Biographical sketch

Scott Ferson, scott@ramas.com, 1-631-751-4350, fax -3435 Applied Biomathematics, 100 North Country Road, Setauket, New York 11733 USA

Scott Ferson is a senior scientist at Applied Biomathematics (www.ramas.com). His research focuses on developing reliable mathematical and statistical tools for risk assessments and on methods for uncertainty analysis when empirical information is very sparse. He holds a Ph.D. from the State University of New York at Stony Brook. He is author of /RAMAS Risk Calc Software 4.0: Risk Assessment with Uncertain Numbers/ (Lewis Publishers). He has over 75 other scholarly publications, including four books and several software packages, in environmental risk analysis and uncertainty propagation. His research has addressed quality assurance for Monte Carlo assessments, exact methods for detecting clusters in small data sets, backcalculation methods for use in remediation planning, and distribution-free methods of risk analysis appropriate for use in information-poor situations.

Ferson is an adjunct professor at Marine Sciences Research Institute at Stony Brook University, and serves on the editorial board of /Human and Ecological Risk Assessment/. He is chair of the conferences and workshops committee of the Society for Risk Analysis and has served on several panels in the US and the Europe.

Keynote Address Abstract

ENVIRONMENTAL CONTAMINATION IN ECOLOGICAL SYSTEMS

Many human activities introduce chemical contaminants into the natural environment. Manufacturing by-products, agricultural fertilizers and pesticides, leachates from mine tailings, combustion residues, waste and effluent streams deliver anthropogenic toxicants and other chemicals into aquatic and terrestrial ecosystems. Planning mitigation and remediation strategies and designing systems for minimum environmental impact require clear assessment of the nature, magnitude and consequence of the impacts of these contaminants. Risk assessors are beginning to appreciate the need to include ecological processes in their assessment models. The need arises because ecological systems have an inherent complexity that can completely erase the effects of an impact or greatly magnify it, depending on the life histories of the biological species involved. This complexity can also delay the consequence of an impact or alter its expression in other ways. Three central themes have emerged in ecological risk assessment:

1) Variability versus incertitude. Natural biological systems fluctuate in time and space, partially due to interactions we understand, but substantially due to various factors that we cannot foresee. The variability of ecological patterns and processes, and our incertitude about them, prevent us from making precise, deterministic estimates of the effects of environmental impacts. Because of this, comprehensive impact assessment requires a probabilistic language of risk that recognizes variability and incertitude, yet permits quantitative statements of what can be predicted. The emergence of this risk language has been an important development in applied ecology over the last decade. A risk-analytic endpoint is a natural summary that can integrate disparate impacts on a biological system.

2) Population-level assessment. In the past, assessments were conducted at the level of the individual organism, or, in the case of toxicity impacts, even at the level of tissues or enzyme function. To justify costly decisions about remediation and mitigation, biologists are often asked “So what?” questions that demand predictions about the consequences of impacts on higher levels of biological organization. Management plans require predictions of the consequent effects on biological populations and ecological communities. Our scientific understanding of community and ecosystem ecology is very limited, however, and quantitative predictions, even in terms of risks, for complex systems would require vastly more data and mechanistic knowledge than are usually available. Extrapolating the results of individual-level impacts to potential effects on the ecosystem may simply be beyond the current scientific capacity of ecology, which still lacks wide agreement about even fundamental equations governing predator-prey interactions. How can we satisfy the desire for ecological relevance when we are limited by our understanding of how ecosystems actually work? As a practical matter, focusing on populations, meta-populations (assemblages of distinct local populations), and short food chains may be a workable compromise between the organism and ecosystem levels. Risk assessment at the population level requires the combination of several technical tools including demographic models, potentially with explicit age, stage or geographic structure, and methods for probabilistic uncertainty propagation, which are usually implemented with Monte Carlo simulation. Meta-populations and short food chains are likely to be at the frontier of what we can address with scientifically credible models over the next decade.

3) Cumulative attributable risk. Assessments should focus on the change in risk due to a particular impact. The risk that a population declines to, say, 50% of its current abundance in the next 50 years is sometimes substantial whether it is impacted by anthropogenic activity or not. Only the potential change in risk, not the risk itself, should be attributed to impact. On the other hand, for environmental protection to be effective, remediation and mitigation must be designed with reference to the cumulative risks suffered by an ecological system from impacts and from all the various stresses present cumulated through time.