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

TitleLegacy of extreme drought and heat on acclimation of mangrove leaf water relations to salinity ARC DP180102969
Collection TypeDataset
Access PrivilegesDivision of Plant Science
DOI - Digital Object Identifier10.25911/61566961e8f69
Metadata LanguageEnglish
Data LanguageEnglish
Significance StatementThe data indicate the importance of year-to-year variation in dry season conditions to the acclimation of leaf water relations to salinity.
Full Description

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.


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/.

Contact Emailholly.beckett@anu.edu.au
Contact AddressPlant Science Division, Research School of Biology, Australian National University, Acton, ACT, 2601, Australia.
Principal InvestigatorHolly A. A. Beckett
SupervisorsMarilyn C. Ball
CollaboratorsHolly A. A. Beckett
Teresa Neeman
Tomás I. Fuenzalida
Callum Bryant
Sara Chica Latorre
Leuwin I. Ovington
Lawren Sack
Patrick Meir
Marilyn C. Ball
Fields of Research060705 - Plant Physiology
Socio-Economic Objective970106 - Expanding Knowledge in the Biological Sciences
Climate Change
Ecological Stress Memory
extreme drought event
Water Relations
Type of Research ActivityPure basic research
Time PeriodDry Season 2016 to Dry season 2019
Date of data creation2016-10
Year of data publication2021
Publisher for CitationThe Australian National University Data Commons
Access Rights TypeOpen
Licence TypeCC-BY - Attribution (Version 4)
Retention PeriodIndefinitely
Extent or Quantity3
Data Size140KB
Data Management PlanNo
Status: Published
Published To:
- Australian National University
- Australian National Data Service
Identifier: anudc:6112
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