Interannual variability in the length–weight relationship can disrupt the abundance–biomass correlation of sea scallop ( <i>Placopecten magellanicus</i> )
Bibliographic record
Abstract
Abstract Fisheries stock assessments often rely on biomass estimates derived from abundance-at-length data and an assumed length–weight relationship (LWR) to establish stock status, which, relative to reference points, informs decisions on harvest rates. While biomass is typically assumed to reflect stock abundance, this assumption can be challenged by abrupt and sustained shifts in the body size of individuals within a stock, such as those arising from environmentally driven changes in the LWR. In the Bay of Fundy, Canada, the sea scallop (Placopecten magellanicus) has recently shown significant interannual variation in its LWR, with corresponding fluctuations in biomass. Using four spatio-temporal generalized additive mixed models, we explore the influence of spatio-temporal and environmental factors on the LWR, shell heights (i.e. size), abundance, and biomass of scallops in the Bay of Fundy, from 2011 to 2023. Results demonstrate that there was a significant shift in the LWR in 2023, which resulted in a large biomass increase (82%), despite only modest increases in shell heights (2%) and abundance (7%). Annual shifts in LWRs can therefore affect the correlation between abundance and biomass. We discuss the broader implications of this finding for stock assessments that depend on a LWR, particularly in the context of climate change and increased volatility in environmental conditions.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".