Long-term assessments of ecological effects of anthropogenic stressors on aquatic ecosystems from paleoecological analyses: challenges to perspectives of lake management
Bibliographic record
Abstract
Paleolimnological techniques are important for determining background ecological conditions and ecosystem responses to stressors when long-term data sets are absent. Research at the Dorset Environmental Sciences Centre (Ontario, Canada) has included paleolimnological studies to determine the effects of anthropogenic stressors on shield lakes, including eutrophication, acidic deposition, and climate change. Diatom-based total phosphorus (TP) inferences suggest that [TP] has declined in some lakes, despite increased inputs from anthropogenic sources. When coupled with siliceous algae-based pH inferences that suggest landscape-scale long-term acidification, these results indicate that the ecological effects of anthropogenic P inputs are being masked by the cumulative effects of multiple stressors. Detailed stratigraphic analyses of lakes that have experienced severe anthropogenic disturbances indicate that despite measured and inferred recovery in epilimnetic [TP] to predisturbance concentrations, pelagic communities have not recovered to predisturbance community composition; profundal communities and the variables associated with water quality (e.g., hypolimnetic oxygen) have also not recorded recovery. This suggests that (i) typical modeling approaches to quantify the effects of anthropogenic inputs on lake water [TP] should be used with caution and (ii) lake management approaches that follow the paradigm of “water quality recovery = biological recovery” may not be applicable to ecosystems being affected by multiple anthropogenic stressors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".