Soil microbial gene diversity in natural broadleaf forests on Gaoligongshan Mountain
Soil microorganisms are among the most important ecosystem components,and they play key roles in biogeochemical cycling,such as carbon and nitrogen cycling.Because of limits in methodology,our understanding of the detailed biological processes in soil microbial communities and their functional activity remains limited.In this study,soil microbial functional gene diversity in broadleaf forests on Gaoligongshan Mountain,Yunnan Province,China,was analyzed by Geochip 2.0,a microbial functional gene array.The array contained 24243 oligonucleotide probes from over 150 functional gene families that targeted key functional genes involved in various ecological and environmental processes,such as carbon and nitrogen cycling.Thus,it provided rapid,specific,sensitive,and potentially quantitative data on microbial communities and was useful for studying the functional diversity and dynamics of microbial communities in different natural environments.A total of 2273 microbial functional genes involved in carbon degradation,carbon fixation,methane oxidation and production,nitrogen cycling,phosphorus utilization,sulphur cycling,organic remediation,metal resistance,energy process,and other categories were detected in five soil samples,and their Shannon-Weaver indexes ranged from 5.39 to 6.91.The soil microbial diversity and abundance in evergreen broadleaf forest was higher than in coniferous and deciduous broadleaf forests.Fifteen and twelve gene categories involved in carbon and nitrogen cycling were observed,respectively.The diversity and abundance of genes involved in carbon and nitrogen cycling differed among the sampling sites.These results showed that different microbial processes involved in element cycling existed in these soil-sampling sites.Canonical correspondence analysis showed that soil organic carbon,soil moisture,soil temperature,and plant diversity may be the key environmental variables shaping the microbial community structure.Variation partitioning analysis showed that soil moisture,soil temperature,and plant Shannon-Weaver index can explain 25.79％,25.83％,and 18.94％ of the community composition,respectively.
NETL
NSTL
万方数据
维普
