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.

Data Use
License
CC0-1.0
Recommended Citation
Zaiats A, Hudson S, Roser A, Roosind A, Barber C, Robb BC, Pendleton BA, Camp MJ, Clark PE, Davidson MM, Frankel-Bricker J, Fremgen-Tarantino M, Forbey JS, Hayden EJ, Richards LA, Rodrigues OK, Caughlin TT. 2021. Unifying community detection across scales from genomes to landscapes [Dataset]. Dryad. https://doi.org/10.5061/dryad.8w9ghx3mf

Funding
National Aeronautics and Space Administration: 80NSCCC17K0738
Idaho State Board of Education: IGEM19-002
Semiconductor Research Corporation: SRC 2018-SB-2842
Idaho Department of Fish and Game: Pittman-Robertson 683 Funds
Sigma Xi Grants-In-Aid
US Bureau of Land Management: L09AC16253
US National Science Foundation: IOS-1258217
US National Science Foundation: DEB-1146194
US National Science Foundation: DEB-1146368
US National Science Foundation and Idaho EPSCoR: OIA-1826801
US National Science Foundation and Idaho EPSCoR: OIA-1757324
US National Science Foundation and Idaho EPSCoR: OIA-1738865
US National Science Foundation: ECCS-1807809