Growth Cost and Transport Efficiency Tradeoffs Define Root System Optimization Across Varying Developmental Stages and Environments in Arabidopsis

Abstract

ABSTRACT Root system architecture (RSA) is central to plant adaptation and fitness, yet the design principles and regulatory mechanisms connecting RSA to environmental adaptation are not well understood. We developed Ariadne, a semi-automated software for quantifying cost-efficiency tradeoffs of RSA by mapping root networks onto a Pareto-optimality framework, which describes the balance between resource transport efficiency and construction cost. Applying Ariadne to Arabidopsis thaliana , we found that root architectures consistently assume Pareto-optimal forms across developmental stages, genotypes, and environmental conditions. Using the Discovery Engine, an engine that combines machine learning together with interpretability techniques, we found developmental stage, the hy5/chl1-5 genotype, and manganese availability as important determinants of the cost-efficiency tradeoff, with manganese exerting a unique influence not observed for other nutrients. These results reveal that RSA plasticity is genetically constrained to cost-efficiency optimal configurations and that developmental and environmental factors shift RSA on the pareto front, with manganese acting as a strong modulator of the transport efficiency and construction cost balance.

Plant phenotyping relevance

RSAのコスト効率トレードオフを定量化する半自動ソフトウェアを開発し、植物形態形質の解析に適用しており、表現型取得・抽出手法が研究の中心である。

Code and data availability

The paper's authors developed the Ariadne software used for all RSA phenotyping and Pareto analysis in this study, and explicitly state it is publicly available on PyPI and provide a GitHub code availability URL. Both are paper-specific, public, actionable code assets. No public phenotype dataset deposit is stated; the