2.5 Statistical analysis
To characterize the physical-chemical differences among the five forest states and reduce these parameters to a limited number of factors, we applied principal components analysis (PCA). Differences between states were examined using biplots displaying the first two PCA factor scores. To analyse the effects of ecosystem degradation and seasonality on soil physical-chemical properties and enzymatic activity, generalized linear models (GLMs) followed by Tukey’s post hoc test were applied. The five forest states and dry/rainy season were established as the independent variables and soil physical-chemical variables, enzymatic activity, and POC and MAOC fractions were the dependent variables. Given that the interaction between forest state and season was not significant for any of the variables, only their principal effects were interpreted. Levene’s test was applied in the GLMs to ensure that the assumption of homogeneity of variance was met. All data are represented as means ± standard error of the mean (SEM).
Pearson correlations were used to assess relationships between variables. For all statistical tests, p < 0.05 was selected as the level of significance. Statistical analyses were performed with R version 4.2.3 (R Core Team 2023) and SPSS v25 for Windows (IBM Corp., Armonk, NY, USA).