Dataset: Suppl 3a: core microbiome
Deployment: Burkepile_FL_Keys

Core microbiome

Principal Investigator:

Deron Burkepile (Florida International University, FIU)

Co-Principal Investigator:

Rebecca Vega Thurber (Florida International University, FIU)

BCO-DMO Data Manager:

Nancy Copley (Woods Hole Oceanographic Institution, WHOI BCO-DMO)

Project:

Cascading interactions of herbivore loss and nutrient enrichment on coral reef macroalgae, corals, and microbial dynamics (HERBVRE)

Data URL:

https://www.bco-dmo.org/dataset-deployment/674404/data

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Description

This dataset contains microbial orders comprising the core microbiome across all samples as measured by taxa prevalence, from Florida Keys National Marine Sanctuary. Cyanobacteria, Bacteroidetes and Actinobacteria were most abundant in all coral genera, with a core set of 13 bacterial orders in ≥95% of all samples. Published in Nature Communications (2016) doi:10.1038/ncomms11833, Supplementary Data 3a.

Natural history of the study site:
This experiment was conducted in the area of Pickles Reef (24.99430, -80.40650), located east of Key Largo, Florida in the United States. The Florida Keys reef tract consists of a large bank reef system located approximately 8 km offshore of the Florida Keys, USA, and paralleling the island chain. Our study reef is a 5-6 m deep spur and groove reef system within this reef tract. The reefs of the Florida Keys have robust herbivorous fish populations and are relatively oligotrophic. Coral cover on most reefs in the Florida Keys, including our site, is 5-10%, while macroalgal cover averages ~15%, but ranges from 0-70% depending on location and season. Parrotfishes (Scaridae) and surgeonfishes (Acanthuridae) are the dominant herbivores on these reefs as fishing for them was banned in 1981. The other important herbivore on Caribbean reefs, the urchin Diadema antillarum, remains at low densities across the Florida Keys following the mass mortality event in 1982-3.

Related Reference:
Zaneveld, J.R., D.E. Burkepile, A.A. Shantz, C. Pritchard, R. McMinds, J. Payet, R. Welsh, A.M.S. Correa, N.P. Lemoine, S. Rosales, C.E. Fuchs, and R. Vega Thurber (2016) Overfishing, nutrient pollution, and temperature interact to disrupt coral reefs down to microbial scales. Nature Communications 7:11833 doi:10.1038/ncomms11833 Supplementary Information

Methods & Sampling

Dataset acquisition description

To identify how algal communities and nutrient pollution affected the coral microbiome, we collected DNA samples from the surface mucus layer of 80 coral colonies (genera Porites, Siderastrea and Agaricia) at approximately monthly intervals. From these samples, 478 were used for 16S ribosomal RNA (rRNA) gene amplicon analyses following quality control (Supplementary Data 2 sheets a–d). (Zaneveld, 2016)

Analysis of microbial β-diversity: Microbial community β-diversity was calculated based on the weighted UniFrac distance matrix67. This is a phylogenetic measure of community similarity that takes into account organismal abundance and phylogeny67. Phylogenetic trees used to calculate this metric were constructed in QIIME 1.8 (ref. 65) through alignment of representative sequences of each OTU with PyNAST against the greengenes core set alignment66, and approximate maximum likelihood phylogenetic inference with FastTree. We considered the pool of distances between samples within each metadata category of interest (for example, algal competition or categories of temperature) using QIIME’s make_distance_boxplots.py. Significance was assessed by non-parametric t-tests, each with 1,000 Monte Carlo permutations (permutation is important in this instance to account for the non-independence of distances). The effect of this procedure is to ask whether different factors increase the dispersion of communities. PCoA plots of β-diversity were visualized in the Emperor software. When multiple categories (for example, different algal types) were tested for effects on β-diversity, the false discovery rate (FDR) for multiple comparisons was controlled at a threshold of q=0.05 using the Bejamini–Hochberg method. (Zaneveld, 2016)

Data Processing Description

Dataset Processing Description

BCO-DMO Processing:
- added conventional header with dataset name, PI name, version date, reference information
- renamed parameters to BCO-DMO standard

More information about this dataset deployment

Funding

Award Number	Funding Source
OCE-1130786	NSF Division of Ocean Sciences

Instruments

None available yet

Parameters

Supplied Name	Supplied description	Supplied Units	Standard Name
taxonomy	microbe taxonomic identification from QIIME Greengenes 13_8	unitless	taxon
all_tmt_95pcent	95% carriage of the microbial order in coral mucus samples across all treatments	unitless	flag
all_tmt_75pcent	75% carriage of the microbial order in coral mucus samples across all treatments	unitless	flag
all_tmt_50pcent	50% carriage of the microbial order in coral mucus samples across all treatments	unitless	flag
core_only_95pcent	95% carriage of the microbial order in coral mucus samples for Greengenes core set only	unitless	flag
core_only_75pcent	75% carriage of the microbial order in coral mucus samples for Greengenes core set only	unitless	flag
core_only_50pcent	50% carriage of the microbial order in coral mucus samples for Greengenes core set only	unitless	flag

Database

Contribute Data

Dataset: Suppl 3a: core microbiome
Deployment: Burkepile_FL_Keys

Dataset acquisition description

Dataset Processing Description

Database

Contribute Data

Dataset: Suppl 3a: core microbiomeDeployment: Burkepile_FL_Keys

Dataset acquisition description

Dataset Processing Description

Dataset: Suppl 3a: core microbiome
Deployment: Burkepile_FL_Keys