Vegetation History and Archaeobotany

, Volume 14, Issue 1, pp 15–30

Multivariate statistical and other approaches for the separation of cereal from wild Poaceae pollen using a large Holocene dataset

  • John C. Tweddle
  • Kevin J. Edwards
  • Nick R. J. Fieller
Original Article


The separation of the pollen of wild Poaceae species from that of domesticated cereal crops is of considerable importance to palynologists studying Holocene vegetational and agricultural change. Studies of the characteristics of modern pollen populations indicate that it may be possible to distinguish cereal pollen from that of many (but not all) undomesticated Poaceae species, though there are few detailed investigations into the applicability of such studies to palaeoecological samples. This paper assesses the reliability of available keys for identifying sub-fossil grass pollen using a large Holocene dataset obtained from a series of well-dated profiles from lowland Yorkshire, England. Pollen within the dataset is classified using the keys of Andersen (Danmarks Geol Undersøgelse, Arbog, 1978, 69–92, 1979) and Küster (1988), and the resulting identifications are compared. The possibilities of combining the two approaches and employing the multivariate statistical techniques of principal component and discriminant analysis to achieve greater confidence of identification are then investigated. Finally, the findings of the above analyses are used to discuss the interpretation of incidences of large Poaceae pollen (i.e. >37 μm grain diameter as measured in silicone oil) within the palynological record, particularly during prehistory. The outcomes of this study will be of relevance to other investigations in which careful identification of large grass pollen is desirable, but where preservation or other factors prohibit accurate or confident identification of pollen surface pattern.


Palynology Holocene Poaceae Cereal pollen Multivariate statistics Yorkshire England 


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Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • John C. Tweddle
    • 1
  • Kevin J. Edwards
    • 2
  • Nick R. J. Fieller
    • 3
  1. 1.Department of Library and Information ServicesThe Natural History MuseumLondonUK
  2. 2.Department of Geography and Environment and Northern Studies CentreUniversity of AberdeenAberdeenUK
  3. 3.Department of Probability and StatisticsThe University of Sheffield, The Hicks BuildingSheffieldUK

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