Literature DB >> 22580039

An imprecise probability approach for the detection of over and underused taxonomic groups with the Campania (Italy) and the Sierra Popoluca (Mexico) medicinal flora.

Caroline S Weckerle1, Stefano Cabras, Maria Eugenia Castellanos, Marco Leonti.   

Abstract

AIM OF THE STUDY: We use the IDM model to test for over- and underuse of plant taxa as source for medicine. In contrast to the Bayes approach, which only considers the uncertainty around the data of medicinal plant surveys, the IDM model also takes the uncertainty around the inventory of the flora into account, which is used for the comparison between medicinal and local floras.
MATERIALS AND METHODS: Statistical analysis of the medicinal flora of Campania (Italy) and of the medicinal flora used by the Sierra Popoluca (Mexico) was performed with the IDM model and the Bayes approach. For Campania 423 medicinal plants and 2237 vascular plant species and for the Sierra Popoluca 605 medicinal plants and 2317 vascular plant species were considered.
RESULTS: The IDM model (s=4) indicates for Campania the Lamiaceae and Rosaceae as overused, and the Caryophyllaceae, Poaceae, and Orchidaceae as underused. Among the Popoluca the Asteraceae and Piperaceae turn out to be overused, while Cyperaceae, Poaceae, and Orchidaceae are underused. In comparison with the Bayes approach, the IDM approach indicates fewer families as over- or underused.
CONCLUSIONS: The IDM model leads to more conservative results compared to the Bayes approach. Only relatively few taxa are indicated as over- or underused. The larger the families (n(j)'s) are, the more similar do the results of the two approaches turn out. In contrast to the Bayes approach, small taxa with most or all species used as medicine (e.g., n(j)=2, x(j)=2) tend not to be indicated as overused with the IDM model.
Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Mesh:

Year:  2012        PMID: 22580039     DOI: 10.1016/j.jep.2012.05.002

Source DB:  PubMed          Journal:  J Ethnopharmacol        ISSN: 0378-8741            Impact factor:   4.360


  3 in total

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  3 in total

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