IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v488y2024ics0304380023003319.html
   My bibliography  Save this article

Information sharing promotes bacterial diversity in oligotrophic environment with low-dose X-ray radiation based on modeling and simulation of agent-based model

Author

Listed:
  • Zhang, Xiaojun
  • Li, Baohuan

Abstract

It remains unclear why bacterial diversity was formed and maintained in oligotrophic environment (OE) with low-dose X-ray radiation (LDXR) due to violation of the competitive exclusion principle in ecology. Based on microbial ecology, bioinformatics, cybernetics, experimental phenomena and data, a new hypothesis was proposed to elucidate information sharing mechanisms driving bacterial community succession with high diversity in OE with LDXR. According to hypothesis, a valid agent-based model (ABM) of cellular automation (CA) was developed to quantitatively describe the hypothesis, and the agent-based simulation sufficiently proved that as the bacterial individuals conduct cooperation to indiscriminately share information of substrate positions with each other in OE with LDXR, it can effectively increase the survival probabilities of all bacterial species, alleviate interspecific competition, and prevent any bacterial species from being dominant, which is beneficial to the coexistence of most species and promotes bacterial diversity during community succession. The results of agent-based simulation are highly similar to the observed phenomena and data in the experiments, therefore sufficiently confirm the proposed hypothesis.

Suggested Citation

  • Zhang, Xiaojun & Li, Baohuan, 2024. "Information sharing promotes bacterial diversity in oligotrophic environment with low-dose X-ray radiation based on modeling and simulation of agent-based model," Ecological Modelling, Elsevier, vol. 488(C).
  • Handle: RePEc:eee:ecomod:v:488:y:2024:i:c:s0304380023003319
    DOI: 10.1016/j.ecolmodel.2023.110601
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380023003319
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2023.110601?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.

    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:ecomod:v:488:y:2024:i:c:s0304380023003319. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: http://www.journals.elsevier.com/ecological-modelling .

    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.