Traditional morphometrics in plant systematics and its role in palm systematics

Botanical Journal of the Linnean Society

, 2006,

151

, 103–111.

© 2006 The Linnean Society of London,

Botanical Journal of the Linnean Society,

2006,

151

, 103–111

103

Blackwell Publishing LtdOxford, UKBOJ

Botanical Journal of the Linnean Society

0024-4074The Linnean Society of London, 2006? 2006

151

?103111

Review Article

MORPHOMETRICS IN PALM SYSTEMATICSA. HENDERSON

*E-mail: [email protected]

The Palms

Guest edited by William J. Baker and Scott Zona

Traditional morphometrics in plant systematics and its role in palm systematics

ANDREW HENDERSON*

New York Botanical Garden, Bronx, NY 10458, USA

Received April 2005; accepted for publication November 2005

A review is given of the role of traditional morphometrics in plant systematics. The three most commonly used tech-niques of data analysis – Cluster Analysis, Principal Component Analysis and Discriminant Analysis – are dis-cussed. The kinds of data that can be taken from palm specimens and the problems of using specimens as datasources are outlined. Published systematic studies of palms using traditional morphometrics are reviewed. Morerecent studies indicate that: hybrid zones between species may be common; infraspecific diversity is greater thanpreviously suspected; there may be more than double the currently accepted number of species; and our currentknowledge of morphological variation in palms is superficial. A procedure for scientific systematics is given, whichincorporates traditional morphometric methods. © 2006 The Linnean Society of London,

Botanical Journal of theLinnean Society

, 2006,

151

, 103–111.

ADDITIONAL KEYWORDS:

Arecaceae – Cluster Analysis – Discriminant Analysis – geometric morphomet-

rics – numerical taxonomy – Principal Component Analysis – Palmae.

INTRODUCTION

Morphometrics can be defined as the quantitativeanalysis of biological form. It has been widely used ina variety of disciplines, including systematics. Thefield has developed rapidly over the last 20 years, tothe extent that we now distinguish between tradi-tional morphometrics (e.g. Marcus, 1990) and themore recent geometric morphometrics (e.g. Rohlf &Marcus, 1993; Adams, Rohlf & Slice, 2004). Geometricmorphometrics, in which information about the rela-tive spatial arrangement of landmarks is preserved, isnot widely used in plant systematics (Jensen, 2003)and has not yet found its way into palm systematics. Itwill not be discussed further here.

In this paper I shall be concerned solely with tradi-tional morphometrics as used by plant systematists,and with the descriptive, as opposed to the phyloge-netic, component of systematics. The data employed

are size and shape variables (i.e. distances or anglesbetween homologous landmarks) and also includequalitative variables, usually taken from herbariumspecimens. I emphasize that my perspective is that ofa herbarium systematist and this bias means I aminterested in both size and shape variables. Othermorphometricians consider that the field concernsonly the analysis of shape (e.g. Rohlf, 1990).

I use the term variable for any qualitative (binaryor multistate) or quantitative (continuous, meristic)attribute. Although I distinguish between qualitativeand quantitative variables, the distinction is notalways clear (Stevens, 1991; Thiele, 1993). Qualitativevariables can also be referred to as characters ortraits, in the sense of the Phylogenetic Species Con-cept (PSC) of Nixon & Wheeler (1990). Characters arequalitative variables that are found in all comparableindividuals within a terminal lineage (i.e. species);traits are qualitative variables that are not distri-buted universally amongst comparable individualswithin such a lineage. I return to this topic later.

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The methods of analysis in traditional morphomet-rics are primarily multivariate statistics. Althoughthe terms morphometrics and multivariate statisticsare sometimes used interchangeably – and someauthors speak of multivariate morphometrics – it ispossible to analyse morphometric data without multi-variate statistical methods (Rohlf, 1990). I give a briefreview of traditional morphometrics in plant systema-tics, and of some of the most commonly used methodsof analysis. I then concentrate on palm systematicsand discuss the kinds of data used, and give examplesof traditional morphometric analyses used to addresssystematic problems in palms. I conclude by outliningwhat I take to be a scientific methodology for descrip-tive systematics, and show how morphometrics is anintegral part of this method.

HISTORICAL OVERVIEW OF TRADITIONAL MORPHOMETRICS IN PLANT SYSTEMATICS

Some of the earliest papers in plant systematics usingmorphometrics were associated with the school ofnumerical taxonomy. The development of this schoolin the late 1950s and its expanding influence in the1960s and 1970s (e.g. Sneath & Sokal, 1973) led to anincrease in the use of morphometrics and multivariatestatistics. This coincided with the increased availabil-ity of computers capable of rapid analysis of largedatasets. However, most of the effort in numerical tax-onomy, and the source of its conflict with other schoolsof systematics, concerned the classification of taxa (i.e.phylogeny), rather than the descriptive component ofsystematics. The unfortunate legacy of numerical tax-onomy is that many researchers associate morphomet-ric methods with the demise of that school ofsystematics. Similarly, many associate the term phe-netic with numerical taxonomy, and phenetics ornumerical phenetics both refer to numerical taxon-omy. I prefer therefore not to use the term phenetic.

In the last 30 years, independently of numerical tax-onomy, there have been numerous studies using mor-phometrics, approached from a number of differentperspectives. Commonly, these methods are used inthe study of species complexes (e.g. Chandler & Crisp,1998; Naczi, Reznicek & Ford, 1998; Raulings & Ladi-ges, 2001; Gengler-Nowak, 2002) and hybrids (e.g.Murrell, 1994; Campbell & Wright, 1996; Marhold

et al

., 2002). Recently, researchers have combinedmolecular and morphological data (e.g. Hansen, Elven& Brochman, 2000; Lihová

et al

., 2004). In most cases,the methods used in these studies have solvedproblems that proved intractable using the methodsof traditional herbarium systematics. Traditionalmonographs and revisions, published in such series as

Flora Neotropica

and

Systematic Botany Monographs

,very seldom employ morphometric methods.

MOST COMMONLY USED MORPHOMETRIC METHODS

There are two important discussions of morphometricmethods as applied to botanical systematics: those ofPimentel (1981) and Crisp & Weston (1993). More gen-eral reviews are provided by Marcus (1990) and James& McCulloch (1990). An excellent and detailed reviewof the field of multivariate statistics, from an ecologi-cal perspective, is that of Legendre & Legendre (1998).The computer program NTSYSpc (Rohlf, 2000) isdesigned specifically for morphometric analysis of sys-tematic data.

Here I discuss morphometric techniques, at least asfar as systematics is concerned, under three headings,depending on the data used and the aims of the study.(1) Cluster Analysis of qualitative data with no

a pri-ori

knowledge of specimen groupings. (2) Ordination ofquantitative data (sometimes with qualitative data)with no

a priori

knowledge of specimen groupings. (3)Discriminant Analysis of quantitative data with

a pri-ori

knowledge of specimen groupings. The first two areexploratory analyses (i.e. they are not necessarilyinferential but merely look for patterns in the data),while the third is confirmatory, with inferential anal-ysis. First, I discuss some of the assumptions of thesemethods, and then give a brief outline of them asapplied in plant systematics.

Difficult problems for systematists using multivari-ate statistics are the underlying assumptions of themethods, particularly if they are being used inferen-tially. The most important requirement for statisticalinference is that the sample be random. Althoughsome authors insist on this (e.g. Marcus, 1990), sam-ples of herbarium specimens cannot be random in thestrict sense – ‘a sample collected from a population insuch a manner that every individual in the populationhas the same probability of being sampled’ (Pimentel,1979). A second requirement for statistical inference isthat data be distributed in a multivariate normal fash-ion. This again is a difficult requirement to meet. Mul-tivariate normality means that each and all variablescombined are normally distributed (Tabachnik &Fidell, 2001). This is not readily tested, although somemultivariate procedures are relatively insensitive todeviations from normality (Tabachnik & Fidell, 2001).Tabachnik and Fidell give discussions of assumptionsfor each multivariate procedure.

Generally, authors of systematic papers using mul-tivariate statistical methods have ignored the require-ments of random sampling and other assumptions.Perhaps the problem is solved by using only noninfer-ential methods. Pielou (1984), for example, makes thedistinction between interpreting the data at handusing cluster analysis and ordination, and using mul-tivariate statistics based on inference. She considers

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the latter activity to be a separate field of enquiry (seealso James & McCulloch, 1990). On the other hand,Tabachnik & Fidell (2001) place much less emphasison the importance of random sampling. They write,‘Use of inferential and descriptive statistics is rarelyan either-or proposition’.

C

LUSTER

A

NALYSIS

Cluster Analysis (CA) is an exploratory tool for classi-fying objects. It is an example of a

Q

-mode type ofanalysis, in which the association amongst objects(specimens) is being assessed (Legendre & Legendre,1998). There are no statistical assumptions about thedata. The most common procedure is for similarobjects to be placed in groups and these in more inclu-sive groups, in a hierarchical manner. Results are usu-ally presented in the form of trees or dendrograms.The greatest development of CA in systematics wasassociated with the school of numerical taxonomy (e.g.Sneath & Sokal, 1973), and there are numerous dif-ferent procedures available.

The two preliminary decisions in CA concern thechoice of an association coefficient and of a clusteringalgorithm. Association matrices for this kind of anal-ysis are produced either by similarity or distance coef-ficients. The simplest similarity coefficient is thesimple matching coefficient. This can be used only forqualitative data (binary or multistate) and CA worksbetter with these kinds of data (Crisp & Weston, 1993;James & McCulloch, 1990). There are many differentclustering algorithms available. The most commonlyused algorithm in systematics is the hierarchical,agglomerative algorithm using averages –

UPGMA

(unweighted pair-group method using arithmeticaverages). Examples of the use of CA in plant system-atics are provided by Binns, Baum & Arnesen (2002),Crisp & Weston (1993), Gengler-Nowak (2002).

In contrast to cluster analysis,

R

-mode analysisassesses the association amongst variables, andbegins with matrices produced by coefficients ofdependence amongst variables. Principal ComponentAnalysis (PCA) is an

R

-mode type of analysis.

P

RINCIPAL

C

OMPONENT

A

NALYSIS

PCA is usually used as an exploratory tool in system-atics. It is a method for rotating the axes of a coordi-nate system such that the first axis (first principalcomponent) passes through the greatest dimension ofthe swarm of data points, and thus accounts for thegreatest amount of variance of any possible axis. Thesecond principal component, orthogonal to the first,accounts for the greatest amount of residual variance,and so on. There are as many components as originalvariables, and these components are linear combina-

tions of the original variables. Rotation of axes is rigid,so that the data points retain their positions relativeto one another. Most of the variance is usually sum-marized by the first few components, and PCA thusreduces a larger number of variables to fewer vari-ables, which are often easier to interpret. PCA is thusdescribed as a dimension reducing method. Scores ofeach specimen on the principal components, usuallythe first two, can be plotted on bivariate scattergrams,allowing visualization of the relative positions of thespecimens. Some authors (e.g. James & McCulloch,1990) consider that PCA should not be used for mul-tiple samples – a restriction which would entirelyeliminate its usefulness in systematics. This opinion isnot shared by others (e.g. Humphries

et al

., 1981).Examples of the use of PCA in plant systematics areprovided by Boyd (2002), Chandler & Crisp (1998),Crisp & Weston (1993) and Naczi

et al

. (1998).If a dataset contains both quantitative and qualita-

tive variables, and is thus unsuitable for analysis withcoefficients of dependence as in PCA, Principal Coor-dinates Analysis (PCoA) can be used. PCoA beginswith any kind of distance matrix amongst specimens(or a similarity matrix transformed to a distancematrix) and is thus a

Q

-mode type of analysis. The dis-tance matrix is often based on Gower’s coefficient(Legendre & Legendre, 1998). PCoA extracts eigenval-ues and eigenvectors from the distance matrix. Dis-tances amongst specimens are maximized on the firstprincipal coordinate, and then on the second coordi-nate, and so on. PCoA thus provides a geometrical rep-resentation of the distances amongst specimens, and itis the visualization of specimens in two- or three-dimensions that is its main purpose.

D

ISCRIMINANT

A

NALYSIS

Also known as Discriminant Function Analysis orCanonical Variate Analysis, Discriminant Analysis(DA) comprises a group of methods rather than a sin-gle procedure (Pimentel, 1979). The kinds of data usedare the same as for PCA, quantitative variables, butthe specimens are identified a priori to groups (i.e.taxa). The first stage in DA is usually a multivariateanalysis of variance (MANOVA), which tests thehypothesis that group centroids are the same. If theMANOVA supports the alternate hypothesis, thatgroup centroids are significantly different, then DAproceeds with two operations – classification and dis-crimination. Some authors (e.g. James & McCulloch,1990) regard the initial, inferential stage, MANOVA,as a separate procedure, so that DA itself becomes anoninferential method.

For classification, DA produces classification, oridentification functions that are used to determine towhich group specimens belong. Results of this may be

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presented in the form of a classification matrix inwhich each specimen is classified according to the clas-sification functions, either correctly according to theoriginal grouping, or into another group. The percent-age of correct classifications is given and this gives anindication of the validity of the original grouping.These functions can also be used for identification, andcan predict group membership of an unidentified spec-imen (assuming it belongs to one of the groups).

For discrimination, DA finds discriminant functions(i.e. linear combinations of variables) that bestdiscriminate amongst the predefined groups, bymaximizing the differences amongst groups whileminimizing variation within groups. Discriminantfunctions can be used to assess the relative impor-tance of the original variables for discriminatingamongst groups.

DA is well suited to systematics. The first stage(MANOVA) may not seem at first sight to be of muchinterest, because the groups have been identified

apriori

on some grounds. However, MANOVA of groupswhich have been delimited by some other dataset, e.g.qualitative data, can be tested by DA using an inde-pendent dataset in the form of quantitative data. Ifthese groups represent species, then the initialMANOVA of DA is a test of species as hypotheses (seelater). DA can be used to show which variables are themost discriminatory, to classify specimens, and toidentify unknown specimens. Examples of the use ofDA in plant systematics are provided by Battaglia &Patterson (2001), Binns

et al

. (2002), Boyd (2002) andMurrell (1994).

TRADITIONAL MORPHOMETRICS IN PALM SYSTEMATICS

In this section I first review the kinds of data used inpalm morphometrics, and some of the problems asso-ciated with their collection. I then review all papers inwhich traditional morphometrics has been used inpalm systematics.

S

OURCES

OF

DATA

The data used in traditional morphometric studies ofpalms are morphological or anatomical, taken fromherbarium specimens. Less often data are takendirectly from living plants. There are several problemsassociated with using specimens as a data source.First, palms are tropical plants, and there are stillnumerous gaps in the collection coverage of tropicalcountries. Even in small, relatively well-collectedcountries such as Panama, there is uneven coverage.These kinds of gaps and the problems they cause aremagnified many times in countries such as Brazil,where enormous areas of the Amazon region have

never been collected for any plants, let alone palms(Henderson, 1995). The consequences of uneven collec-tion density are that distribution gaps may be artefac-tual, leading to erroneous conclusions.

Secondly, palms are woody plants and are oftenlarge and/or spiny and do not fit into the general col-lector’s main purpose – to collect as many specimensas possible. Consequently there are relatively fewpalm collections in herbaria, and those that do existare often fragmentary or incomplete. Poorly collected,pressed and mounted specimens are often useless asdata sources. For example, collectors routinely cut offthe base of the inflorescence, making it impossible tosee the important variables of peduncle length andbract number or type of insertion. Leaves are oftenfolded and pressed in such a way that it is impossibleto score such variables as number, angles, lengths andwidths of leaflets. In general, the larger the size of thepalm, the more incomplete the specimen. The conse-quences of poor specimens are missing data. These arethe bane of multivariate analysis.

Even when complete organs are present on the spec-imen, there may still be problems in their measure-ment. Many organs are distorted as a result ofpressing and drying. Some organs continue to changein size as they age. For example, petioles continue toelongate after the leaf opens (Chazdon, 1991) andrachillae thicken as the fruits develop. Such variablesshould be used with some caution. The extent of onto-genetic allometry in palms (at least beyond the juve-nile stage), and the extent to which this may confoundsystematic studies is not yet clear. Do younger orshorter adult palms have smaller organs than older ortaller adults of the same species? Nor have the poten-tial size differences between sexes of dioecious speciesbeen investigated. The various methods to remove theeffects of size in morphometric studies have beenreviewed by Humphries

et al

. (1981), but these havenot been attempted in palm studies. Indeed, somewould be difficult to apply because we have no mea-sure of overall size (e.g. weight, volume or length) ofpalms, nor a reasonable surrogate.

Despite these problems, one can usually recover areasonable amount of data for analytical purposesusing simple tools such as a protractor, digital cali-pers and a ruler. Typically, 20–40 variables can bemeasured or counted from herbarium specimens, andthere may be several hundred specimens in the sam-ple. Some variables can be taken from specimenlabels, such as stem height or stem diameter,although these are seldom measured accurately inthe field.

The use of leaf anatomical data circumvents theproblem of missing data, because virtually all speci-mens have leaves. The drawback is that it requiresmuch more time and labour to make anatomical sec-


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tions and extract the data, and such data have seldombeen used.

S

YSTEMATIC

STUDIES

OF

PALMS

USING

MORPHOMETRICS

The first morphometric study of a group of palms wasthat of Madulid (1981). This concerned variation inleaf morphology in

Calamus javensis

Blume, and thepurpose was to find morphologically based groupswithin this complex species. Madulid scored 125 spec-imens for 35 leaf variables. Almost half of these werequantitative but were scored as multistate qualitativevariables. Madulid used CA and PCA to analyse thedata, but found few discernible groupings.

Nauman & Sanders (1991) carried out a study of theclassification of species of

Coccothrinax

. Based prima-rily on a literature survey, rather than on specimens,they found 22 variables. All of these, including quan-titative ones, were scored as qualitative (binary ormultistate). Nauman and Sanders used PCoA and CAto analyse the data, and found three main clusters ofspecies.

Pinheiro (1997) studied generic boundaries in thesubtribe Attaleinae. He used 87 qualitative and quan-titative, leaf anatomical variables from 75 herbariumspecimens, representing 30 species. Also includedwere a number of putative hybrids. He used PCA andCA to analyse the data. Although Pinheiro found sev-eral reasonably well-defined groups of species, he con-sidered that extensive hybridization made genericresolution difficult. However, the observed variationappears typical of closely related genera and may notnecessarily be indicative of hybridization. Pinheiro’sstudy is remarkable in that he was the first to use leafanatomy and was able to score many variables andproduce a dataset free of missing data.

Rustiami (1999) analysed species classification insection

Piptospatha

of

Daemonorops

. She used 58specimens to represent 11 previously recognized spe-cies. She scored 39 vegetative and reproductive vari-ables, both qualitative and quantitative. She used CAand PCA to analyse the data, and the result was areduction in the number of species in the section from11 to five.

Mogea (1999) scored 51 morphological, qualitativevariables from 22 species of

Arenga

. He used CA toproduce a classification of species, and this was con-sidered to represent a phylogeny of the genus. At anarbitrarily chosen level of similarity, four groups ofspecies were recognized.

Bayton (2001) analysed the

Calamus hollrungii

Becc. species complex. Using 40 vegetative variablesscored from 137 specimens, he tested an informal clas-sification that recognized seven species within thecomplex. Bayton used PCoA, but got inconclusive

results. One of the problems Bayton encountered wasthat leaf sheath spines provided a suite of six qualita-tive variables, but spines were lacking on somespecimens. Bayton discussed the problem of scoringsuch inapplicable variables. However, Bayton’s use ofStrong & Lipscomb’s (1999) coding regimes is notappropriate. Strong and Lipscomb’s study is orien-tated to phylogenetic analysis and variable coding,using such programs as PAUP and HENNIG86.Clearly, in a morphometric study these scoring con-ventions and programs are not applicable. A betterapproach to the problem of inapplicable variables is totreat them in terms of characters and traits. Based onBayton’s results, leaf sheath armature seems morelikely to be a trait, rather than a character, and thusshould not be used to delimit species. I return to thistopic later.

In four of the six studies discussed above, the aimwas to classify species (Nauman & Sanders, 1991;Mogea, 1999; Pinheiro, 1997; Rustiami, 1999), and inthe other two the purpose was to delimit taxa within aspecies complex (Madulid, 1981; Bayton, 2001). How-ever, all six studies were infused with the philosophyof numerical taxonomy and all suffer because of thatto a greater or lesser extent. Procedures that werecommonly used in numerical taxonomy would not beused today. For example, quantitative variables werescored as qualitative, qualitative variables were usedin PCA, and CA was used to classify species.

Borchsenius’s (1999) paper was something of a turn-ing point in palm systematics, because it was the firstto apply morphometric methods free from the influ-ence of numerical systematics. Borchsenius studiedvariation within the

Geonoma cuneata

H. Wendl. exSpruce species complex. He measured 12 quantitativevariables from stems and leaves from 105 individualliving plants at one site in western Ecuador. At thissite he found four different varieties of

G. cuneata

.Using PCA and DA, he could clearly separate the fourvarieties. However, differences between the varietiesbroke down when he included plants from other sitesin western Ecuador in the analysis. Borchsenius con-cluded that the current varietal classification was notapplicable in western Ecuador, much less throughoutthe total range of

G. cuneata

, from western Ecuador toNicaragua.

Another species complex in

Geonoma

, the Amazo-nian

G. stricta

(Poit.) Kunth, was studied by Hender-son & Martins (2002). They measured 19 quantitativevariables from 238 herbarium specimens and analy-sed the data with PCA, CA and DA. Although theirapproach was different from that of Borchsenius(1999), i.e. they used herbarium specimens as a datasource across the range of the species, as opposed toBorchsenius who used living plants from a limitedpart of the range, their conclusions were similar. The

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current varietal classification of

G. stricta

was unreal-istic, and one was probably not possible based on thedata and methods employed. The main problem wasfound to be in variation in leaf size and shape.

Loo

et al

. (2001) carried out an analysis of the vari-ation within

Licuala glabra

Griff. in PeninsularMalaysia. They studied 74 herbarium specimens andscored 43 vegetative and reproductive variables, bothqualitative and quantitative. They used PCoA withqualitative and quantitative variables, and PCA withquantitative variables. Results showed three cleargroupings of specimens, and these corresponded withcertain mountain ranges in the region. Like that ofBorchsenius (1999), this paper is important in palmsystematics because of its use of appropriate method-ology and conclusive results. Loo

et al

. (1999) usedmolecular data to analyse three populations of

L. glabra

, and used both CA and PCA.Kahn & Gluchy (2002) measured 4393 pistillate

flowers from 135 living plants of

Astrocaryumurostachys

Burret in Amazonian Ecuador. Usingunivariate statistics, they found that pistillateflower morphology varied quantitatively but notqualitatively.

In 2002, Henderson and coworkers began a series ofmorphometric studies on palm genera, mostly fromMesoamerica. Henderson & Ferreira (2002) studiedvariation between and within the two species of

Syn-echanthus

. They used 23 qualitative and quantitativevariables from 355 herbarium specimens, and ana-lysed the data with CA, PCA and DA. They found thatqualitative but not quantitative variables clearly sep-arated the two species. Within

S. warscewiczianus

H.Wendl., there were populations of very small plantsfrom isolated mountains in central Panama. Within

S. fibrosus

(H. Wendl.) H. Wendl., there were threeseparate populations, one in Mexico, a second in Bel-ize, Guatemala, Honduras and Nicaragua, and a thirdin Costa Rica. PCA showed that

S. warscewiczianus

varied mostly in size, and regression showed thatsome of this variation was associated with elevation.In contrast,

S. fibrosus

varied more in shape andshowed no association with elevation.

Henderson (2002) analysed 22 qualitative andquantitative variables from 476 specimens of

Rein-hardtia

, using CA, PCA and DA. In marked contrast toother studies of palms, PCA of quantitative variablesclearly separated the six species. Using regression,Henderson found clinal variation in some variableswith elevation. One species,

R. gracilis

(H. Wendl.)Burret, exhibited considerable variation and sevendifferent populations were distinguished.

The two studies discussed above, on

Synechanthus

and

Reinhardtia

, lacked any systematic content inthat they only discussed variation discernible bymorphometric analysis of data from specimens. The

absence of systematic conclusion can be considered ashortcoming of these studies. In Henderson (2004) andHenderson (2005a), multivariate analysis of specimendata was combined with delimitation of taxa.

In

Hyospathe

(Henderson, 2004), 31 variables from428 specimens were scored. CA was used to dividequalitative variables into characters or traits. Charac-ters only were used to delimit species, and six specieswere recognized, based on groups of specimens withunique combinations of character states. This is anapplication of the Phylogenetic Species Concept (PSC;Nixon & Wheeler, 1990). Variation within each specieswas then analysed using PCA and DA. One specieswas found to be widespread and complex, and wasdivided into six subspecies. Henderson hypothesizedthe existence of a hybrid zone between two speciesalong eastern Andean slopes.

Henderson (2005a) analysed variation within

Calyptrogyne

, for which 35 variables, both qualitativeand quantitative, were scored from 563 specimens.CA, PCA and DA were used to analyse the data. As aresult of the analysis, the number of recognized spe-cies increased from eight to 18, these with 13 subspe-cies. Regression showed evidence of clinal variation ofsome variables with longitude, and there was also evi-dence of the existence of a hybrid zone between twospecies in Costa Rica.

The four papers on

Synechanthus, Reinhardtia,Calyptrogyne

and

Hyospathe

show the development ofmy own thinking on methods in herbarium systemat-ics. A shortcoming of the

Synechanthus

and

Reinhard-tia

papers is that they have no systematic basis: theyare merely analyses of morphological variation. In the

Calyptrogyne

study there is a systematic basis, butcharacters are not distinguished from traits, and spec-imen groups were delimited before a species conceptwas applied. In

Hyospathe

, qualitative variables weredivided into characters and traits, and the PSCapplied.

C

ONCLUSIONS

FROM

RECENT

STUDIES

A review of these recent papers using morphometricmethods on palms leads to several conclusions. Thesederive from the complexity of variation uncovered bymorphometric analysis, a complexity that is notrevealed, or is glossed over, in traditional systematicstudies.

1 Hybrid zones may be common phenomena in palms.Henderson (2004, 2005a) postulated the existence ofzones of intermediacy between two species in both

Hyospathe

and

Calyptrogyne

. In studying

Geonoma

,I have found evidence of a hybrid zone between

G. deversa

(Poit.) Kunth and

G. leptospadix

Trail.Hybrid zones will have important implications


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for systematics, but will need to be investigatedfurther using ecological and genetic data andmethodologies.

2 Subspecific variation in many species of palms isgreater than previously suspected, and is seldomadequately documented by traditional methods.Species such as Synechanthus warscewiczianus andReinhardtia gracilis, as well as many species ofGeonoma, are widespread and exhibit markedmorphological variation, and this often correlateswith geographical disjunction. In such situations,subspecies can be recognized. I think it is useful todistinguish here between these kind of variable,or polytypic, species and polymorphic, or ochlo-,species (Cronk, 1998). Variation in the former canreadily be understood using morphometric methods.Variation in the latter is not so easily resolved.Ochlospecies exhibit great variation that does notcorrelate with geography, and are not amenable tosystematic treatment (Cronk, 1998). There are sev-eral examples from Geonoma, where ochlospecieshave been termed species complexes (G. cuneata:Borchsenius, 1999; G. stricta: Henderson & Mar-tins, 2002). However, I think that ochlospecies maybe relatively uncommon in palms; most species com-plexes are polytypic and may be understood usingmorphometric analysis. I have found examples ofpolytypic species in Geonoma, e.g. G. interrupta(Ruiz & Pav.) Mart.and G. maxima (Poit.) Kunth.

3 Morphometric studies suggest the number of spe-cies of palms may be underestimated. The numberof species recognized in Hyospathe has increasedfrom two to six, these with six subspecies (Hender-son, 2004). In Calyptrogyne the number hasincreased from eight to 18, these with 13 subspecies(Henderson, 2005a). Similar increases are found inGeonoma (A. Henderson, unpubl. data). I estimate,from this admittedly small sample, that there maybe double the currently accepted number of 2300species of palms.

4 Following directly from this, our knowledge of spe-cies-level variation in general is poor. The founda-tion of our systematic knowledge of palms at alllevels rests on our knowledge of species, and yet wehave hardly scratched the surface when it comes tounderstanding morphological variation in palms. Iconsider the main impediment to understandingspecies-level variation is not so much the datasource, i.e. specimens, but rather flawed methodol-ogy. Although there are limitations on the knowl-edge that can be gained from herbarium specimens,it is apparent that when we use morphometricmethods we recover far more information than wedo with traditional methods. Analysis of this infor-mation, combined with a scientific systematicmethod, gives a much improved estimate of species

diversity. Recently I have put forward what Iconsider to be a scientific method for herbariumsystematics, at least its descriptive component(Henderson, 2005b). This method involves severalseparate yet sequential stages, two of which involvemorphometric methods. I give a brief review of thismethod here.

The first stage in a systematic study is to choose aspecies concept (Henderson, 2005b). This should havean appropriate theoretical background and an appli-cable discovery operation – and these two parts of theconcept are closely linked. Herbarium systematistsare therefore constrained in their choice of concept. Bytaking into account theoretical background, discoveryoperation and data (i.e. primarily morphological datataken from specimens), I consider that the PSC is themost appropriate for herbarium systematics. Phyloge-netic species are: ‘the smallest aggregation of popula-tions . . . diagnosable by a unique combination ofcharacter states in comparable individuals’ (Nixon &Wheeler, 1990).

The next stage is to assemble a sample of specimensand to state an initial hypothesis: that a certainnumber of groups are present in the sample. Data aregathered from the specimens and the matrix isconstructed. Qualitative attributes are divided intocharacters and traits using either Population Aggre-gation Analysis (Davis & Nixon, 1992) if applicable, orCA and the sequential removal of suspected traitsfrom the analysis (e.g. Henderson, 2004). Charactersalone are used to produce groups of specimens withunique combinations of character states. At this stagethe initial hypothesis may be accepted or rejected, andin the latter case a new hypothesis proposed. This maybe in the form of a null hypothesis – for example, thatgroup centroids are different – and such a hypothesiscan be tested inferentially using DA of quantitativedata. The PSC is applied to these groups. Quantitativeand geographical variation within species may then beanalysed and subspecies recognized. It is this stage atwhich morphometrics is most useful. Descriptions oftaxa are given in the form of summary statistics of thesample. Ranges, means or medians, confidence inter-vals or coefficients of variation, and sample size, takendirectly from the data matrix, contain informative,unambiguous statements about the sample. Forqualitative data in descriptions, all that is neededare the relevant states of the characters. Finally,names are applied to species according to the rules ofnomenclature.

FUTURE PROSPECTS

Considering the total number of systematic papers onpalms published over the last 30 years, the role of mor-

110 A. HENDERSON

© 2006 The Linnean Society of London, Botanical Journal of the Linnean Society, 2006, 151, 103–111

phometric analysis has been minor. I consider this isdue in part to the legacy of numerical taxonomy. Asshown above, the few palm papers based on this philo-sophical background have not been overly successful.Nevertheless, traditional systematics has not, in myopinion, been entirely successful either. Palm system-atists have, for the most part, ignored the problems ofspecies concepts and species delimitation, and pro-duced monographs and revisions without any recourseto a scientific methodology. The result has been wildlydifferent estimates of the numbers of species of palmsand an unstable systematics that changes with everyrevision. Traditional herbarium systematics of palmsis untenable because of the lack of stated species con-cepts and of a scientific methodology. Recent system-atic revisions employing an explicit methodology givea scientific estimate of species diversity.

I think that if we can move toward a more scientificsystematics of palms, as outlined above, incorporatingmorphometric methods, then we will have a more sta-ble systematics. We will also have a sound basis onwhich to study other problems, such as subspecificvariation, hybrids and hybrid zones, species com-plexes and biogeographical patterns. Furthermore, weshould complete descriptive systematic studies beforewe attempt phylogenetic analysis, although thesethings are often carried out the wrong way round(Wheeler, 2004). Geometric morphometric methodsshould also be applied to systematic problems inpalms. Geographic Information Systems (GIS) tech-nology will become increasingly important in palmsystematics, and this will be especially informativewith morphometric datasets. We will begin to under-stand biogeographical patterns and the associationbetween morphological variables and environmentalvariables. A group of researchers at the University ofAarhus is already active in this field (e.g. Borchsenius& Skov, 1997; Skov & Borchsenius, 1997).

ACKNOWLEDGEMENTS

I thank the organizers of this conference for the oppor-tunity to present this paper.

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Traditional morphometrics in plant systematics and its role in palm systematics