Assessing climate change vulnerability in Canadian marine conservation networks: implications for conservation planning and resilience
Bibliographic record
Abstract
Marine protected areas (MPAs) are critical in safeguarding biodiversity and ecosystem functions under climate change. The long-term effectiveness of these static conservation measures will depend on how well they represent current and future ocean changes. Here, we use the Climate Risk Index for Biodiversity to assess the vulnerability representation of marine ecosystems within the Canadian marine conservation network (CMCN) under two divergent emissions scenarios. We found that MPAs best represent climate vulnerability in Atlantic Canada (85% representativity overall, and 93% in the Gulf of Saint Lawrence under low emissions), followed by the Pacific (78%) and Arctic (63%; lowest in the Eastern Arctic (41% under high emissions) regions). Notably, MPAs with lower climate vulnerability are proportionally overrepresented in the CMCN. Broad-scale geographic targets employed in the Scotian Shelf-Bay of Fundy network planning process achieve over 90% representativity of climate vulnerabilities, underscoring the importance of ensuring habitat representativity and geographic distribution in conservation planning to enhance climate resilience, even if not explicitly prioritized. Moving towards Canada’s target to protect 30% of its waters by 2030, prioritizing representativity and designation of MPAs in currently underrepresented climate-vulnerable regions may be crucial to enhancing the resilience of the CMCN amidst an ever-changing climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".