南洛河流域洛南盆地表土孢粉与植被的关系(英文)

编号 zgly0001584933

文献类型 期刊论文

文献题名 南洛河流域洛南盆地表土孢粉与植被的关系(英文)

作者 张文超  李春海  鹿化煜  田先华  张红艳  雷昉  唐领余 

作者单位 SchoolofGeographicandOceanographicSciences  NanjingUniversity  JiangsuCollaborativeInnovationCenterforClimateChange  StateKeyLaboratoryofLakeScienceandEnvironment  NanjingInstituteofGeographyandLimnology  CAS  CollegeofLifeScien 

母体文献 Journal of Geographical Sciences 

年卷期 2014年03期

年份 2014 

分类号 Q948 

关键词 surfacepollen  vegetationtype  clusteranalysis  PCA  LuonanBasin 

文摘内容 The catchment of South Luohe River in Central China is an important region for investigating modern pollen-environment relationship, because it is located in the transitional zone between south and north China, an environment which is sensitive to climate changes. In this study, 40 surface samples under ten vegetation types were collected to reveal the relationship between pollen assemblages and vegetation. The results show that the surface pollen assemblages reflect the vegetation quite well. In forest topsoils, the average of arboreal pollen content is greater than 40%, and the Selaginella sinensis spore is high. As to sparse forest grassland and shrub community, the average arboreal pollen is 13.2% and 16.6% respectively, and the shrub pollen is relatively higher than that of grassland samples. The grassland and farmland are characterized by low percentage of tree and shrub pollen(<10% and <1%), and high percentage of herbs(>80%). Pinus, Quercus and some other arboreal pollen can indicate the regional vegetation because of their dispersal ability. Quercus pollen is under-representative and so is Pinus. Artemisia pollen is significantly over-represented, has poor correlation with the plant coverage, and may reflect human disturbance. Gramineae can indicate plant quite well, but with low representation. High content of Chenopodiaceae probably suggests human impact. Predominant Selaginella sinensis can be used as an indicator of forest environment. Cluster analysis and principal components analysis of pollen assemblages can distinguish forest and non-forest vegetation well. The former method is better at separating pine and mixed forests, while the latter is more stable and could better differentiate farmland and other non-forest area. The first axis of PCA mainly reflects the humidity.