Accounting for non-stationary stock–recruitment relationships in the development of MSY-based reference points
Bibliographic record
Abstract
Asbtract Stock–recruitment relationships (SRRs) may vary over time due to ecological and anthropogenic impacts, challenging traditional approaches of calculating maximum sustainable yield (MSY)-based reference points that assume constant population traits. We compare seven methods to calculate MSY, FMSY and BMSY by modelling constant, stochastic (uncorrelated), and autocorrelated SRRs using simulations and two case studies [Atlantic cod (Gadus morhua) and American plaice (Hippoglossoides platessoides) on the Grand Bank off Newfoundland, Canada]. Results indicated that the method used to model SRRs strongly affected the temporal pattern of recruitment projection, and the variations generated by autocorrelated SRRs were more similar to observed patterns. When the population productivity had low-frequency and large-magnitude variations, stochastic SRRs generated greater MSY and FMSY estimates than constant or autocorrelated SRRs, while no consistent pattern of BMSY was detected. In the case studies, stochastic and autocorrelated SRRs produced asymmetric relationships between fishing mortality and yield, with higher risk of overfishing by going beyond FMSY. Overall, our results suggest that caution should be taken when calculating MSY-based reference points in highly dynamic ecosystems, and correctly accounting for non-stationary population dynamics could, therefore, lead to more sustainable fisheries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".