# Response of Mangrove Carbon Fluxes to Drought Stress Detected by Photochemical Reflectance Index
## 3 Results
### Correlations between PRI, Environmental Factors, and Carbon Fluxes  
Pearson correlation analyses based on half-hour data (Figure 5) showed that PRI had good relationships with carbon dynamics at the diurnal scale over the four years, showing a positive PRI-LUE relationship and negative PRI-GPP and PRI-NEE relationships. The absolute values of seasonal mean correlation coefficients of diurnal PRI-GPP and PRI-NEE relationships were higher than 0.58. The annual mean values of correlation coefficients between PRI and the three carbon-related variables were the highest in 2020, though the differences were not obvious. Figure 4: Seasonal and inter-annual variations in monthly (dotted) PRI0, sunlit PRI, and seasonal \(\Delta\)PRI from January 2017 to December 2020. Lines indicate the 3-month moving average of monthly values. PRI = photochemical reflectance index. Daily mean data were grouped by different years to analyze the correlations at the seasonal scale (Figure 6). Daily mean values generally showed linear relationships between carbon fluxes and PRI (statistically significant at \(p<0.05\)) for each year. At the seasonal scale, LUE (R\({}^{2}\) = 0.08\(-\)0.43 for the four years) had a positive relationship with PRI while GPP (R\({}^{2}\) = 0.04\(-\)0.29) and NEE (R\({}^{2}\) = 0.07\(-\)0.24) had negative relationships with PRI. The slopes of the correlation between daily PRI and carbon fluxes were steeper in 2020 compared to in previous years. Moreover, the fitted relationships in 2020 were the best with obviously higher R\({}^{2}\) than other years. In addition to the PRI-carbon relationships, the relationships between two PRI components and carbon fluxes were also analyzed. The PRI-carbon relationships were similar to the PRI-carbon relationships, while the relationships between seasonal \(\Delta\)PRI and carbon fluxes were fairly poor. The roles of environmental factors in driving PRI variations and carbon dynamics were examined using the RF approach based on monthly mean data (Figure 7). The OOB prediction errors of the RF models of GPP, NEE, and PRI, were small (\(<\)5%). The RF-based analyses demonstrated that GPP was most correlated with PAR and VPD(t \(-\) 2) among all explanatory variables, and the partial dependence plot confirmed that GPP increased with increasing PAR but decreased with increasing VPD. NEE was most correlated with VPD (t \(-\) 1) and salinity(t \(-\) 1), with NEE decreasing with VPD and salinity. The RF analyses of PRI revealed that dominant explanatory variables included GPP(t + 1), VPD(t \(-\) 1), VPD (t \(-\) 2) and rainfall, where PRI increased with rainfall but decreased with VPD and GPP. Figure 5: Seasonal mean values of daily Person correlation coefficients between half-hourly PRI and carbon-related variables from January 2017 to December 2020. For better comparison, absolute values of correlation coefficients were shown in the figure. PRI = photochemical reflectance index; LUE = light use efficiency; GPP = gross primary production; NEE = net ecosystem exchange. Figure 6: The relationships between daily carbon-related and PRI-related variables: (**a–c**) LUE vs. PRI, PRI0, and \(\Delta\)PRI; (**d–f**) GPP vs. PRI, PRI0, and \(\Delta\)PRI; (**g–l**) NEE vs. PRI, PRI0, and \(\Delta\)PRI; (**g–l**) NEE vs. PRI, PRI0, and \(\Delta\)PRI. Lines in black, green, blue, and red represent values in 2017, 2018, 2019, and 2020, respectively. All the fitting curves were statistically significant (\(p<0.05\)). PRI = photochemical reflectance index; LUE = light use efficiency; GPP = gross primary production; NEE = net ecosystem exchange. Figure 7: The regression performance (**a**,**d**,**g**) of three sets of random forest (RF) analyses and their quantification of relative importance (**b**,**e**,**h**) and affecting direction (**c**,**f**,**i**) for explanatory variables in  
driving the variations of monthly GPP (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)), NEE (\(\mu\)mol m\({}^{-2}\) s\({}^{-1}\)), and PRI.