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).