Are Plant Noises an Important Element in Ecosystems? A Response to a Bi-Directional Research Contribution of Pyke and Karban

A stressed plant emitted around 30 to 50 of these popping or clicking sounds per hour at seemingly random intervals, but unstressed plants emitted far fewer sounds — around one per hour.

So are plant noises an important feature of ecosystems, influencing the behaviour of plants and animals alike? The evidence isn’t yet clear, says Graham Pyke, a retired biologist who specializes in environmental science.

The model produced by the team was 70% accurate and it was used to deduce whether a plant had been cut or if it was water stressed. This result suggests a possible role for the audio monitoring of plants in farming and horticulture.

But rest assured, the bouquet of cut flowers in your vase isn’t screaming at you in pain. There is no evidence that the noise produced by the plants is intentional or a form of communication.

We know about the plant’s responses to stress. Richard Karban, a distinguished professor of entomology at University of California, Davis who studies interactions between herbivores and their host plants, said it’s a useful contribution to the field and to our general appreciation that plants are capable of sophisticated behaviors. He wasn’t involved in the research.

Previously, Hadany’s team has also studied whether plants can ‘hear’ sounds, and found that beach evening-primoses (Oenothera drummondii) release sweeter nectar when exposed to the sound of a flying bee2.

For example, he said the sounds could indicate to a female moth the message that a particular tomato plant is in stress and not suitable to lay eggs on, or feed on.

“Lots of sounds in the world are generated that are not ‘intentional’ signals, but nonetheless can be heard and used by other organisms for their own benefits. Does the concept of communication need to be bi-directional for it to be considered as such? He said it in an email.

Learning to distinguish between unstressed and thirsty plants by machine-learning acoustic emission classification: The tobacco-tomato experiment

The team repeated the experiment with tobacco and tomato plants in a noisier greenhouse environment. After recording the plants, the researchers made a machine-learning algorithm that could differentiate between the unstressed plants, thirsty plants and cut plants.

“The finding that there is information in the acoustic emissions, using neural network classification, is exciting as such (a) technique is fast and can identify data structures that the human eyes or ears cannot,” Robert noted.