Evaluating the design of the first marine protected area network in Pacific Canada under a changing climate
Bibliographic record
Abstract
Marine protected area (MPAs) networks can buffer marine ecosystems from the impacts of climate change by allowing species to redistribute as conditions change and by reducing other stressors. There are, however, few examples where climate change has been considered in MPA network design. In this paper, we assess how climate change considerations were integrated into the design of a newly released MPA network in the Northern Shelf Bioregion in British Columbia, Canada, and then evaluate the resulting network against projected physical and biogeochemical changes and biological responses. We found that representation, replication, and size and spacing recommendations integrated into the design phase were met in most cases. Furthermore, despite varying degrees of projected changes in temperature, dissolved oxygen, and aragonite saturation across the MPA network, suitable habitat for demersal fish species is projected to remain in the network despite some redistribution among sites. We also found that mid-depth MPAs are particularly important for persistence, as fish are projected to move deeper to avoid warming in shallower areas. Our results highlight that a representative MPA network with adequate replication, that incorporates areas of varying climate change trajectory, should buffer against the impacts of climate change.
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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 | 0.005 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".