Intelligent behaviour is regarded as a property of the whole individual plant or animal. Although there is discussion among population ecologists as to whether the plant should be regarded as the genet or an individual ramet because of the modular character and a certain degree of independence of behaviour of individual meristems (White, 1979), I shall assume that the individual is the genet. A consequence of a repetitive modular structure is that the individual ramets might be regarded as being like parallel processors contributing different experiences resulting from different ages to present day decisions.
Learning and memory are the two emergent (holistic) properties of neural networks that involve large numbers of neural cells acting in communication with each other. But, both properties originate from signal transduction processes in individual neural cells. Quite remarkably, the suite of molecules used in signal transduction are entirely similar between nerve cells (Kandel, 2001) and plant cells (Trewavas, 2000; Gilroy and Trewavas, 2001). Most decisions made by plants about growth and development do seem to involve communication between all parts of the plant, but with prominence in the decision given to meristems local to the signal. In the marine snail Aplysia, and probably all animal neural systems, learning and memory are intertwined. Learning results from the formation of new dendrites, and memory lasts as long as the newly formed dendrites themselves (Kandel, 2001). The neural network is phenotypically plastic and intelligent behaviour requires that plastic potential. Plant development is plastic too and is not irreversible; many mature plants can be reduced to a single bud and root and regenerate to a new plant with a different structure determined by the new environmental circumstances.
Adaptively variable behaviour in animals is commonly secured by coordinating different groups of muscles. Individuality in cell and tissue behaviour in plants can underpin behaviour of different, but equal, variety in individual plants, and will be considered later.
Do plants work by rote, incapable of anything but reflexive responses?
The animal reflex arc is invariant under all conditions and a common attitude sees plant behaviour as analogous and likewise automaton, rote and invariant. There are probably at least four reasons for this mistaken perception.
(1) The use of statistics to simplify complex individual behaviour.
Statistics originated as a method to test whether two populations differed significantly as a result of their environmental treatments. However, the wholesale summary of physiological responses through means, averages or medians simply eliminates individual variation on the common, but incorrect, assumption that such variation is only experimental error (Trewavas, 1998). Individual behaviour (as required in the definition of intelligence) is ignored and behaviour thus over-simplified. Quite critically, the mean or average does not usually reflect the behaviour of any individual and is simply a composite population response with meaning only to those who wish to study the behaviour of whole populations. But the behaviour of the mean is commonly assumed to reflect the behaviour of each individual in the whole population, particularly when describing mechanisms. Statistical averaging can seriously mislead as to actual mechanisms in individual plants.
Gravitropic responses illustrate the difficulty. Ishikawa et al. (1991) imposed a gravitational stimulus on young growing roots to produce, some 5–6 h later, the textbook picture of recovery to vertical growth. However, the trajectory of individual roots back to the vertical was far from simple, and Ishikawa et al. (1991) properly recognized five approximate classes of response. Zieschang and Sievers (1991) found the trajectories of individual gravi-responding roots of Phleum pratense too complex to summarize as statistical means. Gravi-responding hypocotyls or coleoptiles can likewise show enormous variations in trajectory back to the vertical (Macleod et al., 1987). Red light, calcium, touch, moisture, oxygen, temperature, ethylene and auxin have all been reported to modify gravitropic bending, illustrating the common observation that physiological phenomena are integrated responses resulting from many environmental influences (Trewavas, 1992). But variations in individual seedling sensitivity to each of these factors increase the variety of individual responses. Rich and Smith (1986) noted similar complexity in initiation time in phototropism, with individual hypocotyls requiring anywhere from 5 to 40 min to initiate response to the same blue light signal. They discuss the problems that averaging incurs in deciding on transduction mechanisms to this signal. Integration of many different environmental influences to produce a final integrated response is a particular feature of the intelligent animal.
(2) Controlled environments during experimentation.
Because the effects of the numerous environmental factors on plant growth and development can be complex, students are taught to examine such complexity by keeping all environmental factors constant except one, which is varied sufficiently strongly to obtain a response. Again, the response is usually summarized statistically. These experimental approaches, which are perfectly valid for asking questions about population behaviour, predispose towards assumptions that responses are reflexive because the signal is imposed until a response is obvious. A good example is water deprivation in which water is withheld until a response is achieved. However, in the wild, a multiplicity of factors affect the response to water deprivation, and the imposition of the stimulus takes place in a constantly changing environmental framework on plants of different age, different genotypes and very different circumstances. Experimentally depriving an animal of water or nutrient for several days and then exposing it to sources of either, would give rise to an apparently reproducible response (particularly when summarized statistically), but no-one would regard such responses as indicating lack of intelligence; far from it.
(3) The capacity to navigate a maze.
One of the hallmarks of intelligent behaviour in the laboratory is the capacity of animals to run successfully through mazes and to receive an eventual reward. But the capacity of plants to grow through an environmental maze is not commonly assumed to represent intelligent behaviour and attracts little attention. Individual branches growing through gaps towards sources of light are an obvious example (Trewavas, 1986b). Numerous studies on rhizomes suggest that higher plants must be able to construct a three-dimensional perspective of their local space and optimize their growth patterns to exploit resources, thus receiving rewards for successful behaviour. To any wild plant the environment represents a continual maze that must be successfully navigated.
Dia-gravitropic rhizomes can certainly sense vertical environmental vectors, either from being buried or from receipt of light near the surface, with vertical growth then being adjusted (Bennet-Clark and Ball, 1951; Maun and Lapierre, 1984). Consistent control of rhizome horizontal direction has been observed, particularly in heterogeneous soil environments, which are extremely common (Farley and Fitter, 1999). Rich soil patches are exploited by increased branching and growth; poor ones are either directly avoided or the rhizome thins to conserve resource use and growth is accelerated to speed the detection of new richer patches (Salzmann, 1985; MacDonald and Lieffers, 1993; Aphalo and Ballare, 1995; Evans and Cain, 1995; Kleijn and Van Groenendael, 1999; Wijesinghe and Hutchings, 1999). Evans and Cain (1995) report that Hydrocotyle rhizomes veer away from patches of grass and thus from competition.
Roots are able to sense humidity gradients and thus also construct a three-dimensional environmental perspective (Takahashi and Scott, 1993). Increased root branching in soil patches rich in nitrate or phosphate indicate a similar ability in environmental perception (Drew et al., 1973). Roots will also take avoidance action when near others (Aphalo and Ballare, 1995). These data, and others, have led to the concept that plants actively forage resources from their environment (Hutchings and deKroon, 1994) using assessment mechanisms similar to those of animals.
Both plants and animals use exploratory behaviour to enhance the chances of survival by optimizing the gathering of food resources, thus maximizing both the potentials for reproduction and the selfish passage of genes into the next generation.
(4) Intelligent behaviour in animals requires the right environmental context for it to be expressed.
A simple (sometimes controversial) way to detect intelligent behaviour in humans is to impose an IQ test. These two factors, environmental context and organism, are both essential in detection and examination of intelligent behaviour. Just as obvious intelligent behaviour is not so easy to detect in caged animals in zoos, it will not be readily observed in laboratory grown plants; in part, because the necessary competitive and variable circumstances to elicit intelligent responses are not present. Intelligence requires both the organism able to compute and the right environmental circumstances to elicit that computation. On that basis, it is not surprising that most observations supporting the concept of plant intelligence come from ecologists studying plant behaviour under conditions more nearly mimicking those of plants in the wild. The observations of Darwin or Von Sachs that suggested similarities between animal behaviour, nervous systems and the behaviour of plants (quotations are to be found in Trewavas, 1999) could represent the lack of controlled growth and laboratory facilities in the 19th century, and thus the likely observation of plants growing under less-controlled and far more realistic circumstances, eliciting intelligent behaviour.