Roles of the Red List of Ecosystems in the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
The Kunming-Montreal Global Biodiversity Framework (GBF) of the UN Convention on Biological Diversity set the agenda for global aspirations and action to reverse biodiversity loss. The GBF includes an explicit goal for maintaining and restoring biodiversity, encompassing ecosystems, species and genetic diversity (goal A), targets for ecosystem protection and restoration and headline indicators to track progress and guide action1. One of the headline indicators is the Red List of Ecosystems2, the global standard for ecosystem risk assessment. The Red List of Ecosystems provides a systematic framework for collating, analysing and synthesizing data on ecosystems, including their distribution, integrity and risk of collapse3. Here, we examine how it can contribute to implementing the GBF, as well as monitoring progress. We find that the Red List of Ecosystems provides common theory and practical data, while fostering collaboration, cross-sector cooperation and knowledge sharing, with important roles in 16 of the 23 targets. In particular, ecosystem maps, descriptions and risk categories are key to spatial planning for halting loss, restoration and protection (targets 1, 2 and 3). The Red List of Ecosystems is therefore well-placed to aid Parties to the GBF as they assess, plan and act to achieve the targets and goals. We outline future work to further strengthen this potential and improve biodiversity outcomes, including expanding spatial coverage of Red List of Ecosystems assessments and partnerships between practitioners, policy-makers and scientists. The Red List of Ecosystems is a headline indicator in the Kunming-Montreal Global Biodiversity Framework. This Progress illustrates how the Red List of Ecosystems can be used by Parties to the Framework to contribute to its implementation and monitoring.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".