Oikos, ,
Apr 17, 2021
Unifying community detection across scales from genomes to landscapes
Stephanie F. Hudon, Andrii Zaiats, 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, Eric J. Hayden, Lora A. Richards, Olivia K. Rodriguez, T. Trevor Caughlin
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.