Discovering Tolerance—A Computational Approach to Assess Abiotic Stress Tolerance in Tomato Under Greenhouse Conditions - Biermann, Robin Tim; Bach, Linh T.; Kläring, Hans-Peter; Baldermann, Susanne; Börnke, Frederik; Schwarz, Dietmar (Frontiers in Sustainable Food Systems, 2022)
Modern plant cultivars often possess superior growth characteristics, but within a limited range of environmental conditions. Due to climate change, crops will be exposed to distressing abiotic conditions more often in the future, out of which heat stress is used as example for this study. To support identification of tolerant germplasm and advance screening techniques by a novel multivariate evaluation method, a diversity panel of 14 tomato genotypes, comprising Mediterranean landraces of Solanum lycopersicum, the cultivar “Moneymaker” and Solanum pennellii LA0716, which served as internal references, was assessed toward their tolerance against long-term heat stress. After 5 weeks of growth, young tomato plants were exposed to either control (22/18°C) or heat stress (35/25°C) conditions for 2 weeks. Within this period, water consumption, leaf angles and leaf color were determined. Additionally, gas exchange and leaf temperature were investigated. Finally, biomass traits were recorded. The resulting multivariate dataset on phenotypic plasticity was evaluated to test the hypothesis, that more tolerant genotypes have less affected phenotypes upon stress adaptation. For this, a cluster-analysis-based approach was developed that involved a principal component analysis (PCA), dimension reduction and determination of Euclidean distances. These distances served as measure for the phenotypic plasticity upon heat stress. Statistical evaluation allowed the identification and classification of homogeneous groups consisting each of four putative more or less heat stress tolerant genotypes. The resulting classification of the internal references as “tolerant” highlights the applicability of our proposed tolerance assessment model. PCA factor analysis on principal components 1–3 which covered 76.7% of variance within the phenotypic data, suggested that some laborious measure such as the gas exchange might be replaced with the determination of leaf temperature in larger heat stress screenings. Hence, the overall advantage of the presented method is rooted in its suitability of both, planning and executing screenings for abiotic stress tolerance using multivariate phenotypic data to overcome the challenge of identifying abiotic stress tolerant plants from existing germplasms and promote sustainable agriculture for the future.