Explaining preemptive acclimation by linking information to plant phenotype

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1093/jxb/erab537. This is version 3 of this Preprint.


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Pedro José Aphalo, Víctor O. Sadras


We review mechanisms for preemptive acclimation in plants and propose a conceptual model linking developmental and evolutionary ecology with the acquisition of information through sensing of cues and signals. The idea is that plants acquire much of the information in the environment not from individual cues and signals but instead from their joint multivariate properties such as correlations. If molecular signalling has evolved to extract such information, the joint multivariate properties of the environment must be encoded in the genome, epigenome and phenome. We contend that multivariate complexity explains why extrapolating from experiments done in artificial contexts into natural or agricultural systems almost never works for characters under complex environmental regulation: biased relationships among the state variables both in time and space create a mismatch between the evolutionary history reflected in the genotype and the artificial growing conditions in which the phenotype is expressed. Our model can generate testable hypotheses bridging levels of organization. In this note we describe the model, its theoretical bases and discuss its implications. We illustrate the hypotheses that can be derived from the model in two cases of preemptive acclimation based on correlations in the environment: the shade avoidance response and acclimation to drought.




Agriculture, Biology, Ecology and Evolutionary Biology, Integrative Biology, Life Sciences


adaptation, cues and signals, daptation, drought, eco-devo, epigenome, genome, information, phenome, preemptive acclimation, preemptive acclimation., shade


Published: 2021-07-07 11:48

Last Updated: 2021-12-06 17:09

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CC-By Attribution-NonCommercial-NoDerivatives 4.0 International

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Data and Code Availability Statement:
This is a literature review and theoretical analysis based on a conceptual model. Only two figures are based on new data from a study just completed. These data are available throught a link in a separate preprint available at https://ecoevorxiv.org/ypqea/.

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