Modeling Tree Survival for Investigating Climate Change Effects

• 成果类型: Article; Early Access
• 署名作者: Augustin, Nicole; Albrecht, Axel; Anaya-Izquierdo, Karim; Davis, Alice; Meining, Stefan; Puhlmann, Heike; Wood, Simon
• 署名单位: University of Edinburgh
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2025.2516181
• 发表日期: 2025
• 关键词: additive-models; time-scale; prediction; CHOICE; pine

摘要

Using German forest health monitoring data we investigate the main drivers leading to tree mortality and the association between defoliation and mortality; in particular (a) whether defoliation is a proxy for other covariates (climate, soil, water budget); (b) whether defoliation is a tree response that mitigates the effects of climate change and (c) whether there is a threshold of defoliation which could be used as an early warning sign for irreversible damage. Results show that environmental drivers leading to tree mortality differ by species, but some are always required in the model. The defoliation effect on mortality differs by species but it is always strong and monotonic. There is some evidence that a defoliation threshold exists for spruce, fir, and beech. We model tree survival with a smooth additive Cox model allowing for random effects taking care of dependence between neighboring trees and nonlinear functions of spatial time varying and functional predictors on defoliation, climate, soil and hydrology characteristics. Due to the large sample size and large number of parameters, we use parallel computing combined with marginal discretization of covariates. We propose a boost forward penalize backward scheme based on combining component-wise gradient boosting with integrated backward selection. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.