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Unifying community detection across scales from genomes to landscapes

Cite this dataset

Zaiats, Andrii et al. (2021). Unifying community detection across scales from genomes to landscapes [Dataset]. Dryad.


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.

Usage notes

Please see ReadMe file and Supporting Information for methodologcal and modeling details.


National Aeronautics and Space Administration, Award: 80NSCCC17K0738

Idaho State Board of Education, Award: IGEM19-002

Semiconductor Research Corporation, Award: SRC 2018-SB-2842

Idaho Department of Fish and Game, Award: Pittman-Robertson 683 Funds

Sigma Xi Grants-In-Aid

United States Department of the Interior, Award: L09AC16253

National Science Foundation, Award: IOS-1258217

National Science Foundation, Award: DEB-1146194

National Science Foundation, Award: DEB-1146368

National Science Foundation, Award: OIA-1826801

National Science Foundation, Award: OIA-1757324

National Science Foundation, Award: OIA-1738865

National Science Foundation, Award: ECCS-1807809

Sigma Xi Grants-In-Aid