## 2 Materials and Methods
### Statistical Analyses  
For all data analyzed, a Kolmogorov-Smirnov test with Lilliefors correction was used to assess normality. Then, we explored the annual and seasonal dynamics of R\({}_{\text{S}}\), TS, SWC and PPT. Monthly means of R\({}_{\text{S}}\), TS and SWC were calculated from the replicated sampling points available in the transects at each site and a Kruskal-Wallis test following by a Dunn multiple comparison post hoc test (\(p<0.05\)) was used to determine the differences of R\({}_{\text{S}}\) across study sites between dry and wet seasons. Additionally, annual C emission estimates were calculated for each soil collar using a time course fitting and area under the curve integration function (SigmaPlot v.12, Systat Software, Inc., Chicago, IL, USA) [73]. Finally, for ecosystem flux and meteorological variables, Spearman pairwise correlations were used to identify the relationships among all the variables and a Kruskal-Wallis test withDunn multiple comparison post hoc test (\(p<0.05\)) was used to determine the differences among sites. Structural equation modeling (SEM) is a multivariate statistical tool that allows testing causal or correlative relationships among different variables. The general SEM model considered a full set of hypotheses based on literature, precedent exploratory analyses and our own experience [11; 70]. Briefly, we hypothesize that the global (K) and net (Q*) radiation would control the evapotranspiration (ET, negatively), vapor pressure deficit (VPD, positively), and air temperature (Tair, positively) regardless of the successional stage [74]. In turn, the VPD and ET would be correlated to gross primary production (GPP, negatively and positively, respectively) at all sites [75]. GPP would be controlled by either K or Q*, depending on the successional stage [76]. A higher ET reduces the residence time of soil moisture, therefore negatively affecting the soil water content (SWC) [77] and a cooling effect that constrains R\({}_{\text{S}}\)[78; 79]. In consequence, SWC together with soil temperature (TS) would be the most important controlling of R\({}_{\text{S}}\)[18]. The variation of GPP across forest succession would be reflected on R\({}_{\text{S}}\), since GPP represents the autotrophic activity of the ecosystem [27]. The general model was applied to each successional stage (i.e., old growth, mid-secondary or early-secondary). The SEM model was tested for the combined years of R\({}_{\text{S}}\) and half-hour ecosystem flux and meteorological observations, which match with the dates and hours when R\({}_{\text{S}}\) measurements were carried out during the sampling campaigns (Table 2). Prior to the SEM analysis, the variables of interest from the dataset of each site were log transformed. Several models were run, and the best-fitted were selected according to the goodness of fit [80]; this procedure was performed to the dataset that integrates all three study sites (general model) and was also performed individually for each study site (i.e., OG, MS and ES). In all cases, to ensure a good model fitting, goodness of fit was assessed according to [PERSON] [81] through the following parameters: (1) a Chi-square (\(\chi^{2}\)) test--this parameter must be nonsignificant \(\chi^{2}\) (\(p>0.05\); df = 1); (2) a root-mean-square error of approximation statistic (RMSEA), whose values must be <0.10; and (3) a goodness-of-fit index (GFI) and the Bentler and Bonett's normed-fit index (NFI), where both estimates must be >0.9. The standardized path regression weights (SRW) were obtained with the maximum likelihood estimation method [82].