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Assessment and calibration of representational bias in soil phytolith assemblages in Northeast China and its implications for paleovegetation reconstruction

Published online by Cambridge University Press:  10 April 2018

Guizai Gao
School of Geographical Science, Northeast Normal University, Changchun 130024, China
Dongmei Jie*
School of Geographical Science, Northeast Normal University, Changchun 130024, China Key Laboratory of Vegetation Ecology, Ministry of Education, Changchun 130024, China
Lidan Liu
School of Geographical Science, Northeast Normal University, Changchun 130024, China
Hongyan Liu
School of Geographical Science, Northeast Normal University, Changchun 130024, China
Dehui Li
School of Geographical Science, Northeast Normal University, Changchun 130024, China
Nannan Li
School of Geographical Science, Northeast Normal University, Changchun 130024, China
Jichen Shi
School of Geographical Science, Northeast Normal University, Changchun 130024, China
Chengcheng Leng
School of Geographical Science, Northeast Normal University, Changchun 130024, China
Zhihe Qiao
Daqing Normal University, Daqing 163000, PR China
*Corresponding author at: School of Geographical Science, Northeast Normal University, Changchun 130024, China. E-mail address: (D.M. Jie).


The assessment and calibration of representational bias in modern soil phytolith assemblages provide the basis for improving interpretation of fossil phytolith assemblages. We studied soil phytolith representation by comparing phytoliths from living plant communities with those from paired surface soils, representing 39 plant communities in Northeast China. Together with the use of representation indices, the 34 and 30 soil morphotypes observed in forest and grassland samples, respectively, were both classified into the following four groups: “Associated types” were similarly represented in soils and in the corresponding species inventory data; “Over-represented types” and “Under-represented types” were respectively over- and under-represented in soils compared to the inventory data; and, in the case of “Special types,” the relationship with the parent plants was unclear. In addition, the diagnostic types exhibited different degrees of representation, while the most common morphotypes were equally represented between grassland samples and forest samples. On this basis, a comparison between the original and corrected soil phytolith indices of the additional 29 soil samples was conducted. The soil phytoliths frequencies corrected by R-values differed between plots with differing plant compositions, and were moderately consistent with actual plant richness in the plot inventory data. We therefore confirmed that R-values are a promising means of correcting soil phytoliths for representational bias in temperate regions. The corrected soil phytoliths can be used to reliably reflect vegetation variability. Overall, our study provides an improved understanding of soil phytolith representation and offers a potential method for improving the accuracy of paleovegetation reconstruction.

Research Article
Copyright © University of Washington. Published by Cambridge University Press, 2018 

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