Leveraging the Red List of Ecosystems for national action on coral reefs through the Kunming-Montreal Global Biodiversity Framework
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 progress of countries towards achieving this goal, and to guide action across the GBF’s targets. Using Kenyan coral reefs, we demonstrate how nations implementing the GBF, can use standardised estimates of ecosystem degradation from RLE assessments to support site-specific management decisions. We undertook a reef-by-reef analysis to evaluate the relative severity of 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 standardised estimates of state to identify reef sites which maintain a better condition through time relative to adjacent sites. Over the past 50 years, Kenya’s coral reefs have degraded across all four ecosystem components. At more than half the monitored sites both parrotfish and grouper abundance declined by more than 50%, while coral cover and macroalgae-coral ratio declined by at least 30%. This resulted in a Vulnerable threat status for coral reefs in Kenya based on degradation (under criterion D of the RLE). The temporal trends in coral cover revealed four sites which maintained an above average condition over their monitoring history (15-25 years). The results can guide management actions to contribute to at least nine 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. We also found that Marine Protected Areas and Locally Managed Marine Areas, which restrict fishing and control gears, had lower declines in groupers compared to unmanaged areas, providing further evidence for their expansion. The RLE has a key role to play in monitoring and meeting the goals and targets of the Global Biodiversity Framework, 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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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".