Factors driving spatial variation in egg survival of an ecologically and culturally important forage fish
Bibliographic record
Abstract
Abstract Low trophic‐level forage fish are experiencing global declines, influencing coupled human–ocean systems worldwide. Along the northwest coast of North America, declining trajectories of Pacific herring (Clupea pallasii) have prompted interest in improving the understanding of its population and community dynamics to better guide future conservation and management strategies. To improve future population estimates and understanding of the ecological factors governing herring egg survival, the magnitude, spatial variation and mechanisms driving herring egg loss rates were quantified. This was achieved by way of repeated observational field surveys and a predator exclusion experiment. Observational surveys revealed that regional egg loss rates (Z) were substantial and ranged from 0.101 ± 0.019 to 0.134 ± 0.028, the equivalent of 88–94% egg loss over a 21 day incubation period, or 50–60% egg loss over 6.8 days, the average time lag between spawn deposition and annual egg surveys. Furthermore, spatial variation was high, with egg loss rates varying 5‐fold among study sites. Depth, time since spawn, and spawn area were primary spatial drivers of egg loss, but predator abundance and exposure were secondarily important. Experimental evidence showed that benthic predation and habitat type were strong drivers of egg loss, suggesting that a high proportion of eggs, particularly those spawned on benthic substrates, are consumed by predators. These results have important conservation implications for managers and ecologists seeking to estimate herring biomass and to understand the environmental influences on predator–prey interactions that affect herring dynamics.
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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 | 0.001 | 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".