Incorporating cyclone risk in the design of marine protected and conserved areas as an ecosystem‐based adaptation approach
Bibliographic record
Abstract
Marine protected and conserved areas (MPCAs) are promoted as an ecosystem-based adaptation (EbA) approach to increase community and ecosystem resilience to climate change. However, traditional approaches to MPCA design typically do not consider climate risk or habitat condition under a climate threat. We used the Great Sea Reef (GSR) in Fiji as a case study to develop a land-sea prioritization framework that links modeled sediment runoff from rainfall during extreme cyclone events to the probability of coral reefs being in good condition. We incorporated this information in an MPCA prioritization scenario intending to achieve 90% certainty of good-condition coral cover under cyclone risk while minimizing cost to fishers and meeting ecosystem conservation targets. We explored the trade-offs between sites selected for protection, the relative opportunity cost to fishers, and the representation of conservation feature targets between the MPCA scenario that included cyclone risk and a baseline scenario that did not. The cyclone risk scenario's best solution required larger areas of protection (5% more GSR area) than the baseline scenario and additional protection in areas with moderate to high probability of good-condition coral cover. Some areas prioritized for protection in the cyclone risk scenario had relatively high turbidity. Large sections around Vanua Levu were consistently selected for protection across both prioritization scenarios due to high concentrations of all ecosystem conservation features, particularly sea turtle feeding grounds. Overall, the cyclone risk MPCA design had a higher fisher opportunity cost but protected a larger amount of ecosystem conservation features and buffered against habitat condition uncertainty. We explored the potential outcomes of expanding on threat-avoidance and cost-effective conservation prioritization by including habitat responses to threats in the prioritization process. Our findings can inform MPCA design during EbA planning in regions at risk from climate change.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".