A recent post on the Gene Expression blog (here) alerts me to an article on selection for human longevity in PLoS One (here).
Explaining ageing is actually harder than it might seem and there is no particular reason to suppose that ageing should occur at all. One explanation, described by Peter Medawar, is that genes with deleterious effects later in life are relatively less effectively selected against because of a general increase in mortality with age (the longer you live the higher the chance of accidental death) and can persist in a mutation-selection balance. Decreases in the intensity of selection with age can also favour genes with beneficial effects earlier in the lifespan and deleterious effects later. This scenario, first described by George Williams, is called antagonistic pleiotropy and can take a number of forms depending on the sorts of trade-offs lying behind these gene effects.
But there is a problem which is unique to humans (ed. it turns out I am wrong that this is unique see here!) and that is the female menopause. As Hamilton pointed out there should be a sudden increase in mortality after the age of menopause in both sexes because an expected fertility of zero effectively negates selection against deleterious genes acting at this time. Arguments aimed to try to fell this “wall of death” are generally focussed on intergenerational transfers and the best known of these is the “grandmother” hypothesis which suggests that the continued presence of post-reproductive females can enhance survivorship of grandchildren and other relatives through the provision of resources and time. In this way longevity is kin selected.
But the PLoS One article by Tuljapurkar et al. offers a new explanation, which is that male reproduction past the age of female menopause can strengthen selection for longevity and assuage the rise to infinite mortality predicted by Hamilton. Collating data from several cultures they show that males continue to show non-zero fertility rates after the age of female menopause (which is not surprising given that males tend to mate with younger females across the board), but also they show that the shape of the fertility function is different in males compared with females. It is not simply an echo of female fertility rates at younger ages, but instead shows an extended tail of male fertility with increasing age. This effect is likely a result of several factors including serial monogamy (wherein males are more likely to re-marry than females), polygyny (in which males have multiple partners) and the existence of high status males (who show persistent sex appeal into old age). It is important to remember that genes for longevity can be shared between the sexes so that theories of ageing should take account of fertility in both sexes. Tuljapurkar et al. explicity modelled the dynamics of fertility in both sexes to produce a model supporting their conclusion that male age-specific fertility rates can explain the persistence of human longevity past the menopause and the absence of a “wall of death”.
This month’s PLoS Biology has an interesting article by Lars Chittka and Thomas Döring in the unsolved mysteries section that addresses a wonderfully simple-sounding question: why do the leaves change colour in the autumn? (Open-access to this article is here).
Chittka and Döring discuss the hypothesis, advanced independently by Hamilton and Brown in 2001 and by Archetti in 2000, that the changing colour of leaves in the fall comprises an honest warning signal to aphids. The hypothesis holds that aphids heed this because the chemicals responsible for bright autumnal colours indicate a plant’s capacity to resist infestation. Further because this signal is costly to produce it becomes invulnerable to misleading signalling and evolutionarily stable. The benefit to the plants of this signal would be that trees adopting it are less likely to suffer parasitism at the beginning of the next season when growth of vegetation resumes.
This is a nice hypothesis. The colours of autumn leaves are partly caused by loss of chlorophyll and this might appear to be a sufficient explanation, but an extra explanation is surely required for the red pigments produced by anthocyanins. This is because expression of these biochemically active compounds specifically increases in the fall.
Nice though it is, the hypothesis is probably wrong. The best knowledge that we have about aphid visual systems suggests that red leaves are probably relatively inconspicuous to them as there is no evidence for the existence of red photoreceptors. On the other hand, the visual processing mechanism that draws aphids to green targets is also in some cases, it would seem, super-stimulated by yellow targets. This spectrally opponent mechanism pits activation of ultraviolet and blue photoreceptors (negative) in the insect eye against activation of green photoreceptors (positive). The relatively low stimulation of the former receptors by yellow leaves effectively makes them greener than green. In summary, the principal two autumnal colours: red and yellow, are cryptic and attractive, respectively, rather than being deterrent in effect.
What makes this article so helpful is that it illustrates the problems with adaptive story telling and the work required to effectively test adaptive hypotheses (also illustrated positively by my post below on superb fairy-wrens). It would appear that we humans are led astray by our red photoreceptors which lead us to find autumn leaves attractive and attention-arresting.
Perhaps evolutionary psychologists can rest easy thinking that as humans trying to understand humans there is less vulnerability to this particular sort of error. I believe this would be a mistake because cross-cultural differences invite similar problems of differences in perception. The take-home message: collect the data and understand the biology and ecology (or culture if you prefer) of the organisms under study! Can anyone describe anthropological data that can be used to refute otherwise plausible adaptive hypotheses?
The other day I heard, through the grapevine, that an esteemed colleague thought that evolutionary psychology had, as a discipline, established and sorted out its theoretical basis. All that remained to be done (which is a big all) was to test individual hypotheses through whatever empirical means were available. A body of knowledge needed building.
My initial reaction to this was to be sceptical. If evolutionary psychology is interpreted in its broadest sense – i.e. the application of evolutionary biology to the behavioural sciences, or more correctly, the behavioural sciences are rightly understood as a sub-discipline of biology – then what of the clash between gene-level selectionists and multi-level and group selectionists, for example? Most of this debate is happening in areas that attempt to describe and account for a phenomenon referred to as culture. Culture is claimed to have a distinct and describable ontological status, and some claim that it can evolve whilst others claim it affects gene-level evolution through processes such as niche construction. Folk appear to be trying to sort something theoretical out here using the standard tools of philosophy as well as mathematical modeling. (And, as you will perhaps deduce from my tone, I think there are some other core theoretical issues lurking in the very establishment of the problem.)
Any how, I do not want to bang on about cultural evolution and its ills here, just yet. But, what I wanted to ask folk was:
1) Do you think my esteemed colleague is correct?
2) If not, then what are the key theoretical challenges that remain for evolutionary psychology?
3) If you think she is correct, then what key experiments need doing in order to falsify this body of theory (or rather, in order to attempt a falsification, for it may, of course, not stand)?
This week’s Science magazine has an interesting study (here for summary) about the relationship between parental investment and cooperative breeding in the superb fairy-wren. Sometimes these birds breed in pairs and sometimes cooperatively.
In cooperative breeding (sometimes unrelated) individuals assist a breeding pair by provisioning their young. It turns out that spotting the benefits of this has been quite hard: the chicks are given more food but they don’t seem to end up any heavier for it. Russell et al. nicely show, with general linear models, how the beneficial effects of increased provisioning are hidden by a corresponding decrease in maternal investment in the egg. It turns out that in cooperatively breeding situations mothers produce lighter eggs with less yolk and this is compensated for by the increased provisioning. This implies that the benefits of cooperative breeding are not to the chicks but to the mothers who may be able to conserve resources for future reproductive attempts. Even more elegantly, Russell et al. reciprocally cross-foster the chicks from pairs to groups and vice versa showing that offspring from eggs hatched in groups and raised by pairs fare particularly poorly.
An interesting and more openly speculative part of the paper is the authors’ explanation as to why it might be that cooperative breeding occurs at all. Apparently delayed reciprocity (whereby helpers specifically receive benefits later) is unstable but benefits accruing directly from augmentation of group size (which may include helping, but also things like predator vigilance) may be the culprit. The authors cite this paper in support. Now male birds tend to disperse less from the natal area than females so we expect males to benefit more from group augmentation and Russell et al. measured increased provisioning to chicks with increasing male numbers. Group augmentation seems to me a fascinating explanation for altruism in groups and this study strikes me as a superb (sorry!) example of the effective testing of adaptive hypotheses.
The “question of the year” at Nature Genetics asks geneticists what they would do if the equivalent of the human genome could be sequenced for one thousand dollars. There are lots of interesting answers from geneticists there. But I think this opens up a more general question for (evolutionary) psychologists (and obviously behaviour geneticists) about what easy access to genetic information could provide. What sorts of questions could be addressed in psychology if genetics was easy and cheap?
Here’s one idea: we could find out how genetically similar friends are to each other (e.g. do they share single nucleotide polymorphisms or SNPs?). This might help address theories about altruism and the green beard effect or it might just stimulate more questions. The green beard effect is an abstraction from the logic of kin selection that shows how altruism can occur between non-relatives by a genetic mechanism. In this scenario a single gene or a group of genes in linkage disequilibrium (i.e. genes that tend to be inherited together) produce two effects: 1. a distinctive trait such as a green beard and 2. a tendency to be altruistic to others with this trait (or nasty to those without). Groups of green-bearded organisms can then start to spread in populations by mutual aid (or through individually costly punishment of outsiders). Evidence for this in non-humans is accumulating (see here, here and here for empirical support) and it would be lovely if such a system could be identified in humans by high-throughput methods.
I’m thinking about the relationship between induction and parsimony. Parsimony is the principle that urges us to adopt the theory which makes the fewest assumptions, when faced with a choice between two or more theories that all equally well explain the evidence presented. One way to understand why this might be justified is that theories which make fewer existence claims are responding better to the evidence which is not presented. This assumes that the absence of evidence for an existence claim is, to some extent, evidence for its absence. The non-presence of some evidence therefore favours the simpler theory (making fewer existence claims) over the others. The problem with this is the problem of induction. We cannot be certain that we are not making an inductive fallacy (roughly speaking assuming that the future will be like the past). Now I said above that absence of evidence is “to some extent” evidence for absence. This indicates that a probabilistic view of parsimony may also be adopted. If a theory postulates the existence of two things each with some probability (independent of the other) then it has a lower probability (multiply them!) than a rival theory which postulates the existence of only one of these. This seems to me to be true regardless of our uncertainty about what sorts of probability distribution underlie reality (although these will affect the usefulness of parsimony). Am I slipping up here or repeating myself? Finally the interesting exception to this is when the prior probability of particular existence claims is increased by the theory within which we labour. I believe evolutionary theory increases the likelihood that developmental mechanisms will be complex. This is simply because we must consider them as products of descent with modification (whether this is achieved by genetic drift or natural selection).
I’m just starting to read “The Black Swan: the impact of the highly improbable” by Nassim Nicholas Taleb. So far, so fun (have a read: UK and US). The title is motivated by the fact that it takes only one observation of a black swan to undermine the supposition that all swans are white even though we may have years of observations to back it up. This is the classic problem of induction.
It looks like a major claim of the author will be that the probability distributions that underlie the majority of events in the natural and social world are non-normal and subject to huge sampling error. Hence the problem of induction is a severe one in everyday life. Also this problem is one that lies at the edge of our knowledge since knowledge is most easily acquired about systems with more predictable properties. I might be over-interpreting here, but I suspect that this leads him to conclude that science communication, story-telling about uncertainty, is a flawed activity that will itself promote unpredictability by making people less savvy about risky and rare events in a globalised, mass-media world.
I find it an interesting to think that what we don’t know is somehow different in kind from what we do know. It is certainly good to be humble about the limits of knowledge in general. But I am also suspicious that there may be radical scepticism in here: something one tends to find surrounding poorly-evidenced and contrarian ideas. So I will be looking out for selective use of the unpredictability idea and for alternatives that massage intuitions in the absence of evidence.