Abstract

Evolutionary processes have been described not only in biology but also for a wide range of human cultural activities including languages and law. In contrast to the evolution of DNA or protein sequences, the detailed mechanisms giving rise to the observed evolution-like processes are not or only partially known. The absence of a mechanistic model of evolution implies that it remains unknown how the distances between different taxa have to be quantified. Considering distortions of metric distances, we first show that poor choices of the distance measure can lead to incorrect phylogenetic trees. Based on the well-known fact that phylogenetic inference requires additive metrics, we then show that the correct phylogeny can be computed from a distance matrix {mathbf {D}} if there is a monotonic, subadditive function zeta such that zeta ^{-1}({mathbf {D}}) is additive. The required metric-preserving transformation zeta can be computed as the solution of an optimization problem. This result shows that the problem of phylogeny reconstruction is well defined even if a detailed mechanistic model of the evolutionary process remains elusive.

Highlights

  • At the most abstract level, evolution can be seen as a consequence of the generation of variation and selection

  • Let us return to Assumption A and characterize distances that derive from additive metrics in a simple manner: Lemma 2 Let be a metric distance matrix, let be an a.m.-consistent function, suppose is invertible, increasing, and subadditive, and let| . | be a matrix norm

  • The correct inference of phylogenetic relationships is possible for additive distances and for the large class of distances that arise from additive metrics with a monotonic metric-preserving function

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Summary

Introduction

At the most abstract level, evolution can be seen as a consequence of the generation of variation and selection. Keywords Cultural evolution · Phylogenetic tree · Additive metric · Metric-preserving functions The basis of distance-based phylogenetic methods is additive metrics, i.e., metrics that are representations of edge-weighted trees.

Results
Conclusion

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