Climate change and nutrient enrichment altering sedimentary diatom assemblages since pre-industrial time: evidence from Canada’s most populated ecozone
Bibliographic record
Abstract
Abstract Lakes worldwide are under threat by a myriad of environmental stressors that have been increasing in number and magnitude. These stressors can be regional such as climate change, or local such as nutrient-rich runoff, invasive species, and road salt contamination, to name but a few. To protect lake ecosystems from further deterioration, we need long-term data to define pre-disturbance baselines and to identify stressors that are causing the greatest ecological changes. Paleolimnology is an effective approach to reconstruct limnological history, providing an important window of lake changes through time. Here, we applied paleolimnological tools to explore the pre-industrial and contemporary diatom assemblage changes of 27 lakes located in the most populated ecozone in Canada, the Mixedwood Plains. We also examined a full sediment core for Lac des Chicots (Southern Québec), aiming to disentangle the impacts of natural versus anthropogenic interactions and to assess their relative effects on the lake’s biotic structure. Our ordination analysis suggests that the Mixedwood Plains lakes have experienced varying lake-specific ecological changes over the past ~ 150 years, with two major trends across most study lakes: (1) a prevalent increase in planktic species, and (2) a rise in mesotrophic/eutrophic taxa in lakes receiving high human impacts. Our case study of Lac des Chicots identifies ecological impacts from both historical natural events and recent human activities, such as cultural eutrophication and climate warming. Overall, our study demonstrates that lakes in the Mixedwood Plains ecozone have experienced marked ecological changes that are mainly associated with human impacts.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".