The shade avoidance syndrome: a non-Markovian stochastic growth model

Area of Science:

• Plant Physiology
• Mathematical Biology
• Ecology

Background:

• Plants in dense populations exhibit shade avoidance syndrome due to light competition.
• Shade avoidance syndrome is regulated by the hormone auxin, influenced by environmental and internal signals.
• Auxin-induced plant growth involves a time lag, suggesting complex regulatory mechanisms.

Purpose of the Study:

• To model plant growth dynamics considering the time lag in auxin signaling.
• To investigate the role of memory effects in plant growth processes.
• To simulate plant height distributions and compare them with real-world observations.

Main Methods:

• Developed a plant growth model based on energy-like function extremization, omitting the Markov property.
• Simulated plant height distributions and growth dynamics.
• Validated the model using experimental data of Arabidopsis thaliana and an independent biomass production model.

Main Results:

• The model successfully generated bimodal and right-skewed height distributions, mirroring natural plant communities.
• Simulated growth dynamics and speed for isolated plants closely matched experimental data for Arabidopsis thaliana.
• The model's growth dynamics demonstrated consistency with established biomass production functions.

Conclusions:

• Plant growth can be effectively modeled by extremizing an energy-like function, particularly when memory effects are considered.
• The findings underscore the significant role of memory effects in plant growth regulation.
• The developed model provides a robust framework for understanding plant growth dynamics and competition.