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Comparison of morphometric techniques for shapes with few homologous landmarks based on machine-learning approaches to biological discrimination

Published online by Cambridge University Press:  08 April 2016

Bert Van Bocxlaer  and
Roland Schultheiß
Bert Van Bocxlaer
Affiliation:
Research Unit Palaeontology, Department of Geology and Soil Science, Ghent University, Krijgslaan 281 S8, B-9000 Ghent, Belgium. E-mail: [email protected]
Roland Schultheiß
Affiliation:
Department of Animal Ecology and Systematics, Justus Liebig University Giessen, Heinrich-Buff-Ring 26-32 (IFZ), D-35392 Giessen, Germany. E-mail: [email protected]
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Abstract

Biometric analyses are useful tools for the study of organisms, their phylogenetic affiliation, and the pattern and rate of their evolution. Various morphometric techniques have been developed to analyze morphological variation, but methodological choices are often made arbitrarily because quantitative comparisons are lacking or inconclusive. Here we address morphometric quantification of taxa with few unambiguously identifiable landmarks (<15), utilizing ornamented and unornamented gastropod shells. Support vector machines were applied to evaluate classification performances of landmark (LMA), elliptic Fourier (EFA), and semi-landmark analysis (SLM). This evaluation is based on the discrimination of between-group differences relative to within-group variation, and thus allows comparing how the techniques treat different types of biological information. The results suggest that EFA performs slightly better than SLM (and certainly LMA) in discerning a priori identified taxa with unornamented shells, but that SLM is significantly superior to other techniques for ornamented shells. Alignment and homology problems may cause the subtle variations in ornamentation to become blurred as noise in EFA, even though EFA is often cited to be able to deal with complex shapes. Performance of LMA depends entirely on how accurately the structure can be covered with landmarks. Guidelines in choosing a morphometric technique in diverse cases are provided.

Type
Articles
Information
Paleobiology , Volume 36 , Issue 3 , Summer 2010 , pp. 497 - 515
Copyright
Copyright © The Paleontological Society 

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References

Literature Cited

Adams, D. C., Rohlf, F. J., and Slice, D. E. 2004. Geometric morphometrics: ten years of progress following the ‘revolution.’ Italian Journal of Zoology 71:5–16.CrossRefGoogle Scholar
Anderson, L. C., and Roopnarine, P. D. 2003. Evolution and phylogenetic relationships of Neogene Corbulidae (Bivalvia; Myoidea) of tropical America. Journal of Paleontology 77:1086–1102.Google Scholar
Bookstein, F. L. 1991. Morphometric tools for landmark data: geometry and biology. Cambridge University Press, Cambridge.Google Scholar
Bookstein, F. L. 1996. Applying landmark methods to biological outline data. Pp. 59–70 in Mardia, K. V., Gill, C. A., and Dryden, I. L., eds. Image fusion and shape variability. University of Leeds, Leeds, U.K. Google Scholar
Bookstein, F. L. 1997. Landmark methods for forms without landmarks: morphometrics of group differences in outline shape. Medical Image Analysis 1:225–243.Google Scholar
Bookstein, F. L. 1998. A hundred years of morphometries. Acta Zoologica 44:7–59.Google Scholar
Bookstein, F. L., Strauss, R. E., Humphries, J. M., Chernoff, B., Elder, R. L., and Smith, G. R. 1982. A comment upon the uses of Fourier methods in systematics. Systematic Zoology 31:85–92.Google Scholar
Brehm, G., and Fiedler, K. 2004. Ordinating tropical moth ensembles from an elevational gradient: a comparison of common methods. Journal of Tropical Ecology 20:165–172.CrossRefGoogle Scholar
Brown, D. S. 1994. Freshwater snails of Africa and their medical importance. Taylor and Francis, London.CrossRefGoogle Scholar
Brown, M. P. S., Grundy, W. N., Lin, D., Cristianini, N., Sugnet, C. W., Furey, T. S., Ares, M., and Haussler, D. 2000. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences USA 97:262–267.Google Scholar
Burges, C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2:121–167.CrossRefGoogle Scholar
Carvajal-Rodríguez, A., Conde-Padín, P., and Rolán-Alvarez, E. 2005. Decomposing shell form into size and shape by geometric morphometric methods in two sympatric ecotypes of Littorina saxatilis . Journal of Molluscan Studies 71:313–318.CrossRefGoogle Scholar
Cheetham, A. H., Sanner, J., and Jackson, J. B. C. 2007. Metrarabdotos and related genera (Bryozoa: Cheilostomata) in the Late Paleogene and Neogene of tropical America. Paleontological Society Memoir 67. Journal of Paleontology 81(Suppl. to No. 1).CrossRefGoogle Scholar
Chessel, D., Dufour, A.-B., and Thioulouse, J. 2004. The ade4 package — I: one-table methods. R News 4:5–10.Google Scholar
Clabaut, C., Bunje, P. M., Salzburger, W., and Meyer, A. 2007. Geometric morphometric analyses provide evidence for the adaptive character of the Tanganyikan cichlid fish radiations. Evolution 61:560–578.Google Scholar
Clarke, K. R. 1993. Non-parametric multivariate analyses of changes in community structure. Austral Ecology 18:117–143.Google Scholar
Conde-Padín, P., Carvajal-Rodríguez, A., Carballo, M., Caballero, A., and Rolán-Alvarez, E. 2007a. Genetic variation for shell traits in a direct-developing marine snail involved in a putative sympatric ecological speciation process. Evolutionary Ecology 21:635–650.Google Scholar
Conde-Padín, P., Grahame, J. W., and Rolán-Alvarez, E. 2007b. Detecting shape differences in species of the Littorina saxatilis complex by morphometric analysis. Journal of Molluscan Studies 73:147–154.Google Scholar
Conde-Padín, P., Caballero, A., and Rolán-Alvarez, E. 2009. Relative role of genetic determination and plastic response during ontogeny for shell-shape traits subjected to diversifying selection. Evolution 63:1356–1363.Google Scholar
Cortes, C., and Vapnik, V. N. 1995. Support vector networks. Machine Learning 20:273–297.Google Scholar
Cox, T. F., and Cox, M. A. A. 2000. Multidimensional scaling. Chapman and Hall, New York.CrossRefGoogle Scholar
Crowley, T. E., Pain, T., and Woodward, F. R. 1964. A monographic review of the Mollusca of Lake Nyasa. Annales du Musée Royal de l'Afrique Centrale, Sciences Zoologiques 131:1–58.Google Scholar
DeCoste, D., and Schölkopf, B. 2002. Training invariant support vector machines. Machine Learning 46:161–190.Google Scholar
Dietz, E. J. 1983. Permutation tests for association between two distance matrices. Systematic Zoology 32:21–26.Google Scholar
Dryden, I. L., and Mardia, K. V. 1998. Statistical shape analysis. Wiley, New York.Google Scholar
Ehrlich, R., Pharr, R. B., and Healy-Williams, N. 1983. Comments on the validity of Fourier descriptors in systematics: a reply to Bookstein et al. Systematic Biology 32:202–206.CrossRefGoogle Scholar
Genner, M. J., Todd, J. A., Michel, E., Erpenbeck, D., Jimoh, A., Joyce, D. A., Piechocki, A., and Pointier, J.-P. 2007. Amassing diversity in an ancient lake: evolution of a morphologically diverse parthenogenetic gastropod assemblage in Lake Malawi. Molecular Ecology 16:517–530.CrossRefGoogle Scholar
Gould, S. J. 1991. The disparity of the Burgess Shale arthropod fauna and the limits of cladistic analysis: why we must strive to quantify morphospace. Paleobiology 17:411–423.Google Scholar
Green, W. D. K. 1996. The thin-plate spline and images with curving features. Pp. 79–87 in Mardia, K. V., Gill, C. A., and Dryden, I. L., eds. Image fusion and shape variability. University of Leeds, Leeds, U.K. Google Scholar
Haines, A. J., and Crampton, J. S. 2000. Improvements to the method of Fourier shape analysis as applied in morphometric studies. Palaeontology 43:765–783.Google Scholar
Hammer, Ø., and Harper, D. A. T. 2006. Paleontological data analysis. Blackwell, Oxford.Google Scholar
Hammer, Ø., Harper, D. A. T., and Ryan, P. D. 2001. PAST: paleontological statistics software package for education and data analysis. Palaeontologia Electronica 4:1–9.Google Scholar
Hastie, T., and Tibshirani, R. 1998. Classification by pairwise coupling. Annals of Statistics 26(1):451–471.CrossRefGoogle Scholar
Hotelling, H. 1933. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology 24:417–441.CrossRefGoogle Scholar
Iwata, H., and Ukai, Y. 2002. SHAPE: a computer program package for quantitative evaluation of biological shapes based on elliptic Fourier descriptors. Journal of Heredity 93:384–385.Google Scholar
Jablonski, D. 1999. The future of the fossil record. Science 284:2114–2116.CrossRefGoogle ScholarPubMed
Jackson, J. B. C., and Cheetham, A. H. 1990. Evolutionary significance of morphospecies: a test with cheilostome Bryozoa. Science 248:579–582.CrossRefGoogle ScholarPubMed
Keerthi, S. S., Shevade, S. K., Bhattacharyya, C., and Murthy, K. R. K. 2001. Improvements to Platt's SMO algorithm for SVM classifier design. Neural Computation 13:637–649.CrossRefGoogle Scholar
Kruskal, J. 1964. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29:1–27.Google Scholar
Kuhl, F. P., and Giardina, C. R. 1982. Elliptical Fourier features of a closed contour. Computer Graphics and Image Processing 18:236–258.Google Scholar
Lestrel, P. E. 1989. Method for analyzing complex two-dimensional forms: elliptical Fourier functions. American Journal of Human Biology 1:149–164.CrossRefGoogle ScholarPubMed
Lestrel, P. E. 1997. Fourier descriptors and their applications in biology. Cambridge University Press, Cambridge.Google Scholar
Louda, S. M., and McKaye, K. R. 1982. Diurnal movements in populations of the prosobranch Lanistes nyassanus at Cape Maclear, Lake Malawi, Africa. Malacologia 23:13–21.Google Scholar
Louda, S. M., Gray, W. N., McKaye, K. R., and Mhone, O. J. 1983. Distribution of gastropod genera over a vertical depth gradient at Cape Maclear, Lake Malawi. Veliger 25:387–392.Google Scholar
Louda, S. M., McKaye, K. R., Kocher, T. D., and Stackhouse, C. J. 1984. Activity, dispersion, and size of Lanistes nyassanus and Lanistes solidus (Gastropoda, Ampullariidae) over the depth gradient at Cape Maclear, Lake Malawi, Africa. Veliger 26:145–152.Google Scholar
Loy, A., Busilacchi, S., Costa, C., Ferlin, L., and Cataudella, S. 2000. Comparing geometric morphometrics and outline fitting methods to monitor fish shape variability of Diplodus puntazzo (Teleostea: Sparidae). Aquacultural Engineering 21:271–283.CrossRefGoogle Scholar
MacLeod, N. 1999. Generalizing and extending the eigenshape method of shape space visualization and analysis. Paleobiology 25:107–138.Google Scholar
Mandahl-Barth, G. 1972. The freshwater Mollusca of Lake Malawi. Revue de Zoologie et de Botanique Africaines 86:257–289.Google Scholar
Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Research 27:209–220.Google Scholar
Marcus, L. F. 1990. Traditional morphometrics. In Rohlf, F. J. and Bookstein, F. L., eds. Proceedings of the morphometries workshop. University of Michigan Museum of Zoology Special Publication 2:77–122.Google Scholar
McCune, B., and Grace, J. B. 2002. Analysis of ecological communities. MjM Software Design, Gleneden Beach, Ore. Google Scholar
Minchin, P. R. 1987. An evaluation of the relative robustness of techniques for ecological ordination. Plant Ecology 69:89–107.Google Scholar
Monti, L., Baylac, M., and Lalanne-Cassou, B. 2001. Elliptic Fourier analysis of the form of genitalia in two Spodoptera species and their hybrids (Lepidoptera: Noctuidae). Biological Journal of the Linnean Society 72:391–400.Google Scholar
Navarro, N., Zatarain, X., and Montuire, S. 2004. Effects of morphometric descriptor changes on statistical classification and morphospaces. Biological Journal of the Linnean Society 83:243–260.Google Scholar
Oksanen, J., Kindt, R., Legendre, P., O'Hara, B., Simpson, G. L., Stevens, M. H. H., and Wagner, H. 2008. Vegan: community ecology package, Version R, package version 1.1-3.Google Scholar
Papadopoulos, L. N., Todd, J. A., and Michel, E. 2004. Adulthood and phylogenetic analysis in gastropods: character recognition and coding in shells of Lavigeria (Cerithioidea, Thiaridae) from Lake Tanganyika. Zoological Journal of the Linnean Society 140:223–240.CrossRefGoogle Scholar
Parsons, K. J., Robinson, B. W., and Hrbek, T. 2003. Getting into shape: an empirical comparison of traditional truss-based morphometric methods with a newer geometric method applied to New World cichlids. Environmental Biology of Fishes 67:417–431.Google Scholar
Platt, J. 1998. Fast training of support vector machines using sequential minimal optimization. Pp. 41–65 in Schölkopf, B., Burges, C., and Smola, A., eds. Advances in Kernel methods: support vector learning. MIT Press, Cambridge.Google Scholar
R Development Core Team 2007. R: a language and environment for statistical computing, Version 2.6. Foundation for Statistical Computing, Vienna.Google Scholar
Read, D. W., and Lestrel, P. E. 1986. Comment on uses of homologous-point measures in systematics: a reply to Bookstein et al. Systematic Zoology 35:241–253.Google Scholar
Reyment, R. A. 1991. Multidimensional paleobiology. Pergamon, Oxford.Google Scholar
Rohlf, F. J. 1990a. Fitting curves to outlines. In Rohlf, F. J. and Bookstein, F. L., eds. Proceedings of the morphometries workshop. University of Michigan Museum of Zoology Special Publication 2:167–177.Google Scholar
Rohlf, F. J. 1990b. Morphometries. Annual Review of Ecology and Systematics 21:299–316.Google Scholar
Rohlf, F. J. 1999. Shape statistics: Procrustes superimpositions and tangent spaces. Journal of Classification 16:197–223.Google Scholar
Rohlf, F. J. 2000. Statistical power comparisons among alternative morphometric methods. American Journal of Physical Anthropology 111:468–478.3.0.CO;2-B>CrossRefGoogle ScholarPubMed
Rohlf, F. J. 2003. tpsSmall, Version 1.20. Department of Ecology and Evolution, State University of New York at Stony Brook.Google Scholar
Rohlf, F. J. 2004. tpsDig 1. Department of Ecology and Evolution, State University of New York at Stony Brook.Google Scholar
Rohlf, F. J. 2006. tpsUtil, file utility program, Version 1.38. Department of Ecology and Evolution, State University of New York at Stony Brook.Google Scholar
Rohlf, F. J. 2008. tpsDig 2, Version 2.12. Department of Ecology and Evolution, State University of New York at Stony Brook.Google Scholar
Rohlf, F. J., and Archie, J. W. 1984. A comparison of Fourier methods for the description of wing shape in mosquitoes (Diptera: Culicidae). Systematic Zoology 33:302–317.Google Scholar
Rohlf, F. J., and Marcus, L. F. 1993. A revolution in morphometrics. Trends in Ecology and Evolution 8:129–132.CrossRefGoogle Scholar
Schölkopf, B., and Smola, A. J. 2001. Learning with kernels: support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge.Google Scholar
Schopf, T. J. M. 1982. A critical assessment of punctuated equilibria. 1. Duration of taxa. Evolution 36:1144–1157.Google Scholar
Schopf, T. J. M., Raup, D. M., Gould, S. J., and Simberloff, D. S. 1975. Genomic versus morphologic rates of evolution: influence of morphologic complexity. Paleobiology 1:63–70.Google Scholar
Schultheiß, R., Van Bocxlaer, B., Wilke, T., and Albrecht, C. 2009. Old fossils-young species: evolutionary history of an endemic gastropod assemblage in Lake Malawi. Proceedings of the Royal Society of London B 276:2837–2846.Google Scholar
Sheets, H. D. 2008. IMP: integrated morphometrics package. Department of Physics, Canisius College, Buffalo, N.Y. Google Scholar
Sheets, H. D., Keonho, K., and Mitchell, C. E. 2004. A combined landmark and outline-based approach to ontogenetic shape change in the Ordovician Trilobite Triarthrus becki . Pp. 67–81 in Elewa, A., ed. Applications of morphometries in paleontology and biology. Springer, New York.Google Scholar
Sheets, H. D., Corvino, K. M., Panasiewicz, J. M., and Morris, S. R. 2006. Comparison of geometric morphometric outline methods in the discrimination of age-related differences in feather shape. Frontiers in Zoology 3:15.Google Scholar
Sidlauskas, B. 2008. Continuous and arrested morphological diversification in sister clades of characiform fishes: a phylomorphospace approach. Evolution 62:3135–3156.Google Scholar
Thompson, D. A. W. 1942. On growth and form. A new edition. University Press and Macmillan, Cambridge, U.K. Google Scholar
van Rijsbergen, C. J. 1979. Information retrieval. Butterworths, London.Google Scholar
Vapnik, V. N. 1979. Estimation of dependences based on empirical data. Springer, New York.Google Scholar
Vapnik, V. N. 1998. Statistical learning theory. Wiley, New York.Google Scholar
Venables, W. N., and Ripley, B. D. 2002. Modern applied statistics with S. Springer, New York.Google Scholar
Vermeij, G. J., and Carlson, S. J. 2000. The muricid gastropod subfamily Rapaninae: phylogeny and ecological history. Paleobiology 26:19–46.Google Scholar
Wagner, P. J. 2002. Phylogenetic relationships of the earliest anisostrophically coiled gastropods. Smithsonian Contributions to Paleobiology 88:1–152.Google Scholar
Wilcox, C. D., Dove, S. B., McDavid, W. D., and Greer, D. B. 2002. ImageTool 3.0 for Windows. University of Texas Health Science Center, San Antonio.Google Scholar
Witten, I. H., and Frank, E. 2005. Data mining: practical machine learning tools and techniques. Morgan Kaufmann, San Francisco.Google Scholar
Wood, A. R., Zelditch, M. L., Rountrey, A. N., Eiting, T. P., Sheets, H. D., and Gingerich, P. D. 2007. Multivariate stasis in the dental morphology of the Paleocene–Eocene condylarth Ectocion . Paleobiology 33:248–260.Google Scholar
Zelditch, M. L., Swiderski, D. L., Sheets, H. D., and Fink, W. L. 2004. Geometric morphometries for biologists: a primer. Elsevier, London.Google Scholar
Zelditch, M. L., Wood, A. R., Bonett, R. M., and Swiderski, D. L. 2008. Modularity of the rodent mandible: integrating bones, muscles and teeth. Evolution and Development 10:756–768.Google Scholar
Zhang, L., Zhou, W., and Jiao, L. 2004. Hidden space support vector machines. IEEE Transactions on Neural Networks 15:1424–1434.Google Scholar
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