DOI: 10.5061/dryad.905qftv01
title: Can oxygen isotopes in tree rings be used to detect stomatal responses to global change?

description: Stomatal conductance (gs) regulates CO2 and water fluxes of
 plants. Although experiments have shown that gs decreases with
 elevated CO2, it is unclear how gs is responding in-situ to
 long-term exposures to rising CO2 and a changing climate. Tree
 ring isotope analysis provides a unique method to assess tree
 ecophysiological responses to long-term exposures to slowly changing
 environmental conditions. In particular, it has been suggested that
 changes in gs can potentially be inferred from tree ring
 stable oxygen isotope ratios (δ18Otrc). Several studies have indeed used
 δ18Otrc trends to conclude that gs has not significantly changed
 from pre-industrial values. However, it remains unclear whether δ18Otrc is
 sufficiently sensitive to detect the magnitude of change in gs expected
 due to CO2 increases and climatic changes. We deposit here the
 climate data and R scripts that we use to evaluate the sensitivity of
 δ18Otrc trends to CO2 and climate-induced changes in gs, and to
 VPD and temperature increases since the beginning of the 20th century,
 using current theoretical models. We find that temporal changes in
 gs only significantly affect δ18Otrc trends when the Péclet
 effect is present, and then only in dry climates. In contrast to the weak
 effects of gs on δ18Otrc trends, we find that temporal increases in VPD
 and temperature, independent of changes in gs, have far greater
 contributions to δ18Otrc trends. Thus, this increasingly popular method
 should be used with caution, because it is highly challenging to
 unambiguously attribute trends in δ18Otrc to changes in gs.
description: # Can oxygen isotopes in tree rings be used to detect stomatal responses
 to global change? Dataset DOI:
 [10.5061/dryad.905qftv01](https://doi.org/10.5061/dryad.905qftv01) ##
 Description of the data and file structure R scripts and climate data used
 for modelling simulations and creating figures. ### Files and variables
 #### File: SI.6_histogram.R **Description:** R script for histogram (Fig.
 SI.6) which compares model-predicted 50-year change in tree ring d18O to
 real 50-year changes in tree ring d18O from 172 chronologies in 3
 published studies. * create xlsx file for Treydte et al (2023) data-
 download
 at [https://doi.org/10.25921/rt6p-df91](https://doi.org/10.25921/rt6p-df91) * create xlsx file for Mathias and Thomas (2021) data- download at [https://doi.org/10.6084/m9.figshare.13541927.v2](https://doi.org/10.6084/m9.figshare.13541927.v2)  #### File: 21.10_script.R **Description:** R script for all models and all other figures.  #### File: data_30.5.25.xlsx **Description:** Climate data downloaded from Climate Explorer used for the main figures. Metadata is on the end page.  ##### Variables * CO2 = atmospheric CO~2~ concentration (unit = ppm) * temp anom = annual air temperature anomaly (unit = °C)  * ea anom = annual atmospheric vapour pressure anomaly (unit = kPa, raw data unit = hPa) * SMA = simple moving average * Empty cells are where there is no data for the variable for a given year #### File: Tropical_data.xlsx **Description:** Tropical climate data for SI.7. Metadata in end page ##### Variables * CO2 = atmospheric CO~2~ concentration (unit = ppm) * temp anom = annual air temperature anomaly (unit = °C)  * ea anom = annual atmospheric vapour pressure anomaly (unit = kPa, raw data unit = hPa) ## Code/software R Studio version 4.5.1 packages: readxl, ggplot2, ggpubr, ggthemes, ggpattern, scales, grid, gridExtra, dplyr, tidyr, cowplot, openxlsx ## Access information Other publicly accessible locations of the data: Data was derived from the following sources: * Climate Explorer: [Climate Explorer: Starting point](https://climexp.knmi.nl/start.cgi) * Treydte et al (2023) data: [https://doi.org/10.25921/rt6p-df91](https://doi.org/10.25921/rt6p-df91) * Mathias & Thomas (2021) data: [https://doi.org/10.6084/m9.figshare.13541927.v2](https://doi.org/10.6084/m9.figshare.13541927.v2)  * Guerrieri et al (2019) data: [https://pasta.lternet.edu/package/data/eml/edi/401/2/2991aaafb4060a461d0eea4c353731c7](https://pasta.lternet.edu/package/data/eml/edi/401/2/2991aaafb4060a461d0eea4c353731c7)