Relationships between vegetation and stomata, and between vegetation and pollen surface soil in Yunnan, Southwest China |
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Authors: | HuaDong Shen ChunHai Li HeWen Wan GuoBang Tong JinSong Liu Johnson Dan |
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Institution: | 15657. State Key Laboratory of Lake Science and Environment, Nanjng Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, 210008, China 25657. University of Chinese Academy of Sciences, Beijing, 100049, China 35657. College of Life Sciences, Anhui University, Hefei, 230039, China 45657. Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Zhengding, 050803, China 55657. Mathematics and Information Science College, Hebei Normal University, Shijiazhuang, 050024, China 65657. The Key Laboratory of Calculation and Application of Hebei Province, Shijiazhuang, 050024, China 75657. Environmental Science, Department of Geography, University of Lethbridge, Alberta, T1K 3M4, Canada
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Abstract: | Surface pollen and stomata of 61 samples collected in a study area ranging from tropical seasonal rainforest to oak forest (Quercus spinosa) in the Yulong Snow Mountain region in Yunnan, China, are used to distinguish vegetation communities. The results show that tropical seasonal rainforest (and mountain rainforest), south subtropical evergreen broad-leaved forest, and Quercus shrub are distinguished effectively from other vegetation types by analysis of surface pollen. The south subtropical evergreen broad-leaved forest, Pinus kesiya forest and evergreen broadleaf forest are distinguished effectively from other types of vegetation by pollen analysis. However, P. yunnanensis forest is not distinguished from other vegetation types, and P. armandii, P. densata forest and temperate deciduous conifer mixed forest are not distinguished. The over-representation of Pinus pollen is the main reason that these vegetation communities are not distinguished from each other. Conifer stomata analysis is an effective tool for identifying and distinguishing different types of coniferous forest, and this method performs well even with a small number of sampling points. |
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