IMHE OpenIR  > 山地表生过程与生态调控重点实验室
Uncertainty of future projections of species distributions in mountainous regions
Tang, Ying1,2; Winkler, Julie A.1; Vina, Andres2,3; Liu, Jianguo2; Zhang, Yuanbin4; Zhang, Xiaofeng5; Li, Xiaohong6; Wang, Fang2; Zhang, Jindong2; Zhao, Zhiqiang2
2018-01-10
Source PublicationPLOS ONE
ISSN1932-6203
Volume13Issue:1
SubtypeArticle
AbstractMultiple 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.
WOS HeadingsScience & Technology
DOI10.1371/journal.pone.0189496
WOS Subject ExtendedScience & Technology - Other Topics
Indexed BySCI
WOS KeywordCLIMATE-CHANGE IMPACT ; DISTRIBUTION MODELS ; GIANT PANDA ; QINLING MOUNTAINS ; UNITED-STATES ; TEMPERATURE ; HABITAT ; CHINA ; VULNERABILITY ; PREDICTIONS
Language英语
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000419689600011
Funding OrganizationNational Science Foundation(BIO-1340812) ; Michigan AgBioResearch
PublisherPUBLIC LIBRARY SCIENCE
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/20823
Collection山地表生过程与生态调控重点实验室
Corresponding AuthorWinkler, Julie A.
Affiliation1.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
Recommended Citation
GB/T 7714
Tang, Ying,Winkler, Julie A.,Vina, Andres,et al. Uncertainty of future projections of species distributions in mountainous regions[J]. PLOS ONE,2018,13(1).
APA Tang, Ying.,Winkler, Julie A..,Vina, Andres.,Liu, Jianguo.,Zhang, Yuanbin.,...&Zhao, Zhiqiang.(2018).Uncertainty of future projections of species distributions in mountainous regions.PLOS ONE,13(1).
MLA Tang, Ying,et al."Uncertainty of future projections of species distributions in mountainous regions".PLOS ONE 13.1(2018).
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