Biostimulation for Enhanced Bioremediation of Crude Oil and Diesel Fuel by Marine Sediment Communities of Canada’s Subarctic: A Microcosm-Simulated Oil Spill Study
Bibliographic record
Abstract
Increases in shipping traffic, future mining, and oil and gas developments represent significant oil spill risks in Canada’s subarctic marine environment. The impact of oil on marine ecosystems and the traditional activities of local indigenous peoples are of major concern. To understand the response of local microbial communities to oil contamination and nutrient biostimulation, microcosm-simulated oil spills contaminated with diesel fuel or crude oil and incubated at 4°C were constructed using marine sediments from Hudson Bay and the Labrador Sea. Changes in microbial community structure, diversity, and composition were monitored by DNA extraction, the amplification of 16S rRNA genes, followed by sequencing and taxonomic classifications. Additionally, hydrocarbon degradation in response to bioremediation was monitored by changes in gas compositions with GC, and through hydrocarbon extractions and GC-MS analysis. Results suggested that petroleum hydrocarbons decreased observed microbial diversity and led to dominance by Gammaproteobacteria in both sediments, where many hydrocarbonoclastic bacteria (HCB) increased heavily in abundance at both sites, including Cycloclasticus, Marinobacter, Oleispira, Paraperlucidibaca, Pseudomonas, Thalassolituus, and Zhongshania. The same OTUs were found to increase in abundance in both high and low nutrient treatments, but biostimulation was found to increase initial rates of biodegradation by accelerating the succession and dominance of these HCB. Increase in the relative abundance of Cycloclasticus was noted as signifying succession in response to hydrocarbon degradation and biostimulation. The Labrador Sea sediment community was found to be more responsive to oil spills and biostimulation mitigation strategies, which could be tied to historical exposures of the community to natural oil seepages in the region. Porticoccus and Oleispira are suggested as robust bioindicators for cold seawater environments contaminated by diesel or crude oil, respectively. A comparison of three PCR primer pairs for HCB detection found 341F/806R was the preferred choice for detecting HCB taxa and assessing environmental baselines in areas at risk of oil spills. Microbial biodiversity baselines and in situ rates of microbial degradation should be included in future environment assessments by industry. Overall, this study provided a first account of key crude oil- and diesel-degrading bacteria among marine sediments in this subarctic region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".