Data from: Stepping inside the niche: microclimate data are critical for accurate assessment of species’ vulnerability to climate change

To assess a species' vulnerability to climate change, we commonly use mapped environmental data that are coarsely resolved in time and space. Coarsely resolved temperature data are typically inaccurate at predicting temperatures in microhabitats used by an organism and may also exhibit spatial bias in topographically complex areas. One consequence of these inaccuracies is that coarsely resolved layers may predict thermal regimes at a site that exceed species' known thermal limits. In this study, we use statistical downscaling to account for environmental factors and develop high-resolution estimates of daily maximum temperatures for a 36 000 km2 study area over a 38-year period. We then demonstrate that this statistical downscaling provides temperature estimates that consistently place focal species within their fundamental thermal niche, whereas coarsely resolved layers do not. Our results highlight the need for incorporation of fine-scale weather data into species' vulnerability analyses and demonstrate that a statistical downscaling approach can yield biologically relevant estimates of thermal regimes.为评估物种对气候变化的脆弱性，当前学界普遍采用时间与空间分辨率均较低的空间制图环境数据。低分辨率气温数据往往无法准确预测生物所处微生境的实际温度，且在地形复杂区域易产生空间偏差。此类精度不足带来的一项后果是，低分辨率图层可能会预测某一站点的热环境状况超出物种已知的耐热阈值。本研究采用统计降尺度（statistical downscaling）方法整合环境影响因子，为一处面积36000平方千米的研究区域构建了38年间的逐日最高气温高分辨率估算结果。随后本研究验证发现，经统计降尺度得到的气温估算值，可始终将目标物种的分布限定在其基础热生态位范围内，而低分辨率图层则无法达成这一效果。本研究结果凸显了将精细尺度气象数据纳入物种脆弱性分析的必要性，同时证明统计降尺度方法可生成具备生物学相关性的热环境状况估算结果。