CONSTRUCTING MORTALITY MODELS FOR NATURAL FOREST STATE III IN FOUR PROVINCES OF THE CENTRAL REGION, VIETNAM - Thi Thu Hien, Cao (Journal of Forestry Science and Technology, 2018)
In this research, the dead trees were defined as the standing trees that died between the two occasions at which measurements were taken. The data on 300 subplots from 12 permanent sample plots were collected. The response variable was the number of dead trees per subplot. The results suggest that we successfully developed the mortality model by using both generalized linear and generalized linear mixed models for count data to address the problem of overdispersion. Arithmetic mean diameter of the subplot, plot basal area, and provinces as a categorical variable were found to be the most significant explanatory variable. With the generalized linear model, we found that the Negative Binomial GLM was the most appropriate model for predicting the number of recruitment for three groups. From using the provinces as a grouping variable, we realized that the mean numbers of dead trees was different in the four different locations, namely the number of dead trees for group 1 and group 2 in Thua Thien Hue were the highest, while in Ha Tinh these were the lowest. With the generalized linear mixed model, the Negative Binomial GLMM solves overdispersion by treating a plot as a random effect. The GLMM with random intercept was selected as the equation for the direct prediction of dead trees across each of the two species groups, and for N. melliferum. The GLMM with a random slope was chosen for S. wightianum.