In the past decade, several large, integrated forest management experiments have been initiated in the Pacific Northwest, partially in response to contentious resource management debates. Their goal is to use alternative silviculture treatments to enhance wildlife habitat, biodiversity, or the conservation of aquatic resources in a manner that is socially acceptable. These randomized-block experiments have one unusual feature: treatment units are commercially operational (13-20 ha). Because the large-scale context is designed into these experiments, results can be directly interpreted at the scale of management that produced the manipulation, eliminating a change-of-scale bias common in smaller management experiments. The considerable advantages of large, operational treatments are accompanied by their own problems, however. Because of the great expense (~US$106/block) and size (50-200 ha) of the experimental blocks, sample size is usually small. This means that statistical power (the probability of correctly rejecting the null hypothesis) is weak across blocks. With few replicates and high variability both within and among these large-scale treatments, investigators face the possibility that differences might only be detectable at untraditionally high significance levels. A second problem with large-scale experiments is pseudoreplication (lack of independence across replicates), which results in the strength of the experimental evidence being overstated. Meta-analysis (a joint hypothesis test across experiments) is proposed as an effective way to increase sample size - and therefore power - while accounting for the different degrees of variation across studies. A test of a common hypothesis about ecosystem management would greatly increase not only the power of the test but the return on investment from these rather expensive experiments.
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