Quantifying the dynamic recovery of plants through stress memory and physiological attractors.

Abstract

Climate change-induced weather variability poses a growing threat to global food security, yet plant resilience is still interpreted through static and reductionist models that treat stress as independent and transient. Here, we introduce a unified quantitative framework grounded in dynamical systems theory. We formalize three novel metrics: (1) the Phenological Weather Memory Index (PWMI), that quantifies exponentially decaying stress memory across developmental stages (with a decay constant α = 0.10 determined by cross‑validation); (2) the Treatment-Weather Resonance Coefficient (TWRC), which measures the alignment of agronomic interventions with favorable weather conditions; and (3) the Physiological State-Space Trajectory (PSST), which maps multi-trait plant physiology into low dimensional attractor basins. Analyzing 288 tomato plants across 24 cultivars under hot, sub-tropical conditions (mean VPD: 2.53 kPa, 65 heat days > 35 °C), we discovered that stress memory is strongly phase-dependent, remaining minimal during vegetative growth (PWMI = 0.009) but increasing sharply during reproductive phase (PWMI = 0.574). Despite the prolonged thermal stress, 97.9% of plants converged into a stable high-yield attractor basin, revealing a fundamental nonlinearity in plant performance. This convergence was driven by dynamic recovery, defined as the capacity of certain cultivars to rapidly forget the stress memory while maintaining internal physiological flexibility. Cultivars such as 'Pony Express', combined low PWMI with effective treatment-weather synchronization, enabling stable productivity under extreme conditions. Together, these results demonstrate that resilience is not a static trait of endurance, but an emergent property arising from temporal synchronization, rapid stress recovery and stable physiological organization. By quantifying stress "forgetting curves" and attractor dynamics, this framework provides a predictive, systems-based foundation for breeding and management strategies that prioritize dynamic recovery over stress tolerance alone.

Plant phenotyping relevance

植物のストレス記憶・回復・生理状態を定量化する新規指標と状態空間フレームワークを中心に提示しており、単なる生理測定ではなく表現型抽出・解析手法の開発に該当する。

Code and data availability

The paper's analysis code is stated to be in a Jupyter notebook embedded in the supplementary material, but no public repository, deposit, or authors' URL is provided; no public phenotype dataset, images, or model checkpoints are linked. The only URL present is the Creative Commons license, which is not a paper asset.

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