Abstract: [Objective] Simulating bark thickness for Betula alnoides plantations can help to estimate its wood volume, timber outturn and bark volume.[Method] The datasets including diameters of outside and inside bark and bark thickness at each stem segment were obtained through stem analysis of B. alnoides trees, and were randomly divided into two parts, about 75% for modeling and 25% for validating. Thirteen models were selected to fit with the above datasets, the least squaresmethod was used to estimate values of parameters, and significances(deviation from zero)of these parameters were examined by student's t test at the 0.05 level. Four statistics indexes such as bias(B), absolute bias(AB), mean square error(MSE) and determination coefficient(R²)were used to evaluate the goodness of fit for those models in which all parameters were significant. Student's paired t test was applied with the validation dataset to check the validity of these functions, and the models were discarded if the estimated values differed significantly from observed values. The presences of multicollinearity and heteroscedasticity were then detected for the remaining models, and the best bark models were ultimately selected for B. alnoides plantations.[Result] Among 13 models, Models(2)and(5)had at least one parameter without significant difference from zero at the 0.05 level, and should not be considered in the further analysis. The four statistical criteria and their relative ranking values were calculated for the remaining 11 models. For modeling bark thickness at breast height, the performances of Model(3)and(4)were almost the same and were better than that of Model(1); Model(7)did better than Model(6)for modeling bark thickness at any height; Model(8) showed better than Model(9)for fitting relative bark thickness; and compared to Models(10)and(12),Models(11)and(13) exhibited better in simulating diameter inside bark. The result of student's paired t test showed that Models(9),(12)and(13)had significant difference between observed and estimated values and should be excluded. Among the remaining 8 models, only Model(4)showed weak multicollinearity, while no multicollinearity existed in the other models. The results of analysis on residual plots and White test indicated that no heteroskedasticity exist in Models(1),(3)and(4),however, the Models(6),(7),(8),(10)and(11)had the heteroskedasticity, which were improved through variable transformation.[Conclusion] Models(3),(7),(8)and(11)were suitable for fitting bark thickness at breast height, bark thickness at any height, relative bark thickness and diameter inside bark, respectively. Due to the easily obtain of diameters at breast height, plantations ages and bark thickness at any height,these models are applicable in the practice of forest survey. Meanwhile, besides tree age, diameter at breast height and height, site factors also affect bark thickness,therefore,these factors should be considered so as to promote accuracy of the simulation on bark thickness for Betula alnoides.

Key words: Betula alnoides, bark regimes, relative ranking system, residual analysis