Surplus production dynamics in declining and recovering fish populations
Bibliographic record
Abstract
Surplus production rates predicted by simple biomass dynamics models are generally expected to follow a simple dome-shaped pattern as population size changes and to show similar trajectories during population decline and recovery. Age-structured models, however, predict substantially lower surplus production rates during population recovery than during decline because of reduced mean fecundity, unless recruitment compensation is very strong. Ecosystem models like Ecosim predict more complex patterns, with reduced production during recoveries due to both age-structure effects and cultivation–depensation effects related to changes in competitor and predator abundances. Production-driven recoveries, where surplus production per biomass is higher during recovery than decline, are predicted in cases where there has been substantial change in overall ecosystem productivity or community structure. 110 case examples illustrate that simple, repeatable relationships between stock size and production are uncommon, and the most common pattern is production-driven change in stock size, where changes in production rate apparently independent of stock size then drive stock increase or decrease. We conclude that nonstationarity in productivity needs to be considered as part of population rebuilding and that empirical estimates of surplus production may provide insight in this process.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".