CYANOBACTERIA PRESENCE IN BOREAL-TEMPERATE LAKES IN RESPONSE TO THE ANTHROPOCENE
Bibliographic record
Abstract
Novel research is needed that links the cumulative effects of human and climate-driven changes on aquatic ecosystems that lead to a greater predominance of cyanobacteria— an ecological state with significant societal consequences. The principal aim of this thesis was to assess how human and climate-mediated change regulate cyanobacteria abundance and their toxins in boreal-temperate lakes across Canada. Using contemporary and historical techniques, I use multiple lines of evidence to infer changes in cyanobacteria and their toxins across spatiotemporal scales and correlate these changes to meteorological and land-use parameters. Paleogenetics was used to reconstruct cyanobacteria communities and their toxin-producing potential (specifically microcystin) over a period spanning approximately 120 years. Novel spectroscopic techniques (Visible Near-infrared Reflectance Spectroscopy (VNIRS)) were also applied to test the widespread applicability of this tool for reconstructing past cyanobacteria abundance. Major findings include: 1) demonstrating risks associated with cyanobacteria depth-differentiation (i.e., deep cyanobacteria layers); 2) land use changes were the dominant factor influencing cyanobacteria abundance but had little effect on microcystin-producing potential; 3) temperature was the dominant factor amplifying the risk of potentially toxigenic cyanobacteria, with elevated microcystin-producing potential being a more recent phenomenon (the 1980s onwards); and 4) the cyanobacteria VNIRS model showed a strong correlation with conventional paleolimnological tools (qPCR) in 80% of study lakes. Highlighting the promise of VNIRS as a routine assessment tool in paleolimnological research. The scientific findings in this thesis build on the current body of literature showing that human activities are now a pervasive force fundamentally reshaping phycological communities in inland waters. Climate warming amplifies the risk of potentially toxigenic cyanobacteria, with cyanobacteria abundance increasing in ‘unconventional’ systems’ (i.e., remote, northern lakes). As society moves forward in an increasingly human and climatically impacted planet, supplementing the existing paradigm for predicting and managing cyanobacteria with climate change adaptation will be necessary to develop a conceptual framework with broader coverage. Furthermore, technological advancements that can override traditional bottlenecks (i.e., time and financial constraints), such as VNIRS, are necessary for achieving equitable access to cyanobacteria diagnostics and ensuring no place is left behind from economic inequality.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".