样方
采样(信号处理)
多样性指数
联营
生物
统计
生态学
物种丰富度
数学
计算机科学
滤波器(信号处理)
灌木
人工智能
计算机视觉
作者
Shuzhen Li,Ye Deng,Xiongfeng Du,Kai Feng,Yueni Wu,Qing He,Zhujun Wang,Yangying Liu,Danrui Wang,Xi Peng,Zhaojing Zhang,Arthur Escalas,Yuanyuan Qu
标识
DOI:10.1016/j.scitotenv.2021.144966
摘要
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 m2 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 m2), 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.
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