Integrating ecosystem connectivity into the design of marine protected area networks
Bibliographic record
Abstract
Marine protected area (MPA) networks are commonly designated to achieve ecosystem-level conservation objectives. To design MPA networks capable of achieving such objectives, there has been a strong emphasis placed on the importance of incorporating connectivity into their design. Approaches to incorporate connectivity, however, have primarily been developed from a population-based, biotic perspective that overlooks the abiotic components of ecosystems. Ecological evidence suggests that ecosystem connectivity - the movement of materials, including organisms, detritus, and inorganic nutrients, between ecosystems - can be a fundamental driver of ecosystem dynamics, emphasizing the need for MPA network design strategies to adopt a broader, ecosystem-based perspective on connectivity. Here, we propose a conceptual framework for integrating ecosystem connectivity into the design of MPA networks, using the best available data. Through the application of our framework to a case study on the Pacific coast of Canada, we present the current state of knowledge regarding benthic ecosystem connectivity in temperate marine environments. We highlight potential interdependencies between benthic marine ecosystems, and present evidence-based rules of thumb for integrating ecosystem connectivity into the design of MPA networks. We discuss our findings in relation to Canada’s MPA network objectives and design guidelines.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".