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

Kiyoshi Umeki, Marc D. Abrams, Keisuke Toyama, Eri Nabeshima

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Article numbere019
Pages (from-to)1-12
Number of pages12
JournalForest Systems
Volume28
Issue number3
DOIs
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Forestry
  • Ecology, Evolution, Behavior and Systematics
  • Soil Science

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