A model for longitudinal data sets relating wind-damage probability to biotic and abiotic factors: a Bayesian approach

• Umeki, Kiyoshi ^[1] ; Abrams, Marc David ^[2] ; Toyama, Keisuke ^[3] ; Nabeshima, Eri ^[4]
  1. [1] Chiba University

     Chiba University

     Chūō-ku, Japón

     
  2. [2] Penn State Milton S. Hershey Medical Center

     Penn State Milton S. Hershey Medical Center

     Township of Derry, Estados Unidos

     
  3. [3] University of Tokyo

     University of Tokyo

     Japón

     
  4. [4] Ehime University

     Ehime University

     Japón

     

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• Localización: Forest systems, ISSN 2171-5068, ISSN-e 2171-9845, Vol. 28, Nº. 3, 2019
• Idioma: inglés
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  • Aim of study: To develop a statistical model framework to analyze longitudinal wind-damage records while accounting for autocorrelation, and to demonstrate the usefulness of the model in understanding the regeneration process of a natural forest.Area of study: University of Tokyo Chiba Forest (UTCBF), southern Boso peninsula, Japan.Material and methods: We used the proposed model framework with wind-damage records from UTCBF and wind metrics (speed, direction, season, and mean stand volume) from 1905–1985 to develop a model predicting wind-damage probability for the study area. Using the resultant model, we calculated past wind-damage probabilities for UTCBF. We then compared these past probabilities with the regeneration history of major species, estimated from ring records, in an old-growth fir–hemlock forest at UTCBF.Main results: Wind-damage probability was influenced by wind speed, direction, and mean stand volume. The temporal pattern in the expected number of wind-damage events was similar to that of evergreen broad-leaf regeneration in the old-growth fir–hemlock forest, indicating that these species regenerated after major wind disturbances.