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Contaminant monitoring programs are designed to determine temporal and geographic trends in residue concentrations. Obvious interpretive problems may arise from analytical changes, inconsistent replication, and other activities that upset a program's continuity. Less obvious difficulties, related to the inherent nonnormality of residue data and the abilities of the data to meet many of the assumptions required by statistical methods, present themselves during the analysis, testing, and interpretation of results.
Statistical analysis of monitoring data is traditionally accomplished by testing the differences between location or time-period means with a two-way factorial analysis of variance (AOV) in which variation accrues from locations, time periods, time × location interaction, and error. Without replication, interaction is usually assumed to be absent when main effects are tested, which is a questionable practice with residue data. Adding the species, species type, or sample type effects to the two-way factorial results in a three-way factorial involving more complex interactions. An alternative to the full factorial AOV—analysis as a partially nested factorial—is presented for use when the species collected are not common to all locations and time periods. All analyses are illustrated by using a selected subset of National Pesticide Monitoring Program data for organochlorine residues in freshwater fish.
Depending on the monitoring program's objectives and how the stations were selected, location effects may be considered random rather than fixed. This may result in a mixed-model AOV and alter the computation of F-ratios for time-period main effects, possibly changing the outcome of significance tests and resulting conclusions regarding temporal trends.
monitoring, organochlorine pesticides, polychlorinated biphenyls, data analysis, analysis of variance, aquatic toxicology, hazard assessment
Fisheries ecologist, U.S. Fish and Wildlife Service, Columbia National Fisheries Research Laboratory, Columbia, Mo.