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

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)

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

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