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首页> 美国卫生研究院文献>Frontiers in Microbiology >Demonstrating microbial co-occurrence pattern analyses within and between ecosystems
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Demonstrating microbial co-occurrence pattern analyses within and between ecosystems

机译：展示生态系统内和生态系统之间的微生物共生模式分析

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Co-occurrence patterns are used in ecology to explore interactions between organisms and environmental effects on coexistence within biological communities. Analysis of co-occurrence patterns among microbial communities has ranged from simple pairwise comparisons between all community members to direct hypothesis testing between focal species. However, co-occurrence patterns are rarely studied across multiple ecosystems or multiple scales of biological organization within the same study. Here we outline an approach to produce co-occurrence analyses that are focused at three different scales: co-occurrence patterns between ecosystems at the community scale, modules of co-occurring microorganisms within communities, and co-occurring pairs within modules that are nested within microbial communities. To demonstrate our co-occurrence analysis approach, we gathered publicly available 16S rRNA amplicon datasets to compare and contrast microbial co-occurrence at different taxonomic levels across different ecosystems. We found differences in community composition and co-occurrence that reflect environmental filtering at the community scale and consistent pairwise occurrences that may be used to infer ecological traits about poorly understood microbial taxa. However, we also found that conclusions derived from applying network statistics to microbial relationships can vary depending on the taxonomic level chosen and criteria used to build co-occurrence networks. We present our statistical analysis and code for public use in analysis of co-occurrence patterns across microbial communities.
机译：共生模式在生态学中用于探索生物之间的相互作用以及环境对生物群落内共存的影响。微生物群落之间共现模式的分析范围从所有群落成员之间的简单成对比较到焦点物种之间的直接假设检验。但是，在同一研究中很少同时在多个生态系统或多个规模的生物组织中研究共现模式。在这里，我们概述了一种针对三种不同规模进行共现分析的方法：社区规模的生态系统之间的共现模式，社区内共生微生物的模块以及嵌套在模块内的共现对微生物群落。为了证明我们的共现分析方法，我们收集了公开可用的16S rRNA扩增子数据集，以比较和对比不同生态系统中不同分类级别的微生物共现。我们发现社区组成和共现的差异反映了社区规模上的环境过滤和一致的成对现象，可用于推断对微生物分类不了解的生态特征。但是，我们还发现，将网络统计数据应用于微生物关系所得出的结论可能会有所不同，具体取决于所选的分类标准和用于构建共现网络的标准。我们介绍了我们的统计分析和代码，供公共使用，以分析整个微生物群落的共现模式。

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