The effect of salinity on branch water relations and stem hydraulic vulnerability in two co-occurring mangrove species ARC DP180102969

Plant mortality due to extremes in drought and salinity is frequently attributed to hydraulic failure (Adams et al., 2017; McDowell et al., 2022). However, hydraulic risk may be managed by flexibly altering storage and release of water within the plant. For instance, when drought or salinity conditions increase, adjustment of water relations parameters such as water storage and hydraulic capacitance can reduce mangrove hydraulic vulnerability (Bryant et al., 2021; Coopman et al., 2021). The role of such water relations parameters in the maintenance of hydraulic function under varied environmental conditions remains poorly resolved, yet is fundamental to understanding species’ survival under increasing drought and salinity (Guadagno et al., 2017; McDowell et al., 2022). This dataset investigates how plasticity in branch water relations and hydraulic vulnerability maintain turgor and stem hydraulic function in two co-occurring mangrove species: Aegiceras corniculatum and Rhizophora stylosa growing at low and high salinity.
We hypothesised that 1) as salinity increases the proportionate contribution of foliage water storage and capacitance to whole-branch water relations will decrease, 2) stem capacitance will increase with the proportion of living tissue (inner bark, cortex, ray parenchyma) in the stem, 3) P12 and P50 will occur at more negative water potentials in plants growing at high salinity compared to plants growing at low salinity, and 4) hydraulic safety margins will be smaller in plants grown at high than low salinity due to a greater change in estuarine water potential than plasticity in P50 as salinity increases.
For full methods see publication: Holly A. A. Beckett, Callum Bryant, Teresa Neeman, Maurizio Mencuccini, and Marilyn C. Ball, (2023). Plasticity in branch water relations and stem hydraulic vulnerability enhances hydraulic safety in mangroves growing along a salinity gradient. Plant, Cell & Environment.
Abridged methods:
Species and site: Sun-exposed, terminal canopy branches less than 1 m in length of Aegiceras corniculatum and Rhizophora stylosa growing naturally along the banks of the Daintree River, Daintree National Park, Far North Queensland, Australia (16.1700° S, 145.4185° E) were collected mid-dry season, August 2019, at two salinities from three estuarine sites: Upper and Middle estuarine sites (low salinity), and Lower estuarine site (high salinity). The low salinity sites had a salinity of 0 parts per thousand (ppt) while the high salinity site had a salinity of 24 ppt measured from surface water samples using a hand-held refractometer (A.S.T. Co. Ltd., Japan). Branches were defined as comprising of foliage (the collection of leaves on the branch) and stem (woody canopy growth to which foliage is attached).
Branch water relations: One branch from each of five trees of each species were collected from high salinity, while one branch from each of 10 trees of A. corniculatum and one branch from each of 7 trees of R. stylosa were collected from low salinity. Branches were recut under 1% seawater perfusion solution for R. stylosa and 5% seawater perfusion solution for A. corniculatum and allowed to rehydrate overnight. Branch water release curves were constructed using a modified bench drying method (Gleason et al., 2014; Bryant et al., 2021) and analysed according to methods described by Bryant et al. (2021). Branch, foliage and stem capacitance and water storage were calculated from both the initial linear slope prior to the water potential at the leaf turgor loss point, describing decline in water content with decline in water potential during drying, and from the total water lost between first to last measurement.
Stem anatomy: From each individual measured for branch water relations, an additional branch was collected for paired stem anatomy. Transverse sections 100 µm thick in A. corniculatum and 200 µm thick in R. stylosa were cut using a sledge microtome G.S.L.1 (S.Lucchinetti, Schenkung Dapples, Zurich, Switzerland) from three stem developmental points. Developmental points were identified based on maturity of bark development, distance from shoot tip and stem diameter to capture variation in anatomy with stem development. Sections were observed under a ZEISS Axiostar Plus epifluorescence microscope (ZEISS, Germany). The Halide Mark II Pro Camera app on an iPhone 8 was used to capture RAW format images. One transverse section from each developmental point was analysed for radial stem tissue layer thickness and area, and vessel lumen area (measured on each vessel within a 0.25 mm² area of xylem tissue) using FIJI image analysis software (Schindelin et al., 2012).
Hydraulic vulnerability curves: The pneumatic method was used to estimate hydraulic vulnerability (Pereira et al., 2016; Zhang et al., 2018; Sergent et al., 2020; Jansen et al., 2022). One sun-exposed canopy branch was collected from each of five trees of A. corniculatum and four trees of R. stylosa from high salinity, and from each of six trees of A. corniculatum and five trees of R. stylosa from low salinity. Trees sampled were 2-4 m tall. Measurement of hydraulic vulnerability and analyses were conducted according to methods described by Bryant et al. (2021).
Statistical analyses: R statistical software package (R Core Team, 2023 version 4.3.1) was used for all statistical analysis. The lmer() function in the lmerTest package (Kuznetsova et al., 2017) was used to run all linear mixed models. To analyse water storage and capacitance at the branch level, a linear mixed model with salinity and species as fixed effects and estuarine site as random intercepts was used. A linear mixed model was then used to analyse contributions to branch water storage and capacitance made by foliage and stem organs, with salinity, species and organ as fixed effects and estuarine site and individual as random intercepts. To assess the significance of main effects and interactions of linear mixed models at the p

极端干旱与盐胁迫导致的植物死亡常被归因于水力衰竭（hydraulic failure）（Adams et al., 2017; McDowell et al., 2022）。然而，植物可通过灵活调控体内水分的储存与释放来应对水力风险。例如，当干旱或盐度升高时，调整水分关系（water relations）参数（如水分储存与水容（hydraulic capacitance））可降低红树的水力脆弱性（hydraulic vulnerability）（Bryant et al., 2021; Coopman et al., 2021）。这类水分关系参数在多变环境下维持水力功能的作用仍未被充分阐明，但却是理解物种在日益加剧的干旱与盐胁迫下存活机制的核心（Guadagno et al., 2017; McDowell et al., 2022）。本数据集旨在探究两种同域分布的红树物种——桐花树（Aegiceras corniculatum）和红海榄（Rhizophora stylosa）——在不同盐度环境下，枝条水分关系与水力脆弱性的表型可塑性如何维持叶片膨压（turgor）与茎干水力功能。

本研究提出如下假说：1）随着盐度升高，叶片水分储存与水容对整枝水分关系的相对贡献将下降；2）茎干水容随茎内活组织（内树皮、皮层、射线薄壁组织）的占比升高而增加；3）与低盐环境下的植株相比，高盐环境植株的P12与P50会出现在更负的水势条件下；4）随着盐度升高，河口水势的变化幅度大于P50的表型可塑性，因此高盐环境下植株的水力安全边际（hydraulic safety margins）小于低盐环境植株。

完整实验方法参见论文：Holly A. A. Beckett、Callum Bryant、Teresa Neeman、Maurizio Mencuccini及Marilyn C. Ball（2023）。《沿盐度梯度分布的红树中，枝条水分关系与茎干水力脆弱性的可塑性提升水力安全》。*Plant, Cell & Environment*。

缩写实验方法：

物种与采样位点：2019年8月旱季中期，于澳大利亚昆士兰州远北区丹翠国家公园丹翠河沿岸（16.1700° S, 145.4185° E）自然生长的桐花树（Aegiceras corniculatum）和红海榄（Rhizophora stylosa）的阳生顶冠枝条（长度小于1米）被采集。采样覆盖3个河口位点：上游与中游河口位点（低盐环境）、下游河口位点（高盐环境）。经手持式折射仪（A.S.T. 有限公司，日本）对表层水样测定，低盐位点的盐度为0‰，高盐位点盐度为24‰。本研究中枝条定义为包含叶片（枝条上所有叶片的集合）与茎干（附着叶片的木质冠部生长部分）。

枝条水分关系测定：从高盐环境的每个物种的5株植株各采集1根枝条；低盐环境下则采集桐花树10株、红海榄7株各1根枝条。将枝条在1%海水灌注液（红海榄）与5%海水灌注液（桐花树）下重新剪切，并置于暗处复水过夜。采用改良的台式干燥法（bench drying method）构建枝条水分释放曲线（Gleason et al., 2014; Bryant et al., 2021），并按照Bryant等（2021）的方法进行分析。枝条、叶片与茎干的水容及水分储存量通过两个维度计算：一是叶片膨压丧失点（turgor loss point）水势之前的初始线性斜率（该斜率描述干燥过程中水分含量随水势降低的下降趋势），二是首次至末次测定间的总失水量。

茎干解剖：从每株完成枝条水分关系测定的植株中额外采集1根枝条，用于配对茎干解剖分析。使用滑动式切片机G.S.L.1（S.Lucchinetti, Schenkung Dapples，瑞士苏黎世），从3个茎发育阶段切取横切片：桐花树切片厚度为100 µm，红海榄为200 µm。发育阶段依据树皮发育成熟度、距梢距离与茎干直径确定，以覆盖茎发育过程中的解剖结构变异。切片在ZEISS Axiostar Plus落射荧光显微镜（ZEISS，德国）下观察，使用iPhone 8的Halide Mark II Pro Camera应用程序采集RAW格式图像。利用FIJI图像分析软件（Schindelin et al., 2012）对每个发育阶段的1个横切片进行分析，测定径向茎组织层厚度与面积，以及导管腔面积（在木质部组织0.25 mm²区域内的每个导管上测量）。

水力脆弱性曲线：采用气动法（pneumatic method）估算水力脆弱性（Pereira et al., 2016; Zhang et al., 2018; Sergent et al., 2020; Jansen et al., 2022）。高盐环境下采集桐花树5株、红海榄4株的阳生顶冠枝条各1根；低盐环境下采集桐花树6株、红海榄5株的阳生顶冠枝条各1根。采样植株树高为2-4米。水力脆弱性的测定与分析按照Bryant等（2021）的方法进行。

统计分析：本研究使用R统计软件包（R Core Team, 2023版本4.3.1）完成所有统计分析。利用lmerTest包中的lmer()函数（Kuznetsova et al., 2017）运行所有线性混合模型（linear mixed models）。为分析枝条水平的水分储存与水容，采用以盐度和物种为固定效应、河口位点为随机截距的线性混合模型。随后使用线性混合模型分析叶片与茎干器官对枝条水分储存及水容的贡献，模型以盐度、物种与器官为固定效应，河口位点与个体为随机截距。为评估线性混合模型主效应及交互效应的显著性，以p