ontophylo: Reconstructing the evolutionary dynamics of phenomes using new ontology‐informed phylogenetic methods

Author:

Porto Diego S.1ORCID,Uyeda Josef2ORCID,Mikó István3ORCID,Tarasov Sergei1ORCID

Affiliation:

1. Finnish Museum of Natural History Helsinki Finland

2. Department of Biological Sciences Virginia Tech Blacksburg Virginia USA

3. University of New Hampshire Durham New Hampshire USA

Abstract

Abstract Reconstructing ancestral states for discrete characters is essential for understanding trait evolution in organisms. However, most existing methods are limited to individual characters and often overlook the hierarchical and interactive nature of traits. Recent advances in phylogenetics now offer the possibility of integrating knowledge from anatomy ontologies to reconstruct multiple discrete character histories. Nonetheless, practical applications that fully harness the potential of these new approaches are still lacking. This paper introduces ontophylo, an R package that extends the PARAMO pipeline to address these limitations. Ontophylo enables the reconstruction of phenotypic entities composed of amalgamated characters, such as anatomical regions or entire phenomes. It offers three new applications: (1) reconstruction of evolutionary rates of amalgamated characters using phylogenetic non‐homogeneous Poisson process (pNHPP) that allows modelling rate variation across tree branches and time; (2) reconstruction of morphospace dynamics; and (3) visualization of evolutionary rates on vector images of organisms. Ontophylo incorporates ontological knowledge to facilitate these applications. Benchmarking confirms the accuracy of pNHPP in estimating character rates under different evolutionary scenarios, and example applications demonstrate the utility of ontophylo in studying morphological evolution in Hymenoptera using simulated data. Ontophylo can be easily integrated with other ontology‐oriented and general‐purpose R packages and offers new opportunities to examine morphological evolution on a phenomic scale using new and legacy data.

Funder

National Science Foundation

Academy of Finland

Publisher

Wiley

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