The individual in the crowd

In this final chapter, we draw together the threads of evolution, causation, development and phylogeny that have run through this book and show how they underlie one of the most striking features of animal lives – their tendency to be social. Tinbergen's four questions have stood the test of time, they are as important as they ever were and as he did in his pioneering book, The Study of Instinct (1951), we shall attempt a synthesis involving them all. Our knowledge of animal behaviour has grown enormously since Tinbergen's time and has spawned whole new disciplines such as behavioural ecology and neuroethology, but there is still much be gained by asking his different kinds of questions about behaviour. Asking how (causally) animals choose their mates, for example, is hugely illuminated by understanding why (in an evolutionary sense) they choose the mates they do, and vice versa. Since virtually all animals are social for at least part of their lives, social behaviour provides an obvious base for a synthesis of many aspects of behaviour across a wide range of species.

When we observe flocks of birds, swarms of insects or herds of antelope, it is easy to lose sight of the individual in the crowd. If a group stays together, it is because individual animals all benefit from staying with one another.

It is 14 years since the last edition and the science or sciences of animal behaviour have progressed enormously. Topics which justified only a brief mention in an introductory text then, for example sperm competition as a factor in mating systems, have prospered to require textbooks of their own. How then to approach a new edition which cannot be allowed to become significantly larger.

It is our conviction that an introduction to the whole field, or at least a substantial part of it, remains as important as ever. Discussing new areas of research is best done from a firm basis of the basic concepts and for us these are still embodied in Niko Tinbergen's 1963 ‘Four Questions for Ethology’ – function, evolution, causation and development. So the plan of our book remains essentially that of the other editions. There has been some extensive rewriting and we have tried to give good coverage to those areas where there have been important advances, notably in the evolution of behaviour and its development. We continue to give extensive references so that readers can easily get into the literature of areas that catch their interest. There is a great deal of new literature to be explored but we have never cited new work unless it really adds something. Often concepts are best illustrated by some of the now classical papers.

Evolution by natural selection is the great unifying concept of biology, so much so that most biologists now feel that, without it, none of the phenomena they study really make sense. Richard Dawkins (1986) provides an excellent modern survey of the great explanatory power of what has come to be called ‘neo-Darwinism’. Natural selection, operating on random, inherited variation has, over the generations, shaped animals to match the environments in which they live. Sometimes adaptation has been achieved through genes, accumulated over many generations, biasing the development of behaviour directly into appropriate responses. In other cases, animals inherit only biases to respond adaptively to their immediate environment so as to acquire, individually, appropriate responses by learning. Much behaviour develops by a mingling of such processes. All through this book we have emphasized the adaptive role of behaviour in an animal's life and so the concepts of ‘evolution’ and ‘adaptation’ have been implicit in much of what has already been said. Now we look at them in more detail.

In fact, not just one but two of Tinbergen's four questions are about the evolution of behaviour. One of them, about ‘adaptiveness’, is about how behaviour that we see present-day animals doing helps them to survive and reproduce. The other, about phylogeny, is about the changes in behaviour that have taken place over evolutionary time.

Just before dawn, a red junglefowl (Gallus gallus) cock crows from his roost in the bushes. Then, as it begins to get light, he makes his way to the ground with his group of three females and starts his daily search for food, scratching in the leaf litter for seeds and, if he is lucky, worms and grubs. In the heat of the day, he will return to the bushes for a siesta, but later he will resume his feeding on the ground, perhaps breaking off to dustbathe, mate with the females or search for water. All the time, he keeps a wary eye out for predators and rival males, ready to disappear into the undergrowth or see off an intruder at a moments' notice. As it gets dark, the group leave the ground in preparation for spending the next night in the relative safety of the bushes.

This simple description of the day in the life of one animal illustrates perfectly just how complex animal behaviour is. At every moment of every day an animal can be said to be making ‘decisions’ about what to do next – choosing to do one thing rather than another, or choosing to stop what it is doing and start something else. And those decisions can be studied at every level ??? from the level of the whole day, in which the decisions are between which parts of the day to be active and which parts to hide or sleep, right down to the moment-to-moment decisions about whether at this second to peck down at a food item or look up to see if there is a predator around. Indeed, we can look at even longer time scales, such as how behaviour changes over a whole year or even a whole lifetime.

Behaviour can be so well adapted to an animal's way of life that the animal seems to ‘know’ what to respond to, what to do and when to do it. Thus partridges and small rodents ‘know’ that the most effective defence against a hawk flying overhead is to flatten themselves on the ground and remain motionless since the hawk, which is very sensitive to movement, is least likely to see them. Most of us have experience of wasps that know about sweet drinks or mosquitoes that know where to find exposed human flesh.

Using the word ‘know’ in this context does not necessarily imply that animals are consciously thinking about what they are doing (although they may be, as we discuss in Chapter 5). Animals may ‘know’ things about their environments in the same way that a heat-seeking missile knows how to find its target or a computer knows how many mistakes you have made in the course of a game. In other words, quite simple unconscious mechanisms are capable of giving rise to what we might refer to as knowledge. When we ask causal questions about behaviour, we are asking what this knowledge is and how the animal uses it.

Learning as part of adaptation

Throughout this book we have made frequent reference to learning as a form of behavioural development retained into adult life and as one pathway by which behaviour becomes adapted to an animal's requirements. Now attention must be focused on learning itself. Learning and memory go together because whilst the former involves changing behaviour as a result of experience, its effects cannot be put to use unless the results of the experience can be stored in some way and recalled the next time they are needed. A discussion of learning will finally lead us on to a consideration of the mental or cognitive abilities of animals and how far they share some of those abilities with ourselves. The issues raised there take us far beyond just learning abilities but it is essential first to consider what are the different types of learning and biologists will want to examine how and whether they are expressed in a range of very different animals. For this reason, Thorpe's (1963) book, although old, remains useful because it does deal with the whole animal kingdom. It covers something of the context and the phenomena associated with learning in molluscs and insects, fish and amphibians, as well as the more familiar studies with birds and mammals.

One of the most remarkable features of living organisms – plant or animal – is the way in which a single-celled zygote, the fertilized egg, is transformed by cell division, cell differentiation and cell movement into the adult form, far more complex and often millions of times larger. Development (often called embryology when it describes the progress from egg to a young but free-living stage) is a most challenging field of research and there has been some remarkable progress in recent years. New insights help us to understand how the sequences of development have been modified as a group of animals has evolved, for example as the vertebrates have evolved through fishes, amphibians and reptiles to birds or mammals. The initial stages must stay the same and modifications can be added only at the end. Thus all vertebrate embryos go through a stage which involves their developing gill clefts. Fish – of course – retain these and so do some amphibians but in reptiles, birds and mammals they close up as other structures pertaining to their future life on land are added on and replace them. Hence the old phrase, ‘ontogeny recapitulates phylogeny’ which retains its power if not taken too literally.

Young animals grow up

The zygote contains all the information necessary to build a new organism, provided that it can develop in and interact with a suitable environment. When we study the development of behaviour, we must obviously concern ourselves with some aspects of embryology – for example, the way in which the basic framework of the nervous system is laid down – but we need to go far beyond this.

The data explosion

We live in a world that is rich in data, ever increasing in scale. This data comes from many different sources in science (bioinformatics, astronomy, physics, environmental monitoring) and commerce (customer databases, financial transactions, engine monitoring, speech recognition, surveillance, search). Possessing the knowledge as to how to process and extract value from such data is therefore a key and increasingly important skill. Our society also expects ultimately to be able to engage with computers in a natural manner so that computers can ‘talk’ to humans, ‘understand’ what they say and ‘comprehend’ the visual world around them. These are difficult large-scale information processing tasks and represent grand challenges for computer science and related fields. Similarly, there is a desire to control increasingly complex systems, possibly containing many interacting parts, such as in robotics and autonomous navigation. Successfully mastering such systems requires an understanding of the processes underlying their behaviour. Processing and making sense of such large amounts of data from complex systems is therefore a pressing modern-day concern and will likely remain so for the foreseeable future.

Machine learning

Machine learning is the study of data-driven methods capable of mimicking, understanding and aiding human and biological information processing tasks. In this pursuit, many related issues arise such as how to compress data, interpret and process it. Often these methods are not necessarily directed to mimicking directly human processing but rather to enhancing it, such as in predicting the stock market or retrieving information rapidly. In this probability theory is key since inevitably our limited data and understanding of the problem forces us to address uncertainty.