Category Archives: Evolution

Winter Anthropology Colloquium, Part 1

I am organizing the colloquium for the Stanford Anthropology department this winter. I believe it may be the first time that a faculty member for the Ecology and Environment group has organized the colloquium since the Blessed Event that merged departments back in 2008 (though I’m not certain of that). There have been a few scheduling glitches, as it seems winter quarter 2015 has the highest density of talks I’ve yet encountered in 11 years at Stanford, but we’re off to a great start. Our first speaker came all the way from the UK to speak to us about social dilemmas and cooperation. Shakti Lamba is an ESRC Research Fellow and Lecturer in Human Behavioural Ecology in the Centre for Ecology and Conservation at the University of Exeter.

Shakti talked about her very exciting work on behavioral norms. She uses a variety of methods, including ethnography, experimental games, and advanced statistical techniques to understand the nature of variation in cooperative norms within and between populations (see, e.g., papers here or here for examples of her work). I generally have mixed feelings about experimental games, but I think there is a small cadre of anthropologists, including Shakti and Drew Gerkey, among others, who use them as a tool for eliciting much richer behavioral and social observations than do most field researchers (whether or not they use experimental games!). I was impressed by the sophistication of her approach, her keen experimental design, and the excellent population thinking that it entails. However, I was most impressed with her coolness and eloquence under some pretty heated questioning from a number of senior faculty members who simply misunderstand evolutionary process.  Looking forward to seeing more of her work, especially forthcoming longitudinal research with Alex Alvergne, in the future!

Here is the poster for her talk:

Lamba_Talk

Seriously, People, It's Selection, Not Mutation!

I just read an excellent piece at Slate.com this morning by Benjamin Hale. He notes that the scariest, most insidious thing about Ebola Virus Disease is that the disease capitalizes on intimate contact for transmission. While diseases such as influenza or cholera are transmitted by casual contact, frequently to strangers, via aerosolized droplets (influenza) or fecally contaminated water (cholera). Caretakers, and especially women, are hit hard by EVD. Hale writes,

…the mechanism Ebola exploits is far more insidious. This virus preys on care and love, piggybacking on the deepest, most distinctively human virtues. Affected parties are almost all medical professionals and family members, snared by Ebola while in the business of caring for their fellow humans. More strikingly, 75 percent of Ebola victims are women, people who do much of the care work throughout Africa and the rest of the world. In short, Ebola parasitizes our humanity.

True, and tragic, enough. But this article falls prey to one of my biggest frustrations with the reporting of science, one that I have written about recently in the context of the current EVD epidemic ravaging West Africa.

In the list Hale presents of the major concerns about EVD, he notes: “The threat of mutation,” citing concern that Ebola virus might become airborne in a news report in Nature and the New York Times article that got me so worked up 10 days ago. Earlier this week, there was yet another longish piece in Nature/Scientific American that mentions “mutation” seven times but never once mentions selection. Or in another Nature piece,  UCSF infectious disease physician Charles Chiu is quoted: “The longer we allow the outbreak to continue, the greater the opportunity the virus has to mutate, and it’s possible that it will mutate into a form that would be an even greater threat than it is right now.” True, mutations accumulate over time. Not true, mutation alone will make Ebola virus a greater threat than it is now. That would require selection.

While the idea of airborne transmission of Ebola virus is terrifying, the development of the ability to be transmitted via droplet or aerosol would be an adaptation on the part of the virus. Adaptations arise from the action of selection on the phenotypic variation. Phenotypes with higher fitness come to dominate the population of entities of which they are a part. In the case of a virus such as Ebola virus, this means that the virus must make sufficient copies of itself to ensure transmission to new susceptible hosts before killing the current host or being cleared by the host’s immune system. While efficient transmission of EVD by aerosol or droplet would be horrible, equally horrible would be an adaptation that allowed it to transmit more efficiently from a dead host. It’s not entirely clear how long Ebola virus can persist in its infectious state in the environment. In a study designed to maximize its persistence (indoors, in the dark, under laboratory conditions), Sagripanti and colleagues found that Ebola virus can persist for six days. Under field conditions, it’s probably much shorter, but CDC suggests that 24 hours in a reasonably conservative estimate.

The lack of a strong relationship between host survival and pathogen transmission is why cholera can be so devastatingly pathogenic. The cholera patient can produce 10-20 liters of diarrhea (known as “rice water stools”) per day. These stools contain billions of Vibrio cholerae bacteria, which enter the water supply and can infect other people at a distance well after the original host has died. The breaking of the trade-off between host mortality and the transmissibility of the pathogen means that the natural break on virulence is removed and the case fatality ratio can exceed 50%. That’s high, kind of like the current round of EVD. Imagine if the trade-off between mortality and transmission in EVD were completely broken…

Changes in pathogen life histories like increased (or decreased) virulence or mode of transmission arise because of selection, not mutation, and this selection results from interactions with an environment that we are actively shaping. Sure, mutation matters because it provides raw material upon which selection can act, but the fact remains that we are talking primarily about selection here. Is this pervasive misunderstanding of the mechanisms of life the result of the war of misinformation being waged on science education in the US? I can’t help but think it must at least be a contributor, but if it’s true, it’s pretty depressing because this misunderstanding is finding its way to some of the world’s top news and opinion outlets.

Selection is What Matters

This has to be a quick one, but I wanted to go on the record is noting my frustration at the current concern that Ebola might “mutate” into something far worse, like a pathogen that is efficiently transmitted by aerosol. For example, Michael Osterholm wrote in the New York Times yesterday, “The second possibility is one that virologists are loath to discuss openly but are definitely considering in private: that an Ebola virus could mutate to become transmissible through the air.”  I heard Morning Edition host David Greene ask WHO Director Margaret Chan last week, “Is this virus mutating in a way that could be very dangerous, that could make it spread faster?”

I agree, Ebola Virus becoming more easily transmitted by casual contact would be a ‘nightmare scenario.’ However, what we need to worry about is not mutation per se, but selection! Yes, the virus is mutating. It’s a thing that viruses do. Ebola Virus is a Filovirus. It is composed of a single strand of negative-sense RNA. Like other viruses, and particularly RNA viruses, it is prone to high mutation rates. This is exacerbated by the fact that RNA polymerases lack the ability to correct mistakes. So mutations happen fast and they don’t get cleaned up. Viruses also have very short generation times and can produce prodigious copies of themselves. This means that there is lots of raw material on which selection can act, because variation is the foundation of selection. Add to that heritability, which pretty much goes without saying since we are talking about the raw material of genetic information here, and differential transmission success and voilà, selection!

And virulence certainly responds to selection. There is a large literature on experimental evolution of virulence. See for example the many citations at the linked to Ebert’s (1998) review in Science here. There are lots of different specific factors that can favor the evolution of greater or lesser virulence and this is where theoretical biology can come in and make some sense of things. Steve Frank wrote a terrific review paper in 1996, available on his website, that describes many different models for the evolution of virulence. Two interesting regularities in the the evolution of virulence may be relevant to the current outbreak of EVD in West Africa. The first comes from a model developed by van Baalen & Sabelis (1995). Noting that there is an inherent trade-off between transmissibility of a pathogen and the extent of disease-induced mortality that it causes (a virus that makes more copies of itself is more likely to be transmitted but more viral copies means the host is sicker and might die), they demonstrate that when the relative transmissibility of a pathogen declines, its virulence will increase. They present a marginal value theorem solution for optimal virulence, which we can represent graphically in the figure below. Equilibrium virulence occurs where a line, rooted at the origin, is tangent to the curve relating transmissibility to disease-induced mortality. When the curve  is shifted down, the equilibrium mortality increases. EVD is a zoonosis and it’s reasonable to think that when it makes the episodic jump into human populations, it is leaving the reservoir species the biology of which it is adapted to and entering a novel species to which it is not adapted. Transmission efficiency very plausibly would decrease in such a case and we would expect higher virulence.

Marginal Value Theorem

The second generality that may be of interest for EVD is discussed by Paul Ewald in his book on the evolution of infectious disease and (1998) paper. Ewald notes that when pathogens are released of the constraint between transmissibility and mortality — that is, when being really sick (or even dead) does not necessarily detract from transmission of the pathogen — then virulence can increase largely without bound. Ewald uses the difference in virulence between waterborne  and directly-transmitted pathogens to demonstrate this effect. At first glance, this seems to contradict the van Baalen & Sabelis model, but it doesn’t really. The constraint is represented by the curve in the above figure. When that constraint is released, the downward-sloping curve becomes a straight line (or maybe even an upward-sloping curve) and transmissibility continues to increase with mortality. There is no intermediate optimum, as predicted by the MVT, so virulence increases to the point where host mortality is very high.

A hemorrhagic fever, EVD is highly transmissible in the secretions (i.e., blood, vomit, stool) of infected people. Because these fluids can be voluminous and because so many of the cases in any EVD outbreak are healthcare workers, family members, and attendants to the ill, we might imagine that the constraints between transmissibility and disease-induced mortality on the Ebola Virus could be released, at least early in an outbreak. As behavior changes over the course of an outbreak — both because of public health interventions and other autochthonous adaptations to the disease conditions — these constraints become reinforced and selection for high-virulence strains is reduced.

These are some theoretically-informed speculations about the relevance of selection on virulence in the context of EVD. The reality is that while the theoretical models are often supported by experimental evidence, the devil is always in the details, as noted by Ebert & Bull (2003). One thing is certain, however. We will not make progress in our understanding of this horrifying and rapidly changing epidemic if all we are worried about is the virus mutating.

Selection is overwhelmingly the most powerful force shaping evolution. The selective regimes that pathogens face are affected by the physical and biotic environments in which pathogens are embedded. Critically, they are also shaped by host behavior. In the case of the current West African epidemic of EVD, the host behavior in question is that of many millions of people at risk, their governments, aid organizations, and the global community. People have a enormous potential to shape the selective regime that will, in turn, shape the pathogen that will infect future victims. This is what we need to be worrying about, not whether the virus will mutate. It saddens and frustrates me that we live in a country where evolution is so profoundly misunderstood that even our most esteemed, and otherwise outstanding sources of information and opinion don’t understand the way nature works and the way that human agency can change its workings for our benefit or detriment.

 

 

On Genetics and Human Behavioral Biology

Nicholas Wade, former science reporter for the New York Times has written a book, A Troublesome Inheritance, in which he argues that large-scale societal differences (e.g., the existence of capitalist democracies in the West or of paternalistic, authoritarian political systems in Asia) may be attributable to small genetic differences that were fixed at a population level through the action of natural selection since the emergence of anatomically modern humans and their subsequent dispersal from Africa. The fixation of these gene variants happened because the continents of Europe, Asia, and Africa (homes of the major “racial” groups) differed in systematic ways. David Dobbs recently reviewed it in the Sunday Review of Books, which prompted a kind of amicus brief letter-to-the-editor from over 120 population geneticists, affirming that Wade’s writing misrepresents the current science of genetics. A full list of the signatories of this letter can be found here. It is a veritable who’s who of contemporary population genetics.

As you might imagine, A Troublesome Inheritance has been quite controversial. A great deal has already been written on this book, both in formal publications and in the science (and economics) blogging ecosystem. To name just a few, Greg Laden, my old homie and fellow TF for Irv DeVore‘s famous Harvard class, Science B-29, Human Behavioral Biology, wrote a brief review here for American Scientist. Columbia statistician and political scientist, Andrew Gelman, wrote a review for Slate.com. Notre Dame professor and frequent contributor of popular work on human evolution, Agustin Fuentes, wrote a critique for Huffington Post, while UNC-C anthropology professor Jonathan Marks wrote a critique for the American Anthropological Association blog, which also appears in HuffPo.

Honestly, I think that Wade’s book is so scientifically weak and ideological (despite his protestations that science should be apolitical) that it is likely to have a very short half-life in contemporary discourse on human diversity and science more broadly. In fact, I have advocated to the editorial boards of professional societies to which I belong not to do anything special about this book since I’m confident it will be soon forgotten for its sheer scientific mediocrity. I find it interesting that the great majority of the people who like the book seem not to be scientists but comment on Wade’s “bravery” for spurning “political correctness” and the like. There are substantial parallels here to public debate over climate change or vaccination: the professional conclusions of the scientists who actually work on the topic only matter when they correspond with the social, political, or economic interests of the parties engaging in the debate. What do geneticists know about genetics anyway? So, it is with some hesitancy that I write about it, but my colleagues’ letter has reminded me of a larger beef I have with the contemporary state of human evolutionary studies. This beef boils down to the fact that most contemporary students of human evolutionary biology know next to nothing about genetics. I’ve actually encountered a number of leading figures in human behavioral biology who maintain an outright hostility toward genetics. This is a topic that my colleague Charles Roseman and I have grumbled about for a few years now. We keep threatening to do something about it, but haven’t quite gotten around to it yet. Perhaps this is a humble start…

This state of affairs is extremely problematic since genetics is the material cause (in the Aristotelean sense) or one of the mechanistic causes (in the Tinbergian sense) of much of the diversity of life. If we are going to make a scientific claim that some observed trait is the result of natural selection, we should be able to have a sense for how such a trait could evolve in the first place. The standard excuse for ignoring genetics in the adaptive analysis of a trait of interest is what Alan Grafen termed the “phenotypic gambit.” The basic idea behind the phenotypic gambit is that natural selection is strong enough to overcome whatever constraints may be acting on it. The phenotypic gambit is a powerful idea and it has yielded some productive work in behavioral ecology. I use it. However, a complete evolutionary explanation of a trait’s existence needs to consider all levels of explanation. In modern terms, and as nicely outlined a letter by Randolph Nesse, we need to answer questions about mechanism, ontogeny, phylogeny, and function. Explanations relying on the phenotypic gambit only address the functional question (i.e., fitness, or what Tinbergen called the “survival value” of the trait).

I could go on about this for a long time, so I will limit myself to three points: (1) complex traits will generally not be created by a single gene, (2) heritability and the response to selection are regularly misunderstood and misapplied, (3) we need to think about the strength of selection and the constancy of selective regimes when making statements about the adaptive evolution of specific traits.

First, we need to get over the whole one-gene thing. Among other things, the types of adaptive arguments that are made particularly for recent human behavioral innovations are simply highly implausible for single genes. There are a variety of formulae for calculating the time to fixation of advantageous alleles that depend on the particulars of the system (e.g., details about dominance, initial frequency, mutation rate). Using the approximation that the number of generations that it takes for the fixation of a highly advantageous allele with selection coefficient s is simply twice the natural logarithm of s divided by s, we can calculate the expected time to fixation for an advantageous allele. With a (very) substantial average selection coefficient of s=0.05 (think of lopping of 5% of the population each generation), the time to fixation of such a highly advantageous allele is about 120 generations generations. That’s over 3,000 years for humans. This is interesting, of course, because it makes the type of recent evolution the John Hawks or Henry Harpending have discussed more than plausible. It makes it hard to imagine how the large changes in presumably complex behavioral complexes in historical time suggested by authors such as Wade or Gregory Clark, author of Farewell to Alms (which I actually find a fascinating book), pretty implausible.

In addition to the population-genetic implausibility of single-locus evolutionary models, complex traits are polygenic, meaning that they are constructed from multiple genes, each of which typically has a small effect. Now, this doesn’t even address the issue of epigenetics, where genotype-environment interactions profoundly shape gene expression and can produce fundamentally different phenotypes in the absence of significant genetic difference, but that’s another post. In many ways, this is good news for people who study whole organisms in a naturalistic context (like human behavioral ecologists!) because it means that we can work with quantitatively-measured trait values and apply regression models to understanding their dynamics. In short, the math is easier though, admittedly, the statistics can be pretty tricky. Further good news: there are lots of people who would probably be happy to collaborate and there are plenty of training opportunities in quantitative genetics through short courses, etc.

The masterful review paper that Marc Feldman and Dick Lewontin wrote for Science in 1975 amid the controversy surrounding Arthur Jensen’s work on the genetics of intelligence, and its implications for racial educational achievement differentials, still applies. Heritability is a systematically misunderstood concept and its misuse seems to surface in policy debates approximately every twenty years. Heritability, in the strict sense, is a ratio of the total phenotypic variance that is attributable to additive genetic variance (i.e., the variance contributed by the mean effect of different alleles). Because total variance of the phenotype is in the denominator of this ratio, heritability is very much a population-specific measure. If a population has low total phenotypic variance because of a uniformly positive environment, for instance, there is more potential for a greater fraction of the total variance to be due to additive genetic variance. Think, for example, about children’s intelligence (as measured through psychometric tests) in a wealthy community with an excellent school district where most parents are college-educated and therefore have the motivation to guide their children to high scholastic achievement, the resources to supplement their children’s school instruction (e.g., hiring tutors or sending kids to enrichment programs), and the study skills and knowledge base to help their children with homework, etc. I have used this example in prior post. Given the relative uniformity of the environment, more of the variation in test scores may be attributable to additive genetic contributions and heritability would be higher than it would be in a more heterogeneous population. This is a hypothetical example, but it illustrates the rather constrained meaning of heritability and the problems associated with its application to cross-population comparisons. It is also suggestive of the problem of effect sizes of different contributions to phenotypic variance. The potential for environmental variance to swamp real additive genetic variance is quite large. What’s a better predictor of life expectancy: having a genetic predisposition to high longevity or living in a neighborhood with a high homicide rate or a endemic cholera in the drinking water supply?

Heritability essentially measures the potential response to selection, everything else being equal. The so-called Breeder’s Equation (Lush 1937) states that the change in a single quantitative phenotype (e.g., height) from one generation to the next is equal to the product of heritability and the force of selection. If there is lots of additive variability in a trait but not much selective advantage to it, the change in the mean phenotype will be small. Similarly, even if selection is very strong, the phenotype will not change much if the amount of additive variance is low. A famous, but frequently misunderstood result, known as Fisher’s Fundamental Theorem shows that the change in fitness is directly proportional to variance in fitness. This is really just a special case of the breeder’s equation, as shown in great detail in Lynch and Walsh’s textbook (and their online draft chapter 6) or in Steve Frank’s terrific book, in which the trait we care about is fitness itself. An important implication of Fisher’s theorem is that selection should deplete variance in fitness — and this makes sense if we think of selection as truncating a distribution. A corollary of Fisher’s theorem is that traits which are highly correlated with fitness should not have high heritability. Oops. Does this mean that intelligence, with its putatively very high heritabilities is not important for fitness?

Everything in the last paragraph applies to the case where we are only considering a single trait. When we consider the joint response of two or more traits to selection, we must account for correlations between traits (technically, additive genetic covariances between the traits). Sometimes these covariances will be positive; sometimes they will be negative. When the additive genetic covariance between two traits is negative, it means that selection to increase the mean of one will reduce the mean of the other. In their fundamental (1983) paper, my Imperial College colleague Russ Lande and Steven Arnold generalized the breeder’s equation to the multivariate case. The response to selection becomes a balancing act between the different force of selection, additive genetic variance, and additive genetic covariance for all the traits. Indeed, this is where constraints come from (or it’s at least one place). Suppose there are two traits (1 and 2) that share a negative covariance. Further suppose that the force of selection is positive for both but is stronger on trait 1 than it is on trait 2. Depending on the amount of genetic variance present, this could mean that the mean of trait 2 will not change or even that the mean could decrease from one generation to the next.

The work of Lande and Arnold (and many others) has spawned a huge literature on evolvability (something that Charles has moved into and that we have some nascent collaborative work on in the area of human life-history evolution). This work is very important for understanding things like the evolution of human psychology. Consider the hypothesis, popular in evolutionary psychology, that the mind is divided into a large number of specific problem-solving “modules,” each of which is the product of natural selection on the outcome of the problem-solving. How do you create so many of these “organs” in a relatively short time frame? Humans last shared a common ancestor with chimpanzees and bonobos around five million years ago and most likely human ancestors until about 1.8 million years ago seem awfully ape-like (and therefore probably not carrying around anything like the human mental toolkit in their heads). One of the key processes responsible for the creation of complex phenotypes is known as modularity (which is a bit confusing since this is also the term that evolutionary psychologists use for these mental organs!) and one of the fundamental mechanisms by which modularity is achieved is through the duplication of sets of genes responsible for existing structures. These duplicated “modules” are less constrained because of their redundancy and can evolve to form new structures. However, the fact that modules are duplicated means that they should experience substantial genetic correlation with their ancestral modules. This makes me skeptical that the diversity of hypothetical structures posited by the massive modularity hypothesis could be constructed by directional selection on each module. There is just bound to be too much correlation in the system to permit it to move in a fine-tuned way toward to phenotypic optimum for each module.

Trade-offs matter for the evolution of phenotypes. While I suspect that very few human evolutionary biologists would argue with that, I think that we generally fall short of considering the impact of trade-offs for adaptive optima. The multivariate breeders’ equation of Lande and Arnold gives us an important (though incomplete) tool for looking at these trade-offs mechanistically. A few authors have done this. The example that comes immediately to mind is Virpi Luumaa and her research group, who have done some outstanding work on the quantitative genetics of human life histories using Finnish historical records.

My third, and last (for now), point addresses the constancy of selection. This is related to the concept of the Environment of Evolutionary Adaptedness (EEA), central to the reasoning of evolutionary psychology. A few years back, I wrote quite a longish piece on this topic and its attendant problems. Note that when we use population-genetic models like the one we discussed above for the expected time to fixation of an advantageous allele, the selection coefficient s is the average value of that coefficient over time. In reality, it will fluctuate, just as the demography of the population selection is working on will vary. Variation in vital rates can have huge impacts on demographic outcomes, as my Stanford colleague Shripad Tuljapurkar has spent a career showing. It can also have enormous effects on population-genetic outcomes, which shouldn’t be too surprising since it’s the population of individuals which is governed by the demography that is passing genetic material from on generation to the next!

When I read accounts of rapid selection that rely heavily on EEA-type environments or the type of generalizations found in the second half of Wade’s book (e.g., Asians live in paternalistic, autocratic societies), my constant-environment alarm bells start to sound. I worry that we are essentializing societies. One of the all-time classic works of British Social Anthropology is Sir Edmund Leach’s groundbreaking Political systems of Highland Burma. Leach found that the social systems of northern Burma were far more fluid than anthropologists of the time typically thought was the case. One of the key results is that there was a great deal of interchange between the two major social systems in northern Burma, the Kachin and and Shan. Interestingly, the Shan, who occupied lowland valleys, practiced wet-rice agriculture, and whose social systems were highly stratified were seen by western observers as being more “civilized” than the Kachin, who occupied the hills, practiced slash-and-burn agriculture, and had much more egalitarian social relations. Leach (1954: 264) writes, “within the general Kachin-Shan complex we have, I claim, a number of unstable sub-systems. Particular communities are capable of changing from one sub-system into another.” Yale anthropologist/political scientist James Scott has extended Leach’s analysis in his recent book, The Art of Not Being Governed, and suggested that the fluid mode of social organization, where people alternate between hierarchical agrarian states, and marginal tribes depending on political, historical, and ecological vicissitudes is, in fact, the norm for the societies of Southeast Asia.

The clear implication of this work for our present discussion is that a single lineage may find some of its members struggling for existence in hierarchical states where the type of docility that Wade suggests should be advantageous would be beneficial, while descendants just a generation or two distant might find themselves in egalitarian societies where physical dominance, initiative, and energy might be more likely to determine evolutionary success. I don’t mean to imply that these generalizations regarding personality-type and evolutionary success are necessarily supported by evidence. The key here is that the social milieux of successive generations could be radically different if the models of Leach and Scott are right (and the evidence brought to bear by Scott is impressive and leads me to think that the models are right). At the very least, this will reduce the average selection differential on the putative genes for personality types that are adapted to particular socio-political environments. More likely, I suspect, it will establish quite different selective regimes — say, for behavioral flexibility through strong genotype-environment interactions!

These are some of the big issues regarding genetics and the evolution of human behavior that have been bothering me recently. I’m not sure how we go about fixing this problem, but a great place to start is by fostering more collaborations between geneticists and behavioral biologists. Of course, this would be predicated on behavioral biologists’ motivation to fully understand the origin and maintenance of phenotypes and I worry that the institutional incentives for this are not in place.

EEID 2014 Wrap-Up

It’s been a long time since I’ve written in monkey’s uncle. Life has gotten pretty busy and my seeming inability to write brief entries has led me to neglect the blog this year. However, I am freshly back from the Ecology and Evolution of Infectious Disease Conference in Fort Collins, Colorado and feel compelled to give my annual run-down. The conference was hosted by friend and colleague Mike Antolin, Sue Vandewoude, and my erstwhile post-doc, now CSU researcher, Dan Salkeld. Nice job, folks, on a very successful conference.

EEID is pretty much the best meeting. As I noted in last year’s post, I love its future-orientation. EEID is a meeting that foregrounds the work of junior scientists and there was, as ever, a tremendous array of human capital on display at this meeting. This drives home to me the importance of investment in professional training and research programs that specifically develop human capital. This community exists in large measure because of the innovative program jointly offered by NSF and NIH. Thanks as ever to the vision and hard work of Josh Rosenthal, Sam Scheiner, and all the funders (e.g., support from The Gates Foundation can be found all around this conference) for this area. It’s always great to catch up with smart, fun friends. Plenty of time was spent talking science and drinking craft beer (what a beer town Ft. Collins is!) with the likes of Peter Hudson, Jessica Metcalf, Ottar Bjornstad, Aaron King, Mike Antolin, Tony Goldberg, Issa Cattadori, Maciej Boni, Marm Kilpatrick and, of course, Dan Salkeld. It was nice to meet and chat, if only briefly, with my sometime remote collaborator Paul Sharp, who gave what I understand to be an extremely stimulating keynote on the complicated and surprising evolution of malaria (alas, I missed it as I was delayed getting to Ft. Collins). I also spent some quality time learning about acquired immunity in dogs with Colin Parrish. This may come in handy for some ideas that Jess Metcalf and I have been playing around with.

There is a great tradition of the EEID hike and closing banquet/dance. Ft. Collins provided a beautiful and challenging hike out in Lory State Park. The view from the top of Arthur’s Peak was pretty amazing.

View from the top of the trail on Arthur's Peak, Lory State Park, Ft. Collins.
View from the top of the trail on Arthur’s Peak, Lory State Park, Ft. Collins.

At Wednesday’s banquet, I’m afraid to say that Princeton once again dominated the dance floor as we all rocked out to the amazing Denver funk/rock/jam band Kinetix (great choice, Mike). The Stanford showing was disappointing in part because of the early departure of some of our most enthusiastic dancers. Don’t get cocky though, Princeton. We’ll be gunning for you next year.

The entirety of Tuesday morning’s session was given over to communicating science. Dan Salkeld warmed up the crowd with some hilarious examples of the reporting frenzy that ensued following the publication of our paper on plague dynamics in prairie dog towns or, more recently, Hillary Young‘s work showing that excluding large ruminants increases rodent density in Kenya. Wow. Dan also used my Stanford colleague Rebecca Bird‘s work as an example of how an unexpected story can engage readers and listeners. My collaborator Tony Goldberg gave a talk that was also not lacking in ridiculous headlines thanks to his “viral” nose-tick work. David Quammen, author of outstanding popular science books such as The Song of the Dodo and Spillover (which Bill Durham and I use for our class on environmental change and emerging infectious disease), gave a terrific presentation in which he consolidated a lot of nice, practical advice on the craft of writing engaging work into 18 points, amply illustrated by anecdotes of characters from our field. Sonia Altizer from the University of Georgia introduced the crowd to the opportunities (and pitfalls) of citizen science and suggested that it might just be possible to engage the public in disease ecology data collection. Some examples she identified included the granddaddy of citizen-science in the US run by the Laboratory of Ornithology at Cornell, the ZomBee Watch at SFSU, and her own Project MonarchHealth. If I had to summarize this session in one pithy phrase, I think it would have to be “Yay, ecologists!”

Quammen took to Twitter to call us out for being behind the curve with respect to social media.

While there were, in fact, a few of us tweeting the occasional tidbit from the conference, I think his general point is valid. This stuff is intrinsically interesting and we can do a much better job communicating to broad publics.

Some talks that really caught my attention.

Ary Hoffmann gave a great talk about the complexities of using bacteria of the genus Wolbachia to control the Aedes mosquitoes that transmit dengue in Australia (and elsewhere). Wolbachia infects mosquitoes and can have a variety of effects on their biology. The reason artificial infection of mosquitoes wit this bacterium seems so promising as a means of biological control is that the offspring of crosses between infected and uninfected mosquitoes are not viable. This is obviously a very substantial fitness cost to the mosquitoes and this creates serious challenges for getting the infected mosquitoes to persist and take over local populations. Hoffmann presented a cool result about the invasibility of infected mosquitoes wherein in the early phases of introduction there is an unstable point in the mosquito dynamics. At this point, if the infected mosquitoes are above a threshold, they will successfully invade, otherwise, they will die out because of the inherent fitness costs of the Wolbachia infection. One policy challenge that arises is that to get a local population of mosquitoes above the invasibility threshold, researchers and vector-control specialists have to sometimes introduce a lot of mosquitoes. This means that the number of mosquitoes locally can increase substantially and, as you can imagine, this isn’t always popular with communities.

Fellow Anthropologist Aaron Blackwell from UCSB gave a fantastic talk on our “old friends”, the helminths (cue the freaky electron micrograph of a helminth’s mouth!). Aaron participates in the Tsimane Health and Life History Project which was started by colleagues Mike Gurven (also at UCSB) and Hilly Kaplan (New Mexico). Using sophisticated multi-state Markov hazard models (go Anthropology!), Aaron showed that co-infection with helminths and Giardia is less frequent than expected among this population that experiences ubiquitous exposure to both pathogens and that, in fact, infection with the one appears to be protective against infection with the other. One of the most provocative results he presented showed that helminth infection actually lowered systolic blood pressure in men by an amount equivalent to the increase that comes from aging ten years. Chronic helminthic infection may be a reason why Tsimane men’s systolic blood pressure does not rise precipitously with age as it does in the US. This result, which may provide fresh insights into the mechanisms of hypertension, a major source of morbidity in the US, struck me as particularly poignant given the demeaning comments made about NSF funding for work among the Tsimane from none other than Lamar Smith (R–TX), the chair of the House Committee on Science, Space, and Technology.

Anna Savage, a post-doc with the National Zoo in Washington DC, gave an awesome talk on the comparative immunogenetics of of frogs with respect to infection with the devastating fungal infection, chytridiomycosis. Chytridiomycosis has been identified as a major cause of amphibian extinction worldwide and Anna showed surprising heterogeneity in immune response across frog species. This is a subject with which I have only passing familiarity, but her talk demonstrated an amazing sophistication in integrating different levels of biological organization and making sense of a dauntingly complex problem. I would wager that Dr. Savage is one to keep an eye on.

The organizers tried a scheduling format that was a bit different from last year, wherein each session started with two half-hour talks generally given by somewhat more senior people. The second half of each session was then given over to brief ten-minute talks, typically delivered by more junior people. This format is nicely in keeping with the great EEID tradition of promoting the research of junior scientists. A few short talks that I found especially interesting included one by Sarah Hamer, from Texas A&M, on Chagas disease in the United States. She presented sobering data from national blood-bank surveillance showing a surprising number of Chagas-infected samples coming from donors with no history of travel to Latin America. When pushed by a questioner, she suggested that she would consider Chagas to be endemic in the US, at least in dogs and possibly even in people. Carrie Cizauskas, formerly of Wayne Getz‘s shop at Berkeley and now with Andy Dobson and Andrea Graham at Princeton, give a nice talk on the role of both stress and sex hormones in mediating macroparasite infection in wild ungulates in Etosha National Park, Namibia. Romain Garnier from Princeton described a very nifty image-processing approach to scanning large volumes of histological slides for indications of infection.

I perhaps didn’t see as many posters as I should have. The problem with the poster sessions is that one keeps running into various people one wants to talk to. I did manage to check out the poster of my former freshman advisee and current Princeton EEB student Cara Brook. She’s got an awesome PhD project studying the multi-host ecology of infectious disease in Malagasy fruit bats.

I’m looking forward to next year’s meeting at the University of Georgia already. I’m also looking forward to resuscitating the pedagogical workshop that used to be a signature feature of this EEID meeting. More on that later…

Ecology and Evolution of Infectious Disease

I am recently back from the 2013 Ecology and Evolution of Infections Disease Conference at Penn State University. This was quite possibly the best meeting I have ever attended, not even for the science (which was nonetheless impeccable), but for the culture. I place the blame for this awesome culture firmly on the shoulders of the leaders of this field and, in particular, the primary motivating force behind the recent emergence of this field, Penn State’s Peter Hudson. Since I had attended the other EEID conference at UGA earlier this Spring (another great conference), I had no intention on attending the Penn State conference this year. Then, one day in late March, Nita Bharti asked me if I was going and mentioned, “You know it’s Pete’s 60th birthday, right?” Well that sealed it; I really had no choice.  I simply had to go if for no other reason than to pay my due respect to this man I admire so greatly. Pete has the most relentless optimism about the future of science and a willingness to make things happen that I have ever encountered and, in this way, has provided me one of my primary role models as a university professor and mentor. He has played a role in developing so many of the brilliant people who make this field so exciting, it’s amazing (just a sample that comes immediately to mind: Ottar Bjornstad, Matt Ferrari, Nita Bharti, Marcel Salathé, Isabella Cattadori, Jamie Lloyd-Smith, Shweta Bansal, Jess Metcalf…). Of course, even as I write this, I realize the joint influence of another major player in the field, Bryan Grenfell, formerly of Penn State but now at Princeton, becomes obvious. A great scientist in his own right, Pete is the master facilitator, providing the support (and institutional interference!) that allows young scholars to thrive. He is a talent-spotter extraordinaire.

The talks that made up the bulk of the scientific program were, for the most part, excellent. The average age of the speakers was about 30, maybe just a bit higher. When one attends an academic conference, one typically expects that the major addresses to the collected masses will be by geezers, er, senior scholars in the field. There was a clear play at inversion of the standard model here though. Speakers were clearly chosen because of their trajectories, not their past achievements.  That’s pretty great. When I went up for tenure at Stanford, I was told that Stanford does not really care about what you have done; it cares about what you will do. Of course, the best information that the university has about your future work is the work you have already done. This conference embodied this spirit by placing the future (and, in many cases, current) leaders of the field in the key speaking roles while some of the biggest names in ecology, population biology, and epidemiology sat happily in the audience (e.g., joining Hudson and Grenfell were Andy Dobson, Andrew Read, Mick Crawley, Charles Godfray, Mike Boots, Mercedes Pascual, Les Real, Matt Thomas, …)

The tone set by these great mentors carries through to the whole culture of the conference, where senior people attended the poster sessions, sat with students at lunches and dinners, and schmoozed at the plentiful open-bar mixers. For example, on the first full day of the conference, there was an afternoon poster session that started at 4:30 (we had been in back-to-back sessions since 8:30). This session was preceded by an hour-long poster-teaser session in which grad students and post-docs got up and presented 60-second (and, as Andrew Read noted, not one nanosecond more) teasers of their posters. Bear in mind, this session was entirely comprised of students and post-docs. It was striking that essentially every seat in the house was occupied and all the major players were present. The teasers were great – many were very funny, including a haiku apparently written by a triatomine bug and translated to us by Princeton EEB student Jennifer Peterson.

After the teasers, the conference went en masse to the fancy new Millenium Science Complex (it turns out that Pete Hudson has physical capital projects in addition to human capital ones!). There, participants milled about the 150 posters. After spending quite a bit of time doing this – and dutifully getting pictures of all my lab with their posters – I thought to check the time and realized it was nearly 6:30. The poster session had been going for two hours and nearly everyone was still there, including all the luminaries. It helped that there was free beer. I tweeted my amazement at this realization:

That is, in fact, Princeton‘s Bryan Grenfell moving fast in the middle of the picture, apparently making a bee-line for Michigan’s Aaron King. Andrew Read is in the far background, talking to a poster-presenter (he has that posture).

Scientific highlights for me included Caroline Buckee‘s talk about measuring mobility in the context of malaria transmission in Kenya and Derek Cummings‘s talk on the Fluscape Project to measure spatial heterogeneity in influenza transmission in China. I am a long-time fan of this project and it’s nice to see the great work that has come out of it. These talks were right in my wheelhouse of interest, but there were plenty other cool ones including Britt Koskella‘s talk on the dynamics of bacteria and phage on tree leaves.

Stanford was exceedingly well represented at this conference. My lab had no fewer than five posters. Ashley Hazel presented on her work with Carl Simon on modeling gonorrhea transmission dynamics in Kaokoland, Namibia. Whitney Bagge presented her work on remote-sensing of rodent-borne disease in Kenya. Alejandro Feged presented work on the transmission dynamics of malaria in the Colombian Amazon among the indigenous Nukak people. Laura Bloomfield presented her remote sensing and spatial analysis work from our project on the spillover of primate retroviruses in Western Uganda. I closed things out with a minimalist poster on simple graphical models for multiple attractors in vector-borne disease dynamics in multi-host ecologies. In addition to my lab group, Giulio De Leo (with whom I have been running a weekly disease ecology workshop at Woods since winter quarter) was there, helping to bridge all sorts of structural holes in our collective collaboration graphs.

The other thing that comes out of these meetings, especially more intimate ones like EEID, is some actual work on collaborative projects. I managed to find some time to sit down and discuss plans with collaborators as well as do some shameless recruitment for my planned re-submission of the Stanford Biodemography Workshops. I’m really excited about some of these collaborations, including one that brings together my two major areas of interest: biodemography and life history theory and infectious disease ecology.

Oh, and I’m convinced that there must be an interpretive dance component to the Ph.D. exam in the Grenfell lab. This is certainly the most parsimonious explanation for much of what I saw Wednesday night.

The Return of Lahontan Cutthroat Trout

The New York Times had a terrific story on Wednesday on the recovery of an endemic trout previously believed to be extinct since the 1940s in Pyramid Lake, Nevada. As I am currently teaching my class, Ecology, Evolution, and Human Health, with its emphasis on adaptation as local process and human-environment interaction, I was happy to see such an excellent story about local adaptation. In a nutshell, the trout was over-fished and also suffered devastating population declines in Pyramid Lake because of predation from introduced brook trout (and other exotic salmonids) and hybridization with introduced rainbows. This is, alas, an all too common story for trout endemics of western North America. A remanent population of Lahontan cutthroats, that were genetically very similar to the original Pyramid stock, was found in a Pilot Peak stream near the Utah border and samples from this population were brought to a USFWS breeding facility in cooperation with the Paiute Nation.  It sounds like the breeding/stocking program has been a tremendous success and the Lahontan cutties have now returned to Pyramid Lake. A big part of the story appears to be the intensive management of the main prey item of Lahontan cutties, the cui-ui sucker, which was devastated  following the construction of the Derby Dam in 1905.

This was all great news, but the thing that really caught my attention (because I’m currently teaching this class that focuses on adaptation) was the fact that the re-introduced Lahontan cutties have thrived so rapidly:

Since November, dozens of anglers have reported catching Pilot Peak cutthroats weighing 15 pounds or more. Biologists are astounded because inside Pyramid Lake these powerful fish, now adolescents, grew five times as fast as other trout species and are only a third of the way through their expected life span.

Can you say adaptation?! There is something about the interaction between this particular cutthroat species and the environment of Pyramid Lake that makes for giant fish as long as the juveniles can escape predation by exotic salmonids and adults can prey on their preferred species. Great news for anglers, great news for the Paiute Nation, great news for ecology.

On Anthropological Sciences and the AAA

I guess the time has rolled around again for my annual navel-gaze regarding my discipline, my place within it, and its future. Two strangely interwoven events have conspired to make me particularly philosophical as we enter into the winter holidays. First, I am in the middle of a visit by my friend, colleague, and former student, Charles Roseman, now an associate professor of anthropology at the University of Illinois, Urbana-Champaign. The second is that the American Anthropological Association meetings just went down in San Francisco and this always induces an odd sense of shock and subsequent introspection.

Charles graduated with a Ph.D. from the Department of Anthropological Sciences (once a highly ranked department according the the National Research Council) in 2005. He was awarded tenure at UIUC, a leading department for biological anthropology, this past year and has come back to The Farm to collaborate with me on our top-secret sleeper project of the past seven years. We’ve made some serious progress on this project since he arrived and maybe I’ll be able to write about that soon too.

The annual AAA meeting is one  that I never attended until about four years ago, coinciding with what we sometimes refer to as “the blessed event,” the remarrying of the two Stanford Anthropology departments. It’s actually a bit of coincidence that I started attending AAAs the same year that we merged but it has largely been business of the new Department of Anthropology that has kept me going back – largely to serve on job search committees. This year, I had two responsibilities that drew me to the AAAs. The first was the editorial board meeting for American Anthropologist, the flagship publication of the association.  I joined the editorial board this year and it seemed a good idea to go and get a feel for what is happening with the journal and where it is likely to head over the next couple years.

My other primary responsibility was chairing a session that was organized by two of my Ph.D. students, Yeon Jung Yu and Shannon Randolph. In addition to Yeon and Shannon, my Ph.D. student Alejandro Feged also presented work from his dissertation research.  All three of these students were actually accepted into Anthsci and are part of the last cohort of students to leave Stanford still knowing the two-department system.

It was a great pleasure to sit in the audience and watch Yeon, Shannon, and Alejandro dazzle the audience with their sophisticated methods, beautiful images, and accounts of impressive, extended — and often hardcore — fieldwork. For her dissertation research, Yeon worked for two years with commercial sex workers in southern China, attempting to understand how women get recruited into sex work and how social relations facilitate their ability to survive and even thrive in a world that is quite hostile to them. Her talk was incredibly professional and theoretically sophisticated. For her dissertation research, Shannon worked in the markets of Yaoundé, Cameroon, trying to understand the motivations for consumption of wild bushmeat. Shannon was able to share with the audience her innovative approaches to collecting data (over 4,000 price points, among other things) on a grey-market activity that people are not especially eager to discuss, especially in the market itself. Alejandro did his dissertation research in the Colombian Amazon, where he investigated the human ecology of malaria in this highly endemic region. His talk demonstrated that the conventional wisdom about malaria ecology in this region — namely, that the people most at risk for infection are adult men who spend the most time in the forest — is simply incorrect for some indidenous popualtions and his time-budget analyses made a convincing case for the behavioral basis of this violation of expectations. This was a pretty heterogeneous collection of talks but they shared the commonality of a very strong methodological basis to the research.

At at time when many anthropologists express legitimate concerns over their professional prospects, I have enormous confidence in this crop of students, all three of whom are regularly asked to do consulting for government and/or non-govermental organizations because of their subject knowledge and methodological expertise. Anthsci graduates — there weren’t that many of them since the department existed for less than 10 years — have done very well in the profession overall. I will list just a couple here whose work I knew well because I was on their committees or their work was generally in my area

In addition to these grad students, I think that it’s important to note the success of the post-docs who worked either in Anthsci or with former Anthsci faculty on projects that started before the merger. Some of these outstanding people include:

In a discipline that is lukewarm at best on the even very notion of methodology, I suspect that students with strong methodological skills — in addition to the expected theoretical sophistication and critical thinking (note that these skills do not actually trade-off) — enjoy a distinct comparative advantage when entering a less-than-ideal job market. Of course, I don’t mean to imply that Anthsci didn’t have its share of graduates who leave the field out of frustration or lack of opportunity or who get stuck in the vicious cycle of adjunct teaching. But this accounting gives me hope. It gives me hope for my both my current and future students and it gives me hope for the field. Maybe I’ll even go to AAAs again next year…

Risk Management: The Fundamental Human Adaptation

It was a conceptually dense week in class.  The first part of the week I spent talking about topics such as ecological complexity, vulnerability, adaptation, and resilience. One of the key take-home messages of this material is that uncertainty is ubiquitous in complex ecological systems.  Now, while systemic uncertainty does not mean that the world is unpatterned or erratic, it does mean that people are never sure what their foraging returns will be or whether they will come down with the flu next week or whether their neighbor will support them or turn against them in a local political fight. Because uncertainty is so ubiquitous, I see it as especially important for understanding human evolution and the capacity for adaptation. In fact, I think it’s so important a topic that I’m writing a book about it.  More on that later…

First, it’s important to distinguish two related concepts.  Uncertainty  simply means that you don’t know the outcome of a process with 100% certainty.  Outcomes are probabilistic.  Risk, on the other hand, combines both the likelihood of a negative outcome and the outcome’s severity. There could be a mildly negative outcome that has a very high probability of occurring and we would probably think that it was less risky than a more severe outcome that happened with lower probability. When a forager leaves camp for a hunt, he does not know what return he will get.  10,000 kcal? 5,000 kcal? 0 kcal? This is uncertainty.  If the hunter’s children are starving and might die if he doesn’t return with food, the outcome of returning with 0 kcal worth of food is risky as well.

Human behavioral ecology has a number of elements that distinguish it as an approach to studying human ecology and decision-making.  These features have been discussed extensively by Bruce Winterhalder and Eric Smith (1992, 2000), among others.  Included among these are: (1) the logic of natural selection, (2) hypothetico-deductive framework, (3) a piecemeal approach to understanding human behavior, (4) focus on simple (strategic) models, (5) emphasis on behavioral strategies, (6) methodological individualism.  Some others that I would add include: (7) ethological (i.e., naturalistic) data collection, (8) rich ethnographic context, (9) a focus on adaptation and behavioral flexibility in contrast to typology and progressivism.  The hypothetico-deductive framework and use of simple models (along with the logic of selection) jointly accounts for the frequent use of optimality models in behavioral ecology. Not to overdo it with the laundry lists, but optimality models also all share some common features.  These include: (1) the definition of an actor, (2) a currency and an objective function (i.e., the thing that is maximized), (3) a strategy set or set of alternative actions, and (4) a set of constraints.

For concreteness’ sake, I will focus on foraging in this discussion, though the points apply to other types of problems. When behavioral ecologists attempt to understand foraging decisions, the currency they overwhelmingly favor is the rate of energy gain. There are plenty of good reasons for this.  Check out Stephens and Krebs (1986) if you are interested. The point that I want to make here is that, ultimately, it’s not the energy itself that matters for fitness.  Rather it is what you do with it. How does a successful foraging bout increase your marginal survival probability or fertility rate? This doesn’t sound like such a big issue but it has important implications. In particular, fitness (or utility) is a function of energy return.  This means that in a variable environment, it matters how we average.  Different averages can give different answers. For example, what is the average of the square root of 10 and 2? There are two ways to do this: (1) average the two values and take the square root (i.e., take the function of the mean), and (2) take the square roots and average (i.e., take the mean of the function). The first of these is \sqrt{6}=2.45. The second is (\sqrt{10} + \sqrt{2})/2=2.29.  The function of the mean is greater than the mean of the function.  This is a result of Jensen’s inequality. The square root function is concave — it has a negative second derivative. This means that while \sqrt{x} gets bigger as x gets bigger (its first derivative is positive), the increase is incrementally smaller as x gets larger. This is commonly known as diminishing marginal utility.

Lots of things naturally show diminishing marginal gains.  Imagine foraging for berries in a blueberry bush when you’re really hungry.  When you arrive at the bush (i.e., ‘the patch’), your rate of energy gain is very high. You’re gobbling berries about as fast as you can move your hands from the bush to your mouth. But after you’ve been there a while, your rate of consumption starts to slow down.  You’re depleting the bush.  It takes longer to pick the berries because you have to reach into the interior of the bush or go around the other side or get down on the ground to get the low-hanging berries.

berryplot

Chances are, there’s going to come a point where you don’t think it’s worth the effort any more.  Maybe it’s time to find another bush; maybe you’ve got other important things to do that are incompatible with berry-picking. In his classic paper, Ric Charnov derived the conditions under which a rate-maximizing berry-picker should move on, the so-called ‘marginal value theorem’ (abandon the patch when the marginal rate of energy gain equals the mean rate for the environment). There are a number of similar marginal value solutions in ecology and evolutionary biology (they all arise from maximizing some rate or another). Two other examples: Parker derived an marginal value solution for the optimal time that a male dung fly should copulate (can’t make this stuff up). van Baalen and Sabelis derived the optimal virulence for a pathogen when the conditional probability of transmission and the contact rate between infectious and susceptible hosts trade off.

So, what does all this have to do with risk? In a word, everything.

Consider a utility curve with diminishing marginal returns.  Suppose you are at the mean, indicated by \bar{x}. Now you take a gamble.  If you’re successful, you move to x_1 and its associated utility.  However, if you fail, you move down to x_0 and its associated utility.  These two outcomes are equidistant from the mean. Because the curve is concave, the gain in utility that you get moving from \bar{x} to x_1 is much smaller than the loss you incur moving from \bar{x} to x_0.  The downside risk is much bigger than the upside gain.  This is illustrated in the following figure:

risk-aversion

When returns are variable and utility/fitness is a function of returns, we can use expected utility as a tool for understanding optimal decisions. The idea goes back to von Neumann and Morgenstern, the fathers of game theory. Expected utility has received some attention in behavioral ecology, though not as much as it deserves.  Stephens and Krebs (1986) discuss it in their definitive book on foraging theory.  Bruce Winterhalder, Flora Lu, and Bram Tucker (1999) have discussed expected utility in analyzing human foraging decisions and Bruce has also written with Paul Leslie (2002; Leslie & Winterhalder 2002) on the topic with regard to fertility decisions.  Expected utility encapsulates the very sensible idea that when faced with a choice between two options that have uncertain outcomes, choose the one with the higher average payoff. The basic idea is that the world presents variable pay-offs. Each pay-off has a utility associated with it. The best decision is the one that has the highest overall expected, or average, utility associated with it. Consider a forager deciding what type of hunt to undertake. He can go for big game but there is only a 10% chance of success. When he succeeds, he gets 10,000 kcal of energy. When he fails, he can almost always find something else on the way back home to bring to camp. 90% of the time, he will bring back 1,000 kcal.  The other option is to go for small game, which is generally much more certain endeavor. 90% of the time, he will net 2,000 units of energy.  Such small game is remarkably uniform in its payoff but sometimes (10%) the forager will get lucky and receive 3,000 kcal. We calculate the expected utility by summing the products of the probabilities and the rewards, assuming for simplicity in this case that the utility is simply the energy value (if we didn’t make this assumption, we would calculate the utilities associated with the returns first before averaging).

Big Game: 0.1*10000 + 0.9*1000 = 1900

Small Game: 0.9*2000 + 0.1*3000 = 2100

Small game is preferred because it has higher expected utility.

We can do a bit of analysis on our utility curve and show something very important about risk and expected utility. I’ll spare the mathematical details, but we can expand our utility function around the mean return using a Taylor series and then calculate expectations (i.e., average) on both sides.  The resulting expression encapsulates a lot of the theory of risk management. Let w(x) indicate the utility associated with return x (where I follow the population genetics convention that fitness is given by a w).

 \overline{w(x)} = w(\bar{x}) + \frac{1}{2} w? \mathrm{Var}(x).

Mean fitness is equal to the fitness of the mean payoff plus a term that includes the variance in x and the second derivative of the utility function.  When there is diminishing marginal utility, this will be negative.  Therefore, variance will reduce mean fitness below the fitness of the mean. When there is diminishing marginal utility, variance is bad. How bad is determined both by the magnitude of the variance but also by how curved the utility curve is.  If there is no curve, utility is a straight line and w?=0.  In that case, variance doesn’t matter.

So variance is bad for fitness.  And variance can get big. One can imagine it being quite sensible to sacrifice some mean return in exchange for a reduction in variance if this reduction outweighed the premium paid from the mean. This is exactly what we do when we purchase insurance or when a farmer sells grain futures.  This is also something that animals with parental care do.  Rather than spewing out millions of gametes in the hope that it will get lucky (e.g., like a sea urchin), animals with parental care use the energy they could spend on lots more gametes and reinvest in ensuring the survival of their offspring. This is probably also why hunter-gatherer women target reliable resources that generally have a lower mean return than other available, but risky, items.

It turns out that humans have all sorts of ways of dealing with risk, some of them embodied in our very biology.  I’m going to come up short in enumerating these because this is the central argument of my book manuscript and I don’t want to give it away (yet)! I hope to blog here in the near future about three papers that I have nearly completed that deal with risk management and the evolution of social systems, reproductive decision-making in an historical population, and foraging decisions by contemporary hunter-gatherers.  When they come out, my blog will be the first to know!

References

Charnov, E. L. 1976. Optimal foraging: The marginal value theorem. Theoretical Population Biology. 9:129-136.

Leslie, P., and B. Winterhalder. 2002. Demographic consequences of unpredictability in fertility outcomes. American Journal of Human Biology. 14 (2):168-183.

Parker, G. A., and R. A. Stuart. 1976. Animal behavior as a strategy optimizer: evolution of resource assessment strategies and optimal emigration thresholds. American Naturalist. 110 (1055-1076).

Stephens, D. W., and J. R. Krebs. 1986. Foraging theory. Princeton: Princeton University Press.

van Baalen, M., and M. W. Sabelis. 1995. The dynamics of multiple infection and the evolution of virulence. American Naturalist. 146 (6):881-910.

Winterhalder, B., and P. Leslie. 2002. Risk-sensitive fertility:The variance compensation hypothesis. Evolution and Human Behavior. 23:59-82.

Winterhalder, B., F. Lu, and B. Tucker. 1999. Risk-sensitive adaptive tactics: Models and evidence from subsistence studies in biology and anthropology. Journal of Archaeological Research. 7 (4):301-348.

Winterhalder, B., and E. A. Smith. 2000. Analyzing adaptive strategies: Human behavioral ecology at twenty-five. Evolutionary Anthropology. 9 (2):51-72.

Response to Selection

I’m done now with the first week of the Spring quarter. It was a bit challenging because I had to attend the PAA meetings in Washington, DC for the latter part of the week, but Brian Wood ably covered for me on Thursday. I thought that I would use the blog as a tool for summarizing one of the key points I want students to take away from this fist week in which we discussed evolution and natural selection.

We spent a good deal of lecture time talking about adaptation.  Specifically, we discussed how adaptation can serve as a foil to typology and essentialism. Adaptation is local and must be seen within its specific environmental and historical context. Adaptations are dynamic because environments are.

Adaptationist thinking is powerful, but can easily be overdone. This is why I also think it is essential to understand the mechanics of selection, something that I’m afraid is not often addressed in introductory evolutionary anthropology classes.  So, in the very first lecture of class, I throw some quantitative genetics (and, thus, some math) at students.  Of course, these are Stanford students, so I’m confident they can handle a little techie-ness every now and then. We specifically discuss the multivariate breeder’s equation, sometimes known as Lande‘s equation:

\Delta \mathbf{\bar{z}} = \mathbf{G \beta}

,

where \Delta \mathbf{\bar{z}} is the change in the mean fitness of a multivariate trait, \mathbf{G} is the additive genetic variance-covariance matrix, and \beta is the selection gradient on \mathbf{\bar{z}}.

In effect, \beta is a vector pointing in the direction of the optimal change in the phenotype. The matrix \mathbf{G} does two things to this gradient pushing \mathbf{\bar{z}} toward its optimum: (1) it scales the response depending on how much additive variance there is in each trait and (2) it rotates it as a function of the covariances between traits. I won’t get too much into matrix multiplication here (this is a very nice reference too). The key point is that \mathbf{G} is a square k \times k matrix (where k is the number of traits we’re looking at) the diagonal elements of which are variances and the off-diagonal elements of which, g_{ij} represent the covariances between traits i and j.   Selection requires variance. Without sufficient variance, even strong selection won’t change the phenotype much between generations.  But variance isn’t all there is to it. When the covariances are positive, there will be substantial indirect selection, and when they are negative, you have genetic constraints at work. Selection may be pointing in a particular direction, but the structure of the trade-offs could very easily mean that you can’t actually get there.

Let’s consider three quick (toy) examples.  Say we have two traits, maybe “length” and “width” (this could be something less vague and insipid: Lande (1979) looks at brain mass and body mass in a serious two-trait example). We will assume that the selection gradient is \mathbf{\beta} = \{0.5, 0.25\}?. That is, the force of selection is twice as high on length as it is on width, but it is pretty strong and positive on both. We’ll demonstrate the effect of variance and constraint in three ways:  (1) more variance in the trait under weaker selection (\mathbf{G_1}), (2) positive covariance between the two traits (\mathbf{G_2}), and (3) negative covariance between the two traits (\mathbf{G_3}).

 \mathbf{G_1} = \left( \begin{array}{cc} 0.33 & 0.00 \\ 0.00 & 0.67 \end{array} \right)

 \mathbf{G_2} = \left( \begin{array}{cc} 0.33 & 0.33 \\ 0.33 & 0.67 \end{array} \right)

 \mathbf{G_3} = \left( \begin{array}{cc} 0.33 & -0.33 \\ -0.33 & 0.67 \end{array} \right)

The figure below plots the response to selection in the three different types of genetic architecture.  The direction of selection is indicated in the grey arrow. If the variances of the two traits were equal to 1 and there were zero covariances, this is where selection would move the phenotype pair (try it). We can see that the response to selection moves toward width (the trait under weaker selection) even when covariances are zero (black arrow).  Why? Because there is more variance for width than there is for length (0.67 \times 0.25 > 0.33 \times 0.5).  This effect becomes more pronounced when there is positive covariance between the traits (blue arrow) — the selection toward width is 0.33 \times 0.5 +0.67 \times 0.25 = 0.3325. When the covariances are negative, we see something cool (red arrow).  The response to selection is small and moves (almost) entirely in the direction of length. This is because the negative covariance between length and width, when acted on by the strong selection on length, all but cancels out the positive response to selection (-0.33 \times 0.5 + 0.67 \times 0.25 = 0.0025).

selection-constraint-plot

This simple demonstration shows that the response to selection can be complex. Making an argument that some trait would be under selection is not sufficient to say that it actually evolved (or will evolve) that way.  Entirely plausible arguments for the direction of selection are made all the time in evolutionary anthropology.  Here is one from a very important paper in paleoanthropology (Lovejoy 1981: 344):

Any behavioral change that increases reproductive rate, survivorship, or both, is under selection of maximum intensity. Higher primates rely on social behavioral mechanisms to promote survivorship during all phases of the life cycle, and one could cite numerous methods by which it theoretically could be increased.  Avoidance of dietary toxins, use of more reliable food sources, and increased competence in arboreal locomotion are obvious examples. Yet these are among the many that have remained under stadong selection throughout much of the course of primate evolution, and therefore unlikely that early hominid adaptation was a product of intensified selection for adaptations almost universal to anthropoid primates.

Arguing for selection without considering trade-offs can get you into trouble.  Selection in the presence of quantitative genetic constraints (or even differential variance in the traits) can produce counter-intuitive results. (Selectionists, don’t dispair. There are ways to deal with this, but it will have to wait for another post). In the case of Lovejoy’s argument, there are good reasons to think that survivorship and reproductive rate are, indeed, strongly negatively correlated. Which is under stronger selection? Which has more additive variance? How strong are the negative covariances?

When we make selectionist or adaptationist arguments, we should always keep in the back of our minds the three questions:

  1. How strong is the force of selection?
  2. How much variance is there on which selection can act?
  3. How is the trait constrained through negative correlations with other traits?

References

Lande, R. A. 1979. Quantitative genetic analysis of multivariate evolution applied to brain: body size evolution. Evolution. 33:402-416.

Lovejoy, C. O. 1981. The origin of man. Science. 211:341-350.