And so we model: the ineffective use of mathematical models in ecological risk assessments.

DEAR SIR: The article, ‘‘Separating the Wheat from the Chaff: The Effective Use of Mathematical Models as Decision Tools,’’ printed in Integrated Environmental Assessment and Management (Glaser and Bridges 2007), gives this author great cause to articulate a vastly contrary perspective on the need for and utility of mathematical models in the field of ecological risk assessment (ERA). I will argue that, in the subject article, modeling is far from being an asset to the ERA process, and I will also endeavor to demonstrate that modeling is often a hindrance to our work in this field. I believe my impetus for writing here, thereby effectively launching a formal debate over the merits of modeling, is the rather apparent bias in the Glaser and Bridges paper. What comes across more than anything is simply a sense of infatuation with models on the part of the authors; the authors can find no shortage of applications for models to facilitate decision making, and the prospect of models generating information that is either incorrect or not serviceable is plainly not discussed. This debate/commentary piece is generally patterned after the series of observations and conclusions in the Glaser and Bridges article, but additional arguments are also supplied to paint a completely different picture of mathematical models as decision tools. It is my perception that the authors have gone awry and arrived at their position through two means: 1) a faulty logic sequence and 2) electing to see so much of the application and utility of mathematical models through a singular contaminants issue, namely sediments that appear to be in need of remediation. The faulty logic sequence is as follows: 1) A plethora of contaminants-based risk-management decisions seemingly need to be made and 2) models are there for the taking and can be adapted to any specific need, ergo, 3) models must be that great panacea that can come to the rescue and allow for those critical risk-management decisions to be made. Although there was most definitely a need to focus on one specific environmental problem so the authors could assert their position while keeping their article to a manageable page length, the authors’ choice of a contaminated sediments (and surface water) context may have been a poor one. It is more often the case where contaminated sediment sites are discussed, as opposed to contaminated terrestrial sites, that many are primed to only hear about how a remediation project will proceed. In electing to focus on modeling utility from a vantage point that occurs far into the risk assessment process (i.e., remedy selection), the reader is easily misled into accepting it as a given that any and all prior health-risk assessment efforts were error free and have gone uncontested. More over, there is a grave oversight in the authors’ treatment of the subject matter amid this difficulty. Mathematical modeling was almost assuredly employed a number of times en route to arriving at that point in the risk assessment process where competing remedial strategies can be considered. Why there is virtually no mention of this in the Glaser and Bridges article may simply reflect the authors’ greater fascination with the remedial side of the risk assessment process. Another very real possibility, and a disturbing one, is that the authors themselves do not recognize what is arguably the true primary use of models. The 1st of the authors’ 12 stated observations/ conclusions is that ‘‘a model is a tool for evaluating alternate hypotheses’’. I would very much contend that, first and foremost, a model is a tool to be used when trying to assess risk, and what models are called on to do routinely for ERAs, no different than for human health assessments, is to calculate essential values—a task that does not even involve hypotheses. Models are used to determine such things as the number of samples needed, the chemical concentration in a given ecosystem compartment, a receptor’s level of risk or hazard, and perhaps a receptor’s safe consumption rate of a dietary item. What I find unfortunate is the authors’ overattention to model use within a remedial decision-making context. Are we always so sure that our risk and hazard estimations are correct that we’re ready now to embrace only such phenomena as ‘‘quantitative analysis of management options,’’ ‘‘projected rate of natural recovery,’’ ‘‘evaluating remedial alternatives,’’ and ‘‘risk-based decision-making,’’ all terms that excessively recur throughout the Glaser and Bridges article? What concerns this author most is the potential for the reader, after digesting the well-intended article, to come away with a skewed perception of model utility. The article is likely to foster the sense that all is well, if not great, with models, so long as we are alerted to certain matters of application (e.g., ‘‘uncertainty, and therefore the need for mathematical models, is greater in systems near background’’) and model limitations (e.g., ‘‘the utility of a model depends on the strength of the constraints placed upon it’’). The reader can easily get the sense that a model is ready for application if we do nothing more than acknowledge certain phenomena about it, such as identifying the constraints that are placed on the model. How does one go about assessing the strengths of the constraints placed on a model, though? Are there formulae for doing this? Borrowing from the language of the authors, how does one, ‘‘understand whether the constraints under which the model operates are sufficiently tight to permit answering management questions’’? Are there units to a tightness scale that the authors have forgotten to include in what they have written? To be fair, the thrust of the Glaser and Bridges article was not intended to supply such details in their defense of model use, but unless the reader can trust that implementable measures actually exist to address such things as ‘‘model constraints and Published on the Web 9/13/2007. Integrated Environmental Assessment and Management — Volume 4733, Number 4—pp. 473–475 2007 SETAC 473