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Using the Space-Time Permutation Scan Statistic to Map Anomalous Diameter Distributions Drawn from Landscape-Scale Forest Inventories

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Abstract:

Landscape-scale tree stem diameter distributions contain information that is potentially useful for evaluating the structural sustainability of forests, describing the impacts of past disturbances, and predicting future forest structure. One obstacle to interpreting diameter distributions at large scales is that typical boundaries used to define populations, such as ecoregions or counties, may not correspond to areas with different diameter distributions. We modified the space-time permutation scan statistic (STPSS), a disease outbreak detection technique, to identify and map areas in Pennsylvania, USA, where diameter distributions based on Forest Inventory and Analysis plots appeared different from the diameter distributions of the state as a whole. We used linear, exponential, and polynomial regressions to model the diameter distributions of the entire state and of areas indicated by the STPSS. The best fit regression models were linear and polynomial as determined by Akaike's information criterion and log likelihood statistics. Regression models confirmed that the STPSS identified areas where the diameter distributions of all species, oaks, and red maple differed from their corresponding statewide populations. Through a nested application of the STPSS at successively smaller spatial scales, we mapped core zones within each area where the difference was greatest.

Keywords: Acer rubrum; Pennsylvania; Quercus alba; Quercus prinus; Quercus rubra; forest health assessment; hot spot detection; size-class distribution

Document Type: Research Article

Publication date: October 1, 2008

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