Login / Signup

Giant taxon-character matrices II: a response to Laing et al. (2017).

Tiago Rodrigues SimõesMichael W CaldwellAlessandro PalciRandall L Nydam
Published in: Cladistics : the international journal of the Willi Hennig Society (2017)
The trend towards big data analyses in evolutionary biology has been observed in phylogenetics via the assembly of giant datasets composed of genomic and phenotypic data. We recently (Simões et al., 2017. Giant taxon-character matrices: Quality of character constructions remains critical regardless of size. Cladistics 33, 198-219) presented a critique of the phylogenetic character concepts used in current morphological datasets, with the caution that giant datasets did not obviate the empirical requirement of rigor in character construction. Laing et al. (2017. Giant taxon-character matrices: The future of morphological systematics. Cladistics, https://doi.org/10.1111/cla.12197) have since argued that we had 'suggested' that large datasets inherently contain flawed characters, and that we had presented a substandard methodology of phylogenetic analysis. Laing et al. concluded by discussing their approach to phylogenetic signal, total evidence and the inevitability of large datasets. We here reply to Laing et al. by reviewing what we actually wrote regarding dataset size, characters and methodology. We show that Laing et al.'s. central premise is unsupported, thus characterizing a Straw Man argument, and deeply misrepresents our original study. In part two, we discuss total evidence and phylogenetic signal issues raised by Laing et al. that are of major consequence to the appropriate construction of large morphological datasets.
Keyphrases
  • big data
  • rna seq
  • rare case
  • artificial intelligence
  • machine learning
  • single cell
  • gene expression
  • risk assessment
  • dna methylation
  • electronic health record
  • deep learning