Analyzing the Effects of Accidental Environmental Impacts: Approaches and Assumptions

Abstract

Because studies of environmental accidents must be initiated after the fact and because the accidents cannot (or should not) be replicated, sampling cannot be entirely randomized, and investigations involve some degree of confounding and pseudoreplication. The study designs that can be used carry methodological limitations and ecological assumptions, which must be considered in evaluating results. The methodological issues relate to consistency in sampling methods and to the adequacy of sampling of levels of environmental disturbance or contamination, while the ecological assumptions derive from spatial and temporal variation in biological resources and the factors that affect them. We assess how these methodological issues and ecological assumptions affect study designs based on before–after comparisons and on single‐time or multiple‐time sampling after an accident. Designs that rest on the assumption of a steady‐state equilibrium in resource‐environment relationships (baseline and time‐series designs) must be interpreted with particular care, and baseline designs are sensitive to the effects of pseudoreplication and inconsistencies in methods. Other designs (pre/post paired samples, impact level‐by‐time, impact trend‐by‐time) assume only that environmental variations are equivalent among areas and/or contamination levels (a dynamic equilibrium) and are less affected by pseudoreplication. Single‐time designs (comparisons between impact and reference sites, between matched paired sites, or over a contamination gradient) have fewer methodological limitations, but assume that other natural factors that may influence the response of a resource are equal among all samples. If measurements of other factors are included in the design, covariance analysis may help to reduce this problem. In evaluating the effects of unplanned environmental impacts, post facto study designs that document both initial effects and subsequent recovery (impact level‐by‐time, impact trend‐by‐time) or that treat effects as continuous rather than categorical variables (gradient or trend designs) may be more useful than before–after comparisons.