Restoration of Zooplankton Communities in Industrially Damaged Lakes: Influences of Residual Metal Contamination and the Recovery of Fish Communities
Bibliographic record
Abstract
Abstract The Sudbury, Ontario, Canada area offers a unique opportunity to develop our understanding of biotic and abiotic lake recovery processes in industrially damaged natural systems. In recent decades, lakes in the Sudbury area have shown improvements in water quality due to decreases in sulfur (S) and metal emissions from area smelters, and reduced acid deposition from more distant sources. However, biological recovery is lagging and mechanisms controlling the lag are not yet clear. Our study examines the roles of two factors, residual metal contamination and altered fish predation, on zooplankton community recovery. Data collected over three decades on six study lakes were analyzed using redundancy analysis ( RDA ) and partial RDA 's to assess historical and present relationships of water chemistry and fish abundance with zooplankton community recovery. Continuing metal toxicity appears to be the primary cause of the absence of some zooplankton species, particularly Daphnia spp. from metal‐contaminated lakes. Conversely, once water quality is suitable and abundant planktivores reestablish, fish planktivory becomes a factor affecting Daphnia spp. establishment. The introduction of piscivores into these lakes may be necessary to facilitate the return of many Daphnia species. Further reductions in metal toxicity will also assist with the complete recovery of zooplankton communities. Studying natural systems over several decades allows us to better understand the intricate steps involved with recovery of industrially damaged lakes, and this knowledge will greatly benefit future restoration efforts in other industrially damaged systems .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".