Modeling diameter distribution of natural oriental beech stands in the Almus region using the Weibull distribution

Oriental beech (Fagus orientalis Lipsky) ranks second among hardwood tree species in terms of area in Türkiye and ranks first in industrial wood production with a rate of 15%. In this context, reliable and accurate information on the status, growth and development characteristics of these forests is needed to create future planning and strategies for both the protection and sustainable management of Oriental beech forests. The information to be obtained with the help of diameter distribution models, which is one of the most important components of growth and yield models, can reveal possible stand structures that will emerge because of different silvicultural interventions to be applied to the stands. The Weibull distribution is used extensively by many researchers for the modeling of diameter distributions because it has a more flexible structure than other models and has a cumulative distribution function in closed form. In this study, diameter distribution models were developed for natural, even-aged, and pure oriental beech stands in the Tokat-Almus region using the Weibull distribution. Two different approaches, namely the Seemingly Unrelated Regression (SUR) and Modified Cumulative Distribution Function Regression (MCDF) to estimate the coefficients of the regression equation used for the estimation of the parameters of the Weibull distribution function. Four different parameter estimation methods, namely parameter estimation method, moment- and percentile-based parameter recovery methods, and hybrid method were used. According to the results, it was determined that the models developed with the MCDF approach were more successful than the SUR approach. In terms of parameter estimation methods, it was determined that the moment-based method in the models developed with the SUR approach, and the hybrid method in terms of MCDF gave the most successful results. The poorest performer was the parameter estimation method developed within the SUR approach.