Evaluating factors affecting the distribution and timing of Pacific herring Clupea pallasii spawn in British Columbia
Bibliographic record
Abstract
Pacific herring Clupea pallasii spawn in nearshore areas in late winter to early spring, but factors influencing the timing and spatial distribution of spawning are not well known. We modeled the temporal and spatial distribution of spawning for 5 herring stocks in British Columbia from egg deposition surveys conducted from 1988-2018 using different sets of environmental predictors and modeling methods. Random forest modeling showed that the timing of spawning in each year was mainly influenced by the number of daylight hours being >10.5, cumulative degree days >100 and salinity at 30.5. The spatial distribution of spawning tended to occur at consistent locations over time. Results showed that the probability of spawning occurring at a transect in a given year was largely determined by the biomass of herring and location of the transect relative to the center of spawning. Environmental factors at individual transects played a much smaller role in determining spawn distribution. There was mixed evidence for spatial expansion of spawning distribution in years of high biomass, with some stocks, such as Haida Gwaii, showing no expansion of the spawning area in years of higher biomass. Since Pacific herring recruitment has been linked to their ability to time larval hatch to spring bloom timing, future warming temperatures may result in earlier herring spawning relative to the spring bloom. This will increase the probability of mismatch with prey, impacting larval herring starvation, growth and perhaps mortality, leading to reductions in recruitment to these important stocks.
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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 | 0.003 |
| 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 | 0.001 | 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".