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Unifying community detection across scales from genomes to landscapes

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 and Resources

FieldValue
Modified
2022-03-10
Release Date
2022-03-03
Publisher
Identifier
7d957cb2-7eff-4416-8f09-b2ccfc4f720b
Language
English (United States)
License
Author
Andrii Zaiats, Stephanie F. Hudon, Anna Roser, Anand Roopsind, Cristina Barber, Brecken C. Robb, Britt A. Pendleton, Meghan J. Camp, Patrick E. Clark, Merry M. Davidson, Jonas Frankel-Bricker, Marcella Fremgen-Tarantino, Jennifer Sorensen Forbey, et al.
Contact Name
Trevor Caughlin
Contact Email
Public Access Level
Public
DOI
10.5061/dryad.8w9ghx3mf
Data available on:: 
Sunday, April 11, 2021