Spatial Autocorrelation in Growth of Undisturbed Natural Pine Stands Across Georgia

Abstract

Abstract Moran's I statistic measures the spatial autocorrelation in a random variable measured at discrete locations in space. Permutation procedures test the null hypothesis that the observed Moran's I value is no greater than that expected by chance. The spatial autocorrelation of gross basal area increment is analyzed for undisturbed, naturally regenerated stands in three Georgia forest types: loblolly, shortleaf, and slash pine. The analysis uses 0.4-ha permanent sample plots from a forest inventory that included two remeasurement intervals (1961-1972 and 1972-1982). We present a new statistic for exploratory spatial analyses, and this statistic revealed an anomalous cluster of unusually slow-growing shortleaf pine plots occurred in the mountains 100 km north of Atlanta. A regression model was used to predict gross basal area increment as a function of variables that describe local stand conditions, and no significant spatial autocorrelations existed in the regression residuals. This result suggests that the anomalous cluster of slow-growing plots can be explained by the spatial distribution of local stand conditions rather than spatial patterns of other possible causes such as air pollution, although alternative interpretations are possible. For. Sci. 40(2):314-328.