Impact of the use of different temperature‐dependent larval development functions on estimates of potential large‐scale connectivity of American lobster
Bibliographic record
Abstract
Abstract The way in which the effect of temperature on the development rate of crustacean larvae is simulated in larval dispersal models potentially impacts the inferences made about population recruitment and connectivity. In this study, we contrasted dispersal and connectivity predictions made by a large‐scale dispersal model of American lobster (Homarus americanus H. Milne Edwards, 1837) larvae using three temperature‐dependent larval development functions proposed in the literature: (1) “warm‐source lab”, (2) “warm‐source field”, and (3) “cold‐source lab”. Differences in predictions using each function were contrasted in the northern (colder) and southern (warmer) portions of the species' range. Using these different development functions resulted in significant and marked differences (61.3–162.4 km in the north and 30.9–81.9 km in the south) in the distances dispersed by larvae from hatch to settlement. In general, predicted self‐seeding, retention, and local connectivity were increased, and predicted connectivity among distant locations was decreased, when a function predicting faster development was used. The field‐derived function predicted much less connectivity and decreased dispersal overall than both lab‐derived functions. The cold‐source lab function predicted more retention in northern regions, but less in southern regions, than the warm‐source lab function. Our findings indicate the need for more studies to quantify the rate at which lobster larvae develop in nature, including how this may vary over space and time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".