Cross-scale multivariate analysis of physiological responses to high temperature in two tropical crops with C₃ and C₄ metabolism

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• 作者：Hilton F. Ví ; tolo^a ; Gustavo M. Souza^a ; ^{gustavo@unoeste.br} ; Joaquim A.G. Silveira^b
• 关键词：Brachiaria brizantha ; Glycine max ; Oxidative stress ; Photosynthesis ; Plant growth ; Principal component analysis
• 刊名：Environmental and Experimental Botany
• 出版年：2012
• 期刊代码：39_00988472
• 类别：abs
• 出版时间：August, 2012
• 卷：80
• 期：Complete
• 页码：54-62
• 文件大小：661 K

摘要

The concept of hierarchical levels and its implications for biological organisation have been recognised for many years, and various hierarchical models of biological organisation have been proposed. However, the increased tendency to analyse biological systems from the molecular perspective has overshadowed the integrated view of living beings. On the other hand, plant ecophysiological studies have shown that variations in environmental factors can affect the plant from the molecular to the whole-plant level in different ways. The main objective of this study was to demonstrate that a cross-scale multivariate approach is important to support more consistent conclusions about plant-environment interactions. A study with two tropical crops (a C₃ and a C₄ species) was performed to test the effects, in a single experiment, of high temperatures across different levels of plant organisation from the molecular to the whole-plant level. Simulations using principal component analysis (PCA) on variables grouped according to the nature of the data were performed to investigate the effect of the observational scale on the interpretation of plant responses to high temperature. Despite an efficient photosynthetic response to elevated temperature, indicating an acclimatisation of the photosynthetic apparatus, the results of growth analysis showed a significant reduction in the biomass of both species and in leaf area. The resulting PCAs showed that the distinct differences between the responses of each species to the two temperature regimes were dependent on the specific data set observed. Despite causal associations between the physiological variables in different temperature regimes, the PCA results demonstrate that focusing on specific datasets markedly influences the interpretation of plant environmental response. Thus, we suggest that the multivariate approach could improve the differentiation between a global disruptive disturbance and a local physiological adjustment within a plant.