AimMost process‐based forest simulation models used to project the impacts of climate change on forest dynamics rely on thermal transfer functions (TTF) that describe the relationship between temperature and growth. However, these functions have faced criticism, undermining model forecasts and highlighting the need for more robust TTF based on large empirical datasets. In this study, we modelled growth response to growing degree‐days (GDD) of common tree species in eastern North America using an unprecedented dataset of over 1 million tree records from 29,809 permanent sample plots.LocationCanada and the United States.Time Period1958–2018.Major Taxa StudiedTree species.MethodsWe used quantile regression analysis to build TTF by modelling tree growth response to GDD for 16 tree species using a widely distributed data across North America. The newly fitted TTF were then used to project near‐term (2041–2070 time period) growth responses to climate warming and were compared with TTF currently applied in published modelling studies.ResultsOur results support the assumption of a parabolic growth response curve to GDD, but challenge the assumption of optimal growth occurring at the centre of the species' climatic range (as assumed by the climatic envelope approach commonly used to develop TTF). Compared to our empirically derived TTF, the TTF used in four well‐established, published forest simulation models tend to overestimate the negative impact of climate warming on the growth of cold‐adapted, boreal tree species, while underestimating it for some temperate species.Main ConclusionsOur results indicate that the selected published forest models often underestimate the optimum temperature under which maximum tree growth occurs for cold‐adapted, boreal species, suggesting greater resilience to climate change than previously forecasted. We recommend the application of this empirical approach to other tree species and integrating these more realistic parameters into existing modelling frameworks to improve ecological forecasting.

中文翻译：

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北方树的生长可能比以前考虑的更能耐受变暖：对未来建模研究的影响


Aim大多数基于过程的森林模拟模型用于预测气候变化对森林动态的影响，都依赖于描述温度与生长之间关系的热传递函数 （TTF）。然而，这些功能面临批评，破坏了模型预测，并强调了基于大型经验数据集的更稳健的 TTF 的必要性。在这项研究中，我们使用来自 29,809 个永久样本地的超过 100 万条树木记录的前所未有的数据集，对北美东部常见树种的生长响应进行了建模。地点加拿大和美国.时间段1958–2018.主要分类群研究的树种。方法我们使用分位数回归分析来构建 TTF，方法是使用北美广泛分布的数据对 16 个树种的树木生长对 GDD 的反应进行建模。然后使用新拟合的 TTF 来预测近期（2041-2070 年时间段）增长对气候变暖的反应，并与目前已发表的建模研究中应用的 TTF 进行比较。结果我们的结果支持对 GDD 的抛物线生长响应曲线的假设，但挑战了最佳生长发生在物种气候范围中心的假设（如通常用于发展 TTF 的气候包络法所假设的那样）。与我们根据经验得出的 TTF 相比，四个成熟的、已发布的森林模拟模型中使用的 TTF 往往高估了气候变暖对寒冷适应的北方树种生长的负面影响，而低估了某些温带物种的负面影响。主要结论我们的结果表明，选定的已发表森林模型经常低估了适应寒冷的北方物种树木生长最大的最佳温度，这表明对气候变化的适应能力比以前预测的要强。我们建议将这种实证方法应用于其他树种，并将这些更现实的参数整合到现有的建模框架中，以改善生态预测。