The emergence of high-throughput DNA sequencing methods provides unprecedented opportunities to further unravel bacterial biodiversity and its worldwide role from human health to ecosystem functioning. However, despite the abundance of sequencing studies, combining data from multiple individual studies to address macroecological questions of bacterial diversity remains methodically challenging and plagued with biases. Here, using a machine-learning approach that accounts for differences among studies and complex interactions among taxa, we merge 30 independent bacterial data sets comprising 1,998 soil samples from 21 countries. Whereas previous meta-analysis efforts have focused on bacterial diversity measures or abundances of major taxa, we show that disparate amplicon sequence data can be combined at the taxonomy-based level to assess bacterial community structure. We find that rarer taxa are more important for structuring soil communities than abundant taxa, and that these rarer taxa are better predictors of community structure than environmental factors, which are often confounded across studies. We conclude that combining data from independent studies can be used to explore bacterial community dynamics, identify potential 'indicator' taxa with an important role in structuring communities, and propose hypotheses on the factors that shape bacterial biogeography that have been overlooked in the past.

中文翻译：

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在全球土壤的独立研究中检测细菌群落的宏观生态模式。

高通量 DNA 测序方法的出现为进一步揭示细菌生物多样性及其从人类健康到生态系统功能的全球作用提供了前所未有的机会。然而，尽管有大量的测序研究，但结合来自多个单独研究的数据来解决细菌多样性的宏观生态问题在系统上仍然具有挑战性，并且受到偏见的困扰。在这里，我们使用机器学习方法来解释研究之间的差异和分类群之间的复杂相互作用，我们合并了 30 个独立的细菌数据集，其中包括来自 21 个国家的 1,998 个土壤样本。虽然以前的荟萃分析工作集中在细菌多样性测量或主要分类群的丰度上，我们表明，可以在基于分类的水平上组合不同的扩增子序列数据来评估细菌群落结构。我们发现稀有类群比丰富的类群对构建土壤群落更重要，并且这些稀有类群比环境因素更能预测群落结构，环境因素在研究中经常被混淆。我们得出结论，结合独立研究的数据可用于探索细菌群落动态，确定在构建群落中具有重要作用的潜在“指标”分类群，并就过去被忽视的影响细菌生物地理学的因素提出假设。并且这些稀有的分类群比环境因素更能预测群落结构，环境因素经常在研究中混淆。我们得出结论，结合独立研究的数据可用于探索细菌群落动态，确定在构建群落中具有重要作用的潜在“指标”分类群，并就过去被忽视的影响细菌生物地理学的因素提出假设。并且这些稀有的分类群比环境因素更能预测群落结构，环境因素经常在研究中混淆。我们得出结论，结合独立研究的数据可用于探索细菌群落动态，确定在构建群落中具有重要作用的潜在“指标”分类群，并就过去被忽视的影响细菌生物地理学的因素提出假设。