The global protected area network does not harbor genetically diverse populations
Bibliographic record
Abstract
Global biodiversity conservation targets include expanding protected areas and maintaining genetic diversity within species by 2030. However, the extent to which existing protected areas capture genetic diversity within species is unclear. We examined this question using a global sample of nuclear population-level genetic data comprising georeferenced genotypes from 2,513 local populations, 134,183 individuals, and 176 species of mammals and marine fish. We found that the existing protected area network does not capture populations with higher than average genetic diversity, and populations within protected areas are not more genetically differentiated than populations sampled elsewhere. This general trend does not preclude their effectiveness for specific species or regions currently, or in the future. While it may be desirable to prioritize regions with high genetic diversity when designating new protected areas, we caution that this will not be possible in many of the most at-risk regions of the world due to a lack of data. Continued focus on minimizing population decline and maintaining connectivity between protected areas remain essential considerations in area-based conservation for mediating genetic diversity loss.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".