Quantifying marine larval dispersal to assess MPA network connectivity and inform future national and transboundary planning efforts
Bibliographic record
Abstract
Abstract A Marine Protected Area (MPA) network, in which multiple reserves are designated in a region, can promote the protection of biodiversity across space. To be effective as a network, the design must consider whether MPAs are likely to be connected through the movement of individuals of species of interest. Additionally, network design may explicitly incorporate design features that promote biodiversity in unprotected habitats through the dispersal or spillover of multiple species. Patterns of dispersal and the ability of MPAs to function as an interacting network, however, are difficult to estimate at broad and transboundary spatial scales, and therefore connectivity is often not fully integrated in the design and assessment of MPA networks. Here, we model the dispersal of multiple nearshore species to estimate the potential connectivity of the existing MPAs in British Columbia, Canada, including connections to MPAs in the United States by simulating dispersal using a biophysical model with regional oceanographic currents. We found that MPAs in BC potentially meet connectivity design criteria for nearshore invertebrate species: the majority of MPAs (65-90%) are likely to exchange individuals (i.e. functional connectivity) and support persistent metapopulations, and more than half the unprotected coast (55-85%) receives a large proportion of the larvae produced in MPAs. Furthermore, we found that species’ dispersal abilities and the level of exposure of an MPA to open ocean can predict dispersal distance when we account for the random effects of dispersal location and season. Therefore, future predictions of connectivity are possible based on these core biological and physical attributes, without running new simulations. Together, these analyses provide a robust and novel assessment of multi-species connectivity that can support the design of new MPAs with transboundary connectivity on the northwest coast of North America.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".