Effects of marine reserve age on fish populations: a global meta‐analysis
Bibliographic record
Abstract
Summary 1. Marine reserves are widely used for conservation and fisheries management. However, there is debate surrounding the speed of population recovery inside reserves and how recovery differs among species. Here, we determine how reserve effectiveness in enhancing fish density changes with reserve age. We also examine how the effects of protection vary between fished and non‐fished species and among species of different body sizes, which we use as a proxy for life history and ecology. 2. We meta‐analysed over 1000 ratios of fish densities (inside : outside reserves) taken from reserves of 1–26 years old from around the world. 3. Overall, older reserves were more effective than younger reserves, with fish densities increasing within reserves by ∼5% per annum relative to unprotected areas. Reserves older than 15 years consistently harboured more fish compared with unprotected areas; younger reserves were less reliably effective. 4. Large, fished species responded strongly and positively to protection in old (>15 years) and, unexpectedly, in new and young (≤10 years) reserves. Small, fished species and non‐fished species of all sizes showed weaker responses to protection that did not vary predictably with reserve age. 5. We expected large fish to respond more slowly to protection than smaller species. We also expected small species to decline after large fish had recovered (i.e. trophic cascades). Neither prediction was supported. 6. Synthesis and applications. Our meta‐analyses demonstrate that, globally, old reserves are more effective than young reserves at increasing fish densities. Our results imply that reserves should be maintained for up to 15 years following establishment, even if they initially appear ineffective. If protection is maintained for long enough, fish densities within reserves will recover and such benefits will be particularly pronounced for large, locally fished 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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.026 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".