Estimating Physiological Dose Optima in Plant Radiohormesis Through Censoring-Aware Hierarchical Bayesian Modeling

Abstract

Background: Low doses of ionizing radiation can enhance germination and early seedling vigor in many plant species, but dose–response curves are often measured over a narrow dose range. As a result, the empirical dose of maximal stimulation (D^*) is frequently recorded at the limits of that range, so estimates that treat those boundary values as exact can be biased. Objectives: Using dose–response data from Nicotiana tabacum, a censoring-aware framework is developed to obtain biologically interpretable estimates of  D^* across developmental traits. Methods: The approach combines a censored regression formulation with a hierarchical Bayesian model that partially pools information across varieties and developmental traits while preserving the differences between them. Results: Posterior inference showed a consistent pattern: germination and early seedling traits had lower optimal doses, whereas biomass-related traits were shifted into the upper part of the tested range, which suggests distinct radiosensitivity regimes across developmental stages. Conclusion: The sample is small, so the framework is presented as a means of estimating stimulation optima together with the uncertainty that boundary effects induce, to guide the design of pre-sowing irradiation trials.