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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Computational capability of ecological dynamics.

Masayuki Ushio1,2,3, Kazufumi Watanabe4, Yasuhiro Fukuda5

  • 1Hakubi Center, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan.

Royal Society Open Science
|April 24, 2023
PubMed
Summary

Ecological dynamics can be a computational resource. Researchers developed ecological reservoir computing (ERC) frameworks to show that ecological networks can perform computations and predict future dynamics, demonstrating real-time computation in a unicellular organism population.

Keywords:
computational capabilityecological dynamicsecological networksmachine learningneural networkreservoir computing

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Area of Science:

  • Ecology
  • Computational Science
  • Machine Learning

Background:

  • Ecological dynamics are driven by complex networks.
  • Artificial networks are used for machine learning, but ecological networks' computational potential is unexplored.

Purpose of the Study:

  • To investigate if ecological networks possess computational capabilities.
  • To develop frameworks for using ecological dynamics as a computational resource.

Main Methods:

  • Developed two computational/empirical frameworks based on reservoir computing.
  • Implemented *in silico* ecological reservoir computing (ERC) to reconstruct and process ecological dynamics.
  • Utilized real-time ERC with *Tetrahymena thermophila* population dynamics as a computational resource, using temperature as input.

Main Results:

  • *In silico* ERC successfully predicted near-future chaotic dynamics and emulated nonlinear dynamics.
  • Real-time ERC demonstrated synchronized dynamics and near-future predictions of empirical time series.
  • Provided the first empirical evidence of population-level phenomena performing real-time computations.

Conclusions:

  • Ecological dynamics can be harnessed as a computational resource.
  • Findings open new research avenues in computational science and ecology regarding the use, evolution, and maintenance of computational capabilities in ecosystems.