Characteristics of the modern pollen assemblages from different vegetation zones in Northeast China: Implications for pollen-based climate reconstruction

  • Qiaoyu CuiEmail author
  • Yan ZhaoEmail author
  • Feng Qin
  • Chen Liang
  • Quan Li
  • Rongwei Geng
Research Paper


Northeast China is an essential area for studying the strength of East Asian Summer Monsoon (EASM), due to its northernmost location in EASM domain. However, the lack of sufficient modern pollen data in this region hinders an effective interpretation of fossil pollen records and quantitative vegetation/climate reconstructions. Here, 44 surface pollen samples from forest, steppe, and meadow were used to explore pollen-vegetation-climate relationships. Cluster analysis, species indicator analysis, and principal components analysis, were used to identify the discontinuous and continuous trends in pollen dataset. In addition, correlation analysis and boosted regression trees were used to investigate primary explanatory variables, while coinertia analysis and redundancy analysis to examine pollen-vegetation and pollen-climate correlations respectively. Our results show that: (1) vegetation can be well represented by surface pollen assemblages, i.e. forest is characterized by a high proportion of tree pollen (>70%) dominated by Betula (>40%) along with Alnus, Larix, and Pinus, whereas Steppe by herb pollen (>80%), dominated by Artemisia, Chenopodiaceae; (2) significant correlations exist between pollen assemblages and mean annual temperature and then mean annual precipitation; (3) pollen ratios of Artemisia/Chenopodiaceae and arboreal/non-arboreal can respectively be used as good indicators of humidity and temperature in Northeast China.


Northeast China Modern pollen assemblages Pollen-vegetation relationships Pollen-climate relationships Climate reconstruction 


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This study was supported by the National Key R & D Program of China (Grant No. 2016YFA0600501), the Strategic Priority Research Program of Chinese Academy of Sciences (Grant No. XDA20070101) and the National Natural Science Foundation of China (Grant Nos. 41572353, 41401228 & 41690113).


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Copyright information

© Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources ResearchChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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