Identifying biodiversity knowledge gaps for conserving South Africa’s endemic flora
Bibliographic record
Abstract
Abstract As a megadiverse country with a rapidly growing population, South Africa is experiencing a biodiversity crisis: natural habitats are being degraded and species are becoming threatened with extinction. In an era of big biodiversity data and limited conservation resources, conservation biologists are challenged to use such data for cost-effective conservation planning. However, while extensive, key genomic and distributional databases remain incomplete and contain biases. Here, we compiled data on the distribution of South Africa’s > 10,000 endemic plant species, and used species distribution modelling to identify regions with climate suitable for supporting high diversity, but which have been poorly sampled. By comparing the match between projected species richness from climate to observed sampling effort, we identify priority areas and taxa for future biodiversity sampling. We reveal evidence for strong geographical and taxonomic sampling biases, indicating that we have still not fully captured the extraordinary diversity of South Africa’s endemic flora. We suggest that these knowledge gaps contribute to the insufficient protection of plant biodiversity within the country—which reflect part of a broader Leopoldean shortfall in conservation data.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".