Predicting tree recruitment with negative binomial mixture models

摘要：

　　Tree recruitment model plays 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 forecast 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.

关键词： tree recruitment negative binomial model zero negative binomial model Hurdle negative binomial model Chinese Pine

作者: 张雄清 雷渊才 段爱国

作者单位: 中国林业科学研究院林业研究所北京 100091;国家林业局林木培育重点实验室 北京 100091 中国林业科学研究院资源信息研究所 北京 100091

会议类型: 国内会议

会议名称: 第十届中国林业青年学术论坛

会议地点: 南京

会议语种:英文

页码: 1-13

在线出版日期: 2012-09-01（万方平台首次上网日期，不代表论文的发表时间）