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    BIOMASS EQUATIONS FOR CALABRIAN PINE IN THE MEDITERRANEAN REGION OF TURKEY
    (Croatian Forestry Soc, 2016) Sönmez, Turan; Kahriman, Aydin; Sahin, Abdurrahman; Yavuz, Mehmet
    The aim of this study was to develop allometric equations for the estimation of above-ground biomass components of Calabrian pine (Pinus brutia Ten.) tree in the Mediterranean Region of Turkey. Using regression analysis, different allometric equations were fitted for the tree components of the above-ground biomass using diameter at breast height (dbh) and tree height as estimators. Two hundred and ninety-two trees between 0.4 and 63.0 cm in dbh were randomly sampled throughout 292 natural, pure Calabrian pine stands in Turkey's Mediterranean Region, where it forms diverse stand structures. Finally, the allometric equations were developed for the tree components of the Calabrian pine tree for the stem, bark, branch, needle and total above-ground biomass. The stem, bark and total biomass equations explained more than 90% of the observed variability, while the branch and needle biomass equations explained 82% and 65%, respectively.
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    A novel approach to selecting a competition index: the effect of competition on individual-tree diameter growth of Calabrian pine
    (Canadian Science Publishing, Nrc Research Press, 2018) Kahriman, Aydin; Sahin, Abdurrahman; Sönmez, Turan; Yavuz, Mehmet
    In this study, we evaluated the performance of 18 competition indices composed of nine distance-dependent and nine distance-independent indices in explaining the variation in individual-tree diameter growth of Calabrian pine (Pinus brutia Ten.) in the central Mediterranean region of Turkey. The data were obtained from 432 sample plots with varying stand age, site index, and stand density. To evaluate the performance of each competition index, the mean square error reduction approach was used relative to the noncompetition. Also, this study compared fixed and mixed effects models to analyze diameter growth. Statistical analyses showed that the best distance-independent competition indices performed as well as the best distance-dependent competition indices. The distance-independent competition index of Schroder and Gadow (1999; Can. J. For. Res. 29(2): 280-283, doi:10.1139/x98-199) performed best and is recommended for use in future growth and yield models to be used in the central Mediterranean region of Turkey. Also, the best selection of competitive neighbors was achieved using the area of influence overlap method, whereas the fixed-radius and angle count sampling methods had no significant improvement in quantifying the competition effects. On the other hand, all mixed effects models provided much better fits than their fixed model counterparts.
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    Site index models for Calabrian pine in the central Mediterranean region of Turkey
    (Taylor & Francis Inc, 2018) Kahriman, Aydin; Sönmez, Turan; von Gadow, Klaus
    Stem analysis data of 432 trees were obtained from even-aged, pure natural stands of Calabrian pine in the central Mediterranean Region of Turkey. Eight dynamic site equations derived with the Generalized Algebraic Difference Approach (GADA) were compared, based on autoregressive analysis and a thorough evaluation of the goodness of fit. We used generalized nonlinear least squares methods for model fitting. The adjusted coefficients of determination (0.9825-0.9842), root-mean-square errors (0.8004-0.8435 m), and Akaike's information criterion differences (0-145) indicated a good fit of the eight site index equations. The Hossfeld equation (M3) provided the best result. The Durbin-Watson test statistic did not reveal an autocorrelation issue while the Hossfeld equation provided a satisfactory solution to the serial correlation problem in stem analysis data as time series using autoregressive modeling. This study presents new site index models for Calabrian pine forests in the central Mediterranean region of Turkey where it is the most important commercial tree species. The site index equation, based on the Hossfeld model is recommended for height growth prediction and site classification of Calabrian pine stands in the central Mediterranean region of Turkey, providing a new basis for growth prediction and yield estimation in these important forest ecosystems.