The ongoing question of how to best analyze microbial community datasets is paramount to deducing the processes that affect the composition and function of microbial communities. The type of information and metric used to measure biological diversity in any study of microbial diversity is a decision that must be well-justified prior to hypothesis PD0325901 price testing instead of being made arbitrarily based solely on which metrics are popularly used by plant and animal ecologists. This justification, in turn, should be
based on evidence produced by work, such as this study, that has systematically tested the efficacy and utility of these diversity metrics under a range of situations. Availability of supporting data The R code adapted from Leinster & Cobbold [17] and used to calculated diversity profiles is available see more for download and use at https://gist.github.com/darmitage. The hypersaline lake viruses raw sequencing reads are available in the NCBI BioProject (accession number PRJNA81851, http://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA81851). The subsurface
bacteria dataset is available at: http://banfieldlab.berkeley.edu/SOM/yelton2012/. Acknowledgements Funding for this project was provided by a National Science Foundation Grant (#1050680) to Sandy Andelman and Julia Parrish: The Dimensions of Biodiversity Distributed Graduate Seminar (DBDGS). HMD was funded by a National Science Foundation Graduate Research Fellowship. Funding for JBE and the hypersaline lake virus study was provided by National Science Foundation award 0626526 and Department of Energy award DE-FG02-07ER64505.
JK was funded by a NASA – Harriett G. Jenkins Pre-Doctoral Fellowship and a Mycological Society of America – NAMA Memorial Fellowship. The authors would like to thank S. Andelman, J. Parrish, C. Maranto, R. Sewell Nesteruk, J. Prosser, T. Bruns, and all other DBDGS participants for their input throughout the project. Electronic supplementary material Additional file 1: Table S1: – Results of the community composition analyses (Jaccard and Unifrac) for the four environmental microbial community datasets. Figure S1. – Acid mine drainage bacteria and archaea (GAIIx) diversity profiles. Figure S2. Paclitaxel price – Hypersaline lake viruses methyltransferase diversity profiles. Figure S3. – Hypersaline lake viruses concanavalin A-like glucanases/lectins diversity profiles. Figure S4. – Substrate-associated soil fungi forest diversity profiles. Figure S5. – Acid mine drainage bacteria and archaea (HiSeq) phylogenetic (UniFrac) and taxonomic (Jaccard) hierarchical dissimilarity clusters. Figure S6. – Acid mine drainage bacteria and archaea (GAIIx) phylogenetic (UniFrac) and taxonomic (Jaccard) hierarchical dissimilarity clusters. Figure S7.