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Applications of Self-Organizing Maps for Ecomorphological Investigations through Early Ontogeny of Fish

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  • Tommaso Russo
  • Michele Scardi
  • Stefano Cataudella

Abstract

We propose a new graphical approach to the analysis of multi-temporal morphological and ecological data concerning the life history of fish, which can typically serves models in ecomorphological investigations because they often undergo significant ontogenetic changes. These changes can be very complex and difficult to describe, so that visualization, abstraction and interpretation of the underlying relationships are often impeded. Therefore, classic ecomorphological analyses of covariation between morphology and ecology, performed by means of multivariate techniques, may result in non-exhaustive models. The Self Organizing map (SOM) is a new, effective approach for pursuing this aim. In this paper, lateral outlines of larval stages of gilthead sea bream (Sparus aurata) and dusky grouper (Epinephelus marginatus) were recorded and broken down using by means of Elliptic Fourier Analysis (EFA). Gut contents of the same specimens were also collected and analyzed. Then, shape and trophic habits data were examined by SOM, which allows both a powerful visualization of shape changes and an easy comparison with trophic habit data, via their superimposition onto the trained SOM. Thus, the SOM provides a direct visual approach for matching morphological and ecological changes during fish ontogenesis. This method could be used as a tool to extract and investigate relationships between shape and other sinecological or environmental variables, which cannot be taken into account simultaneously using conventional statistical methods.

Suggested Citation

  • Tommaso Russo & Michele Scardi & Stefano Cataudella, 2014. "Applications of Self-Organizing Maps for Ecomorphological Investigations through Early Ontogeny of Fish," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-9, January.
  • Handle: RePEc:plo:pone00:0086646
    DOI: 10.1371/journal.pone.0086646
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    Cited by:

    1. Tommaso Russo & Lorenzo D'Andrea & Antonio Parisi & Stefano Cataudella, 2014. "VMSbase: An R-Package for VMS and Logbook Data Management and Analysis in Fisheries Ecology," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-18, June.
    2. Hancui Zhang & Shuyu Chen & Jun Liu & Zhen Zhou & Tianshu Wu, 2017. "An incremental anomaly detection model for virtual machines," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-23, November.

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