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  1. Abstract

    Recent work examining nematode and tardigrade gut microbiomes has identified species-specific relationships between host and gut community composition. However, only a handful of species from either phylum have been examined. How microbiomes differ among species and what factors contribute to their assembly remains unexplored. Cyanobacterial mats within Antarctic Dry Valley streams host a simple and tractable natural ecosystem of identifiable microinvertebrates to address these questions. We sampled 2 types of coexisting mats (i.e., black and orange) across four spatially isolated streams, hand-picked single individuals of two nematode species (i.e.,Eudorylaimus antarcticusandPlectus murrayi) and tardigrades, to examine their gut microbiomes using 16S and 18S rRNA metabarcoding. All gut microbiomes (bacterial and eukaryotic) were significantly less diverse than the mats they were isolated from. In contrast to mats, microinvertebrates’ guts were depleted of Cyanobacteria and differentially enriched in taxa of Bacteroidetes, Proteobacteria, and Fungi. Among factors investigated, gut microbiome composition was most influenced by host identity while environmental factors (e.g., mats and streams) were less important. The importance of host identity in predicting gut microbiome composition suggests functional value to the host, similar to other organisms with strong host selected microbiomes.

  2. Abstract Background

    Understanding the factors that influence microbes’ environmental distributions is important for determining drivers of microbial community composition. These include environmental variables like temperature and pH, and higher-dimensional variables like geographic distance and host species phylogeny. In microbial ecology, “specificity” is often described in the context of symbiotic or host parasitic interactions, but specificity can be more broadly used to describe the extent to which a species occupies a narrower range of an environmental variable than expected by chance. Using a standardization we describe here, Rao’s (Theor Popul Biol, 1982., Sankhya A, 2010. ) Quadratic Entropy can be conveniently applied to calculate specificity of a feature, such as a species, to many different environmental variables.


    We present our R packagespecificityfor performing the above analyses, and apply it to four real-life microbial data sets to demonstrate its application. We found that many fungi within the leaves of native Hawaiian plants had strong specificity to rainfall and elevation, even though these variables showed minimal importance in a previous analysis of fungal beta-diversity. In Antarctic cryoconite holes, our tool revealed that many bacteria have specificity to co-occurring algal community composition. Similarly, in the human gut microbiome, many bacteria showed specificity tomore »the composition of bile acids. Finally, our analysis of the Earth Microbiome Project data set showed that most bacteria show strong ontological specificity to sample type. Our software performed as expected on synthetic data as well.


    specificityis well-suited to analysis of microbiome data, both in synthetic test cases, and across multiple environment types and experimental designs. The analysis and software we present here can reveal patterns in microbial taxa that may not be evident from a community-level perspective. These insights can also be visualized and interactively shared among researchers usingspecificity’s companion package,specificity.shiny.

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