Estimating a thermal constant of spawning to explain spawning time of Pacific herring Clupea pallasii across space and time
Bibliographic record
Abstract
Explaining variation in life history phenology requires us to disentangle environmental-dependent variability from that caused by adaptive change across time and space. Here, we offer thermal time models (models measuring time in temperature units) as tools to understand the spawning dynamics of small pelagic fish, such as Pacific herring Clupea pallasii. We hypothesised that thermal time explains the annual timing of spawning of Pacific herring across space and time. By testing this hypothesis, we identified developmental constants (thermal constants of spawning) that can be used to make spawning time predictions. We examined spatio-temporal changes in Pacific herring spawning time over a 69 yr period (1941-2010) across 6 regions off British Columbia (BC), Canada. We estimated the degree-days (DD, °C-days) from the onset of gonadal maturation to spawning by combining spawning time estimates with distribution-specific temperature estimates. We then fitted models to explore how DD to spawning can be used to explain observed spawning time patterns across space and time and identified temperature-independent sources of variability (e.g. adaptive differences among regions, spawner size). We found that, even though Pacific herring often spawned ∼5 d later with each increasing degree in latitude, the average thermal time in DD to spawning was ∼1700°C-days. We also found that DD to spawning explains linear variation in spawning time across years for some regions of the BC Pacific herring. Thermal time models can aid in predictions of environmental responses and forecasts of life-history phenology in a changing climate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".