Forecasted changes to the timing of Pacific herring Clupea pallasii spawn in a warming ocean
Bibliographic record
Abstract
Pacific herring Clupea pallasii are a critical commercial and subsistence fish species and play a keystone role in the ecology and culture of the North Pacific. The annual herring spawn, in which mature herring migrate nearshore to deposit eggs along the coastline, is an important event linked to the migration of seabirds and marine mammals as well as a subsistence harvest for Alaska Natives and First Nations in British Columbia. Previous work has suggested that environmental variables and broad teleconnection indices play a role in the magnitude and phenology of spawning; however, the effects of these drivers have not been examined in the context of future climate scenarios. Here, we modeled variability in the timing of herring spawn across British Columbia and Southeast Alaska using survey data from 1951-2022. We created a model using Pacific teleconnection indices, sea surface temperature (SST), tidal height, and lagged data to predict spawn date anomalies (SDAs) across 9 spawning regions. SDAs were significantly affected by the Oceanic Niño Index, Pacific Decadal Oscillation, SST, and lagged SDAs. We then used this model to predict SDAs using projected SST from climate models and bootstrapped teleconnection data from 2025-2100. Future herring spawn timing trends earlier on average with warming SSTs, although the magnitude is relatively small, occurring 9 d earlier on average by 2100. This changing phenology, though small, varied by region and may have ecosystem-level ramifications and create timing mismatch for migratory species. However, our findings also reinforce the importance of other physical factors not measured in this study, such as photoperiod, which drive herring spawn timing.
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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 | 0.001 | 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".