Changes in Algal Species Composition Affect Juvenile Sockeye Salmon Production at Woss Lake, British Columbia: A Lake Fertilization and Food Web Analysis
Bibliographic record
Abstract
Abstract In recent decades, the abundance of sockeye salmon Oncorhynchus nerka in the Nimpkish River watershed (Vancouver Island) has declined by more than 80%. To improve sockeye salmon abundance, one of three Nimpkish nursery lakes (Woss Lake, 13.6 km2) was fertilized; a second (Vernon Lake, 8.4 km2) was maintained as an unmanipulated reference. For 3 years (2000–2002), we assessed changes in phytoplankton, zooplankton, and juvenile sockeye salmon and then used food web analysis to model the fate of fertilizer phosphorus (FP) as it moved up the food web to juvenile sockeye salmon. As the experiment progressed, we found two distinctly different results. During the first 2 years (2000 and 2001), fertilization was associated with higher concentrations of P, chlorophyll, and phytoplankton. However, because most of the algae were inedible diatoms (principally Rhizosolenia eriensis), only 8% of the algal standing stock was available to zooplankton and less than 0.1% of the FP was incorporated into sockeye salmon biomass via limnetic pathways. The result was that juvenile sockeye salmon growth rates in the treated and control lakes were not significantly different. During the third year (2002), a bloom of the diatom Leptocylindrus increased Woss Lake edible algal biomass by a factor of 7; zooplankton production tripled, and juvenile sockeye salmon production increased by 19%. Consequently, Woss Lake juvenile sockeye salmon grew twice as fast as they did in untreated Vernon Lake, and juvenile sockeye salmon fall weights were 50% higher than those recorded before the lake was fertilized. We conclude that for north Pacific coastal lakes, bottom-up effects resulting from changes in phytoplankton species composition can profoundly alter rates of juvenile sockeye salmon production. Regulation of algal species composition through manipulations of fertilizer composition and application timing might be used to reduce blooms of nuisance algae and increase age-0 sockeye salmon yield.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".