Salt Marsh Bacterial Communities before and after the Deepwater Horizon Oil Spill

Coastal salt marshes along the northern Gulf of Mexico shoreline received varied types and amounts of weathered oil residues after the 2010 Deepwater Horizon oil spill. At the time, predicting how marsh bacterial communities would respond and/or recover to oiling and other environmental stressors was difficult because baseline information on community composition and dynamics was generally unavailable. Here, we evaluated marsh vegetation, physicochemistry, flooding frequency, hydrocarbon chemistry, and subtidal sediment bacterial communities from 16S rRNA gene surveys at 11 sites in southern Louisiana before the oil spill and resampled the same marshes three to four times over 38 months after the spill. Calculated hydrocarbon biomarker indices indicated that oil replaced native natural organic matter (NOM) originating from Spartina alterniflora and marine phytoplankton in the marshes between May 2010 and September 2010. At all the studied marshes, the major class- and order-level shifts among the phyla Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria occurred within these first 4 months, but another community shift occurred at the time of peak oiling in 2011. Two years later, hydrocarbon levels decreased and bacterial communities became more diverse, being dominated by Alphaproteobacteria (Rhizobiales), Chloroflexi (Dehalococcoidia), and Planctomycetes Compositional changes through time could be explained by NOM source differences, perhaps due to vegetation changes, as well as marsh flooding and salinity excursions linked to freshwater diversions. These findings indicate that persistent hydrocarbon exposure alone did not explain long-term community shifts.IMPORTANCE Significant deterioration of coastal salt marshes in Louisiana has been linked to natural and anthropogenic stressors that can adversely affect how ecosystems function. Although microorganisms carry out and regulate most biogeochemical reactions, the diversity of bacterial communities in coastal marshes is poorly known, with limited investigation of potential changes in bacterial communities in response to various environmental stressors. The Deepwater Horizon oil spill provided an unprecedented opportunity to study the long-term effects of an oil spill on microbial systems in marshes. Compared to previous studies, the significance of our research stems from (i) a broader geographic range of studied marshes, (ii) an extended time frame of data collection that includes prespill conditions, (iii) a more accurate procedure using biomarker indices to understand oiling, and (iv) an examination of other potential stressors linked to in situ environmental changes, aside from oil exposure.

Oil residue contamination of continental shelf sediments of the Gulf of Mexico

We have investigated the distribution of a heavy oil residue in the coastal sediments of the Gulf of Mexico. The amount of the contamination was determined by high-temperature pyrolysis coupled with the Gas Chromatography-Mass Spectrometry (GCMS) of air-dried sediments. The pyrolysis products contain straight-chain saturated and unsaturated hydrocarbons, such as dodecane and 1-dodecene, resulting in a very characteristic pattern of double peaks in the GCMS. Hydrocarbons containing 8 to 23 carbon atoms were detected in the pyrolysis products. Using thermal pyrolysis we have found that the sediment samples collected along Texas, Louisiana, and Mississippi shores contain no detectable traces of oil residue, but most of the samples collected along Alabama and Florida shores contain ~200ppm of heavy oil residue.

Interpreting comprehensive two-dimensional gas chromatography using peak topography maps with application to petroleum forensics

Background

Comprehensive two-dimensional gas chromatography \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) provides high-resolution separations across hundreds of compounds in a complex mixture, thus unlocking unprecedented information for intricate quantitative interpretation. We exploit this compound diversity across the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) topography to provide quantitative compound-cognizant interpretation beyond target compound analysis with petroleum forensics as a practical application. We focus on the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) topography of biomarker hydrocarbons, hopanes and steranes, as they are generally recalcitrant to weathering. We introduce peak topography maps (PTM) and topography partitioning techniques that consider a notably broader and more diverse range of target and non-target biomarker compounds compared to traditional approaches that consider approximately 20 biomarker ratios. Specifically, we consider a range of 33–154 target and non-target biomarkers with highest-to-lowest peak ratio within an injection ranging from 4.86 to 19.6 (precise numbers depend on biomarker diversity of individual injections). We also provide a robust quantitative measure for directly determining “match” between samples, without necessitating training data sets.

Results

We validate our methods across 34 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) injections from a diverse portfolio of petroleum sources, and provide quantitative comparison of performance against established statistical methods such as principal components analysis (PCA). Our data set includes a wide range of samples collected following the 2010 DeepwaterHorizon disaster that released approximately 160 million gallons of crude oil from the Macondo well (MW). Samples that were clearly collected following this disaster exhibit statistically significant match \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(99.23 \pm 1.66 )\,\%$$\end{document}(99.23±1.66)% using PTM-based interpretation against other closely related sources. PTM-based interpretation also provides higher differentiation between closely correlated but distinct sources than obtained using PCA-based statistical comparisons. In addition to results based on this experimental field data, we also provide extentive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) biomarker ROI image of the MW pre-spill sample (sample \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\#1$$\end{document}#1 in Additional file 4: Table S1). We compare the robustness of the cross-PTM score against peak location variability in both dimensions and compare the results against PCA analysis over the same set of simulated images. Detailed description of the simulation experiment and discussion of results are provided in Additional file 1: Section S8.

Conclusions

We provide a peak-cognizant informational framework for quantitative interpretation of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) topography. Proposed topographic analysis enables \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(GC \times GC)$$\end{document}(GC×GC) forensic interpretation across target petroleum biomarkers, while including the nuances of lesser-known non-target biomarkers clustered around the target peaks. This allows potential discovery of hitherto unknown connections between target and non-target biomarkers.

Electronic supplementary material

The online version of this article (doi:10.1186/s13065-016-0211-y) contains supplementary material, which is available to authorized users.