Developing a framework to estimate dynamic reference points for American lobster using a thermally explicit spawning stock biomass/recruitment relationship
Bibliographic record
Abstract
Management of marine species often relies on biological reference points (BRPs): threshold and target indicators that trigger management actions. These BRPs are usually based on the biology of the species and rarely consider environmental effects. Under climate change, this is problematic as many biological/physiological relationships are assumed temporally static. This spurious assumption can lead to inaccurate management practices. To combat this problem, recent research highlights the importance of developing temporally dynamic BRPs. This paper sees the development of a dynamic BRP calculator to inform management of levels of spawning biomass necessary to sustain the desired future levels of recruitment given forecasted climate scenarios. We test this calculator on American lobster ( Homarus americanus) of the Gulf of Maine and Georges Bank. Results for lobster indicate a temperature-driven, but complex, spawning biomass/recruitment relationship. Increased warming scenarios appear to yield overall higher recruitment per spawning biomass and dynamic BRPs calculated under these scenarios reveal that smaller population levels can sustain management-desired recruitment levels. This study highlights the importance of developing dynamic BRPs for fisheries management in a changing environment.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".