The use of connectivity in the design of networks of marine protected areas
Bibliographic record
Abstract
Marine protected areas (MPAs) are an area-based conservation strategy commonly used to safeguard marine biodiversity and ecosystem services. Population connectivity governs the exchange of individuals among spatially fragmented habitats and is an essential criterion in the design of MPAs. However, detailed computational methods for connectivity are inconsistently applied in management decisions. We reviewed the scientific and management literature to explore the use of connectivity in MPAs located in countries with advanced marine spatial planning. Only 7.8% of 739 MPAs considered connectivity as an ecological criterion, although it has been increasingly used since 2007, suggesting progress in spatial conservation planning towards the use of ecological conservation objectives. In most cases, connectivity was measured implicitly using either rules of thumb or size and spacing guidelines. Of the MPAs that considered connectivity, 67% were for state marine conservation areas or reserves in California and commonwealth marine reserves in Australia. This pattern indicates substantial geographic biases and significant differences in conservation planning and prioritization among countries. We suggest that the incorporation of connectivity in conservation planning needs to become more accessible to practitioners. Prioritizing connectivity as an ecologically important criterion in MPA design will more adequately address metapopulation persistence and recovery.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".