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

  • Kiyoshi Umeki Graduate School of Horticulture, Chiba University, 648 Matsudo, Matsudo, Chiba, 271-8510 Japan http://orcid.org/0000-0003-4412-2117
  • Marc David Abrams 307 Forest Resources Building, Department of Ecosystem Science and Management, Penn State University, University Park, PA 16802 USA
  • Keisuke Toyama The University of Tokyo Chiba Forest, 770 Amatsu, Kamogawa, Chiba, 299‒5503 Japan http://orcid.org/0000-0003-4069-3895
  • Eri Nabeshima Faculty of Agriculture, Ehime University, Tarumi, Matsuyama, Ehime, 790-8566 Japan


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.

Research highlights: The model framework presented in this study can accommodate data with temporal interdependencies, and the resultant model can predict past and future patterns in wind disturbances. Thus, we have provided a basic model framework that allows for better understanding of past forest dynamics and appropriate future management planning.

Keywords: dendrochronology; tree regeneration; wind-damage probability model; wind disturbance.

Abbreviations used: intrinsic CAR model (intrinsic conditional autoregressive model); MCMC (Markov chain Monte Carlo); 16 compass points = N, NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, NNW (north, north-northeast, northeast, east-northeast, east, east-southeast, southeast, south-southeast, south, south-southwest, southwest, west-southwest, west, west-northwest, northwest, north-northwest, respectively); UTCBF (the University of Tokyo Chiba Forest).


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Author Biography

Kiyoshi Umeki, Graduate School of Horticulture, Chiba University, 648 Matsudo, Matsudo, Chiba, 271-8510 Japan

Graduate School of Horticulture

Associate Professor


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How to Cite
UmekiK., AbramsM. D., ToyamaK., & NabeshimaE. (2019). A model for longitudinal data sets relating wind-damage probability to biotic and abiotic factors: a Bayesian approach. Forest Systems, 28(3), e019. https://doi.org/10.5424/fs/2019283-15200
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