Leveraging the Red List of Ecosystems for action on coral reefs through the Kunming‐Montreal Global Biodiversity Framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Countries have committed to conserving and restoring ecosystems after signing the Kunming‐Montreal Global Biodiversity Framework (GBF). The IUCN Red List of Ecosystems (RLE) will serve as a headline indicator to track countries' progress toward achieving this goal. Using Kenyan coral reefs, we demonstrate how nations implementing the GBF can use standardized estimates of ecosystem degradation from RLE assessments to support site‐specific management decisions. We undertook a reef‐by‐reef analysis to evaluate the relative decline of four key ecosystem components over the past 50 years: hard corals, macroalgae, parrotfish, and groupers. Using the two benthic indicators, we also calculated standardized estimates of state to identify reef sites which maintain a better condition through time relative to adjacent sites. Kenya's coral reefs have degraded across all four ecosystem components. At more than half the monitored sites parrotfish and grouper abundance declined by more than 50%, while coral cover and macroalgae‐coral ratio declined by at least 30%. This resulted in an Endangered threat status for coral reefs in Kenya (under criterion D of the RLE). The results can guide management actions related to 9 of the 23 GBF targets. For example, we identified several sites with relatively healthy benthic and fish communities as candidate areas for protection measures under Target 3. The RLE has a key role to play in monitoring and meeting the goals and targets of the GBF, and our work demonstrates how using the wealth of data within these assessments can inform local‐scale ecosystem management and amplify the GBF's impact.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it