Towards a semantic approach to numerical tree inference in phylogenetics. (10th March 2017)

• Record Type: Journal Article
• Authors: Vogt, Lars
• Is Part Of: Cladistics. Volume 34:Number 2(2018:Apr.)
• Page Start: 200
• Page End: 224
• Publication Date: 2017-03-10
• Subjects: Cladistic analysis -- Periodicals; 578.012
• DOI: 10.1111/cla.12195
• Languages: English
• ISSNs: 0748-3007

Abstract

Abstract: Conventional approaches to phylogeny reconstruction require a character analysis step prior to and methodologically separated from a numerical tree inference step. The former results in a character matrix that contains the empirical data analysed in the latter. This separation of steps involves various methodological and conceptual problems (e.g. homology assessment independent of tree inference and character optimization, character dependencies, discounting of alternative homology hypotheses). In morphology, the character analysis step covers the stages of morphological comparative studies, homology assessment and the identification and coding of morphological characters. Unfortunately, only the last stage requires some formalism, whereas the preceding stages are commonly regarded to be pre‐rational and intuitive, which is why their reproducibility and analytical accessibility is limited. Here, I introduce a rational for a semantic approach to numerical tree inference that uses sets of semantic instance anatomies as data source instead of character matrices, thereby avoiding the above‐mentioned problems. A semantic instance anatomy is an ontology‐based description of the anatomical organization of a specimen in the form of a semantic graph. The semantic approach to numerical tree inference combines and integrates the steps of character analysis and numerical tree inference and makes both analytically accessible and communicable. Before outlining first steps for a research programme dedicated to the semantic approach to numerical tree inference, I discuss in detail the methodological, conceptual, and computational challenges and requirements that first have to be dealt with before adequate algorithms can be developed.