The Extended Phenotype: The Long Reach of the Gene (Popular Science) (25 page)

Gould comes closer to the truth here, but the truth is subtler, as I hope to show in
Chapter 13
. The point was alluded to in the previous chapter. Briefly, the sense in which genes may be said to ‘caucus’ and form ‘alliances’ is the following. Selection favours those genes which succeed
in the presence of other genes, which in turn succeed in the presence of them
. Therefore mutually compatible sets of genes arise in gene-pools. This is more subtle and more useful than to say that ‘we call the resultant object a body’.

Of course
genes are not directly visible to selection.
Obviously
they are selected by virtue of their phenotypic effects, and certainly they can only be said to
have
phenotypic effects in concert with hundreds of other genes. But it is the thesis of this book that we should not be trapped into assuming that those phenotypic effects are best regarded as being neatly wrapped up in discrete bodies (or other discrete vehicles). The doctrine of the extended phenotype is that the phenotypic effect of a gene (genetic replicator) is best seen as an effect upon the world at large, and only incidentally upon the individual organism—or any other vehicle—in which it happens to sit.

This is a chapter about practical research methodology. There will be those who accept the thesis of this book at a theoretical level but who will object that, in practice, field workers find it useful to focus their attention on
individual
advantage. In a theoretical sense, they will say, it is right to see the natural world as a battleground of replicators, but in real research we are obliged to measure and compare the Darwinian fitness of individual organisms. I want to discuss a particular piece of research in detail to show that this is not necessarily the case. Instead of comparing the success of individual organisms, it is often in practice more useful to compare the success of ‘strategies’ (Maynard Smith 1974) or ‘programs’ or ‘subroutines’, averaged across the individuals that use them. Of the many pieces of research that I could have discussed, for instance the work on ‘optimal foraging’ (Pyke, Pulliam & Charnov 1977; Krebs 1978), Parker’s (1978a) dungflies, or any of the examples reviewed by Davies (1982), I choose Brockmann’s study of digger wasps purely because I am very familiar with it (Brockmann, Grafen & Dawkins 1979; Brockmann & Dawkins 1979; Dawkins & Brockmann 1980).

I shall use the word ‘program’ in exactly the same sense as Maynard Smith uses ‘strategy’. I prefer ‘program’ to ‘strategy’ because experience warns that ‘strategy’ is quite likely to be misunderstood, in at least two different ways (Dawkins 1980). And incidentally, following the
Oxford English Dictionary
and standard American usage, I prefer ‘program’ to ‘programme’ which appears to be a nineteenth-century affectation imported from the French. A program (or strategy) is a recipe for action, a set of notional instructions that an animal seems to be ‘obeying’, just as a computer obeys its program. A computer programmer writes out his program in a language such as Algol or Fortran, which may look rather like imperative English. The machinery of the computer is so set up that it behaves as if
obeying these quasi-English instructions. Before it can run, the program is translated (by computer) into a set of more elementary ‘machine language’ instructions, closer to the hardware and further from easy human comprehension. In a sense it is these machine language instructions that are ‘actually’ obeyed rather than the quasi-English program, although in another sense both are obeyed, and in yet another sense neither is!

A person watching and analysing the behaviour of a computer whose program had been lost might, in principle, be able to reconstruct the program or its functional equivalent. The last four words are crucial. For his own convenience he will write the reconstructed program in some particular language—Algol, Fortran, a flow chart, some particular rigorous subset of English. But there is no way of knowing in which, if any, of these languages the program was originally written. It may have been directly written in machine language, or ‘hard-wired’ into the machinery of the computer at manufacture. The end result is the same in any case: the computer performs some useful task such as calculating square roots, and a human can usefully treat the computer
as if
it was ‘obeying’ a set of imperative instructions written out in a language that is convenient for humans to understand. I think that for many purposes such ‘software explanations’ of behaviour mechanisms are just as valid and useful as the more obvious ‘hardware explanations’ favoured by neurophysiologists.

A biologist looking at an animal is in somewhat the same position as an engineer looking at a computer running a lost program. The animal is behaving in what appears to be an organized, purposeful way, as if it was obeying a program, an orderly sequence of imperative instructions. The animal’s program has not actually been lost, for it never was written out. Rather, natural selection cobbled together the equivalent of a hard-wired machine code program, by favouring mutations that altered successive generations of nervous systems to behave (and to learn to change their behaviour) in appropriate ways. Appropriate means, in this case, appropriate for the survival and propagation of the genes concerned. Nevertheless, although no program was ever written down, just as in the case of the computer running a program which has been lost, it is convenient for us to think of the animal as ‘obeying’ a program ‘written’ in some easily understood language such as English. One of the things we can then do is to imagine alternative programs or subroutines which might ‘compete’ with each other for ‘computer time’ in the nervous systems of the population. Though we must treat the analogy with circumspection, as I shall show, we can usefully imagine natural selection as acting directly on a pool of alternative programs or subroutines, and treat individual organisms as temporary executors and propagators of these alternative programs.

In a particular model of animal fighting, for example, Maynard Smith (1972, p. 19) postulated five alternative ‘strategies’ (programs):

1 Fight conventionally; retreat if opponent proves to be stronger or if opponent escalates.

2 Fight at escalated level. Retreat only if injured.

3 Start conventionally. Escalate only if opponent escalates.

4 Start conventionally. Escalate only if opponent continues to fight conventionally.

5 Fight at escalated level. Retreat before getting hurt if opponent does likewise.

For the purpose of computer simulation it was necessary to define these five ‘strategies’ more rigorously, but for human understanding the simple imperative English notation is preferable. The important point for this chapter is that the five strategies were thought of as if they (rather than individual animals) were competing entities in their own right. Rules were set up in the computer simulation for the ‘reproduction’ of successful strategies (presumably individuals adopting successful strategies reproduced and passed on genetic tendencies to adopt those same strategies, but the details of this were ignored). The question asked was about strategy success, not individual success.

A further important point is that Maynard Smith was seeking the ‘best’ strategy in only a special sense. In fact he was seeking an ‘evolutionarily stable strategy’ or ESS. The ESS has been rigorously defined (Maynard Smith 1974), but it can be crudely encapsulated as a strategy that is successful when competing with copies of itself. This may seem an odd property to single out, but the rationale is really very powerful. If a program or strategy is successful, this means that copies of it will tend to become more numerous in the population of programs and will ultimately become almost universal. It will therefore come to be surrounded by copies of itself. If it is to remain universal, therefore, it must be successful when competing against copies of itself, successful compared with rare different strategies that might arise by mutation or invasion. A program that was not evolutionarily stable in this sense would not last long in the world, and would therefore not present itself for our explanation.

In the case of the five strategies listed above, Maynard Smith wanted to know what would happen in a population containing copies of all five programs. Was there one of the five which, if it came to predominate, would retain its numerical superiority against all comers? He concluded that program number 3 is an ESS: when it happens to be very numerous in the population no other program from the list does better than program 3 itself (actually there is a problem over this particular example—Dawkins 1980, p. 7—but I shall ignore it here). When we talk of a program as ‘doing better’ or as being ‘successful’ we are notionally measuring success as capacity to propagate copies of the same program in the next generation: in reality this
is likely to mean that a successful program is one which promotes the survival and reproduction of the animal adopting it.

What Maynard Smith, together with Price and Parker (Maynard Smith & Price 1973; Maynard Smith & Parker 1976), has done is to take the mathematical theory of games and work out the crucial respect in which that theory must be modified to suit the Darwinist’s purpose. The concept of the ESS, a strategy that does relatively well against copies of itself, is the result. I have already made two attempts at advocating the importance of the ESS concept and explaining its broad applicability in ethology (Dawkins 1976a, 1980), and I shall not repeat myself unnecessarily here. Here my purpose is to develop the relevance of this way of thinking for the subject of the present book, the debate about the level at which natural selection acts. I shall begin by recounting a specific piece of research that used the ESS concept. All the facts I shall give are from the field observations of Dr Jane Brockmann, reported in detail elsewhere and briefly mentioned in
Chapter 3
. I shall have to give a brief account of the research itself before I can relate it to the message of this chapter.

Sphex ichneumoneus
is a solitary wasp, solitary in the sense that there are no social groups and no sterile workers, although the females do tend to dig their nests in loose aggregations. Each female lays her own eggs, and all labour on behalf of the young is completed before the egg is laid—the wasps are not ‘progressive provisioners’. The female lays one egg in an underground nest which she has previously provisioned with stung and paralysed katydids (long-horned grasshoppers). Then she closes up that nest, leaving the larva to feed on the katydids, while she herself starts work on a new nest. The life of an adult female is limited to about six summer weeks. If one wished to measure it, the success of a female could be approximated as the number of eggs that she successfully lays on adequate provisions during this time.

The thing that especially interested us was that the wasps seemed to have two alternative ways of acquiring a nest. Either a female would dig her own nest in the ground, or she would attempt to take over an existing nest which another wasp had dug. We called these two behaviour patterns
digging
and
entering
, respectively. Now, how can two alternative ways of achieving the same end, in this case two alternative ways of acquiring a nest, coexist in one population? Surely one or the other ought to be more successful, and the less successful one should be removed from the population by natural selection? There are two general reasons why this might not happen, which I will express in the jargon of ESS theory: firstly, digging and entering might be two outcomes of one ‘conditional strategy’; secondly, they might be equally successful at some critical frequency maintained by frequency-dependent selection—part of a ‘mixed ESS’ (Maynard Smith 1974, 1979). If the first possibility were correct, all wasps would be programmed with the same
conditional rule: ‘If X is true, dig, otherwise enter’; for instance, ‘If you happen to be a small wasp, dig, otherwise use your superior size to take over another wasp’s burrow’. We failed to find any evidence for a conditional program of this or any other kind. Instead, we convinced ourselves that the second possibility, the ‘mixed ESS’, fitted the facts.

There are in theory two kinds of mixed ESS, or rather two extremes with a continuum between. The first extreme is a balanced polymorphism. In this case, if we want to use the initials ‘ESS’, the final S should be thought of as standing for
state
of the population rather than strategy of the individual. If this possibility obtained, there would be two distinct kinds of wasps, diggers and enterers, who would tend to be equally successful. If they were not equally successful, natural selection would tend to eliminate the less successful one from the population. It is too much to hope that, by sheer coincidence, the net costs and benefits of digging would exactly balance the net costs and benefits of entering. Rather, we are invoking frequency-dependent selection. We postulate a critical equilibrium proportion of diggers,
p*
, at which the two kinds of wasps are equally successful. Then, if the proportion of diggers in the population falls below the critical frequency, diggers will be favoured by selection, while if it rises above the critical frequency enterers will be favoured. In this way the population would hover around the equilibrium frequency.

It is easy to think of plausible reasons why benefit might be frequency-dependent in this way. Clearly, since new burrows come into existence only when diggers dig them, the fewer diggers there are in the population the stronger will be the competition among enterers for burrows, and the lower the benefit to a typical enterer. Conversely, when diggers are very numerous, available burrows abound and enterers tend to prosper. But, as I said, frequency-dependent polymorphism is only one end of a continuum. We now turn to the other end.

At the other end of the continuum there is no polymorphism among individuals. In the stable state all wasps obey the same program, but that program is itself a mixture. Every wasp is obeying the instruction, ‘Dig with probability
p
, enter with probability 1 –
p
’; for instance, ‘Dig on 70 per cent of occasions, enter on 30 per cent of occasions’. If this is regarded as a ‘program’, we could perhaps refer to digging and entering themselves as ‘subroutines’. Every wasp is equipped with both subroutines. She is programmed to choose one or the other on each occasion with a characteristic probability,
p
.