The Extended Phenotype: The Long Reach of the Gene (Popular Science) (11 page)

McCleery (1978), in an agreeably comprehensible introduction to the McFarland school of ethological optimality theory, mentions H. A. Simon’s concept of ‘satisficing’ as an alternative to optimizing. If optimizing systems are concerned with maximizing something, satisficing systems get away with doing just enough. In this case, doing enough means doing enough to stay alive. McCleery contents himself with complaining that such ‘adequacy’ concepts have not generated much experimental work. I think evolutionary theory entitles us to be a bit more negative
a priori
. Living things are not selected for their capacity simply to stay alive; they are staying alive in competition with other such living things. The trouble with satisficing as a
concept is that it completely leaves out the competitive element which is fundamental to all life. In Gore Vidal’s words: ‘It is not enough to succeed. Others must fail.’

On the other hand ‘optimizing’ is also an unfortunate word because it suggests the attainment of what an engineer would recognize as the best design in a global sense. It tends to overlook the constraints on perfection which are the subject of this chapter. In many ways the word ‘meliorizing’ expresses a sensible middle way between optimizing and satisficing. Where
optimus
means best,
melior
means better. The points we have been considering about historical constraints, about Wright’s adaptive landscapes and about rivers following the line of immediate least resistance, are all related to the fact that natural selection chooses the better of present available alternatives. Nature does not have the foresight to put together a sequence of mutations which, for all that they may entail temporary disadvantage, set a lineage on the road to ultimate global superiority. It cannot refrain from favouring slightly advantageous available mutations now, so as to take better advantage of superior mutations which may arrive later. Like a river, natural selection blindly meliorizes its way down successive lines of immediately available least resistance. The animal that results is not the most perfect design conceivable, nor is it merely good enough to scrape by. It is the product of a historical sequence of changes, each one of which represented, at best, the
better
of the alternatives that happened to be around at the time.

‘If there were no constraints on what is possible, the best phenotype would live for ever, would be impregnable to predators, would lay eggs at an infinite rate, and so on’ (Maynard Smith 1978b). ‘An engineer, given carte blanche on his drawing board could design an “ideal” wing for a bird, but he would demand to know the constraints under which he must work. Is he constrained to use feathers and bones, or may he design the skeleton in titanium alloy? How much is he allowed to spend on the wings, and how much of the available economic investment must be diverted into, say, egg production?’ (Dawkins & Brockmann 1980). In practice, an engineer will normally be given a specification of minimum performance such as ‘The bridge must bear a load of ten tons … The aeroplane wing must not break until it receives a stress three times what would be expected in worst-case turbulent conditions; now go ahead and build it as cheaply as you can.’ The best design is the one that satisfies (‘satisfices’) the criterion specification at the least cost. Any design that achieves ‘better’ than the specified criterion performance is likely to be rejected, because presumably the criterion could be achieved more cheaply.

The particular criterion specification is an arbitrary working rule. There is
nothing magic about a safety margin of three times the expected worst-case conditions. Military aircraft may be designed with more risky safety margins than civilian ones. In effect, the engineer’s optimization instructions amount to a monetary evaluation of human safety, speed, convenience, pollution of the atmosphere, etc. The price put on each of these is a matter of judgement, and is often a matter of controversy.

In the evolutionary design of animals and plants, judgement does not enter into it, nor does controversy except among the human spectators of the show. In some way, however, natural selection must provide the equivalent of such judgement: risks of predation must be evaluated against risks of starving and benefits of mating with an extra female. For a bird, resources spent on making breast muscles for powering wings are resources that could have been spent on making eggs. An enlarged brain would permit a finer tuning of behaviour to environmental details, past and present, but at a cost of an enlarged head, which means extra weight at the front end of the body, which in turn necessitates a larger tail for aerodynamic stability, which in turn … Winged aphids are less fecund than wingless ones of the same species (J. S. Kennedy, personal communication). That every evolutionary adaptation must cost something, costs being measured in lost opportunities to do other things, is as true as that gem of traditional economic wisdom, ‘There is no such thing as a free lunch’.

Of course the mathematics of biological currency-conversion, of evaluating the costs of wing muscle, singing time, predator-vigilance time, etc., in some common currency such as ‘gonad equivalents’, are likely to be very complex. Whereas the engineer is allowed to simplify his mathematics by working to an arbitrarily chosen minimum threshold of performance, the biologist is granted no such luxury. Our sympathy and admiration must go out to those few biologists who have attempted to grapple with these problems in detail (e.g. Oster & Wilson 1978; McFarland & Houston 1981).

On the other hand, although the mathematics may be formidable, we don’t need mathematics to deduce the most important point, which is that any view of biological optimization that denies the existence of costs and trade-offs is doomed. An adaptationist who looks at one aspect of an animal’s body or behaviour, say the aerodynamic performance of its wings, while forgetting that efficiency in the wings can only be bought at a cost which will be felt somewhere else in the animal’s economy, would deserve all the criticism he gets. It has to be admitted that too many of us, while never actually denying the importance of costs, forget to mention them, perhaps even forget to think about them, when we discuss biological function. This has probably provoked some of the criticism that has come our way. In an earlier section I quoted Pittendrigh’s remark that adaptive organization was a ‘patchwork of makeshifts’. We must also not forget that it is a tangle of compromises (Tinbergen 1965).

In principle, it would seem a valuable heuristic procedure to
assume
that an animal is optimizing something under a given set of constraints, and to try to work out what those constraints are. This is a restricted version of what McFarland and his colleagues call the ‘reverse optimality’ approach (e.g. McCleery 1978). As a case study I shall take some work with which I happen to be familiar.

Dawkins and Brockmann (1980) found that the digger wasps (
Sphex ichneumoneus
) studied by Brockmann behaved in a way that a naive human economist might have criticized as maladaptive. Individual wasps appeared to commit the ‘Concorde Fallacy’ of valuing a resource according to how much they had already spent on it, rather than according to how much they could get out of it in the future. Very briefly, the evidence is as follows. Solitary females provision burrows with stung and paralysed katydids which are to serve as food for their larvae (see
Chapter 7
). Occasionally two females find themselves provisioning the same burrow, and they usually end up fighting over it. Each fight goes on until one wasp, thereby defined as the loser, flees from the area, leaving the winner in control of the burrow and all the katydids caught by both wasps. We measured the ‘real value’ of a burrow as the number of katydids which it contained. The ‘prior investment’ by each wasp in the burrow was measured as the number of katydids which she, as an individual, had put into it. The evidence suggested that each wasp fought for a time proportional to her own investment, rather than proportional to the ‘true value’ of the burrow.

Such a policy has great human psychological appeal. We too tend to fight tenaciously for property which we have put great effort into acquiring. The fallacy gets its name from the fact that, at a time when sober economic judgement of future prospects counselled abandoning the developing of the Concorde airliner, one of the arguments in favour of continuing with the half completed project was retrospective: ‘We have already spent so much on it that we cannot back out now.’ A popular argument for prolonging wars gave rise to the other name for the fallacy, the ‘Our boys shall not have died in vain’ fallacy.

When Dr Brockmann and I first realized that digger wasps behaved in like manner, I was, it has to be confessed, a little disconcerted, possibly because of my own past investment of effort (Dawkins & Carlisle 1976; Dawkins 1976a) in persuading my colleagues that the psychologically appealing Concorde Fallacy was, indeed, a fallacy! But then we started to think more seriously about cost constraints. Could it be that what appeared to be maladaptive was better interpreted as an optimum,
given certain constraints?
The question then became: Is there a constraint such that the wasps’ Concordian behaviour is the best they can achieve under it?

In fact the question was more complicated than that, because it was necessary to substitute Maynard Smith’s (1974) concept of evolutionary
stability (‘ESS’—see
Chapter 7
) for that of simple optimality, but the principle remains that a reverse optimality approach might be heuristically valuable. If we can show that an animal’s behaviour is what would be produced by an optimizing system working under constraint X, maybe we can use the approach to learn something of the constraints under which animals actually do work.

In the present case it seemed that the relevant constraint might be one of sensory capacity. If the wasps, for some reason, cannot count katydids in the burrow, but can metre some aspect of their own hunting efforts, there is an asymmetry of information possessed by the two combatants. Each one ‘knows’ that the burrow contains at least
b
katydids, where
b
is the number she herself has caught. She may ‘estimate’ that the true number in the burrow is larger than
b
, but she does not know how much larger. Under such conditions Grafen (in preparation) has shown that the expected ESS is approximately the one originally calculated by Bishop and Cannings (1978) for the so-called ‘generalized war of attrition’. The mathematical details can be left aside; for present purposes what matters is that the behaviour expected by the extended war of attrition model would look very like the Concordian behaviour actually shown by the wasps.

If we were interested in testing the general hypothesis that animals optimize, this kind of
post hoc
rationalization would be suspect. By
post hoc
modification of the details of the hypothesis, one is bound to find a version which fits the facts. Maynard Smith’s (1978b) reply to this kind of criticism is very relevant: ‘… in testing a model we are
not
testing the general proposition that nature optimizes, but the specific hypotheses about constraints, optimization criteria, and heredity’. In the present case we are making a general assumption that nature does optimize within constraints, and testing particular models of what those constraints might be.

The particular constraint suggested—inability of the wasp’s sensory system to assess the contents of a burrow—is in accordance with independent evidence from the same population of wasps (Brockmann, Grafen & Dawkins 1979; Brockmann & Dawkins 1979). There is no reason to regard it as an irrevocably binding limitation for all time. Probably the wasps could evolve the capacity to assess nest contents, but only at a cost. Digger wasps of the related species
Ammophila campestris
have long been known to make an assessment of the contents of each of their nests every day (Baerends 1941). Unlike
Sphex
, which provisions one burrow at a time, lays an egg, then fills the burrow in with soil and leaves the larva to eat the provision on its own,
Ammophila campestris
is a progressive provisioner of several burrows concurrently. A female tends two or three growing larvae, each in a separate burrow, at the same time. The ages of her various larvae are staggered, and their food needs are different. Every morning she assesses the current contents of each burrow on a special early morning ‘inspection round’. By
experimentally changing the contents of burrows, Baerends showed that the female adjusts her whole day’s provisioning of each burrow according to what it contained at the time of her morning inspection. The contents of the burrow at any other time of day have no effect on her behaviour, even though she is provisioning it all day. She appears, therefore, to use her assessment faculty sparingly, switching it off for the rest of the day after the morning inspection, almost as though it was a costly, power-consuming instrument. Fanciful as that analogy may be, it surely suggests that the assessment faculty, whatever it is, may have overhead running costs, even if (G. P. Baerends, personal communication) these consist only in the time consumed.

Sphex ichneumoneus
, not being a progressive provisioner, and tending only one burrow at a time, presumably has less need than
Ammophila
for a burrow-assessment faculty. By not attempting to count prey in the burrow, it can save itself not only the running expenditure that
Ammophila
seems so careful to ration; it can also save itself the initial manufacturing costs of the necessary neural and sensory apparatus. Probably it could benefit slightly from having an ability to assess burrow contents, but only on the comparatively rare occasions when it finds itself competing for a burrow with another wasp. It is easy to believe that the costs outweigh the benefits, and that selection has therefore never favoured the evolution of assessment apparatus. I think this is a more constructive and interesting hypothesis than the alternative hypothesis that the necessary mutational variation has never arisen. Of course we have to admit that the latter might be right, but I would prefer to keep it as a hypothesis of last resort.