Predicting tree recruitment with negative binomial mixture models

• 作 者: 张雄清[1]雷渊才[2]段爱国[1]
• 机 构: 中国林业科学研究院林业研究所北京 100091
• 出 处: 第十届中国林业青年学术论坛
• 发表日期: 2012年期

　　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.