Sockeye salmon (<i>Oncorhynchus nerka</i>) nursery lake fertilization: Review and summary of results
Bibliographic record
Abstract
We reviewed 24 sockeye salmon (Oncorhynchus nerka) nursery lake experiments that involved whole-lake fertilization with appropriate treatment and control years. We found that: 21 of 21 studies showed that fertilization was associated with increased chlorophyll a concentrations, 16 of 16 showed increased zooplankton biomasses, 16 of 16 demonstrated increased average smolt weights, and 11 of 13 showed increased smolt biomasses. Studies involving assessments of egg-to-smolt survival were rare, but all (4 of 4) showed increased survival rates. Studies involving increased smolt-to-adult survival (i.e., marine survival) were even rarer, but all (3 of 3) showed that lake fertilization and increased smolt size were associated with increased marine survival. Several fertilization studies reported problems, and some offered solutions. For instance, when whole-lake fertilization stimulated the growth of blue-green algae, fertilizer with higher nitrogen to phosphorus ratios was used to control the problem. Conversely, when high nitrogen to phosphorus ratios were associated with blooms of ungrazable diatoms, notably Rhizosolenia eriensis, reduced nitrate concentrations were recommended. To date, solutions designed to constrain the growth of both blue-green algae and Rhizosolenia blooms remain elusive. Some studies showed that when both mysids (large invertebrate planktivores) and juvenile sockeye inhabit the same lake, sockeye suffer from a competitive disadvantage and mysids consume 8090% of the available zooplanktonic food production. Similarly, a small number of studies demonstrated that competition from sticklebacks (Gasterosteus aculeatus) adversely affected sockeye growth rates, and although the problem remains unresolved, ongoing work in lakes containing kokanee (O. nerka), suggests that stocked cutthroat trout (Salmo clarki) may be capable of controlling stickleback densities through predation. Despite all of these difficulties, in almost all cases, when lakes were fertilized with various mixtures of inorganic nitrogen and phosphorus, pelagic food web bottom-up control was strong enough and predictable enough to ensure that sockeye smolt biomass increased. We conclude that sockeye nursery lake fertilization is a technique that can contribute usefully to both the enhancement and conservation of sockeye salmon populations. Key words: sockeye salmon, lake fertilization, bottom-up, aquatic food web.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".