Gulf of Mexico hydrocarbon seep communities: VI. Patterns in community structure and habitat

The southern Gulf of Mexico (sGoM) is highly susceptible to receiving environmental impacts due to the recent increase in oil-related activities. In this study, we assessed the changes in the bacterioplankton community structure caused by a simulated oil spill at mesocosms scale. The 16S rRNA gene sequencing analysis indicated that the initial bacterial community was mainly represented by Gamma-proteobacteria, Alpha-proteobacteria, Flavobacteriia, and Cyanobacteria. The hydrocarbon degradation activity, measured as the number of culturable hydrocarbonoclastic bacteria (CHB) and by the copy number of the alkB gene, was relatively low at the beginning of the experiment. However, after four days, the hydrocarbonoclastic activity reached its maximum values and was accompanied by increases in the relative abundance of the well-known hydrocarbonoclastic Alteromonas. At the end of the experiment, the diversity was restored to similar values as those observed in the initial time, although the community structure and composition were clearly different, where Marivita, Pseudohongiella, and Oleibacter were detected to have differential abundances on days eight–14. These changes were related with total nitrogen (p value = 0.030 and r2 = 0.22) and polycyclic aromatic hydrocarbons (p value = 0.048 and r2 = 0.25), according to PERMANOVA. The results of this study contribute to the understanding of the potential response of the bacterioplankton from sGoM to crude oil spills.

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Modelling community structure and species co-occurrence using fishery observer data

ICES Journal of Marine Science ◽

10.1093/icesjms/fsw033 ◽

2016 ◽

Vol 73 (7) ◽

pp. 1750-1763 ◽

Cited By ~ 2

Author(s):

Jeffrey Robert Pulver ◽

Hui Liu ◽

Elizabeth Scott-Denton

Keyword(s):

Cluster Analysis ◽

Community Structure ◽

Gulf Of Mexico ◽

Reef Fish ◽

Efficient Method ◽

Probabilistic Models ◽

Distinct Species ◽

Common Species ◽

Species Relationships ◽

Correlation Measure

Abstract In this study, we modelled fishery observer data to compare methods of identifying community structure using cluster analyses to determine stratifications and probabilistic models for examining species co-occurrence in the Gulf of Mexico deepwater reef fish fishery. Comparing cluster analysis methods, the correlation measure of dissimilarity in combination with average agglomerative linkage was the most efficient method for determining species relationships using simulated random species as a comparison tool. Cluster analysis revealed distinct species stratifications and in combination with multiscale bootstrapping generated probabilities indicating the strength of stratifications in the fishery. A more parsimonious approach with probabilistic models was also developed to quantify pairwise species co-occurrence as random, positive, or negative based on the observed vs. expected fishing sets with co-occurrence. For the most common species captured, the probabilistic models predicted positive or negative co-occurrence between 84.2% of the pairwise combinations examined. These methods provide fishery managers tools for determining multispecies quota allocations and offer insights into other bycatch species of interest.

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