Fuzzy Statistical and Modeling Approach to Ecological Assessments

Abstract

Increasing attention on the extreme sensitivity of ecological systems to environmental insults has been changing the traditional view that humans are the most sensitive species. Ecological risk assessment, especially the relatively unexplored area of applied ecotoxicology, has been developed to meet this need. However, characterization, quantification, estimation, and prediction of ecological risks at multiple scales are often very difficult. Valuation of ecosystem components and scaling from the laboratory toxicity bioassay or intensive investigations of single sites (or relatively small geographic areas) to population, community, ecosystem or landscape level are also ill-defined. Some uncertainty is unavoidable in ecologists’ assessment and prediction about ecological systems, simply because uncertainty emerges whenever information pertaining to the situation is deficient in some respect. It may be incomplete, imprecise, fragmentary, not fully reliable, vague, contradictory, or deficient in some other way. In these situations, unexpected risks and/or environmental changes may result from decisions that must be made. There are various information deficiencies resulting in different types of uncertainty. Traditionally, the only well-developed mathematical apparatus for dealing with uncertainty in ecological risk assessment has been probability theory (Suter and Barnthouse, 1993). However, the probabilistic approach alone cannot represent uncertainties attached to systems for which some deterministic dynamical characteristics are unknown or deliberately ignored, as well as uncertainties attached to their mathematical model. We have recognized that uncertainty is a multidimensional concept. Which of its dimensions are actually manifested in a description of an ecological situation is determined by the mathematical theory employed. The challenge now is to introduce and/or develop new methods to address these concerns.