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, Rd 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, Rd 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 Rd adjusted to a standard reference temperature, often 25°C (Rd,25). Some existing hypotheses explain and predict the temporal and spatial down-regulation of Rd,25 in response to warming (see Table 1).

Table 1. The four hypotheses evaluated in this study

*Notes: b25 and Rfix are estimated as the mean value of all field measurements in their work.

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 Rd,25. We calculated the predictive ability (R2) and root mean square error (RMSE) of each hypothesis driven by differing factors compared to the observed Rd,25, both temporally (in B4WarmED) and spatially (in GlobResp+LCE). 

Fig. 1 Scatter plots of natural-log transformed Rd,25 between observations and predictions according to the three hypotheses. The temporal observations are from the B4WarmED dataset (a-c) and the spatial observations are from the combined GlobResp and LCE datasets (d-f). Mean values are the average of all species within the sampling day (doy_mean, a-c) or site (site_mean, d-f), respectively. The solid black line is the fitted line of the robust regression. R2 and RMSE are the weighted coefficient of determination and weighted root mean square error between observed Rd,25 and predicted Rd,25.

The two hypotheses that account for the effect of Tnight, (H3) explained the variability of doy-mean Rd,25 derived from B4WarmED consistently better than H2, which considered only the effect of Vcmax,25 or H1, which took into account the effects of Nleaf and Tdaily (Fig. 1a-c). The observed doy-mean Rd,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 Rd,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 Rd,25 varying from 0.57 to 1.52 μmol CO2 m–2 s–1. The range of variation in doy-mean Rd,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 Rd,25 (Fig. 1d-f) was best explained by H2 (R2 = 0.65). Tdaily and Nleaf (H1), and Tnight alone (H3), represented the geographic variation of Rd,25 less well (R2= 0.48, 0.36) than H2.

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

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


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