Alleviating Growth and Recruitment Overfishing through Simple Management Changes: Insights from an Overexploited Long-Lived Fish
Bibliographic record
Abstract
Abstract Growth and recruitment overfishing can co-occur when a fishery is subjecting small and immature fish in conjunction with adult fish to excessive exploitation rates such that it reduces the spawning biomass to the point where recruitment is significantly impaired. Such conditions are generally evident in open-access fisheries and are especially detrimental to long-lived species as they reach maturity at older ages. Here, we investigate the conditions of a long-lived lutjanid, Malabar Blood Snapper Lutjanus malabaricus, in Kuwait waters, for which catches declined by about 95% between 1995 and 2009 with negligible recovery afterward, yet exploitation rates are likely high and remain hardly regulated. Using an age-structured model and length and age distributions for over 47,000 Malabar Blood Snapper, we (1) underscore the impacts of recruitment and growth overfishing on fish biomass and catch and (2) demonstrate the efficacy of improving the size limit policy to address both issues. The proportion of small fish (length-classes <50 cm; age-classes = 1–4 years) in the catch rose from 40–50% in 1981 to over 70% between 1992 and 1998, indicating growth overfishing. Due to the selection of immature fish at high exploitation rates, the age-structured model showed that recruitment dropped virtually linearly with decreasing biomass by the mid-1990s, implying recruitment overfishing. Future scenarios show that by increasing the current mean age at vulnerability (1 year or 34 cm) to the age at first maturity (5 years or 61 cm), biomass and catch would increase by at least 300% and 130%, respectively, relative to status quo. Biomass would rebuild to higher levels if exploitation rates are regulated at sustainable levels. Our study highlights the importance of simple management changes in alleviating both types of overfishing, particularly when open-access conditions cannot be rapidly remedied due to weak management institutions.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".