Why I Don’t like Kin Selection

Sorry this is so long.  To paraphrase Mark Twain Blaise Pascal, I would have written less, but I didn’t have time.

Up to this point I have been slogging through the details of why the phenotypic perspective is a good idea, and how it resolves a bunch of technical issues around how evolution works.   One of the truisms of teaching is that nobody gets it on the first pass, so I will occasionally be going back over the technical details of the phenotypic approach, but at this point it is time to change gears. It turns out that the true strength of the phenotypic perspective is that it suddenly resolves a suite of issues that have plagued evolutionary biologists for a long time. Some of the issues are minor things like: what is an individual (hint: you get to decide), why sex (hint: genes are slaves to phenotypes, and have no rights), the origin of life (hint: the phenotype always comes first), why is DNA the molecule of inheritance (hint: the unstable enslave the stable), and a host of other equally trivial questions in biology. Dang, when we get done with this, evolutionary biologists won’t have anything to fight about. Sadly a lot of this isn’t published, so I have to come to grips with how I feel about writing down unpublished ideas. On the other hand, until the phenotypic approach is embraced by somebody other than me these ideas will never get published. . . .

This week, however, I figure I should talk about kin selection. By this point it should be obvious that I am no fan of kin selection or inclusive fitness. And, while I do like Hamilton’s work in general, a lot of his models just aren’t very good. My postdoc adviser, David Mertz, referring to optimal foraging theory, once claimed that Robert MacArthur set ecology back 100 years. I always loved that statement, both because I understood what he meant, and because it couldn’t possibly be true. MacArthur had a tendency to ignore important issues and produce models that were aesthetically appealing but unsatisfying to the deep thinkers in the field. Second, population ecology wasn’t 100 years old, so it implied that MacArthur had reset the field back to before it started. Well, I guess I rather feel a bit the same way about Hamilton when it comes to social evolution. The real damage that Hamilton did 50 years ago when he published his model is that people somehow thought that inclusive fitness was useful, and a gigantic field grew up that has actively interfered with our ability to understand social evolution. So what is it that I don’t like about kin selection?

macarthur hamilton

Robert MacArthur (Left), William Hamilton (Right). Both MacArthur’s optimal foraging theory and Hamilton’s kin selection theory are optimality models. Both suffer from the limitations that are inherent to the optimality approach.

Kin selection is an optimality approach. Optimality approaches have a way of providing interesting insights but then grinding to a halt when efforts are made to apply them to experimental systems. The case in point is optimal foraging theory (Back to MacArthur. Maybe Mertz and I were cut from the same cloth). MacArthur and Pianka (1966. Am Nat 100:603-609) developed the first optimal foraging model, which was an effort to solve the problem of what an organism should do to maximize energy intake within the constraints of the ecology of the organism. Such constraints eventually included such things as search time for food, handling time etc. This was an enlightening model in that it really focused on the idea that organisms can be thought of as solving the problem of maximizing food intake while minimizing risks and costs. This led first to a large number of models on optimal foraging theory, and second, the realization that real organisms basically never follow the optimal solution. The solution to the lack of fit to real data was a series of ever more complex essentially post hoc theoretical solutions, e.g., they are maximizing limiting resources not calories, they are avoiding secondary compounds etc. The bottom line is that today you will frequently find simple optimal foraging models used as a starting point for more nuanced theoretical and experimental studies, but you would be hard pressed to find ANY papers that are solely about an optimal foraging model. I asked a colleague about this, and her response was that people probably stopped because it was just not very useful. This is the fate of optimality models of all sorts. They provide nice qualitative insights, but they simply are not very useful. Kin selection models are no exception. Yes, Haldane’s famous statement about being willing to sacrifice his life for two brothers or eight cousins is a nice qualitative insight (among other things it demonstrates that Haldane had a time machine so that he could travel to the future to bask in Hamilton’s brilliance).   However, that is as far as ANY kin selection study has ever gotten. Read a few. They do crazy hard research on the behavior of prairie dogs or slime molds, then at the end they say something to the effect of “and this is consistent with a model of kin selection.” There are never any numbers telling us just how consistent or anything else. How close does your organism have to be to the optimum before your theory is supported? Optimality models don’t provide that insight.

Note added later:  I originally attributed the two brothers, eight cousins comment to Dobzhansky.  This was simply me writing too fast.  In fact, this story may be apocryphal.  Thanks to Trevor Pierce for pointing this error out.

Kin selection can only focus on altruism. ALL kin selection models are about the evolution of altruism. To a kin selectionist altruism is when an individual increases the fitness of another individual at the expense of their own fitness. To a multilevel selectionist altruism is when group and individual selection are acting in opposition. There are ample models showing the equivalence of these two statements (e.g., Goodnight 2013 Evolution 67, 1539). Because kin selection is an optimality model the only time it is interesting to study social behavior is when the two levels are acting in opposition. Multilevel selection is much richer than this. There are plenty of times when two or more levels of selection act in the same direction. Many of these would be very interesting, however, biologists tend to ignore them because they are outside the realm of what can be studied using kin selection models.

Kin selection is a genic model.   The way that Hamilton originally developed his model, and the way it is virtually always used is based on shared genes. Individuals are altruistic towards other individuals because they are relatives and thus might share genes. Relatedness is a proxy for the probability of shared genes, but there are other means of detecting genetic similarity, such as the “greenbeard” model (Jeeze I hate that term. I hope who ever invented that term burns in hell, that is, if atheists can burn in hell). The problem is that the world doesn’t work that way. Models be damned, there is no “altruism” gene. Models that start with the assumption of a single locus with an altruistic and selfish allele are ok as a starting point for qualitative thinking, but totally useless in the real world. The problem is that the genic nature of kin selection does not give us a way to move beyond that. Sure you find lots of times where modelers will define “x” to be some measure of the genome, but if we are going to use it in the real world we need to know WHAT aspect of the genome. Also there have been attempts to move to a phenotypic based kin selection model, but these have always failed, mainly because they always go back to the thought that shared phenotype means shared genes and somewhere buried down there is an altruism gene. On top of this there are plenty of cases of culturally based altruism. Soldiers are famous for acts of altruism among genetically unrelated members of the same unit. What makes them similar is culture not genes. Genic models cannot handle this, and kin selection is no exception.

Kin selection uses a linear additive genetic model. The one thing we know for a fact is that there are tons of evolutionarily important interactions in the biological world. These include dominance, epistasis, and indirect genetic effects of all sorts. We also know that these have profound effects on selection, and especially multilevel selection. With its focus on single altruism genes and gene sharing kin selection models are relegated to the world of one behavior, one gene. Relatedness (r) in kin selection models tends to be the proportion of genes shared. In fact, “r” equates to the fraction of variance among (kin) groups, that is a measure of similarity. If there are interactions, particularly indirect genetic effects, “r” may be much larger than the proportion of genes shared.

Kin selection assumes that the cost and benefit are the same trait. In kin selection models the trait is “altruistic” versus “selfish” which has fitness consequences on the individual (cost) and on its partners (benefit). This works fine for single locus traits that are the focus of kin selection models; however, real traits are polygenic. In a polygenic setting the cost and the benefit must be considered separate, but genetically correlated, traits. Consider two individuals, both altruists, but one is a more efficient altruist than the other. That is the efficient one can help at less cost to itself. In this case both individuals give the same benefit (B), but different costs (C). This is not possible in a single locus model, but it is an expected result for polygenic models. In kin selection models, because they are the same trait determined by a single locus the only thing that can change the equation is r, which is strictly a measure of the proportion of shared genes. For a polygenic trait when the group and individual trait are considered to be separate correlated traits, I have shown that “r” is the ratio of heritabilities for the group and individual level trait. (Goodnight 2005 Population Ecology 47, 3-12.). If we take, for example, a typical metazoan, the cells within an organism are nearly genetically identical, thus the within individual heritability for cell level traits is very nearly zero. On the other hand the heritability of organismal traits is what ever it is, and very likely non-zero. In this case “r” is the ratio of the heritabilites of the organismal trait to the cell trait, which could be a very large number. In the kin selection world “r” goes from zero to one, in my world it goes from zero to infinity.

Nobody has ever or will ever measure the strength of kin selection. Kin selection is an optimality approach, and tells you what the best solution is. However, the best solution doesn’t mean much if it is unattainable. It may be unattainable for many reasons. There may not be genetic variation for it. It may be opposed by selection acting on something else.  Selection on it may be so weak as to be meaningless.  How do we address this? Well the typical way is to measure the strength of selection, and the heritability of a trait. If we do this we have data to specifically address these questions.  For example there are numerous examples of studies showing that opposing selection on different traits or different life-stages prevent a trait from changing. Central to these studies is comparing the strength of opposing selection and seeing if the actual value of the trait corresponds to the value predicted by the estimated competing rates of evolution. There are plenty of other studies in which selection is technically found to be operating, but it is so weak that it can be disregarded, thus, knowing the strength of selection is essential to being able to interpret its importance. Because kin selection is an optimality model it is not and cannot be used to measure the strength of kin selection. Indeed, because kin selection is apparently defined by Hamilton’s rule it is not at all clear what we might mean by the strength of kin selection. Unfortunately, until we know the strength of kin selection relative to other evolutionary forces any conclusions drawn from kin selection studies will be nothing more than “just so” stories.

Kin selection confounds three different things. From a multilevel selection perspective Hamilton’s rule consists of three different elements, cost, which is the strength of individual selection, benefit, which is the strength of group selection, and r, which, for a trait and the group mean of a trait, is the fraction of variance among groups. More generally, it is the squared correlation between the group and individual trait. If it is a phenotypic selection model that would be the phenotypic correlation, if it is a genetic model it is the additive genetic correlation. The problem is that kin selection mushes these three things, group selection, individual selection, and variance explained by the group trait into a single value. The question is how does one interpret this? It tells us nothing about whether or not kin selection is important since it tells us nothing about the strength of selection. It tells us where a trait should evolve to, but if a trait is at that predicted optimum it gives no guidance as to whether that value is due to kin selection, due to something else that just happened to be at the same optimum, or if it is just passing through as it evolves to some other value. Further there are three parameters that can be manipulated, C, B and r, so for any given optimum we can presumably make a three dimensional surface of values of these parameters that will all provide the same optimal trait value. Thus we potentially can’t even easily compare two populations at the same optimum.

There are other minor concerns I could raise, but I am up to twice my normal length for a blog post so I will stop. In closing, I will say I am not going to dismiss kin selection as useless any more than I will dismiss optimal foraging theory as useless. However, like optimal foraging theory, it appears to mainly be useful in making broad stroke qualitative predictions that can be used in the introduction, or in a laudatory paragraph about how wonderful Hamilton is at the end of a paper. If you want to make quantitative statements about selection in real world populations that will contribute to our understanding of social evolution multilevel selection might be a better choice.

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