IDEAS home Printed from https://ideas.repec.org/a/eee/thpobi/v148y2022icp22-27.html
   My bibliography  Save this article

When can we reconstruct the ancestral state? A unified theory

Author

Listed:
  • Ho, Lam Si Tung
  • Dinh, Vu

Abstract

Ancestral state reconstruction is one of the most important tasks in evolutionary biology. Conditions under which we can reliably reconstruct the ancestral state have been studied for both discrete and continuous traits. However, the connection between these results is unclear, and it seems that each model needs different conditions. In this work, we provide a unifying theory on the consistency of ancestral state reconstruction for various types of trait evolution models. Notably, we show that for a sequence of nested trees with bounded heights, the necessary and sufficient conditions for the existence of a consistent ancestral state reconstruction method under discrete models, the Brownian motion model, and the threshold model are equivalent. When tree heights are unbounded, we provide a simple counter-example to show that this equivalence is no longer valid.

Suggested Citation

  • Ho, Lam Si Tung & Dinh, Vu, 2022. "When can we reconstruct the ancestral state? A unified theory," Theoretical Population Biology, Elsevier, vol. 148(C), pages 22-27.
    • Handle: RePEc:eee:thpobi:v:148:y:2022:i:c:p:22-27
    DOI: 10.1016/j.tpb.2022.09.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040580922000648
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tpb.2022.09.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lambert, Amaury & Stadler, Tanja, 2013. "Birth–death models and coalescent point processes: The shape and probability of reconstructed phylogenies," Theoretical Population Biology, Elsevier, vol. 90(C), pages 113-128.
    2. Ho, Lam Si Tung & Dinh, Vu & Nguyen, Cuong V., 2019. "Multi-task learning improves ancestral state reconstruction," Theoretical Population Biology, Elsevier, vol. 126(C), pages 33-39.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Steel, Mike, 2015. "Reflections on the extinction–explosion dichotomy," Theoretical Population Biology, Elsevier, vol. 101(C), pages 61-66.
    2. Lambert, Amaury, 2018. "The coalescent of a sample from a binary branching process," Theoretical Population Biology, Elsevier, vol. 122(C), pages 30-35.
    3. Crespo, Fausto F. & Posada, David & Wiuf, Carsten, 2021. "Coalescent models derived from birth–death processes," Theoretical Population Biology, Elsevier, vol. 142(C), pages 1-11.
    4. Ignatieva, Anastasia & Hein, Jotun & Jenkins, Paul A., 2020. "A characterisation of the reconstructed birth–death process through time rescaling," Theoretical Population Biology, Elsevier, vol. 134(C), pages 61-76.
    5. Wiuf, Carsten, 2018. "Some properties of the conditioned reconstructed process with Bernoulli sampling," Theoretical Population Biology, Elsevier, vol. 122(C), pages 36-45.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:thpobi:v:148:y:2022:i:c:p:22-27. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/intelligence .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.