The Extent and Spatial Scale of Connectivity Among Reef Fish Populations: Implications for Marine Protected Areas Designated for Fisheries Enhancement
Bibliographic record
Abstract
Enthusiasm for the use of no-take marine protected areas (MPAs) as management tools for the protection and enhancement of coral reef fishes is widespread. However, evidence that such marine reserves actually enhance fishery yields is limited, primarily because of difficulties in quantifying the exchange of individuals—especially larvae—between local populations within and outside the protected area. Knowledge of the extent and spatial scale of this connectivity is of vital importance for the effective design and implementation of marine reserves intended as fishery management tools. We review our current understanding of connectivity among coral reef populations, including the role of important determining factors such as pelagic larval duration, larval behavior, and hydrodynamics. We also discuss artificial and natural tagging methods that potentially can be used to track movements of larvae between marine reserves and surrounding waters. To illustrate the application of such methods, we discuss ECONAR (Ecological CONnections Among Reefs), a new, regional-scale research project designed to measure the extent of connectivity among populations of coral reef fishes in the Mesoamerican Barrier Reef System.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".