Medicinal plants have been used by local communities to treat all sorts of diseases, and this unique indigenous knowledge has been documented in various studies. However, using this vast knowledge to formulate and test hypothesis in ethnobotany is not yet a common practice in the discipline despite recent calls for more hypothesis-driven ethnobotanical researches. Here, we collected ethnobotanical data on 811 woody plant species in the Mpumalanga Province of South Africa to test the non-random hypothesis of medicinal plant selection, which predicts a positive correlation between the size of plant families and the number of medicinal plants in the families. We tested this hypothesis by fitting the commonly used simple linear regression model and the negative binomial model. Our analysis confirmed the hypothesis and revealed that some plant families are over-utilised—i.e. contain more medicinal plants than expected. The identification of over-utilised families is the first step towards the prioritisation of research efforts for drug discovery. The proportion of over-utilised families ranges from 50% (linear regression with untransformed data) and 55% (linear regression after log–log transformation) to 34% (negative binomial model). With the simple linear model and untransformed data, the top over-utilised families are Fabaceae (residual = + 34.44), Apocynaceae (+ 5.82) and Phyllanthaceae (+ 5.53). The log-transformed model confirms these three families as the top over-utilised families but in a slightly different sequence: Fabaceae (+ 1.55), Phyllanthaceae (+ 0.83) and Apocynaceae (+ 0.79). However, using the negative binomial model, Fabaceae is no longer even part of the top 10 over-utilised families, which are now Phyllanthaceae (+ 2.09), Apocynaceae (+ 1.51), Loganiaceae (+ 1.48), Rhamnaceae (+ 1.48), Sapotaceae (+ 1.48), Oleaceae (+ 1.39), Salicaceae (+ 1.39), Clusiaceae (+ 1.30), Boraginaceae (+ 1.28) and Lamiaceae (+ 1.18). This suggests that the relative medicinal value of some families may have been over-estimated in comparison with others. Our study is an illustration of the need to apply appropriate model while testing ethnobotanical hypotheses to inform priority setting for drug discovery.
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The first author gratefully acknowledges funding from the Global Excellence and Stature (GES) and a PhD merit bursary both offered by the University of Johannesburg, in South Africa. We acknowledge the South Africa’s National Research Foundation for funding (Grants Nos: 103944; 112113; 111195) to Dr K. Yessoufou.
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Diagnostic plots showing non-normality of the residuals of untransformed data (XLS 122 kb)
Diagnostic plots showing improved normality of the residuals of untransformed data (PDF 27 kb)
Checklist of all plants recorded and their medicinal status in Mpumalanga province, South Africa. 1, at least one medicinal use is documented for a species in the province; 0, no known medicinal use is recorded for a givens species (PDF 27 kb)
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Muleba, I., Yessoufou, K. & Rampedi, I.T. Testing the non-random hypothesis of medicinal plant selection using the woody flora of the Mpumalanga Province, South Africa. Environ Dev Sustain 23, 4162–4173 (2021). https://doi.org/10.1007/s10668-020-00763-5
- Ethnobotanical hypothesis
- Generalised linear model with negative binomial