“Big” concerns with high latitude marine protected areas (MPAs): trends in connectivity and MPA size
Bibliographic record
Abstract
The success of marine protected areas (MPAs) as fisheries management tools in tropical latitudes has generated interest in their applicability and potential elsewhere. Here we suggest that dispersal and gene flow in marine fish populations (a primary biological consideration for marine reserve design) increases with latitude. For example, north temperate fish species at latitudes between 40° and 45° had about three times greater dispersal potential (planktonic larval duration (PLD), n = 96 species) and genetic homogeneity (FST, n = 100 species) than fish species near equatorial regions. Using the PLD and FST relationships, dispersal increases at a rate of ~8% per degree of latitude north or south of the equator. Therefore tropical MPAs should not serve as direct scalar templates in other regions, but rather should be used as a basis against which higher-latitude MPAs should be scaled. However, a review of 429 existing MPAs indicates that no such changes in reserve size have been implemented with respect to latitude. Fisheries managers must be prepared and willing to implement MPAs at large scales in high latitudes, either as single reserves or in a network, or else we lose the legitimacy of a new and promising management tool for conserving marine biodiversity in cold ocean regions.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".