Category Archives: Human Ecology

On Anthropological Sciences and the AAA

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

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

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

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

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

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

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

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

This is Just What Greece Needs

Greece was officially deemed malaria-free in 1974. Recent reports, however, suggest that there is ongoing autochthonous transmission of of Plasmodium vivax malaria. According to a brief report from the Mediterranean Bureau of the Italian News Agency (ANSAmed), 40 cases of P. vivax malaria have been reported in the first seven months of 2012. Of these 40, six had no history of travel to areas known to be endemic for malaria transmission. The natural inference is thus that they acquired it locally (i.e., “autochthonously”) and that malaria may be back in Greece.

More detail on the malaria cases in Greece can be found on this European Centre for Disease Prevention and Control website. The actual ECDC report on autochthonous malaria transmission in Greece can be found here. A point in that report that is not mentioned in the ANSAmed newswire is that 2012 marks the third consecutive year in which autochthonous transmission has been inferred in Greece. So much for Greece being malaria-free.

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.

 

Get Off the Sexual Network

When I was in Uganda last month, I was talking with collaborators, field assistants, villagers, taxi drivers, bartenders – pretty much anyone who would listen – about social networks, I was struck by what a sophisticated understanding of social networks my average interlocutor had.  As part of our project examining the risk of zoonotic disease spillover in rural Uganda, we are gathering data on individual people’s personal networks. We are interested in contact networks, for sure, but we are also examining people’s social capital – the resources to which an individual has access for instrumental action that are embedded in his or her social network. There are generally two classes of definitions of social capital used in the literature. The first, made famous by Robert Putnam‘s book, Bowling Alone, is really a measure of community solidarity. How cohesive are communities and how does this contribute to individuals’ and communities’ welfare?  The definition I typically employ is attributable to Bourdieau and a host of other scholars, especially Nan Lin. This definition emphasizes both the networked nature of social capital and the instrumentality of it.

The reasoning behind doing a social capital inventory in conjunction with our study of zoonotic disease spillover risk is to have a thorough description of the “state” of individuals. Social surveys typically measure income, household wealth, land holdings, etc. One measures such things in a social survey because one is interested in the economic state of the individual or household in which she is embedded. Social capital is a measure of economic – and social – well-being for people where many of the resources that they need to succeed, or even just get by, are not specifically located in the household or with the individual. We suspect that people in rural Uganda will vary in the amount of social capital they have and that this may be a major axess of vulnerability.

So, there I am, talking to anyone who would listen about the best way to gather information on personal social networks and it turns out that everyone I spoke with was amazingly familiar with the whole concept of social networks. The catch is, the networks with which they are familiar are a special type of networks – sexual networks. When I asked how everyone seemed to know so much about sexual networks, they pointed me to a public-service advertising campaign for which the tag line is “get off the sexual network.” Despite the Central African origin of HIV-1, Uganda was an early center for the epidemic. However, as noted by Stoneburner and Lowbeer, in their important 2004 paper, Uganda experienced substantial – and early – decline in HIV-1 incidence because of health communication through social networks. They write:

The response in Uganda appears to be distinctively associated with communication about acquired immunodeficiency syndrome (AIDS) through social networks. Despite substantial condom use and promotion of biomedical approaches, other African countries have shown neither similar behavioral responses nor HIV prevalence declines of the same scale. The Ugandan success is equivalent to a vaccine of 80% effectiveness. (Stoneburner & Lowbeer 2004)

I definitely need to check out the current state of the art to see if other countries in Sub-Saharan Africa have now experienced similar public health gains as a result of network-oriented interventions.

Based on my rather unsystematic sample, I’d say that this campaign has really worked raise people’s understanding of relational interconnectedness.  I was not able to get a picture of the huge billboards on the Kampala-Entebbe Highway (because it was always dark when I drove by them) but the TV ad is available on youtube. On the one hand, this is really great (both for the obvious public health reasons and because people seem to have a good understanding of webs of social relations). On the other hand, it will probably mean we will need to work hard to clarify what types of networks we mean when we gather our network data.

Three Questions About Norms

Well, it certainly has been a while since I’ve written anything here. Life has gotten busy with new projects, new responsibilities, etc. Yesterday, I participated in a workshop on campus sponsored by the Woods Institute for the Environment, the Young Environmental Scholars Conference. I was asked to stand-in for a faculty member who had to cancel at the last minute. I threw together some rather hastily-written notes and figured I’d share them here (especially since I spoke quite a bit of the importance for public communication!).

The theme of the conference was “Environmental Policy, Behavior, and Norms” and we were asked to answer three questions: (1) What does doing normative research mean to you? (2) How do your own norms and values influence your research? (3) What room and role do you see for normative research in your field? So, in order, here are my answers.

What does doing normative research mean to you?

I actually don’t particularly like the term “normative research” because it sounds a little too much like imposing one’s values on other people. I am skeptical of the imposition of norms that have more to do with (often unrecognized) ideology and less about empirical truth – an idea that was later reinforced by a terrific concluding talk by Debra Satz. If I can define “normative” to mean with the intent to improve people’s lives, then OK.  Otherwise, I prefer to do “positive” research.

For me, normative research is about doing good science. As a biosocial scientist with broad interests, I wear a lot of hats. I have always been interested in questions about the natural world, and (deep) human history in particular. However, I find that the types of questions that really hold my interest these days are more and more engaged in the substantial challenges we face in the world with inequality and sustainability. In keeping with my deep pragmatist sympathies, I increasingly identify with Charles Sanders Pierce‘s idea that given the “great ocean of truth” that can potentially be uncovered by science, there is a moral burden to do things that have social value. (As an aside, I think that there is social value in understanding the natural world, so I don’t mean to imply a crude instrumentalism here.) In effect, there is a lot of cool science to be done; one may as well do something of relevance.  I personally have little patience for people who pursue racist or otherwise socially divisive agendas and cloak their work in a veil of  free scientific inquiry.  This said, I worry when advocacy interferes with intellectual fairness or an unwillingness to accept that one’s position is not actually true.

I think that we are fooling ourselves if we believe that our norms somehow don’t have an effect on our research.  Recognizing what these norms that shape your research – whether implicitly or explicitly – helps you manage your bias. Yes, I said manage. I’m not sure we can ever completely eliminate it. I see this as more of a management of a necessary trade-off, drawing an analogy between the practice of science and a classic problem in statistics, between bias and variance. The more biased one is, the less variance there is in the outcome of one’s investigation. The less bias, the greater the likelihood that results will differ from one’s expectations (or wishes). Recognizing how norms shape our research also deals with that murky area of pre-science: where do our ideas for what to study come from?

How do your own norms and values influence your research?

Some of the the norms that shape my own research and teaching include:

transparency: science works best when it is open. This places a premium on sharing data, methods, and communicating results in a manner that maximizes access to information. As a simple example, this norm shapes my belief that we should not train students from poor countries in the use of proprietary software (and other technologies) that they won’t be able to afford when they return to their home countries when there are free or otherwise open-source alternatives.

fairness: this naturally includes a sense of social justice or people playing on an equal playing field, but it also includes fairness to different ideas, alternative hypotheses, the possibility that one is wrong. This type of fairness is essential for one’s credibility as a public intellectual in science (particularly supporting policy), as noted eloquently in this interview with Dick Lewontin.

respect for people’s ultimate rationality: Trying to understand the social, ecological, and economic context of people’s decision-making, even if it violates our own normative – particularly market-based economic – expectations.

flexibility: solving real problems means that we need to be flexible in our approach, willing to go where the solutions lead us, learning new tools and collaborating. Flexibility also means a willingness to give up on a research program that is doing harm.

good-faith communication: I believe that there is no room for obscurantism in the academy of the 21st century. This includes public communication. There are, of course, complexities here with regard to the professional development of young scholars.  One of the key trade-offs for young scholars is the need for professional advancement (which comes from academic production) and activism, policy, and public communication. Within the elite universities, the reality is that neither public communication nor activism count much for tenure. However, as Jon Krosnick noted, tenure is a remarkable privilege and, while it may seem impossibly far away for a student just finishing a Ph.D., it’s not really. Once you prove that you have the requisite disciplinary chops, you have plenty of time to to use tenure for what it is designed for (i.e., protecting intellectual freedom) and engaging in critical public debate and communication.

humility: solving problems (in science and society) means caring more about the answer to a problem than one’s own pet theory. Humility is intimately related to respect for others’ rationality.  It also means recognizing the inherently collaborative nature of contemporary science: giving credit where it is due, seeking help when one is in over one’s head, etc. John DeGioia, President of Georgetown University, quoted St. Augustine in his letter of support for Georgetown Law Student, Sandra Fluke against the crude attacks by radio personality Rush Limbaugh and I think those words are quite applicable here as well.  Augustine implored his interlocutors to “lay aside arrogance” and to “let neither of us assert that he has found the truth; let us seek it as if it were unknown to both.” This is not a bad description of the way that science really should work.

What room and role do you see for normative research in your field?

I believe that there is actually an enormous amount of room for normative research, if by “normative research,” we mean research that has the potential to have a positive effect on people’s lives. If instead we mean imposing values on people, then I am less sure of its role.

Anthropology is often criticized from outside the field, and to a lesser extent, from within it for being overly politicized. You can see this in Nicholas Wade’s critical pieces in the New York Times Science Times section following the American Anthropological Association’s executive committee excising of the word “science” from the field’s long-range planning document. Wade writes,

The decision [to remove the word ‘science’ from the long-range planning document] has reopened a long-simmering tension between researchers in science-based anthropological disciplines — including archaeologists, physical anthropologists and some cultural anthropologists — and members of the profession who study race, ethnicity and gender and see themselves as advocates for native peoples or human rights.

This is a common sentiment. And it is a complete misunderstanding. It suggests that scientists can’t be advocates for native peoples or human rights.  It also suggests that one can’t study race, ethnicity, or gender from a scientific perspective.  Both these ideas are complete nonsense.  For all the leftist rhetoric, I am not impressed with the actual political practice of what I see in contemporary anthropology. There is plenty of posturing about power asymmetries and identity politics but it is always done in such a mind-numbingly opaque language and with no apparent practical tie-in to policies that make people’s lives better. And, of course, there is the outright disdain for “applied” work one sees in elite anthropology departments.

Writing specifically about Foucault, Chomsky captured my take on this whole mode of intellectual production:

The only way to understand [the mode of scholarship] is if you are a graduate student or you are attending a university and have been trained in this particular style of discourse. That’s a way of guaranteeing…that intellectuals will have power, prestige and influence. If something can be said simply, say it simply, so that the carpenter next door can understand you. Anything that is at all well understood about human affairs is pretty simple.

Ultimately, the simple truths about human affairs that I find anyone can relate to are subsistence, health, and the well-being of one’s children. These are the themes at the core of my own research and I hope that the work I do ultimately can effect some good in these areas.

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.

fpi-ts-1990-2011-1

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.

berryplot

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

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

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

risk-aversion

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

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

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

Small game is preferred because it has higher expected utility.

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

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

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

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

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

References

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

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

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

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

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

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

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

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

Complexity and Nihilism

This week in class I tried to take on the topic of complexity, as in “complex systems theory.”  Complexity is a very important topic in human ecology, and biosocial science more generally.  It’s also a topic that worries me a bit. It worries for two reasons. First, it seems all too easy for people to fall in with the cult of complexity and I believe that the weight of evidence shows very clearly that people are not at their best when they are associated with cults. If a perspective on science provides novel (especially testable!) insights, then I’m all for it. When it takes on the doctrinaire elements of a religion, then I’m less convinced of its value.  The second reason complexity worries me is clearly related to the first. I am continually frustrated by anthropologists who, when confronted with complexity, throw their hands up and say it’s too complex to make predictions, why bother to do science or understand the principles underlying the system?  You’d need to be trained as a theoretical physicist to understand the theory and people who think they understand something are just deluding themselves (or at least the rest of us) with a masculinist, hegemonic fantasy anyway. Let’s just tell a narrative (preferably peppered with some mind-numbing post-structuralist social theory). Better, perhaps, that we describe history. I think that this view is misguided to say the least (though I agree that history is fundamentally important).

There are three very influential reviews, all written for the Annual Review of Anthropology (when Bill Durham was editor, might I add), by eminent ecological anthropologists that have fed this perspective. Ian Scoones, Steve Lansing, and William Baleé each wrote a review between 1999 and 2006 more or less on the topic of complexity in human ecology. Scoones (1999) reviewed the ‘New Ecology’ and its implications for the social science. Lansing (2003) introduced complexity proper , and Baleé (2006) wrote about ‘Historical Ecology.’ I think its probably fair to say that each of these authors has a different sensibility regarding the role of science in anthropology.

Baleé advocates for the perspective of historical ecology, which emphasizes historical contingency and human agency in shaping landscapes.  He seems to conflate systems ecology with an equilibrium episteme, noting that historical ecology is ‘at odds with systems ecology’ (Baleé 2006: 81) for the latter approach’s inability to allow human agency to increase biodiversity in some cases.  This is an odd critique, since there is nothing inherent in any systems theory of ecological dynamics that makes this the case.  He is also critical of island biogeography theory of MacArthur & Wilson (1967) because of its lack of attention to human agency as a cause of species invasions. Again, there is nothing inherent in island biogeography theory — or its modern inheritor, metapopulation biology — that excludes human agency as a mechanism for colonization. Presumably, the interested anthropologist could construct a model that included human facilitation of species invasions and explore both the transient and asymptotic (e.g., equilibrium) properties of this model.

Systems ecology, according to Baleé’s review, may have provided mathematical rigor to human ecology but it was static, ahistorical, and neglected political processes, a point first noted by Wolf in his Europe and the People without History. While it is certainly true that cultural ecologists studied relatively unstratified cultures (typically in isolation of other parts of the (human) world economic system), once again, there is nothing intrinsic in cultural ecology that makes this necessary. The idea of a cultural core (“the constellation of features which are most closely related to subsistence activities and economic arrangements” (Steward 1955:37)), central to Steward’s cultural ecology, is entirely applicable to stratified societies. It is more complex but that doesn’t make it irrelevant. Similarly, it seems that Steward’s multilinear evolutionary theory of culture, with its focus on broad cross-cultural patterns but emphasis of local particularities is also largely compatible with the tenets of historical ecology. I think that it is a fundamental misapprehension that every anthropologist who studies subsistence of face-to-face groups, following in the tradition of Julian Steward, is unaware of the larger political entanglements of foraging, farming, or pastoral people in a larger world political-economic system (see, e.g., Doug Bird‘s nice essay on the politics of Martu foraging). There is just a conditionality — or ‘bracketing’ if you prefer the phenomenological term — of subsistence activities.  Given that the Martu or Hadza (or whoever) forage, how do they go about doing it? What are the consequences for landscapes in which they are embedded? These are legitimate, important, and interesting questions.  So are questions about broader political economy.  A little secret: They’re not mutually exclusive.

Lansing writes about complex systems proper, and about the phenomenon of emergence in particular.  Emergence occurs when order arises solely out of local interactions and in the absence of central control. I agree completely with Lansing that an investigation of emergence is an important endeavor in ecological anthropology and, indeed, anthropology more generally. My concern that emerges from Lansing’s paper is simply the idea that we have no hope of understanding anything without really complex nonlinear models — models that are so complex they can only be instantiated in agent-based simulations. While I am engaged in the ideas of complex systems, I am not quite ready to give up on many traditional forms of analysis that use linear models. As we will see below, the devil is in the details in complex systems models and I don’t think it’s good for science to deprive ourselves of important suites of tools because of a priori assumptions about the nature of the systems we study. This statement should not be interpreted to mean that I think this is what Lansing is doing. I do worry about anthropologists who read this review being scared away from formal ecological analysis because the nonlinearity sounds scary.

It is Scoones (1999) who makes the most extreme statements about the consequences of complexity for human ecology.  Regarding the three unifying themes around which the new human ecology was coalescing, he writes (1999: 490), “Third is the appreciation of complexity and uncertainty in social-ecological systems and, with this, the recognition of that prediction, management, and control are unlikely, if not impossible.” I think that this statement, while it may be an accurate description of some unifying themes in recent human ecology is simply incorrect and more than a bit nihilist. In all fairness, Scoones goes on to ask what the alternatives to the usual practice are (1999: 495):

So, what is the alternative to such a managerialist approach? A number of suggestions have been made. They generally converge around what has been termed “adaptive management” (Holling 1978, Walters 1976). This approach entails incremental responses to environmental issues, with close monitoring and iterative learning built into the process, such that thresholds and surprises can be responded to (Folke et al 1998).

This is a fair statement, which is rather at odds with the previous quote. If prediction and management are impossible, why is adaptive management a viable replacement?  Does adaptive management not entail making predictions and, well, managing? Of course it does.

I have a series of critical questions that must be addressed before we accede to excessive complexity and stop trying to understand the process underlying human ecology.

  1. With nonlinearity (as with stochasticity), the devil is in the details. What is the shape of the response? Sometimes nonlinear models are remarkably linear over the relevant parameter space and time scope.  Sometimes they’re not.  We don’t know unless we ask.
  2. What is the strength of the response? With nonlinearity, the thing that matters for the difficulty in prediction, sensitivity to initial conditions, etc. is the strength of response. Sometimes this strength is not that high and linear models work amazingly well.
  3. How big are the possible perturbations? We might be able to make quite good predictions if perturbations are small. Of course, we shouldn’t assume that perturbations are always small (as much classical analysis does).  This is an empirical question.
  4. What is the effect of random noise?  Some of the deterministic models with exotic dynamics collapse into pretty standard models in the presence of noise.  Of course, sometimes randomness makes prediction even harder — this is partly a function of the previous three points (i.e., the shape of nonlinearity, the strength of the response, and the size of perturbations).

A couple figures can illustrate two of these points.  Consider the following hypothetical recruitment plot.  On the x-axis, I have plotted the population size, while on the  y-axis, I have plotted the number of recruits born. Suppose that the actual underlying process for recruitment was density-dependent (i.e., was nonlinear), as indicated by the dashed line. In this particular hypothetical case, you would not do all that badly with a linear model (solid line).  As we move across three orders of magnitude, the difference in recruitment between the linear and nonlinear models is two births. The process of recruitment is nonlinear (i.e., it’s density-dependent) but you would do just fine with predictions based on a linear model.

linear-nonlin-comp

Taking up on Bob May’s classic (1976) paper, we can use the logistic map (a discrete-time logistic population growth model)  to look at strength of response.  The logistic map is given by the following nonlinear difference equation X_{t+1} = a X_t (1 ? X_t). We can plot the relationship between X_t and X_{t+1}.  This shows the classic symmetric, humped recruitment curve characteristic of the logistic model.  Where a line X_{t+1} = X_t intersects the recruitment curve, the model has a fixed point. The stability of these fixed points is determined by the slope of the tangent line at the intersection of the curves. If the absolute value of this slope is greater than one, perturbations from the fixed point will grow — the model is unstable.  If the absolute value of this slope is less than one, then any trajectory in the neighborhood will return to the fixed point. The parameters used to make these figures create a complex 2-point series (i.e., the population oscillates between two fixed points) on the left-hand case, while for the right-hand case, there is a simple fixed point. By cranking up the parameter a in the logistic map, we can induce more and more exotic dynamics.  However, the key point here is that if the response is weak enough, the dynamics are not especially exotic at all. Note that we start to get the interesting behavior at values of a>3, or a tripling of population size each time step.  Human populations do not grow nearly this fast.  Not even close. This isn’t to say that some human processes with nonlinear dynamics don’t have very strong responses, but clearly not all must. Population growth is a pretty important problem for human ecology, and it’s dynamics are unlikely to be really exotic.  Maybe we can use some simple models to understand human population dynamics?  See last week’s post on the work of Tuljapurkar and colleagues for some exemplary contemporary work.

response-strength

So, there are two cases where understanding the nature of the nonlinearity makes an enormous difference in how we make predictions and otherwise understand the system.  Sometimes nonlinear models are effectively linear over important ranges of parameter space.  Sometimes the response of a nonlinear model is small enough that the system shows very predictable, well-mannered dynamics. But just so you don’t think that I don’t think complexity is an issue, let’s look at one more example.  This model is from a classic study by Hastings and Powell (1991) showing chaos in a simple model of a food chain.

The model has three species: producer, primary consumer, secondary consumer; and it is a simple chain (secondary consumer eats primary consumer eats producer). Hastings and Powell chose the model parameters to be biologically realistic — there’s nothing inherently wacky about the way the model is set up. Using the same parameters that they use to produce their figure 2, I numerically solved their equations (using deSolve in R).  The first plot shows the dynamics in time, with the bizarre oscillations in all three species.

series

In the second figure, I reproduce (more or less) their three-dimensional phase plot, which takes time out of the plot and instead plots the three population series directly against each other.

3d-phase

Finally, I plot some pair-wise phase-plots, which are easier to visualize than the false 3D image above.

phase-planes

On the whole, we see very complex behavior in a rather simple food chain. Hastings and Powell (1991: 901-902) summarize their findings: (1) contrary to conventional wisdom, they suggest that chaos need not be rare in nature, (2) chaotic behavior “need not lead to an erratic and unpatterned trajectory in time that one might infer from the usual (not mathematical) connotation of the word ‘chaos'” and (3) time scales matter tremendously — over short time scales, the behavior of the system is quite regular.

For me, the greatest lesson from the complex systems approach is the need to understand the specific details.  Contrary to the inclination to throw up one’s hands at the thought of a science of human ecology (let alone putting this science into practice with sensible management policies), it seems that the issues raised here mean that we should study these systems more, attempting to understand both their historical trajectories and the principles upon which they are organized. By all means, let’s jettison old-fashioned ideas about typology and homeostasis in nature.  No need to keep around the clockworks metaphor of ecological succession or the idea that the Dobe !Kung are Pleistocene remnants. Ecosystems, landscapes, whatever term you want to use, don’t necessarily tend toward equilibria. Uncertainty is ubiquitous. People are part of these systems and have been for a long time. Good, we’re agreed.  But can we please not give up on using all the scientific tools we have at our disposal to understand these complex systems in which human beings are embedded? Anthropologists have much to contribute to this area, not the least of which is long-term, place-based research on human-environmental systems.

The lesson of prediction over the short-term is another issue that comes up repeatedly in the complex systems literature.  I think that the work of George Sugihara and colleagues is especially good on this front. I have blogged (here and here) about a paper on which he is a co-author before (I should note that in this paper they suggest ways to make predictions of catastrophic events in complex systems with noise — just sayin’). There is a nice, readable article in Scientific American on his work on fisheries that summarizes the issues. This work combines so many things that I like (demography, fish, statistics, theoretical ecology, California), it’s a bit scary. Another nice, readable piece that also describes some of Sugihara’s work in finance can be found in SEED magazine here.

This post is already too long.  I clearly will need to write about the other topic for the week, risk and uncertainty, at a later date.

References

Baleé, W. 2006. The research program of historical ecology. Annual Review of Anthropology. 35:75-98.

Hastings, A., and T. Powell. 1991. Chaos in a three-species food chain. Ecology. 72 (3):896-903.

Lansing, J. S. 2003. Complex adaptive systems. Annual Review of Anthropology. 32:183-204.

MacArthur, R. H., and E. O. Wilson. 1967. The theory of island biogeography. Princeton: Princeton University Press.

May, R. M. 1976. Simple Mathematical-Models with Very Complicated Dynamics. Nature. 261 (5560):459-467.

Scoones, I. 1999. New Ecology and the social sciences: What prospects for a fruitful engagemnt? Annual Review of Anthropology. 28:479-507.

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:

 \frac{dN}{dt} = rN (1 ? \frac{N}{K})

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).

pearl-badfit

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.