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Journal of Ethnopharmacology 116 (2008) 422–430

Patterns of medicinal plant use: An examinationof the Ecuadorian Shuar medicinal flora using

contingency table and binomial analyses

Bradley C. Bennett a,∗, Chad E. Husby a,b

a Department of Biological Sciences and Center for Natural Products,Florida International University, Miami, FL 33199, USAb Montgomery Botanical Center, Miami, FL 33156, USA

Received 18 September 2007; received in revised form 7 December 2007; accepted 9 December 2007Available online 23 December 2007

bstract

thnopharmacological relevance: Botanical pharmacopoeias are non-random subsets of floras, with some taxonomic groups over- or under-epresented. Moerman [Moerman, D.E., 1979. Symbols and selectivity: a statistical analysis of Native American medical ethnobotany, Journal ofthnopharmacology 1, 111–119] introduced linear regression/residual analysis to examine these patterns. However, regression, the commonly-mployed analysis, suffers from several statistical flaws.im of the study: We use contingency table and binomial analyses to examine patterns of Shuar medicinal plant use (from Amazonian Ecuador).aterials and methods: We first analyzed the Shuar data using Moerman’s approach, modified to better meet requirements of linear regression

nalysis. Second, we assessed the exact randomization contingency table test for goodness of fit. Third, we developed a binomial model to test foron-random selection of plants in individual families.esults: Modified regression models (which accommodated assumptions of linear regression) reduced R2 to from 0.59 to 0.38, but did not eliminatell problems associated with regression analyses. Contingency table analyses revealed that the entire flora departs from the null model of equal

roportions of medicinal plants in all families. In the binomial analysis, only 10 angiosperm families (of 115) differed significantly from the nullodel. These 10 families are largely responsible for patterns seen at higher taxonomic levels.onclusions: Contingency table and binomial analyses offer an easy and statistically valid alternative to the regression approach.2007 Elsevier Ireland Ltd. All rights reserved.

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eywords: Linear regression; Binomial analysis; Medicinal plants use patterns

. Introduction

A basic question that ethnobiology asks is, “What resourceso people use?” A far more interesting question is, “Why doeople use particular plant or animal species?” Few researchersddress this topic. The “Why” question encompasses a variety ofossible explanations at both proximate and ultimate levels. Forxample, Fittonia albivenis (Lindl. ex Veitch) Brummitt (Acan-

haceae) may be used to treat liver ailments because any of theollowing.

∗ Corresponding author. Tel.: +1 3053483586; fax: +1 3053481986.E-mail address: bennett@fiu.edu (B.C. Bennett).

aaba

378-8741/$ – see front matter © 2007 Elsevier Ireland Ltd. All rights reserved.oi:10.1016/j.jep.2007.12.006

dual analysis; Shuar

. Tradition (e.g., grandmother used it)

. Efficacy (e.g., the plant actually works)

. Diffusion (e.g., other nearby ethnic groups utilize the plant)

. Abundance and commonness (e.g., the plant is available andwidespread)

. Form–function relationship or doctrine of signatures (e.g.,the leaves resemble the liver)

. Taxonomic affiliation (e.g., other species of Acanthaceaeprovide useful medicines)

Here, we focus on the relationship between taxonomic affili-

tion and medicinal plant selection. Traditional pharmacopoeiasre not random selections of the available flora. Rather, they areiased across taxonomic ranks. Certain genera, families, orders,nd even higher taxonomic levels are more likely to be employed

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han others. Most analyses have concentrated on familialomparisons. Lamiaceae, Asteraceae, Poaceae, Fabaceae, Mal-aceae, Rutaceae, Apaiceae, Brassicaceae, and Rosaceae, forxample, dominate the list of introduced medicinal plants inorthern South America (Bennett and Prance, 2000). The other0 families contribute less than 50% of the species in the intro-uced pharmacopoeia.

Moerman (1979, 1991, 1996) introduced the use of linearegression analysis to examine patterns of medicinal plant useased on taxonomic affiliation. The method, which is easy tomploy, takes the following approach. (1) Linearly regress theumber of species in a family against the number of medici-al species in the family for a specific geographic region. (2)nterpret least squares line as a measure of average relation-hip between family size and number of medicinal species. (3)se regression residuals to assess medicinal over- or under-tilization of groups.

The regression approach seeks to quantify the degree to whichfamily has an unexpectedly high or low number of medicinal

pecies relative to the overall proportion of medicinal speciesn the flora (averaged by the least squares regression line). Auggested advantage of the regression approach in the analysisf medicinal floras is that species counts overemphasize largeamilies while the percentage of medicinal species per familyan overemphasize small ones (Moerman, 1989).

Moerman’s pioneering work inspired scores of other studiesncluding Kapur et al. (1992), Moerman et al. (1999), Leontit al. (2003), Bourbonnais-Spear et al. (2004), Shepard (2004),nd Treyvaud Amiguet et al. (2006). Remarkably similar pat-erns emerged. Asteraceae (1), Apaiceae (2), Rosaceae (4), andamiaceae (9) ranked among the top medicinal plant families

n North America (Moerman, 1996). Asteraceae (3), Lamiaceae22), and Apiaceae (33) were over utilized in Belize (Treyvaudmiguet et al., 2006). Asteraceae (1) and Lamiaceae (5) also

anked high in an analysis of a Mexican pharmacopoeia (Leontit al., 2003).

Despite the apparent convergence of results using theegression approach, these analyses suffer from significantethodological problems. Most importantly, the implicit goal of

he approach, to assess the degree to which a given family departsrom the expected proportion of medicinal species for the flora aswhole, is statistically inferential but the method is not. This isecause the regression method lacks tests of statistical hypothe-es and has no measure of uncertainty for estimates or controlf error rate associated with over- or under-representation. Theegression approach requires a subjective assessment of whatonstitutes a large or small residual. Moreover, there is no initialtatistical assessment of whether more families depart from theverall proportion for the flora than one would expect due tohance (i.e., no assessment of whether there is an associationetween family and the proportion of medicinal species). Thisnalysis is required before proceeding to analyses of individualamilies.

Contingency table and binomial tests provide simple, alter-ative methods to assess the patterns of medicinal plant usageor a given flora. Assessing whether distribution of medicinalpecies among families departs significantly from a homoge-

tate

opharmacology 116 (2008) 422–430 423

eous null distribution is an appropriate way to begin analyzingflora. This is analogous to analyzing the overall significance

f an ANOVA model before testing the significance of individ-al parameters within the model. Here we describe the use ofcontingency table analysis of the overall flora combined withinomial analyses of individual plant taxa to examine patternsf medicinal plant use with data from the Shuar of Ecuador.e examine patterns at the rank of family (using the most cur-

ent APG family circumscriptions) as well as those at higheraxonomic ranks.

. Methodology

.1. Data set

We analyzed data from the Shuar of Amazonian EcuadorBennett et al., 2002). The Shuar are one of five Jıvaroanroups in the eastern montana of Ecuador and Peru. Mosthuar reside in Ecuador’s Morona-Santiago Province. With bet-

er access to markets during the past few decades, the Shuarre expanding their subsistence base to include cattle, tim-er, and cash crops. Nonetheless, horticulture and hunting areignificant contributors to their livelihood and Manihot escu-enta Crantz (Euphorbiaceae) remains their most importantrop.

The Shuar use 210 species for medicine (Bennett et al.,002). Nine families (out of 119) provide 40% of their medicinalpecies: Lamiaceae, Solanaceae, Fabaceae, Malvaceae, Aster-ceae, Piperaceae, Rubiaceae, Cyperaceae, and Euphorbiaceae.everal species have multiple medicinal uses. Most notable areyperus articulatus L. and Cyperus prolixus Kunth (Cyper-ceae), Melissa officinalis L. (Lamiaceae), Piper peltatum L.nd Piper aduncum L. (Piperaceae), and Urera baccifera (L.)audich. ex Wedd. (Urticaceae).

.2. Regression approach

We first analyzed the data employing Moerman’s regressionpproach using Microsoft Excel’s LINEST linear regressionunction. The first model included all Shuar data. The sec-nd contained only angiosperm families (minus one outlier,hich had undue influence on regression parameters) and used

he number of non-medicinal plants per family (instead ofhe total number of species per family) as the independentariable. In a final model, we used a

√Y + 0.5 transforma-

ion of the dependent variable (number of medicinal plantser family) in an attempt to correct non-homogeneity of vari-nce.

.3. Contingency table approach—overall flora

Expected values were calculated under the null hypothesis

hat medicinal and non-medicinal species are allocated within

family according to the proportion of medicinal species inhe flora as a whole: expected number of species in a cat-gory = (total # of species in a family × (# of species in a

424 B.C. Bennett, C.E. Husby / Journal of Ethn

Table 1Sample contingency table from the Shuar data

Family Medicinal species Non-medicinal species Row totals

AcanthaceaeObserved 14 4 18Expected 14.57 3.43

AchariaceaeObserved 2 0 2Expected 1.62 0.38

AlliaceaeObserved 1 0 1

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ategory/total # of species)). For example, in a sample dataet including three families (Table 1), the expected numberf medicinal species in Acanthaceae is 14.57 (=18 × (17/21)).hus, the null hypothesis for the flora as a whole is: H0:i = si × (�mi/�si), where Mi is the expected number of medic-

nal species in family i and si is the number of speciesn family i, �mi is the total number of medicinal speciesn the flora, and �si is the total number of species in theora.

To assess the degree of departure of the flora from the homo-eneous null hypothesis (i.e., no association between family androportion of medicinal plants), we performed a Goodness ofit test, based on the classic chi-square goodness of fit statisticX2 = (observed-expected)2/expected)) using the free statisticaloftware package R (version 2.5.1, 2007, R Project for Statisticalomputing, R Code is available from authors). Because manyxpected values were less than 5 in our example (and in the Shuaredicinal flora as a whole), the typical chi-square distribution

pproximation is not reliable. However, an exact randomiza-ion test for Goodness of Fit (approximated via Monte Carloimulation) eliminated this problem (Agresti, 2001). A small p-alue for this test indicates a significant departure by the floras a whole from the null hypothesis of a uniform proportion ofedicinal species among families.

.4. Binomial approach—individual families (and higherevel taxa)

If the number of medicinal plants for total flora departsrom the null model, individual taxa can be examined. Our null

ypothesis (H0,i: Mi = pflora × si) is that plants belonging to fam-ly i are no more likely to be used medicinally than would be thease for the flora as a whole. In other words, the proportion ofedicinal plants in family i (pi) equals the proportion of medici-

Siet

able 2inear regression models for number of medicinal plants per family vs. number of sp

odel Data Dependent varia

All families MAngiosperm families (minus Lamiaceae) MAngiosperm families (minus Lamiaceae) SQRT (M + 0.5)

= number of species per family (S), M = number of medicinal species per family, SQ

opharmacology 116 (2008) 422–430

al plants in the total flora (pflora = �mi/�si). The two alternativeypotheses are H1: Mi > pflora × si and H2: Mi < pflora × si. Theest statistic under H0,i has a binomial (Si, pflora) distribution,here si is the size of family i. Using this approach, under0,i one treats each species within a family as an independenternoulli trial (a random trial with two possible outcomes), withrobability pflora of having a medicinal use.

Of the 851 randomly collected species, the Shuar utilized10 (ca. 25%) for therapeutic purposes. Multiplying 0.25 byhe number of species per taxon yielded the expected numberf medicinal species in that taxon. For example, five (of theine sampled species) of Amaranthaceae were used medicinally.he expected number of medicinal species in the family is 2.25

9 × 0.25). Since, the 2.75 difference between the observed andxpected values are positive, Amaranthaceae is numerically (butot necessarily statistically) over represented.

To test the significance of individual variation from a uniformroportion of medicinal plants among families, we calcu-ated binomial p-values for over- or under-representation using

icrosoft Excel’s BINOMDIST function. BINOMDIST yieldshe probability that there are X or fewer successes (a) andhe probability that there are exactly X number of successesb). The probability of X or more successes is (1 − a) + b (forur purposes, the number of “successes” equals the number ofedicinal species). Continuing with the Amaranthaceae exam-

le, the probability of five or more medicinal species is 0.0489.herefore, Amaranthaceae is significantly over represented at= 0.05. For taxa that had fewer medicinal species than pre-icted, we employed the binomial probability of X or feweruccesses.

.5. Taxonomy

Most analyses of medicinal plants have focused on plantamilies, often using dated family circumscriptions such asronquist (1981). During the 1990s, molecular data, espe-ially the plastid gene rbcL (e.g., Chase et al., 1993), providedew information that revolutionized phylogenetic reconstruc-ion. The Angiosperm Phylogeny Group (APG) published aeminal paper in 1998 that often differed with contemporaneouslassifications (e.g., Cronquist, 1981; Thorne, 1992; Takhtajan,997). The initial APG (1998) system comprised 462 fami-ies arranged in 40 putatively monophyletic orders and a fewonophyletic higher groups. APG II (2003) provided a revised

huar data from Bennett et al. (2002) according to APG fam-ly circumscriptions (Stevens, 2001, onwards) and used Soltist al. (2005) and Judd et al. (2002) to determine higher levelaxonomy.

ecies per family

ble Independent variable Regression R2

S M = 0.215S + 0.277 0.59S − M M = 0.219S + 0.506 0.45S − M M = 0.056S + 1.005 0.38

RT = square root.

B.C. Bennett, C.E. Husby / Journal of Ethn

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ig. 1. Medicinal species per family vs. total species per family (for all families).

. Results

.1. Regression analysis

Using the standard approach of Moerman, we generated threeignificant linear regressions (Table 2). The first regression,hich included all families and used the total number of specieser family (medicinal and non-medicinal species), accountedor 59% of the variation in the number of medicinal specieser family (Fig. 1). The second regression, using the number ofon-medicinal species per family as the predictor variable andemoving one outlier (Lamiaceae had a substantial influencen the regression) had lower explanatory value (R2 = 0.45). Toccommodate increasing variance of Y with increasing valuesf X, we transformed Y (number of medicinal species) using aY + 0.5 transformation. This improved the latter problem but

2 decreased to 0.38.

.2. Contingency table analysis

The exact Goodness of Fit Test on the contingency table forhe Shuar flora as a whole revealed a significant departure fromomogeneity (P < 0.001), indicating that medicinal species areot evenly distributed among families in the Shuar flora. Due tohis significant result, we proceeded with a binomial analysis toxamine deviations of individual families and orders from theull proportion of medicinal species.

.3. Binomial analysis

In the binomial analysis, only 10 of the 115 angiospermamilies differed significantly from the null model. Arecaceae,romeliaceae, Melastomataceae, and Sapotaceae were under

epresented. Amaranthaceae, Apiaceae, Costaceae, Lamiaceae,iperaceae, and Zingiberaceae were over represented (Table 3).f α increased from 0.05 to 0.1, Gesneriaceae and (0.07)

alvaceae (0.09) would contain more medicinal species thanredicted by the null model. The six over represented familiesat α = 0.05) can be ranked based on the difference betweenbserved and expected values in the following order: Lami-

4

opharmacology 116 (2008) 422–430 425

ceae, Piperaceae, Zingiberaceae, Apiaceae, Amaranthaceae,ostaceae.

When the three major angiosperm clades are considered,he monocots supplied fewer medicinal species than woulde expected due to under representation by non-Commelinidonocots. Magnoliids and Eudicots did not differ significantly

rom the null model (Table 4). In the Eudicots, the Caryophyl-id and Asterid clades were over-represented with respect toherapeutic species. The remaining three clades, Ranunculales,antales, and Rosids did not differ from the null model (Table 5).ithin the Rosids, only the diverse Myrtales clade, with

ewer medicines than expected, differed from the null model.he pattern within the asterids was more complex. Cornalesid not differ from null expectations. Ericales was underrep-esented, while both euasterid clades were over-representedTable 6).

The Shuar species comprise 38 orders. Of these, 27 did notiffer from the null model. Five were under represented andix were over represented (Table 7). Asparagales (monocot,on-commelinid), Arecales (Commelinid monocot), MyrtalesRosids), Sapindales (Eurosids II), and Ericales (Asterid andister to core asterids) were under represented. PiperalesMagnoliid complex), Zingiberales (Commeliniid monocot),aryophyllales (Caryophyllid), Malvales (Eurosids II), Lami-les (Euasterids I), and Apiales (Euasterids II) were overepresented. The number of medicinal plants in these cladesargely explains the patterns found in the higher clades.

. Discussion

.1. Regression analysis

The three significant linear regressions accounted for8–59% of the variation in the number of medicinal specieser family. The first regression, following Moerman’s stan-ard approach accounted for the greatest variation, but it muste regarded with caution. The predictor variable, number ofpecies, includes a component it is predicting (i.e., the num-er of medicinal species). The second regression, employingon-medicinal species per family as the predictor, still suffersrom the problem of increasing variance with increasing valuesf X. The

√Y + 0.5 transformation used in the final regression

artly alleviates this problem but it does not solve the problemf repeated values of Y, preventing use of parametric regressionethods.Sokal and Rohlf (1969) discuss the assumptions of inferential

inear regression analysis:

. The independent variable X is measured without error.

. The expected value for the variable Y for any given value Xis described by the linear function μy = α + βX.

. For any given values of X the Y are independently and nor-mally distributed. This relationship can be expressed by the

equation Y = α + βX + ε, where ε is a normally distributederror term with a mean of zero.

. The samples along the regression line are homescedastic, thatis, they have a common variance σ2. In other words, the vari-

426 B.C. Bennett, C.E. Husby / Journal of Ethnopharmacology 116 (2008) 422–430

Table 3Shuar medicinal plants arranged by family

Family S M E M − E Probabilitiesof M or fewer

Probabilitiesof M or more

Significance

Acanthaceae 14 4 3.5 0.50 0.74 0.48 nsAchariaceae 2 0 0.5 −0.50 0.56 1.00 nsAlliaceae 1 0 0.3 −0.25 0.75 1.00 nsAlstromeriaceae 1 0 0.3 −0.25 0.75 1.00 nsAmaranthaceae 9 5 2.3 2.75 0.99 0.05 MoreAmaryllidaceae 2 0 0.5 −0.50 0.56 1.00 nsAnacardiaceae 1 0 0.3 −0.25 0.75 1.00 nsAnnonaceae 5 2 1.3 0.75 0.90 0.37 nsApiaceae 4 4 1.0 3.00 1.00 0.00 MoreApocynaceae 7 2 1.8 0.25 0.76 0.56 nsAquifoliaceae 1 1 0.3 0.75 1.00 0.25 nsAraceae 31 5 7.8 −2.75 0.18 0.92 nsAraliaceae 1 0 0.3 −0.25 0.75 1.00 nsArecaceae 26 0 6.5 −6.50 0.00 1.00 FewerAristolochiaceae 1 1 0.3 0.75 1.00 0.25 nsAsteraceae 28 9 7.0 2.00 0.86 0.25 nsBalsaminaceae 2 0 0.5 −0.50 0.56 1.00 nsBegoniaceae 6 3 1.5 1.50 0.96 0.17 nsBignoniaceae 11 3 2.8 0.25 0.71 0.54 nsBixaceae 1 1 0.3 0.75 1.00 0.25 nsBoraginaceae 10 1 2.5 −1.50 0.24 0.94 nsBromeliaceae 20 0 5.0 −5.00 0.00 1.00 FewerBrunelliaceae 1 0 0.3 −0.25 0.75 1.00 nsBurseraceae 9 1 2.3 −1.25 0.30 0.92 nsCactaceae 4 1 1.0 0.00 0.74 0.68 nsCampanulaceae 2 1 0.5 0.50 0.94 0.44 nsCannabaceae 11 2 2.8 −0.75 0.46 0.80 nsCannaceae 2 0 0.5 −0.50 0.56 1.00 nsCaricaceae 5 2 1.3 0.75 0.90 0.37 nsCaryocaraceae 2 0 0.5 −0.50 0.56 1.00 nsCaryophyllaceae 1 1 0.3 0.75 1.00 0.25 nsCelastraceae 1 0 0.3 −0.25 0.75 1.00 nsChrysobalanceae 4 0 1.0 −1.00 0.32 1.00 nsCleomaceae 2 1 0.5 0.50 0.94 0.44 nsClusiaceae 9 1 2.3 −1.25 0.30 0.92 nsCombretaceae 1 1 0.3 0.75 1.00 0.25 nsCommelinaceae 8 3 2.0 1.00 0.89 0.32 nsConvolvulaceae 4 0 1.0 −1.00 0.32 1.00 nsCostaceae 3 3 0.8 2.25 1.00 0.02 MoreCucurbitaceae 14 2 3.5 −1.50 0.28 0.90 nsCyclanthaceae 4 1 1.0 0.00 0.74 0.68 nsCyperaceae 14 6 3.5 2.50 0.96 0.11 nsDioscoreaceae 1 0 0.3 −0.25 0.75 1.00 nsElaeocarpaceae 2 0 0.5 −0.50 0.56 1.00 nsEricaceae 7 2 1.8 0.25 0.76 0.56 nsEuphorbiaceae 18 6 4.5 1.50 0.86 0.28 nsFabaceae 53 11 13.3 −2.25 0.30 0.81 nsGentianaceae 2 0 0.5 −0.50 0.56 1.00 nsGesneriaceae 7 4 1.8 2.25 0.99 0.07 nsHaemodoraceae 1 1 0.3 0.75 1.00 0.25 nsHeliconiaceae 3 0 0.8 −0.75 0.42 1.00 nsHernandiaceae 1 0 0.3 −0.25 0.75 1.00 nsHypericaceae 2 0 0.5 −0.50 0.56 1.00 nsLacistemaceae 1 0 0.3 −0.25 0.75 1.00 nsLamiaceae 18 13 4.5 8.50 1.00 0.00 MoreLauraceae 13 3 3.3 −0.25 0.58 0.67 nsLaxmanniaceae 1 0 0.3 −0.25 0.75 1.00 nsLecythidaceae 6 2 1.5 0.50 0.83 0.47 nsLinderniaceae 2 1 0.5 0.50 0.94 0.44 nsLoasaceae 1 0 0.3 −0.25 0.75 1.00 nsLoganiaceae 2 0 0.5 −0.50 0.56 1.00 nsLoranthaceae 4 2 1.0 1.00 0.95 0.26 nsLythraceae 3 0 0.8 −0.75 0.42 1.00 ns

B.C. Bennett, C.E. Husby / Journal of Ethnopharmacology 116 (2008) 422–430 427

Table 3 ( Continued)

Family S M E M − E Probabilitiesof M or fewer

Probabilitiesof M or more

Significance

Malpighiaceae 4 2 1.0 1.00 0.95 0.26 nsMalvaceae 26 10 6.5 3.50 0.96 0.09 nsMarantaceae 7 1 1.8 −0.75 0.44 0.87 nsMarcgraviaceae 1 0 0.3 −0.25 0.75 1.00 nsMelastomataceae 38 5 9.5 −4.50 0.06 0.98 FewerMeliaceae 10 2 2.5 −0.50 0.53 0.76 nsMenispermaceae 2 0 0.5 −0.50 0.56 1.00 nsMoraceae 26 5 6.5 −1.50 0.34 0.82 nsMusaceae 1 1 0.3 0.75 1.00 0.25 nsMyristicaceae 9 1 2.3 −1.25 0.30 0.92 nsMyrsinaceae 1 0 0.3 −0.25 0.75 1.00 nsMyrtaceae 12 1 3.0 −2.00 0.16 0.97 nsNyctaginaceae 3 1 0.8 0.25 0.84 0.58 nsOchnaceae 2 0 0.5 −0.50 0.56 1.00 nsOlacaceae 3 0 0.8 −0.75 0.42 1.00 nsOnagraceae 2 0 0.5 −0.50 0.56 1.00 nsOrchidaceae 23 3 5.8 −2.75 0.14 0.95 nsOxalidaceae 2 1 0.5 0.50 0.94 0.44 nsPapveraceae 1 0 0.3 −0.25 0.75 1.00 nsPassifloraceae 3 0 0.8 −0.75 0.42 1.00 nsPhyllanthaceae 4 0 1.0 −1.00 0.32 1.00 nsPhytoloccaceae 1 0 0.3 −0.25 0.75 1.00 nsPicramniaceae 1 0 0.3 −0.25 0.75 1.00 nsPiperaceae 18 9 4.5 4.50 0.99 0.02 MorePlantaginaceae 3 2 0.8 1.25 0.98 0.16 nsPoaceae 22 4 5.5 −1.50 0.32 0.84 nsPolygalaceae 1 1 0.3 0.75 1.00 0.25 nsPolygonaceae 4 1 1.0 0.00 0.74 0.68 nsPortulacaceae 1 1 0.3 0.75 1.00 0.25 nsRanunculaceae 1 0 0.3 −0.25 0.75 1.00 nsRhamnaceae 1 1 0.3 0.75 1.00 0.25 nsRosaceae 2 1 0.5 0.50 0.94 0.44 nsRubiaceae 30 9 7.5 1.50 0.80 0.33 nsRutaceae 7 1 1.8 −0.75 0.44 0.87 nsSalicaceae 8 2 2.0 0.00 0.68 0.63 nsSantalaceae 2 0 0.5 −0.50 0.56 1.00 nsSapindaceae 7 0 1.8 −1.75 0.13 1.00 nsSapotaceae 16 0 4.0 −4.00 0.01 1.00 FewerScrophulariaceae 1 1 0.3 0.75 1.00 0.25 nsSiparunaceae 1 0 0.3 −0.25 0.75 1.00 nsSmilacaceae 2 1 0.5 0.50 0.94 0.44 nsSolanaceae 43 12 10.8 1.25 0.74 0.39 nsStaphyleaceae 1 0 0.3 −0.25 0.75 1.00 nsSymplocaceae 1 0 0.3 −0.25 0.75 1.00 nsTheophrastaceae 3 0 0.8 −0.75 0.42 1.00 nsUrticaceae 5 3 1.3 1.75 0.98 0.10 nsVerbenaceae 7 3 1.8 1.25 0.93 0.24 nsViolaceae 3 1 0.8 0.25 0.84 0.58 nsVitaceae 3 1 0.8 0.25 0.84 0.58 nsVochysiaceae 1 0 0.3 −0.25 0.75 1.00 nsXyridaceae 1 0 0.3 −0.25 0.75 1.00 nsZingiberaceae 7 5 1.8 3.25 1.00 0.01 MoreUnknown 8 1 2.0 −1.00 0.37 0.90 nsFerns & Allies 22 2 5.5 −3.50 0.06 0.99 FewerLichens 2 1 0.5 0.50 0.94 0.44 nsMosses 2 2 0.5 1.50 1.00 0.06 ns

Total 851 210

S = number of species per family (S), M = number of medicinal species per family, E = expected number of medicinal species per family (where E = 0.25*S),M − E = difference between observed and expected number of medicinal plants. The next two columns provide the binomial probabilities of finding M or fewermedicinal species (probabilities of M or fewer) and M or more medicinal species (Pr of M or more). ns = not significant, fewer = significantly fewer medicinal speciesat P < 0.05, more = significantly more medicinal species at P < 0.05 for a one-tailed binomial test. Data from Bennett et al. (2002).

428 B.C. Bennett, C.E. Husby / Journal of Ethnopharmacology 116 (2008) 422–430

Table 4Shuar medicinal plants in three major angiosperm clades

Clade S M E M − E Probabilities of M or fewer Probabilities of M or more Significance

Magnoliids 48 16 12.0 4.00 0.93 0.12 nsMonocots 181 34 45.3 −11.25 0.03 0.98 FewerCommelinid clade (115) (24) 28.8 −4.75 0.18 0.87 nsOther monocots (66) (10) 16.5 −6.50 0.04 0.98 FewerEudicots 588 154 147.0 7.00 0.76 0.27 ns

Total 817 204

S = number of species per clade (S), M = number of medicinal species per clade, E = expected number of medicinal species per clade (where E = 0.25*S),M − E = difference between observed and expected number of medicinal plants. The next two columns provide the binomial probabilities of finding M or fewerm probas a one-

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edicinal species (probabilities of M or fewer) and M or more medicinal species (pecies at P < 0.05, more = significantly more medicinal species at P < 0.05 for

ance around the regression line is constant and independentof the magnitude of X or Y.

Regression-residual analysis of medicinal floras fails to meetll of these assumptions. For one, the independent variable X isost often obtained from published floras that are not perfectly

ongruent with the medicinal flora. Second, the assumption ofomescedasticity is violated. Variance becomes substantiallyarger as X increases. Furthermore, the distributions of X and

are non-normal since X and Y are discrete count data andvalues (number of medicinal species) often contain multiple

epeating values, especially zero. The frequency of zero val-es means the distribution is not only non-normal, but also notpproximate by any continuous distribution. Of greater signifi-ance, the predictor variable (number of species) per family haswo components: the number of medicinal species per familylus the remaining species in the family. Therefore, the relation-hip is described by the equation Y = α + β (Y + n) + ε, where ns the number of non-medicinal species. One would thereforexpect X to be a predictor of Y, since one of its components is. Despite this significant violation of statistical practice (cf.,ewis-Beck, 1993), the regression residual analysis has beenmployed frequently.

.2. Contingency table/binomial analyses

The contingency table analysis showed that the number ofedicinal plants in the Shuar flora as a whole departs sig-

sfec

able 5huar medicinal plants in the major eudicot clades

lade S M E M − E Prob

anunculales 4 0 1.0 −1.00 0.32aryophyllid clade 23 10 5.8 4.25 0.99antales 9 2 2.3 −0.25 0.60osids 317 68 79.3 −11.25 0.08sterids 235 74 58.8 15.25 0.99

otal 588 154

= number of species per clade (S), M = number of medicinal species per clade− E = difference between observed and expected number of medicinal plants. Thedicinal species (probabilities of M or fewer) and M or more medicinal species (proba

pecies at P < 0.05, more = significantly more medicinal species at P < 0.05 for a one-

bilities of M or more). ns = not significant, fewer = significantly fewer medicinaltailed binomial test. Data from Bennett et al. (2002).

ificantly (P < 0.001) from homogeneity. This indicates thatedicinal species are not distributed among families accord-

ng to a uniform proportion. While this result is intuitive,ontingency table analysis allows for the objective testing ofhe significance of the departure from a non-uniform distribu-ion.

The second step in our method, using the binomial approach,evealed only 10 families that differed from the null model withespect to the number of medicinal plant species. Of the six over-epresented families, Apiaceae, Amaranthaceae, Lamiaceae arebundant in pharmacopoeias throughout the world. Costaceae,iperaceae, and Zingiberaceae are among the most importantedicinal families in the tropics. These 10 families are largely

esponsible for the patterns seen at higher taxonomic levels. Forxample, over representation of medicinal plants in Zingiber-ceae and Costaceae accounted for over representation in theingiberales.

The over-represented families also ranked high using theegression-residual approach. However, there is no statisti-al clarity as to their residual ranking. In addition, someamilies with positive residuals (e.g., Cyperaceae, Rubiaceae,uphorbiaceae) have numbers of medicinal plants that do notiffer significantly from null expectations. Cyperaceae is a sur-rise. It seldom ranks high in lists of medicinal species. With

ix of its 11 species being employed medicinally it barelyailed to reach significance at α = 0.1. In the Amazon, sev-ral Cyperus species are widely employed medicinally. Knownommonly as piripiri, these species are associated with an

abilities of M or fewer Probabilities of M or more Significance

1.00 ns0.04 More0.70 ns0.94 ns0.01 More

, E = expected number of medicinal species per clade (where E = 0.25*S),e next two columns provide the binomial probabilities of finding M or fewerbilities of M or more). ns = not significant, fewer = significantly fewer medicinaltailed binomial test. Data from Bennett et al. (2002).

B.C. Bennett, C.E. Husby / Journal of Ethnopharmacology 116 (2008) 422–430 429

Table 6Shuar medicinal plants in three major rosid and asterid clades with significantly fewer or more medicinal species than expected

Clade S M E M − E Probabilities of M or fewer Probabilities of M or more Significance

RosidsMyrtales 57 7 14.3 −7.25 0.01 0.99 Fewer

AsteridsEricales 37 4 9.3 −5.25 0.03 0.99 FewerEuasterids I 161 55 40.3 14.75 1.00 0.01 MoreEuasterids II 36 15 9.0 6.00 0.99 0.02 More

Total 549 141

S = number of species per clade (S), M = number of medicinal species per clade, E = expected number of medicinal species per clade (where E = 0.25*S),M − E = difference between observed and expected number of medicinal plants. The next two columns provide the binomial probabilities of finding M or fewermedicinal species (probabilities of M or fewer) and M or more medicinal species (probabilities of M or more). ns = not significant, fewer = significantly fewermedicinal species at P < 0.05, more = significantly more medicinal species at P < 0.05 for a one-tailed binomial test. Data from Bennett et al. (2002).

Table 7Shuar medicinal plants in orders with significantly fewer or more medicinal species than expected

Order S M E M − E Probabilities of M or fewer Probabilities of M or more Significance

Piperales 19 10 4.8 5.25 1.00 0.01 MoreAsparagales 27 3 6.8 −3.75 0.07 0.98 FewerArecales 26 0 6.5 −6.50 0.00 1.00 FewerZingiberales 23 10 5.8 4.25 0.99 0.04 MoreCaryophyllales 23 10 5.8 4.25 0.99 0.04 MoreMyrtales 57 7 14.3 −7.25 0.01 0.99 FewerMalvales 27 11 6.8 4.25 0.98 0.05 MoreSapindales 34 4 8.5 −4.50 0.05 0.98 FewerEricales 37 4 9.3 −5.25 0.03 0.99 FewerLamiales 63 31 15.8 15.25 1.00 0.00 MoreApiales 5 4 1.3 2.75 1.00 0.02 More

Total 817 204

S = number of species per order (S), M = number of medicinal species per order, E = expected number of medicinal species per order (where E = 0.25*S),M ts. Thm probas a one-

ei

amcrscve

sbats(

fMfr

eElouFaBrmraatfl

ftd

− E = difference between observed and expected number of medicinal planedicinal species (probabilities of M or fewer) and M or more medicinal species (

pecies at P < 0.05, more = significantly more medicinal species at P < 0.05 for

rgot-like fungus, which is responsible for their biological activ-ty.

Murtaugh (2007) advocates for simplicity in statisticalnalyses, arguing that straightforward techniques are lessistake-prone, easier to understand, require fewer arbitrary

hoices by the analyst, and facilitate repeatability by otheresearchers. Exact contingency table and binomial analyses aretraightforward and are easily calculated using standard statisti-al software or free web applications. The large number of zeroalues (for number of medicinal plants) is not a problem forither of these methods.

Our binomial approach yields results similar to the “meta-ignificance” analysis of Moerman and Estabrook (2003). This isecause the “meta-significance” randomization analysis is actu-lly an approximation to the exact binomial approach. Thus,here is no need to use the approximate computationally inten-ive method, METASIG, developed by Moerman and Estabrook2003).

The contingency table/Binomial approach opens up many

urther possibilities for enriching analyses of pharmacopoeias.

ultiple comparison adjustments, such as the standard Bon-erroni method, can be employed to control the overall errorate (α) for the large number of hypothesis tests typically

ctsG

e next two columns provide the binomial probabilities of finding M or fewerbilities of M or more). ns = not significant, fewer = significantly fewer medicinaltailed binomial test. Data from Bennett et al. (2002).

mployed in analyses of medicinal floras (cf., Moerman andstabrook, 2003). The binomial analysis allows one to calcu-

ate exact confidence intervals for the estimated proportionsf medicinal species in each family to provide a measure ofncertainty for these estimates (Hollander and Wolfe, 1999).urthermore, the contingency table and binomial approachesllow great potential flexibility for analyzing medicinal floras.oth of these methods can be readily extended to incorpo-

ate multiple categories for plants (e.g., >10, 5–10, 1–4, or 0edicinal uses). Contingency tables can have any number of

ows and columns and the binomial test can be extended tomultinomial test for three or more categories. The binomial

pproach also allows for testing other null proportions, such ashe proportion of medicinal species in a family from anotherora.

Our final comment pertains to taxonomy. Older floras oftenollowed Engler and Prantl’s family circumscriptions. Sincehe 1980s, most have utilized Cronquist’s (1981) familialelimitations. Modern systematics has introduced significant

hanges in these older circumscriptions. Analyses of pat-erns of medicinal plant use should incorporate the latestystematic treatments outlined by the Angiosperm Phylogenyroup.

4 Ethn

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30 B.C. Bennett, C.E. Husby / Journal of

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