Regression analyses of southern African ethnomedicinal plants: informing the targeted selection of bioprospecting and pharmacological screening subjects

• Published in: Journal of ethnopharmacology Vol. 119; no. 3; pp. 356 - 364
• Main Authors: Douwes, E., Crouch, N.R., Edwards, T.J., Mulholland, D.A.
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 28.10.2008; Amsterdam; New York: Elsevier
• Subjects: Africa, Southern; Animals; bioactive properties; bioassays; Bioprospecting; chemistry; classification; data analysis; Databases, Factual; Drug Evaluation, Preclinical; Drug Evaluation, Preclinical - statistics & numerical data; ethnobotany; Ethnomedicinal plant selection; flora; fractionation; Humans; indigenous knowledge; Least-Squares Analysis; medicinal plants; medicinal properties; Medicine, African Traditional; Models, Statistical; plant architecture; plant biochemistry; plant exploration and collection; Plants, Medicinal; Plants, Medicinal - chemistry; Plants, Medicinal - classification; Regression analyses; Regression Analysis; South Africa; Southern Africa; statistics & numerical data; Southern Africa; Africa, Southern; South Africa; Bioprospecting; Southern Africa; Ethnomedicinal plant selection; Regression analyses
• DOI: 10.1016/j.jep.2008.07.040
• ISSN: 0378-8741

Regression analyses of local medicinal floras are considered potentially useful when prioritising candidate plant taxa for pharmacological/bioprospecting investigations. To identify plant orders and subsequently families within the highly diverse ethnomedicinal flora of southern Africa, towards which biases by traditional healers are demonstrable. Taxa so identified can subsequently be weighted appropriately in semi-quantitative selection systems. Plant data sourced from the SANBI MedList database, the most comprehensive inventory of ethnomedicinal plants for the Flora of southern Africa region were grouped by order. A least squares regression analysis was applied to test the null hypothesis that the use of these plants by traditional healers is strictly random. Of ‘hot’ orders subsequently identified, characteristics of taxa therein were assessed to better determine the roles played by (i) growth forms, and (ii) inherent chemical diversity, in plant selections by ethnomedicinal practitioners. Analyses identified seven principally ‘hot’ plant orders (Malpigiales, Fabales, Gentianales, Asteraceae, Solanales, Malvales and Sapindales) and ‘hot’ families therein from a total of 55 regional ethnomedicinal orders. Five ‘cold’ ethnomedicinal orders (Rosales, Proteales, Poales, Asparagales and Caryophyllales) were shown to be significantly less represented in the medicinal flora than predicted. No clear growth form preferences were identified across orders. The presence of highly diverse bioactives was evident in the ‘hottest’ plant families from ‘hot’ plant orders. These 12 outliers identified by the regression analyses allowed for the falsification of the null hypothesis. Indications are that ‘hot’ taxa are selected traditionally on the basis of bioactivity, which is reflected in chemical diversity.