Extreme weather can have significant impacts on plant species demography; however, most studies have focused on responses to a single or small number of extreme events. Long‐term patterns in climate extremes, and how they have shaped contemporary distributions, have rarely been considered or tested. BIOCLIM variables that are commonly used in correlative species distribution modelling studies cannot be used to quantify climate extremes, as they are generated using long‐term averages and therefore do not describe year‐to‐year, temporal variability. We evaluated the response of 37 plant species to base climate (long‐term means, equivalent to BIOCLIM variables), variability (standard deviations) and extremes of varying return intervals (defined using quantiles) based on historical observations. These variables were generated using fine‐grain (approx. 250 m), time‐series temperature and precipitation data for the hottest, coldest and driest months over 39 years. Extremes provided significant additive improvements in model performance compared to base climate alone and were more consistent than variability across all species. Models that included extremes frequently showed notably different mapped predictions relative to those using base climate alone, despite often small differences in statistical performance as measured as a summary across sites. These differences in spatial patterns were most pronounced at the predicted range margins, and reflect the influence of coastal proximity, continentality, topography and orographic barriers on climate extremes. Species occupying hotter and drier locations that are exposed to severe maximum temperature extremes were associated with better predictive performance when modelled using extremes. Understanding how plant species have historically responded to climate extremes may provide valuable insights into our understanding of contemporary distributions and help to make more accurate predictions under a changing climate.

中文翻译：

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使用每月时间序列数据生成的气候极端变量可改善植物物种的预测分布

极端天气可能会对植物物种人口统计学产生重大影响；但是，大多数研究都集中在对单个或少量极端事件的响应上。极少有人考虑或测试极端气候下的长期格局及其对当代分布的影响。相关物种分布建模研究中常用的BIOCLIM变量不能用于量化极端气候，因为它们是使用长期平均值生成的，因此无法描述逐年的时间变异性。我们根据历史观测资料评估了37种植物对基础气候（长期平均值，相当于BIOCLIM变量），变异性（标准差）和变化的回报间隔（使用分位数定义）的极端值的响应。这些变量是使用细粒度（约 250 m），时间序列的温度和降水数据为39年中最热，最冷和最干燥的月份。与单独的基本气候相比，极端事件在模型性能方面提供了显着的附加改进，并且比所有物种的变异性都更加一致。相对于仅使用基准气候的模型，包含极端值的模型通常显示出明显不同的映射预测，尽管统计性能之间通常存在很小的差异，以不同站点的汇总来衡量。这些空间格局的差异在预计的范围边缘最为明显，反映了沿海邻近性，大陆性，地形和地形障碍对极端气候的影响。使用极端模型进行建模时，处于极端高温条件下且处于较热和较干燥位置的物种与更好的预测性能相关。了解植物物种在历史上如何应对极端气候可能为我们对当代分布的理解提供有价值的见解，并有助于在气候变化的情况下做出更准确的预测。