4.4: Inclusive fitness, kin and group selection, and social evolution
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The question that troubled Darwin (and others) was, how can evolutionary processes produce this type of social, self-sacrificing behavior? Consider, for example, the behavior of bees. Worker bees, who are sterile females, “sacrificed themselves to protect their hives” even though they do not themselves reproduce123. Another example, taken from the work of R.A. Fisher (1890–1962), involved the evolution of noxious taste as a defense against predators. Assuming that the organisms eaten by predators did not benefit from this trait, how could the trait of “distastefulness” arise in the first place? If evolution via natural selection is about an individual’s differential reproductive success, how are such traits possible? W.D. Hamilton (1936–2000) provided the formal answer, expressed in the equation r times b > c (defined by Sewall Wright (1889–1988)), where “b” stands for the benefit of the trait to the organism and others, “c” stands for the cost of the trait to the individual and “r” indicates the extent to which two organisms within the population are related to one another (see above).
Let us think some more about what this means. How might active cell death in bacterial cells be beneficial evolutionarily? In this case, reproduction is asexual and the cell’s or organism’s neighbors are likely to be closely related. In fact, they are likely to be clonally related, that is sets of cells or organisms derived from a common parent in an asexual manner. Aside from occasional mutations, the cells and organisms within a clone are genotypically identical. Their genotypic similarity arises from the molecular processes by which the genetic material (DNA) replicates and is delivered to the two daughter cells. We can characterize the degree of relationship or genotypic similarity through their r value, the coefficient of relationship. In two genetically identical organisms, r = 1. Two unrelated organisms, with minimum possible genotypic similarity would have an r very close to, but slightly larger than 0 (you should be able to explain why r is not equal to 0). Now let us return to our cost-benefit analysis of a trait’s effect on reproductive success. As we introduced before, each trait has a cost = c to the organism that produces it, as well as a potential benefit = b in terms of reproductive success. Selection leads to a trait becoming prevalent or fixed within a population if b > c. But this equation ignores the effects of a trait on other related and neighboring organisms. In this case, we have to consider the benefits accrued by these organisms as well. Let us call the benefit to the individual as a result of their cooperative/altruistic behavior = bi and the benefit to others/neighbors = bo. To generate our social equation, known as Hamilton’s rule (see above), we need to consider what is known as the inclusive fitness, namely the benefits provided to others as a function of their relationship to the cooperator. So b > c becomes bi + r x bo > c. This leads to the conclusion that a trait can evolve if the cost to the cell or organism that displays it, in terms of metabolic, structural, or behavioral impact on its own reproductive ability, is offset by a sufficiently large increase in the reproductive success of individuals related to it. The tendency of an organism to sacrifice itself for another will increase (be selected for) provided that the reproductive success of closely enough related organisms is increased sufficiently. We will see that we can apply this logic to a wide range of situations and it provides an evolutionary mechanism driving the appearance and preservation of various social behaviors.
That said, the situation can be rather more complex. Typically, to work, inclusive fitness requires a close relationship to the recipient of the beneficial act. So how can we assess this relationship? How does one individual “know” (that is, how is its behavior influenced by the degree of relationship to others) that it is making a sacrifice for its relatives and not just a bunch of (semi-) complete strangers? As social groups get increasingly large, this becomes a more and more difficult task. One approach is to genetically link the social trait (e.g., altruistic behavior) to a physically discernible trait, like smell or a detectable structure. This is sometimes called a “green beard” trait. Individuals that cooperate (that is, display social behavior) with other organisms do so only when the green beard trait is present. The presence of the green beard trait indicates that the organism is related to the cooperator. Assume a close linkage between the two traits (social and visible), one can expect social behavior from an apparent (distantly related) stranger. In some cases, a trait may evolve to such a degree that it becomes part of an interconnected set of behaviors. Once, for example, humans developed a brain sufficiently complex to do what it was originally selected for (assuming that it was brain complexity that was selected, something we might never know for sure), this complexity may have produced various unintended byproducts. Empathy, self-consciousness, and a tendency to neurosis may not be directly selected for but could be side effects of behavioral processes or tendencies that were. As a completely unsupported (but plausible) example, the development of good memory as an aid to hunting might leave us susceptible to nightmares. Assume, for the moment (since we are speculating here), that empathy and imagination are “unintended” products of selective processes. Once present, they themselves can alter future selection pressures and they might not be easy to evolve away from, particularly if they are mechanistically linked to a trait that is highly valued (that is, selected for). The effects of various genetic mutations on personality and behavior strongly supports the idea that such traits have a basis in one’ s genotype. That said, this is a topic far beyond the scope of this book.
Contributors and Attributions
Michael W. Klymkowsky (University of Colorado Boulder) and Melanie M. Cooper (Michigan State University) with significant contributions by Emina Begovic & some editorial assistance of Rebecca Klymkowsky.