Large-scale management experiments in the moist maritime forests of the Pacific Northwest

Abstract

Several large, integrated forest management experiments have been initiated in the Pacific Northwest this past decade, 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. Seven of these large-scale multi-resource silvicultural experiments are examined and evaluated, in light of previous experience with large-scale experiments. All seven employ randomized block designs with replicated treatment units large and practical enough to be commercially operational (most treatment units are 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$ 10 6/block) and size (50–200 ha) of the experimental blocks, sample size is small ( n<7 blocks) on all but one experiment. This means that statistical power (the probability of correctly rejecting the null hypothesis) will be 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. This is a concern for three of the experiments because their blocks are located in relatively small geographic areas. 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. Looking for commonality, all seven studies are examining the effect of alternative silvicultural on both wildlife habitat and biodiversity. 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. In addition to small sample sizes, large variability, and pseudoreplication, other problems common to large-scale experiments are evident. Forest growth experiments are inherently long-term because they are dominated by slow processes with strong transient dynamics. Investigators are faced with institutional and academic demands for short-term results that not only are publishable but also can justify the large investments. The realities of the timber-sale process delayed or eliminated several blocks on at least three of the experiments. Randomization becomes a serious concern for the forest manager, because a clearcut or heavy removal treatment could be assigned to a highly visible location that might be socially unacceptable.