Molecular characterization of the hydrocarbon biodegradation process on Canadian Arctic beaches
Bibliographic record
Abstract
The warming effect of climate change is causing a decrease in sea ice across the Arctic Ocean, especially in the Canadian high Arctic. This is expected to cause an increase in shipping traffic through the Northwest Passage. This comes with the risk of environmental damage to the region with one of the largest concerns being a hydrocarbon spill from a passing ship. Due to the remoteness of the Canadian high Arctic, the response in case of such a spill will be slow and the spilled hydrocarbons might reach the shoreline. Simpler remediation strategies could be preferrable due to the limited resources available for a cleanup in this area. One of said strategies is bioremediation using the native microbial communities that inhabit these beaches. Limited research has been carried out to understand the hydrocarbon biodegradative capabilities of Arctic microbes on shorelines. This limits our understanding of the microbial community and the metabolic processes these microbes are carrying out in response to the presence of hydrocarbons in their environment. This is crucial information that needs to be obtained to understand whether bioremediation will be an effective cleanup strategy. This thesis aims to narrow this knowledge gap by utilizing state-of-the-art molecular techniques to describe the potential and actualized in situ hydrocarbon biodegradation capabilities of Arctic shoreline microorganisms.Initially, I first performed a metagenomic survey of the baseline microbial communities inhabiting 9 Arctic beaches from 4 regions of the Canadian Arctic archipelago to understand the genomic potential available in those beaches before any spill has occurred. The presence of known hydrocarbon-degrading taxa as well as genes involved in hydrocarbon degradation pathways, especially those associated with the degradation of short, medium, and long-chain alkanes was identified. I then compared metagenome-assembled genomes from the survey with genomes of isolates obtained from the same beaches that can grow using hydrocarbons as a source of carbon.To further corroborate the results of the survey, in situ mesocosm experiments consisting of hydrophobic netting covered with three fuels commonly used by the shipping industry: Marine diesel, Bunker C, and Ultra Low Sulfur Fuel Oil (ULSFO) were performed. These nettings were deployed in Assistance Bay (Cornwallis Island, Nunavut) for a month. I then performed 16S rRNA gene amplicon, metagenomic, and metatranscriptomic sequencing on the netting to characterize how the native microbial community of this beach responds when fuels are added to the beach. A shift in the community composition towards taxa commonly associated with hydrocarbon degradation as well as the presence of multiple genes these taxa are using to metabolize aliphatic and aromatic hydrocarbons was observed. Analysis of the remaining fuel showed that 14 – 78% of the compounds were biodegraded, with Marine diesel having the highest degradation and Bunker C having the lowest.Finally, a second round of in situ mesocosm experiments was performed to determine whether allowing biodegradation to occur for a whole year along with the addition of fertilizers would further stimulate the Assistance Bay beach microbiota and lead to higher Marine diesel and ULSFO biodegradation. After a year, I observed more defined differences in the microbial communities of the fuel treatments compared to the controls, but no noticeable differences between the communities of the two fuels, suggesting that similar microbes can metabolize both kinds of fuel. There was no effect of the addition of fertilizers for the microbial composition or the biodegradation performance. The increased duration of the experiment also did not result in more of the fuel being biodegraded, with similar percentages observed after a year (33 – 72%) which could have been caused by the decrease in metabolic activity of most microbes under the sub-zero temperatures experienced in the prolonged Arctic winter
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".