Metabarcoding has offered unprecedented insights into microbial diversity. In many studies, short DNA sequences are binned into consecutively lower Linnaean ranks, and ranked groups (e.g., genera) are the units of biodiversity analyses. These analyses assume that Linnaean ranks are biologically meaningful and that identically ranked groups are comparable. We used a metabarcode dataset for marine planktonic diatoms to illustrate the limits of this approach. We found that the 20 most abundant marine planktonic diatom genera ranged in age from 4 to 134 million years, indicating the non-equivalence of genera because some have had more time to diversify than others. However, species richness was largely independent of genus age, suggesting that disparities in species richness among genera were better explained by variation in rates of speciation and extinction. Taxonomic classifications often do not reflect phylogeny, so genus-level analyses can include phylogenetically nested genera, further confounding rank-based analyses. These results underscore the indispensable role of phylogeny in understanding patterns of microbial diversity.
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Subject categories: Microbial ecology and functional diversity of natural habitats
Integrated genomics and post-genomics approaches in microbial ecologyReferences have been reordered. Please check.OK.
Rinke C, Schwientek P, Sczyrba A, Ivanova NN, Anderson IJ, Cheng J-F, et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature. 2013;499:431–7.
De Vargas C, Audic S, Henry N, Decelle J, Mahé F, Logares R, et al. Eukaryotic plankton diversity in the sunlit ocean. Science. 2015;348:1261605.
Malviya S, Scalco E, Audic S, Vincent F, Veluchamy A, Poulain J, et al. Insights into global diatom distribution and diversity in the world’s ocean. P Natl Acad Sci USA. 2016;113:E1516–25.
Pleijel F, Rouse GW. Ceci n’est pas une pipe: Names, clades and phylogenetic nomenclature. J Zool Syst Evol Res. 2003;41:162–74.
Sundberg PER, Pleijel F. Phylogenetic classification and the definition of taxon names. Zool Scr. 1994;23:19–25.
Cantino PD, de Queiroz K (2010). PhyloCode: A Phylogenetic Code of Biological Nomenclature. Ohio University. Athens, Ohio. https://www.ohio.edu/PhyloCode/PhyloCode2a.pdf
Harvey PH, Pagel MK. The Comparative Method in Evolutionary Biology. Oxford: Oxford University Press; 1991.
Stadler T, Rabosky DL, Ricklefs RE, Bokma F. On age and species richness of higher taxa. Am Nat. 2014;184:447–55.
Magallón S, Sanderson MJ. Absolute diversification rates in angiosperm clades. Evolution. 2001;55:1762–80.
Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Structure and function of the global ocean microbiome. Science. 2015;348:1261359.
Nakov T, Beaulieu JM and Alverson AJ (2018). Accelerated diversification is related to life history and locomotion in a hyperdiverse lineage of microbial eukaryotes (Diatoms, Bacillariophyta). New Phytol. https://doi.org/10.1111/nph.15137
Lazarus D, Barron J, Renaudie J, Diver P, Türke A. Cenozoic planktonic marine diatom diversity and correlation to climate change. PLOS ONE. 2014;9:e84857.
Mann DG, Vanormelingen P. An inordinate fondness? The number, distributions, and origins of diatom species. J Eukaryot Microbiol. 2013;60:414–20.
Kociolek JP, Balasubramanian K, Blanco S, Coste M, Ector L, Liu Y et al. (2018). DiatomBase. http://www.diatombase.org Accessed 18 Feb 2018.
Alverson AJ, Beszteri B, Julius ML, Theriot EC. The model marine diatom Thalassiosira pseudonana likely descended from a freshwater ancestor in the genus Cyclotella. BMC Evol Biol. 2011;11:125.
Wiese R, Renaudie J, Lazarus DB. Testing the accuracy of genus-level data to predict species diversity in Cenozoic marine diatoms. Geology. 2016;44:1051–4.
Kociolek JP. Taxonomy and ecology: further considerations. Proc Calif Acad Sci. 2005;56:99–106.
This work was supported by a grant from the Simons Foundation (403249, AJA). This material is also based upon work supported by the National Science Foundation (NSF) under Grant no. DEB-1353131. This research used computational resources available through the Arkansas High Performance Computing Center, which was funded through multiple NSF grants and the Arkansas Economic Development Commission.