Conditional inference trees in the assessment of tree mortality rates in the transitional mixed forests of Atlantic Canada

Abstract

Long-term predictions of forest dynamics, including forecasts of tree growth and mortality, are central to sustainable forest-management planning. Although often difficult to evaluate, tree mortality rates under different abiotic and biotic conditions are vital in defining the long-term dynamics of forest ecosystems. In this study, we have modeled tree mortality rates using conditional inference trees (CTREE) and multi-year permanent sample plot data sourced from an inventory with coverage of New Brunswick (NB), Canada. The final CTREE mortality model was based on four tree- and three stand-level terms together with two climatic terms. The correlation coefficient (R2) between observed and predicted mortality rates was 0.67. High cumulative annual growing degree-days (GDD) was found to lead to increased mortality in 18 tree species, including Betula papyrifera, Picea mariana, Acer saccharum, and Larix laricina. In another ten species, including Abies balsamea, Tsuga canadensis, Fraxinus americana, and Fagus grandifolia, mortality rates tended to be higher in areas with high incident solar radiation. High amounts of precipitation in NB’s humid maritime climate were also found to contribute to heightened tree mortality. The relationship between high GDD, solar radiation, and high mortality rates was particularly strong when precipitation was also low. This would suggest that although excessive soil water can contribute to heightened tree mortality by reducing the supply of air to the roots, occasional drought in NB can also contribute to increased mortality events. These results would have significant implications when considered alongside regional climate projections which generally entail both components of warming and increased precipitation.

Evaluation of Ecological Function Restoration Effect for Degraded Natural Forests in Xiaoxinganling, China

“…The horizontal and vertical structures are more complex [42], the canopy is more obvious, the utilization rate of light in the forest is higher, the intensity of light in the forest decreases gradually with the height, the proportion of direct light in the forest is lower, and the proportion of scattered light in the forest is higher and reasonably distributed [43,44]. At the same time, the amount of forest litter on the surface of mixed forests is greater and more complex than that of pure forests [45], which is more conducive to increasing the number and species of soil microorganisms [46,47]. The decomposition of forest litter can improve the soil and effectively improve the physical and chemical properties of the soil [48,49].…”

Intra‐daily variation of Escherichia coli concentrations in agricultural irrigation ponds

“…Conditional inference trees use hypothesis testing to determine covariates responsible for splitting the dataset into homogenous groups with respect to the output variable ( E. coli concentrations). This approach resolves complications of overfitting and covariate selection bias associated with traditional regression trees and uses a predetermined significance level (α = .05) as a split‐stopping criterion (Guan et al., 2021). The only constraint used in creating the cTREEs was that at least 10 samples must be present in the terminal nodes.…”

An Analysis of the Factors Affecting Forest Mortality and Research on Forecasting Models in Southern China: A Case Study in Zhejiang Province

“…By contrast, modelling stand-level mortality directly addresses these limitations. Previous studies have underscored the advantages of employing standlevel prediction models, emphasizing critical considerations in model selection and the choice of independent variables, asserting that mortality formulas should incorporate more detailed indicators such as tree species and terrain conditions at the stand level [18][19][20]. Furthermore, the integration of tree mortality with machine learning algorithms is relatively uncommon.…”