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).