Data from: Towards a common methodology for developing logistic tree mortality models based on ring-width data

Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based studies have aimed to derive growth–mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of alive and death observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. We assess the implications of key methodological decisions when developing tree-ring based growth–mortality relationships using logistic mixed-effects regression models. As examples, we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site), and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth–mortality relationships on the statistical sampling scheme used, (2) determining the type of explanatory growth variables that should be considered, and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring-based mortality models was reasonably high for all three species (area under the receiving operator characteristics curve, AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth–mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth–mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth–mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models.树木死亡率是塑造森林动态的核心过程，因此对树木死亡概率指示指标的需求日益增长。近数十年来，基于树轮（tree-ring）的相关研究愈发聚焦于生长-死亡率函数的推导，其中绝大多数采用逻辑回归模型。然而，由于采样策略（包含存活与死亡观测样本的数量及特征）、纳入的解释性生长变量类型（水平、趋势等）、以及使各生长变量区分能力达到最优的时间窗口长度（存活/死亡观测前的年数）存在多样性差异，此类研究的结果难以进行跨研究比较与综合。 本研究针对采用混合效应逻辑回归模型（logistic mixed-effects regression models）构建基于树轮的生长-死亡率关系时，关键方法学决策所带来的影响展开评估。作为示例，我们使用已发表的树轮数据集，涉及欧洲冷杉（Abies alba，13个样地）、假山毛榉（Nothofagus dombeyi，1个样地）以及无梗花栎（Quercus petraea，1个样地）。本研究基于固定采样量开展，旨在实现三大目标：（1）评估生长-死亡率关系对所采用统计采样方案的依赖性；（2）确定应纳入的解释性生长变量类型；（3）识别用于计算这些变量的最优时间窗口长度。 针对这三个物种，基于树轮的死亡率模型表现均较为优异：受试者工作特征曲线（receiving operator characteristics curve）下面积（AUC）>0.7。对于欧洲冷杉与假山毛榉两个物种而言，生长水平变量是死亡率概率最关键的预测因子，而无梗花栎则需纳入生长趋势变量。此外，用于计算各生长变量的时间窗口长度存在高度不确定性，且依赖于采样方案——部分生长-死亡率关系会随树木年龄发生变化。 本研究通过校正与采样相关的主要偏倚，得以确定可靠的物种特异性生长-死亡率关系。研究结果强调，采用与研究问题相一致的采样策略至关重要。建立一套通用方法以构建可靠的生长-死亡率关系，是提升我们对跨物种树木死亡率的理解、并改进其在动态植被模型中表征的重要一步。