# On the Uses of an Interdisciplinary Ph.D.

Today, I participated in a panel — along with super-smart colleagues Alex Konings and Kabir Peay — for the first-year Ph.D. students in the E-IPER program, an interdisciplinary, graduate interdepartmental program (IDP) at Stanford. As is the idiom for any E-IPER event, we spent a lot of time fretting about interdisciplinarity: what it means, how you achieve it, what costs it entails for jobs, etc.

I expressed the slightly heretical opinion that we should not pursue interdisciplinarity for interdisciplinarity’s sake. What matters — both in terms of the science and more instrumental outcomes such as getting published, getting a job, getting tenure — are questions. Yes, questions. One should ask important questions that people care about. Why are there so many species in the tropics? Where do pandemic diseases come from and how can we best control them? Does democracy and the rule of law provide the best approach to governance? How do people adapt to a changing climate?

Where the interdisciplinary Ph.D. program comes in is it provides students the opportunity to pursue whatever tools and approaches are required to answer the question in the best way possible. You don’t need to use a particular approach because that’s what people in your field do. Sometimes the best thing to do will be totally interdisciplinary; sometimes it will look a bit more like what someone in a disciplinary program would do. Always lead with the question.

Answering important questions using the best tools available is probably the best route to managing the greatest risk of an interdisciplinary degree. This risk, of course, is the difficulty in getting a job when you don’t look like what any given department had in mind when they wrote a job ad. The best way to manage this risk is simply to be excellent. If your work is strong enough, the specific discipline of your Ph.D. doesn’t really matter. Now, there are certainly some disciplines that are more xenophobic than others (anthropology and economics come immediately to mind), but if your work is really outstanding, the excuse that you don’t have the right degree for a given job gets much more tenuous. Two people who come immediately to mind are my colleague David Lobell and my sometime collaborator and former Stanford post-doc Marcel Salathé.

Is David a geographer? Geologist? Economist? Doesn’t really matter because he’s generally recognized as being a smart guy doing important work. Similarly with Marcel: population geneticist? Epidemiologist? Computer scientist? Who cares? He has important things to say and gets recognized for it.

Now, alas, we can’t all be David and Marcel, but we can strive to ask important scientific questions and let these questions lead us to both the skills and the bodies of knowledge we need. These then form the foundation of our research careers. Interdisciplinarity then is about following the question. It is not an end to itself.

# 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.

# 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…

# 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!

# On Global State Shifts

This is a edited version of a post I sent out to the E-ANTH listserv in response to a debate over a recent paper in Nature and the response to it on the website “Clear Science,” written by Todd Meyers. In this debate, it was suggested that the Barnosky paper is the latest iteration of alarmist environmental narratives in the tradition of the master of that genre, Paul Ehrlich. Piqued by this conversation, I read the Barnosky paper and passed along my reading of it.

The Myers’s piece on the “Clear Science” web site is quite rhetorically clever. Climate-change deniers have a difficult task if they want to convincingly buck the overwhelming majority of reputable scientists on this issue. Myers uses ideas about the progress of science developed by the philosopher Thomas Kuhn in his classic book, The Structure of Scientific Revolutions. By framing the Barnosky et al. as mindlessly toeing the Kuhnian normal-science line, he has come up with a shrewd strategy for dealing with the serious scientific consensus around global climate change. Myers suggests that “Like scientists blindly devoted to a failed paradigm, the Nature piece simply tries to force new data to fit a flawed concept.”

I think that a pretty strong argument can be made that the perspective represented in the Barnosky et al. paper is actually paradigm-breaking. For 200 years the reigning paradigm in the historical sciences has been uniformitarianism. Hutton’s notion — that processes that we observe today have always been working — greatly extended the age of the Earth and allowed Lyell and Darwin to make their remarkable contributions to human understanding. This same principle allows us to make sense of the archaeological record and of ethnographic experience. It is a very useful foil for all manner of exceptionalist explanatory logic and I use it frequently.

However, there are plenty of ways that uniformitarianism fails. If we wanted to follow the Kuhnian narrative, we might say that evidence has mounted that leads to increased contradictions arising from the uniformitarian explanatory paradigm. Rates of change show heterogeneities and when we trying to understand connected systems characterized by extensive feedback, our intuitions based on gradual change can fail, sometimes spectacularly. This is actually a pretty revolutionary idea, apocalyptic popular writings aside, in mainstream science.

Barnosky et al. draw heavily on contemporary work in complex systems. The theoretical paper (Scheffer et al. 2009) upon which the Barnosky paper relies heavily represents a real step forward in the theoretical sophistication of this corpus and does so by making unique and testable predictions about systems approaching critical transitions. I have written about it previously here.

The most difficult part of projecting the future state of complex systems is that human element. This leads too many physical and biological scientists to simply ignore social and behavioral inputs. This said, there are far too few social and behavioral scientists willing to step up and do the hard collaborative work necessary to make progress on this extremely difficult problem. The difficulty of projecting human behavior often leads to projections of the business-as-usual variety and, unfortunately, these are often mischaracterized by the media and other readers. Such projections simply assume no change in behavior and look at the consequences some time down the line. A business-as-usual projection actually provides a lot of information, albeit about a very hypothetical future. What if things stayed the way they are? Yes, behavior changes. People adapt. Agricultural production becomes more efficient. Prices increase, reducing demand and allowing sustainable substitutes. Of course, sometimes things get worse too. Despite tremendous global awareness and lots of calls to reduce greenhouse gas emissions, carbon emissions have continued to rise. So, there is nothing inherently flawed about a business-as-usual projection. We just need to be clear about what it means when we use one.

A criticism that emerged on the list is that Barnosky et al. is essentially “an opinion piece.” However, the great majority of the Barnosky et al. paper is, in fact, simply a review. There are numerous facts to be reviewed: biodiversity has declined, fisheries have crashed, massive amounts of forest have been converted and degraded, the atmosphere has warmed. They are facts. And they are facts in which many vested interests would like to sow artificial uncertainty for political purposes. Positive things have happened too (e.g., malaria eradication in temperate climes, increased food security in some places that used to be highly insecure, increased agricultural productivity — though this may be of dubious sustainability), though these are generally on more local scales and, in some cases, may simply reflect exporting the problems to rich countries to the Global South. The fact that they are not reviewed does not mean that the paper belongs in an hysterical chicken-little genre.

A common critique of the doomsday genre is the certainty with which the horrible outcomes are framed. The Barnosky paper is suffused with uncertainty. In fact, this is the main point I take away from it! The first conclusion of the paper is that “it is essential to improve biological forecasting by anticipating critical transitions that can emerge on a planetary scale and understanding how such global forcings cause local changes.” This suggests to me that the authors are acknowledging massive uncertainty about the future, not saying that we are doomed with certainty. Or how about: “the plausibility of a future planetary state shift seems high, even though considerable uncertainty remains about whether it is inevitable and, if so, how far in the future it may be”?

Myers writes “they base their conclusions on the simplest linear mathematical estimate that assumes nothing will change except population over the next 40 years. They then draw a straight line, literally, from today to the environmental tipping point.” This is a profoundly misleading statement. Barnosky et al. are using the fold catastrophe model discussed in Scheffer et al. (2009). The Scheffer et al. analysis of the fold catastrophe model uses some fairly sophisticated ideas from complex systems theory, but the ideas are relatively simple. The straight line that so offends Myers arises because this is the direction of the basin of attraction. In the figure below, I show the fold-catastrophe model. The abcissa represents the forcing conditions of the system (e.g., population size or greenhouse gas emissions). The ordinate represents the state of the system (e.g., land cover or one of many ecosystem services). The sideways N represents an attractor — a more general notion of an equilibrium. The state of the system tends toward this curve whenever it is perturbed away.

The region in the interior of the fold (indicated by the dashed line) is unstable while the upper and lower tails (indicated by solid lines) are stable and tend to draw perturbations from the attractor toward them. The grey arrows indicate the basin of attraction. When the system is perturbed off of the attractor by some random shock, the state tends to move in the direction indicated by the arrow. When the state is forced all the way down the top arc of the fold, it enters a region where a relatively small shock can send the state into a qualitatively different regime of rapid degradation. This is illustrated by the black arrow (a shock) pushing the state away from point F2. The state will settle again on the attractor, but a second shock will send the state rapidly down toward the bottom arm of the fold (point F1). Note that this region of the attractor is stable so it would take a lot of work to get it back up again (e.g., reduce population or drastically reduced total greenhouse gasses). This is what people mean when they colloquially refer to a “global tipping point.”

This is the model. It may not be right, but thanks to Scheffer et al. (2009), it makes testable predictions. By framing global change in terms of this model, Barnosky et al. are making a case for empirical investigation of the types of data that can falsify the model. Maybe because of the restrictions placed on them by Nature (and these are severe!), maybe because of some poor choices of their own, they include an insufficiently explained, fundamentally complex figure that a critic with clear interests in muddying the scientific consensus can sieze on to dismiss the whole paper as just more Ehrlich-style hysteria.

For me — as I suspect for the authors of the Barnosky et al. paper — massive, structural uncertainty about the state of our planet, coupled with a number of increasingly well-supported models of the behavior or nonlinear systems (i.e., not simply normal science) strongly suggests a precautionary principle. This is something that the economist Marty Weitzman suggested in his (highly technical and therefore not widely read) paper in 2009 and that I have written about before here and here. This is not inflammatory fear-mongering, nor is it grubbing for grant money (I wish it were that easy!). It is responsible scientists doing their best to communicate the state of the science within the constraints of society and the primary mode of scientific communication. Let’s not be taken in by writers pretending to present “just the facts” in a cool, detached manner but who actually have every reason to try to foment unnecessary uncertainty about the state of our world and impugn the integrity of people doing their level best to understand a rapidly changing planet.

References

Kuhn, T. 1962. The Structure of Scientific Revolutions. Chicago: University of Chicago Press.

Scheffer, M., J. Bascompte, W. A. Brock, V. Brovkin, S. R. Carpenter, V. Dakos, H. Held, E. H. van Nes, M. Rietkerk, and G. Sugihara. 2009. Early-Warning Signals for Critical Transitions. Nature. 461 (7260):53-59.

Weitzman, M. L. 2009. On Modeling and Interpreting the Economics of Catastrophic Climate Change. The Review of Economics and Statistics. XCI (1):1-19.

# New Grant, Post-Doc Opportunity

Biological and Human Dimensions of Primate Retroviral Transmission
One of the great enduring mysteries in disease ecology is the timing of the AIDS pandemic. AIDS emerged as a clinical entity in the late 1970s, but HIV-1, the retrovirus that causes pandemic AIDS, entered the human population from wild primates many decades earlier, probably near the turn of the 20th century. Where was HIV during this long interval? We propose a novel ecological model for the delayed emergence of AIDS. Conceptually, in a metapopulation consisting of multiple, loosely interconnected sub-populations, a pathogen could persist at low levels indefinitely through a dynamic balance between localized transmission, localized extinction, and long-distance migration between sub-populations. This situation might accurately describe a network of villages in which population sizes are small and rates of migration are low, as would have been the case in Sub-Saharan Africa over a century ago.
We will test our model in a highly relevant non-human primate system. In 2009, we documented three simian retroviruses co-circulating in a metapopulation of wild red colobus monkeys (Procolobus rufomitratus) in Kibale National Park, Uganda, where we have conducted research for over two decades. We will collect detailed data on social interactions, demography, health, and infection from animals in a core social group.
We will also study a series of 20 red colobus sub-populations, each inhabiting a separate, isolated forest fragment. We will determine the historical connectivity of these sub-populations using a time series of remotely sensed images of forest cover going back to 1955, as well as using population genetic analyses of hypervariable nuclear DNA markers. We will assess the infection status of each animal over time and use viral molecular data to reconstruct transmission pathways.
Our transmission models will define the necessary conditions for a retrovirus to persist, but they will not be sufficient to explain why a retrovirus might emerge. This is because human social factors ultimately create the conditions that allow zoonotic diseases to be transmitted from animal reservoirs and to spread. We will therefore conduct an integrated analysis of the root eco-social drivers of human-primate contact and zoonotic transmission in this system. We will study social networks to understand how social resources structure key activities relevant to human-primate contact and zoonotic transmission risk, and we will explore knowledge, beliefs, and perceptions of human-primate contact and disease transmission for a broad sample of the population. We will reconcile perceived risk with actual risk through a linked human health survey and diagnostic testing for zoonotic primate retroviruses.
The ultimate product of our research will a data-driven set of transmission models to explain the long-term persistence of retroviruses within a metapopulation of hosts, as well as a linked analysis of how human social factors contribute to zoonotic infection risk in a relevant Sub-Saharan African population. Our study will elucidate not only the origins of HIV/AIDS, but also how early-stage zoonoses in general progress from “smoldering” subclinical infections to full-fledged pandemics.

I am thrilled to report that our latest EID project proposal, Biological and Human Dimensions of Primate Retroviral Transmission, has now been funded (by NIAID nonetheless!).  I will briefly describe the project here and then shamelessly tack on the full text of our advertisement for a post-doc to work as the project manager with Tony Goldberg, PI for this grant, in the College of Veterinary Medicine, University of Wisconsin, Madison.  This project will complement the ongoing work of the Kibale EcoHealth Project. The research team includes: Tony, Colin Chapman (McGill), Bill Switzer (CDC), Nelson Ting (Iowa), Mhairi Gibson (Bristol), Simon Frost (Cambridge), Jennifer Mason (Manchester), and me. This is a pretty great line-up of interdisciplinary scholars and I am honored to be included in the list.

Biological and Human Dimensions of Primate Retroviral Transmission

One of the great enduring mysteries in disease ecology is the timing of the AIDS pandemic. AIDS emerged as a clinical entity in the late 1970s, but HIV-1, the retrovirus that causes pandemic AIDS, entered the human population from wild primates many decades earlier, probably near the turn of the 20th century. Where was HIV during this long interval? We propose a novel ecological model for the delayed emergence of AIDS. Conceptually, in a metapopulation consisting of multiple, loosely interconnected sub-populations, a pathogen could persist at low levels indefinitely through a dynamic balance between localized transmission, localized extinction, and long-distance migration between sub-populations. This situation might accurately describe a network of villages in which population sizes are small and rates of migration are low, as would have been the case in Sub-Saharan Africa over a century ago.

We will test our model in a highly relevant non-human primate system. In 2009, we documented three simian retroviruses co-circulating in a metapopulation of wild red colobus monkeys (Procolobus rufomitratus) in Kibale National Park, Uganda, where we have conducted research for over two decades. We will collect detailed data on social interactions, demography, health, and infection from animals in a core social group.

We will also study a series of 20 red colobus sub-populations, each inhabiting a separate, isolated forest fragment. We will determine the historical connectivity of these sub-populations using a time series of remotely sensed images of forest cover going back to 1955, as well as using population genetic analyses of hypervariable nuclear DNA markers. We will assess the infection status of each animal over time and use viral molecular data to reconstruct transmission pathways.

Our transmission models will define the necessary conditions for a retrovirus to persist, but they will not be sufficient to explain why a retrovirus might emerge. This is because human social factors ultimately create the conditions that allow zoonotic diseases to be transmitted from animal reservoirs and to spread. We will therefore conduct an integrated analysis of the root eco-social drivers of human-primate contact and zoonotic transmission in this system. We will study social networks to understand how social resources structure key activities relevant to human-primate contact and zoonotic transmission risk, and we will explore knowledge, beliefs, and perceptions of human-primate contact and disease transmission for a broad sample of the population. We will reconcile perceived risk with actual risk through a linked human health survey and diagnostic testing for zoonotic primate retroviruses.

The ultimate product of our research will a data-driven set of transmission models to explain the long-term persistence of retroviruses within a metapopulation of hosts, as well as a linked analysis of how human social factors contribute to zoonotic infection risk in a relevant Sub-Saharan African population. Our study will elucidate not only the origins of HIV/AIDS, but also how early-stage zoonoses in general progress from “smoldering” subclinical infections to full-fledged pandemics.

Post Doctoral Opportunity

The Goldberg Lab at the University of Wisconsin-Madison invites applications for a post-doctoral researcher to study human social drivers of zoonotic disease in Sub-Saharan Africa.   The post-doc will be an integral member of a new, international, NIH-funded project focused on the biological and human dimensions of primate infectious disease transmission in Uganda, including social drivers of human-primate contact and zoonotic transmission.  This is a unique opportunity for a post-doctoral scholar with training in the social sciences to study human-wildlife conflict/contact and health and disease in a highly relevant ecological setting.  The following criteria apply.

1. Candidates must have completed or be near to completing a PhD in the social sciences, in a discipline such as anthropology, geography, sociology, behavioral epidemiology, or a relevant discipline within the public health fields.
2. Candidates must have a demonstrated interest in health and infectious disease.
3. Candidates must have prior field experience in Sub-Saharan Africa.
4. Candidates must be willing to relocate to Madison, Wisconsin for three years.
5. Candidates must be willing to spend substantial time abroad, including in Sub-Saharan Africa and at partner institutions in the United Kingdom.
6. Candidates must have experience with collection and analysis of both quantitative and qualitative data.  Familiarity with methods such as social network analysis, GIS, participatory methods, and survey design would be advantageous.

The successful candidate will help lead a dynamic international team of students and other post-docs in a multi-institutional, multidisciplinary project.  Duties involve a flexible combination of fieldwork, analyses, and project coordination, in addition to helping to mentor students from North America, Europe, and Africa.  The successful applicant will be expected to explore new research directions of her/his choosing, assisted by a strong team of collaborators.

University of Wisconsin-Madison is a top-notch institution for research and training in the social and health sciences.  Madison, WI, is a vibrant city with outstanding culture and exceptional opportunities for outdoor recreation.

Applicants should send a current CV, a statement of research interests and qualifications (be sure to address the six criteria above), and a list of three people (names, addresses, e-mails) who can serve as references.

Materials and inquiries should be sent to Dr. Tony L. Goldberg (tgoldberg@vetmed.wisc.edu).  Application materials must be received by September 12, 2011 for full consideration; the position is available starting immediately and requires a three-year commitment.

# Guess What: Food Prices Still Near All-Time Highs

The FAO Food Price Index (FPI) remains at near record-highs, and this at a time when record droughts and calamitous famine threaten the Horn of Africa. Using the latest data from the FAO FPI page, I plot here the FPI time series from 1990-2011.

World food prices are high and have remained so since the beginning of this year, though there have been some pretty dramatic swings between 2008 and now.  There is some argument that the real problem for poverty alleviation is actually price volatility and not high prices per se.  However, a recent paper in Foreign Affairs by Barrett and Bellemare argues that the problem for the world’s poor is really high prices (a more complete working paper can be found here). I find their arguments quite persuasive. Among these, the authors wryly note “Perhaps not coincidentally, [commentators’ and politicians’] emphasis on tempering price volatility favors the same large farmers who already enjoy tremendous financial support from G-20 governments.”

# 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.

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:

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).

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.

# Models of Human Population Growth

The logistic equation is a model of population growth where the size of the population exerts negative feedback on its growth rate. As population size increases, the rate of increase declines, leading eventually to an equilibrium population size known as the carrying capacity.  The time course of this model is the familiar S-shaped growth that is generally associated with resource limitation. This model has only two parameters: $r$ is the intrinsic growth rate and $K$ is the carrying capacity. The rate of increase in the population declines as a linear function of population size.  In symbols:

When the population size is very small (i.e., when $N$ is close to zero), the term in the parentheses is approximately one and population growth is approximately exponential.  When population size is close to the carrying capacity (i.e., $N \approx K$), the term in parentheses approaches zero, and population growth ceases. It is straightforward to integrate this equation by partial fractions and show that resulting solution is indeed an S-shaped, or sigmoid, curve.

Raymond Pearl was a luminary in human biology.  A professor at Johns Hopkins University, a founder of the Society for Human Biology and the International Union for the Scientific Study of Population (IUSSP), Pearl also re-discovered the logistic growth model (which was originally developed by the great Belgian mathematician Pierre François Verhulst).  In the logistic model, Pearl believed he had found a universal law of biological growth at its various levels of organization.  In his book, The Biology of Population Growth, Pearl wrote:

… human populations grow according to the same law as do the experimental populations of lower organisms, and in turn as do individual plants and animals in body size. This is demonstrated in two ways: first by showing as was done in my former book “Studies in Human Biology,” that in a great variety of countries all of the recorded census history which exists is accurately described by the same general mathematical equation as that which describes the growth of experimental populations; second, by bringing forward in the present book the case of a human population-the indigenous native population of Algeria-which has in the 75 years of its recorded census history practically completed a single cycle of growth along the logistic curve.

In addition to Algeria, Pearl fit the logistic model to the population of the United States from 1790-1930. The fit he produced was uncanny and he confidently predicted that the US population would level out at 198 million, since this was the best-fit value of $K$ in his analysis.  I have plotted the US population size (from the decennial census) as black points below, with Pearl’s fitted curve in grey. We can see that the curve fits incredibly well for the period 1790-1930 (the span to which he fit the data), but the difference between prediction and empirical reality becomes increasingly large after 1950 (yep, that would be thanks to the Baby Boom).

Why does the logistic model fail so spectacularly in this case (and many others)?

The logistic model is phenomenological, rather than mechanistic. A phenomenological model is a mathematical convenience that we use to describe some empirical observations, but has no foundations in mechanisms or first principles. Such models can be useful when theory is lacking to explain some phenomenon or when the mathematics that would be required to model the mechanisms is too complicated. You can make a prediction from a phenomenological model, but I wouldn’t bet the farm on that prediction. In the absence of an actual understanding of the mechanisms producing the population change, the predictions can go horribly wrong, as we see in the case of Raymond Pearl’s fit.

Specifically, the logistic model  fails to consider mechanisms of population regulation. When density increases, what is affected?  Birth rates? Death rates? The $r$ parameter in the logistic model is simply the difference in the gross birth and death rates when there are no conspecifics present.  In general, when the birth rate exceeds the death rate, a population increases.  The linear decrease in $r$ with increasing population size presumably can come about by either the birth rate decreasing or the death rate increasing.  The logistic model is indifferent to the specific cause of slowing.  It just stops increasing when $N=K$. Is it possible that, in real populations, increasing the death rate and decreasing the birth rate might have qualitatively different effects on population growth? We’ll see.

This probably goes without saying, but there is no capacity for the positive feedbacks with population size. In her classic work, The Conditions of Agricultural Growth, Danish economist Esther Boserup noted that population growth often stimulates innovation. Population pressure might cause an agricultural group that has run out of land to intensify cultivation by improving the land or multi-cropping, thereby facilitating even greater population growth.  Various authors, including Ken Wachter and Ron Lee (both at Berkeley) and Jim Wood at Penn State have noted that real populations probably incorporate both Malthusian (i.e., conditions leading to increased mortality, decreased fertility, and general misery with increased population size) and Boserupian phases in their dynamics.  Wood coined the term “MaB Ratchet” (MaB = Malthus and Boserup) which describes the following dynamic: Malthusian pressure incites  Boserupian innovation, relaxing negative feedback and allowing further population growth.  While a population is undergoing a Boserupian expansion, quality of life improves. Alas, given enough time, the population will always return to “the same level of marginal immiseration.” (Wood 1998: 114). Such complex regimes of positive and negative population feedback are not a possibility .

One final problem with the logistic model is that there is no structure — all individuals are identical in terms of their effect on and contribution to population growth. Human vital rates vary predictably – and substantially – by age, sex, geographic region, urban vs. rural residence, etc. And then there’s the issue of unequal resource distribution.  All individuals in a population are hardly equal in their consumption (or production) and so we should hardly expect each to exert an identical force on population growth.

So are there better alternative models for human population growth that incorporate the sensible idea that as populations push the limits of their resource base, growth should slow down and eventually cease? There is now.  My Stanford colleague and collaborator in various endeavors, Shripad Tuljapurkar, has a series of papers in which he and his students develop mechanistic population models for agricultural populations that specifically link age-specific vital rates (i.e., survivorship, fertility), agricultural production and labor, and specific (age-specific) metabolic needs for individuals engaged in heavy physical labor.  The models start with an optimal energy supply for survival and reproduction.  As food gets more scarce, mortality increases and fertility decreases.  The model has an equilibrium where birth and death rates balance. A key feature of the model is the idea of the food ratio, which is the number of calories available to consume in a given year relative to the number of calories needed to maximize survival and fertility. The food ratio tells us how hungry the population is. In the first of a series of three papers, Lee and Tuljapurkar (2008) develop this model and show how changes in mortality, fertility, and agricultural productivity actually all have distinct effects on the population growth rate, equilibrium, and how hungry people are at equilibrium. Analysis of their model yielded the following results:

• Increasing agricultural productivity or the amount of time spent working on agricultural production increases the food ratio, while keeping the population growth rate largely unchanged
• Increasing baseline survival increases the food ratio but decreases the population growth rate
• Decreasing fertility only decreases the growth rate – the food ratio remains unchanged

So, we see that it is possible that increasing the death rate and decreasing the birth rate might have qualitatively different effects on population growth. In fact, it seems quite likely, given Lee & Tulja’s model.

We don’t, as yet, have the kind of test that we gave Raymond Pearl’s application of the logistic model to US population size. It would be very nice if we could use the Lee-Tulja model to make a prediction about the future dynamics of some population (and its distribution of hunger) and challenge this prediction with data not used for fitting the model in the first place. This said, I think that theoretical exercise alone is enough to demonstrate the importance of moving beyond phenomenological population models whenever possible. We are unlikely to make accurate predictions or understand the response of population to environmental and social changes in the absence of mechanistic models.

References

Lee, C. T., and S. Tuljapurkar. 2008. Population and prehistory I: Food-dependent population growth in constant environments. Theoretical Population Biology. 73:473–482.

Wood, J. W. 1998. A theory of preindustrial population dynamics: Demography, economy, and well-being in Malthusian systems. Current Anthropology. 39 (1):99-135.

# Ecology, Evolution, and Human Health

Yesterday, I spent most of the day collecting content for my upcoming classes this spring and getting the course web sites together.  For the first time in a while, I will (officially) be teaching two classes in one quarter (which effectively means teaching three or four when I add the other things like lab meetings in).  The first is our graduate class on statistics in the anthropological sciences.  I taught something like this back in the old department (i.e., Anthropological Sciences) but haven’t taught it in years (though a Google search for “department of anthropological sciences stanford” turns up the syllabus for this class).  It is technically a requirement for Ph.D. students in the Ecology and Environment focus within Anthropology, so it’s about time.  It will be fun to teach again and we’re looking to use the class as a platform to develop resources for anthropologists doing statistical work (more later).

The other class that I will be teaching starting next week is Ecology, Evolution, and Human Health, a class I first taught last year. This class is meant to be an introduction to the Ecology and Environment undergraduate focus in Anthropology.  I’m actually really looking forward to teaching it again.  The course material forms the core of a book I am writing on human population biology and my attempts at improving the lectures has done wonders for my writing output of late.  We’ll see what happens when the quarter actually starts. Hopefully, between trips to Rwanda and Tanzania and moving into Arroyo House this summer, I will find time to finish it!

Back in December, when the is-anthropology-science kerfuffle was going strong, I wrote a blog post in which I suggested that if you want to feel good about the future of scientific anthropology (which, I admit, can sometimes be difficult, even for an obstinate optimist), all you need to do is look at the great work coming from the new generation of trans-disciplinary anthropologists (and other biosocial scientists).  At the time, I put together a short list of people whose work I greatly admire.  These included:

• Craig Hadley at Emory on food security and psychological well-being
• Amber Wutich at ASU on vulnerability, water security, and common-pool resources
• Lance Gravlee at UF on the embodiment of racial discrimination and its manifestations in health
• Brooke Scelza at UCLA on parental investment and childhood outcomes
• Dan Hrushka at ASU on how cultural beliefs, norms and values interact with economic constraints to produce health outcomes
• Crickette Sanz at Washington University on multi-ape ecology of the Goualougo Triangle, Republic of Congo
• Herman Pontzer at CUNY on measuring daily energy expenditures in hunter-gatherers
• Rebecca and Douglas Bird on subsistence and signaling among Martu foragers

In preparing for Anthro 31, I started to put together a list of links to people doing the kind of work we will discuss.  In a pique of obsessiveness yesterday, I greatly expanded that list.  It occurred to me that this list is somewhat orphaned in an obscure directory for a particular class I occasionally teach and that it would make sense to share it more generally.  So, here we go, copied wholesale from my class links page (though that page still contains links to books, professional societies, and other resources for students interested in human ecology, demography, health, etc.):

There are a number of excellent practicing anthropologists who maintain science blogs. Among these are Kate Clancy‘s (UIUC) Context and Variation, Daniel Lende and Greg Downey‘s Neuroanthropology, Julienne Rutherford‘s AAPA BANDIT, and Patrick Clarkin’s blog dedicated to biological anthropology, war and health, growth nutrition. Along with Rebecca Stumpf, Kate Clancy is also the director of the Laboratory for Evolutionary Endocrinology (which has its own blog) at the University of Illinois.

Upon further reflection, I think that the University of Illinois has to be a major contender for best place to study biological anthropology. Wow, they’ve got an amazing group of biological anthropologists there. Stanley Ambrose, Kate Clancy, Paul Garber, Lyle Konigsberg, Steve Leigh, Ripan Malhi, John Polk, Charles Roseman, Laura Shackelford, Rebecca Stumpf. Too many to link to directly. I don’t know all of them, but the ones I know are outstanding. Yipes! I think they may be plotting to take over the field.

Back to the blog front, you can always count on gems of anthropological, evolutionary, and political wisdom from Greg Laden as well.

Susan C. Antón (NYU) and Josh Snodgrass (Oregon) organize the Bones and Behavior Working Group, the goal of which is to foster greater synthesis across the different sub-areas of biological anthropology. Of particular interest are their standardized protocols for anthropometry.

Mario Luis Small, at the University of Chicago, has done some really outstanding work measuring how social institutions affect social capital and the impact such differences in social capital actually have for people’s well-being.

Richard Bribiescas is the author of Men: Evolutionary and Life History and is director of the Reproductive Ecology Laboratory at Yale. Yale is also now the home to Catherine Panter-Brick who also happens to be the senior editor for medical anthropology at Social Science and Medicine.

A number of excellent human biologists find their home in the Laboratory for Human Biology Research at Northwestern. This includes Bill Leonard, Thom McDade, and Chris Kuzawa. Rumor has it that alumna Elizabeth Sweet is moving back to Northwestern as well. She is doing truly innovative work integrating the rigorous analysis of biomarkers of health (and a bicultural perspective favored by the Northwestern group) and the political economy of economic and social disparities — really getting at how inequality ‘gets under the skin.’  I really look forward to seeing what comes from her future research.

Karen Kramer, in the department formerly known as (Biological) Anthropology at Harvard, is a real leader in integrating evolutionary, demographic, and economic perspectives on human reproduction and the life histories.

Patrick Clarkin at UMass, Boston has a very interesting research program employing biocultural and evolutionary models to understand the effects of war on nutrition and growth among SE Asian diaspora. UMass, Boston is also home to Colleen Nyberg who does great work on acculturation and health, the psychobiology of stress and HPA function, and growth and development.

Julienne Rutherford at the University of Illinois, Chicago School of Dentistry works on the role of the intrauterine environment on health. Of particular interest for this class is her collaborative work on understanding the epigenetic regulation of placental systems of amino acid transport as part of the Cebu Longitudinal Study in the Philippines. UIC also has a number of excellent human biologists scattered about in anthropology, including Betsy Abrams and Crystal Patil, Epidemiology (Bob Bailey) and Community Health Sciences (Nadine Peacock).

Let’s not forget our friends across The Pond. Durham may have lost Catherine Panter-Brick to Yale, but they got a number of new folks who, when combined with the veterans, make it a very appealing place to study ecological/evolutionary anthropology. Among the faculty there are my colleagues Gillian Bentley, Rebecca Sear, and Frank Marlowe, and numerous others. Rebecca does very sophisticated work in anthropological demography, while Frank is one of the leading ethnographers of contemporary hunter-gatherers (and my collaborator on our Hadza demography project).

Ruth Mace, in my opinion, does some of the best work in human behavioral ecology right now and she keeps churning out top students at UCL.

I’m looking forward to working with Mhairi Gibson at Bristol on our new project on the transmission dynamics of primate retroviruses and human-wildlife contact in Uganda. She has done excellent work on the behavioral ecology of reproduction and parental investment in Ethiopia.

I will also mention a number of excellent researchers who teach classes that are relevant to Ecology, Evolution, and Human Health:

Mark Moritz at Ohio State University has established a Hunter-Gatherer Wiki is conjunction with his course on Hunter-Gatherers. Mark came and gave a terrific talk on livestock exchanges among FulBe pastoralists at the MAPSS colloquium this year.

Mike Gurven at UCSB teaches a course on the behavioral ecology of hunter-gatherers. Mike does some of the most interesting biodemographic work out there these days.

Bruce Winterhalder at UC Davis, a founding father of human behavioral ecology, has a very interesting course on classics in cultural ecology.

Claudia Valeggia, at Penn, does great work among the Toba people of Argentina teaches a class on reproductive ecology.

Lots of good people. Lots of good work.  Surely, there is reason for optimism…