How performance metric choice influences individual tree mortality model selection

Abstract

Understanding tree mortality is crucial to understand forest dynamics and is essential for growth models and simulators. Although factors such as competition, drought, and pathogens drive mortality, their underlying mechanisms remain difficult to model. While substantial attention has focused on selecting appropriate algorithms and covariates, evaluating individual tree mortality models also requires careful selection of performance criteria. This study compares seven different metrics to assess their impact on model evaluation and selection. Results show that candidate models exhibited varying performances across metrics and that the choice of metric significantly influences the selection of the best model. When no confusion matrix was available, the area under the precision-recall curve (AUCPR) emerged as a more reliable alternative to the area under the ROC curve (AUC), offering a more informative assessment for imbalanced datasets. When a confusion matrix was available, Cohen’s Kappa coefficient (K) and Matthews correlation coefficient (MCC) outperformed accuracy-based metrics, providing a fairer evaluation of both live and dead tree classifications. These findings emphasize the importance of choosing appropriate evaluation standards to enhance mortality model assessment and ensure reliable predictions in forestry applications.