# Integrating the Social Sciences with the Environmental and Earth Sciences

Seven years ago, I was invited to participate in a panel at NIH in Bethesda charged with evaluating the joint NSF/NIH interdisciplinary program on the Ecology of Infectious Disease. While there was an explicit call for the participation of social and behavioral sciences in the call for proposals, very few social scientists were getting involved in this remarkable program. Having participated in a wide array of similarly interdisciplinary panels, I knew that this was a common dilemma: the architects of the panel (whether it is a panel evaluating grant proposals, an interdisciplinary symposium, or an edited volume), who are typically natural scientists of some sort, make a good-faith effort to bring social scientists into the fold, but generally have little luck. Through a series of slightly hilarious miscommunications and travel snafus, I was unable to attend the meeting in Bethesda. I holed up for a weekend in a cottage in Santa Fe (where I had been participating in an panel the previous week) and wrote a document on how researchers working on the ecology of infectious disease could engage the social sciences and social scientists. As I contemplate my new role in the School of Earth, Energy, and the Environment at Stanford, it seems like a propitious time to revisit this white paper.

The stakes for involving the social sciences in environmental research – broadly construed – are high. Massive – potentially existential – problems like climate change, emerging pandemic disease, and large-scale extinction have both human drivers and enormous consequences for human welfare. This said, there are precious few social scientists – people charged with understanding human behavior and societies – who are engaged in research on environmental problems. This problem is particularly acute at elite institutions such as leading research universities.

Since human behavior is central to many aspects of most environmental problems, the contributions of social scientists to work on environmental problems is important and, quite possibly, necessary for dealing with the major problems associated with this domain of research. Understanding environmental problems such as climate change is obviously of major significance for state actors (e.g., governments, regulatory bodies)  and, ultimately, people more generally. Why then is it so difficult to engage social scientists in these research questions? This seems all the more puzzling given the amount of money potentially available for this research, particularly when compared to the funding available within the social science disciplines. There is clearly a collective action problem here: the generation of a public good that could come from the cooperation of social scientists and natural scientists is being inhibited somehow. Presumably, there would be benefits for social scientists who chose to collaborate with natural scientists on important environmental problems. Why then are we stuck with the collective action problem?

Sometimes, there is an explicit attempt to get social scientists to do the bidding of natural scientists in promoting social or cultural change for their desired ends. The eminent Stanford ecologist, Paul Ehrlich  has called for research into the mechanisms that change social norms, suggesting that there is an urgency to changing norms because of mounting environmental problems. The irony here, of course, is that in trying to engage social scientists in research on the environment, Ehrlich and other interested natural scientists needs to induce a change in social norms.

In an essay reviewing models for changing social norms,  Ehrlich and the great Princeton ecologist Simon Levin note that they did not even attempt to address how asymmetries of power or social networks affect the spread of social norms. Unfortunately, this is exactly the problem facing natural scientists trying to engage social scientists and models that fail to acknowledge these factors are doomed to failure. Within both the academy and society more broadly, there are distinct power asymmetries across scientific fields and, in general, social science fields are on the losing end of such power asymmetries. The great majority of social scientists can not compete with natural scientists with respect to research funding or the prestige (or volume) of of their publications.

When power/prestige gradients are steep, disciplines are likely to become insularized. An adaptive response to a collective’s inability to compete across disciplines is to, consciously or not, collude in agreeing that the only relevant opinions about the quality/volume of individual scholars’ research are other members of the scholars’ discipline. There are institutional practices that can facilitate this (e.g., the manner in which promotions are managed). I suggest that insularized disciplines will also fetishize theory above all other intellectual outcomes. Theory becomes fetishized at the expense of answering interesting and important questions or developing new methodologies for answering important questions. There are few checks on the degree to which theory can become abstruse and convoluted when its development becomes decoupled from answering questions. There comes a point where only very narrow specialists can ever hope to understand the intricacies of a particular theoretical tradition and be successful. Emphasizing theoretical development above all else within a discipline is thus a path toward disciplinary insularity and is the enemy of both interdisciplinarity and problem-focused research.

Social science disciplines do not gain prestige or other within-field benefits from engaging in the substance of human-environmental interactions. This arises in part because of the dynamics of differentiation from higher-prestige science disciplines engaged in these questions. There is also a positive feedback. Way back when I first came to Stanford, I was at a party where most of the other party-goers were political scientists. At the time, I was struck by the fact that there didn’t seem to be anyone in the department who studied environmental politics. I took the opportunity that this party afforded to ask a fellow assistant professor in that department why this was the case. His answer was simply “because no one could ever get tenure at Stanford studying environmental politics.” This conversation piqued my interest and I have now had a similar conversation with quite a few economists and political scientists. While not everyone is as blunt as my interlocutor in 2003, most have agreed broadly that working on environmental questions is not the route to professional advancement and these topics are therefore avoided by promising junior scholars trying to forge research careers.

I should probably note that economics provides an interesting exception to the power/prestige hierarchy. It’s really a topic that deserves its own post, so I won’t get into it too much here, but I think that economics is an exception that proves the rule. While the discipline is certainly more prestigious than, say, anthropology (!), I think that it’s hard to imagine a more insular discipline that fetishizes theory – and its mathematical accoutrements – more and in which professional incentives are absolutely not aligned with the interests of interdisciplinary, problem-based science.

Another phenomenon that has become a barrier to genuine interdisciplinary engagement for social scientists is the tendency for scientists from high-prestige disciplines to dabble in social science. The hilariously half-assed surveys that some scientists field when they want to get at the “human dimensions” of their problem come immediately to mind.

At a more structural level, I think about network science. When one attends the Sunbelt Social Networks Conference, one can frequently hear grumbling about how a bunch of physicists have swooped in and created what is sometimes known as a “new science” of networksIt is rare to find citations to the substantial social science literature on the topics many physicists write on other than the token citations to Milgram or possibly Simmel. New terms for well-described phenomena are invented and go largely to cultural fixation. Prior work (often 20 years old) is ignored. Papers on social networks get published in Physical Review D rather than established technical journals like Social Networks, Computational and Mathematical Organization Theory, or the Journal of Mathematical Sociology. Within-discipline citations are circular. Concepts having little interest to social scientists (for good reason) go to fixation and demand being addressed despite dubious relevance (e.g., “scale-free” networks).

I am actually of two minds about this phenomenon. On the one hand, it would be nice if this “new science” did a better job acknowledging that smart people have been working on these topics for quite a long time. On the other hand, I think that we need to have more more than the small handful of methodological innovators who work on social network analysis from within social sciences departments. The volume of quality work coming out of the physical sciences is almost certainly greater than that coming out of social science departments. A big part of this is the social organization of science (see below), but surely part of this is about getting smart people to work on important problems. We need to have more social scientists who are willing to engage in the general science literature where the visibility is greater (e.g., compare the citation patterns and general visibility of Science vs. CMOT!). Social scientists need to be willing to take the risk of publishing their strongest results in high-prestige general science journals like Science, Nature, PNAS. Yes, we will usually get rejected, but that’s no different from the experience of natural scientists, and we are certain to never get into these high-impact journals if we never even try.

I do not, in any way, want to decry the engagement of high-prestige natural scientists with the social sciences. Indeed, this is something we desperately need! But it needs to be real engagement rather than either dilettantism or intellectual imperialism. Three examples of physicists who switched disciplines and had enormous positive effects come immediately to mind: Harrison White (Sociology, Columbia), Bob May (Epidemiology, Ecology and Evolutionary Biology, Oxford), and my sometime mentor, Shripad Tuljapurkar (Demography/Population Biology, Stanford). These are all scholars who took the substance and the history of their new disciplines seriously and have made enormous contributions. My amazing Ph.D. student Mike Price is a physicist-turned-anthropologist who is poised to make some truly fundamental contributions to anthropology, evolutionary biology, and economics.

A key issue that has not been sufficiently addressed in the differential funding, productivity, and status within universities is the social organization of science. The natural sciences are generally structured for productivity: organized labs, large groups working toward a common research goal, substantial division of labor. This social organization of science certainly interacts with institutional structures. For example, the allocation of teaching load and the manner in which activities (i.e., lab meetings, co-taught classes) are credited often differs systematically between the natural and social sciences. For the most part, social scientists still follow a more individualist model of scholarly production. Papers may be written with students, but research groups, if they exist, are not necessarily structured for production toward a common research goal. My own situation is instructive on this topic. As an anthropologist, I have always had more of a natural-science culture to my research group. This said, my “lab” has always been more a loose confederation of people more or less interested in similar things, than a group focused on a clearly-articulated research goal.  There was a point not that long ago when I had Ph.D. students simultaneously working on the following topics:  bushmeat in Cameroon, sex workers in China, malaria ecology in the Colombian Amazon, water security in Caribbean Colombia, sago horticulture in West Papua, disease transmission networks in Uganda, rodent population cycling and hantavirus transmission, TB in South Africa, food sharing in Nunavik. Now that I’m based in a natural science department, there is hope for some more coherence.

Really Inviting Social Scientists to the Table: A Power-Inversion Strategy

We have a situation where lower-prestige disciplines effectively opt out of competing with high-prestige ones, where runaway theory fetishism institutionally insulates scholars interested in similar phenomena from each other, and where substantive applications to problems of human-environment interaction are institutionally blocked. How do we get social scientists engaged?

The simple answer is that professional incentives of social-science researchers (particularly junior ones) and the institutional and societal priorities of solving vital problems involving the environment and human well-being need to be aligned. The first step is to get social scientists to the table to foster an environment of collaboration. Being mindful of the power dynamics across fields, collaboration opportunities need to be framed in terms of categories of thought and research questions of intrinsic interest to social scientists.  This may sound trivial, but it is key that this framing be consistent with the autochthonous development of ideas within the social sciences, rather than (even well-intentioned) natural scientists’ conceptions of what social science is. Even though an RFP or other invitation may seem like something in which social scientists should be interested to natural scientists and program officers, it may not obviously address institutionally important or interesting questions, theories, or methodologies from the social scientists’ perspective.

Remember, the system of professional reward works reasonably well from any individual social science researcher’s perspective. We expect agents to be risk-averse and such risk-aversion in this context leads to the collective action problem that we are forgoing a public good of increased understanding – and maybe even the ability to positively intervene in – significant environmental challenges of humanitarian, social, and economic import. By framing questions in terms of existing research themes in the social sciences, we may be able to overcome the risk-aversion because properly framed research opportunities should not be professionally risky.

Some areas within social science of relevance to the environmental sciences include:

• political economy, global-to-local political relationships, the development of power asymmetries – particularly in regard to access to resources, health, etc.
• equity, justice, property rights, and social movements
• trust, governance, conflict
• consumption, social, cultural and symbolic capital
• institutions
• migration, indigeneity, and ethnicity
• markets, commodities, motivations, values and cosmologies, and time horizons

In brief, if we want social scientists to become engaged with research generally seen as beneficial from a societal perspective, we have to let them “do their thing” first and let the natural science do the complementing. Rather than asking how social science can contribute to natural science research agendas, we must sometimes ask how natural science can contribute to social science research agendas. Some examples from infectious disease ecology: How can thinking about the emergence in the western hemisphere of Zika virus help us understand the development of trust or its implications for governance? How do neoliberal economic policies promote the emergence of Nipah virus of Japanese encephalitis? Why does the Indonesian government refuse to provide H5N1 samples to the US CDC or WHO? This certainly doesn’t mean that it always has to work this way, but it must work this way sometimes if progress is to be made.

I do think that natural scientists and social scientists need to be able to sit down and put together intellectually strong, multi-disciplinary research projects together. However, the way to get social scientists engaged in the first place is to frame the research possibilities in terms that are relevant to them. From here, real interdisciplinarity can be achieved.

# AAA Post-Mortem

Well, it’s been a long time and there are bunch of things I should really catch up on here. I spent last year on sabbatical in the very remote location, at least half a mile from my house, of the Center for Advanced Study in the Behavioral Sciences, where I was working on various book manuscripts that seem to grow in number the more I write. Then there’s the fact that I changed Earth Systems Science. This is a topic that clearly requires a bit of explication at some point, but now is not the time. I figured I’d break back into the blog by doing my semiregular, highly-selective review of the American Anthropological Association meetings.

The meetings this year were in Minneapolis, which is a lovely city, but maybe not the best place for a conference that meets in mid-November. Apparently, the weather was beautiful for the first couple days. However, I had spent the beginning of the week in Atlanta at the meeting of the American Society for Tropical Medicine and Hygiene (that was a very interesting meeting, but this post is about AAA). This meant that I had to fly in late Friday evening before my Saturday session. By this time, the weather had changed to something slightly more horrifying. While my flight seemed like a relatively long-haul (4.5 hours from SFO), we nonetheless flew in a regional jet. A storm had moved into the Twin Cities area by Friday afternoon and our little plane got tossed around quite a bit in our approach to MSP. In fact, we flew in a holding pattern for about an hour before the pilot came on intercom and said that we “we’re going to try to land” Try? It was a white-knuckle landing in which we experienced more yaw than I care to remember. There are many reasons that I’m grateful that our try at landing was successful, and high among those is the fact that I didn’t relish the thought of climbing back through that unsettled air if we had failed on our first pass.

Once safely on the ground, I was able to take the lovely (and cheap!) light rail straight to downtown, about three blocks from the conference venue. I don’t know what one calls the precipitation that was falling at this point (sleet? freezing rain? wintery mix?), but it was quite a shock for this adopted Californian.

The meeting was held in the Minneapolis Convention Center, a cavernous space that the 5,000 or so anthropologists didn’t come close to filling. This gave the conference a bit of a ghost-town feeling. Adventitious encounters were minimal and I definitely saw far fewer anthropologists of different stripes than I have at past meetings. The registration process was surprisingly efficient (maybe because I was registering on what I thought was the last day and there wasn’t much of a crowd). Rather than the standard canvas bag, we were given fluorescent green plastic (?) bags. I have actually wondered whether I hallucinated this, since I threw mine out immediately and have been completely unable to find a picture on one on the internets, but Rex over at Savage Minds has also commented on the bizarre bags (alas, I don’t think they were soy fiber, Rex).

Then there were the conference badges. Not so much badges as they were bibs — a bonus to the messy eaters in our midst! They were bright red (if you are lucky enough to be a AAA member) and were reminiscent of those travel wallets that are designed to fit a passport and shout “I’m a tourist, please harass me!” Too bad I left my fanny-pack at home. Once again, AAA astounds me with its tone-deaf marking of outsiders. Oh, you’re not a member of our club? Then wear this other-colored bib to display your status for all to see! I’ve been to many different societies’ conferences and AAA is the only one where I’ve experienced this practice.

If we’re being perfectly honest here, I’m not big on conference talks. I generally try to minimize the number of attend. Let’s face it, most conference talks are not great and when you have to submit your talk nine months before the conference, freshness of material tends not to be super high. I spend most of my time at conferences meeting with people: planning a paper with co-authors, strategizing with program officers, meeting with editors, catching up with former students, dining and drinking with colleagues I haven’t seen in years. This is the really productive work of an academic conference. Nonetheless, I was curious what AAA had to offer. I found the online program so difficult to use that I gave up on even trying to find talks I wanted to see (this is another topic that Rex takes up hilariously — I couldn’t agree more on his review). This tool was clearly not designed by anyone who actually attends academic conferences. The more I use it to write this piece, the more comically dysfunctional I realize it is. Who thinks it’s a good idea to hide the author names in a search result? See the screenshot below for an example of what gets returned from a search for Anthropology and Environment Society talks:

I organized a session for the Evolutionary Anthropology Society entitled, “Evolutionary Anthropological Approaches to Inequality.” It seemed to me that there is a group of human behavioral ecologists out there doing long-term ethnographic work, measuring economic and demographic variables, and thinking hard about how people adapt in a rapidly-changing world, and that these scholars might have some real insights into the mechanisms generating economic inequality. I was right.

One of my goals for this session was to beat the bushes for contributions from scholars other than the usual suspects. Don’t get me wrong; I’m very fond of the usual suspects in EAS, but there is a lot of great work being done that is both relevant to our section’s mission and engages different communities of scholars. I had some success recruiting different people in the initial call for papers. Unfortunately, when these new folks saw how much it costs to present a paper at AAA (the conference is very expensive, particularly when you adjust for the overall quality of the scientific program), they backed out. Fortunately, we had enough people still signed up that we were able to retain our session (apparently, you only need four speakers to stay on the program). When faced with a sparse session, the organizer has a few options: add a discussant, give speakers longer slots, open up a discussion. I decided to opt for yet another strategy. We wildcatted three late-breaking talks by junior researchers. Obviously, these talks didn’t make the program (not sure that’s much of a penalty though, given what a mess the program was), but it still gave the speakers the opportunity to present their research in front of a crowd of at least 50 (we had a great turn-out for the entire session).

The line-up for the session included a number of stars from EAS (well, and me):

1. Paul Hooper (Emory), “If You Know What Feeds Hierarchy, then You Can Starve It”
2. Siobhán Mattison (UNM), “Market Integration, Kinship, and Social Inequality Among the Mosuo of Southwest China”
3. me (Stanford),”State-Dependence, Uncertainty, and the Economic Rationality of Poor People”
4. Katie Starkweather (MPI), “Subsistence Strategies: Risk, Reward and Gender Roles for the Boat-Dwelling Shodagor of Bangladesh”

We missed Tamas David-Barrett, who had to deal with some emergent business back home. Our late-breaking talks were contributed by Elspeth Ready (Stanford), E’lana Jordan (Stanford), and Dave Nolin (Missouri). All were great. This was E’lana’s debut, as she is just back from the field, and she killed it. Elspeth was, as ever, dazzling in her analytical sophistication and the general bad-assery of her fieldwork.

The other EAS session, “Finding Insights in the Field: Ethnographic Experience and the Scientific Process,” organized by former student and current Omidyar post-doc at SFI, Elly Power, was also terrific. All the talks in this session were very good and the attendance was excellent. Naturally, I was particularly partial to the incredibly sophisticated analysis that Elly presented to close out this excellent session.

This has now happened enough times at AAA that it has tweaked that paranoid part of my brain. Our EAS session was scheduled at the same time as a great-looking session sponsored by Anthropology and the Environment Society called “Emergent Landscapes, Disturbance Ecology, and New Approaches in Ecological Anthropology.” Friends and colleagues involved in this session included Mark Moritz (Ohio State), Steve Lansing (Nanyang Technological University, Singapore), Brian Codding (Utah), Sean Downey (Maryland), and Kathy Galvin (Colorado State), among others. This session would have been of great interest to many in EAS, but, unless you’re Hermione Granger, you can only be in one session at a time. It was particularly frustrating because the editor for the new journal, Nature Human Behavior, also had to choose between sessions. These are constituencies who have natural affinities and we should work on getting them to coordinate somehow, AAA scheduling be damned.

Once again, there was apparently nothing newsworthy at AAA, as a Google news search turns up no hits from the actual meeting. This is a big difference between AAA and other major professional meetings, where new discoveries or novel analyses make their way into different quarters of the news media. The usual defensive response to this critique is that anthropology is more a humanities discipline (which, of course, is itself debatable) and, as such, doesn’t lend itself to “discoveries” or press releases. I don’t buy that. A similar search for the MLA, for example, turns up quite a few stories. I think it’s something about which we should be concerned as a discipline. While I am heartened by some of the work I saw at AAA (or from which I was structurally blocked from seeing but about which I heard in conversations with speakers later that evening), I really wonder about the relevance of our discipline as a whole. In principle, I believe the world really needs anthropology — perhaps now more than ever. But, in practice, I’m not sure what anthropologists are doing is what the world needs.

Among other things, I wonder if we really need to have meetings in convention centers. The public spaces seemed particularly sparse this year and many of the sessions I walked by looked like they had more speakers than audience members. I know it’s hard, but maybe we need to conduct a bit of quality control. Of course, I should be careful what I ask for. Given the fact that EAS is not a powerful section of AAA, we would almost certainly lose, even though our sessions are very well attended. This may sound heretical, but maybe we should collect data on session attendance and factor that into which sessions get included. Surely, the number of people who actually attend a session is a better measure of interest than the number of people who belong to a given section. In my experience, both EAS, BAS, and Anthropology and the Environment would do quite well on that criterion, even if they are relatively small sections.

I will probably keep going to AAA, at least occasionally, not because I think it’s a good conference in general. I will go because there is a core of great young researchers in EAS and I want to continue to support them, even if AAA is, at best, an uneasy home for them.

# Winter Anthropology Colloquium, Part 2

We had the second of our speakers in the winter anthropology colloquium Friday. Daniel Nettle came on Friday. Daniel’s talk was co-sponsored by the Institute for Research in the Social Sciences. Daniel is a human behavioral ecologist with extremely broad interests and a penchant for using HBE as a tool for studying social inequality and human health. Somehow, we’d never met before. I’m glad that’s been taken care of now. Of the dozens of things that Daniel could have talked about, he chose to talk about his ethnographic project in Newcastle on Tyne.

Given my interests in demography and epidemiology, I’ve seen lots of talks on social deprivation, inequality, neighborhood effects, etc., but Daniel’s talk showed a refreshing creativity. A large fraction of the data he presented came from deceptively simple ethological methods. I think that there is a lot that both the methods and theory of behavioral ecology and ethology have to offer studies of social inequality and health. Of course, I’m not alone in this belief. Mhairi Gibson (my collaborator in Uganda) and David Lawson (this week’s speaker!) published a terrific  book last year on the application of HBE to applied problems.

Much of the work Daniel’s work in this area is published in open-access journals (e.g., here and here). I’m intrigued by the relatively new journal, PeerJ, where he has published a number of papers now, and am planning to submit something there soon.

The flyer for Daniel’s talk:

# 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.

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.

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…

# Aedes aegypti in San Mateo County

The mosquito, Aedes aegypti, which is the vector for a number of world scourges (e.g., dengue, yellow fever), has been found in San Mateo County (just across San Francisquito Creek from Stanford) for the first time since 1979. That makes three counties in California where the mosquito has been found. While not a panic-inducing development, it would be most excellent if the good people of San Mateo and Santa Clara counties would make sure their yards are free of mosquito breeding habitat!

# 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 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.

# Ecology and Evolution of Infectious Disease, 2013

I am recently back from the Ecology and Evolution of Infectious Disease (EEID) Principal Investigators’ Meeting hosted by the Odum School of Ecology at the University of Georgia in lovely Athens. This is a remarable event, and a remarkable field, and I can’t remember ever being so energized after returning from a professional conference (which often leave me dismayed or even depressed about my field). EEID  is an innovative, highly interdisciplinary funding program jointly managed by the National Science Foundation and the National Institutes of Health. I have been lucky enough to be involved with this program for the last six years. I’ve served on the scientific review panel a couple times and am now a Co-PI on two projects.

We had a big turn-out for our Uganda team in Athens and team members presented no fewer than four posters. The Stanford social networks/human dimensions team (including Laura Bloomfield, Shannon Randolph and Lucie Clech) presented a poster (“Multiplex Social Relations and Retroviral Transmission Risk in Rural Western Uganda”) on our preliminary analysis of the social network data. Simon Frost’s student at Cambridge, James Lester, presented a poster (“Networks, Disease, and the Kibale Forest”) analyzing our syndromic surveillance data. Sarah Paige from Wisconsin presented a poster on the socio-economic predictors of high-risk animal contact (“Beyond Bushmeat: Animal contact, injury, and zoonotic disease risk in western Uganda”) and Maria Ruiz-López, who works with Nelson Ting at Oregon, presented a poster on their work on developing the resources to do some serious population genetics on the Kibale red colobus monkeys (“Use of RNA-seq and nextRAD for the development of red colobus monkey genomic resource”).

Parviez Hosseini, from the EcoHealth Alliance, also presented a poster for our joint work on comparative spillover dynamics of avian influenza (“Comparative Spillover Dynamics of Avian Influenza in Endemic Countries”). I’m excited to get more work done on this project which is possible now that new post-doc Ashley Hazel has arrived from Michigan. Ashley will oversee the collection of relational data in Bangladesh and help us get this project into high gear.

The EEID conference has a unique take on poster presentations which make it much more enjoyable than the typical professional meeting. In general, I hate poster sessions. Now, don’t get me wrong: I see lots of scientific value in them and they can be a great way for people to have extended conversations about their work. They can be an especially great forum for students to showcase their work and start the long process of forming professional networking. However, there is an awkwardness to poster sessions that can be painful for the hapless conference attender who might want, say, to walk through the room in which a poster session is being held. These rooms tend to be heavy with the smell of desperation and one has to negotiate a gauntlet of suit-clad, doe-eyed graduate students desperate to talk to anyone who will listen about their work. “Please talk to me; I’m so lonely” is what I imagine them all saying as I briskly walk through, trying to look busy and purposeful (while keeping half an eye out for something really interesting!).

The scene at EEID is much different. All posters go up at the same time and the site-fidelity of poster presenters is the lowest I have ever seen. It has to be since, if everyone stuck by their poster, there wouldn’t be anyone to see any of them! What this did was allow far more mixing than I normally see at such sessions and avoid much of the inherent social awkwardness of a poster session. Posters also stayed up long past the official poster session. I continued to read posters for at least a day after the official session ended. Of course, it helps that there was all manner of great work being presented.

There were lots of great podium talks too. I was particularly impressed with the talks by Charlie King of Case Western on polyparasitism in Kenya, Maria Diuk-Wasser of Yale on the emergence of babesiosis in the Northeast, Jean Tsao (Michigan State) and Graham Hickling‘s (Tennessee) joint talk on Lyme disease in the Southeast, and Bethany Krebs’s talk on the role of robin social behavior in West Nile Virus outbreaks. Laura Pomeroy, from Ohio State, represented one of the other few teams with a substantial anthropological component extremely well, talking about the transmission dynamics of foot-and-mouth disease in Cameroon. Probably my favorite talk of the weekend was the last talk by Penn State’s Matt Thomas. They done awesome work elucidating the role of temperature variability on the transmission dynamics of malaria.

It turns out that this was the last EEID PI conference. Next year, the EEID PI conference will be combined with the other EEID conference which was originally organized at Penn State (and is there again this May). This combining of forces is, I’m sure, a good thing as it will reduce confusion and probably make it more likely that all the people I want to see have a better chance of showing up. I just hope that this new, larger conference retains the charms of the EEID PI conference.

EEID is a new, interdisciplinary field that has grown thanks to some disproportionately large contributions of a few, highly energetic people. One of the principals in this realm is definitely Sam Scheiner, the EEID program officer at NSF.  The EEID PI meeting has basically been Sam’s baby for the past 10 years. Sam has done an amazing job creating a community of interdisciplinary scholars and I’m sure I speak for every researcher who has been heavily involved with EEID when I express my gratitude for all his efforts.

# On The Dilution Effect

A new paper written by Dan Salkeld (formerly of Stanford), Kerry Padgett (CA Department of Public Health), and myself just came out in the journal Ecology Letters this week.

One of the most important ideas in disease ecology is a hypothesis known as the “dilution effect”. The basic idea behind the dilution effect hypothesis is that biodiversity — typically measured by species richness, or the number of different species present in a particular spatially defined locality — is protective against infection with zoonotic pathogens (i.e., pathogens transmitted to humans through animal reservoirs). The hypothesis emerged from analysis of Lyme disease ecology in the American Northeast by Richard Ostfeld and his colleagues and students from the Cary Institute for Ecosystem Studies in Millbrook, New York. Lyme disease ecology is incredibly complicated, and there are a couple different ways that the dilution effect can come into play even in this one disease system, but I will try to render it down to something easily digestible.

Lyme disease is caused by a spirochete bacterium Borrelia burgdorferi. It is a vector-borne disease transmitted by hard-bodied ticks of the genus >Ixodes. These ticks are what is known as hemimetabolous, meaning that they experience incomplete metamorphosis involving larval and nymphal stages. Rather than a pupa, these larvae and nymphs resemble little bitty adults. An Ixodes tick takes three blood meals in its lifetime: one as a larva, once as a nymph, once as an adult. At different life-cycle stages, the ticks have different preferences for hosts. Larval ticks generally favor the white-footed mouse (Peromyscus leucopus) for their blood meal and this is where the catch is. It turns out that white-footed mice are extremely efficient reservoirs for Lyme disease. In fact, an infected mouse has as much as a 90% chance of transmitting infection to a larva feeding on it. The larvae then molt into nymphs and overwinter on the forest floor. Then, in spring or early summer a year after they first hatch from eggs, nymphs seek vertebrate hosts. If an individual tick acquired infection as a larva, it can now transmit to its next host. Nymphs are less particular about their choice of host and are happy to feed on humans (or just about any other available vertebrate host).

This is where the dilution effect comes in. The basic idea is that if there are more potential hosts such as chipmunks, shrews, squirrels, or skunks, there are more chances that an infected nymph will take a blood meal on a person. Furthermore, most of these hosts are much less efficient at transmitting the Lyme spirochete than are white-footed mice. This lowers the prevalence of infection and makes it more likely that it will go extinct locally. It’s not difficult to imagine the dilution effect working at the larval stage blood-meal too: if there are more species present (and the larvae are not picky about their blood meal), the risk of initial infection is also diluted.

In the highly-fragmented landscape of northeastern temperate woodlands, when there is only one species in a forest fragment, it is quite likely that it will be a white-footed mouse. These mice are very adaptable generalists that occur in a wide range of habitats from pristine woodland to degraded forest. Therefore, species-poor habitats tend to have mice but no other species. The idea behind the dilution effect is that by adding different species to the baseline of a highly depauperate assemblage of simply white-footed mice, the prevalence of nymphal infection will decline and the risk for zoonotic infection of people will be reduced.

It is not an exaggeration to say that the dilution-effect hypothesis is one of the two or three most important ideas in disease ecology and much of the explosion of interest in disease ecology can be attributed in part to such ideas. The dilution effect is also a nice idea. Wouldn’t it be great if every dollar we invested in the conservation of biodiversity potentially paid a dividend in reduced disease risk? However, its importance to the field or the beauty of the idea do not guarantee that it is actually scientifically correct.

One major issue with the dilution effect hypothesis is its problem with scale, arguably the central question in ecology. Numerous studies have shown that pathogen diversity is positively related to overall biodiversity at larger spatial scales. For example, in an analysis of global risk of emerging infectious diseases, Kate Jones and her colleagues form the London Zoological Society showed that globally, mammalian biodiversity is positively associated with the odds of an emerging disease. Work by Pete Hudson and colleagues at the Center for Infectious Disease Dynamics at Penn State showed that healthy ecosystems may actually be richer in parasite diversity than degraded ones. Given these quite robust findings, how is it that diversity at a smaller scale is protective?

We use a family of statistical tools known as “meta-analysis” to aggregate the results of a number of previous studies into a single synthetic test of the dilution-effect hypothesis. It is well known that inferences drawn from small samples generally have lower precision (i.e., the estimates carry more uncertainty) than inferences drawn from larger samples. A nice demonstration of this comes from the classical asymptotic statistics. The expected value of a sample mean is the “true mean” of a normal distribution and the standard deviation of this distribution is given by the standard error, which is defined as the standard deviation of the distribution divided by the square root of the sample size. Say that for two studies we estimate the standard deviation of our estimate of the mean to be 10. In the first study, this estimate is based on a single observation, whereas in the second, it is based on a sample of 100 observations. The estimated of the mean in the second study is 10 times more precise than the estimate based on the first because $10/\sqrt{1} = 10$ while $10/\sqrt{100} = 1$.

Meta-analysis allows us to pool estimates from a number of different studies to increase our sample size and, therefore, our precision. One of the primary goals of meta-analysis is to estimate the overall effect size and its corresponding uncertainty. The simplest way to think of effect size in our case is the difference in disease risk (e.g., as measured in the prevalence of infected hosts) between a species rich area and a species poor area. Unfortunately, a surprising number of studies don’t publish this seemingly basic result. For such studies, we have to calculate a surrogate of effect size based on the reported test statistics of the hypothesis that the authors report. This is not completely ideal — we would much rather calculate effect sizes directly, but to paraphrase a dubious source, you do a meta-analysis with the statistics that have been published, not with the statistics you wish had been published. On this note, one of our key recommendations is that disease ecologists do a better job reporting effect sizes to facilitate future meta-anlayses.

In addition to allowing us to estimate the mean effect size across studies and its associated uncertainty, another goal of meta-analysis is to test for the existence of publication bias. Stanford’s own John Ioannidis has written on the ubiquity of publication bias in medical research. The term “bias” has a general meaning that is not quite the same as the technical meaning. By “publication bias”, there is generally no implication of nefarious motives on the part of the authors. Rather, it typically arises through a process of selection at both the individual authors’ level and the institutional level of the journals to which authors submit their papers. An author, who is under pressure to be productive by her home institution and funding agencies, is not going to waste her time submitting a paper that she thinks has a low chance of being accepted. This means that there is a filter at the level of the author against publishing negative results. This is known as the “file-drawer effect”, referring to the hypothetical 19 studies with negative results that never make it out of the authors’ desk for every one paper publishing positive results. Of course, journals, editors, and reviewers prefer papers with results to those without as well. These very sensible responses to incentives in scientific publication unfortunately aggregate into systematic biases at the level of the broader literature in a field.

We use a couple methods for detecting publication bias. The first is a graphical device known as a funnel plot. We expect studies done on large samples to have estimates of the effect size that are close to the overall mean effect because estimates based on large samples have higher precision. On the other hand, smaller studies will have effect-size estimates that are more distributed because random error can have a bigger influence in small samples. If we plot the precision (e.g., measured by the standard error) against the effect size, we would expect to see an inverted triangle shape — or a funnel — to the scatter plot. Note — and this is important — that we expect the scatter around the mean effect size to be symmetrical. Random variation that causes effect-size estimates to deviate from the mean are just as likely to push the estimates above and below the mean. However, if there is a tendency to not publish studies that fail to support the hypothesis, we should see an asymmetry to our funnel. In particular, there should be a deficit of studies that have low power and effect-size estimates that are opposite of the hypothesis. This is exactly what we found. Only studies supporting the dilution-effect hypothesis are published when they have very small samples. Here is what our funnel plot looked like.

Note that there are no points in the lower right quadrant of the plot (where species richness and disease risk would be positively related).

While the graphical approach is great and provides an intuitive feel for what is happening, it is nice to have a more formal way of evaluating the effect of publication bias on our estimates of effect size. Note that if there is publication bias, we will over-estimate our precision because the studies that are missing are far away from the mean (and on the wrong side of it). The method we use to measure the impact of publication bias on our estimate of uncertainty formalizes this idea. Known as “trim-and-fill“, it uses an algorithm to find the most divergent asymmetric observations. These are removed and the precision of the mean effect size is calculated. This sub-sample is known as the “truncated” sample. Then a sample of missing values is imputed (i.e., simulated from the implied distribution) and added to the base sample. This is known as the “augmented” sample. The precision is then re-calculated. If there is no publication bias, these estimates should not be too different. In our sample, we find that estimates of precision differ quite a bit between the truncated and augmented samples. We estimate that between 4-7 studies are missing from the sample.

Most importantly, we find that the 95% confidence interval for our estimated mean effect size crosses zero. That is, while the mean effect size is slightly negative (suggesting that biodiversity is protective against disease risk), we can’t confidently say that it is actually different than zero. Essentially, our large sample suggests that there is no simple relationship between disease risk and biodiversity.

On Ecological Mechanisms One of the main conclusions of our paper is that we need to move beyond simple correlations between species richness and disease risk and focus instead on ecological mechanisms. I have no doubt that there are specific cases where the negative correlation between species richness and disease risk is real (note our title says that we think this link is idiosyncratic). However, I suspect where we see a significant negative correlation, what is really happening is that some specific ecological mechanism is being aliased by species richness. For example, a forest fragment with a more intact fauna is probably more likely to contain predators and these predators may be keeping the population of efficient reservoir species in check.

I don’t think that this is an especially controversial idea. In fact, some of the biggest advocates for the dilution effect hypothesis have done some seminal work advancing our understanding of the ecological mechanisms underlying biodiversity-disease risk relationships. Ostfeld and Holt (2004) note the importance of predators of rodents for regulating disease. They also make the very important point that not all predators are created equally when it comes to the suppression of disease. A hallmark of simple models of predation is the cycling of abundances of predators and prey. A specialist predator which induces boom-bust cycles in a disease reservoir probably is not optimal for infection control. Indeed, it may exacerbate disease risk if, for example, rodents become more aggressive and are more frequently infected in agonistic encounters with conspecifics during steep growth phases of their population cycle. This phenomenon has been cited in the risk of zoonotic transmission of Sin Nombre Virus in the American Southwest.

I have a lot more to write on this, so, in the interest of time, I will end this post now but with the expectation that I will write more in the near future!

# New Publication, Emerging infectious diseases: the role of social sciences

This past week, The Lancet published a brief commentary I wrote with a group of anthropologist-collaborators. The piece, written with Craig Janes, Kitty Corbett, and Jim Trostle, arose from a workshop I attended in lovely Buenos Aires back in June of 2011. This was a pretty remarkable meeting that was orchestrated by Josh Rosenthal, acting director of the Division of International Training and Research at the Fogarty International Center at NIH, and hosted in grand fashion by Ricardo Gürtler of the University of Buenos Aires.

Our commentary is on a series of papers on zoonoses, a seemingly unlikely topic for about which a collection of anthropologists might have opinions. However, as we note in our paper, social science is essential for understanding emerging zoonoses. First, human social behavior is an essential ingredient in $R_0$, the basic reproduction number of an infection (The paper uses the term “basic reproductive rate,” which was changed somewhere in production from the several times I changed “rate” to “number”). Second, we suggest that social scientists who participate in primary field data collection (e.g., anthropologists, geographers, sociologists) are in a strong position to understand the complex causal circumstances surrounding novel zoonotic disease spill-overs.

We note that there are some challenges to integrating the social sciences effectively into research on emerging infectious disease. Part of this is simply translational. Social scientists, natural scientists, and medical practitioners need to be able to speak to each other and this kind of transdisciplinary communication takes practice. I’m not at all certain what it takes to make researchers from different traditions mutually comprehensible, but I know that it’s more likely to happen if these people talk more. My hypothesis is that this is best done away from anyone’s office, in the presence of food and drink. Tentative support for this hypothesis is provided by the wide-ranging and fun conversations over lomo y malbec. These conversations have so far yielded at least one paper and laid the foundations for a larger review I am currently writing. I know that various permutations of the people in Buenos Aires for this meeting are still talking and working together, so who knows what may eventually come of it?