Ecosystem distribution profiling of bacteria from a unique hypersaline sediment (sabkha) reveals ecological specialization among communities in the environment
- Published
- Accepted
- Subject Areas
- Biodiversity, Biogeography, Bioinformatics, Ecology, Microbiology
- Keywords
- Hypersaline environments, biogeography, Ecosystem distribution, 16S rRNA profiles, Earth Microbiome, Specialists
- Copyright
- © 2017 Wan et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2017. Ecosystem distribution profiling of bacteria from a unique hypersaline sediment (sabkha) reveals ecological specialization among communities in the environment. PeerJ Preprints 5:e3286v1 https://doi.org/10.7287/peerj.preprints.3286v1
Abstract
Advances in genomic sequencing technologies resulted in massive microbial diversity data (16S ribosomal gene sequences, rDNA) being generated in every possible environment. However, the majority of microorganisms have never been cultured, and therefore, nor cataloged. This poses a problem for molecular microbial ecologists because a large portion of the marker sequences can not be taxonomically resolved past the phylum taxon level. This tells very little about who or what these microorganisms are doing in relation to their environment. Our study describes an approach to assist in drawing ecological information from a sample when the taxon resolution is poor. We generated 16S rDNA libraries from a hypersaline marine sediment (coastal Sabkha) and saline mangrove soil in Abu Dhabi and then compared the compositional features to a database of 20,470 publicly available microbial community profiles (comprising the entire Earth Microbiome Project, EMP) that were annotated with terms from the Environmental Ontology (EnvO). An accurate taxonomic classification was not possible for 80% of the Sabkha operational taxonomic units (OTUs) beyond phylum level with widely used taxonomy classification tools, but habitat profiling performed on the community revealed strong links to bacterial assemblages of soil and marine origins. To capture the notion of generalist vs. specialist formally, we developed an algorithm to derive empirical probability distributions of OTUs over ecosystems from observed occurrences in the sample database, which then give rise to OTU-specific ecosystem entropies. We observed very low average ecosystem entropy of the Sabkha in contrast to other environmental samples. Based on this concept, the Sabkha community, while of midrange alpha diversity, presented largely specialist characteristics, with most OTUs identified to be unique to the Sabkha habitat. This finding is further corroborated by the observation that the Sabkha sample is unique with respect to the EMP-derived dataset (which contains 74 hypersaline and thousands of marine samples), as a comprehensive UniFrac similarity search did not yield any significant matches. Finally, we show that the ecosystem entropy formalism, which intrinsically accounts for the ability of OTUs to cross ecosystem borders according to a context database, is a novel, informative tool to describe and identify extreme environments in addition to conventional ecological diversity measures.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Mangrove soil raw sequence data (fastq)
V4 region from amplicon sequencing of microbial community, isolated from Mangrove soil, Abu Dhabi.