RESEARCH ARTICLE OPEN ACCESS A model for longitudinal data sets relating wind-damage probability to biotic and abiotic factors: a Bayesian approach Kiyoshi Umeki (Umeki, K) 1 , Marc D. Abrams (Abrams, MD) 2 , Keisuke Toyama (Toyama, K) 3 and Eri Nabeshima (Nabeshima, E) 4 1 Graduate School of Horticulture, Chiba University, 648 Matsudo, Matsudo, Chiba, 271-8510 Japan. 2 307 Forest Resources Building, Department of Ecosystem Science and Management, Penn State University, University Park, PA 16802 USA. 3 The University of Tokyo Chiba Forest, 770 Amatsu, Kamogawa, Chiba, 299‒5503 Japan. 4 Faculty of Agriculture, Ehime University, Tarumi, Matsuyama, Ehime, 790-8566 Japan. 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 fr–hemlock forest at UTCBF. Main results: Wind-damage probability was infuenced 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 fr–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. Additional 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). Authors´ contributions: Conceived and designed the study: KU and MDA. Collected data: KU, MDA, KT, and EN. Analyzed data: KU. Wrote the paper: KU. Revised the manuscript: MDA, KT, and EN. Citation: Umeki, K., Abrams, M.D., Toyama, K., Nabeshima, E. (2019). A model for longitudinal data sets relating wind-damage probability to biotic and abiotic factors: a Bayesian approach. Forest Systems, Volume 28, Issue 3, e019. https://doi.org/10.5424/ fs/2019283-15200 Supplementary material: (Tables S1, S2; Figures S1–S5 and Appendices A1, A2) accompany the paper on FS’s website. Received: 23 May 2019. Accepted: 15 Nov 2019. Copyright © 2019 INIA. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International (CC-by 4.0) License. Competing interests: The authors have declared that no competing interests exist. Correspondence should be addressed to Kiyoshi Umeki: umeki@faclty.chiba-u.jp Forest Systems 28 (3), e019, 12 pages (2019) eISSN: 2171-9845 https://doi.org/10.5424/fs/2019283-15200 Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Funding agencies/Institutions Project / Grant Ministry of Education, Culture, Sports, Science, and Technology of Japan 23380085, 23380079, and 26450205 Japan Society for the Promotion of Science (to the second author) Guest Researcher Award Introduction Wind is a major disturbance in the forests of the Japanese archipelago and in other parts of the world (e.g. Nakashizuka & Yamamoto, 1987; Peterson, 2000; Svoboda et al., 2014). Wind causes damage to established trees and provides opportunities for trees to regenerate in natural forests (Abrams & Orwig, 1996; Abrams et al., 1999). Many important features of natural forests, including structure, diversity, and dynamics, are infuenced by wind disturbances (e.g. Ulanova, 2000; Mitchell, 2013). However, these disturbances cause signifcant economic losses for forest managers (e.g. Peterson, 2000). Therefore, understanding wind disturbance in a given forest is key to ecologically and economically sustainable management. Furthermore,