Tag Archives: Evolution

The Key to the Survival of the Human Species?

Perhaps it’s just me being a bit groggy from jet-lag, but I just read one of the most bizarre things I think I have ever seen in the New York Times.  There is a generally very interesting article by Sarah Kershaw on so-called “cougars,” older women who have sexual relationships with younger men. It was the first I had ever heard the term – shows what I know. As the article concludes, Kershaw makes the following statement:

The paradox, of course, is that the older-woman relationship makes perfect sense when it comes to life expectancy, with women outliving men by an average of five years. But with men’s fertility far outlasting women’s, biology makes the case for the older-man scenario, and recent research has even suggested that older men having children with younger women is a key to the survival of the human species.

Say what?! Survival of the species??

It’s a pretty strange statement that strangely lacks attribution, particularly given how well referenced all the other scholarly work discussed in the article is.  I wonder if it isn’t a vague allusion to the work of my colleague Shripad Tuljapurkar who has shown that systematic differences in mean age of childbearing would mitigate the so-called “wall of death” predicted by W.D. Hamilton’s famous paper on the evolution of senescence.

Stanford Workshop in Biodemography

On 29-31 October, we will be holding our next installment of the Stanford Workshops in Formal Demography and Biodemography, the result of an ongoing grant from NICHD to Shripad Tuljapurkar and myself.  This time around, we will venture onto the bleeding edge of biodemography.  Specific topics that we will cover include:

  • The use of genomic information on population samples
  • How demographers and biologists use longitudinal data
  • The use of quantitative genetic approaches to study demographic questions
  • How demographers and biologists model life histories

Information on the workshop, including information on how to apply for the workshop and a tentative schedule, can be found on the IRiSS website. We’ve got an incredible line-up of international scholars in demography, ecology, evolutionary biology, and genetics coming to give research presentations.

The workshop is intended for advanced graduate students (particularly students associated with NICHD-supported Population Centers), post-docs, and junior faculty who want to learn about the synergies between ecology, evolutionary biology, and demography. Get your applications in soon — these things fill up fast!

Always a Bridesmaid, Never a Bride

Well, it’s happened again.  My work has been written up in Science but I am not mentioned.  I’m actually not that concerned this time — we’re going to submit the paper for publication soon. I’ve been telling myself (and other people) that this thing we’ve ben working on (all the while being very cryptic about what this thing exactly is) is important.  Every once in a while, I wonder if I’ve just been fooling myself.  The fact that this work has been written up in Science the day after the paper was presented at the Montreal Conference on Retroviruses and Opportunistic Infections suggests to me that it is, indeed, important.

Nearly Neutral Networks and Holey Adaptive Landscapes

My holiday reading on modularity has led me into some of the literature on the evolution of complexity.  Some of the most interesting work in theoretical biology that I’ve read in a while relates to the ideas of nearly neutral networks and holey adaptive landscapes, an area developed by Sergey Gavrilets at the University of Tennessee.  The various papers of his to which I refer can be accessed on his website.  I find his explanations very clear, but recognize that this work is fairly technical stuff and my understanding of it is greatly facilitated by previous experience with similar models in the context of epidemics on networks (believe it or not). Nonetheless, a reasonably accessible introduction can be found in his 2003 chapter, “Evolution and speciation in a hyperspace: the roles of neutrality, selection, mutation and random drift.” I have based much of my discussion here on this paper along with his 1997 paper in JTB.

The father of American population genetics and Modern Evolutionary Synthesis pioneer Sewall Wright first came up with the metaphor of the adaptive landscape in 1932.  The basic idea is a kind of topographic map where the map coordinates are given by the genotype and the heights above these coordinates are given by the fitnesses associated with particular genotype combinations.  A landscape, of course, is a three dimensional object.  It has a length, a width (or latitude and longitude) and height.  This particular dimensionality turns out to be very important for this story.

A major concern that arises from the idea of an adaptive landscape is how populations get from one peak to another.  In order to do this, they need to pass through a valley of low fitness and this runs rather counter to our intuitions of the way natural selection works.  The usual way around this apparent paradox is to postulate that populations are small and that random genetic drift (which will be more severely felt in small populations) moves the population away from its optimal point on the landscape.  Once perturbed down into a valley by random forces, there is the possibility that the population can climb some other adaptive peak.

This is a slightly unsatisfying explanation though.  Say that we have a finite population of a diploid organism characterized by a single diallelic locus. Finite populations are subject to random drift. The size of the population is N.  Assume that the fitnesses are w_{AA}=1, w_{Aa}=1-s, and w_{aa}=1. This is actually a very simple one-dimensional adaptive landscape with peaks at the ends of the segment and a valley in between.  Assume that the resident population is all AA.  What happens to a mutant a allele? We know from population genetics theory that the probability that a completely neutral (i.e., s=0) mutant allele reaching fixation is 1/2N.  Russ Lande has shown that when the s>0 this probability becomes:

 U = \frac{1}{2N} \frac{e^{-Ns}\sqrt{4Ns/\pi}}{erf(\sqrt{Ns})},

where erf() is the error function  erf(t) = 2/\sqrt{\pi} \int_0^t e^{-y^2} dy.

When Ns=20 (say a population size of 200 and a fitness penalty of s=0.1), this probability is approximately U=10^{-8}.  So for quite modest population size and fitness disadvantage for the heterozygote, the probability that the population will drift from AA to aa is very small.  This would seem to spell trouble for the adaptive landscape model.

Gavrilets solved this conundrum — that moving between peaks on the adaptive landscape appears to require the repeated traversing of very low-probability events — apparently by thinking a little harder about the Wrightean metaphor than the rest of us.  Our brains can visualize things very well in three dimensions.  Above that, we lose that ability.  Despite all the Punnett squares we may have done in high school biology, real genotypes, of course, are not 2 or 3 dimensional.  Instead, even the simplest organism has a genotype space defined by thousands of dimensions.  What does a thousand dimensional landscape look like? I haven’t the faintest idea and I seriously doubt anyone else does either.  Really, all our intuitions about the geometry of such spaces very rapidly disappear when we go beyond three dimensions. 

Using percolation theory from condensed matter physics, Gavrilets reveals a highly counter-intuitive feature of such spaces’ topology. In particular, there are paths through this space that are very nearly neutral with respect to fitness.  This path is what is termed a “nearly neutral network.” This means that a population can drift around genotype space moving closer to particular configurations (of different fitness) while nonetheless maintaining the same fitness.  It seems that the apparent problem of getting from one fitness peak to another in the adaptive landscape is actually an artifact of using low-dimensional models. In high-dimensional space, it turns out there are short-cuts between fitness peaks.  Fitness wormholes?  Maybe.

Gavrilets and Gravner (1997) provide an example of a nearly neutral network with a very simple example motivated by Dobzhansky (1937).  This model makes it easier to imagine what they mean by nearly neutral networks in more realistic genotype spaces.

Assume that fitness takes on one of two binary values: viable and non-viable. This assumption turns our space into a particularly easy type of structure with which to work (and it turns out, it is easy to relax this assumption).  Say that we have a three diallelic loci (A, B, and C).  Say also that we have reproductively isolated “species” whenever there is a difference of two homozygous loci — i.e., in order to be a species a genotype must differ from the others by at least homozygous loci.  The reproductive isolation that defines these species is enforced by the fact that individuals heterozygous at more than one locus are non-viable. While it may be a little hard to think of this as a “landscape”, it is.  The species nodes on the cube are the peaks of the landscape.  The valleys that separate them are the non-viable nodes on the cube face.  Our model for this is a cube depicted in this figure.

Now using only the visible faces of our projected cube, I highlight the different species in blue.

The cool thing about this landscape is that there are actually ridges that connect our peaks and it is along these ridges that evolution can proceed without us needing to postulate special conditions like small population size, etc. The paths between species are represented by the purple nodes of the cube.  All the nodes that remain black are non-viable so that an evolutionary sequence leading from one species to another can not pass through them.  We can see that there is a modest path that leads from one species to another — i.e., from peak to peak of the fitness landscape. Note that we can not traverse the faces (representing heterozygotes for two loci) but have to stick to edges of the cube — the ridges of our fitness landscape.  There are 27 nodes on our cube and it turns out that 11 of them are viable (though the figure only shows the ones visible in our 2d projection of the cube).

So much for a three-dimensional genotype space. This is where the percolation theory comes in. Gavrilets and Gravner (1997) show that as we increase the dimensionality, the probability that we get a large path connecting different genotypes with identical fitness increases.  Say that the assignment of fitness associated with a genotype is random with probability p that the genotype is viable and 1-p that it is non-viable.  When p is small, it means that the environment is particularly harsh and that very few genotype combinations are going to be viable. In general, we expect p to be small since most random genotype combinations will be non-viable. Percolation theory shows that there are essentially two regimes in our space.  When p<p_c, where p_c is a critical threshold probability, the system is subcritical and we will have many small paths in the space.  When  p>p_c, we achieve criticality and a giant component forms, making a large viable evolutionary path  traversing many different genotypes in the space.  These super-critical landscapes are what Gavrilets calls “holey”. Returning to our three dimensional cube, imagine that it is a chunk of Swiss cheese.  If we were to slice a face off, there would be connected parts (i.e., the cheese) and holes.  If we were, say, ants trying to get across this slice of cheese, we would stick to the contiguous cheese and avoid the holes. As we increase the dimensionality of our cheese, the holes take up less and less of our slices (this might be pushing the metaphor too far, but hopefully it makes some sense).

A holey adaptive landscape holds a great deal of potential for evolutionary change via the fixation of single mutations.  From any given point in the genotype space, there are many possibilities for evolutionary change.  In contrast, when the system is sub-critical, there are typically only a couple of possible changes from any particular point in genotype space.

To get a sense for sub-critical and supercritical networks, I have simulated some random graphs (in the graph theoretic sense) using Carter Butts‘s sna package for R.  These are simple 1000-node Bernoulli graphs (i.e., there is a constant probability that two nodes in the graph will share an undirected edge connecting them).  In the first one, the probability that two nodes share an edge is below the critical threshold p_c.


We see that there are a variety of short paths throughout the graph space but that starting from any random point in the space, there are not a lot of viable options along which evolution can proceed. In contrast to the sub-critical case, the next figure shows a similar 1000-node Bernoulli graph with the tie probability above the critical threshold — the so-called “percolation threshold.”

Here we see the coalescence of a giant component.  For this particular simulated network, the giant component contains 59.4% of the graph.  In contrast, the largest connected component in the sub-critical graph contained 1% of the nodes.  The biological interpretation of this graph is that there are many viable pathways along which evolution can proceed from many different parts of the genotype space. Large portions of the space can be traversed without having to pass through valleys in the fitness landscape.

This work all relates to the concept of evolvability, discussed in the excellent (2008) essay by Massimo Pigliucci.  Holey adaptive landscapes make evolvability possible.  The ability to move genotypes around large stretches of the possible genotype space without having to repeatedly pull off highly improbable events means that adaptive evolution is not only possible, it is likely.  In an interesting twist, this new understanding of the evolutionary process provided by Gavrilets’s work increases the role of random genetic drift in adaptive evolution.  Drift pushes populations around along the neutral networks, placing them closer to alternative adaptive peaks that might be attainable with a shift in selection.

Super cool stuff.  Will it actually aid my research?  That’s another question altogether…

Another fun thing about this work is that this is essentially the same formalism that Mark Handcock and I used in our paper on epidemic thresholds in two-sex network models. I never cease being amazed at the utility of graph theory.


Dobzhansky, T. 1937. Genetics and the Origin of Species. New York: Columbia University Press.

Gavrilets, S. 2003. Evolution and speciation in a hyperspace: the roles of neutrality, selection, mutation and random drift. In Crutchfield, J. and P. Schuster (eds.) Towards a Comprehensive Dynamics of Evolution – Exploring the Interplay of Selection, Neutrality, Accident, and Function. Oxford University Press. pp.135-162.

Gavrilets, S., and J.Gravner. 1979. Percolation on the fitness hypercube and the evolution of reproductive isolation. Journal of Theoretical Biology 184: 51-64.

Lande, R.A. 1979. Effective Deme Sizes During Long-Term Evolution Estimated from Rates of Chromosomal Rearrangement. Evolution 33 (1):234-251.

Pigliucci, M. 2008. Is Evolvability Evolvable? Nature Genetics 9:75-82.

Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and selection in evolution. Proceedings of the 6th International Congress of Genetics. 1: 356–366.


More on Buller and Evolutionary Psychology

This is an ongoing series of meditations on evolutionary psychology inspired by my recent reading of David Buller’s piece in Scientific American.  I have been thinking quite a bit in the last year about the relationship between evolutionary psychology, human behavioral ecology, and evolutionary genetics, and maybe these ruminations will help me get my thoughts clear on these difficult topics.  Caveat utilitor: these are not fully formed ideas but the blog is a useful device for organizing my sketches.

I found an interesting  critique of Philosopher of Science and evolutionary psychology critic David Buller‘s book, Adapting Minds. Edouard Machery and H. Clark Barrett wrote an extended, critical review of Buller’s 2005 book in the journal Philosophy of Science.

I must admit that I find myself torn on some of these debates. I am sympathetic to many of the criticisms voiced by Buller, but think that some of the rebuttals are quite compelling as well. For example, Buller is highly critical of work on child homicide by Martin Daly and Margo Wilson of McMaster University.  Daly and Wilson, in a series of famous studies, suggest that child homicide (a rare event) is much more likely to be perpetrated by step-parents (including boyfriends).  The explanation for why this might be relates to the existence of an anti-cuckoldry mechanism in men’s brains. Given the enormous obligate investment — generally on the part of two parents — entailed in the successful recruitment of human offspring, cuckoldry represents a potentially enormous fitness cost for human men.

In one study of child homicides in Canada between 1974 and 1990, Daly and Wilson calculated a risk-ratio that child homicides are perpetrated by step-parents vs. (putative) biological parents of 123.7.  Buller suggests that such results might simply arise because of ascertainment bias in the reporting of child homicide.  Specifically, he suggests that the cause of death listed on a child’s death certificate is far less likely to be homicide if the act was perpetrated by a biological parent. In support of this argument, he cites a paper by Crume et al. (2002) which compared cause-of-death as listed on the death certificate with the cause determined by a interagency multidisciplinary child fatality review team.  This team reviewed child deaths in the state of Colorado and found that a substantial number of likely homicides were not reported as such.  They were then able to investigate which attributes of (alleged) perpetrators made ascertainment more or less likely.  They found that homicides committed by non-relatives (including boyfriends) were 8.41 times more likely to be recorded as such than were those committed by parents. Of 152 death at the hands of parents only 65 were correctly ascertained while 87 were not.  For the 51 deaths attributable to non-relatives, 44 were correctly ascertained while seven were not.  This yields an odds ratio of (44*87)/(65*7)=8.41 that non-relatives will be correctly ascertained compared to parents (the OR changes to 8.71 following multivariate adjustment — it is this number that is discussed in the various papers). This seems pretty damning (and suggests there are major problems detecting fatal violence against children).  However, one point from this paper that Buller does not note in his critique (at least his 2005 paper in Trends in Cognitive Sciences) is that the odds of ascertainment for non-parent relatives — including step-parents — is not significantly different from unity. That is, the group that includes step-parents is as likely to be ascertained as biological parents.  My understanding is that Daly and Wilson’s analysis applies to step-parents as well as boyfriends.  The theory certainly predicts this.

My sense is that Buller is reaching a little too far in this critique. While I would hardly consider myself an expert on the topic, I have always thought quite highly of Daly and Wilson’s demographic work on homicide.  One of my students is currently relying heavily on their Chicago mortality study published in BMJ.  That is something I do have some expertise in and I think it is excellent.  What I want to know is this: what is the counterfactual to the Daly & Wilson work?  How many child deaths would need to be re-classified in order to have ascertainment bias be sufficient to account for their observed differences?  Daly & Wilson (2007) do just this sort of counterfactual calculation.  They assume that step-fathers are always caught, whereas biological fathers are never caught.  According to their calculation, such a scenario would imply that there were 500 unaccounted-for paternal murders to yield the observed rates.  This is where the problem comes in.  There simply aren’t 500 deaths each year to children under five in Canada in that period that aren’t due to congenital defects or infectious disease.  Mortality among the young is rare in developed countries. Clearly, not all of the effect that Daly & Wilson report can be attributed to ascertainment bias.  There seems to be some there there.

I think that this over-reaching is a shame.  The critiques that Buller levels in his recent Scientific American piece are serious and deserve to be taken seriously. Here, I specifically mean the idea that an analysis of the Pleistocene will yield significant clues for understanding the design of the human mind and that evolutionary psychology will be much use in helping us understand unique and universal human traits.  The tone of this debate (on both sides) seems to preclude serious consideration of these important concerns.

As I mentioned in my previous post, I find the latter problem particularly troubling because it suggests that there are some things we can never know about human evolution in a scientific way.  Depending on the question, one possible solution to this problem is something Marc Hauser used to talk about in Science B-29 at Harvard.  The problem was how to use evolutionary tools to explain the unique phenomenon of human language.  While human language is clearly a unique, derived trait — and therefore in a difficult position with respect to scientific explanation — there are features of human language (e.g., those described by Hockett in his design features of human language) that are shared across multiple species, making them amenable to the comparative method.  If we limit ourselves to specific autapomorphies — as Buller apparently wants us to when it comes to Human Nature — then we are sunk.  If we can find features of our cognition that are shared across species and look, as Darwin first suggested, at convergent solutions to similar problems across species, then we may have some hope of understanding the unique whole of human cognition. Of course, we can’t do this for cognitive features that have arisen since the Pleistocene because we only have one remnant of the hominin clade left (us).

Regarding our ability to understand the design of the human brain based on our knowledge of the environment of Pleistocene hunter-gatherers, Machery and Barrett (2006: 236) write that Pleistocene hominins experienced a “reduction in sexual dimorphism in body size due to increased pair bonding and male investment in offspring and corresponding reduction in male-male competition.” While I happen to agree with this point (and have two new papers either submitted or in prep elaborating my take on this particular phenomenon), it is, in fact, conjectural.  There is nothing to stop us from forming hypotheses about the mechanisms or functional consequences of human behavior that result from this conjecture, and there might be substantial value in doing so.  Nonetheless, I think it’s important to note that it is hardly certain that the cause of the reduction in sexual dimorphism among Pleistocene hominins (something we are pretty sure of) was pair bonding.  I’m afraid to say that I am not the least bit confident that we will ever know this for certain.

Why do we think that Pleistocene hominins were “pair bonded”?  We know that sexual size dimorphism is a correlate of mating system.  Polygynous mammals tend to be sexually dimorphic.  The more polygynous, the more dimorphic.  Presumably, this arises through intra-male mating competition, where size matters for the outcome of agonistic encounters.  As detailed in our 1999 paper, the best paleontological evidence we have suggests that there was a substantial reduction in both sexual size dimorphism and dimorphism in canine teeth (another strong correlate of polygyny among Primates) with the emergence of the genus Homo.  This reduction in sexual dimorphism is attributed by many authors, ourselves included, as a signal of a change in mating system toward increased monogamy.  Does monogamy necessarily mean pair-bonding?  Not necessarily. (again, I do think it’s true in this case and hopefully, I will finish the paper in which I discuss the details of this argument soon)  There is also the issue that humans are not much different in terms of sexual size dimorphism from chimpanzees, whose mating system is completely promiscuous.  Our teeth may rescue us here.  Chimpanzees are quite sexually dimorphic in their canine teeth.  But how do you weigh the importance of canines as a weapon in a species that makes tools, including weapons that allow it to kill from a distance?

My point here is that there is a good deal of uncertainty about basic aspects of Pleistocene hominin behavior.  This uncertainty is unlikely to ever be completely resolved.  As a result, I’m not convinced that looking for clues about human behavior and the design of the human brain in the behavior of Pleistocene hominins is necessarily the most efficient of productive avenue for understanding our psychology. I don’t take the absolutist position that Buller seems to take that there is nothing to be learned about the present by studying the deep past (i.e., it is more than “pure guesswork”).  I like the iterative approach of working between hypothesis generation and empirical test that Machery and Barrett describe and think that it sounds an awful lot like the process that most scientists employ in their work and it sounds like the way individuals adapt to dynamic environments.

I’ll end this ramble with a question: Do you have to be an evolutionary psychologist to believe in Human Nature?  Buller seems to think so and to think that it’s a bad idea.  I don’t think of myself as an evolutionary psychologist, but I do think there is such a thing as Human Nature.  I am struck by the fact that despite the dizzying array of cultural diversity that is manifested by our species, a smile is a smile, embarrassment is embarrassment, and a look of consternation is a look of consternation.  We might find different things amusing, mortifying, or distressing but pretty much people everywhere experience these emotions and, because of our theory of mind, recognize them in others.  The work of Eckman, Eibl-Eibesfeldt, and Fernald, to name a few, is pretty compelling in this regard.  Do we have a cheater-detection module that was engineered in the Pleistocene?  Maybe.  Honestly, I don’t care that much, but I do think that denying the existence of Human Nature is done at our collective peril.


Buller, D. J. (2005). Evolutionary psychology: the emperor’s new paradigm. Trends in Cognitive Sciences, 9(6), 277-283.

Crume, T. L., DiGuiseppi, C., Byers, T., Sirotnak, A. P., & Garrett, C. J. (2002). Underascertainment of Child Maltreatment Fatalities by Death Certificates, 1990–1998. Pediatrics, 110(2), 1-6.

Daly M, Wilson M (2007) Is the “Cinderella effect” controversial? A case study of evolution-minded research and critiques thereof. In C Crawford & D Krebs, eds., Foundations of evolutionary psychology. Mahwah NJ: Erlbaum.

Machery, E., & Barrett, H. C. (2006). Essay Review: Debunking Adapting Minds. Philosophy of Science, 73, 232-246.

Wilson, M., & Daly, M. (1997). Life expectancy, economic inequality, homicide, and reproductive timing in Chicago neighbourhoods. British Medical Journal, 314(7089), 1271-1274.

Buller on Evolutionary Psychology

Relentless critic of evolutionary psychology, David Buller recently wrote a piece in Scientific American outlining the critique he has developed over the last several years against this particular flavor of human evolutionary studies.  The author of Adapting Minds lists four ideas from contemporary evolutionary psychology (EP) that he suggests are fallacious:

  1. Analysis of Pleistocene Adaptive Problems Yields Clues to the Mind’s Design
  2. We Know, or Can Discover, Why Distinctively Human Traits Evolved
  3. “Our Modern Skulls House a Stone Age Mind”
  4. The Psychological Data Provide Clear Evidence for Pop EP

In my graduate seminar on Evolutionary Theory for the Anthropological Sciences, we read Buller’s more technical (2005) critique of EP.  I find myself largely in agreement with his criticisms and, importantly, when I disagree with him, I think it is for interesting reasons.  

The first of these critiques is, in my opinion, the most far-reaching and damning. The Pleistocene, the geological epoch that lasted from around 1.8 million to 10,000 years before present, takes on the role as a mythical age of creation for EP. You see, the Pleistocene represents out species “Environment of Evolutionary Adaptedness” (EEA), a concept derived from developmental psychology and particularly John Bowlby, the father of attachment theory.  In the words of Tooby and Cosmides (1990: 386-387), the EEA “is not a place or a habitat, or even a time period.  It is a statistical composite of the adaptation-relevant properties of ancestral environments ecounted by members of ancestral populations, weighted by their frequency and fitness-consequences.”

The key question, as Buller notes, is what would such a statistical composite look like for humans?  The insight that is regularly trotted out is that humans (hominins really) were everywhere hunter-gatherers until about 10,000 years ago — and were mostly hunter-gatherers for some substantial period after that! So, what do we know about hunter-gatherers?  Much to our collective loss, most of what we know about hunter-gatherers comes from the study of highly marginalized populations.  This is because states, with their potential economic surpluses, large populations sizes, and hierarchical social organization (read: efficient militaries) pushed hunter-gatherers into marginal habitats throughout the world as they moved across the landscape.  Nonetheless, the hunter-gatherer populations that we know about are a remarkably diverse lot.  A terrific reference cataloging some of this diversity is Robert Kelly’s (1995) The Foraging Spectrum.  In my specific area of interest (i.e., biodemography), Mike Gurven and Hilly Kaplan have recently written a very interesting paper on the diversity of hunter-gatherer patterns of mortality.  In this figure, taken from Gurven and Kaplan’s paper, we can catch a glimpse of the variability just in hunter-gatherer demography.

Humans are clearly quite different from chimpanzees.  The point of Gurven and Kaplan’s paper is that the existence of elderly within our societies is not simply an artefact of the modern industrialized world.  Old-age is as much a part of the human life cycle as is childhood.  Given the long potential lifespans of people in all the sampled populations, there is nonetheless a remarkable diversity in life expectancy (the average number of years lived by a person in the population) portrayed in this figure, considering that these are all groups without access to modern medicine.  There are people who live in arid lands of Sub-Saharan Africa (!Kung, Hadza), South American forests (Ache, Tsimane, Yanomamo) and South American grasslands (Hiwi).  Life expectancy at age 5, (\stackrel{\circ}{e}_5) varies by as much as 30%.  The basic point here is that even in something as basic as age-specific schedules of mortality and fertility, different hunter-gatherer groups are very different from each other (note that the Ache and !Kung differ in their total fertility rates by a factor of nearly two).

In all likelihood, our Pleistocene ancestors, like the sample of hunter-gatherer societies discussed in Kelly (1995) or Gurven and Kaplan (2007), lived in diverse habitats, engaged in diverse economic activities within the rubric of hunting and gathering, had diverse social structures, met with diverse biotic and abiotic environmental challenges to survival and reproduction, and dealt with diverse hostile and harmonious relations with conspecifics outside of their natal groups or communities. In other words, it’s hard to imagine what neat statistical generalizations about hunter-gatherer lifeways — and the selective forces they entailed — could emerge from such diversity. People lived in face-to-face societies.  People had to integrate disparate sources of information to make decisions about fundamentally uncertain phenomena. There was probably a sexual division of labor, though not necessarily the same one everywhere. There were women and men. Probably some other things too, but not that many.  Robert Foley (1996) has a nice critique of what he sees as an extreme simplification of the Pleistocene hunter-gatherer lifeways under the rubric of the EEA.  

Another related problem with the EEA line of thinking is this idea that somehow selection stopped when humans developed agriculture.  10,000 years, while brief in the grand scheme of things, is still not exactly evolutionary chump change.  That span represents anywhere from 350-450 human generations.  This is, in fact, plenty of time for selection to act.  We know from genome scans done in the lab of Jonathan Pritchard, for example, that there is extensive evidence for rapid, recent selection in humans. New, complex psychological mechanisms?  Probably not, but we should nonetheless not fall into the trap of thinking that somehow evolution stopped for our species 10,000 years ago.

Buller’s second fallacy (“We Know, or Can Discover, Why Distinctively Human Traits Evolved”) is a deeply difficult problem in human evolution. I’m afraid that my current thinking on this problem leads me to the same pessimistic conclusion that Buller and his colleague Jonathan Kaplan come to: There are just some things that we can’t know (scientifically) about human evolution.  This arises from the fact that our species is the only member of our genus and we are separated from our sister species by nearly six million years. As Dick Lewontin first noted in 1972, despite our dizzying cultural and social diversity, we are an amazingly homogenous species genetically.  I suspect that what this means is that the standard conceit of EP (one that Buller is highly critical of), that humans are everywhere the same critter, is probably true.  Unique (and universal) phenomena present science with a particular explanatory challenge. Buller is spot on when he criticizes EP for wanting it both ways.  On the one hand, EP sees a robust and universal human nature (an idea to which I am sympathetic, by the way).  On the other, EP sees strong selection driving the evolution of diverse psychological mechanisms.  The unpleasant reality is that if selection on psychological mechanisms were, in fact, that strong and pervasive, we should expect contemporary heterogeneity in the expression of such adaptations across different populations.  This is a topic that University of Illinois anthropological geneticist Charles Roseman and I have talked about quite a bit and have a very slowly gestating manuscript in which we discuss this and other ideas.  I know of no convincing evidence that such variation exists and for this and other reasons, I remain a steadfast skeptic of the idea that natural selection has shaped all these important psychological mechanisms independently and with precision to the tasks to which they are supposed to represent engineering solutions.

Buller’s argument for fallacy #3 (“Our Modern Skulls House a Stone Age Mind”) is, I think, a little unfair.  The major argument he makes on this point is that some of our psychological mechanisms did not, in fact, arise in our Pleistocene hunter-gatherer ancestors, but are of a more ancient, primate (or even mammalian) nature.  Honestly, I doubt that this point would elicit many complaints by anyone of the so-called Santa Barbara school. Sometimes critics — myself included — make a little too much of the it-all-evolved-in-the-Pleistocene bit.  I think this is one example of that.  Tooby and Cosmides have themselve argued that the EEA can be thought of as working at a variety of time scales.  The emotional systems described by Jaak Panksepp and used by Buller in his critique — Care, Panic and Play — are all pretty basic ones for a social species.  Indeed, the emotional system of panic almost certainly pre-dates complex sociality.  The EEA argument, as laid out by John Tooby and Irv DeVore (1987) and then by Tooby and Cosmides (1990), is essentially one of evolutionary lag: complex adaptations to past environments are carried forward into the present.  When a system retains its function, the scale of such lag can be large.  Think about bilateral symmetry or the tetrapod bauplan. I think that a fair assessment of Santa Barbara style EP reveals that there is nothing contradictory about the existence of primitive (in the sense of pleisiomorphic) emotional systems in contemporary humans.

Another small point of departure between Buller’s critique and my own thinking on the matter is his discussion of David Buss‘s work on sexual jealousy.  Now, I should be perfectly clear here.  I happen to think that the whole sex-differences in sexual preferences thing is the most overplayed finding in all of evolutionary science.  In class, I refer to this work as Men-Are-From-Mars Evolutionary Psychology.  The basic idea is to take whatever tired sexual stereotype that you’d hear in a second rate stand-up comedian’s monologue, or read about in airport bookstore self-help tracts and dress it up as the scientifically proven patrimony of our evolutionary past.  Ugh.  No, the part of Buller’s argument with which I disagree is his apparent take on decision-making. Buller writes, “According to Pop EP, many cultural differences stem from a common human nature responding to variable local conditions.”  I guess I’m not so clear as to what’s wrong with such a statement.  Isn’t that really what he argues in the previous paragraphs when he suggests that women and men have a fundamentally similar reaction to sexual jealousy?  On this he writes, “Instead both sexes could have the same evolved capacity to distinguish threatening from nonthreatening infidelities and to experience jealousy to a degree that is proportional to the perceived threat to a relationship in which one has invested mating effort.”  An evolutionary psychology that took seriously environmental (including cultural) variability and combined it with some preferences over risk and uncertainty and a generalized calculus of costs and benefits: Now that would be interesting!  Of course, I’d call that behavioral ecology.

Regarding fallacy #4 (“The Psychological Data Provide Clear Evidence for Pop EP”) more generally, I think that Buller is right on.  The evidence for many of these so-called psychological adaptations is pretty weak.  There is general contempt for population genetics among the smarter (and there are smart ones) evolutionary psychologists with whom I have talked and general ignorance among the less gifted.  I think this contempt and/or ignorance is expressed to the detriment of scientific progress in EP.  Buller’s point that cross-cultural differences are sometimes greater than inter-sexual differences in the psychological traits that are putative adaptations for sex-specific reproductive strategies, while not specifically substantiated, is pretty devastating.  This is where population genetics comes in.  Thinking about within vs. between population variance is a very important step in understanding the evolutionary forces at work.

The complex organ that is the human brain is certainly the result of selection.  As George Williams reminds us, selection is the only evolutionary mechanism that can produce this type of complexity. So, like Buller, I agree that there must be an evolutionary psychology.  Our various complaints are with the evolutionary psychology that Buller labels “Pop EP.”  It’s all too easy to be critical. Developing scientific theories for phenomena as complex as those surrounding the evolution of our species is a difficult task and takes ingenuity, courage, and, of course, thick skin. Among the various practitioners of EP of whom Buller is particularly critical, I think that John Tooby and Leda Cosmides are smart people who manifest all these qualities.  A fallacy of contemporary discourse — one that is all too easily seen in anthropological meetings —  is that people who disagree intellectually must hold each other in contempt or otherwise dislike each other.  I disagree with much of current EP but I also think there are some interesting ideas among practitioners of EP, once we get beyond the trite Men-Are-From-Mar/Women-Are-From-Venus stereotypes.

Detailing where I think the action is in an interesting evolutionary psychology is at the very least another long blog post.  Some areas that I think are promising and/or under-studied include: detailed analyses of cultural transmission dynamics, understanding how people integrate diverse types of information to form decisions with fitness consequences, and understanding how people weigh risk and uncertainty.  I have a lot more to say on these topics, so I think it will have to wait for future posts…


Buller, D. J. (2005). Evolutionary psychology: the emperor’s new paradigm. Trends in Cognitive Sciences, 9(6), 277-283.

Foley, R. (1996). The adaptive legacy of human evolution: A search for the environment of evolutionary adaptedness. Evolutionary Anthropology, 4, 194-203.

Gurven, M., & Kaplan, H. (2007). Longevity Among Hunter-Gatherers: A Cross-Cultural Examination. Population and Development Review, 33(2), 321–365.

Kelly, R. L. (1995). The Foraging Spectrum: Diversity in Hunter-Gatherer Lifeways. Washington DC: Smithsonian Institution Press.

Lewontin, R. C. (1972). The apportionment of human genetic diversity. Evolutionary Biology, 6, 381-398.

Voight, B. F., Kudaravalli, S., Wen, X., & Pritchard, J. K. (2006). A map of recent positive selection in the human genome. PLoS Biology, 4(3), e72. Epub 2006 Mar 2007.

Tooby, J., & Cosmides, L. (1990). The Past Explains the Present – Emotional Adaptations and the Structure of Ancestral Environments. Ethology and Sociobiology, 11(4-5), 375-424.

Tooby, J., & DeVore, I. (1987). The reconstruction of hominid behavioral evolution through strategic modeling. In W. Kinzey (Ed.), Primate Models of Hominid Behavior. Stony Brook: SUNY Press.

Gadgil & Bossert (1970)

I am currently teaching a class entitled “Demography and Life History Theory.”  For this class, we read the classic paper by Madhav Gadgil and Bill Bossert, “Life Historical Consequences of Natural Selection.” In preparing for class, I re-read this paper for about the twelfth time.  Something happened this time.  It really dawned on me what a spectacularly important paper this is.  Just about every important theme in life history theory is addressed in this paper and the analyses remain remarkably relevant.

One of the fundamental ideas this paper brought to life history theory is thinking about the convexity of the functions that describe both the fitness benefits and costs associated with the degree of reproductive effort.  In particular, Gadgil & Bossert show that iteroparity (i.e., repeated breeding) can only evolve if the function relating benefit to effort is concave or the function relating cost to effort is convex.  The figure shows a concave profit function and a linear cost.  Clearly, the maximal value of the difference between the profit and cost happens at some intermediate level of effort \theta_j.

Profit (solid line) and cost (dashed line) as a function of reproductive effort at at age j.

Maddeningly, Gadgil & Bossert invert the terms “convex” and “concave.”  I’m sure there is a good historical explanation for this, but contemporary usage indicates that a continuous, twice differentiable function f(\theta) is convex if f?(\theta)>0.  That is a convex function shows increasing marginal returns to effort, whereas a concave function shows diminishing marginal returns to effort.

Their analysis focuses on a discrete-time form of Lotka’s characterisitc equation:

 1 = \sum_0^n e^{-mx} l_x b_x

where m is the Maulthusian parameter (the intrinsic rate of increase, r, in a density-independent population), l_x is the fraction surviving to age x, b_x is the birth rate (in daughters) to females age x.

What makes this approach so interesting and important is that the vital rates are functions of reproductive effort \theta_x at each age.  In addition to l_x and b_x being  functions of effort, they are also functions of “satisfaction” \psi_x, a measure of environmental quality.  Fertility at age x increases with effort and satisfaction.  Survivorship and growth decrease with effort and increase with satisfaction.  Fertility also increases with body size.

Gadgil and Bossert then maximize m subject to the biological constraints on intrinsic mortality, growth rate, and initial size, and the environmental constraints of satisfaction and mortality due to predation. They used an automatic computer to numerically solve their optimization problem.  They derive a number of quite general results for age-structured populations.

  1. Only when the profit function is concave or the cost function is convex can repeated breeding be optimal.
  2. Reproductive effort increases with age in repeated breeders.  George Williams arrived at a similar conclusion in 1966, but failed to consider the possibility that the fitness profit could decline with age.  Gadgil & Bossert arrive at this result with a much more general approach than that used by Williams.
  3. When mortality increases following some age j, reproductive effort increases for ages less than j.
  4. When reproductive potential increases slowly with size, reproductive effort will be lower at maturity, rise with age, and growth will continue beyond maturity.
  5. A uniform change in mortality — affecting all ages equally — will have no direct impact on reproductive effort.
  6. If the population is resource-limited, such a uniform change in mortality will increase satisfaction \psi_j with its consequent effects, including a lowering of age at first breeding and increase in reproductive effort.

I would have to say that this is the desert island paper for life history theory.  If you only ever read one paper (conditional on having the mathematics background to make it worthwhile), this is the one to read, even after 38 years.

Evolution of a Bourgeois Temperment?

So, I’m teaching a graduate-level class in evolutionary theory this quarter. Given my druthers, I would have run a rather technical class in which we would discuss quantitative genetics, optimality models, game theory, multi-level evolution… Stuff like that. Well, we’ve done a bit of that but, due to popular demand, I actually took out two weeks on game theory and optimality models, and instead we are reading Gregory Clark‘s new book, A Farewell to Alms, in which argues that the Industrial Revolution may have its roots in quite recent biological evolution. Nicholas Wade wrote a review of the book in the New York Times that a number of students and I found intriguing. In this review, Wade quotes Clark as saying,

Through the long agrarian passage leading up to the Industrial Revolution, man was becoming biologically more adapted to the modern economic world.

We’ll see… Regardless of what I think of the book (which I’ve not yet read, but will do so along side the students starting next week), it seemed like an interesting case on which to bring to bear our new-found analytical skills in evolutionary theory. More later…