Thanks to visit codestin.com
Credit goes to github.com

Skip to content

sustentarea/logonia

Logônia

Project Status: Active - The project has reached a stable, usable state and is being actively developed. FAIR checklist badge fair-software.eu License: GPLv3

Overview

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.

If you find this project useful, please consider giving it a star! GitHub repo stars

How It Works

Logônia runs on a grid of patches, where each patch represents a piece of soil that can host a plant. Patches correspond to a specific geographic area and store historical climate data.

Each simulation step represents one month. Over time, plants grow, reproduce, and age. These processes are controlled by sliders on the model’s interface. Climate conditions directly influence growth probability, adding realism and complexity to the simulation.

Climate Data

The model uses Historical Monthly Weather Data from WorldClim 2.1 (Fick & Hijmans, 2017) for a region of the Brazilian Amazon Forest.

This dataset provides 12 monthly values per year for 1951–2024, based on downscaled data from CRU-TS-4.09, developed by the Climatic Research Unit at the University of East Anglia (Harris et al., 2020). The variables are: Average Minimum Temperature (°C) (A), Average Maximum Temperature (°C) (B), and Total Precipitation (mm) (C).

The dataset can be reproduced using Quarto notebooks in the qmd folder of the model repository, adapted from those in the LogoClim model.

Energy and Growth Probability

Growth probability is determined by a logistic regression model that incorporates patch-level climate variables. The probability follows the equation below:

$$ p(\text{tmin, tmax, prec}) = \cfrac{1}{1 + e^{- (\beta_{0} + \beta_{\text{tmin}} \text{tmin} + \beta_{\text{tmax}} \text{tmax} + \beta_{\text{prec}} \text{prec})}} $$

A logônia gains or loses energy at each step according to the following rules:

  • If a random number between 0 and 1 is less than or equal to the growth probability for the current patch, the plant gains the number of energy points defined by the energy-gain slider.
  • If the probability is below 0.25 and does not meet the above condition, the plant loses 1 energy point.
  • Otherwise, its energy remains unchanged.

If a plant gets to 0 points of energy it dies.

Growth Phases

A logônia plant develops through three phases: seedling, juvenile, and adult. Each has distinct shapes and energy thresholds.

As a seedling, the logônia can only grow and age. Once it accumulates 10 energy points, it becomes a juvenile.

As a juvenile, it continues to grow and age. When it reaches 30 energy points, it advances to its final stage: an adult.

As an adult, the logônia gains the ability to reproduce.

Reproduction

Adult plants can reproduce asexually by randomly colonizing unoccupied patches:

  • If an adult has at least 30 energy points, and a random number between 0 and 1 is less than or equal to the reproduction-rate slider, a new seedling is created in a randomly selected patch with 0 age and 1 point of energy.
  • If the selected patch is already occupied, the seedling dies immediately.

When a logônia occupy a patch, the patch color changes to brown to indicate it was once taken.

Senescence

Plants age by 1 month per step. Age is shown by color, fading from lime to brown. They die when they reach their maximum age of 100 months or run out of energy.

How to Use It

See a showcase of the model at the following link: https://youtu.be/kzfTuw9U-wI?si=ztyGvA5_ztb5dew9

Setup

To get started, ensure you have NetLogo installed. This model was developed with NetLogo 7.0.0. Use this version or newer for best compatibility.

The model relies on the NetLogo extensions LevelSpace and String. These extensions are installed automatically when the model is run for the first time.

Downloading the Model

You can download the latest release of the model from its GitHub releases page. For the development version, you can clone or download this GitHub repository directly.

Important

Download all repository files, not only the nlogox folder.

Running the Model

Once everything is ready, open the logonia.nlogox file located in the nlogox folder, click Setup and then Go to start. No extra installation steps are needed.

Refer to the Info tab in the model for additional details.

How to Cite

To cite Logônia please use the following format:

Vartanian, D., Garcia, L., & Carvalho, A. M. (2025). Logônia: Plant growth response model in NetLogo [Computer software]. https://doi.org/10.17605/OSF.IO/TPY7S

A BibTeX entry for LaTeX users is:

@Misc{vartanian2025,
  title = {Logônia: Plant growth response model in NetLogo},
  author = {{Daniel Vartanian} and {Leandro Garcia} and {Aline Martins de Carvalho}},
  year = {2025},
  doi = {10.17605/OSF.IO/TPY7S},
  note = {Computer software}
}

How to Contribute

Contributions are welcome! Whether you want to report bugs, suggest features, or improve the code or documentation, your input is highly valued. Please check the issues tab for existing issues or to open a new one.

License

Copyright (C) 2025 Daniel Vartanian

Logônia is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free Software
Foundation, either version 3 of the License, or (at your option) any later
version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with
this program. If not, see <https://www.gnu.org/licenses/>.

Acknowledgments

We gratefully acknowledge Stephen E. Fick, Robert J. Hijmans, and the entire WorldClim team for their outstanding work in creating and maintaining the WorldClim datasets.

We also thank the Climatic Research Unit at the University of East Anglia and the UK Met Office for providing the CRU-TS-4.09 dataset, a key source of historical climate data.




This work was developed with support from the Sustentarea Research and Extension Center at the University of São Paulo (USP).



This work was supported by the Department of Science and Technology of the Secretariat of Science, Technology, and Innovation and of the Health Economic-Industrial Complex (SECTICS) of the Ministry of Health of Brazil, and the National Council for Scientific and Technological Development (CNPq) (grant no. 444588/2023-0).