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Random sampling in metagenomic sequencing leads to overestimated spatial scaling of microbial diversity
Author(s) -
Tu Qichao
Publication year - 2020
Publication title -
environmental microbiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.954
H-Index - 188
eISSN - 1462-2920
pISSN - 1462-2912
DOI - 10.1111/1462-2920.14973
Subject(s) - biology , metagenomics , microbial ecology , sampling (signal processing) , ecology , spatial ecology , microbial population biology , spatial variability , beta diversity , spatial analysis , spatial heterogeneity , statistics , biodiversity , mathematics , computer science , bacteria , genetics , filter (signal processing) , gene , computer vision , biochemistry
Summary Revealing the spatial scaling patterns of microbial diversity is of special interest in microbial ecology. One critical question is whether the observed spatial turnover rate truly reflect the actual spatial patterns of extremely diverse microbial communities. Using simulated mock communities, this study suggested that the currently observed microbial spatial turnover rates were overestimated by random sampling processes associated with high‐throughput metagenomic sequencing. The observed z values were largely contributed by accumulated microbial taxa due to cumulative number of samples. This is a crucial issue because microbial communities already have very low spatial turnover rate due to the small size and potential cosmopolitism nature of microorganisms. Further investigations suggested a linear relationship between the observed and expected z values, which can be applied to remove random sampling noises from the observed z values. Adjustment of z values for data sets from six American forests showed much lower spatial turnover rate than that before adjustment. However, the patterns of z values among these six forests remained unchanged. This study suggested that our current understanding of microbial taxa–area relationships could be inaccurate. Therefore, cautions and efforts should be made for more accurate estimation and interpretation of microbial spatial patterns.