On-line control of movement in plants

Artificial and biological supports are different for pea plants

Previous studies on the kinematics of pea plants' ascent and attach behavior have demonstrated that the signature of their movement varies depending on the kind of support. So far, these studies have been confined to artificial supports (e.g. wooden sticks). Little is known regarding the conditions under which pea plants could rely on biological supports (e.g. neighboring plants) for climbing toward the light. In this study, we capitalize on the 3D kinematic analysis of movement to ascertain whether pea plants scale their kinematics differently depending on whether they aim for artificial or biological support. Results suggest that biological support determines a smoother and more accurate behavior than that elicited by the artificial one. These results shed light on pea plants' ability to detect and classify the properties of objects and implement a movement plan attuned to the very nature of the support. We contend that such differences depend on the augmented multisensory experience elicited by the biological support.

Ascent and Attachment in Pea Plants: A Matter of Iteration

Pea plants (Pisum sativum L.) can perceive the presence of potential supports in the environment and flexibly adapt their behavior to clasp them. How pea plants control and perfect this behavior during growth remains unexplored. Here, we attempt to fill this gap by studying the movement of the apex and the tendrils at different leaves using three-dimensional (3D) kinematical analysis. We hypothesized that plants accumulate information and resources through the circumnutation movements of each leaf. Information generates the kinematical coordinates for the final launch towards the potential support. Results suggest that developing a functional approach to grasp movement may involve an interactive trial and error process based on continuous cross-talk across leaves. This internal communication provides evidence that plants adopt plastic responses in a way that optimally corresponds to support search scenarios.

'United we stand, divided we fall': intertwining as evidence of joint actions in pea plants

In life, it is common for almost every kind of organism to interact with one another. In the human realm, such interactions are at the basis of joint actions, when two or more agents syntonize their actions to achieve a common goal. Shared intentionality is the theoretical construct referring to the suite of abilities that enable such coordinated and collaborative interactions. While shared intentionality has become an important concept in research on social cognition, there is controversy surrounding its evolutionary origins. An aspect still unexplored but promising to bring new insights into this open debate is the study of aneural organisms. To fill this gap, here we investigate whether climbing plants can act jointly to achieve a common goal, i.e. reaching the light. We examined Pisum Sativum plants growing intertwined when there is a need to climb but a potential support is not present in the environment. Three-dimensional kinematic analysis of their movement revealed a coordinated and complementary behaviour. They tend to coordinate their movement in time and space to achieve a joint climbing. By deliberately extending the context in which a joint action takes place, we pay tribute to the complex nature of this social phenomenon. The next challenge for the field of joint action is to generate a perspective that links coordination mechanisms to an evolutionary framework across taxa.

Plant Intelligence from a Comparative Psychology Perspective

After being subjected to years of debates regarding the possibility that plants possess some form of intelligence, many admit to needing to close their eyes and to breathe mindfully when having to listen to the same arguments yet again [...].

Decision-Making Underlying Support-Searching in Pea Plants

Finding a suitable support is a key process in the life history of climbing plants. Those that find a suitable support have greater performance and fitness than those that remain prostrate. Numerous studies on climbing plant behavior have elucidated the mechanistic details of support-searching and attachment. Far fewer studies have addressed the ecological significance of support-searching behavior and the factors that affect it. Among these, the diameter of supports influences their suitability. When the support diameter increases beyond some point, climbing plants are unable to maintain tensional forces and therefore lose attachment to the trellis. Here, we further investigate this issue by placing pea plants (Pisum sativum L.) in the situation of choosing between supports of different diameters while their movement was recorded by means of a three-dimensional motion analysis system. The results indicate that the way pea plants move can vary depending on whether they are presented with one or two potential supports. Furthermore, when presented with a choice between thin and thick supports, the plants showed a distinct preference for the former than the latter. The present findings shed further light on how climbing plants make decisions regarding support-searching and provide evidence that plants adopt one of several alternative plastic responses in a way that optimally corresponds to environmental scenarios.

Classifying Circumnutation in Pea Plants via Supervised Machine Learning

Climbing plants require an external support to grow vertically and enhance light acquisition. Climbers that find a suitable support demonstrate greater performance and fitness than those that remain prostrate. Support search is characterized by oscillatory movements (i.e., circumnutation), in which plants rotate around a central axis during their growth. Numerous studies have elucidated the mechanistic details of circumnutation, but how this phenomenon is controlled during support searching remains unclear. To fill this gap, here we tested whether simulation-based machine learning methods can capture differences in movement patterns nested in actual kinematical data. We compared machine learning classifiers with the aim of generating models that learn to discriminate between circumnutation patterns related to the presence/absence of a support in the environment. Results indicate that there is a difference in the pattern of circumnutation, depending on the presence of a support, that can be learned and classified rather accurately. We also identify distinctive kinematic features at the level of the junction underneath the tendrils that seems to be a superior indicator for discerning the presence/absence of the support by the plant. Overall, machine learning approaches appear to be powerful tools for understanding the movement of plants.

Kinematic Evidence of Root-to-Shoot Signaling for the Coding of Support Thickness in Pea Plants

Plants such as climbers characterized by stems or tendrils need to find a potential support (e.g., pole, stick, other plants or trees) to reach greater light exposure. Since the time when Darwin carried out research on climbing plants, several studies on plants’ searching and attachment behaviors have demonstrated their unique ability to process some features of a support to modulate their movements accordingly. Nevertheless, the strategies underlying this ability have yet to be uncovered. The present research tries to fill this gap by investigating how the interaction between above- (i.e., stems, tendrils, …) and below-ground (i.e., the root system) plant organs influences the kinematics of their approach-to-grasp movements. Using three-dimensional (3D) kinematic analysis, we characterized the movements of pea plants (Pisum sativum L.) as they leaned towards supports whose below- and above-ground parts were characterized by different thicknesses (i.e., thin below- thick above-ground, or the opposite). As a control condition, the plants were placed next to supports with the same thickness below and above ground (i.e., either entirely thin or thick). The results suggest that the information regarding below- and above-ground parts of a support appears to be integrated and modulates the reach-to-grasp behavior of the plant. Information about the support conveyed by the root system seems to be particularly important to achieve the end-goal of movement.

Sowing the seeds of intentionality: Motor intentions in plants

Motor intention/intentionality has been investigated from a wide variety of perspectives: some researchers have, for example, been focusing on the purely physical and mechanical aspects underlying the control of action, while others have been concentrating on subjective intentionality. Basically, all approaches ranging from the neuroscientific to phenomenological-inspired ones have been used to investigate motor intentions. The current study set out to examine motor intentions in connection to plant behavior utilizing the final goal of plant action as the definition of its motor intention. Taking a wide-angle approach, the first part of the review is dedicated to examining philosophical and psychological studies on motor intentions. Recent data demonstrating that plant behavior does indeed seem goal-directed will then be reviewed as we ponder the possibility of purposeful or intentional plant responses to stimuli and stress conditions in their environment. The article will draw to a close as we examine current theories attempting to explain plants' overt behavior and corresponding covert representations.

Can Plants Move Like Animals? A Three-Dimensional Stereovision Analysis of Movement in Plants

Simple Summary

Intrigued by the ability of climbing peas to detect and grasp structures such as garden reeds, we adapted a method classically used to investigate the grasping movement of animals to the study of grasping movements in plants. We used time-lapse photography to document the behavior of pea plants, grown in the vicinity of a support pole. Using this footage, we analyzed the kinematics of tendrils growth and found that their approach and grasp exhibited movement signatures comparable to those characterizing the reach-to-grasp movement of animals. Through our method it may be possible to demonstrate that plants may be more sentient than we give them credit for: namely, they may possess the ability to act intentionally.

Abstract

In this article we adapt a methodology customarily used to investigate movement in animals to study the movement of plants. The targeted movement is circumnutation, a helical organ movement widespread among plants. It is variable due to a different magnitude of the trajectory (amplitude) exhibited by the organ tip, duration of one cycle (period), circular, elliptical, pendulum-like or irregular shape and the clockwise and counterclockwise direction of rotation. The acquisition setup consists of two cameras used to obtain a stereoscopic vision for each plant. Cameras switch to infrared recording mode for low light level conditions, allowing continuous motion acquisition during the night. A dedicated software enables semi-automatic tracking of key points of the plant and reconstructs the 3D trajectory of each point along the whole movement. Three-dimensional trajectories for different points undergo a specific processing to compute those features suitable to describe circumnutation (e.g., maximum speed, circumnutation center, circumnutation length, etc.). By applying our method to the approach-to-grasp movement exhibited by climbing plants (Pisum sativum L.) it appears clear that the plants scale movement kinematics according to the features of the support in ways that are adaptive, flexible, anticipatory and goal-directed, reminiscent of how animals would act.