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<p>The Greek fir (<i>Abies cephalonica</i> Loudon), is a significant species in Greece of high ecological and economic value. However, despite its high importance, a notable lack of allometric models regarding its basic dendrometric attributes at tree level can be observed. The current study aimed to fill this knowledge gap. Based on a sample of 2873 well-documented fir trees from 47 sample plots in Parnassos Mt., in central Greece, nonlinear mixed-effects allometric models for the prediction of total height, crown width, height to live crown, and maximum crown width have been developed. The proposed models presented reasonable predictive performance, explaining 60% to 87% of the tree attributes variance in all cases, yielding modest prediction errors, while satisfying all the statistical assumptions in terms of fitting. The inclusion of statistically significant stand-level covariates improved the model's ability to explain a specific systematic variation in the response tree variables. The proposed models provided detailed information on tree crown characteristics, constituting valuable tools in a wider frame of sustainable forest management, as they can be linked with a range of forest models and products, offering insights into the complex interactions among tree attributes.</p>

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Allometric management models for Greek fir (Abies cephalonica Loudon) at Parnassos Mt., Greece

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月13日

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  • Evdoxia Syrmpa,
  • Dimitra Papadopoulou,
  • Thekla Tsitsoni,
  • Dimitrios I. Raptis

摘要

The Greek fir (Abies cephalonica Loudon), is a significant species in Greece of high ecological and economic value. However, despite its high importance, a notable lack of allometric models regarding its basic dendrometric attributes at tree level can be observed. The current study aimed to fill this knowledge gap. Based on a sample of 2873 well-documented fir trees from 47 sample plots in Parnassos Mt., in central Greece, nonlinear mixed-effects allometric models for the prediction of total height, crown width, height to live crown, and maximum crown width have been developed. The proposed models presented reasonable predictive performance, explaining 60% to 87% of the tree attributes variance in all cases, yielding modest prediction errors, while satisfying all the statistical assumptions in terms of fitting. The inclusion of statistically significant stand-level covariates improved the model's ability to explain a specific systematic variation in the response tree variables. The proposed models provided detailed information on tree crown characteristics, constituting valuable tools in a wider frame of sustainable forest management, as they can be linked with a range of forest models and products, offering insights into the complex interactions among tree attributes.