Incorporating natural variability in biological reference points and population dynamics into management of Atlantic salmon (Salmo salar L.) stocks returning to home waters
Bibliographic record
Abstract
Following advice from the International Council for the Exploration of the Seas and North Atlantic Salmon Conservation Organization, Irish salmon stocks have been managed on a river-by-river basis since 2007 with biological reference points (BRPs) based on maximum sustainable yield (MSY). A method for estimating BRPs at the river scale and the associated variability arising from observed variability in population structures and fecundities is presented here. Calculations of BRPs (referred to as conservation limits, CLs) were updated and their natural variability was included. Angling logbooks provided new river-specific weight data to give sea age and fecundity ranges, and improved estimates of river-wetted areas, to account for available nursery habitat for juveniles and river-specific carrying capacities, were introduced. To transport BRPs, Bayesian stock–recruitment analysis was re-run with an updated list of monitored rivers and smolt ages. Results were converted to salmon numbers per river in Monte Carlo simulations incorporating the variability in sea ages and fecundities. Minimum sample size rules were implemented to reduce sampling error effects. Results showed that average total CL increased by 7%, average one sea-winter (1SW) CL decreased by 5% and average multi-sea-winter (MSW) CL increased by 157%. Differences were attributed to increases in wetted areas, MSW proportions, and changes in both 1SW and MSW fecundities. While some changes were large, we believe that these updated CLs provide more accurate estimates and with associated confidence limits they are more robust, river-specific, and readily incorporated into stock assessments. As a significant improvement on their predecessors, they represent a major development for the conservation and management of salmon stocks. Additionally, the approach described is portable across stocks and has the potential to be implemented in other jurisdictions to improve the management of Atlantic salmon. Finally, this method of incorporating variation has application for the development of BRPs and management of other species.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".