3.2 Principal Factors of Stress and Riparian Health Condition
PCA is widely used for data matrix transformation and allows the researcher to understand the relationship between indicators by condensing the dimensionality of the data using factor analysis. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy and Bartlett’s test of sphericity were used to check data legitimacy and consequently determine PCA suitability. Both tests verified that our data were statistically valid for PCA. The KMO tests resulted in scores of 0.858 and 0.731 for RHIs and stress indicators, respectively, which were higher than a score of 0.6, whereas Bartlett’s tests appeared as 0.000, which was lower than 0.05. PCA results for RHIs and stress indicators are presented in Figures 6, respectively. Based on the initial eigenvalues, three components (holding 14 out of 27 indicators and accounting for 65.24% of the total variation) were extracted from the riparian zone health indicators, as shown in Figure 6A. These were confirmed as sufficient and authentic for parallel analysis using two additional steps—screen plot and Monte Carlo PCA. The first component (F1) was mostly condensed for PC1, H1, PC6, PC3b, R1, Er1 and Er3a, while four RHIs (Ex4, Ex1b, Ex3, and Ex2) and three RHIs (Er3b, Er3c, and Er3a) were shown in the second component (F2) and third component (F3). Following the same selection criteria, three components (holding 7 out of 13 indicators and accounting for 70.90 % of the total variation within the TGDR) were extracted from the stress indicators, as shown in Figure 6B. The rotated component matrix for stress indexing indicated that P8b, P9, P8a, and P3a, were strongly linked, based on similarly high values, in the first component (F1), and two stress indicators (P7 and P10) were shown in the second component (F2), with one indicator (P8c) in third component F3.
[Figure 6 to be inserted about here]