Five useful techniques for analysing palynological data

Abstract

Palynologists often produce large quantitative data sets that can seldom be matched by other types of paleontological data. Although palynological data are subject to study by analytical techniques to answer questions regarding evolution, paleoclimate, and biogeography, the use of palynological data has often been qualitative, thus limiting their interpretation. Here, five techniques that can be used with palynological data are presented. These deal with diversity (number of species, evenness, diversity indices, and abundance distribution models), comparing similarities among samples, building a composite section, constructing species ranges, and estimating edge effects. The code necessary to perform these techniques has been included using R for Statistical Computing. R is an open-source and powerful statistical software available freely to anyone worldwide.