## 3 Results
### Statistical Analysis  
According to Table 3, the non-grouping demonstrated non-normal distribution for the variables, as they presented a \(p\)-value \(>5\%\) from the Shapiro-Wilk test [68]. However, the data were normal when grouped by year for GPP and Rainfall, and when grouped in classes for CO\({}_{2}\)Flux and Rainfall (precipitation). It was not possible to verify the normality of the grouping in classes for GPP, because WB and UBL data showed null values, as they do not represent vegetative classes [41]. The EBF, GL and CL represented the greatest variabilities for CO\({}_{2}\)Flux, and PW for Rainfall within the years 2013 to 2018. The Shapiro-Wilk grouped normality test indicates that statistics could be applied to normal data distribution to GPP or Rainfall over the years. The same occurred in the case of the Rainfall variable in different land use classes (Table 3). Complementary to the Shapiro-Wilk normality test, the histogram showed the data distribution and in which position was its highest frequency. This was observed between 20 and 40 gC m\({}^{-2}\), for the GPP, from -1 to 2.5 umol m\({}^{-2}\) s\({}^{-1}\), for CO\({}_{2}\)Flux, and from 1600 to 2200 mm, for Rainfall variables. Based on the histogram, it was also possible to identify unexpected values and trends (Figure S5). Since the multivariate Shapiro-Wilk normality test and the histogram (Table 3 and Figure S5) presented non-parametric data [69; 71], we decided to apply the Kruskal-Wallis and Dunn tests to compare groups of classes. The boxplots grouped by classes and by years were performed (Figure 4). The median values of the classes and years groups are shown in Table S1. \begin{table} \begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Vavible**} & \multicolumn{10}{c}{**Grouped by Classes**} \\ \cline{2-13}  & \multicolumn{2}{c}{**Non-**} & \multirow{2}{*}{**EBF**} & \multirow{2}{*}{**DRF**} & \multirow{2}{*}{**MF**} & \multirow{2}{*}{**WSV**} & \multirow{2}{*}{**SV**} & \multirow{2}{*}{**GL**} & \multirow{2}{*}{**PW**} & \multirow{2}{*}{**CL**} & \multirow{2}{*}{**UBL**} & \multirow{2}{*}{**WB**} \\  & & & & & & & & & & & & \\ \hline
**GPP** & 0.000 *** & - & - & - & - & - & - & - & - & - & - \\
**CO\({}_{2}\)Flux** & 0.000 *** & 0.000 *** & 0.936 & 0.658 & 0.204 & 0.874 & 0.047 ** & 0.474 & 0.007 *** & 0.692 & 0.464 \\ Rainfall & 0.000 *** & 0.793 & 0.786 & 0.436 & 0.952 & 0.229 & 0.577 & 0.0794 * & 0.695 & 0.117 & 0.424 \\ \hline \multicolumn{13}{c}{**Grouped by Year**} \\ \cline{2-13}  & \multicolumn{2}{c}{**2009**} & \multicolumn{2}{c}{**2010**} & \multicolumn{2}{c}{**2011**} & \multicolumn{2}{c}{**2012**} & \multicolumn{2}{c}{**2013**} & \multicolumn{2}{c}{**2014**} & \multicolumn{2}{c}{**2015**} & \multicolumn{2}{c}{**2016**} & \multicolumn{2}{c}{**2017**} & \multicolumn{2}{c}{**2018**} & \multicolumn{2}{c}{**2019**} \\ \hline
**GPP** & 0.483 & 0.406 & 0.439 & 0.390 & 0.506 & 0.567 & 0.470 & 0.493 & 0.478 & 0.421 & 0.398 \\
**CO\({}_{2}\)Flux** & 0.004 *** & 0.007 *** & 0.005 *** & 0.000 *** & 0.000 *** & 0.005 *** & 0.008 *** & 0.008 *** & 0.005 *** & 0.006 *** \\ Rainfall & 0.192 & 0.824 & 0.715 & 0.2067 & 0.007 *** & 0.015 ** & 0.002 *** & 0.006 *** & 0.058 * & 0.062 * & 0.144 \\ \hline \hline \end{tabular}   
* *Differences between land use classes were observed for GPP (X\({}^{2}\) = 103.96), CO\({}_{2}\)Flux (X\({}^{2}\) = 106.73) and Rainfall (X\({}^{2}\) = 65.12) by the Kruskal-Wallis test (\(p\) < 0.05). The [PERSON]'s post-hoc test showed that differences in GPP were between EBF and CL, GL, UBL, PW and WB. The DBF and WSV classes were different from UBL, PW and WB. MF and SV were different from UBL and WB. It is also interesting to observe that EBF did not differ from the DBF, MF, SV and WSV classes, all these considered as forest classes, which had medians of 52.90, 34.68, 31.53, 31.32 and 39.84 gC m\({}^{-2}\), respectively. Outliers below the minimum values were observed for SV, DBF, CL and PW, of 23.53, 22.90, 16.88 and 0.43 gC m\({}^{-2}\), in 2016, respectively (Figure 4a and Table S1).