Sketching the Human Microbiome Biogeography with DAR (Diversity-Area Relationship) Profiles
SAR (species area relationship) is a classic ecological theory that has been extensively investigated and applied in the studies of global biogeography and biodiversity conservation in macro-ecology. It has also found important applications in microbial ecology in recent years thanks to the breakthroughs in metagenomic sequencing technology. Nevertheless, SAR has a serious limitation for practical applications—ignoring the species abundance and treating all species as equally abundant. This study aims to explore the biogeography discoveries of human microbiome over 18 sites of 5 major microbiome habitats, establish the baseline DAR (diversity-area scaling relationship) parameters, and perform comparisons with the classic SAR. The extension from SAR to DAR by adopting the Hill numbers as diversity measures not only overcomes the previously mentioned flaw of SAR but also allows for obtaining a series of important findings on the human microbiome biodiversity and biogeography. Specifically, two types of DAR models were built, the traditional power law (PL) and power law with exponential cutoff (PLEC), using comprehensive datasets from the HMP (human microbiome project). Furthermore, the biogeography “maps” for 18 human microbiome sites using their DAR profiles for assessing and predicting the diversity scaling across individuals, PDO profiles (pair-wise diversity overlap) for measuring diversity overlap (similarity), and MAD profile (for predicting the maximal accrual diversity in a population) were sketched out. The baseline biogeography maps for the healthy human microbiome diversity can offer guidelines for conserving human microbiome diversity and investigating the health implications of the human microbiome diversity and heterogeneity.
KeywordsSpecies area relationship (SAR) Diversity-area relationship (DAR) profile Maximal accrual diversity (MAD) profile Pair-wise diversity overlap (PDO) profile Biogeography of human microbiome Power law Self similarity Scale invariance
I am indebted to DD Ye, LW Li, and J. Li, from the Chinese Academy of Sciences, for their computational help.
ZS Ma designed and conducted the study and wrote the paper. The author read and approved the final manuscript for submission.
This study received funding from the following sources: National Science Foundation of China (Grant No. 71473243) and Yun-Ridge Industry Technology Leader Grant, a China-US International Cooperation Project on Genomics/Metagenomics Big Data.
Compliance with Ethical Standards
Conflict of Interest
The author declares that he has no conflict of interest.
No permission is needed since the datasets involved in this study were already published and publically available at http://hmpdacc.org.
- 1.Baas-Becking LGM (1934) Geobiologie of inleiding tot de milieukunde. (eds) W.P. Van Stockum & Zoon N.V, Den Haag [Netherlands]Google Scholar
- 11.Fierer N. (2008). Microbial biogeography: patterns in microbial diversity across space and time. In: Accessing uncultivated microorganisms: from the environment to organisms and genomes and back. Zengler K. (editor). ASM Press, Washington DC pgs. 95–115Google Scholar
- 12.Gotelli NJ. Chao A (2013) Measuring and estimating species richness, species diversity, and biotic similarity from sampling data. In: Levin SA (ed) Encyclopedia of biodiversity, vol 5, 2nd ed. Academic Press, p 195–211Google Scholar
- 21.Jones SE, Cadkin TA, Newton RJ, McMahon KD (2012) Spatial and temporal scales of aquatic bacteria beta diversity. Front Microbiol 318:64–73Google Scholar
- 25.Ma ZS (2018) DAR (diversity–area relationship): extending classic SAR (species–area relationship) for biodiversity and biogeography. Ecol Evol. https://doi.org/10.1002/ece3.4425
- 45.van der Gast CJ (2013) Microbial biogeography and what Baas Becking should have said. Microbiol Today 40:108–111Google Scholar
- 48.Whiteson KL, Bailey B, Bergkessel M, Conrad D, Delhaes L, Felts B, Harris JK, Hunter R, Lim YW, Maughan H, Quinn R, Salamon P, Sullivan J, Wagner BD, Rainey PB (2014) The upper respiratory tract as a microbial source for pulmonary infections in cystic fibrosis: parallels from island biogeography. Am J Respir Crit Care Med 189:1309–1315CrossRefPubMedPubMedCentralGoogle Scholar
- 51.Ye CX, Hill C, Ruan J, Ruan, Ma ZS (2016) DBG2OLC: efficient assembly of large genomes using Iong erroneous reads of the third generation sequencing technologies. http://www.nature.com/articles/srep31900