4.6 Article

Phylogenetic Patterns of Extinction Risk in the Eastern Arc Ecosystems, an African Biodiversity Hotspot

Journal

PLOS ONE
Volume 7, Issue 10, Pages -

Publisher

PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0047082

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Funding

  1. University of Johannesburg through the African Centre for DNA Barcoding

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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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