Sparse genetic data limit biodiversity assessments in protected areas globally
Bibliographic record
Abstract
Global conservation targets include protecting genetic diversity within species. Yet few studies have assessed whether protected areas (PAs) include genetically diverse populations across species globally. A first step is understanding the availability of population genetic data that could be used in these assessments. We surveyed georeferenced population‐level nuclear (as opposed to mitochondrial or plastid‐based) genetic data across continents and marine biomes (36,354 populations, 2809 species) and found substantial geographic and taxonomic gaps. Most data were concentrated in Europe and North America, with major gaps in Africa and Asia. For most taxonomic groups, data were available for <1% of described species. Globally, 52.08% of the total areal extent of PAs lacked genetically sampled populations. These gaps in data availability highlight the need for targeted genetic data collection, harmonization, and sharing to improve genetic diversity monitoring and conservation planning. Combined with proxy‐based genetic indicators, such data are needed to inform PA assessments, bolster area‐based conservation initiatives like 30 × 30, and support achievement of global genetic conservation targets.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".