We present an optimum sampling design for the detection of environmental impact. Unlike other approaches, we stress the importance of replication through time, particularly in variable environments. We provide a statistical model and a rationale for incorporating the variability between sampling locations over time into the error term of the analysis, together with the instantaneous replicate variability. As a result, a determination of statistical significance will correspond more closely to biological significance. The sampling design incorporates two levels of replication, instantaneous at a single time and place, and over time. These levels of replication must be optimized, depending on their relative variability and the marginal cost of collecting each kind of sample. Finally, we review a power test procedure to determine the number of replicates necessary to detect a change of a predicted magnitude. We feel that environmental monitoring is an important source of feedback about the results of alternative strategies of resource utilization. As such, it is an essential part of any attempt to use or develop natural resources wisely. The concept of a predicted change is central to the design of successful and cost effective monitoring programs. These programs should be designed, from inception, to have a specified probability of detecting a predicted change of specified magnitude. Without this design, monitoring programs run the risk, on the one hand, of having little or no chance of detecting anything but catastrophic changes, or, on the other, of sampling far in excess of what is necessary to test reasonable hypotheses. We have integrated relevant ecological, statistical, and managemen concerns in the presentation of methods to avoid these problems in the design of environmental monitoring programs.
907--05 (Indirekte Bedeutung der Wälder (Wohlfahrtswirkungen). Natur- und Umweltschutz. Erhebungen "Surveys") 524.63 (Stichprobenverfahren einschl. Punktstichprobe. (Vgl. auch 521.62))
