In 2016 we reported evidence for associative learning in plants (Gagliano et al., 2016). In view of the far-reaching implications of this finding we welcome the attempt made by Markel to replicate our study (Markel, 2020). However, as we discuss here, the protocol employed by Markel was unsuitable for testing for associative learning.
Testing for associative learning relies on the pairing of an unconditioned stimulus (US) with a conditioned stimulus. To be effective, the stimulus used as an US must invariably elicit a response (in Pavlov's classical experiment in dogs the presentation of food elicited invariably salivation). In our study (Gagliano et al., 2016) we used blue light as the US which caused consistently a growth of the plant in the direction of the last presentation of the light (100% phototropic response). This was not the case in the study by Markel, where only a slight bias towards the last presentation of light was obtained (Markel, 2020). Since light was not an effective US in the study by Markel, it is not surprising that no distinct associative learning was observed.
In our study we also encountered conditions in which light was not an effective US. Thus, in the second series of our experiments, we tested the response of the plants in different circadian phases (light, light-dark, dark: see Figure 3 of Gagliano et al., 2016). Whereas the 100% phototropic response was obtained in the light phase, it was attenuated or abolished in the other two protocols. Consequently, no associative learning could be shown in those conditions.
We offer the following potential explanation for the lack of a consistent phototropism in Markel, 2020. Our study was conducted inside a completely dark 5.3 m2 room, where individual Y-mazes were positioned at ample distance (~20 cm radius) from each other. This was necessary to ensure that a plant inside its maze could only receive the blue light we directionally delivered within each maze at set specific times, and was completely shielded from light sources elsewhere. The lack of darkness in the study by Markel (see Figure 1—figure supplement 1C in Markel, 2020) is a major departure from our original design. We surmise that by inadvertently allowing individual plants to be exposed to light arriving from multiple sources within a 1.5 m2 growth cabinet (e.g. light leaking from mazes positioned too close to each other or reflecting from the chamber’s walls), the set up used by Markel could have resulted in random growth patterns, unrelated to the behaviour the experimental treatments were designed to test for, thereby confounding the results.
No data was generated for this study.
- Monica Gagliano
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Christian S Hardtke, University of Lausanne, Switzerland
- Daeyeol Lee, Johns Hopkins University, United States
- Received: July 16, 2020
- Accepted: September 3, 2020
- Version of Record published: September 10, 2020 (version 1)
© 2020, Gagliano et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Gagliano et al. (Learning by association in plants, 2016) reported associative learning in pea plants. Associative learning has long been considered a behavior performed only by animals, making this claim particularly newsworthy and interesting. In the experiment, plants were trained in Y-shaped mazes for 3 days with fans and lights attached at the top of the maze. Training consisted of wind consistently preceding light from either the same or the opposite arm of the maze. When plant growth forced a decision between the two arms of the maze, fans alone were able to influence growth direction, whereas the growth direction of untrained plants was not affected by fans. However, a replication of their protocol failed to demonstrate the same result, calling for further verification and study before mainstream acceptance of this paradigm-shifting phenomenon. This replication attempt used a larger sample size and fully blinded analysis.
Groups of animals inhabit vastly different sensory worlds, or umwelten, which shape fundamental aspects of their behaviour. Yet the sensory ecology of species is rarely incorporated into the emerging field of collective behaviour, which studies the movements, population-level behaviours, and emergent properties of animal groups. Here, we review the contributions of sensory ecology and collective behaviour to understanding how animals move and interact within the context of their social and physical environments. Our goal is to advance and bridge these two areas of inquiry and highlight the potential for their creative integration. To achieve this goal, we organise our review around the following themes: (1) identifying the promise of integrating collective behaviour and sensory ecology; (2) defining and exploring the concept of a ‘sensory collective’; (3) considering the potential for sensory collectives to shape the evolution of sensory systems; (4) exploring examples from diverse taxa to illustrate neural circuits involved in sensing and collective behaviour; and (5) suggesting the need for creative conceptual and methodological advances to quantify ‘sensescapes’. In the final section, (6) applications to biological conservation, we argue that these topics are timely, given the ongoing anthropogenic changes to sensory stimuli (e.g. via light, sound, and chemical pollution) which are anticipated to impact animal collectives and group-level behaviour and, in turn, ecosystem composition and function. Our synthesis seeks to provide a forward-looking perspective on how sensory ecologists and collective behaviourists can both learn from and inspire one another to advance our understanding of animal behaviour, ecology, adaptation, and evolution.