Priorities for protected area expansion so nations can meet their Kunming‐Montreal Global Biodiversity Framework commitments
Bibliographic record
Abstract
Abstract As part of the Kunming‐Montreal Global Biodiversity Framework (K‐M GBF), signatory nations of the Convention on Biological Diversity (CBD) aim to protect at least 30% of the planet by 2030 (Target 3). This bold ambition has been widely celebrated and its implementation seen as pivotal for the overall success of K‐M GBF. However, given that many CBD signatory nations prioritised quantity (e.g., area) over quality (e.g., important areas for biodiversity) when attempting to meet their 2010 CBD Aichi protected area commitments, it is critical that nations focus on protecting those terrestrial, inland waters and marine areas that have the best chance of halting and reversing biodiversity loss and thus contribute to Goal A of the K‐M GBF. Here we provide a review on the type of areas that nations need to prioritise when implementing Target 3 that relates to area ‘quality’: areas of particular importance for biodiversity and ecosystem functions and services, are effectively conserved and managed through ecologically representative, well‐connected and equitably governed systems . We show that data is available for 12 distinct biodiversity conservation and ecosystem service elements that can be mapped and, if conserved, will (with appropriate management) help meet the broad intention of Target 3. We highlight examples of the planning methods available that can be utilized so these areas can be targeted for protection. We discuss issues related to trade‐offs regarding how to prioritise amongst them as well as to operationalise some of the vaguer concepts like ‘representation’ and ‘ecosystem functions and services’ so that they achieve the best outcomes for biodiversity.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".