Abstract

Vegetation reconstructions from palaeoecological records depend on adequate understanding of relationships between modern pollen, vegetation and climate. A key parameter for quantitative vegetation reconstructions is the Relative Pollen Productivity (RPP). Differences in both environmental and methodological factors are known to alter the RPP estimated significantly, making it difficult to determine whether the underlying pollen productivity does actually vary, and if so, why. In this paper, we present the results of a replication study for the Bashang steppe region, a typical steppe area in northern China, carried out in 2013 and 2014. In each year, 30 surface samples were collected for pollen analysis, with accompanying vegetation survey using the “Crackles Bequest Project” methodology. Sampling designs differed slightly between the two years: in 2013, sites were located completely randomly, whilst in 2014 sampling locations were constrained to be within a few km of roads. There is a strong inter-annual variability in both the pollen and the vegetation spectra therefore in RPPs, and annual precipitation may be a key influence on these variations. The pollen assemblages in both years are dominated by herbaceous taxa such as Artemisia, Amaranthaceae, Poaceae, Asteraceae, Cyperaceae, Fabaceae and Allium. Artemisia and Amaranthaceae pollen are significantly over-represented for their vegetation abundance. Poaceae, Cyperaceae and Fabaceae seem to have under-represented pollen for vegetation with correspondingly lower RPPs. Asteraceae seems to be well-represented, with moderate RPPs and less annual variation. Estimated Relevant Source Area of Pollen (RSAP) ranges from 2000 to 3000 m. Different sampling designs have an effect both on RSAP and RPPs and random sample selection may be the best strategy for obtaining robust estimates. Our results have implications for further pollen-vegetation relationship and quantitative vegetation reconstruction research in typical steppe areas and in other open habitats with strong inter-annual variation.

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