Detecting long‐term changes in stomatal conductance: challenges and opportunities of tree‐ring δ18O proxy

There is growing scientific consensus that rising atmospheric CO2 concentrations have caused an increase in forestwater-use efficiency (WUE), but the magnitude of this effect and the relative roles of enhanced photosynthesis vs reduced stomatal conductance (gs) are still under debate (Walker et al., 2020). In Guerrieri et al. (2019) we addressed this by combining 30 years of tree-ring stable carbon (dC) and oxygen (dO) isotope data with a water–carbon optimality model for 12 tree species at eight sites within the AmeriFlux network in the US. The results suggested that enhanced photosynthesis has been widespread, while reduced gs was more common at sites experiencing frequent water limitation. In this issue of New Phytologist, Lin et al. (2022) challenges these findings based on three criticisms pertaining to the use and interpretation of dO data. While the physiological interpretation of dO in tree rings is indeed complex, it remains the only proxy – in conjunction with dC – that can be used to reconstruct mechanisms through which WUE changes in response to global change drivers, including atmospheric CO2. Here, we address the criticisms raised by Lin et al. (2022), with the aim of moving the discussion forward and hence encouraging the use of tree-ring dO as a proxy to constrain plant and forest ecophysiology and the response to environmental changes beyond climate variability. Criticism 1: Lin et al. (2022) questioned the approach employed in Guerrieri et al. (2019) to estimate temporal changes in dO in precipitation (dOP), which relies on equations presented in Barbour et al. (2001) (‘B01 model’ hereafter) to assess spatial dOP. In particular, Guerrieri et al. (2019) used temperature and precipitation (both as annual or growing season means) and elevation as predictors. They considered eight sites across North America (most of the sites were in Canada, and thus did not overlap with the sites studied inGuerrieri et al., 2019) where dOP values were available from the global network of isotopes in precipitation (GNIP) for the same time window we considered in Guerrieri et al. (2019). The nonsignificant relationship between estimated and measured dOP found at those sites led the authors to conclude that the B01 model may not be adequate for the estimation of the isotopic signature of precipitation, and that this would, therefore, likely be the case for our sites. We acknowledge that the time for space substitution of the B01 model bears uncertainties that can limit the interpretation of our results. Yet, in the absence of dOP observations, such an approach is valuable, and we applied it carefully, and in the limited context of our study sites: inferring the directionality of the trend in the transpiration (E)–gs relationship. In Guerrieri et al. (2019), the d OP estimates were validated with 2 years of dO measurements in soil water (dOsw) at two sites with contrasting climates, with the assumption that dOsw will reflect d OP. As shown in supporting information fig. S11 (Guerrieri et al., 2019), the estimates of dOP were in good agreement with measured dOsw, particularly at the site in Florida, where precipitation patterns may be more complex compared to those of other northeasternUS sites, due to the humid subtropical climate. Moreover, we know that measurements made in the first 10 cm of the soil may not accurately reflect dOP due to the effect of evaporation, thus leading to a deviation between modelled dOP and measured d Osw; but this did not seem to particularly affect the site in Florida, which had sandy soil and a higher air temperature than theHarvard Forest in northeasternUS. TheB01modelmaynot provide a robust estimate of the absolute or interannual variability of dOP values; however, temperature and precipitation remain two of the most important factors that affect dOP, and these factors provide reliable predictions of global dOP such as those used in isoscapes (Bowen, 2010). More sophisticated approaches were recently proposed to estimate spatial and temporal changes in precipitation dO (Terzer et al., 2013; Allen et al., 2018 at global scale; Nelson et al., 2021 for Europe), whichwill greatly contribute to reducing the uncertainties in source water dO estimates. When dOP data are not available, estimating dOP variations following the B01 model remains a valuable and more simple approach to accounting for the variation in source water dO in the temporal trend of tree ring dO, and to improving the detection of tree physiological signals, when the focus is the long-term pattern (rather than intra-annual) reconstruction of source water dO. At most of the sites considered by Lin et al. (2022) (see their fig. 1), the estimate from the B01 model and the observed dOP values showed a consistent directionality of trend (given the positive Pearson coefficient for all but two sites), despite the statistical significance of the correlations. Indeed, values from GNIP usually derive from monthly precipitation water (including snow) collection and therefore have a high temporal resolution which can substantially differ from (smoothed) estimates based on integrated periods such as mean growing season or mean annual temperature and precipitation. A critical point regarding dOP concerns the different water sources trees can access, that is, different origins (seasonal) and soil depths (Brinkmann et al., 2018; Allen et al., 2019; Goldsmith et al., 2022). We addressed this, though only at two sites (with contrasting climates) where data on soil and xylemwater dOwere available (fig. S9 inGuerrieri et al., 2019). The estimated dOP fell within the confidence interval of both measured soil and xylem