Computational Model Library

{Logônia}: Plant Growth Response Model in NetLogo (1.1.0)

Logônia is a NetLogo model that simulates the growth response of a fictional plant, logônia, under different climatic conditions. The model uses climate data from WorldClim 2.1 and demonstrates how to integrate the LogoClim model through the LevelSpace extension.

Logônia follows the FAIR Principles for Research Software (Barker et al., 2022) and is openly available on the CoMSES Network and GitHub.

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Release Notes

  • Updated Juvenile color in Logônias for better contrast.
  • Modified climate variable inspection: the model now halts if a switch is toggled during the simulation.
  • Upgraded LogoClim to v2.1.0.
  • Revised 12-month moving average for climate variables to use values directly from LogoClim.
  • Fixed 12-month moving average counter for Logônia plants.
  • Fixed logistic-regression to return false when encountering false values.
  • Refactored code for improved readability and maintainability.
  • Updated the documentation.

Associated Publications

Vartanian, D., Garcia, L., & Carvalho, A. M. (2025). {LogoClim}: WorldClim in NetLogo [Computer software]. https://doi.org/10.17605/OSF.IO/EAPZU

{Logônia}: Plant Growth Response Model in NetLogo 1.1.0

Logônia is a NetLogo model that simulates the growth response of a fictional plant, logônia, under different climatic conditions. The model uses climate data from WorldClim 2.1 and demonstrates how to integrate the LogoClim model through the LevelSpace extension.

Logônia follows the FAIR Principles for Research Software (Barker et al., 2022) and is openly available on the CoMSES Network and GitHub.

Release Notes

  • Updated Juvenile color in Logônias for better contrast.
  • Modified climate variable inspection: the model now halts if a switch is toggled during the simulation.
  • Upgraded LogoClim to v2.1.0.
  • Revised 12-month moving average for climate variables to use values directly from LogoClim.
  • Fixed 12-month moving average counter for Logônia plants.
  • Fixed logistic-regression to return false when encountering false values.
  • Refactored code for improved readability and maintainability.
  • Updated the documentation.

Version Submitter First published Last modified Status
1.1.0 Daniel Vartanian Tue Sep 16 08:55:14 2025 Tue Sep 16 08:55:19 2025 Published
1.0.0 Daniel Vartanian Sat Sep 13 07:35:14 2025 Tue Sep 16 08:47:07 2025 Published

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