## 2 Materials and Methods
### Data Analysis  
The influence of main factors on soil respiration (Rs, \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)) was analyzed using a nested linear mixed model (LMM) with woodland type as the fixed factor (two levels: young and mature woodland), measurement day (date) as the repeated fixed factor (17 levels, to study the seasonality in Rs), and collar nested within woodland as the random factor. Random variance components were calculated using restricted maximum likelihood estimation (RMILE). Two variance structures were considered (compound symmetry and continuous AR1). Bayesian Information Criterion (BIC) and Akaike Information Criterion (AIC) were used as criteria for the selection of the best model. Mean values were compared using [PERSON]'s LSD post-hoc test (\(\alpha\) = 0.05). Linear mixed models were made using the nlme package in R [40]. It the model, \"woodland type\" (W) was a confounded effect of both maturity (age) and soil quality (depth and water retention capacity). For this, \"woodland\" represents the productivity of the juniper sites. To analyze the relationships among the total soil CO\({}_{2}\) efflux (Rs, \(\mu\)mol CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\)), soil temperature (Ts, \({}^{\circ}\)C), and/or soil water content (Sw, %) throughout the seasons included in the study period, the following bivariate model was fitted [41]:  
\[\text{Rs}=exp^{(\beta_{0}+\beta_{0}^{\prime}W)+(\alpha+\alpha^{\prime}W)\times \text{Ts}}\;Sw^{(\beta_{1}+\beta_{1}^{\prime}W)}\times\varepsilon \tag{1}\]  
In Model 1, the effect of the juniper woodland type on the soil CO\({}_{2}\) efflux was included through the dummy variable \"W\" (W = 0 for mature woodland, and W = 1 for young woodland). Model 1 shows the exponential relationship between cell respiration and temperature [42] and adds the influence of soil moisture on this relationship. In the model, Ts was standardized by 10 \({}^{\circ}\)C (Ts/10) to obtain the Q\({}_{10}\) value [43]. When the soil water content (Sw) was not significant and was therefore removed from Model 1, we obtained a univariate model to describe the sensitivity of Rs to soil temperature, described by the coefficient Q\({}_{10}\) (temperature coefficient):  
\[\text{Rs}=exp^{(\beta_{0}+\beta_{0}^{\prime}W)+(\alpha+\alpha^{\prime}W)\times \text{Ts}}\;\times\varepsilon \tag{2}\]  
where \(\alpha\) and \(\alpha^{\prime}\) are the coefficients of temperature sensitivity of soil respiration for mature and young woodlands, respectively. The temperature coefficient, Q\({}_{10}\) (rate of variation of Rs when the soil temperature increased by 10 \({}^{\circ}\)C), was calculated from the univariate function as [41; 43]: Q\({}_{10}=e^{(\alpha+\alpha^{\prime}W)}\), \(\alpha\) for mature woodland and \(\alpha+\alpha^{\prime}\) for young woodland, if W was significant. Thus, significant equations in function of the woodland type could be formulated in their exponential forms [5; 44]: \(\text{Rs}=\text{Rr}\times\text{Q}_{10}^{\frac{T_{0}-T_{0}}{10}}\), where \(\text{Rr}\) is the respiration at the reference temperature (Tr), which was fixed at 0 \({}^{\circ}\)C in our study. Models and Q\({}_{10}\) values were estimated for the short term, i.e., seasons (\"seasonality\" of Q\({}_{10}\)), and the long term (yearly, i.e., the annual whole data for all seasons without division by years [16]) for each woodland area. The selection of significant parameters in Model 1 was carried out by means of logarithmic transformation (linearization of the exponential function [44; 45]) and stepwise regression [46]: \(\log(\text{Rs})=\left(\beta_{0}+\beta_{0}^{\prime}W\right)+(\alpha+\alpha^{ \prime}W)\times\text{Ts}+(\beta_{1}+\beta_{1}^{\prime}W)\;\ln(\text{Sw})+\varepsilon\). This corresponds with the comparison of two regression lines as well as the study of significant differences in Q\({}_{10}\) between the two woodlands during the seasons [44]. In all regressions fitted, coefficients with a value of \(p\) < 0.05 were considered significant [46].