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Characterizing Spatial Patterns of Trees Using Stem-Mapped Data

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Procedures for stem-mapping trees on fixed area plots are described. Two statistical procedures for analyzing spatial patterns of the completely mapped tree data are presented. These procedures, known as nearest neighbor analysis and Ripley's K(d) function, consider the cumulative distributions of distances between trees, compared to a distance distribution for a point pattern generated by a random process. Nearest neighbor uses tree-to-nearest-tree distances, and Ripley's K(d) considers distances between all pairs of trees. Both procedures include edge correction schemes for trees near plot boundaries. The two analyses were applied to stem-mapped data from several old-growth study plots to investigate spatial interactions within and between groups of canopy trees. Patterns for trees in different mortality, size, and competitive classes were analyzed separately. Results showed that between-tree competitive interactions drive forest patterns from clustering toward regularity. There was also evidence that canopy gaps are an important mechanism in the regeneration process. The examples illustrate how spatial pattern analysis can be useful in describing and interpreting complicated development processes that result from competitive interactions between individual trees. For. Sci. 39(4):756-775.

Keywords: Monte Carlo confidence envelopes; Nearest neighbor analysis; Ripley's K(d) analysis; old growth

Document Type: Journal Article

Affiliations: Research Forester, Intermountain Research Station, USDA Forest Service, Moscow, ID 83843

Publication date: November 1, 1993

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