Managing for Coexistence of Kokanee and Trophy Lake Trout in a Montane Reservoir
Bibliographic record
Abstract
Abstract Kokanee Oncorhynchus nerka and Lake Trout Salvelinus namaycush are stocked for sportfishing in lakes and reservoirs throughout the western United States and Canada. However, where the two species co-occur, unsustainable levels of predation by Lake Trout can lead to declines in kokanee abundance and declines in Lake Trout growth and body condition. Such declines occurred in Blue Mesa Reservoir, Colorado. In 2009, managers began removing Lake Trout (<740 mm TL) in an attempt to sustain the hatchery-dependent kokanee population while still providing a trophy Lake Trout fishery. To evaluate this and other strategies for achieving the dual management goals, we developed age-structured kokanee and Lake Trout population models and linked them to a bioenergetics model of Lake Trout predation. We found that the existing level of Lake Trout removal (0.23; ages 4–9) is insufficient to prevent further decline and ultimately the extirpation of the kokanee population. If removal of age-4–9 Lake Trout is intensified to 0.38, the kokanee population would persist; however, removal would have to be increased to 0.63 to allow a return to historic kokanee abundance. Focusing removal on age-4 Lake Trout (0.78) would allow for persistence of kokanee and would leave more trophy Lake Trout for anglers, suggesting that the two goals are compatible under some circumstances. However, management costs of balancing kokanee with trophy Lake Trout are high and put both fisheries at risk unless Lake Trout abundance is controlled. Received October 31, 2013; accepted May 5, 2014
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".