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The benefits of the third dimension – What can we learn from the z-axis in 3D geometric morphometrics based on sailfin silversides (Telmatherinidae)?

Benjamin Wasiljew1*, Jobst Pfaender2, Benjamin Wipfler1 and Fabian Herder1
  • 1 Zoological Research Museum Alexander Koenig (LG), Germany
  • 2 Naturkundemuseum Potsdam, Germany

2D geometric morphometric methods based on photographs or X-ray images are well established to quantify shape variation in single structures or overall body morphology (Zelditch et al., 2004; Lawing & Polly, 2010). They are highly efficient to detect even small-scaled variation (Adams et al., 2004) and hence provide excellent tools for the quantification of traits, in a wide range of evolutionary ecology applications (Gould, 2014). Although a loss of information can obviously be expected when using two dimensional methods to study three dimensional objects, 2D analysis are still state of the art in vertebrate morphology approaches (Buser et al., 2018). Especially in ichthyological studies, 2D methods are widely used (Jamniczky, et al., 2015). The popularity of 2D methods is not surprising since the efforts for collecting and analyzing three dimensional data are substantially higher compared to two dimensional imaging techniques (Cardini, 2014; Gould, 2014). However, 3D imaging techniques have recently undergone a rapid development and thus 3D geometric morphometrics analyses are becoming increasingly efficient (Wake, 2012). Advantages of 3D over 2D geometric morphometrics for capturing structures of biological relevance, increased accuracy and reduced distortion appear plausible (Buser et al., 2018). Here, we investigate the gain of information incorporating the third dimension in the case of three closely related fish species, and in the light of costs, effort, time expenditure and data size. This work compares the methodology and quantifies the results of 2D and 3D geometric morphometrics on the example of three laterally compressed morphospecies of Sailfin Silversides (Telmatherinidae) from the Malili-Lakes of Sulawesi (Indonesia). The morphometric analyzes are based on 2D X-ray images, 2D maximum intensity projections (Fig. 1) and 3D surface renders (Fig. 2) of the head. Our findings indicate that the repeatability is higher while the measurement error is lower in 3D compared to 2D geometric morphometrics. The z-axis contributes significantly to the variance of the 3D dataset which implies that the third dimension provides a major gain of information in our approach. Our results further indicate that 3D geometric morphometrics is capable of projecting small-scaled morphological differences between closely related species more precisely than a 2D analysis. However, our results display that working with µCT data consumes a manifold amount of time and data space compared to 2D images. Several additional software packages are required to create the necessary surface models and the costs of a µCT scanner exceed the expenses for an X-ray scanner by far. In conclusion, we are nevertheless convinced that the increased accuracy, the gain of information and the loss of distortion in 3D geometric morphometrics surpass the low-effort advantages of 2D geometric morphometrics even for laterally compressed animals. The opportunity of using performed µCT scans for further detailed analyses is another often neglected benefit of 3D imaging (Shi et al., 2018). Future improvement of µCT scanners and post-processing software will help to reduce the disadvantages of 3D imaging to a minimum which may result in 3D geometric morphometrics becoming the standard method for analyzing body shape in vertebrates in the near future. Fig. 1 + Fig. 2: Locations of the ten homologous landmarks placed on the heads of “Roundfin” Telmatherina specimens in 2D and 3D. Head shape is described by 10 Landmarks: 1 = anterior tip of premaxilla; 2 = nasal⁄maxilla joint; 3 = nasal⁄neurocranium joint; 4 = lacrimal process; 5 = dorsal neurocranium process; 6 = posterior dorsal point of neurocranium; 7 = posterior ventral end of articular; 8 = most posterior-ventral point of eye socket; 9 = most anterior-ventral point of eye socket; 10 = posterior ventral end of articular.

Figure 1
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Figure 2
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Acknowledgements

We would like to thank the Heinrich-Böll-Stiftung e.V. for funding the first author B. Wasiljew via a PhD scholarship. We are also thankful to C. Koch, P. Rühr, S. Wesel and F. W. Miesen for technical support and advice concerning 2D and 3D imaging techniques. The late R. K. Hadiaty enabled earlier fieldwork for obtaining the focal specimens in Indonesia. The study greatly benefited from discussions and constructive suggestions by J. Schwarzer, L. Hilgers, T. Spanke and J. Flury.

References

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Keywords: 3D µCT imaging, Cranial morphology, Geometric morphometrics (GM), Landmarks, Malili Lakes, sailfin silversides, Shape Analysis, Telmatherina, 2D x-ray imaging

Conference: XVI European Congress of Ichthyology, Lausanne, Switzerland, 2 Sep - 6 Sep, 2019.

Presentation Type: Oral

Topic: MORPHOLOGY, ONTOGENY AND PALAEONTOLOGY

Citation: Wasiljew B, Pfaender J, Wipfler B and Herder F (2019). The benefits of the third dimension – What can we learn from the z-axis in 3D geometric morphometrics based on sailfin silversides (Telmatherinidae)?. Front. Mar. Sci. Conference Abstract: XVI European Congress of Ichthyology. doi: 10.3389/conf.fmars.2019.07.00083

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Received: 21 Jul 2019; Published Online: 14 Aug 2019.

* Correspondence: Mr. Benjamin Wasiljew, Zoological Research Museum Alexander Koenig (LG), Bonn, Germany, b.wasiljew@leibniz-zfmk.de