What Factors Control Acclimation of Leaf Respiration?

About a quarter of the carbon taken up globally in photosynthesis is released to the atmosphere via respiration from plant leaves (Wang et al., 2020). This flux is three times larger than anthropogenic CO₂ emissions from all sources combined (Friedlingstein et al., 2022), and is a key target for model predictions of the global carbon cycle. As an enzyme-catalysed process, R_d increases near-exponentially with warming on a time scale of minutes to hours. This instantaneous response by itself, if sustained, would cause a strong positive feedback between the global carbon cycle and climate warming (Collalti et al., 2020). However, R_d acclimates to environmental changes on time scales from days (Atkin et al., 2000) to weeks (Reich et al., 2016) by down- or up-regulation of the basal respiration rate, which is the value of R_d adjusted to a standard reference temperature, often 25°C (R_d,25). Some existing hypotheses explain and predict the temporal and spatial down-regulation of R_d,25 in response to warming (see Table 1).

_{Table 1. The four hypotheses evaluated in this study}

Here we test these hypotheses using observations of changes through the seasonal cycle in a warming experiment (B4WarmED dataset) and an extensive global data set documenting spatial patterns (GlobResp and LCE datasets) in R_d,25. We calculated the predictive ability (R²) and root mean square error (RMSE) of each hypothesis driven by differing factors compared to the observed R_d,25, both temporally (in B4WarmED) and spatially (in GlobResp+LCE). 

The two hypotheses that account for the effect of T_night, (H3) explained the variability of doy-mean R_d,25 derived from B4WarmED consistently better than H2, which considered only the effect of V_cmax,25 or H1, which took into account the effects of N_leaf and T_daily (Fig. 1a-c). The observed doy-mean R_d,25 varied from 0.57 to 2.17 μmol CO₂ m^–2 s^–1. H1 displayed the smallest changes amongst the three hypotheses (Fig. 1a-c), with estimated doy-mean R_d,25 varying more narrowly (between 1.11 and 1.64 μmol CO₂ m^–2 s^–1) and a general overestimation.

Compared to H1, H2 better demonstrated the variation in the smaller respiration rate, but underestimated the larger respiration rate with estimated doy-mean R_d,25 varying from 0.57 to 1.52 μmol CO₂ m^–2 s^–1. The range of variation in doy-mean R_d,25 under H3, from 0.68 to 2.19 μmol CO₂ m^–2 s^–1, was basically consistent with the observed range. Geographic variation in observed site-mean R_d,25 (Fig. 1d-f) was best explained by H2 (R² = 0.65). T_daily and N_leaf (H1), and T_night alone (H3), represented the geographic variation of R_d,25 less well (R²= 0.48, 0.36) than H2.

These observations suggest that the proposed relationship between V_cmax and R_d,25 based on spatial patterns alone is too simple, and that a fuller explanation should consider both V_cmax and nighttime influences on the acclimation of R_d. An accepted quantitative theory is needed to better explain and predict the temporal and spatial variations of R_d,25.

This work is going to be submitted. We will provide the DOI when it is accepted.

References:

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