Can data collected from marine protected areas improve estimates of life-history parameters?
Bibliographic record
Abstract
One of the argued research-related benefits of marine protected areas (MPAs) to fisheries management is that because there is no fishing inside of an MPA, it may be possible to precisely estimate the rate of natural mortality and better determine growth and maturity rates, parameters that are often prespecified in stock assessments. This study assesses the degree to which having an MPA increases the ability to estimate these parameters in a integrated stock assessment model, Stock Synthesis; how long it would take for these benefits to be reflected in improved estimates of management quantities; and the extent to which these improvements will be reduced or lost if there is movement of adults (i.e., spillover) from the MPA to the fished area. A two-area, age- and length-structured simulation model is used to examine these benefits on estimation performance for Stock Synthesis. Given the data and process assumptions explored here, the extent of improvement in estimation of growth and maturity parameters with data collected from MPAs is small, but estimation of natural mortality is substantially improved compared with directly estimating these parameters using fishery data. The extent of this improvement depends on the degree of spillover and the complexity of the assessment model.
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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 | 0.020 | 0.112 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".