Identification of taxa-specific responses to bioremediation treatments in hydrocarbon-contaminated Arctic soils
Notice bibliographique
Résumé
A warming climate and improved technology have allowed northern countries to more thoroughly explore and exploit Arctic resources.This increased activity has led to an elevated risk of petroleum contamination, and consequently, there is a need to develop strategies to effectively and efficiently degrade these contaminants on site.While many Arctic soil microorganisms are known to naturally metabolize petroleum hydrocarbons in contaminated sites, a process known as bioremediation, treatments directed at stimulating the hydrocarbon-degrading activity of these microbes (e.g.nutrient amendments) have varied in effectiveness.The objective of this study was to determine whether microbial taxa respond equally to disturbances of the soil environment by hydrocarbon contaminants and nutrient amendments, and whether the most efficient hydrocarbon degraders are naturally stimulated.To determine whether the bacteria inhabiting contaminated Arctic soils assimilate added nitrogen equally, a novel 15N-stable isotope probing approach was developed.After a month of in situ incubation, it was determined that many hydrocarbon-degrading bacteria had incorporated the added nitrogen, but to varying extents.The Alphaproteobacteria most effectively used the added nitrogen, as determined by both 16S rRNA and alkB gene enrichment, and this was noteworthy given that they were not expected to be the most effective hydrocarbon-degrading group.To assess whether the relative abundance of bacterial taxa in hydrocarboncontaminated soils was determined by soil characteristics as opposed to hydrocarbondegrading ability, 18 soils from across the Arctic were collected and treated with diesel and monoammonium phosphate.Bacterial diversity and community composition were Thank you first to my supervisor, Dr. Charles Greer.The freedom that you gave me was frightening at first, but made me independent in ways that I hadn't been before.Thank you for supporting me, giving me the chance to fulfill a dream by traveling to the Arctic, and helping to shape even my strange ideas.Your positive and approachable nature is a huge bonus, and your door was always open when I needed input.Thanks also to my co-supervisor Dr. Lyle Whyte for valuable input, and for providing finances that allowed me to attend a conference outside of Montreal.Thanks to Dr. Étienne Yergeau for lessons in statistics, bioinformatics, and creating scientific papers.Thank you also for your help with networking, building my CV, and helping me to find a way to continue my career in Montreal.It was a great privilege to work with one of the rising stars in microbial ecology.Thank you to the staff and visitors of CFS-Alert,
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».