Restoration of a Montane Reservoir Fishery via Biomanipulation and Nutrient Addition
Bibliographic record
Abstract
Abstract An innovative coupling of nutrient addition and biomanipulation strategies was applied to Wahleach Reservoir in southwestern British Columbia to successfully restore fish populations. Kokanee Oncorhynchus nerka were historically abundant in the reservoir, but the population collapsed over 2 decades. This decline in kokanee abundance and a concurrent decline in the catch rates of rainbow trout Oncorhynchus mykiss coincided with the occurrence of trophic depression in the aging reservoir. It also coincided with the onset of food competition with threespine sticklebacks Gasterosteus aculeatus that were illegally introduced in the 1970s. In 1995 through 2000, the reservoir was fertilized with liquid fertilizer with compositions of 10–34–0 and 28–0−0 at an annual P load of up to 225 mg/m2 of lake water (late May through September) at a molar N:P ratio of 25 in 1995, declining to 13 in 2000. Up to 50,000 age-0 kokanee (up to 4.5 g) and approximately 20,000 triploid cutthroat trout O. clarkii were released annually into the reservoir over 4 years. The nutrient addition increased the biomass of fish food organisms, particularly the cladoceran Daphnia rosea. During the years of fish stocking, the abundance of threespine sticklebacks declined by 94%, in part because of predation by the introduced cutthroat trout. Three age-classes of kokanee were reestablished in the reservoir from the stocking efforts. The cutthroat trout grew to sizes of up to 45 cm. With a sustained population of rainbow trout, the two trout species supported a revitalized recreational fishery that also included kokanee in the final year of study. Our results suggest that bottom-up and top-down control mechanisms can be linked to achieve fish population restoration in ultra-oligotrophic reservoirs subject to trophic depression and predominance by introduced nuisance species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".