Local analysis of tree competition and growth

The relationships between the Local Indicator of Spatial Association (LISA) and traditional tree competition indices and individual tree growth were investigated. The results show that like most of the competition indices, LISA had moderate correlations with tree basal area growth. For predicting the tree basal area growth in a linear regression model, the local G(i) performed better than many (73%) competition indices at a plot aggregation level and had higher explanatory power than most (91%) competition indices at an individual plot level. LISA also had linear and strong relationships with some traditional competition indices, such as the Lorimer index. The relationships were stronger ((rho) over cap > 0.90) at an individual plot level than for all plots combined ((rho) over cap > 0.75). More importantly, LISA could be statistically tested to identify local clusters of trees of similar or dissimilar sizes, even though there was no discernible pattern as summarized by a global statistic of spatial autocorrelation. These significant "hot spots" (clusters of trees of similar sizes) or "cold spots" (clusters of trees of dissimilar sizes) indicated subareas in a forest stand where the competition among trees may be more severe than the average. Therefore, LISA can replace the traditional competition indices for exploring the competitive status of neighboring trees, investigating the relationships between tree competition and growth, and estimating individual tree growth as a predictor variable in a forest growth simulator. The hot spots or cold spots identified by LISA provide useful information for the design of silvicultural and management treatments, such as selection thinning. Furthermore, LISA can be readily incorporated into visualization tools, such as a geographic information system (GIS), because it provides georeferenced information at a local level. FOR. SCI. 49(6):938-955.