Influences of Spawning Habitat Characteristics and Interstitial Predators on Lake Trout Egg Deposition and Mortality
Bibliographic record
Abstract
Abstract To understand the factors affecting natural recruitment of lake trout Salvelinus namaycush, we evaluated natural egg deposition, the rate of egg loss of seeded eggs, and the relationship of interstitial predators to egg mortality at a protected nearshore lake trout spawning area in Lake Michigan. Egg mortality and predator densities were evaluated with collection bags that were buried above the drop‐off on spawning substrate at 1‐, 3‐, and 9‐m depths. Habitat selection by spawning lake trout was probably related to the coverage by periphyton and zebra mussels Dreissena polymorpha given that abiotic characteristics of the spawning habitat such as slope (55– 65°), interstitial depth (30–50 cm), and substrate type did not differ across depths. The results of seeding eggs during spawning and recovering them throughout the incubation period (2–177 d) indicated that egg mortality was extremely high early in the spawning period: Over 40% of seeded eggs were lost by 2 d and over 80% of the eggs were lost after only 2 weeks. The rate of egg loss declined significantly after the spawning period, possibly as a result of declining water temperature, which caused reduced predator activity, and ice cover, which reduced the impact of physical disturbance. The greatest proportions of seeded eggs were recovered at the shallowest depths (12.5 ± 1.2% [mean ± SE] at 1 m and 9.0 ± 1.5% at 3 m), where predator densities averaged 11.4 ± 1.8/m2; a significantly smaller proportion was recovered at 9 m (3.9 ± 1.2), where predator densities were highest (22.3 ± 2.0/m2). Because lake trout preferred the shallowest depth for spawning and predation was lowest at this depth, we conclude that this strategy improved the probability of egg survival.
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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".