Quantifying the tape of life: Ancestry-based metrics provide insights and intuition about evolutionary dynamics
A peer-reviewed article of this Preprint also exists.
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Abstract
Fine-scale evolutionary dynamics can be challenging to tease out when focused on broad brush strokes of whole populations over long time spans. We propose a suite of diagnostic metrics that operate on lineages and phylogenies in digital evolution experiments with the aim of improving our capacity to quantitatively explore the nuances of evolutionary histories in digital evolution experiments. We present three types of lineage measurements: lineage length, mutation accumulation, and phenotypic volatility. Additionally, we suggest the adoption of four phylogeny measurements from biology: depth of the most-recent common ancestor, phylogenetic richness, phylogenetic divergence, and phylogenetic regularity. We demonstrate the use of each metric on a set of two-dimensional, real-valued optimization problems under a range of mutation rates and selection strengths, confirming our intuitions about what they can tell us about evolutionary dynamics.
Cite this as
2018. Quantifying the tape of life: Ancestry-based metrics provide insights and intuition about evolutionary dynamics. PeerJ Preprints 6:e26883v1 https://doi.org/10.7287/peerj.preprints.26883v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
Author comment
This paper describes a new suite of metrics for summarizing the long-term evolutionary dynamics of lineages. It also points out some useful metrics used by biologists that we suggest should be used in computational evolution as well. We have submitted this paper to a peer-reviewed conference: Artificial Life 2018.
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Additional Information
Competing Interests
Charles Ofria is an Editor for PeerJ Computer Science.
Author Contributions
Emily Dolson conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, performed the computation work, authored or reviewed drafts of the paper, approved the final draft.
Alexander Lalejini conceived and designed the experiments, performed the experiments, analyzed the data, prepared figures and/or tables, performed the computation work, authored or reviewed drafts of the paper, approved the final draft.
Steven Jorgensen conceived and designed the experiments, performed the computation work, authored or reviewed drafts of the paper, approved the final draft.
Charles Ofria conceived and designed the experiments, performed the computation work, authored or reviewed drafts of the paper, approved the final draft.
Data Deposition
The following information was supplied regarding data availability:
The code and data are available on Github: https://github.com/stevenjson/ALife2018-Lineage.
Funding
This research was supported by the National Science Foundation (NSF) through the BEACON Center (Cooperative Agreement DBI-0939454), Graduate Research Fellowships to ED and AL (Grant No. DGE-1424871), and NSF Grant No. DEB-1655715 to CO. Michigan State University provided computational resources through the Institute for Cyber-Enabled Research and the Digital Scholarship Lab. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF or MSU. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
