. 本文涉及到的叶片功能性状

Traits

Abbr.

性状

Unit

Definition

Leaf turgor loss point

πₜₗₚ

膨压丧失点

MPa

Leaf water potential at which turgor pressure is zero, which is a determinant of the tolerance of leaves to drought stress

Osmotic potential at full rehydration

πo

饱和渗透势

MPa

Solute concentration in cells

Modulus of elasticity

ε

弹性模量

MPa

Wall stiffness, calculated from symplastic water content

Apoplastic fraction

af

质外体含水量

%

Extracellular water content

Total relative water content at turgor loss point

RWCₜₗₚ

膨压丧失点相对含水量

%

Leaf hydration at which cells become flaccid

Leaf mass per area

LMA

比叶重/比叶质量

g∙cm–2

Leaf dry mass per area, a key leaf functional trait that relates to plant performance such as growth and defense

Minimum leaf water potential

Ψmin

叶片最小水势

MPa

The lowest midday leaf water potential experienced by a plant species during a year

Leaf water potential at 50% loss of hydraulic conductance

P50

叶片水力导度丧失50%时水势

MPa

An estimation of the vulnerability of leaf hydraulic conductance to decreasing leaf water potential

Leaf hydraulic safety margin

HSMleaf

叶片水力安全边界

MPa

The difference between Ψmin and P50minP50), which indicates potential risk of hydraulic failure in leaves

Specific leaf area

SLA

比叶面积

cm2∙g–1

Leaf area per dry mass, a key leaf functional trait that relates to plant performance such as growth and defense

Leaf density

LD

叶片密度

g∙cm–3

Leaf dry mass per volume, which is one of the components of SLA and relates to photosynthesis and resistance

Maximum CO2 assimilation rate

Amax

最大光合速率

nmol∙g–1∙s–1

The maximum CO2 assimilation capacity of leaves measured during the wet season under optimal conditions

Leaf life span

LLS

叶片寿命

month

The time elapsed between leaf emergence and fall, which is considered as a balance between lifetime carbon gain and its cost

2.2. P50的定义与水力重要性

P50 (MPa)是指木质部水力导度损失50%时的水势(如表1),是衡量木质部栓塞抗性的核心指标[4]。近年全球研究表明,P50与森林对干旱的脆弱性密切相关:一项涵盖226个物种的研究发现,约70%的森林物种在干旱胁迫下表现出狭窄的水力安全边界(<1 MPa) (如表1),且这种脆弱性在全球森林生物群落中趋于一致[4]。研究还指出,水力安全边界大小与年平均降水量无关,这意味着,无论是干旱还是湿润的生物群落(如热带雨林),其森林都面临着相似的水力失效风险。这一发现解释了为何干旱引发的森林衰退不仅发生在干旱区,也频繁出现在以往被认为水分充足的湿润森林中[4]

对北美西部19种栎树水力的研究显示,超过90%的物种在自然条件下维持着正常的水力安全边界,即它们所经历的最低水势并未达到导致严重栓塞(>50%)的阈值[12]。这些栎树进化出了强大的栓塞抗性,并与植株高度、叶型等性状以及气孔调控行为协同作用,形成了有效避免水力失效的整体策略,从而在各种生境下都保持了较宽的水力安全边界[12]。总体而言,裸子植物通常具有更宽的安全边界(更保守的策略)。研究结果显示,42%的被子植物存在负安全边界(即最小水势Ψₘᵢₙ比P50更负),而裸子植物中这一比例仅为6% [4]。这表明许多被子植物采取“高风险”策略,可能依赖于栓塞修复能力来应对周期性干旱。另有研究表明,裸子植物的致死水势范围(−1.5 MPa至−14.7 MPa,均值为−8.2 MPa)整体低于被子植物(均值约–6.0 MPa),这表明裸子植物可能比被子植物具有更高的理论耐旱极限[13]

3. πₜₗₚP50与植物功能性状的协同关系

3.1. 与叶经济谱的关联

πₜₗₚ与一系列叶片碳经济学性状协同变化。研究表明,更负的πₜₗₚ通常与较低的比叶面积(SLA)、较低的最大光合速率(Amax)、较高的叶片密度(LD)以及较长的叶片寿命(LLS)相关联[10] [14] [15]。这反映了植物在干旱环境中的“保守型”资源投资策略。因此,πₜₗₚ可用来预测物种在“快速–慢速”叶片经济谱中的位置,即具有更负πₜₗₚ的物种倾向于向构建质地坚韧、寿命长的叶片投入更多碳资源,属于“慢速”策略端。同时,P50也与木材密度(WD)和导水效率存在显著的权衡关系,较高的导水效率常伴随较低的栓塞抗性[16]

综合来看,速生型物种通常具有高气孔导度和高光合速率,但其水力安全边界较窄,抗空穴化能力较弱;而慢生型物种则通常木材致密、导管细小,具备更强的抗空穴化能力,更能适应贫瘠或干旱环境。这表明植物水力性状与叶经济谱相互关联,为理解物种分布和生态演化策略提供了新的视角。

3.2. 叶片与叶水力性状的协调

πₜₗₚ与叶片水力安全边界(HSMleaf)呈显著正相关[10]。水力安全边界被定义为叶片最小水势(Ψmin)与P50的差值(更保守的计算可采用Ψmin与叶片水力导度损失88%时的水势P88之差),是评估水力脆弱性的关键指标[4] [12]。由于HSMleaf测定耗时且困难,πₜₗₚ可作为其替代指标,用于预测热带森林中干旱引起的树木死亡率[17]

全球研究显示,约70%的物种具有较窄甚至为负的水力安全边界,表明其水力系统在干旱中极易受损[4]πₜₗₚ较高的物种倾向于表现出等水行为(较强的气孔调节),在干旱期间能维持相对较高的叶片水势,从而具有较高的水力安全边界[10]。茎叶异速生长研究进一步表明,叶片大小与茎秆导水效率、机械强度之间存在显著的权衡关系,这种结构–功能协调影响了植物对干旱的适应策略[18]

4. πₜₗₚ的可塑性及其生态意义

4.1. πₜₗₚ的可塑性

全球整合分析显示,绝大多数物种在干旱季节或干旱处理下会通过渗透调节降低其πₜₗₚ,平均变化幅度约为−0.44 MPa [19]。然而,这种可塑性仅能解释干旱后πₜₗₚ总变异的16%,干旱前的πₜₗₚ (即物种固有值)仍是预测干旱耐受性的更强指标。πₜₗₚ的可塑性在不同生物群落间无显著差异,且与气候变量无显著相关[19]。作物和野生植物在πₜₗₚ可塑性上无显著差异,但在作物品种间,可塑性可能是决定抗旱性差异的关键因素[19]。这为通过筛选高可塑性品种以提高作物的抗旱性提供了思路[19]

πₜₗₚ被证明是预测干旱存活率的可靠指标。在热带低地森林,πₜₗₚ是幼苗干旱抗性的核心指标[20],即πₜₗₚ越负(细胞能在更低水势下保持膨压),幼苗干旱存活率越高(一项针对16个树种的研究中,πₜₗₚ最负的树种存活率达85%,而最高的仅30%)。这一规律在实验干旱与自然厄尔尼诺干旱中均成立,表明πₜₗₚ是热带木本植物干旱抗性的稳定预测指标[20]πₜₗₚP50及最小叶片水势显著相关,反映了从枝条到叶片组织在干旱耐受性上的协同性[21]。这种水力性状与碳经济性状的耦合,支持了“整株植物经济谱”假说[22]

4.2. P50的可塑性与遗传稳定性

πₜₗₚ不同,P50在物种内表现出较低的可塑性。对地中海松树的研究发现,P50在不同种群间的遗传变异有限,表明其可能是一个相对较为保守的性状[23]。这一特性限制了植物通过表型可塑性快速适应气候变化的能力,尤其是在干旱加剧的背景下。此外,有研究表明,在持续干旱条件下,具有较低P50 (即更强栓塞抗性)的树种,其光合作用和蒸腾作用对干旱的抵抗力及干旱过后的恢复力也更强[5]。值得注意的是,在该研究中,气孔行为、πₜₗₚ、水力安全边界等其他耐旱相关性状均未能解释树种在死亡率和恢复力上的种间差异,即它们对抵抗力和恢复力无显著影响[5]。这一结果表明,亚热带常绿树种可能并未像干旱-半干旱地区物种那样发展出多重有效的耐旱机制,这使得亚热带常绿阔叶林在未来面临极端干旱事件(如复合干旱热浪或台风)时可能异常脆弱。

4.3. πₜₗₚP50在干旱存活与森林衰退预测中的应用

1) 作为干旱存活预测指标。在热带低地森林中,πₜₗₚ是预测幼苗干旱存活率的关键指标,πₜₗₚ越负的物种干旱存活率越高[9] [20]。类似地,P50被证明是预测亚热带常绿阔叶林树种在持续干旱中死亡率与恢复力的关键性状[5]

2) 在生态建模与气候变化预测中的潜力。πₜₗₚP50等水力性状整合到植被模型中,可以显著提升对森林干旱响应预测的准确性[11],并更好地评估森林群落对干旱的脆弱性[10]。例如,基于水力安全边界的模型已成功应用于预测喀斯特森林树种生长与死亡风险[24]。未来的研究应进一步整合根系水力性状、非结构性碳水化合物动态等多维度信息,以构建更全面的植物干旱响应模型。

5. 结论

核心水力性状P50πₜₗₚ是连接植物个体生理与生态系统动态的关键纽带。它们不仅可以反映植物的干旱耐受能力,还通过与其它水力性状和碳经济性状的协同关联,指示了物种在“快速–慢速”资源经济谱中的位置。在全球变化导致干旱加剧的背景下,准确测定并应用πₜₗₚP50,将为理解物种分布格局、预测群落动态以及制定有效的生态系统保护策略提供至关重要的科学工具。

6. 研究展望

未来的研究应重点关注以下方向:

1) 多器官整合:同时测定叶、茎、根等器官的水力性状,以全面揭示整株植物的干旱适应策略。

2) 种内变异与可塑性机制:深入探究水力性状可塑性的遗传基础与环境调控机制。

3) 动态模型开发:将水力性状的季节动态与表型可塑性纳入生态系统模型,以提升模型的预测能力。

4) 全球变化情景下的验证:在不同气候区开展长期控制实验与观测,验证水力性状在预测物种分布与群落动态中的普适性。

参考文献

[1] Torres‐Ruiz, J.M., Cochard, H., Delzon, S., Boivin, T., Burlett, R., Cailleret, M., et al. (2023) Plant Hydraulics at the Heart of Plant, Crops and Ecosystem Functions in the Face of Climate Change. New Phytologist, 241, 984-999. [Google Scholar] [CrossRef] [PubMed]
[2] Brodribb, T.J., Powers, J., Cochard, H. and Choat, B. (2020) Hanging by a Thread? Forests and Drought. Science, 368, 261-266. [Google Scholar] [CrossRef] [PubMed]
[3] Salomón, R.L., Wu, H., López, R., Martinez‐Arias, C., Sobrino‐Plata, J., Torres‐Ruiz, J.M., et al. (2025) The Sequence of Drought‐Driven Stomatal Closure, Stem Xylem Embolism, Dehydration, and Aquaporin Gene Expression Differs among Species. Physiologia Plantarum, 177, e70619. [Google Scholar] [CrossRef
[4] Choat, B., Jansen, S., Brodribb, T.J., Cochard, H., Delzon, S., Bhaskar, R., et al. (2012) Global Convergence in the Vulnerability of Forests to Drought. Nature, 491, 752-755. [Google Scholar] [CrossRef] [PubMed]
[5] Shao, J.J., Zhou, X., Zhang, P., Zhai, D., Yuan, T., Li, Z., et al. (2022) Embolism Resistance Explains Mortality and Recovery of Five Subtropical Evergreen Broadleaf Trees to Persistent Drought. Ecology, 104, e3877. [Google Scholar] [CrossRef] [PubMed]
[6] Zhang, X., Ma, S., Hu, H., Li, F., Bao, W. and Huang, L. (2024) A Trade-Off between Leaf Hydraulic Efficiency and Safety across Three Xerophytic Species in Response to Increased Rock Fragment Content. Tree Physiology, 44, tpae010. [Google Scholar] [CrossRef] [PubMed]
[7] Tyree, M.T. and Hammel, H.T. (1972) The Measurement of the Turgor Pressure and the Water Relations of Plants by the Pressure-Bomb Technique. Journal of Experimental Botany, 23, 267-282. [Google Scholar] [CrossRef
[8] Bartlett, M.K., Scoffoni, C., Ardy, R., Zhang, Y., Sun, S., Cao, K., et al. (2012) Rapid Determination of Comparative Drought Tolerance Traits: Using an Osmometer to Predict Turgor Loss Point. Methods in Ecology and Evolution, 3, 880-888. [Google Scholar] [CrossRef
[9] Bartlett, M.K., Scoffoni, C. and Sack, L. (2012) The Determinants of Leaf Turgor Loss Point and Prediction of Drought Tolerance of Species and Biomes: A Global Meta‐Analysis. Ecology Letters, 15, 393-405. [Google Scholar] [CrossRef] [PubMed]
[10] Zhu, S., Chen, Y., Ye, Q., He, P., Liu, H., Li, R., et al. (2018) Leaf Turgor Loss Point Is Correlated with Drought Tolerance and Leaf Carbon Economics Traits. Tree Physiology, 38, 658-663. [Google Scholar] [CrossRef] [PubMed]
[11] Kunert, N., Zailaa, J., Herrmann, V., Muller‐Landau, H.C., Wright, S.J., Pérez, R., et al. (2021) Leaf Turgor Loss Point Shapes Local and Regional Distributions of Evergreen but Not Deciduous Tropical Trees. New Phytologist, 230, 485-496. [Google Scholar] [CrossRef] [PubMed]
[12] Skelton, R.P., Anderegg, L.D.L., Diaz, J., Kling, M.M., Papper, P., Lamarque, L.J., et al. (2021) Evolutionary Relationships between Drought-Related Traits and Climate Shape Large Hydraulic Safety Margins in Western North American Oaks. Proceedings of the National Academy of Sciences, 118, e2008987118. [Google Scholar] [CrossRef] [PubMed]
[13] Liang, X., Ye, Q., Liu, H. and Brodribb, T.J. (2020) Wood Density Predicts Mortality Threshold for Diverse Trees. New Phytologist, 229, 3053-3057. [Google Scholar] [CrossRef] [PubMed]
[14] Pérez-Harguindeguy, N., Díaz, S., Garnier, E., Lavorel, S., Poorter, H., Jaureguiberry, P., et al. (2013) New Handbook for Standardised Measurement of Plant Functional Traits Worldwide. Australian Journal of Botany, 61, 167-234. [Google Scholar] [CrossRef
[15] Nadal, M., Clemente‐Moreno, M.J., Perera‐Castro, A.V., Roig‐Oliver, M., Onoda, Y., Gulías, J., et al. (2023) Incorporating Pressure-Volume Traits into the Leaf Economics Spectrum. Ecology Letters, 26, 549-562. [Google Scholar] [CrossRef] [PubMed]
[16] Gleason, S.M., Westoby, M., Jansen, S., Choat, B., Hacke, U.G., Pratt, R.B., et al. (2015) Weak Tradeoff between Xylem Safety and Xylem‐Specific Hydraulic Efficiency across the World’s Woody Plant Species. New Phytologist, 209, 123-136. [Google Scholar] [CrossRef] [PubMed]
[17] Su, R., Liu, H., Wang, C., Zhang, H. and Cui, J. (2022) Leaf Turgor Loss Point Is One of the Best Predictors of Drought-Induced Tree Mortality in Tropical Forest. Frontiers in Ecology and Evolution, 10, Article ID: 974004. [Google Scholar] [CrossRef
[18] Fan, Z., Sterck, F., Zhang, S., Fu, P. and Hao, G. (2017) Tradeoff between Stem Hydraulic Efficiency and Mechanical Strength Affects Leaf-Stem Allometry in 28 Ficus Tree Species. Frontiers in Plant Science, 8, Article No. 1619. [Google Scholar] [CrossRef] [PubMed]
[19] Bartlett, M.K., Zhang, Y., Kreidler, N., Sun, S., Ardy, R., Cao, K., et al. (2014) Global Analysis of Plasticity in Turgor Loss Point, a Key Drought Tolerance Trait. Ecology Letters, 17, 1580-1590. [Google Scholar] [CrossRef] [PubMed]
[20] Álvarez‐Cansino, L., Comita, L.S., Jones, F.A., Manzané‐Pinzón, E., Browne, L. and Engelbrecht, B.M.J. (2022) Turgor Loss Point Predicts Survival Responses to Experimental and Natural Drought in Tropical Tree Seedlings. Ecology, 103, e3700. [Google Scholar] [CrossRef] [PubMed]
[21] Bartlett, M.K., Klein, T., Jansen, S., Choat, B. and Sack, L. (2016) The Correlations and Sequence of Plant Stomatal, Hydraulic, and Wilting Responses to Drought. Proceedings of the National Academy of Sciences, 113, 13098-13103. [Google Scholar] [CrossRef] [PubMed]
[22] Reich, P.B. (2014) The World‐Wide “Fast-Slow” Plant Economics Spectrum: A Traits Manifesto. Journal of Ecology, 102, 275-301. [Google Scholar] [CrossRef
[23] Lamy, J., Delzon, S., Bouche, P.S., Alia, R., Vendramin, G.G., Cochard, H., et al. (2013) Limited Genetic Variability and Phenotypic Plasticity Detected for Cavitation Resistance in a mediterranean Pine. New Phytologist, 201, 874-886. [Google Scholar] [CrossRef] [PubMed]
[24] Aritsara, A.N.A., Ni, M., Wang, Y., Yan, C., Zeng, W., Song, H., et al. (2023) Tree Growth Is Correlated with Hydraulic Efficiency and Safety across 22 Tree Species in a Subtropical Karst Forest. Tree Physiology, 43, 1307-1318. [Google Scholar] [CrossRef] [PubMed]