Spatial Planning Insights for Philippine Coral Reef Conservation Using Larval Connectivity Networks
Bibliographic record
Abstract
The marine habitats of the Philippines are recognized to be some of the most biodiverse systems globally yet only 1.7% of its seas are designated as marine protected areas (MPAs) with varying levels of implementation. Many of these MPAs were established based on local-scale conservation and fisheries objectives without considering larger-scale ecological connections. The connectivity of reefs through larval dispersal is important in the regional-scale resilience against anthropogenic disturbances and is considered a significant criterion in planning for MPAs. In this study, we provide insights into the delineation of ecologically connected MPA networks using larval dispersal modeling and network analysis. We characterized the network properties of the Philippine coral reefs, organized as 252 reef nodes, based on the larval connectivity networks of a branching coral, sea urchin, and grouper. We then evaluated the distribution of the existing 1,060 MPAs relative to the connectivity patterns. All reef nodes were found to be highly interconnected with a mean shortest path ranging from 1.96 to 4.06. Reef nodes were then ranked according to their relative importance in regional connectivity based on five connectivity indices. Despite the between-organism and between-index variability in rankings, there were reefs nodes, mostly located offshore and at major straits, which consistently ranked high. We found that the distribution of existing MPAs partially capture some of the regional connectivity functions but there is a spatial mismatch between the primarily coastal MPAs and the high-ranking reef nodes. Furthermore, network partitioning identified subnetworks and dispersal barriers. The existing MPAs were found to be disproportionately distributed to a few subnetworks and that the largest subnetworks do not contain the greatest number of MPAs. Considering these gaps, we suggest expanding the coverage of protected areas especially in underrepresented reef networks to meaningfully capture national-scale connectivity and meet global conservation objectives.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".