Chemotaxonomy as a tool for interpreting the cryptic diversity of Poaceae pollen

Adele C.M Julier,Phillip E Jardine,Angela L Coe,William D Gosling,Barry H Lomax,Wesley T Fraser

doi:10.1016/j.revpalbo.2016.08.004

Abstract

The uniform morphology of different species of Poaceae (grass) pollen means that identification to below family level using light microscopy is extremely challenging. Poor taxonomic resolution reduces recoverable information from the grass pollen record, for example, species diversity and environmental preferences cannot be extracted. Recent research suggests Fourier Transform Infra-red Spectroscopy (FTIR) can be used to identify pollen grains based on their chemical composition. Here, we present a study of twelve species from eight subfamilies of Poaceae, selected from across the phylogeny but from a relatively constrained geographical area (tropical West Africa) to assess the feasibility of using this chemical method for identification within the Poaceae family. We assess several spectral processing methods and use K-nearest neighbour (k-nn) analyses, with a leave-one-out cross-validation, to generate identification success rates at different taxonomic levels. We demonstrate we can identify grass pollen grains to subfamily level with an 80% success rate. Our success in identifying Poaceae to subfamily level using FTIR provides an opportunity to generate high taxonomic resolution datasets in research areas such as palaeoecology, forensics, and melissopalynology quickly and at a relatively low cost.

Highlights

The correct identification of pollen grains is an important factor in any research area that uses pollen assemblages to make inferences about vegetation
These research areas can be as diverse as palaeoecology (Germeraad et al, 1968; Mander and Punyasena, 2014), forensics (Horrocks et al, 1998; Mildenhall et al, 2006) and melissopalynology (Herrero et al, 2002; Martin, 2005), as they all share a reliance upon the taxonomic resolution of pollen identification to maximise the accuracy and usefulness of their data
As different window sizes may be optimal for different regions of the spectrum, and this study aimed to provide a simple tool for pollen identification and used the whole spectrum, a window size of 11 was chosen; this falls within the range of optimal values defined by Zimmermann and Kohler (2013)

Summary

Introduction

The correct identification of pollen grains is an important factor in any research area that uses pollen assemblages to make inferences about vegetation. Looking further back into geological time, palynological research has played a fundamental role in understanding plant origination and radiation (e.g. the origin and radiation of vascular plants (Rubinstein et al, 2010), and the radiation of the angiosperms (Lupia et al, 1999)), and shaped our understanding of how the terrestrial biosphere responded to mass extinction events (Looy et al, 2001; Tschudy et al, 1984) This highly diverse group of studies all shares a reliance upon the taxonomic resolution of pollen identification to maximise the accuracy and usefulness of their data.

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