The elephant has a versatile extra limb, says S.Ananthanarayanan.

The elephant’s bulk has worked against endowing the animal with delicate fingers. But it has a trunk that amply compensates

Paule Dagenais, Sean Hensman, Valérie Haechler and Michel C Milinkovitch, from the University of Geneva, the Swiss Institute of Bioinformatics, and Adventures with Elephants, in Bela, South Africa, describe in the journal, Current Biology, how the elephant trunk has evolved, as they say in the paper, “to be a spectacular organ for delicate to heavy object manipulation as well as social and sensory functions.” Understanding this evolution could point the way to doing something similar in the laboratory, the authors say

The paper starts out by drawing attention to a specific feature of the elephant trunk. And what is this? It is that the elephant’s trunk is not a limb with a skeleton, like an arm or the fingers of the hand, but consists only of muscles. And these muscles work not by the action of levers, like limbs do, but by hydraulic forces exerted by liquid deformations. This method of working is found in many parts of the animal world. Instances are the tentacles of the squid or the arms of the octopus. Closer home, this is the way we push food down to the stomach and through the intestines. A familiar organ like this, with has no bones, one that we consciously use, is the tongue. And as the tongue, and the muscles of the mouth, and in the throat, can produce complex speech, or the modulated tones of a trained singer, we can see that muscular control can be more refined than the fingers of a concert pianist, or the limbs of a gymnast.

And so it is with the trunk of the elephant. “It can manipulate a single blade of grass but also carry loads up to 270 kilograms,” say the authors of the paper. The American writer, Herman Melville, in his novel, Moby Dick, in marvelling at the delicacy of touch that the sperm whale has in its tail, was reminded of the elephant. This is what he says: “What tenderness there is in that preliminary touch! Had this tail any prehensile power, I should straightway bethink me of Darmonodes’ elephant that so frequented the flower-market, and with low salutations presented nosegays to damsels, and caressed…”

The elephant’s trunk is a large organ, often over 3 metres long, and it is capable of great manipulation. The paper mentions functions like breathing, smelling, feeling by touch, vocalising, siphoning and spraying water, spraying dust, and handling things, pushing or carrying logs, or using suction to pick up the smallest of objects. The elephant needs to extend the trunk to its full length, to reach a distant object, or to draw in the trunk, and to move things from side to side, the trunk sometimes needs to create a bend, like an elbow, to mimic a skeletal joint.

The human-made, mechanical equivalent may be earth-moving equipment, which have a jib made of many sections. Control of this metal limb, however, needs sophisticated electronics and computing. And yet, the range of movement possible is hardly comparable. Animal limbs, of course, are capable of more complex movement, but the levers and joints through which these come about finally place limitations. Except that the elephant trunk, which has no bones or joints, enjoys near-infinite flexibility.

Except, again, the paper points out, that this flexibility arises out of a very huge number of muscle fragments and associated nerve signals, which could lead to an overwhelming information load. Hence, as is done in the field of artificial intelligence systems, the paper says, a strategy is required to bring down the complexity of the tasks to be handled. The problem that arises in artificial intelligence is because the task in AI is to keep track of a huge number of factors on which a course of action to be decided may depend. For instance, if the task before the AI system is to guide a car that it is driving down a street, the method is to use a camera which feeds, say, a million pixels to the computer every second. The computer is first ‘trained’, with the camera switched on and the car driven by a human driver. During this process, the computer ‘learns’ to associate pixel-patterns that it ‘sees’ with the kind of action the human driver takes. After this ‘learning,’ when the computer has to drive on its own, it is able to process the pixels that it ‘sees’ and take the correct driving decisions.

The trouble with this, and other tasks that AI performs, is that there is a huge information processing load, and not all the information has the same importance. For instance, it is usually the space directly in the path of the moving car, rather than stationary things in the periphery, that are immediately important. In this and other applications, for instance forecasting market trends, it is possible to group many factors, as so called principal components, so that the total number of independent variables to consider is reduced – and hence reduce the complexity.

In the same way, the authors say, the exceedingly great number of units to be considered when looking at how the elephant is able to control its trunk can be brought down by considering the movements of the trunk as composed of a lesser number of ‘building blocks’ of movement. In order to show that this indeed is the case, the authors analysed the gamut of movements of the elephant trunk, their motivation, their speed, their purpose, and found that there existed a pattern, a method that underlay what the trunk achieved.

The method they employed was to assign specific tasks to two adult, African elephants, and to record their trunk movements with high speed cameras, followed by analysis using special software, software that had been successfully used in motion picture animation. The method, called motion-capture, takes shots of real people or action and then develops animation. The technique, which trades creation of animation on the drawing board for adaptation of the real thing, was used with resounding success in the animated films, Lord of the Rings and Avatar.

The same method was used to analyse the elements of the trunk movements, and the result was that simple movements, for instance, of extending, bending and twisting, in a variety of tasks, could be broken down to just 17 primary behaviours. The findings were supplemented by anatomical studies and medical imaging, to demonstrate the relationship between muscle movement economy and the primary units of motion that were discovered.

There is a striking similarity, the paper says, in the method of the elephant trunk and the way the arm of the octopus manages tasks and movements. As elephants and octopi are separated by a billion years of evolution and rely on widely different nervous systems, it is tempting, the paper says, to consider that the principles of breaking complexity down to primitives are basic to the mechanics and functioning of living things.

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