CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), School of Environmental Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China.
CAS Key Laboratory of Environmental Biotechnology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; Institute for Marine Science and Technology, Shandong University, Qingdao, China.
Sci Total Environ. 2021 May 1;767:144966. doi: 10.1016/j.scitotenv.2021.144966. Epub 2021 Jan 30.
Due to the massive quantity and broad phylogeny, an accurate measurement of microbial diversity is highly challenging in soil ecosystems. Initially, the deviation caused by sampling should be adequately considered. Here, we attempted to uncover the effect of different sampling strategies on α diversity measurement of soil prokaryotes. Four 1 m sampling quadrats in a typical grassland were thoroughly surveyed through deep 16S rRNA gene sequencing (over 11 million reads per quadrat) with numerous replicates (33 soil sampling cores with total 141 replicates per quadrat). We found the difference in diversity was relatively small when pooling soil cores before and after DNA extraction and sequencing, but they were both superior to a non-pooling strategy. Pooling a small number of soil cores (i.e., 5 or 9) combined with several technical replicates is sufficient to estimate diversities for soil prokaryotes, and there is great flexibility in pooling original samples or data at different experimental steps. Additionally, the distribution of local α diversity varies with sampling core number, sequencing depth, and abundance distribution of the community, especially for high orders of Hill diversity index (i.e., Shannon entropy and inverse Simpson index). For each grassland soil quadrat (1 m), retaining 100,000 reads after taxonomic clustering might be a realistic option, as these number of reads can efficiently cover the majority of common species in this area. Our findings provide important guidance for soil sampling strategy, and the general results can serve as a basis for further studies.
由于微生物的数量庞大且具有广泛的系统发育，因此准确测量土壤生态系统中的微生物多样性极具挑战性。首先，应充分考虑采样带来的偏差。在这里，我们试图揭示不同采样策略对土壤原核生物α多样性测量的影响。在一个典型的草原中，我们对四个 1 m 的采样小区进行了彻底调查，通过深度 16S rRNA 基因测序（每个小区超过 1100 万条读长）进行了大量重复（每个小区 33 个土壤采样芯，总共 141 个重复）。我们发现，在提取和测序前以及提取和测序后对土壤芯进行混合时，多样性的差异相对较小，但都优于非混合策略。混合少量土壤芯（即 5 或 9 个）并结合多个技术重复足以估计土壤原核生物的多样性，并且在混合原始样本或在不同实验步骤的数据方面具有很大的灵活性。此外，局部α多样性的分布随采样芯数量、测序深度和群落的丰度分布而变化，特别是对于高阶 Hill 多样性指数（即 Shannon 熵和 Simpson 倒数指数）。对于每个草原土壤小区（1 m），在分类聚类后保留 100,000 条读长可能是一个现实的选择，因为这些读长数量可以有效地覆盖该地区的大多数常见物种。我们的研究结果为土壤采样策略提供了重要的指导，并且一般结果可以作为进一步研究的基础。
