Patterns of medicinal plant use: an examination of the Ecuadorian Shuar medicinal flora using contingency table and binomial analyses.
BACKGROUND
Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-represented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal of Ethnopharmacology 1, 111-119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-employed analysis, suffers from several statistical flaws.


OBJECTIVE
We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador).


METHODS
We first analyzed the Shuar data using Moerman's approach, modified to better meet requirements of linear regression analysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test for non-random selection of plants in individual families.


RESULTS
Modified regression models (which accommodated assumptions of linear regression) reduced R(2) to from 0.59 to 0.38, but did not eliminate all problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal proportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the null model. These 10 families are largely responsible for patterns seen at higher taxonomic levels.


CONCLUSIONS
Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.
Bennett
Bradley C
BC
Department of Biological Sciences and Center for Natural Products, Florida International University, Miami, FL 33199, USA. bennett@fiu.edu
Husby
Chad E
CE
D004484:Ecuador; D020834:Ethnopharmacology / Q000706:statistics & numerical data*; D029001:Herbal Medicine / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D008519:Medicine, Traditional; D010946:Plants, Medicinal