Implementing a 30 % area-based target could achieve near-complete biodiversity representation in the Hengduan Mountains, China
Bibliographic record
Abstract
Despite their limitations, it is generally accepted that area-based conservation targets are essential. We explored the potential gains in biodiversity representation if the Kunming-Montreal 30% target was implemented to optimal benefit in the Hengduan Mountains (HDM) bioregion, China. Using high high-resolution (5×5 km²) seed plant species distribution and phylogeny, we created a composite index of biodiversity using six commonly used metrics. These priority biodiversity zones were then enhanced by inclusion of centers of paleo- and neo-endemism identified using the CANAPE method. High-value zones of biodiversity and endemism were then compared with existing nature reserve areas to identify priority conservation gaps. The existing nature reserves covering 12% of the HDM represent 57% of total bioregional taxonomic and 41% of phylogenetic diversity. If an additional 18% of total area were located to coincide with eight priority regions identified, we estimate representation of bioregional taxonomic and phylogenetic biodiversity would both exceed 96%. Explicitly incorporating priority biodiversity with centers of endemism showed these do not always coincide spatially and allowed more specific articulation of conservation aims and threatening processes. We conclude that even within this global biodiversity hotspot, with appropriate optimization a 30% area-based conservation target could provide near-complete representation of bioregional 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".