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林业科学 ›› 2019, Vol. 55 ›› Issue (12): 84-92.doi: 10.11707/j.1001-7488.20191209

• 论文与研究报告 • 上一篇    下一篇

海南尖峰岭热带山地雨林60 hm2动态监测样地土壤微生物物种估算

唐欣1,刘世荣1,*,许涵2,张于光1   

  1. 1. 中国林业科学研究院森林生态环境与保护研究所 北京 100091
    2. 中国林业科学研究院热带林业研究所 广州 510520
  • 收稿日期:2018-02-28 出版日期:2019-12-25 发布日期:2020-01-02
  • 通讯作者: 刘世荣
  • 基金资助:
    国家国际科技合作专项(2015DFA31440);国家自然科学基金项目(31290223);林业公益性行业科研专项(201404201)

Estimation of Soil Microbial Species in a 60 hm2 Dynamic Monitoring Plot of Tropical Mountain Rain Forest in Jianfengling, Hainan, China

Xin Tang1,Shirong Liu1,*,Han Xu2,Yuguang Zhang1   

  1. 1. Research Institute of Forest Ecology, Environment and Protection, CAF Beijing 100091
    2. Research Institute of Tropical Forestry, CAF Guangzhou 510520
  • Received:2018-02-28 Online:2019-12-25 Published:2020-01-02
  • Contact: Shirong Liu
  • Supported by:
    国家国际科技合作专项(2015DFA31440);国家自然科学基金项目(31290223);林业公益性行业科研专项(201404201)

摘要:

目的: 选取海南岛尖峰岭国家级自然保护区内60 hm2大型固定森林样地作为研究区域,利用高通量测序技术和统计推断估算方法,对土壤细菌和真菌的物种丰富度进行估算,比较5种参数和非参数估算方法的精度。方法: 通过Illumina MiSeq测序技术,获得大样地内的500个(40 m×30 m)样方的0~10 cm土壤层的细菌16S rRNA和真菌的ITS2 rRNA基因序列。通过参数估计量SAC和SAD和非参数估计量Chao2、ICE和Jackknife1,对样地内土壤微生物群落OTU丰富度进行估算;并基于OTU在所有样方中的丰度和出现频率,利用R包iNEXT分析样品覆盖率。结果: 从500个样品中分别获得属于土壤细菌和真菌的高质量DNA序列条数分别为14 000 317和7 656 130条,这些序列数据被鉴定为细菌和真菌OTU数量分别有42 873个和22 923个。比较5种参数和非参数估计量估算结果,发现各估计量之间的估算结果相差较小且都呈现略高于观察到的物种数,其中非参数Chao2的估算结果最精确。Chao2准确地外推估算出细菌的OTU个数为42 828,真菌的OTU个数为23 137。iNEXT分析结果表明,本研究测序所获得的序列数和采集的土壤样品对样地内土壤微生物类群的覆盖率均高达99%以上,特别是基于土壤微生物OTU丰度的样品覆盖率均达100%。结论: 1)非参数Chao2是所有估计量中最优的估计量,该估计量表现出较低的外推误差;2)要准确估算微生物的丰富度,至少需要281个土壤样品和742 767条DNA序列才覆盖99%的土壤细菌OTU丰富度,而对土壤真菌群落的OTU丰富度则至少需要386个土壤样品和383 189条DNA序列。本研究可为后期海南尖峰岭热带山地雨林60 hm2大样地土壤微生物多样性研究提供数据基础,为更好地估算微生物多样性提供方法、采样量和测序深度等方面的参考。

关键词: 热带山地雨林, 土壤微生物, 高通量测序, 物种丰富度

Abstract:

Objectve: How to accurately estimate species richness in some specific area is a basic scientific question in ecological research. In this study, a 60 hm2 large-scale forest dynamic plot in Jianfengling National Nature Reserve of Hainan was selected as the typical mountain rain forest for quantification of soil microbial diversity. Species richness of soil bacteria and fungi were estimated using high-throughput sequencing and statistical inference estimation methods. Method: A total of 500 soil samples were collected from 0-10 cm soil layers in each 40 m×30 m quadrat in this 60 hm2 large-scale plot. Illumina MiSeq sequencing platform was used to measure the bacterial 16S rRNA and fungal ITS2 (internal transcribed spacer 2) rRNA gene sequences of the soil samples. Parametric estimators SAC (species accumulative curve) and SAD (species abundance distribution), and non-parametric estimators Chao2, ICE (Incidence-based Coverage Estimator) and Jackknife1 were used to estimate the OTU richness of soil microbial community in all samples, and their effectiveness was evaluated. Based on the abundance and frequency of OTU in all samples, the sample coverage was analyzed by using R package iNEXT (iNterpolation and EXTrapolation of Hill number). Result: From the 500 soil samples, the number of high-quality DNA sequences belonging to soil bacteria and fungi was 14 000 317 and 7 656 130, respectively. These sequence data were identified as 42 873 and 22 923 OTUs for soil bacterial and fungal communities, respectively. Comparing the estimation results based on the five parameters and nonparametric estimators, it was found that the difference among the five estimators was small, and all of them slight overestimated the number of observed species. Non-parametric Chao2 estimator was the best estimator to accurately estimate the soil bacterial and fungal OTU richness, with 42 828 bacterial OTUs and 23 137 fungal OTUs in the 60 hm2 plot. The result of iNEXT analysis showed that the number of obtained sequences and the collected soil samples in the study covered more than 99% of the microbial populations in the 60 hm2 plot. In particular, abundance-based sample coverage reached 100%. Conclusion: Non-parametric Chao2 was the best estimator of all five estimators, which showed a lower extrapolation error. The iNEXT analysis showed that at least 281 soil samples and 742 767 DNA sequences were required to cover 99% of the soil bacteria OTU richness, while at least 386 soil samples and 383 189 DNA sequences were needed to estimate the OTU richness of soil fungal communities. In sum, the present study provides a basis for investigating the soil microbial diversity in a 60 hm2 large-scale plot in the tropical mountain rain forest of Jianfengling, Hainan Island and provides references for estimating methods, sample volumes, and sequencing depth for better estimation of microbial diversity.

Key words: tropical mountain rain forest, soil microbe, high-throughput sequencing, species richness

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