The following is an invited guest post by Dr. Matthew Chew. The bias in research commentary in Nature by Daniel Sarewitz has attracted a lot of comments on the Nature site and Ecolog*. We felt that the issue deserves more discussion. Here, Dr. Chew presents his take on the issue.Matthew K. Chew, Ph.D. Arizona State University School of Life Sciences Center for Biology and Society http://asu.academia.edu/MattChew
Recently in Nature (485:149) my ASU colleague Daniel Sarewitz warned that research is being distorted by a ‘powerful cultural belief’ that ‘progress in science means the continual production of positive findings,’ generating a ‘pervasive bias’ due to ‘a lack of incentives to report negative results, replicate experiments or recognize inconsistencies, ambiguities and uncertainties.’ He judged the resulting flow of false, weak or too-narrowly positives to be effectively useless. Sarewitz went on to suggest that bias toward positive results introduced ‘systematic error’ and was likely to prevail ‘in any field that seeks to predict the behaviour of complex systems [including] economics, ecology, environmental science [and] epidemiology.’ Lumping various “e-” disciplines into a single culture may give their practitioners false comfort that Sarewitz is tarring research with too broad a brush. Nevertheless, ecologists would be rash to invoke insular exceptionalism as a pretext for dismissing his concerns.
What would constitute a ‘positive’ bias in ecology? Well, what hypothesis do ecologists want to support? Very rarely can one find an ecologist whose personal motivations exclude management goals of any kind. In the simplest terms, ecologists often want to save things. Leopold, Carson, Cousteau, Ehrlich, Attenborough, Wilson and others taught us well. Most of us knew before studying ecology how important it is to save things. With experience we’re likely to settle for demonstrating why a particular something could do with a bit of saving; but one way or another, ecologists want to help nature reveal what’s going wrong. It is axiomatic to ecologists that things are going wrong. Making that obvious to everyone is a primary motivation. No one will fret over a non-problem.
If Sarewitz and I are both correct, ecological bias is a double-edged sword. On one edge its influence renders ecological findings (especially predictions) suspect. On the other, that very bias provides a collective identity that draws ecologists together. During his stirring 2008 keynote address to the Ecological Society of America, Lord Robert May declared “This is us, not some natural event.” There was no mistaking the responsibilities being ‘us’ (humans: part of the problem) gave ‘us’ (ecologists) for finding a solution. Is being biased for good, important reasons acceptable if it actually renders our findings useless? My answer is a sympathetic, unequivocal no. So far, so good. But we have a more difficult bias to contemplate.
Having one foot in ecology and the other in history, I find that historians and ecologists face similar challenges: ad hoc methods; indirectly observed, inferred and partial data; actively uncooperative or poorly bounded objects of study; and the need to concoct a coherent narrative from disparate elements. We shouldn’t wonder at that, because ecology is a firmly rooted successor to Enlightenment natural history— the history, that is, of everything but people. Charles Elton’s 1927 definition of ecology as ‘scientific natural history’ might have been glib, but it profoundly bracketed the aspirations and prospects of the field.
History is not science. Historians embrace contingency, while scientists strive to eliminate it. Historians describe succeeding conditions, but do not view particular sequences as inevitable, repeatable, or typical. They hypothesize unique events based on unique actions under unique conditions. There are no laws of history unless teleological commitments (religious, ethical or philosophical) are deployed to interpret events. Historians simplify their stories by emphasizing important actors and events, but not to generate predictive models. Scientists seek to predict conditions or events under the deterministic assumption that given identical circumstances, what happened once will happen again. Replicating results makes science ahistorical. Matter and energy don’t grow or learn or make decisions. Mathematical rectitude is expected.
Many objects ecologists inherited from natural history—continents, climates, soils, species, organisms—are products of historical contingency. While subject to physical laws, they ‘lack’ interchangeability, even with themselves at other times. Some can learn and make decisions. They never quite repeat the past.
The crux of the problem: To make natural history scientific, ecologists must construct ahistorical objects by subsuming a mess of contingent exceptions by using statistical approximations that apply in general but never in particular. The only alternative is treating historically contingent populations, ‘communities’ and ecosystems as ahistorical objects. Scientific simplifications—models—are meant to facilitate prospective manipulation. Our ‘science’ bias drives us to de-nature the natural objects and phenomena we seek to save, in order to analyse them by methods we are taught to deem appropriately scientific. Ecology is sometimes dismissed as a ‘soft’ science, but society demands hard facts. Appeasing society by de-naturing and de-historicizing natural history to make ecology harder leaves…what?
Ecology’s most intractable problems tend to bubble up repeatedly. Contingency is no exception. In his 1999 Oikos (84:177-192) review ‘Are there general laws in ecology?’ John H. Lawton concluded contingencies were relatively manageable in micro- and macro-scale studies, but the [community level] ‘middle ground is a mess’. He offered no solution beyond implying that community ecologists need to tolerate—even revel in—messiness. That seems an explicit admission that community-level studies are natural histories. It does not license sloppy research or reverting to qualitative romanticism, but it does require leaving ample room for ‘unknown unknowns’. Since conservation concerns and interventions often involve community scale processes, conservation must incorporate flexibility even in identifying basic goals and objectives. Rather than apologizing for ‘soft’ forecasting on a case-by-case basis as the ‘best available’ science, perhaps it’s time for ecologists to recognize contingency as one of our most robust, general, even positive findings.
* Search ‘sarewitz’ on Ecolog here.