Because of the complexity of the plant’s responses to water deficit, researchers attempt to find simplified models to understand critical aspects of the problem, searching for single indicators that would allow evaluate the effects of environmental changes on the whole plant. However, this reductionist approach often used in plant sciences makes difficult to achieve systemic emergent behaviours. On the other, currently a new class of models and epistemology have called attention on fundamental properties of complex systems. These properties, named “emergent properties”, are observed in a larger scale of the system and cannot be observed or inferred from the smaller scales of observation in the same system. Herein, we propose that multivariate statistical analysis by principal components (PCA) can be a suitable tool to quantify global responses to environmental disturbances, allowing a kind of specific and partially quantitative assessment of emergent properties. From an experimental study with plants under water deficit, ours results showed that the classical approach of individual analysis of different data sets might provide different interpretations about the effects of water deficit in plants. Thus, the search for a single indicator for determining the behaviour of plants to environmental perturbations hampers a more accurate diagnosis and a deeper understanding of the interactions between plants and the environment. Our study supports the hypothesis that a cross-scale multivariate analysis is an appropriate method to establish models for the systemic understanding of the complex interactions between plants and their changing environment.
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