Data from: Fine-grain, large-domain climate models based on climate station and comprehensive topographic information improve microrefugia detection

Large-domain species distribution models (SDMs) fail to identify microrefugia, as they are based on climate estimates that are either too coarse or that ignore relevant topographic climate-forcing factors. Climate station data are considered inadequate to produce such estimates, a viewpoint we challenge here. Using climate stations and topographic data, we developed three sets of large-domain (450,000 km²), fine-grain (50 m) temperature grids accounting for different levels of topographic complexity. Using these fine-grain grids and the Worldclim data, we fitted SDMs for 78 alpine species over Sweden, and assessed over- versus underestimations of local extinction and area of microrefugia by comparing modelled distributions at species' rear edges. Accounting for well-known topographic climate-forcing factors improved our ability to model fine-scale climate, despite using only climate station data. This approach captured the effect of cool air pooling, distance to sea, and relative humidity on local-scale temperature, but the effect of solar radiation could not be accurately accounted for. Predicted extinction rate decreased with increasing spatial resolution of the climate models and with increasing number of topographic climate-forcing factors accounted for. About half of the microrefugia detected in the most topographically complete models were not detected in the coarser SDMs and in the models calibrated from climate variables extracted from elevation only. Although major limitations remain, climate station data can potentially be used to produce fine-grain topoclimate grids, opening up the opportunity to model local-scale ecological processes over large domains. Accounting for the topographic complexity encountered within landscapes permits the detection of microrefugia that would otherwise remain undetected. Topographic heterogeneity is likely to have a massive impact on species persistence, and should be included in studies on the effects of climate change.

大区域物种分布模型（Species Distribution Models，SDMs）无法识别微避难所（microrefugia），因其基于的气候估算要么空间分辨率过粗，要么忽略了关键的地形气候驱动因子。气候站点数据被普遍认为不足以生成此类气候估算，本文对此传统观点提出了挑战。本研究借助气候站点与地形数据，构建了三套覆盖45万平方千米大区域、空间分辨率为50米的温度格网，分别考量了不同等级的地形复杂度。利用这套高分辨率温度格网与Worldclim数据，我们为瑞典境内的78个高山物种拟合了物种分布模型，并通过对比各物种种群分布后缘的模拟结果，评估了局域灭绝事件与微避难所面积的高估与低估情况。尽管仅使用气候站点数据，但纳入已知的地形气候驱动因子后，我们对精细尺度气候的模拟能力得到了显著提升。该方法能够有效体现冷空气堆积、距海距离与相对湿度对局域温度的调控作用，但无法准确表征太阳辐射的影响。随着气候模型空间分辨率的提升，以及纳入的地形气候驱动因子数量增多，预测得到的物种灭绝率呈下降趋势。在地形因子最完备的模型中检测到的微避难所中，约有一半在分辨率较粗的物种分布模型，以及仅基于高程提取气候变量构建的模型中未被检出。尽管仍存在诸多主要局限，但气候站点数据有望用于生成高分辨率的地形气候格网，为大区域范围内局域尺度生态过程的模拟提供了新的可能。纳入景观内部的地形复杂度，能够检测到常规模拟方法无法发现的微避难所。地形异质性很可能对物种存续产生重大影响，因此应将其纳入气候变化影响相关研究之中。