Set for success: ecological factors facilitating restoration of self-sustaining Lake Trout (Salvelinus namaycush) populations in acid-damaged lakes
Bibliographic record
Abstract
Sudbury, Ontario, Canada is a site of extreme biodiversity loss due to widespread acidification from over a century of metal mining and smelting. Lake trout (Salvelinus namaycush) were the most widely and severely impacted of the resident sportfish but with massive emission reduction in recent years, their populations have since shown significant signs of recovery. The objective of my study was to identify conditions associated with lake trout recolonization and recruitment by conducting fish and water quality surveys on 31 oncedamaged lakes across Sudbury’s historic acid deposition zone. Lake trout biomass and odds of lake trout recruitment success increased in lakes with more depth of usable lake trout habitat, higher zooplankton biomass and a lower concentration of dissolved organic carbon. A history of hatchery stocking of lake trout was a top predictor of total lake trout biomass in standardized gillnet surveys but did not emerge in top models for predicting natural recruitment or the total biomass of natural lake trout in the lake. These results demonstrate the importance of lake-specific ecological factors in the reestablishment of lake trout populations, regardless of whether the source of the population was hatchery stocking, migration from neighbouring lakes or residual populations that survived acidification. Overall, my study shows evidence that Sudbury’s historically damaged lakes have been extensively recolonized and are no longer limited by acidic conditions. In many cases, they have shifted to simplified fish communities in which zooplankton may be a primary prey source for lake trout. Water chemistry factors, in particular the increase in concentration of dissolved organic carbon and the associated decrease in water clarity also emerged as potential factors shaping lake trout recovery in my study lakes.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".