文献类型：期刊文献

英文题名：Predicting tree recruitment with negative binomial mixture models

作者：Zhang, Xiongqing[1] Lei, Yuancai[1] Cai, Daoxiong[2] Liu, Fengqiang[3]

通信作者：Lei, YC[1]

机构：[1]Chinese Acad Forestry, Res Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Chinese Acad Forestry, Expt Ctr Trop Forestry, Pingxiang 532600, Guangxi, Peoples R China;[3]Swedish Univ Agr Sci, So Swedish Forest Res Ctr, S-23238 Alnarp, Sweden

年份：2012

卷号：270

起止页码：209-215

外文期刊名：FOREST ECOLOGY AND MANAGEMENT

收录：;EI(收录号：20121114855318);Scopus(收录号：2-s2.0-84862807136);WOS:【SCI-EXPANDED(收录号:WOS:000302986900024)】；

基金：The authors express their appreciation to State Forestry Administration (SFA), the Ministry of Science and Technology (MOST) and National Natural Science Foundation of China (NSFC) for fiscal support (Project Research Grants No. 201204510, No. 2005DIB5JI42 and No. 31170588) and the Inventory Institute of Beijing Forestry for its data. The authors also thank the two anonymous reviewers for improving the scientific quality of this manuscript and the editors for their careful work.

语种：英文

外文关键词：Tree recruitment; Negative binomial model; Zero-inflated negative binomial model; Hurdle negative binomial model; Chinese pine

摘要：Tree recruitment models play an important role in simulating stand dynamic processes. Periodic tree recruitment data from permanent plots tend to be overdispersed, and frequently contain an excess of zero counts. Such data have commonly been analyzed using count data models, such as Poisson model, negative binomial models (NB), zero-inflated models, and Hurdle models. Negative binomial mixture models (zero-inflated negative binomial model, ZINB; Hurdle negative binomial model, HNB) including NB model were used in this study to predict tree recruitments of Chinese pine (Pinus tabulaeformis) in Beijing. ZINB model and HNB model were suitable for dealing with excess zero counts, for which two equations are created: one predicting whether the count occurs (logistic function) and the other predicting differences on the occurrence of the count (NB model). Based on the model comparisons, the results showed that negative binomial mixture models performed well in modeling tree recruitment, and ZINB model was the best model of negative binomial mixture models. (C) 2012 Elsevier B.V. All rights reserved.