Uncertainty of future projections of species distributions in mountainous regions

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  • Tang, Ying
    • Other Affiliation: Center for Systems Integration and Sustainability; Department of Fisheries and Wildlife; Michigan State University
  • Winkler, Julie A.
    • Other Affiliation: Department of Geography; Environment; and Spatial Sciences; Michigan State University
  • Viña, Andrés
    • Affiliation: College of Arts and Sciences, Department of Geography
    • Other Affiliation: Center for Systems Integration and Sustainability; Department of Fisheries and Wildlife; Michigan State University
  • Liu, Jianguo
    • Other Affiliation: Center for Systems Integration and Sustainability; Department of Fisheries and Wildlife; Michigan State University
  • Zhang, Yuanbin
    • Other Affiliation: Institute of Mountain Hazards and Environment; Chinese Academy of Sciences
  • Zhang, Xiaofeng
    • Other Affiliation: Shaanxi Forestry Department
  • Li, Xiaohong
    • Other Affiliation: Tianshui Normal University
  • Wang, Fang
    • Other Affiliation: Center for Systems Integration and Sustainability; Department of Fisheries and Wildlife; Michigan State University
  • Zhang, Jindong
    • Other Affiliation: Center for Systems Integration and Sustainability; Department of Fisheries and Wildlife; Michigan State University
  • Zhao, Zhiqiang
    • Other Affiliation: Center for Systems Integration and Sustainability; Department of Fisheries and Wildlife; Michigan State University
Abstract
  • 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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  • Article
Rights statement
  • In Copyright
Journal title
  • PloS One
Journal volume
  • 13
Journal issue
  • 1
Page start
  • e0189496
Language
  • English
ISSN
  • 1932-6203

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