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, 109 (50), 20286-91

Dynamic Autoinoculation and the Microbial Ecology of a Deep Water Hydrocarbon Irruption

Affiliations

Dynamic Autoinoculation and the Microbial Ecology of a Deep Water Hydrocarbon Irruption

David L Valentine et al. Proc Natl Acad Sci U S A.

Abstract

The irruption of gas and oil into the Gulf of Mexico during the Deepwater Horizon event fed a deep sea bacterial bloom that consumed hydrocarbons in the affected waters, formed a regional oxygen anomaly, and altered the microbiology of the region. In this work, we develop a coupled physical-metabolic model to assess the impact of mixing processes on these deep ocean bacterial communities and their capacity for hydrocarbon and oxygen use. We find that observed biodegradation patterns are well-described by exponential growth of bacteria from seed populations present at low abundance and that current oscillation and mixing processes played a critical role in distributing hydrocarbons and associated bacterial blooms within the northeast Gulf of Mexico. Mixing processes also accelerated hydrocarbon degradation through an autoinoculation effect, where water masses, in which the hydrocarbon irruption had caused blooms, later returned to the spill site with hydrocarbon-degrading bacteria persisting at elevated abundance. Interestingly, although the initial irruption of hydrocarbons fed successive blooms of different bacterial types, subsequent irruptions promoted consistency in the structure of the bacterial community. These results highlight an impact of mixing and circulation processes on biodegradation activity of bacteria during the Deepwater Horizon event and suggest an important role for mixing processes in the microbial ecology of deep ocean environments.

Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
An analysis of the autoinoculation effect through comparison of two water parcels with single- (red) and double- (blue) exposure histories. (A) Trajectories of two water parcels for 150 d starting April 23, 2010. (B) Time course of cumulative hydrocarbon input to the parcels at the proportions shown in Table S1. (C) Time course of hydrocarbon flux into the two parcels at the proportions shown in Table S1. (D) Time course of dissolved oxygen concentration in the parcels attributed to hydrocarbon respiration. (E) Time course of respiration rate linked to methane consumption in the two parcels. (F) Time course of bacterial growth for organisms consuming methane (Met and Met′). (G) Time course of respiration rate linked to consumption of 25 nonmethane hydrocarbons in the two parcels. (H) Time course of bacterial growth for organisms consuming 25 nonmethane hydrocarbons (excludes Met and Met′). Units for bacterial abundance are shorthand for micromoles carbon per liter.
Fig. 2.
Fig. 2.
Comparison of microbial community dynamics in water parcels with different exposure histories, all starting April 23, 2010. (A) Time course change in hydrocarbon flux (red) and respiration rate (blue) shown in Top, dissolved oxygen concentration (red) and bacterial abundance (blue) shown in Middle, and relative composition of the microbial community shown in Bottom for a parcel experiencing a single exposure. (B) Time course changes as described for A for a parcel experiencing a triple exposure. (C) Time course changes as described in A for a parcel experiencing a double exposure. (D) Tabulated legend identifying the OMTs, their putative phylogenetic affiliation, and their ecosystem function.
Fig. 3.
Fig. 3.
Impact of recirculation on hydrocarbon abundance, bacterial population, and metabolism. (A and B) Spatial distribution of bacterial abundance before (A) and during (B) the early stages of an autoinoculation event. (C) Time course change in average abundance for bacteria consuming nonmethane hydrocarbons and the average summed concentration of these chemicals integrated over the computational domain. (D and E) Spatial distribution of hydrocarbon respiration rate before (D) and during (E) the early stages of the same autoinoculation event. (F) Time course change in the average respiration rate for bacteria consuming nonmethane hydrocarbons, integrated over the computational domain. Because the size of the computation domain is 2° latitude × 2° longitude, a small number of parcels exit the domain near the end of the simulation.
Fig. 4.
Fig. 4.
Comparison of physical and biological features. (A) Spatial distribution of bacterial abundance on June 13, 2010. (B) Hypergraph map on June 13, 2010. (C) Spatial distribution of bacterial abundance on June 30, 2010. (D) Hypergraph map on June 30, 2010. For the hypergraphs, blue indicates mesohyperbolic regions, red indicates mesohyperbolicity with shear, and green indicates elliptic regions in which eddies are formed. The scaled units for the hypergraphs are defined in ref. .
Fig. 5.
Fig. 5.
Spatial and temporal context for development of bacterial communities reported by Hazen et al. (13) for May 30, 2010. (A) Spatial distribution of bacterial abundance for primary hydrocarbon consumers on May 30, 2010. (B) Spatial distribution of the dissolved oxygen anomaly formed from hydrocarbon respiration on May 30, 2010. (C) Spatial distribution of total hydrocarbons (sum of 26 compounds or classes) on May 30, 2010. (D) Spatial distribution of respiration rate for hydrocarbon oxidation on May 30, 2010. (E) Seven-day hypergraph for May 30, 2010, showing mixing regimens and highlighting a mesoelliptic zone where an eddy has set up to the southwest of the wellhead. Scaled units are defined in ref. . (F) One hundred fifty-day trajectory of a water parcel located near the position of sampling (13) on May 30, 2010. (G) Time course changes for the water parcel for which the trajectory is shown in F (as per Fig. 2); May 30, 2010 is shown as a dashed line. The location of the Macondo well is denoted by a black circle in A–E. Samples collected by Hazen et al. (13) on this day were within the symbol denoting the wellhead. Note the log scales for A, B, and D.

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