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

TL;DR

This paper presents a non-Markovian stochastic model for plant growth that accounts for memory effects in shade avoidance syndrome, successfully fitting experimental data and revealing bimodal height distributions.

Contribution

It introduces a novel non-Markovian model for plant growth that incorporates time lag effects of auxin signaling, advancing understanding of shade avoidance mechanisms.

Findings

Model accurately fits Arabidopsis growth data

Simulated height distributions are bimodal and skewed

Growth dynamics align with biomass production models

Abstract

Plants at high population density compete for light, showing a series of physiological responses known as the shade avoidance syndrome. These responses are controlled by the synthesis of the hormone auxin, which is regulated by two signals, an environmental one and an internal one. Considering that the auxin signal induces plant growth after a time lag, this work shows that plant growth can be modelled in terms of an energy-like function extremization, provided that the Markov property is not applied. The simulated height distributions are bimodal and right skewed, as in real community of plants. In the case of isolated plants, the theoretical expressions for the growth dynamics and the growth speed excellently fit experimental data for Arabidopsis thaliana. Moreover, the growth dynamics of this model is shown to be consistent with the biomass production function of an independent model. These results suggest that memory effects play a non-negligible role in plant growth processes.