Uncertainty of future projections of species distributions in mountainous regions

被引:27
|
作者
Tang, Ying [1 ,2 ]
Winkler, Julie A. [1 ]
Vina, Andres [2 ,3 ]
Liu, Jianguo [2 ]
Zhang, Yuanbin [4 ]
Zhang, Xiaofeng [5 ]
Li, Xiaohong [6 ]
Wang, Fang [2 ]
Zhang, Jindong [2 ]
Zhao, Zhiqiang [2 ]
机构
[1] Michigan State Univ, Dept Geog Environm & Spatial Sci, E Lansing, MI 48824 USA
[2] Michigan State Univ, Dept Fisheries & Wildlife, Ctr Syst Integrat & Sustainabil, E Lansing, MI 48824 USA
[3] Univ N Carolina, Dept Geog, Chapel Hill, NC USA
[4] Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu, Sichuan, Peoples R China
[5] Shaanxi Forestry Dept, Xian, Shaanxi, Peoples R China
[6] Tianshui Normal Univ, Tianshui, Gansu, Peoples R China
来源
PLOS ONE | 2018年 / 13卷 / 01期
基金
美国国家科学基金会;
关键词
CLIMATE-CHANGE; DISTRIBUTION MODELS; GIANT PANDA; QINLING MOUNTAINS; TEMPERATURE; HABITAT; VULNERABILITY; PREDICTIONS; COMPLEXITY; EXPANSION;
D O I
10.1371/journal.pone.0189496
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
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页数:23
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