Tumajer Jan, Begović Krešimir, Čada Vojtěch, Jenicek Michal, Lange Jelena, Mašek Jiří, Kaczka Ryszard J, Rydval Miloš, Svoboda Miroslav, Vlček Lukáš, Treml Václav
Department of Physical Geography and Geoecology, Faculty of Science, Charles University, Prague, Czech Republic.
Faculty of Forestry and Wood Science, Department of Forest Ecology, Czech University of Life Science, Prague, Czech Republic.
Glob Chang Biol. 2023 Jan;29(2):462-476. doi: 10.1111/gcb.16470. Epub 2022 Oct 20.
Radial tree growth is sensitive to environmental conditions, making observed growth increments an important indicator of climate change effects on forest growth. However, unprecedented climate variability could lead to non-stationarity, that is, a decoupling of tree growth responses from climate over time, potentially inducing biases in climate reconstructions and forest growth projections. Little is known about whether and to what extent environmental conditions, species, and model type and resolution affect the occurrence and magnitude of non-stationarity. To systematically assess potential drivers of non-stationarity, we compiled tree-ring width chronologies of two conifer species, Picea abies and Pinus sylvestris, distributed across cold, dry, and mixed climates. We analyzed 147 sites across the Europe including the distribution margins of these species as well as moderate sites. We calibrated four numerical models (linear vs. non-linear, daily vs. monthly resolution) to simulate growth chronologies based on temperature and soil moisture data. Climate-growth models were tested in independent verification periods to quantify their non-stationarity, which was assessed based on bootstrapped transfer function stability tests. The degree of non-stationarity varied between species, site climatic conditions, and models. Chronologies of P. sylvestris showed stronger non-stationarity compared with Picea abies stands with a high degree of stationarity. Sites with mixed climatic signals were most affected by non-stationarity compared with sites sampled at cold and dry species distribution margins. Moreover, linear models with daily resolution exhibited greater non-stationarity compared with monthly-resolved non-linear models. We conclude that non-stationarity in climate-growth responses is a multifactorial phenomenon driven by the interaction of site climatic conditions, tree species, and methodological features of the modeling approach. Given the existence of multiple drivers and the frequent occurrence of non-stationarity, we recommend that temporal non-stationarity rather than stationarity should be considered as the baseline model of climate-growth response for temperate forests.
径向树木生长对环境条件敏感，使得观测到的生长增量成为气候变化对森林生长影响的重要指标。然而，前所未有的气候变率可能导致非平稳性，即树木生长响应与气候随时间的脱钩，这可能会在气候重建和森林生长预测中引入偏差。关于环境条件、物种以及模型类型和分辨率是否以及在何种程度上影响非平稳性的发生和程度，人们知之甚少。为了系统地评估非平稳性的潜在驱动因素，我们汇编了分布在寒冷、干燥和混合气候区的两种针叶树种——欧洲云杉和欧洲赤松的年轮宽度年表。我们分析了欧洲的147个站点，包括这些物种的分布边缘以及适中的站点。我们校准了四个数值模型（线性与非线性、日分辨率与月分辨率），以根据温度和土壤湿度数据模拟生长年表。气候-生长模型在独立的验证期内进行测试，以量化其非平稳性，该非平稳性是基于自抽样传递函数稳定性测试进行评估的。非平稳性的程度在物种、站点气候条件和模型之间有所不同。与具有高度平稳性的欧洲云杉林分相比，欧洲赤松的年表显示出更强的非平稳性。与在寒冷和干燥物种分布边缘采样的站点相比，具有混合气候信号的站点受非平稳性的影响最大。此外，日分辨率的线性模型与月分辨率的非线性模型相比，表现出更大的非平稳性。我们得出结论，气候-生长响应中的非平稳性是一种多因素现象，由站点气候条件、树种和建模方法的方法学特征相互作用驱动。鉴于存在多种驱动因素且非平稳性频繁发生，我们建议应将时间非平稳性而非平稳性视为温带森林气候-生长响应的基线模型。
