Tang Ying, Winkler Julie A, Viña Andrés, Liu Jianguo, Zhang Yuanbin, Zhang Xiaofeng, Li Xiaohong, Wang Fang, Zhang Jindong, Zhao Zhiqiang
Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, Michigan, United States of America.
Center for Systems Integration and Sustainability, Department of Fisheries and Wildlife, Michigan State University, East Lansing, Michigan, United States of America.
PLoS One. 2018 Jan 10;13(1):e0189496. doi: 10.1371/journal.pone.0189496. eCollection 2018.
Multiple factors introduce uncertainty into projections of species distributions under climate change. The uncertainty introduced by the choice of baseline climate information used to calibrate a species distribution model and to downscale global climate model (GCM) simulations to a finer spatial resolution is a particular concern for mountainous regions, as the spatial resolution of climate observing networks is often insufficient to detect the steep climatic gradients in these areas. Using the maximum entropy (MaxEnt) modeling framework together with occurrence data on 21 understory bamboo species distributed across the mountainous geographic range of the Giant Panda, we examined the differences in projected species distributions obtained from two contrasting sources of baseline climate information, one derived from spatial interpolation of coarse-scale station observations and the other derived from fine-spatial resolution satellite measurements. For each bamboo species, the MaxEnt model was calibrated separately for the two datasets and applied to 17 GCM simulations downscaled using the delta method. Greater differences in the projected spatial distributions of the bamboo species were observed for the models calibrated using the different baseline datasets than between the different downscaled GCM simulations for the same calibration. In terms of the projected future climatically-suitable area by species, quantification using a multi-factor analysis of variance suggested that the sum of the variance explained by the baseline climate dataset used for model calibration and the interaction between the baseline climate data and the GCM simulation via downscaling accounted for, on average, 40% of the total variation among the future projections. Our analyses illustrate that the combined use of gridded datasets developed from station observations and satellite measurements can help estimate the uncertainty introduced by the choice of baseline climate information to the projected changes in species distribution.
多种因素给气候变化下物种分布的预测带来了不确定性。用于校准物种分布模型以及将全球气候模型(GCM)模拟结果降尺度到更精细空间分辨率时所选用的基线气候信息会带来不确定性,这对于山区而言是一个特别值得关注的问题,因为气候观测网络的空间分辨率往往不足以探测这些地区陡峭的气候梯度。我们使用最大熵(MaxEnt)建模框架以及分布在大熊猫山区地理范围内的21种林下竹类物种的出现数据,研究了从两种截然不同的基线气候信息来源获得的预测物种分布差异,一种来自粗尺度站点观测的空间插值,另一种来自精细空间分辨率的卫星测量。对于每种竹类物种,分别针对两个数据集校准MaxEnt模型,并将其应用于使用德尔塔方法降尺度的17个GCM模拟结果。与使用相同校准的不同降尺度GCM模拟之间相比,使用不同基线数据集校准的模型在竹类物种预测空间分布上的差异更大。就按物种预测的未来气候适宜面积而言,通过多因素方差分析进行量化表明,用于模型校准的基线气候数据集以及通过降尺度得到的基线气候数据与GCM模拟之间的相互作用所解释的方差之和,平均占未来预测总变化的40%。我们的分析表明,结合使用从站点观测和卫星测量得出的网格化数据集,有助于估计因基线气候信息选择而给预测物种分布变化带来的不确定性。