Investigating Long-term Environmental Trends in Central Ontario Lakes Impacted by Cyanobacterial Blooms
Bibliographic record
Abstract
Cyanobacterial blooms degrade water quality by increasing turbidity, causing taste and odour problems, depleting deep-water oxygen concentrations, and producing toxins – all of which can alter aquatic food webs, and depreciate the social and economic value of waterbodies. A common driver of blooms is nutrient enrichment; however, climate-related factors, like surface water temperature, intensity and duration of thermal stratification, precipitation, and wind speed, are also important predictors. Consequently, climate change is expected to increase the spatial extent, severity, frequency, and duration of cyanobacterial blooms. Reports of blooms have increased in recent decades in Canadian lakes, but a lack of long-term monitoring hinders attempts to identify the causes. This thesis examined environmental indicators preserved in lake sediment cores to reveal multi-century trends in water quality and investigate drivers for these recent cyanobacterial blooms. In a remote oligotrophic lake, marked increases in cyanobacterial microfossils in surficial sediments and an increasing trend in primary production since ~1930 CE, in the absence of nutrient enrichment, suggest a climatic driver for recent unprecedented Dolichospermum blooms. In three rural northeastern Ontario lakes, eutrophication in ~1930 CE potentially associated with forest removal and settlement was tracked in diatom assemblages in two of the lakes. However, bloom occurrence in these lakes over a half-century later was associated with distinct diatom species shifts, indicative of enhanced thermal stratification. In Callander Bay, Lake Nipissing, a climate-mediated shift from polymictic conditions to sustained summertime stratification, and increased bottom water anoxia and internal nutrient loading since ~2000 CE, are linked to recent cyanobacterial prevalence. At eight additional sites across Lake Nipissing, diatoms in modern and pre-industrial era sediments revealed strikingly similar assemblage shifts indicative of lake-wide enhanced thermal stratification, indicating more favourable conditions for blooms. Estimates of baseline nutrient and hypolimnetic oxygen concentrations derived in this study can be used to guide management targets. Collectively, increasing primary production tracked over the last several decades (without parallel increases in nutrient enrichment) across all study lakes has likely occurred due to regional warming and a longer ice-free growing season, and invokes climate change as an important driver of cyanobacterial blooms in temperate lakes.
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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 | 0.001 | 0.000 |
| 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".