The role of dispersal and demography in determining the efficacy of marine reserves
Bibliographic record
Abstract
Marine reserves are rapidly becoming an important tool for protection and recovery of depleted marine populations. However, the relative value of reserves to particular species is strongly dependent on its life history and behavior. We present a general conceptual framework for considering dispersal in simple demographic models. This framework includes transition matrices that consist of two age-structured models connected by transition probabilities for general migration, ontogenetic shifts, and recruitment in both a reserve and an unprotected area. We show that life history characteristics and perturbation analysis can be used to predict changes in growth rate due to a decrease in adult mortality resulting from a marine reserve for different levels and types of dispersal. Reserves enhanced growth rate for all species irrespective of net dispersal between the reserve and surrounding matrix habitat, but the efficacy of reserves relative to catch reduction depended significantly on the magnitude and sign of net dispersal across the reserve boundary. Patterns of reserve efficacy across different dispersal types were strongly species specific. Given the paucity of spatially explicit data for many marine systems and species, this simple approach represents a first step in applying life history information to advance current theory and provide practical considerations for marine reserve management.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| 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".