Changes in Key Biodiversity Area networks following national comprehensive assessments
Bibliographic record
Abstract
Key Biodiversity Areas (KBAs) are sites of significance for the global persistence of biodiversity. Based on the Global Standard for the Identification of Key Biodiversity Areas (KBA Standard), published in 2016, sites are currently being assessed for KBA designation in a growing number of countries across the world. For these assessments, the KBA criteria are applied to all species and ecosystems with available data. We reviewed the first comprehensive assessments of 11 countries and compared the KBA network before and after assessments. The mean (SD) number of KBAs per country increased by 69.6% (102.1), and the mean total extent of KBAs per country increased by 164.2% (150.7). More than half of the KBAs in 2024 had >50% of their area outside the 2019 KBAs, indicating a substantial increase in KBA extent (54.0% [18.8] of KBAs). The mean proportion of each KBA covered by protected or conserved areas decreased from 56.2% (20.2) to 44.5% (15.5), owing to the incorporation of unprotected sites in the KBA network. On average, 41.1% (14.0) of sites in each country (mean 44.5 [46.4] sites per country) and 47.2% (20.5) of new KBA area after the assessment were completely unprotected, indicating that many of the new sites were not recognized in national protected area networks as significant for biodiversity before the assessment. Making a comprehensive assessment of KBAs increased the combined coverage of protected and conserved area networks from 25.4% (10.6) to 32.0% (13.1) in each country and thus contributed to reducing biodiversity loss. Therefore, comprehensive assessments of KBAs led to a substantially increased number and extent of recognized sites of importance for biodiversity published in the World Database of KBAs. Where such assessments have not been made, many important areas for biodiversity may be overlooked. We therefore encourage other nations to update their KBA networks to inform efforts to meet the goals and targets of the Kunming-Montreal Global Biodiversity Framework.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".