Although forest tree pattern-dynamics has long been a focus for ecological theory, many aspects of basic analysis remain problematic. This paper describes, examines and illustrates an ‘extreme-value’ approach to clump detection. Simulations demonstrate that the approach, though simple, is sensitive and well suited to identifying aggregation, even in small data sets. The test is complementary to the χ2 approach and significantly outperforms it when high-density clusters form a relatively small fraction of the total area. This difference holds true as density declines, though the power of both tests decreases and the χ2 test eventually fails. Though powerful, the extreme-value tests are slightly conservative. The approach is adaptable to other null distributions and applications. An illustration uses tree data from a Ugandan forest plot with records from 1939 to 1992. One plausible explanation for observed stem increases in this plot is an unusually high incidence of large tree-fall events. Evidence for this is sought through spatial localization of various stem populations. Chi-square tests detect non-random density patterns in the populations that comprise ‘all stems > 10 cm diameter recorded in the 1992 survey’, and ‘all recruits in 1976’. In the extreme-value test three populations are found to be clumped, these are ‘pioneers > 10 cm diameter recorded in the 1992 survey’, ‘all recruits in 1992’ and again ‘all recruits in 1976’. These patterns, particularly the pioneer trees and stem recruitment, signal a ‘gap-dynamic process’ of tree regeneration. Despite this, the trends observed in the Ugandan plot appear unlikely to be caused by tree-falls alone. Various technical and ecological aspects of the extreme-value approach and tree spatial analyses are discussed.
