Increased environmentally driven recruitment variability decreases resilience to fishing and increases uncertainty of recovery
Bibliographic record
Abstract
Abstract Independent of the effects of spawning-stock biomass (SSB), environmental variability in juvenile production, driven by factors such as temperature and food supply, have considerable potential to influence population resilience to fishing and depletion. Here, we analyse 18 time-series of Atlantic cod (Gadus morhua) stocks and empirically estimate this “environmental variability” in recruit-per-spawner (RPS) ratios. We then investigate the role of environmental recruitment variability on population resilience to fishing and ability to recover following depletion. To this end, cod population dynamics are simulated through a period of fishing, followed by a period of recovery, with alternative scenarios of recruitment variability and autocorrelation within it. The major effect of environmental recruitment variability is manifested through uncertainty. Firstly, the higher the recruitment variability, the shorter and less variable the time required for the population to decline below 15% of its carrying capacity, K. Secondly, higher variability leads to higher uncertainty in recovery time. Both these patterns are further strengthened by autocorrelation. Our findings suggest that increased environmental recruitment variability decreases resilience to fishing and increases uncertainty in recovery, thus challenging some traditional views that variability confers high productivity and rapid ability to recover from collapse.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".