Document Type
Article
Publication Date
6-2021
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.
Copyright Statement
This is the peer reviewed version of the following article:
Hudon, S.F.; Zaiats, A.; Roser, A.; Roopsind, A.; Barber, C.; Robb, B.C.; . . . and Caughlin, T.T. (2021). "Unifying Community Detection Across Scales from Genomes to Landscapes". Oikos, 130(6), 831-843. https://doi.org/10.1111/oik.08393
which has been published in final form at https://doi.org/10.1111/oik.08393. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Supplementary Material - Appendix S1
Publication Information
Hudon, Stephanie F.; Zaiats, Andrii; Roser, Anna; Roopsind, Anand; Barber, Cristina; Robb, Brecken C.; . . . and Caughlin, T. Trevor. (2021). "Unifying Community Detection Across Scales from Genomes to Landscapes". Oikos, 130(6), 831-843. https://doi.org/10.1111/oik.08393
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