2.3. Statistical Methods
According to situations and objectives, multivariate statistical techniques are used extensively in ecological and conservation studies to explain the environmental and land-use changes in riparian zones, for example, the algorithms found in Bombino et al. (2019), McIntosh (2015), and Zema et al. (2018). In this study, we used techniques including the Kruskal-Wallis non-parametric alternative to the analysis of variance (ANOVA), principal component analysis (PCA), Pearson correlation, and cluster analysis (CA). The Kruskal-Wallis test is a standard procedure used in scientific and non-scientific disciplines to analyze differences between means (McIntosh, 2015). The level of significance was established at both p < 0.01 and p < 0.05. All the transects were considered spatially independent in terms of group and indicator indexing for the rural, rural–urban transitional, and urban areas. PCA (factor analysis) is used in ecological and conservation studies to extract key elements, factors, and indicators from multidimensional data. This process is effective in clustering the indicators by creating diverse groups. Pearson correlation is a statistical metric used to determine linear relationships and measures the strength and direction between two random variables or indicators (Zhou et al., 2016). This method is used in data classification and analysis for various indices in scientific and non-scientific research (Pavanello et al., 2015; Zhou et al., 2016). CA is used to group similar or dissimilar characteristics among diverse random variables to establish parallel functionality (Bombino et al., 2019; Zema et al., 2018). In our study, the agglomerative hierarchical cluster (AHC) method was used to determine CA. Origin release 2021 (Northampton, MA, USA) was used for statistical analyses and graphing.