Investigating freshwater bacterial diversity, community composition and function in hundreds of Canadian Lakes
Bibliographic record
Abstract
Freshwater bacterial communities play important roles in global biogeochemical cycling and aquatic food webs, yet bacterial diversity, community composition, and community metabolism in freshwater ecosystems remain less explored compared to terrestrial and marine \necosystems. In this thesis work, I investigated bacterial communities in hundreds of lakes located across Canada, a country that contains millions of lakes. Utilizing 16S rRNA and metagenomic techniques, this research explores diversity patterns, community composition and functional \ncapabilities of lake bacterial communities and links variation in these three components to human-mediated alterations, specifically watershed land use types within lake watersheds. \n \nIn the first research chapter, I performed an investigation of communities in 403 lakes from seven ecozones. I identified distinct bacterial diversity patterns between western (Semi-Arid Plateaux, Prairies, and \nBoreal Plains ecozones) and eastern (Boreal Shield, Mixedwood Plains, Atlantic Maritimes, and Atlantic Highlands ecozones) Canada. The identified pattern was primarily influenced by lake physicochemistry including productivity, ion concentration, and lake depth. Bacterial community structure was influenced particularly by lake pH and trophic state. \n \nIn the next research chapter, I expanded the study to 621 lakes across 12 ecozones and explored variation in diversity and community composition patterns in relation to water quality and land use. Total phosphorus (TP) \nwas identified as a key variable shaping community composition, with notable shifts occurring at 110 μg/L TP. Variation in bacterial communities within the Prairies ecozone were driven by agriculture while urbanisation played a role in structuring community composition within the \nPacific Maritimes ecozone. \n \nIn the final research chapter, I investigated bacterial functional capabilities using gene-centric metagenomics. Physicochemical parameters emerged as top predictors of variation in functional gene composition, with xenobiotics biodegradation and metabolism notably influenced. Overall, the research presented in this thesis demonstrates that bacterial diversity, community composition, and community function exhibit variations across continental and regional scales that can be attributed to within-lake conditions and watershed land use types.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".