A confidence interval analysis of sampling effort, sequencing depth, and taxonomic resolution of fungal community ecology in the era of high-throughput sequencing.

High-throughput sequencing technology has helped microbial community ecologists explore ecological and evolutionary patterns at unprecedented scales. The benefits of a large sample size still typically outweigh that of greater sequencing depths per sample for accurate estimations of ecological inferences. However, excluding or not sequencing rare taxa may mislead the answers to the questions ‘how and why are communities different?’ This study evaluates the confidence intervals of ecological inferences from high-throughput sequencing data of foliar fungal endophytes as case studies through a range of sampling efforts, sequencing depths, and taxonomic resolutions to understand how technical and analytical practices may affect our interpretations. Increasing sampling size reliably decreased confidence intervals across multiple community comparisons. However, the effects of sequencing depths on confidence intervals depended on how rare taxa influenced the dissimilarity estimates among commu...

高通量测序（High-throughput sequencing）技术助力微生物群落生态学家以前所未有的规模探索生态与进化模式。针对生态学推断（ecological inferences）的精准估算而言，增大样本量所带来的收益通常仍优于提升单样本测序深度所能获得的增益。然而，排除稀有类群（rare taxa）或未对其开展测序，则可能会误导"群落间差异的成因与形成机制"这一问题的解答。本研究以叶内生真菌（foliar fungal endophytes）的高通量测序数据为研究案例，通过设置不同的采样力度（sampling efforts）、测序深度与分类学分辨率（taxonomic resolutions），评估生态学推断的置信区间（confidence intervals），以探究技术操作与分析流程如何影响我们对实验结果的解读。在多组群落对比实验中，增大样本量可稳健地缩小置信区间。然而，测序深度对置信区间的影响，取决于稀有类群对群落间相异性估算（dissimilarity estimates）的作用……