## 3 Results
### Thermodynamics of SOM Microbial Decomposition
#### 3.2.1 Thermodynamic State Variables and Soil Microbial Diversity
Assuming CR represents the enthalpy change of Reaction (4), \(\Delta_{\tau}\)HSOM; Gibbs energy change for Reaction (4) is also obtained by [PERSON] and [PERSON]’s correlation, \(\Delta_{\tau}\)GSOM, as well as entropy change for Reaction (4), obtained by Equation (3), \(\Delta_{\tau}\)SSOM. Uncertainties for the degree of reduction and Gibbs energy change estimated from [PERSON] and [PERSON]’s correlation are the same as reported in Tables 2 and 3. Standard deviation in CR samples is determined from two replicates of each sample. Results in Table 4 show changes in the CR from LF to M samples and among samples from different sites. Those differences can be explained by, or assumed as, changes in microbial metabolism. LF samples from the same site in the UK (ROG, BW, and NF) show decomposition of substrates at higher, the same, and lower degree of reduction than carbohydrates. All LF samples from separate sites in Ireland have similar CR values in the range reported for carbohydrate catabolism. Mineral samples from the UK show lower and more stable CR values than LF samples, suggesting a change in the microbial metabolism, characterized now by biodcomposition of substrates more oxidized than carbohydrates or by a higher component of anaerobic metabolism as depth increases. Mineral samples from Ireland show higher CR values than the LF ones, but in the carbohydrate range in all cases. Gibbs energy follows the same trend as that of CR since the entropy change contributes little to Gibbs energy at 25 \({}^{\circ}\)C (Table 4). Therefore, aerobic SOM biodecomposition is ruled mainly by the enthalpy of the reactions. Consequently, values for Gibbs energy change in M samples from the UK are less negative than those from Ireland, and the entropy change is lower, suggesting that substrates being catabolized in mineral UK samples are less structurally complex than the rest of samples. These features suggest SOM at different degree of decomposition in all samples. How these thermodynamic state variables contribute to the variance among samples can be studied by PCA analysis, as shown in Figure 2 and by clustering in Figure 3.  
Figure 2: PCA plot showing the distribution of soil samples along with the two principal components. Samples ROGLF, BWLE, and NFLF are from the UK. Samples DCLF, GLF, and KLF come from Ireland. All of them represent the soil LF layer and scatter on the left side of the plot. Mineral samples from the UK (ROGM, BWM, and NFM) appear concentrated in the quadrant limited by the positive \(x\) and negative \(y\)-axis. Mineral samples from Ireland (DCM, GM, and KM) scatter along the positive x and y-axis quadrant. PCA shows that 99% of the variance can be explained by two principal components, principal component 1 (PC1; 62.86%) and principal component 2 (PC2; 37.14%). The thermodynamic state variables for Reaction (1), representing the SOM thermodynamic properties, are the ones with higher contribution to component 1, while the thermodynamic state variables determined for Reaction (4), representing SOM microbial catabolism, are the ones with higher contribution to component 2. The distribution of samples in the PCA plot in Figure 2 suggests spatial variation in their thermodynamic properties that can be attributed to spatial changes in the composition of soil organic substrates. Clusters by the thermodynamic state variables can be observed in Figure 3. There is a clear differentiation between the LF layers and the mineral samples. Soil thermodynamic properties vary with soil depth and denote different states of SOM transformation. Mineral samples from the UK and Ireland form two different clusters, while the LF samples are more overlapped. All these samples come from the same forest ecosystem, oak. Although SOM decomposition originates from oak leaves in all cases, the thermodynamic properties suggest diverse products from the decomposition.