Statistical Analysis
Cluster analysis and multivariate statistical studies have been used to classify the groupings of phytoclasts, palynomorphs, and Amorphous OM according to dominance and sub-dominance in order to elaborate the palynofacies and sediment texture (Grimm, 1987, 1990; Legendre & Legendre, 2012). The palynofacies were counted until 400-600 observations were made and converted into percentages; thereafter CONISS cluster analysis was performed to identify different zones based on the particulate organic matter distribution in TILIA ver. 1.7. Among the multivariate analyses, Principal Component Analysis (PCA) was performed using CANOCO 5.0. The CONISS was performed to create discrete group linkages based on their similarity while ordination analysis using PCA has been used for validating the variables contributing to the typical relationship between varying entities mainly between palynofacies and grain size (Legendre & Legendre, 2012). PCA is a multivariate statistical technique using an orthogonal transformation method to obtain a set of correlated variables into a set of orthogonal, uncorrelated axes called principal components (James & McCulloch, 1990; Legendre & Legendre, 1998; Robertson et al., 2001; Gotelli & Ellison, 2004). The application of PCA for the analysis of community composition data or gradient analysis is greatly acknowledged (Šmilauer & Lepš, 2014). It practically allows plant community ecology to relate the abundance of various components to diverse environmental gradients that are key controlling factors such as temperature, water availability, light, and sediment texture, or their closely correlated representatives (Šmilauer & Lepš 2014).