Marine spatial planning to enhance coral adaptive potential
Bibliographic record
Abstract
Abstract Ocean warming interacts with local stressors to negatively affect coral reefs. The adaptive capacity of reefs to survive these stressors is driven by ecological and evolutionary processes occurring at multiple spatial scales. Marine protected area (MPA) networks are one solution that can address both local and regional threats, yet the impacts of MPA network design on adaptive processes remains unclear. In this paper, we used an eco-evolutionary model to simulate hypothetical MPA configurations in the Caribbean, Southwest Pacific and Coral Triangle under projected warming. We found that protecting thermal refugia (i.e., cooler reefs) largely benefited corals inside the refugia while other reefs declined. In contrast, protecting a diverse habitat portfolio led to increased coral cover both inside and outside of the MPA network. We then quantified the thermal habitat and connectivity representations of reefs both inside and outside existing MPA networks across each region. Most strikingly, reefs in current MPA networks in the Southwest Pacific and Coral Triangle are approximately 2 °C cooler than reefs outside the MPA networks, while the Caribbean’s MPA network is approximately 1 °C warmer than reefs outside the network, based on mean temperatures from 2008-2018. These results suggest that the Caribbean MPA network is poised to protect sources of warm-adapted larvae but not destinations, and the opposite is true of the Southwest Pacific and Coral Triangle. Our results suggest that 1) by protecting sites with particular temperature and connectivity characteristics, marine spatial planning may alter eco-evolutionary processes to enhance or inhibit the adaptive capacity of a reef network and 2) the distribution, extent, and effectiveness of local interventions have the potential to affect regional distributions of coral cover beyond what would be expected from local benefits alone, due to the potentially wide-reaching effects of larval dispersal and gene flow.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".