Data from: Does one model fit all? patterns of beech mortality in natural forests of three European regions

Large uncertainties characterize forest development under global climate change. Although recent studies have found widespread increased tree mortality, the patterns and processes associated with tree death remain poorly understood, thus restricting accurate mortality predictions. Yet, projections of future forest dynamics depend critically on robust mortality models, preferably based on empirical data rather than theoretical, not well-constrained assumptions. We developed parsimonious mortality models for individual beech (Fagus sylvatica) trees and evaluated their potential for incorporation in Dynamic Vegetation Models (DVMs). We used inventory data from nearly 19′000 trees from unmanaged forests in Switzerland, Germany and Ukraine, representing the largest dataset used to date for calibrating such models. Tree death was modelled as a function of size and growth, i.e., stem diameter (dbh) and relative basal area increment (relBAI), using generalized logistic regression accounting for unequal re-measurement intervals. To explain the spatial and temporal variability in mortality patterns, we considered a large set of environmental and stand characteristics. Validation with independent datasets was performed to assess model generality. Our results demonstrate strong variability in beech mortality that was independent of environmental or stand characteristics. Mortality patterns in Swiss and German strict forest reserves were dominated by competition processes as indicated by J-shaped mortality over tree size and growth. The Ukrainian primeval beech forest was additionally characterized by windthrow and a U-shaped size-mortality function. Unlike the mortality model based on Ukrainian data, the Swiss and German models achieved good discrimination and acceptable transferability when validated against each other. We thus recommend these two models to be incorporated and examined in DVMs. Their mortality predictions respond to climate change via tree growth, which is sufficient to capture the adverse effects of water availability and competition on the mortality probability of beech under current conditions.全球气候变化背景下，森林动态发展面临极大不确定性。尽管近期研究已观测到树木死亡率普遍上升，但与树木死亡相关的模式与过程仍未得到充分阐释，这限制了死亡率预测的准确性。然而，未来森林动态的预测高度依赖可靠的死亡率模型，理想情况下应基于实证数据而非约束不足的理论假设。我们针对单株欧洲山毛榉（Fagus sylvatica）构建了简约死亡率模型，并评估了其纳入动态植被模型（Dynamic Vegetation Models, DVMs）的潜力。我们使用了来自瑞士、德国与乌克兰未管理森林的近19000株树木的清查数据，这是目前用于校准此类模型的规模最大的数据集。我们以树木大小与生长指标（即胸径（diameter at breast height, dbh）与相对基面积增量（relative basal area increment, relBAI））为因变量，采用考虑不等复测间隔的广义逻辑回归构建树木死亡预测模型。为阐释死亡率模式的时空变异特征，我们纳入了大量环境与林分特征变量。我们通过独立数据集开展验证，以评估模型的泛化能力。研究结果表明，欧洲山毛榉死亡率存在显著变异，且该变异与环境或林分特征无关。瑞士与德国严格保护森林的死亡率模式以竞争过程为主导，表现为树木大小与生长量对应的死亡率呈J型分布。乌克兰原始山毛榉林的死亡率模式则额外受到风倒干扰影响，且其大小-死亡率函数呈U型分布。与基于乌克兰数据构建的死亡率模型不同，瑞士与德国的模型在互相验证时表现出良好的区分能力与可接受的迁移性。因此，我们建议将这两个模型纳入动态植被模型并开展进一步测试。这些模型的死亡率预测可通过树木生长响应气候变化，足以在当前气候条件下捕捉水分可获得性与竞争对山毛榉死亡概率的负面影响。