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MORPHOJ: an Integrated Software Package for Geometric Morphometrics Molecular Ecology Resources (2011) 11, 353–357 doi: 10.1111/j.1755-0998.2010.02924.x COMPUTER PROGRAM NOTE MORPHOJ: an integrated software package for geometric morphometrics CHRISTIAN PETER KLINGENBERG Faculty of Life Sciences, University of Manchester, Michael Smith Building, Manchester M13 9PT, UK Abstract Increasingly, data on shape are analysed in combination with molecular genetic or ecological information, so that tools for geometric morphometric analysis are required. Morphometric studies most often use the arrangements of morphological landmarks as the data source and extract shape information from them by Procrustes superimposition. The MORPHOJ software combines this approach with a wide range of methods for shape analysis in different biological contexts. The program offers an integrated and user-friendly environment for standard multivariate analyses such as principal compo- nents, discriminant analysis and multivariate regression as well as specialized applications including phylogenetics, quantitative genetics and analyses of modularity in shape data. MORPHOJ is written in Java and versions for the Windows, Macintosh and Unix ⁄ Linux platforms are freely available from http://www.flywings.org.uk/MorphoJ_page.htm. Keywords: geometric morphometrics, multivariate statistics, phylogeny, Procrustes superimposition, quantitative genetics, shape Received 19 July 2010; revision received 8 September 2010; accepted 10 September 2010 Morphometric analyses are in increasingly widespread superimposed landmarks can be used in multivariate sta- use in conjunction with analyses of molecular data. tistical analyses to address a wide range of biological Examples of such combined uses of data include map- questions (e.g. Klingenberg 2010). ping of shape data onto phylogenies inferred from DNA The MORPHOJ software is based on this approach and sequences, quantitative genetic analyses based on geneal- aims to provide a flexible and user-friendly platform for ogies extracted from molecular marker information, or a broad range of morphometric analyses for two- or assessments of the effects of hybridization on shape three-dimensional landmark data. The goal is to provide asymmetry. Therefore, flexible tools for morphometric a single, integrated environment for geometric morpho- analysis are required by a growing community of users metrics so that users can concentrate on the biological in various areas of ecology and evolutionary biology. and statistical aspects of the analyses. As far as possible, The most widespread approach in geometric morpho- the program automatically keeps track of properties such metrics is to represent each specimen by the relative posi- as the symmetry and dimensionality of landmark config- tions of morphological landmarks that can be located urations and adjusts the analyses as needed. MORPHOJ precisely and establish a one-to-one correspondence implements the standard range of multivariate tech- among all specimens included in the analysis. Shape is niques that are widely used in geometric morphometrics then defined as all the geometric information about a as well as a number of more specialized or newer meth- configuration of landmarks except for its size, position ods. This study introduces the MORPHOJ software and and orientation (Dryden & Mardia 1998). The shape gives an overview of the methods it contains. information is extracted by a procedure called Procrustes superimposition, which removes variation in size, posi- Implemented methods tion and orientation from the data on landmark coordi- nates, and which is at the core of geometric After landmark data have been imported (from text files morphometrics (Goodall 1991; Bookstein 1996; Dryden & or files in the formats of other morphometric programs), Mardia 1998; Zelditch et al. 2004). The coordinates of the the first step in a morphometric analysis is to extract shape information from the data with a Procrustes super- Correspondence: Christian Peter Klingenberg, imposition (Dryden & Mardia 1998). MORPHOJ imple- E-mail: [email protected] ments a full Procrustes fit and projection onto the tangent Ó 2010 Blackwell Publishing Ltd 354 COMPUTER PROGRAM NOTE space to the shape space (Dryden & Mardia 1998), which special challenges (Mardia et al. 2000; Klingenberg et al. produces a new set of shape variables that can be used in 2002). MORPHOJ separates the shape variation of such further analyses. Information on the size of the landmark structures into components of symmetric and asymmet- configuration is retained in the data set and available for ric variation, which provide information that is relevant subsequent analyses (centroid size and log-transformed to different biological questions (Klingenberg et al. centroid size; Dryden & Mardia 1998). After this initial 2002). The symmetric component represents the shape step, a wide range of analyses can be used to analyse variation among individuals and is the focus for shape variation or to relate it to other information. answering many biological questions, whereas the After extracting shape information, it is usually help- asymmetry component tends to be used in more special- ful to a search for outliers in the data, for which ized analyses, for instance as a measure of developmen- MORPHOJ provides a graphical user interface (including tal instability in contexts such as hybridization (Mikula the capability to repair mistakes where landmarks have & Machola´n 2008). For such uses, MORPHOJ provides been recorded in the wrong order). If the user has digi- measures of individual asymmetry (Klingenberg & tized specimens repeatedly, it is possible to quantify Monteiro 2005), which can be correlated with external measurement error relative to the effects of biological variables such as ecological factors or measures of interest by using Procrustes ANOVA (Klingenberg & hybridization from molecular markers. Another use for McIntyre 1998; Klingenberg et al. 2002). This is particu- data on morphological asymmetry, employed in a rap- larly important if a study is focusing on subtle effects, idly growing number of studies, is for characterizing such as variation within populations or left—right the developmental basis of morphological integration asymmetry. (Klingenberg 2008). The standard methods of multivariate statistics that MORPHOJ offers several methods for studying morpho- are in widespread use in morphometrics are imple- logical integration and modularity (Klingenberg 2008). In mented in MORPHOJ. Principal component analysis can be addition to general methods such as principal compo- used to examine the main features of shape variation in a nents, partial least squares and matrix correlation, which sample and as an ordination analysis for examining the can be used for characterizing and comparing patterns of arrangement of specimens in morphospace. Canonical integration, MORPHOJ also offers specialized methods for variate analysis provides a different type of ordination assessing hypotheses of modularity in landmark data analysis, which maximizes the separation of specified (Klingenberg 2009). groups (species, ecotypes, etc.), and discriminant analysis Quantitative genetic studies are increasingly feasible, with cross-validation indicates whether groups can be even in natural populations when pedigree information distinguished reliably. The patterns of shape variation is available from behavioural observations or molecular can be compared between groups by matrix correlation markers (McGuigan 2006; Wilson et al. 2010), and the and assessed statistically with matrix permutation tests, methods for such studies have been extended to shape which have been adapted specifically to geometric analyses (Klingenberg & Leamy 2001; Klingenberg & morphometric data (Klingenberg & McIntyre 1998; Klin- Monteiro 2005; Myers et al. 2006; Klingenberg et al. genberg et al. 2002). 2010a). MORPHOJ does not contain methods for estimat- Covariation of shape with other types of variables is ing genetic covariance matrices and similar statistics, for an important aspect of morphometric studies, and which there are specialized software packages such as MORPHOJ offers several techniques that can be used in this VCE (Groeneveld et al. 2008) or Wombat (Meyer 2007) context. Multivariate regression analysis can be used for that are freely available and can handle high-dimen- assessing allometry or other relationships between vari- sional data as they are produced in geometric morpho- ables, such as shape changes over time, which can be metrics, but MORPHOJ can import genetic and studied by regressing shape on size or on time (Monteiro environmental covariance matrices estimated by those 1999; Drake & Klingenberg 2008). Partial least-squares packages and use them in specifically morphometric analysis is a method for studying associations between analyses. The current release of MORPHOJ includes meth- sets of variables and can be used in contexts such as eco- ods for finding shape variables of maximal or minimal morphology (Adams & Rohlf 2000) or morphological heritability, useful for identifying genetic constraints on integration (Klingenberg & Zaklan 2000; Klingenberg evolution, and for simulating hypothetical selection sce- et al. 2001). It also can provide interesting insights con- narios that can help in assessing genetic integration in a cerning the covariation of shape and genetic markers structure (Klingenberg & Leamy 2001; Martı´nez-Abadı´as (e.g. allele dosage of microsatellite markers; unpublished et al. 2009; Klingenberg et al. 2010a). Methods
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