Evaluating individual-based tree mortality modeling with temporal observation data collected from a large forest plot

Abstract

In forests, tree mortality strongly determines forest dynamics, creates gaps for recruitment and contributes to the coexistence of tree species. Individual-based models (IBMs) of tree mortality are key submodels in forest gap models and have been shown to drive simulated long-term forest dynamics. However, tree mortality IBMs remain poorly evaluated at the stand scale, particularly quantitatively, due to the lack of adequate tree mortality demographic data. Tree mortality dynamic data have often been absent from previous mortality IBMs, resulting in difficulty during model evaluation at the stand scale. The goals of this study were (1) to develop a spatially-explicit tree mortality IBM derived from the FORSKA forest gap model and (2) to evaluate its performance by both qualitative and quantitative fits based on a 10-year interval of tree mortality demographic data under a 9-ha forest dynamics plot (FDP) in an old-growth temperate forest in Northeast China. The results showed that, for model qualitative evaluation, the observed dead trees were mainly distributed in the southwest and northeast parts, but the predicted dead trees were centrally clustered only in the southwest part of the FDP. For model quantitative evaluation, the overall model error (Error (%)) for all trees was −10.5%. However, the absolute values of model error for some species (i.e., Acer spp., Betula spp., Tilia spp., Fraxinus mandschurica, and Ulmus spp.) and some diameter at breast height (DBH) classes (i.e., 30–40, 40–50, 50–60, 60–70, and ≥70 cm) were greater than or equal to 20%. Our study highlights that the tree mortality IBM at the stand scale still requires further improvement to enhance the model performance and that the FDPs could provide crucial data support for initialization, parameterization and evaluation in the tree mortality IBM at the stand scale in forest dynamic modeling.