Unifying community detection across scales from genomes to landscapes-猫眼课题宝

DOI：

10.1111/oik.08393

发表时间：

2021-04

期刊：

Oikos

影响因子：

3.4

通讯作者：

中科院分区：

环境科学与生态学2区

文献类型：

作者：

Stephanie F. Hudon;A. Zaiats;Anna V. Roser;A. Roopsind;Cristina Barber;B. Robb;Britt Pendleton;Meghan J. Camp;Patrick E. Clark;Merry M. Davidson;Jonas Frankel‐Bricker;Marcella R. Fremgen-Tarantino;J. Forbey;Eric J. Hayden;Lora A. Richards;Olivia K. Rodríguez;T. T. Caughlin-T.

生物多样性科学包括多个学科和生物尺度，从分子到景观。然而，生物多样性数据通常是用特定学科的方法单独分析的，将得出的推论限制在单一的范围内。为了克服这一问题，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，包括从元基因组学、代谢组学、田间生态学和遥感产生的数据。使用主题模型，我们演示了不同数据集中的群落检测如何为相互作用的植物和草食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有关的分子、有机体和景观级别的群落成员，从肠道微生物到饲料质量。最后，我们对如何使用主题建模来设计跨规模研究，以促进检测、监测和管理生物多样性的整体方法进行了未来的展望。

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.

Unifying community detection across scales from genomes to landscapes

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