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

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  • 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
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    References listed on IDEAS

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    1. Zhang, Zhibin & Yan, Chuan & Krebs, Charles J. & Stenseth, Nils Chr., 2015. "Ecological non-monotonicity and its effects on complexity and stability of populations, communities and ecosystems," Ecological Modelling, Elsevier, vol. 312(C), pages 374-384.
    2. Hu, Dawei & Du, Xiaojie & Li, Leyuan & Sun, Yi & Zhang, Jinhui & Bai, Fanlu, 2016. "Robust stability mechanism of an artificial ecosystem based on biological mutations and synergies driven by ecological information," Ecological Modelling, Elsevier, vol. 319(C), pages 208-217.
    3. Eric D. Kelsic & Jeffrey Zhao & Kalin Vetsigian & Roy Kishony, 2015. "Counteraction of antibiotic production and degradation stabilizes microbial communities," Nature, Nature, vol. 521(7553), pages 516-519, May.
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