Arrive, survive and thrive: essential stages in the re-colonization and recovery of zooplankton in urban lakes in Sudbury, Canada
Bibliographic record
Abstract
The recovery of lakes from severe, historical acid and metal pollution requires that colonists of extirpated species arrive, survive and subsequently thrive. We employed 40 year records from weekly to monthly crustacean zooplankton samples from Middle and Clearwater lakes near Sudbury, Canada, to identify the main mechanistic bottlenecks in this recovery process. While both lakes now have circum-neutral pH, acidity decreased more rapidly in Middle Lake because of past liming interventions, while Clearwater Lake, being larger and supporting more housing, likely receives more zooplankton colonists than Middle Lake. Community richness increased much faster in Middle Lake than in Clearwater Lake, at 1.6 vs 0.9 species decade-1, respectively. Richness has recovered in Middle Lake, when assessed against a target of 9-16 species collection-1 determined from regional reference lakes, but it has not yet recovered in Clearwater Lake. Species accumulation curves and a metric of annual persistence show that this difference is a product not of greater rates of species introduction into Middle Lake, but rather to their greater annual persistence once introduced. Greater annual persistence was associated with better habitat quality (i.e., lower acid and metal toxicity) in Middle Lake, particularly early in the record, and lower planktivore abundance, more recently. These results support a growing consensus that ecological recovery of zooplankton from acidification and metal pollution does not depend strongly on propagule introduction rates which are adequate, but rather on propagule persistence, in lake-rich, suburban landscapes such as those near Sudbury.
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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 | 0.001 |
| Science and technology studies | 0.002 | 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".