Individual Tree Mortality Model for Slash Pine in Florida: A Mixed Modeling Approach

Abstract

Tree mortality is an important biological process and should be incorporated in forest growth simulation models to improve their accuracy and biological authenticity. We developed individual tree mortality models for slash pine using data from north central Florida. We first fit mortality models with only fixed effects using a logistic model and then added a random effect to account for the multilevel nature of the data. We used a generalized linear mixed modeling (GLMM) framework to compare the outcomes of the two fitting processes. Predictions from both models were evaluated using receiver operating characteristics (ROC) curves. Area under the ROC curve was higher for predictions from the GLMM compared with the fixed effects logistic model. Subject-specific responses (including plot-level random effects in the model of individual trees) from the GLMM were better at predicting mortality. Similar results were obtained after performing a cross-validation of the models. Although the fixed effects accounted for regular mortality because of suppression and competition for resources, the plot-level random effect accounted for the effects of other unmeasured plot-level variables. In our models, dbh, height, competition, site index, and basal area per hectare were significant predictors.