DOI: 10.5061/dryad.kkwh70shp
title: Soils drive convergence in the regulation of vascular tension in land plants

description: 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.
description: 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).
description: # Soils drive convergence in the regulation of vascular tension in land
 plants Dataset DOI:
 [10.5061/dryad.kkwh70shp](https://doi.org/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
 *Windows*11 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/](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](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/](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](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](https://r-datatable.com). Pedersen T (2025). *patchwork: The Composer of Plots*. R package version 1.3.1, [https://patchwork.data-imaginist.com](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](https://doi.org/10.32614/CRAN.package.svglite), R package version 2.2.1, [https://CRAN.R-project.org/package=svglite](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](https://doi.org/10.1111/ele.12962) * Bartlett et al. (2012): [https://doi.org/10.1111/j.1461-0248.2012.01751.x](https://doi.org/10.1111/j.1461-0248.2012.01751.x) * Choat et al. (2012): [https://doi.org/10.1038/nature11688](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](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](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](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](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.