A review of the application of coral reef condition indicators in conservation planning
Bibliographic record
Abstract
Monitoring data have shown declines in the condition of coral reefs. However, it is unclear how such monitoring data are used to inform spatial conservation planning decisions to slow and prevent further declines. At least six of the Kunming–Montreal Global Biodiversity Framework Targets (1, 2, 3, 8, 14 and 21) would be improved if coral condition monitoring was better connected to spatial conservation planning. Here, we review published literature for coral condition monitoring in coral reef conservation planning to identify why, how, and which indicators are used to determine coral reef condition. Across 219 monitoring studies, we found 159 different indicators have been used to monitor coral reef condition. While 48% of the studies recommended the use of monitoring data to improve conservation outcomes, only 10% used condition data to inform a spatial conservation plan, and 14% used condition data to measure a plan’s impact. Although monitoring data are useful for improving conservation outcomes, they are rarely applied in conservation planning. By synthesising the existing data and protocols, we provide recommendations of how to address and change the current disconnect between coral reef monitoring and informing spatial actions focusing on data democratisation to see increased data in decision making.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".