Storlie Collin, Merino-Viteri Andres, Phillips Ben, VanDerWal Jeremy, Welbergen Justin, Williams Stephen
Centre for Tropical Biodiversity and Climate Change, College of Marine and Environmental Science, James Cook University, Townsville, Queensland 4810, Australia
Centre for Tropical Biodiversity and Climate Change, College of Marine and Environmental Science, James Cook University, Townsville, Queensland 4810, Australia Museo de Zoología, Escuela de Biología, Pontificia Universidad Católica del Ecuador, Quito, Ecuador.
Biol Lett. 2014 Sep;10(9). doi: 10.1098/rsbl.2014.0576.
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 km(2) 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.
为评估一个物种对气候变化的脆弱性，我们通常使用在时间和空间上分辨率较低的环境数据地图。分辨率较低的温度数据在预测生物体所使用的微生境中的温度时通常不准确，并且在地形复杂的地区可能也会表现出空间偏差。这些不准确的一个后果是，分辨率较低的图层可能会预测出一个地点的热状况超出物种已知的热极限。在本研究中，我们使用统计降尺度法来考虑环境因素，并对一个38年期间的36000平方公里研究区域的日最高温度进行高分辨率估算。然后我们证明，这种统计降尺度法所提供的温度估算能始终将重点物种置于其基本热生态位范围内，而分辨率较低的图层则不能。我们的结果凸显了将精细尺度的气象数据纳入物种脆弱性分析的必要性，并证明统计降尺度法能够得出与生物学相关的热状况估算。
