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. https://doi.org/10.5061/dryad.8w9ghx3mf
Abstract
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.
Funding
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