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Microbiome succession during ammonification in eelgrass bed sediments

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RT @casettron: Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://…
RT @casettron: Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://…
RT @casettron: Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://…
RT @casettron: Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://…
RT @casettron: Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://…
Microbiome succession during ammonification in eelgrass bed sediments https://t.co/HoXSivSXxJ
RT @casettron: Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://…
RT @casettron: Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://…
RT @casettron: Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://…
RT @casettron: Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://…
Preprint: Microbiome succession during ammonification in eelgrass sediments by @phylogenomics & I et al @MooreFound https://t.co/shkMWlcpe6
228 days ago
Microbiome succession during ammonification in eelgrass bed sediments https://t.co/1Gt96ZV7av
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Supplemental Information

Kruskal-Wallis tests on alpha diversity metrics

We used Kruskal-Wallis tests with 9999 permutations to assess whether alpha diversity was significantly different between categories. We used four different measurements of alpha diversity (observed number of OTUs, Chao1, Shannon Inverse Simpson). Categories examined included timepoint, eelgrass status (one genotype, multiple genotypes or none present), eelgrass initial relatedness (low, medium, high), eelgrass final richness and plot location.

DOI: 10.7287/peerj.preprints.2956v1/supp-1

Post-hoc Dunn tests assessing alpha diversity over time

Alpha diversity was determined to be significantly different across timepoints (Table 1).We examined which timepoint comparisons were stochastically dominant using the Dunn test on four different measurements of alpha diversity (observed number of OTUs, Chao1, Shannon Inverse Simpson). Timepoint 1 (initial samples), 2 (7 days), 3 (13 days), and 4 (19 days).

DOI: 10.7287/peerj.preprints.2956v1/supp-2

PERMANOVA results of beta diversity during microbial succession

PERMANOVA tests were performed to find significant differences in microbial beta diversity, calculated as the Weighted Unifrac distance metric, between different categorical variables including initial plot treatment (number of genotypes x level related), eelgrass plot richness, eelgrass initial level related (low, medium, high), eelgrass genotypic evenness, eelgrass status (one genotype, multiple genotypes or none present), timepoint, block (A-L), eelgrass richness, spot (1-6) and plot location (block x spot).

DOI: 10.7287/peerj.preprints.2956v1/supp-3

Pair-wise PERMANOVA results of beta diversity over time

Comparing microbial community structure between pair-wise timepoints using multiple beta diversity metrics (Weighted Unifrac, Unweighted Unifrac, Bray Curtis) to assess at which timepoints, the communities differed significantly. Timepoint 1 (initial samples), 2 (7 days), 3 (13 days), and 4 (19 days).

DOI: 10.7287/peerj.preprints.2956v1/supp-4

PERMANOVA tests on initial beta diversity

PERMANOVA tests were used to look for significant differences in microbial beta diversity, calculated as the Weighted Unifrac distance metric, between different categorical variables at timepoint #1. The categorical variables tested included initial plot treatment (number of genotypes x level related), eelgrass plot richness, eelgrass initial level related (low, medium, high), eelgrass genotypic evenness, eelgrass status (one genotype, multiple genotypes or none present), block (A-L), eelgrass richness and spot (1-6).

DOI: 10.7287/peerj.preprints.2956v1/supp-5

Mantel test results correlating microbial beta diversity throughout succession with measured variables

Mantel tests were used to identify significant correlations between microbial beta diversity, calculated as Bray Curtis dissimilarities, and different quantitative variables including ammonification rate (µmol NH4-N/L sediment/d), total belowground biomass (g/plot), total aboveground biomass (g/plot) and total biomass (g/plot).

DOI: 10.7287/peerj.preprints.2956v1/supp-6

Mantel test results correlating initial microbial beta diversity with measured variables

Mantel tests were used to identify significant correlations between microbial beta diversity, calculated as Bray Curtis dissimilarities, and different quantitative variables at timepoint #1. The quantitative variables tested include ammonification rate (µmol NH4-N/L sediment/d), total belowground biomass (g/plot), total aboveground biomass (g/plot), total biomass (g/plot), rhizome biomass (g/plot), root biomass (g/plot), Rao’s Q, eelgrass genotypic evenness, eelgrass Shannon Diversity, eelgrass average relatedness, plot detritus standing stock (g/plot) from prior months (June, July, August) and plot decomposition rate.

DOI: 10.7287/peerj.preprints.2956v1/supp-7

Kruskal-Wallis tests of mean relative abundance of taxonomic orders over time

The average relative abundance of taxonomic orders was compared between timepoints using Bonferroni corrected Kruskal-Wallis tests.

DOI: 10.7287/peerj.preprints.2956v1/supp-8

Post-hoc Dunn tests of mean relative abundance of taxonomic orders over time

Post-hoc Dunn tests were were performed on taxonomic orders that were found to have significantly different mean relative abundances across timepoints using Kruskal-Wallis tests (Table S8). These tests were used to identify which timepoint comparisons showed stochastic dominance. Only sequential timepoint comparisons are shown here. Timepoint 1 (initial samples), 2 (7 days), 3 (13 days), and 4 (19 days).

DOI: 10.7287/peerj.preprints.2956v1/supp-9

Mean, standard deviation and standard error of the relative abundances of taxonomic orders over time

Only orders with a mean relative abundance of greater than or equal to 2 percent are show here.

DOI: 10.7287/peerj.preprints.2956v1/supp-10

Additional Information

Competing Interests

Jonathan A. Eisen is an academic editor for PeerJ.

Author Contributions

Cassandra L Ettinger analyzed the data, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.

Susan L Williams conceived and designed the experiments, performed the experiments, reviewed drafts of the paper, performed ammonification experiment, helped write paper.

Jessica M Abbott conceived and designed the experiments, performed the experiments, reviewed drafts of the paper, experimental design of eelgrass field experiment, helped with ammonification experiment.

John J Stachowicz reviewed drafts of the paper, advised on experimental design, edited drafts of paper.

Jonathan A Eisen contributed reagents/materials/analysis tools, reviewed drafts of the paper, advised on data analysis, edited drafts of paper.

DNA Deposition

The following information was supplied regarding the deposition of DNA sequences:

This 16S rRNA sequencing project has been deposited in GenBank under accession no. PRJNA350672.

Data Deposition

The following information was supplied regarding data availability:

Coil, David; Eisen, Jonathan; Stachowicz, Jay; Green, Jessica; Holland-Moritz, Hannah; Lang, Jenna (2014): The Seagrass Microbiome. figshare.

https://doi.org/10.6084/m9.figshare.1014334.v1

Funding

This work was funded by a grant from the Gordon and Betty Moore Foundation (GBMF333) “Investigating the co-evolutionary relationships between seagrasses and their microbial symbionts.” The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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