Legacy of extreme drought and heat on acclimation of mangrove leaf water relations to salinity ARC DP180102969

Increasing tree mortality has been observed globally in response to extreme drought in all forest types, including mangroves. Large scale mangrove mortality will have severe consequences for the ecosystems, communities and industries reliant on these forests. This data collection reports measurements of leaf water relations parameters before and after extreme drought followed by extreme flooding in two co-occurring mangroves Aegiceras corniculatum (L.) Blanco and Rhizophora stylosa Griff., growing along an estuarine salinity gradient of the Daintree River, Daintree National Park, Far North Queensland, Australia (16.1700°S, 145.4185°E). Three hypotheses were tested: 1) that species distributed over a broad salinity gradient that varies in space and time exhibit plasticity in fundamental water relations to maintain turgor and water content, 2) that severe but non-lethal dry season conditions lead to greater leaf salinity tolerance in the subsequent dry season, providing evidence of ecological stress memory, and 3) that plant cell turgor can be maintained with water supply from soil; however, achieving full hydration with increasing salinity requires greater inputs from atmospheric water sources. Study design: two to five leaves, each from different trees of both species, A. corniculatum and R. stylosa, were collected from three sites along the bank of the Daintree River, designated High, Mid and Low salinity to reflect differences in salinity regimes according to their estuarine position. Initial measurements of mid-dry season leaf water relations were collected for R. stylosa in October 2016 from High and Mid salinity sites and in late July from the Low Salinity sites, while samples of A. corniculatum were collected in August 2018 from all salinity sites. These were contrasted with measurements of leaf water relations collected in August 2019 in both species from all three sites. Between the 2018 and 2019 collection periods the Daintree River area experienced a severe drought followed by a record-breaking flood. For analysis samples of A. corniculatum and R. stylosa collected during 2016-18 are henceforth called the 'pre-drought/flood' group and those collected in 2019 the 'post-drought/flood' group. Pressure-volume curves (leaf water content in relation to the leaf water potential during dehydration) were constructed and analysed for each leaf according to methods described by Nguyen et al. (2017). Statistical analyses were performed using the R statistical software package (R Core Team, 2020 version 4.0.2). The base model for all pressure-volume parameters was full factorial with species, condition (pre-drought/flood and post-drought/flood) and salinity site (High, Mid and Low) as fixed factors, including all interactions. Significance of main effects and interactions at the p<0.05 level was assessed using the Anova() function in the car package. Model fit was assessed using the Shapiro-Wilk test and where necessary, dependent variables were log transformed to account for non-normality of model residuals. Differences between model estimated marginal means and trendlines were assessed post hoc using the emmeans R package (Lenth, 2020), the Tukey method for p-value adjustment. References Lenth, R. V. 2020. emmeans: Estimated Marginal Means, aka Least-Squares Means. https://CRAN.R-project.org/package=emmeans. Nguyen, H. T., Meir, P., Wolfe, J., Mencuccini, M. & Ball, M. C. (2017). Plumbing the depths: extracellular water storage in specialized leaf structures and its functional expression in a three-domain pressure-volume relationship. Plant, Cell & Environment 40: 1021-1038. 10.1111/pce.12788 R Core Team. 2020. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

全球范围内所有森林类型（包括红树林）均已观测到树木死亡率因极端干旱而上升的现象。大规模红树林死亡事件，将对依赖此类森林的生态系统、社区及产业造成严重负面影响。

本数据集收录了澳大利亚昆士兰州远北部戴恩特里国家公园戴恩特里河河口盐度梯度带（16.1700°S，145.4185°E）上两种同域分布红树林——桐花树（Aegiceras corniculatum (L.) Blanco）和红海榄（Rhizophora stylosa Griff.）——在极端干旱后遭遇极端洪水前后的叶片水分关系参数测量数据。

本研究共验证三项假说：1）分布于随空间与时间动态变化的宽幅盐度梯度中的物种，其基础水分关系具备可塑性，可维持细胞膨压与叶片水分含量；2）严重但非致死的旱季条件，会提升后续旱季的叶片盐度耐受能力，为生态胁迫记忆提供实证依据；3）植物细胞可通过土壤供水维持膨压，但随着盐度升高，实现完全水合则需要更多大气水源的输入。

研究设计：沿戴恩特里河河岸设置高、中、低三个盐度位点，以反映河口不同位置的盐度差异。从两个物种的不同植株上各采集2至5片叶片。2016年10月，研究人员从高、中盐度位点采集红海榄的旱季中期叶片水分关系数据，并于同年7月末从低盐度位点采集该物种的对应样本；2018年8月，从全部三个盐度位点采集桐花树的样本。将上述数据与2019年8月从两个物种的全部三个位点采集的叶片水分关系测量数据进行对比。在2018年至2019年的采样间隔期间，戴恩特里河流域经历了极端干旱后又遭遇破纪录的洪水。本研究将2016—2018年采集的桐花树与红海榄样本命名为“干旱/洪水前组”，2019年采集的样本则归类为“干旱/洪水后组”。

实验方法：采用Nguyen等人（2017）所述方法，为每片叶片构建并分析压力-体积曲线（pressure-volume curves）——即脱水过程中叶片水分含量与叶水势（leaf water potential）的关系曲线。统计分析使用R统计软件包（R Core Team，2020年版本4.0.2）完成。所有压力-体积参数的基础模型为全因子模型，以物种、处理组（干旱/洪水前与干旱/洪水后）和盐度位点（高、中、低）作为固定因子，并纳入所有交互项。采用car包中的“Anova()”函数检验主效应及交互项在p<0.05水平下的显著性。使用Shapiro-Wilk检验评估模型拟合度，必要时对因变量进行对数转换以校正模型残差的非正态性。事后比较采用emmeans R包（Lenth，2020）结合Tukey法进行p值校正，以评估模型估计边际均值与趋势线之间的差异。

参考文献：
[1] Lenth, R. V. 2020. emmeans: 估计边际均值（Estimated Marginal Means，又称最小二乘均值）. https://CRAN.R-project.org/package=emmeans.
[2] Nguyen, H. T., Meir, P., Wolfe, J., Mencuccini, M. & Ball, M. C. (2017). 探究深度：特化叶片结构中的胞外水分储存及其在三域压力-体积关系中的功能表达. 植物、细胞与环境（Plant, Cell & Environment）40: 1021-1038. 10.1111/pce.12788
[3] R Core Team. 2020. R：统计计算语言与环境（R: A Language and Environment for Statistical Computing）. 奥地利维也纳R统计计算基金会（R Foundation for Statistical Computing, Vienna, Austria）. https://www.R-project.org/.