School of Geographical Sciences and Urban Planning, Arizona State University,Tempe, AZ 85287-5302, USA.
Glob Chang Biol. 2013 Feb;19(2):473-83. doi: 10.1111/gcb.12051. Epub 2012 Nov 9.
Recent studies suggest that species distribution models (SDMs) based on fine-scale climate data may provide markedly different estimates of climate-change impacts than coarse-scale models. However, these studies disagree in their conclusions of how scale influences projected species distributions. In rugged terrain, coarse-scale climate grids may not capture topographically controlled climate variation at the scale that constitutes microhabitat or refugia for some species. Although finer scale data are therefore considered to better reflect climatic conditions experienced by species, there have been few formal analyses of how modeled distributions differ with scale. We modeled distributions for 52 plant species endemic to the California Floristic Province of different life forms and range sizes under recent and future climate across a 2000-fold range of spatial scales (0.008-16 km(2) ). We produced unique current and future climate datasets by separately downscaling 4 km climate models to three finer resolutions based on 800, 270, and 90 m digital elevation models and deriving bioclimatic predictors from them. As climate-data resolution became coarser, SDMs predicted larger habitat area with diminishing spatial congruence between fine- and coarse-scale predictions. These trends were most pronounced at the coarsest resolutions and depended on climate scenario and species' range size. On average, SDMs projected onto 4 km climate data predicted 42% more stable habitat (the amount of spatial overlap between predicted current and future climatically suitable habitat) compared with 800 m data. We found only modest agreement between areas predicted to be stable by 90 m models generalized to 4 km grids compared with areas classified as stable based on 4 km models, suggesting that some climate refugia captured at finer scales may be missed using coarser scale data. These differences in projected locations of habitat change may have more serious implications than net habitat area when predictive maps form the basis of conservation decision making.
最近的研究表明，基于细尺度气候数据的物种分布模型（SDMs）可能会比粗尺度模型对气候变化的影响提供明显不同的估计。然而，这些研究在尺度如何影响预测物种分布的结论上存在分歧。在崎岖的地形中，粗尺度气候网格可能无法在构成某些物种的微生境或避难所的尺度上捕获地形控制的气候变化。虽然更细尺度的数据被认为可以更好地反映物种所经历的气候条件，但很少有正式的分析研究表明模型分布如何随尺度而变化。我们为加利福尼亚植物区系特有、不同生活型和分布范围大小的 52 个植物物种，在最近和未来气候条件下，在 2000 倍的空间尺度（0.008-16 平方公里）上建模。我们通过分别将 4 公里气候模型下推到三个更精细的分辨率（基于 800、270 和 90 米数字高程模型）并从中得出生物气候预测因子，为当前和未来气候生成了独特的数据集。随着气候数据分辨率变得越来越粗糙，SDM 预测的栖息地面积越来越大，而精细和粗糙尺度预测之间的空间一致性则逐渐减少。这些趋势在最粗糙的分辨率下最为明显，并且取决于气候情景和物种的分布范围大小。平均而言，与 800 米数据相比，SDM 预测在 4 公里气候数据上的稳定栖息地增加了 42%（预测当前和未来气候适宜栖息地之间的空间重叠量）。我们发现，与基于 4 公里模型的分类相比，基于 90 米模型推广到 4 公里网格的稳定预测区域之间只有适度的一致性，这表明在使用较粗尺度数据时可能会错过一些在更细尺度上捕获的气候避难所。与净栖息地面积相比，预测的栖息地变化位置的这些差异在预测图构成保护决策的基础时可能具有更严重的影响。
