monkey's uncle

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On Global State Shifts

July 5th, 2012 · No Comments · Climate Change, Human Ecology

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.

 

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