Quantifying marine larval dispersal to assess MPA network connectivity and inform future national and transboundary planning efforts
Bibliographic record
Abstract
A marine protected area (MPA) network of multiple reserves can protect biodiversity across space, but to be effective, network configuration should support dispersal among MPAs as well as spillover to unprotected habitats. The ability of MPAs to function as an interacting network of populations connected by dispersal, however, is difficult to estimate at broad spatial scales, and therefore, connectivity is often not integrated in MPA design. Here, we simulate passive larval dispersal using a biophysical model to estimate potential transboundary network connectivity of MPAs in western Canada and United States. Drift time was varied to represent generic functional groups of nearshore species with planktonic larvae and sedentary adult stages. We found that MPAs potentially act as an interacting network and meet connectivity design criteria for nearshore invertebrate species, many MPAs (65%–90%) possibly exchange individuals, and a third of larvae from MPAs (25%–32%) source areas of the unprotected coast. This analysis provides a first approximation of multispecies connectivity to inform ongoing transboundary MPA design, and it can be used as a foundation for future model development.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".