2.9 | Acclimation analyses
To evaluate acclimation of photosynthetic parameters we compared posterior distributions of TOpt of P400, P800, VCMax, and JMax, and of the activation energies of treatment and control plants. Stomatal acclimation was assessed by comparing TOpt of P400 and P270 (and P800and P505), and by analyzing changes in the Ci/ Ca ratio. We further compared the relationship between gs and VPD between treatments (see below). Acclimation of respiration was determined with the set temperature method and the homeostasis method (Atkin, Bruhn & Tjoelker 2005; Slot & Kitajima 2015): The set temperature method compares respiration (R) of control and treatment leaves at a set temperature (here, 30°C):
\(\text{Acclim}_{\text{SetTemp}}=\frac{R_{\text{Control\ }}at\ 30C}{R_{\text{Treatment}}at\ 30C}\)Eqn.
AcclimSetTemp > 1.0 indicates thermal acclimation. The homeostasis method determines the degree of homeostasis:
\(\text{Acclim}_{\text{Homeo}}=\frac{R_{\text{\ Control}}\text{\ at\ }T_{\text{Control}}}{R_{\text{Treatment}}\text{\ at\ }T_{\text{Treatment}}}\)Eqn. 1
When AcclimHomeo ≈ 1.0 respiration rates are homeostatic across conditions and respiration has fully acclimated; when AcclimHomeo < 1.0, acclimation, if any, is imperfect.
2.10 | Curve fitting and statistical analyses
We fit Eqns 2, 3 and 6 using a Bayesian framework with the MCMC sampler Stan using the R libraries ’rstan’ (Stan Development team 2018) and ‘brms’ (Bürkner 2018) with R version 3.5, which facilitated a more thorough exploration of the uncertainty of the parameter estimates. The full models used for Eqns. 2, 3 and 6 contained terms for all treatments; control parameters were estimated as the basis, with parameters for treatment plants, plants transferred from control to treatment conditions, and from treatment to control conditions being estimated as deviations from the controls (see Table S1). The code used to fit these models and to generate Figs. 1−3 and 7 is available at https://github.com/sw-rifai/Tabebuia_rosea_thermal_co2_acclim. Informed priors (Table S2) were used to constrain kOpt, POpt, Ha, and TOpt. These priors were based on literature (e.g. Medlyn et al., 2002; Slot & Winter 2017b), selected to be realistic (i.e. only positive values), and were refined to avoid multimodal posterior fits. Attempts at estimation of the Hd parameter produced bi-modal posterior distributions of other model parameters (e.g. TOpt) and so Hd was fixed at 200 kJ mol–1following the example of Medlyn et al. (2002). Each model was fit using four chains with 2000 iterations during warmup, and a subsequent 4000 iterations during sampling. ΔS was calculated from TOptand Ha estimates for each iteration with Eqn 5. Models were checked to ensure convergence (R̂~ 1), posterior distributions were unimodal, and that posterior predictive checks could approximate the distribution of the response variable. Comparisons of plant-level parameters between control, treatment, and transfer effect plants were made by comparing the distribution of credible intervals between groups.
Treatment effects on the temperature response of the JMax to VCMax ratio, and the VPD response of stomatal conductance were determined with ANCOVA, with temperature and VPD as the respective covariate. To visualize treatment effects on the VPD response of stomatal conductance and the temperature responses of stomatal conductance and the initial A-Cislopes used to assess Rubisco activase status, temperature responses were fitted with generalized additive models using cubic regression splines fit with restricted maximum likelihood in the ‘mgcv’ package (Wood 2017) for R, and 95% confidence intervals were approximated by plotting curves ± 2 standard errors.