Evolution of movement rate increases the effectiveness of marine reserves for the conservation of pelagic fishes
Bibliographic record
Abstract
Current debates about the efficacy of no-take marine reserves (MR) in protecting large pelagic fish such as tuna and sharks have usually not considered the evolutionary dimension of this issue, which emerges because the propensity to swim away from a given place, like any other biological trait, will probably vary in a heritable fashion among individuals. Here, based on spatially explicit simulations, we investigated whether selection to remain in MRs to avoid higher fishing mortality can lead to the evolution of more philopatric fish. Our simulations, which covered a range of life histories among tuna species (skipjack tuna vs. Atlantic bluefin tuna) and shark species (great white sharks vs. spiny dogfish), suggested that MRs were most effective at maintaining viable population sizes when movement distances were lowest. Decreased movement rate evolved following the establishment of marine reserves, and this evolution occurred more rapidly with higher fishing pressure. Evolutionary reductions in movement rate led to increases in within-reserve population sizes over the course of the 50 years following MR establishment, although this varied among life histories, with skipjack responding fastest and great white sharks slowest. Our results suggest the evolution of decreased movement can augment the efficacy of marine reserves, especially for species, such as skipjack tuna, with relatively short generation times. Even when movement rates did not evolve substantially over 50 years (e.g., given long generation times or little heritable variation), marine reserves were an effective tool for the conservation of fish populations when mean movement rates were low or MRs were large.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".