Biodiversity science encompasses multiple disciplines and biological scales from molecules to landscapes. Nevertheless, biodiversity data are often analyzed separately with discipline-specific methodologies, constraining resulting inferences to a single scale. To overcome this, we present a topic modeling framework to analyze community composition in cross-disciplinary datasets, including those generated from metagenomics, metabolomics, field ecology and remote sensing. Using topic models, we demonstrate how community detection in different datasets can inform the conservation of interacting plants and herbivores. We show how topic models can identify members of molecular, organismal and landscape-level communities that relate to wildlife health, from gut microbes to forage quality. We conclude with a future vision for how topic modeling can be used to design cross-scale studies that promote a holistic approach to detect, monitor and manage biodiversity.

中文翻译：

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从基因组到景观的跨尺度统一社区检测

生物多样性科学涵盖从分子到景观的多个学科和生物尺度。然而，生物多样性数据通常使用特定学科的方法进行单独分析，将由此产生的推论限制在单一尺度上。为了克服这个问题，我们提出了一个主题建模框架来分析跨学科数据集中的群落组成，包括从宏基因组学、代谢组学、野外生态学和遥感生成的数据集。使用主题模型，我们展示了不同数据集中的社区检测如何为相互作用的植物和食草动物的保护提供信息。我们展示了主题模型如何识别与野生动物健康相关的分子、有机体和景观级社区的成员，从肠道微生物到草料质量。