Prioritizing Key Resilience Indicators to Support Coral Reef Management in a Changing Climate
Bibliographic record
Abstract
Managing coral reefs for resilience to climate change is a popular concept but has been difficult to implement because the empirical scientific evidence has either not been evaluated or is sometimes unsupportive of theory, which leads to uncertainty when considering methods and identifying priority reefs. We asked experts and reviewed the scientific literature for guidance on the multiple physical and biological factors that affect the ability of coral reefs to resist and recover from climate disturbance. Eleven key factors to inform decisions based on scaling scientific evidence and the achievability of quantifying the factors were identified. Factors important to resistance and recovery, which are important components of resilience, were not strongly related, and should be assessed independently. The abundance of resistant (heat-tolerant) coral species and past temperature variability were perceived to provide the greatest resistance to climate change, while coral recruitment rates, and macroalgae abundance were most influential in the recovery process. Based on the 11 key factors, we tested an evidence-based framework for climate change resilience in an Indonesian marine protected area. The results suggest our evidence-weighted framework improved upon existing un-weighted methods in terms of characterizing resilience and distinguishing priority sites. The evaluation supports the concept that, despite high ecological complexity, relatively few strong variables can be important in influencing ecosystem dynamics. This is the first rigorous assessment of factors promoting coral reef resilience based on their perceived importance, empirical evidence, and feasibility of measurement. There were few differences between scientists' perceptions of factor importance and the scientific evidence found in journal publications but more before and after impact studies will be required to fully test the validity of all the factors. The methods here will increase the feasibility and defensibility of including key resilience metrics in evaluations of coral reefs, as well as reduce costs. Adaptation, marine protected areas, priority setting, resistance, recovery.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.001 |
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".