Spatial incongruence among hotspots and complementary areas of tree diversity in southern <scp>A</scp>frica
Bibliographic record
Abstract
Abstract Aim Biodiversity hotspots have important roles in conservation prioritisation, but efficient methods for selecting among them remain debated. Location Southern Africa. Methods In this study, we used data on the dated phylogeny and geographical distribution of 1400 tree species in southern Africa to map regional hotspots of species richness (SR), phylogenetic diversity (PD), phylogenetic endemism (PE), species endemism (CWE), and evolutionary distinctiveness and global endangerment (EDGE). In addition, we evaluated the efficiency of hotspots in capturing complementary areas of species richness and phylogenetic diversity. We examined the spatial overlap among hotspots for each metric, and review how well one metric may serve as a surrogate for others. We then evaluated the effectiveness of current conservation areas in capturing these different facets of diversity and complementary areas. Lastly, we explored the environmental factors influencing the distribution of these diversity metrics in southern Africa. Results We reveal large spatial incongruence between biodiversity indices, resulting in unequal representation of PD, SR, PE, CWE and EDGE in hotspots and currently protected areas. Notably, no hotspot area is shared among all five measures, and 69% of hotspot areas were unique to a single diversity metric. Areas selected using complementarity are even more dispersed, but capture rare diversity that is overlooked by the hotspot approach. Main conclusions An integrative approach that considers multiple facets of biodiversity is needed if we are to maximise the conservation of tree diversity in southern Africa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".