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Comparative Study
, 109 (39), 15835-40

Phylogenies Reveal Predictive Power of Traditional Medicine in Bioprospecting

Comparative Study

Phylogenies Reveal Predictive Power of Traditional Medicine in Bioprospecting

C Haris Saslis-Lagoudakis et al. Proc Natl Acad Sci U S A.


There is controversy about whether traditional medicine can guide drug discovery, and investment in bioprospecting informed by ethnobotanical data has fluctuated. One view is that traditionally used medicinal plants are not necessarily efficacious and there are no robust methods for distinguishing those which are most likely to be bioactive when selecting species for further testing. Here, we reconstruct a genus-level molecular phylogenetic tree representing the 20,000 species found in the floras of three disparate biodiversity hotspots: Nepal, New Zealand, and the Cape of South Africa. Borrowing phylogenetic methods from community ecology, we reveal significant clustering of the 1,500 traditionally used species, and provide a direct measure of the relatedness of the three medicinal floras. We demonstrate shared phylogenetic patterns across the floras: related plants from these regions are used to treat medical conditions in the same therapeutic areas. This finding strongly indicates independent discovery of plant efficacy, an interpretation corroborated by the presence of a significantly greater proportion of known bioactive species in these plant groups than in random samples. We conclude that phylogenetic cross-cultural comparisons can focus screening efforts on a subset of traditionally used plants that are richer in bioactive compounds, and could revitalize the use of traditional knowledge in bioprospecting.

Conflict of interest statement

The authors declare no conflict of interest.


Fig. 1.
Fig. 1.
Phylogenetic clustering of medicinal floras. Maximum likelihood phylogenetic trees of the floras of the Cape of South Africa (A), Nepal (B), and New Zealand (C), including 80%, 86%, and 88% of the local flora at the genus level, respectively. Traditional medicinal plant use is not scattered randomly, but is concentrated in certain parts of the phylogenetic trees (red branches).
Fig. 2.
Fig. 2.
Concentration of medicinal plants in phylogenetic hot nodes. Blue and orange bars show percentage of total and medicinal flora included in hot nodes, respectively. Green bars show percentage gain in medicinal plants included in hot nodes. Gain is the difference between the number of medicinal plants in hot nodes, and the number of medicinal plants expected in a random sample of the same size. Within region data (A and B) show the average from the separate values for each of the three regions (Nepal, the Cape of South Africa, and New Zealand). Among regions data (C and D) show the average of pair-wise comparisons on a combined phylogenetic tree of the three floras. These comparisons assessed whether hot nodes from one region can predict medicinal plant use from the other two. Both within region (A and B) and among regions (C and D) hot nodes contain a higher percentage of the medicinal than of the total flora. In all cases (A–D) there is a gain in medicinal hits, compared with a random sample. We show medicinal plants are concentrated in few hot nodes, which overlap noticeably among regions.

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