The use of multivariate PCA dataset in identifying the underlying drivers of critical stressors, looking at global problems through a local lens.

Palynology-based multivariate datasets including geological, ecological, and geochemical data identified the relative importance of the underlying drivers of critical stressors to coastal wetlands by identifying and distinguishing between fluvial flooding, saline water intrusion, delta switching, and the landward migration of coastal plants. A sediment core was retrieved using a vibracorer from an intermediate marsh in Lake Salvador, Louisiana, USA. X-ray Fluorescence (XRF) quantified fluvial and marine elemental concentrations (Cl, Sr, Ca, Mn, K, Ti, Fe, Zn, Zr, Br). Palynology-based agglomerate hierarchical analysis of thirty-two pollen taxa was employed to define ecological clusters. The implementation of multivariate principal component analysis (PCA) to geochemical and ecological variables inferred the source of sedimentary material by correlating four taxonomic groups (floodplain trees, upland trees, tidal freshwater herbs, and inland herbs) to specific geochemical signatures and facilitated the testing of potential correlations between geo- and hydrological-conditions and the six ecosystems (interdistributary, delta-plain, deltaic lake, bottomland and swamp forests, freshwater marsh, and intermediate marsh) depicted in each PCA biplot. The PCA scores quantified the relative importance of multiple variables. The squared cosine function, which demonstrates the relative importance of a variable for a given observation, was used to estimate the representation of each variable on the principal component biplots. Multivariate statistical datasets can be valuable to any scientist working across the spectrum of environmental and planetary science fields as a means of identifying the relative importance of diverse background parameters in controlling ecological and environmental conditions. This methodology is applicable across both natural and social sciences as a means of distinguishing natural and anthropogenic impacts.