Connectivity modelling of areas closed to protect vulnerable marine ecosystems in the northwest Atlantic
Bibliographic record
Abstract
Over the course of the past decade, in response to United Nations General Assembly resolutions calling for the protection of vulnerable marine ecosystems (VMEs), the Northwest Atlantic Fisheries Organization has closed 14 areas around the high-seas portion of Grand Bank and Flemish Cap to protect deep-sea coral and sponge habitats from impacts by bottom-contact fishing gears. Structural and functional connectivity for those areas were not explicitly considered in the area-selection process. We applied a particle-tracking model in each of four seasons to produce dispersal trajectories at the surface and 100 m from start points within the closed areas. These were run in forecast and hindcast modes to identify dispersal kernels. Currents at the surface, 100 m, 1000 m and “on bottom” were examined under an independent model (NEMO) to infer structural connectivity among the areas at relevant depths not available in the particle-tracking model. Spawning times and planktonic larval duration of the dominant sponges, sea pens and gorgonian corals were then considered to evaluate the trajectories as bio-physical models, while species distribution models identified potential source populations from hindcast projections. Five of the 14 areas, including the three largest closures, showed particle retention, with three others showing retention within 10 km of their boundaries. The regional pattern of currents and their topographic forcing emerged as a strong structuring agent. A system of weakly-connected closed areas to protect sea pen VMEs on Flemish Cap was identified. The conducted approach illustrates the added value of assessing/modelling networking properties when designing MPAs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".