Phylogenetic Patterns of Extinction Risk in the Eastern Arc Ecosystems, an African Biodiversity Hotspot
Bibliographic record
Abstract
There is an urgent need to reduce drastically the rate at which biodiversity is declining worldwide. Phylogenetic methods are increasingly being recognised as providing a useful framework for predicting future losses, and guiding efforts for pre-emptive conservation actions. In this study, we used a reconstructed phylogenetic tree of angiosperm species of the Eastern Arc Mountains - an important African biodiversity hotspot - and described the distribution of extinction risk across taxonomic ranks and phylogeny. We provide evidence for both taxonomic and phylogenetic selectivity in extinction risk. However, we found that selectivity varies with IUCN extinction risk category. Vulnerable species are more closely related than expected by chance, whereas endangered and critically endangered species are not significantly clustered on the phylogeny. We suggest that the general observation for taxonomic and phylogenetic selectivity (i.e. phylogenetic signal, the tendency of closely related species to share similar traits) in extinction risks is therefore largely driven by vulnerable species, and not necessarily the most highly threatened. We also used information on altitudinal distribution and climate to generate a predictive model of at-risk species richness, and found that greater threatened species richness is found at higher altitude, allowing for more informed conservation decision making. Our results indicate that evolutionary history can help predict plant susceptibility to extinction threats in the hyper-diverse but woefully-understudied Eastern Arc Mountains, and illustrate the contribution of phylogenetic approaches in conserving African floristic biodiversity where detailed ecological and evolutionary data are often lacking.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".