Life history responses of yellow perch (Perca flavescens) to mass removal
Bibliographic record
Abstract
This study evaluates the life history responses of yellow perch to mass removal and the potential for population recovery. We removed approximately 94% of a perch population from Nepawin Lake, a 35 hectare oligotrophic lake in Algonquin Provincial Park, Ontario, as part of a study designed to enhance the recruitment success of brook trout. Several response variables were examined both before and after mass removal: (1) condition, which includes growth, diet and overall condition responses, and (2) reproduction, which includes size at maturity and fecundity. We examine the question of whether compensatory life history responses in the yellow perch will overcome brook trout predation leading to a reestablishment of a high density perch population. Results showed that prior to the manipulation, perch exhibited a narrow size distribution, high dietary overlap, and low condition, typifying a stunted population. After mass removal, the perch population remained in a narrow size distribution, exhibited decreased growth rates for older age classes, showed increased mean condition and increased consumption of zooplankton in all size classes. Perch also exhibited increased size at maturity and decreased fecundity immediately following the mass removal. A time lag is expected before compensatory recruitment is possible in the population, but it is likely that the perch will recover from the mass removal because of strong age 0+ and 1+ cohorts. However, stunting and bottlenecking may still occur in the population. Continued monitoring and management is necessary to observe further changes to the perch population dynamics in Nepawin Lake.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".