Soils drive convergence in the regulation of vascular tension in land plants
Data files
Jan 08, 2026 version files 1.01 MB
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anderegg_Tr_psi_md_psi_pd_available.csv
733.10 KB
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bartlett2012.xlsx
33.58 KB
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choat2012.xls
203.78 KB
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MatlabCode.m
5.57 KB
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OptimalLeafSuction_psi-gs-crit.R
21.60 KB
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README.md
7.63 KB
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Run-SOL-Model-in-R.R
7.58 KB
Abstract
Terrestrial vascular plants operate under negative water potential, which results in hydraulic tension in the vascular system. Vascular tension varies with transpiration and soil drying, and it is regulated by stomata, pressure-activated valves on the leaf surface. We hypothesize that soil physical constraints drive convergence in the operational range of leaf vascular tension. Based on a meta-analysis of 19 diverse species, we find that stomatal regulation of transpiration is activated when leaf vascular tension reaches a narrow target of 1.3±0.6 MPa. This value matches the range (1.4±0.6 MPa) predicted from an optimal soil water extraction model. Optimality in plant vascular tension appears to have evolved by selection for a narrow range of osmotic pressure in the leaves of diverse AASAQAQSAspecies growing across enormously variable environments.
Dataset DOI: 10.5061/dryad.kkwh70shp
Description of the data and file structure
Files and variables
File: anderegg_Tr_psi_md_psi_pd_available.csv
Description: This dataset is a subset of the dataset collected by Anderegg et al. (2018), filtered for species that contain at least transpiration and leaf water potential measurements (predawn & midday).
Variables
- Species: Species name; Trmmol: transpiration rate in (mmol m-2 s-1); LWP: (midday) leaf water potential in MPa; LWPpredawn: (predawn) leaf water potential in MPa, among others.
Missing Values: Note that missing data values are indicated by multiple identifiers, such as "-9999", "NA" and empty cells, as the original data structure was kept unchanged.
File: bartlett2012.xlsx
Description: The original dataset compiled by Bartlett et al. (2012), containing observations of leaf osmotic pressure, etc., among others.
Variables
- Species name and leaf osmotic pressure (πo) in MPa, among others.
Missing Values: Note that missing data values are indicated by empty cells - the original data structure was kept unchanged.
File: choat2012.xls
Description: The original dataset compiled by Choat et al. (2012) contains observations of minimum leaf water potentials, as well as leaf water potentials at 50% loss in xylem conductivity, among others.
Variables
- Species name, minimum leaf water potential (ψmin midday, in MPa), and leaf water potential at 50% loss in xylem hydraulic conductivity (ψ50, in MPa), among others.
Missing Values: Note that missing data values are indicated by empty cells - the original data structure was kept unchanged.
File: MatlabCode.m
Description: Performs simulations based on the soil-plant hydraulic model developed by Carminati & Javaux (2020) for Figures 1C-E. Note that we also provide an open-source alternative, "Run-SOL-Model-in-R.R", of the soil-plant hydraulic model code contained in this file.
File: OptimalLeafSuction_psi-gs-crit.R
Description: Creates Figure 1B; requires the dataset by Anderegg et al. (2018) - "anderegg_Tr+psi_md+psi_pd_available.csv" - and adds the two crop species from Tardieu & Simonneau (1998).
File: Run-SOL-Model-in-R.R
Description: This is an open-source alternative of the soil-plant hydraulic model code developed by Carminati & Javaux (2020), provided in the MATLAB file - "MatlabCode.m".
All data and codes essential to the analysis (and Figure 1) are available. Final figure editing was done using Inkscape v1.3.2 (Inkscape Project, 2023).
Figure 1B is created by "OptimalLeafSuction_psi-gs-crit.R". It requires the dataset by Anderegg et al. (2018) - "anderegg_Tr+psi_md+psi_pd_available.csv" - and adds the two crop species from Tardieu & Simonneau (1998).
Figure 1C-E is created by "MatlabCode.m". In addition to the data in B, it requires the datasets by Bartlett et al. (2012) - "bartlett2012.xlsx" - and Choat et al (2012) - "choat2012.xls".
As an addition to the MATLAB code provided, we also provide an open-source alternative - "Run-SOL-Model-in-R.R" - of the model code developed by Carminati & Javaux (2020), Trends in Plant Science.
Code/software
Software requirements
Operating System
Our codes are tested on Windows11 operating systems using R v.4.5.0 (R Core Team, 2025), Matlab R2022a (The MathWorks Inc., 2022). Figure editing was performed using Inkscape v1.3.2 (Inkscape Project, 2023).
Package Dependencies
R: Packages loaded are tidyverse v2.0.0 (Wickham et al., 2019), data.table v1.17.6 (Barrett et al., 2025), patchwork v1.3.1 (Pedersen, 2025) and svglite v2.2.1 (Wickham et al., 2025).
Hardware requirements
Our codes require only a standard computer with enough RAM to support the operations defined by the user. For minimal performance, this will be a computer with about 2 GB of RAM. Some operations, such as performing the simulations with the large set of varying hydraulic parameters (Figure 1E), may benefit from more computer performance.
References:
Inkscape Project. (2023). Inkscape (Version 1.3.2). Retrieved from https://inkscape.org/release/inkscape-1.3.2/.
The MathWorks Inc. (2022). MATLAB version: 9.13.0 (R2022b), Natick, Massachusetts: The MathWorks Inc. https://www.mathworks.com
R Core Team. (2025) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing.
https://www.r-project.org/
Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL,
Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019).
“Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686
https://doi.org/10.21105/joss.01686.
Barrett T, Dowle M, Srinivasan A, Gorecki J, Chirico M, Hocking T, Schwendinger B, Krylov I (2025). data.table: Extension of
data.frame. R package version 1.17.6, https://r-datatable.com.
Pedersen T (2025). patchwork: The Composer of Plots. R package version 1.3.1, https://patchwork.data-imaginist.com.
Wickham H, Henry L, Pedersen T, Luciani T, Decorde M, Lise V (2025). svglite: An 'SVG' Graphics Device.
doi:10.32614/CRAN.package.svglite https://doi.org/10.32614/CRAN.package.svglite,
R package version 2.2.1,
https://CRAN.R-project.org/package=svglite.
Access information
Other publicly accessible locations of the data:
- Anderegg et al. (2018): https://doi.org/10.1111/ele.12962
- Bartlett et al. (2012): https://doi.org/10.1111/j.1461-0248.2012.01751.x
- Choat et al. (2012): https://doi.org/10.1038/nature11688
Data was derived from the following sources:
- Anderegg, W.R.L. et al. (2018) ‘Woody plants optimise stomatal behaviour relative to hydraulic risk’, Ecology Letters, 21(7), pp. 968–977. Available at: https://doi.org/10.1111/ele.12962.
- 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(5), pp. 393–405. Available at: https://doi.org/10.1111/j.1461-0248.2012.01751.x.
- Carminati, A. and Javaux, M. (2020) ‘Soil Rather Than Xylem Vulnerability Controls Stomatal Response to Drought’, Trends in Plant Science, 25(9), pp. 868–880. Available at: https://doi.org/10.1016/j.tplants.2020.04.003.
- Choat, B. et al. (2012) ‘Global convergence in the vulnerability of forests to drought’, Nature, 491(7426), pp. 752–755. Available at: https://doi.org/10.1038/nature11688.
- Tardieu, F. and Simonneau, T. (1998) ‘Variability among species of stomatal control under fluctuating soil water status and evaporative demand: modelling isohydric and anisohydric behaviours’, Journal of Experimental Botany, 49, pp. 419–432.
No new materials were generated in this study, but data were taken from the literature. The main data is a subset of the dataset collected by Anderegg et al. (2018), complemented with two crop species from Tardieu & Simonneau (1998). These data contain time-point measurements of transpiration rate and leaf water potentials of 19 species in total (for many species, there are additional variables measured). Additionally, we compare observations of initial transpiration reduction (leaf water tension when stomatal regulation is activated) with (i) predictions of soil water extraction by plants using the soil-plant hydraulic model developed by Carminati & Javaux (2020); (ii) measurements of leaf osmotic pressures collected by Bartlett et al. (2012); (iii) observations of minimum leaf water potentials, as well as leaf water water potentials at 50% loss in xylem conductivity compiled by Choat et al. (2012).