Too often, the fruits of years of research effort yield peer-reviewed publications that are greeted by the resounding echo of … silence.
We were therefore extremely pleased to find our recent article was not only read, but genuinely understood and actively supported by the most exacting of all readers, the Editor in Chief of the highly respected journal BioScience. The article in question was our recent review article entitled Leaps of Faith: How implicit assumptions compromise the utility of ecosystem models, wherein we examined how assumptions and uncertainty were treated by the 60 most popular articles describing ecosystem models that were intended to inform management.
In his editorial Models are Not Toys, Dr. Timothy Beardsley focused squarely on our article, (re)articulating our main points with such eloquence that it was clear he fully and completely "got it". Rather than confuse his message by paraphrasing, we simply reproduce the bulk of that editorial directly.
"Mathematical modeling has become central to much of biology and related sciences, to the extent that ecosystem studies that omit to describe a model of their phenomena of interest might even seem underdeveloped. The power of well-constructed models hardly needs defending—their ability to provide a vivid sense of what is going on in a complex situation is felt intuitively. Yet the fascination of modeling can easily lead the unwary into a fantasy of neatly interacting parameters that cease to meaningfully represent the messy, hard-to-measure real word. The temptation is to test just the most interesting model predictions and assumptions—the ones that yield the results we hope to find—and to forget possibly unwelcome complications at the interpretation stage.
For sure, there may be good reasons to ... develop a model that captures some important features of the world even when the model is incomplete in significant ways. Science has made vast strides by employing this reductionist strategy, as Gregr and Chan acknowledge in their important article ... Yet when attempts are made to draw management conclusions from models that are described without a full account of the assumptions and uncertainties that went into them, danger lurks.
Gregr and Chan's survey of articles purportedly relevant to ocean ecosystem management yielded results that can fairly be called “shocking.” “Ecosystem” articles left all assumptions implicit 39 percent of the time, and “social–economic” articles articulated no assumptions 68 percent of the time. The problems may be linked to the increasing fragmentation of modelers into specialized communities that are familiar with their own assumptions—but fail to explain them to outsiders. Gregr and Chan also remark on the “widespread faith in data quality and suitability.” Understandable, given the difficulty of gathering custom environmental data, but faith should have no place in science.
The authors provide a valuable typology of assumptions and uncertainties that all ecosystem modelers would do well to use as a checklist when planning and reporting their research. Unstated assumptions and uncertainties, besides causing unwise management actions, could bring this vital field into disrepute. And there is no reason to think the ocean scientists are more at fault than those who study other ecosystems."
We could not have said it better ourselves. But we are thrilled we articulated it clearly enough, and that this topic was seen as critical enough, for at least one senior Editor to find the work compelling enough to recognize the importance and contribution of our review. It is that sort of validation that keeps scientists motivated, and makes us feel that our work, however slowly, is making its way into the fabric of scientific understanding.
The complete editorial is accessible online at BioScience. The article is available on request from EG.
Beardsley, T. M. (2015). "Models Are Not Toys." BioScience 65(1): 3.
Gregr, E. J. and K. M. A. Chan (2015). "Leaps of faith: How implicit assumptions compromise the utility of ecosystem models for decision-making." BioScience 65(1): 43-54.