عنوان مقاله [English]
نویسنده [English]چکیده [English]
In the current research, the performance of ordinary least square regression model was studied for the task of predicting a key forest structural parameter – tree height – across a study area in Tarbiat Modares University forest research station using a series of edaphic and topographic variables. For this purpose, 123 0.1 ha circular sample plots were established and total height and diameter of Fagus orientalis Lipsky trees with DBH ≥ 7.5 cm within each plot was recorded along with elevation, azimuth and slope of the ground. Also, at the center and four geographical aspects of sample plot, soil samples from first layer (0-10 cm) were taken and mixed for analyzing several soil variables. The results showed the OLS model performed moderately well based on R-squared, but exhibited clear signs of spatial autocorrelation (Moran’s I =0.168). Adding spatial weighted matrix in spatial simultaneous autoregressive models resulted in removing autocorrelation and statistically significant improvement in model fit. Comparison of spatial lag and error SAR models using AIC, log-likelihood and R squared indicated that error SAR model performs better than lag SAR model. These analyses underscore the importance of controlling for spatial autocorrelation in forest ecology studies and furnish guidelines for future modeling of species performance in relation to environmental predictors.