Development of artificial neural network (ANN) models to estimate stem tapers of individual trees in mixed Fagus orientalis and Abies nordmanniana subsp. Equi-trojani stands distributed in Karabük region of Turkey, and comparison of the ANN models with stem taper equations were aimed in this study. The measurements were obtained from 516 sample trees (238 for Oriental beech and 278 for Kazdağı fir) in mixed stands of Karabük region. The measurements included diameter at breast height, tree height, diameter at stump height, and diameters at intervals of 1 m along the stem. In total, 45 ANN models and four stem taper equations were developed. Estimation performances of ANN models and stem taper equations were compared using relative rankings according to seven goodness-of-fit criteria. As a result, the ANN models were more successful in estimation of stem taper for both tree species. The most successful ANN model structures were (i) the model using logistic function in hidden layer with 10 nodes and hyperbolic tangent function in output layer for Fagus orientalis, and (ii) the model using logistic function in hidden layer with 10 nodes and linear function in output layer for Abies nordmanniana subsp. Equi-trojani.

Generalized height (h)–diameter (d) models were developed for Crimean pine stands in the Kastamonu Regional Directorate of Forestry, northwestern Turkey. Nine generalized h-d models were fitted and evaluated based on data set consisting of 2697 h-d measurements obtained from 274 sample plots located in three ecoregions of the studied region. The best results were obtained by modified Richards h-d model accounting for the 93% of the total variance in height–diameter relationship. It is a known fact that h-d relationship can vary in ecological conditions, especially for regions with large areas and various ecological conditions such as Kastamonu. To investigate the differences of height–diameter relationship among the ecoregions located in Kastamonu region, nonlinear extra sum of squares method was performed. The test results showed that there were significant differences, especially between coastal and the inland ecoregions. When compared to reduced model with the same set of parameters for the whole region, full model with a different set of parameters for each ecoregion achieved better accuracy in estimating tree height. Besides, applying h-d model fitted for the coastal ecoregion to the others, or vice versa, could lead under- or over-estimates of tree heights by about 1.8%.

Investigation and numerical determination of the effects of ecologically different regions on forest growth are important issues to be considered for sustainable forest management. In this study, differences in growth patterns of Crimean pine (Pinus nigra subsp. pallasiana [Lamb.] Holmboe) stands among the ecoregions located in the Kastamonu region of northwestern Turkey were investigated. For this purpose, stand-level growth models for dominant height, number of trees, basal area, and volume were fitted using the data from 274 sample plots and 293 stem analysis in even-aged, pure, and productive Crimean pine stands. The nonlinear extra sum of squares method and graphical examinations revealed that growth characteristics of these Crimean pine stands were affected by ecoregional differences. Ecoregion-based growth models, which are more flexible and have the ability to reflect the ecoregional differences in growth patterns, were found to be superior to regional models. Applying general models without any ecoregional classification resulted in biases of 5.3%, 23.1%, 5.5%, and 6.2% in the estimates of dominant height, number of trees, basal area, and volume of Crimean pine stands, respectively. The results showed that ecological differences must be considered in studies on forest growth modeling and forest management. Investigation and numerical determination of the effects of ecoregional differences on forest growth are important issues to be considered for sustainable forest management. In this study, differences in growth patterns (dominant height, number of trees, stand basal area, and stand volume) of Crimean pine (Pinus nigra subsp. pallasiana [Lamb.] Holmboe) stands among the ecoregions located in the Kastamonu region of northwestern Turkey were investigated. Growth characteristics of the Crimean pine stands in the coastal ecoregion showed significant differences to those in the interior. Therefore, it was found necessary to use ecoregion-based growth models instead of a regional model for the entire region.

Kastamonu yöresi sarıçam (Pinus sylvestris L.) meşcerelerinin çap dağılımlarının modellendiği bu çalışmada, bazı meşcere özelliklerinin (ağaç sayısı, orta çap, sıklık, bonitet sınıfı, kapalılık, gelişim çağı ve meşcere tipi) en uygun dağılım fonksiyonunun belirlenmesi üzerine olan etkileri de araştırılmıştır. Çalışmada Orman Genel Müdürlüğü arşivinden temin edilen 890 adet örnek alan verisinden yararlanılmış ve çap dağılımlarının modellenmesinde Beta, Gamma (2 ve 3 parametreli), Johnson SB, Log-normal (2 ve 3 parametreli), Normal ve Weibull (2 ve 3 parametreli) olasılık yoğunluk fonksiyonları kullanılmıştır. En başarılı dağılım fonksiyonunun belirlenmesinde Kolmogorov-Smirnov (KS), Anderson-Darling (AD), Ki-kare (X2) ve Hata İndeksi (e) ölçütleri kullanılmış ve fonksiyonlar bu ölçütlere göre her bir örnek alan için başarı sıralamasına tabi tutulmuştur. En uygun fonksiyonun seçimi üzerine meşcere özelliklerinin etkileri ise Kruskal-Wallis ve Ki-kare testleri yardımıyla değerlendirilmiştir. Sonuç olarak, Johnson SB fonksiyonunun en başarılı olasılık yoğunluk fonksiyonu olduğu belirlenmiştir. Olasılık yoğunluk fonksiyonlarının en başarılı bulunma durumları üzerinde ağaç sayısı, orta çap, kapalılık, gelişim çağı ve meşcere tipi etkili bulunurken, sıklık ve bonitet sınıfının herhangi bir etkisinin olmadığı belirlenmiştir.