Osmotic stress decreases complexity underlying the electrophysiological dynamic in soybean.

Studies on plant electrophysiology are mostly focused on specific traits of single cells. Inspired by the complexity of the signalling network in plants, and by analogy with neurons in human brains, we sought evidence of high complexity in the electrical dynamics of plant signalling and a likely relationship with environmental cues. An EEG-like standard protocol was adopted for high-resolution measurements of the electrical signal in Glycine max seedlings. The signals were continuously recorded in the same plants before and after osmotic stimuli with a -2 MPa mannitol solution. Non-linear time series analyses methods were used as follows: auto-correlation and cross-correlation function, power spectra density function, and complexity of the time series estimated as Approximate Entropy (ApEn). Using Approximate Entropy analysis we found that the level of temporal complexity of the electrical signals was affected by the environmental conditions, decreasing when the plant was subjected to a low osmotic potential. Electrical spikes observed only after stimuli followed a power law distribution, which is indicative of scale invariance. Our results suggest that changes in complexity of the electrical signals could be associated with water stress conditions in plants. We hypothesised that the power law distribution of the spikes could be explained by a self-organised critical state (SOC) after osmotic stress.