Networks of marine protected areas under fluctuating connectivity: The importance of within-MPA dynamics
Bibliographic record
Abstract
Abstract The design of marine protected areas (MPAs) has been optimized under assumptions of spatially and temporally homogeneous larval dispersal, despite complex spatiotemporal patterns displayed by ocean currents. Here we studied the effect of dispersal variability on the effectiveness of MPA networks across scales. We adopted a nested approach integrating the dynamics of both within and among MPA connectivity into a stochastic metapopulation model and first derived metapopulation persistence (required reproductive effort) and stability over MPA networks by partitioning within and among MPA contributions in relation to the spatial resolution of within-MPA connectivity. We applied this framework over a range of dispersal traits (spawning time and pelagic larval duration) and MPA network configurations, based on simulated biophysical connectivity along the northeast Pacific coast. Our results show how within-MPA dynamics affect predictions based on parameters of MPA networks such as MPA size, spacing, and pelagic larval duration. Increasing within-MPA spatial resolution predicted increasing population persistence and stability independently of other network properties. High-resolution within-MPA dynamics also predicted a negative relationship between species persistence and MPA spacing while that relationship was non-monotonic under low-resolution within-MPA dynamics. Our analysis also resolved the role of pelagic larval duration for scaling up within-MPA dynamics to MPA networks: species with short larval duration led to increasing network stability with MPA spacing while the opposite was observed for species with long larval duration. Our study stresses the importance of integrating fluctuating larval connectivity, both within and among MPAs, and more specifically suggest the benefit of small and nearby MPAs under increasing ocean variability.
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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 | 0.005 |
| 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 | 0.001 | 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".