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

Forest Systems

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Title A model for longitudinal data sets relating wind-damage probability to biotic and abiotic factors: a Bayesian approach
 
Creator Umeki, Kiyoshi
Abrams, Marc David
Toyama, Keisuke
Nabeshima, Eri
 
Description 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).
 
Publisher INIA
 
Contributor the Ministry of Education, Culture, Sports, Science, and Technology of Japan
the Japan Society for the Promotion of Science
Chris Bouma, Penn State University
the University of Tokyo Chiba Forest
Faculty of Horticulture, Chiba University
 
Date 2019-12-19
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
 
Format application/pdf
text/html
application/xml
 
Identifier http://revistas.inia.es/index.php/fs/article/view/15200
10.5424/fs/2019283-15200
 
Source Forest Systems; Vol 28, No 3 (2019); e019
Forest Systems; Vol 28, No 3 (2019); e019
2171-9845
 
Language eng
 
Relation http://revistas.inia.es/index.php/fs/article/view/15200/4554
http://revistas.inia.es/index.php/fs/article/view/15200/4570
http://revistas.inia.es/index.php/fs/article/view/15200/4569
http://revistas.inia.es/index.php/fs/article/downloadSuppFile/15200/14535
http://revistas.inia.es/index.php/fs/article/downloadSuppFile/15200/14536
http://revistas.inia.es/index.php/fs/article/downloadSuppFile/15200/14537
 
Rights info:eu-repo/semantics/openAccess
Copyright (c) 2019 Forest Systems
 

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