Assessing the potential for demographic restoration and assisted evolution to build climate resilience in coral reefs
Bibliographic record
Abstract
Interest is growing in developing conservation strategies to restore and maintain coral reef ecosystems in the face of mounting anthropogenic stressors, particularly climate warming and associated mass bleaching events. One such approach is to propagate coral colonies ex situ and transplant them to degraded reef areas to augment habitat for reef-dependent fauna, prevent colonization from spatial competitors, and enhance coral reproductive output. In addition to such "demographic restoration" efforts, manipulating the thermal tolerance of outplanted colonies through assisted relocation, selective breeding, or genetic engineering is being considered for enhancing rates of evolutionary adaptation to warming. Although research into such "assisted evolution" strategies has been growing, their expected performance remains unclear. We evaluated the potential outcomes of demographic restoration and assisted evolution in climate change scenarios using an eco-evolutionary simulation model. We found that supplementing reefs with pre-existing genotypes (demographic restoration) offers little climate resilience benefits unless input levels are large and maintained for centuries. Supplementation with thermally resistant colonies was successful at improving coral cover at lower input levels, but only if maintained for at least a century. Overall, we found that, although demographic restoration and assisted evolution have the potential to improve long-term coral cover, both approaches had a limited impact in preventing severe declines under climate change scenarios. Conversely, with sufficient natural genetic variance and time, corals could readily adapt to warming temperatures, suggesting that restoration approaches focused on building genetic variance may outperform those based solely on introducing heat-tolerant genotypes.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".