Effects of Climate, Limnological Features and Watershed Clearcut Logging on Long-Term Variation in Zooplankton Communities of Boreal Shield Lakes
Bibliographic record
Abstract
In Canada, climate change and forest harvesting may both threaten the ecological integrity of boreal lakes. To disentangle the effects of natural variation in climate and lake environments from those of logging, we evaluated long-term variation (1991–2003) in zooplankton communities of six boreal lakes in Ontario. We monitored concomitantly changes in zooplankton abundance and composition in three undisturbed and three harvested lakes, five years prior and eight years after watershed clearcut logging. We tested the hypothesis that long-term natural variation in climate and lake environments will be more important drivers of zooplankton community changes than short-term impacts of logging. We used space/time interaction tests and asymmetric eigenvector maps to model zooplankton responses to environmental changes and logging. Year-to-year variation in zooplankton abundance and composition were almost an order of magnitude whereas among-lake variation was stable through time. Breakpoints in time series of zooplankton in each lake were not directly related to logging. Climatic and limnological features were the most important drivers of long-term variation in the zooplankton community, shading the effect of logging. These results highlight the need to better understand the pressures exerted by climate change on boreal lake ecosystems in the context of anthropogenic pressure, such as logging.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".