IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0189496.html
   My bibliography  Save this article

Uncertainty of future projections of species distributions in mountainous regions

Author

Listed:
  • Ying Tang
  • Julie A Winkler
  • Andrés Viña
  • Jianguo Liu
  • Yuanbin Zhang
  • Xiaofeng Zhang
  • Xiaohong Li
  • Fang Wang
  • Jindong Zhang
  • Zhiqiang Zhao

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.

Suggested Citation

  • Ying Tang & Julie A Winkler & Andrés Viña & Jianguo Liu & Yuanbin Zhang & Xiaofeng Zhang & Xiaohong Li & Fang Wang & Jindong Zhang & Zhiqiang Zhao, 2018. "Uncertainty of future projections of species distributions in mountainous regions," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-23, January.
  • Handle: RePEc:plo:pone00:0189496
    DOI: 10.1371/journal.pone.0189496
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189496
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0189496&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0189496?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Julie A. Winkler, 2016. "Embracing Complexity and Uncertainty," Annals of the American Association of Geographers, Taylor & Francis Journals, vol. 106(6), pages 1418-1433, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jing Zhen & Xinyuan Wang & Qingkai Meng & Jingwei Song & Ying Liao & Bo Xiang & Huadong Guo & Chuansheng Liu & Ruixia Yang & Lei Luo, 2018. "Fine-Scale Evaluation of Giant Panda Habitats and Countermeasures against the Future Impacts of Climate Change and Human Disturbance (2015–2050): A Case Study in Ya’an, China," Sustainability, MDPI, vol. 10(4), pages 1-19, April.
    2. Wen-Dong Xie & Jia Jia & Kai Song & Chang-Li Bu & Li-Ming Ma & Ge-Sang Wang-Jie & Quan-Liang Li & Heng-Qing Yin & Feng-Yi Xu & Dui-Fang Ma & Xin-Hai Li & Yun Fang & Yue-Hua Sun, 2022. "Comparative Habitat Divergence and Fragmentation Analysis of Two Sympatric Pheasants in the Qilian Mountains, China," Land, MDPI, vol. 11(12), pages 1-14, November.
    3. Egarter Vigl, Lukas & Marsoner, Thomas & Schirpke, Uta & Tscholl, Simon & Candiago, Sebastian & Depellegrin, Daniel, 2021. "A multi-pressure analysis of ecosystem services for conservation planning in the Alps," Ecosystem Services, Elsevier, vol. 47(C).
    4. Moullec, Fabien & Barrier, Nicolas & Drira, Sabrine & Guilhaumon, François & Hattab, Tarek & Peck, Myron A. & Shin, Yunne-Jai, 2022. "Using species distribution models only may underestimate climate change impacts on future marine biodiversity," Ecological Modelling, Elsevier, vol. 464(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Julie A. Winkler & Logan Soldo & Ying Tang & Todd Forbush & David S. Douches & Chris M. Long & Courtney P. Leisner & C. Robin Buell, 2018. "Potential impacts of climate change on storage conditions for commercial agriculture: an example for potato production in Michigan," Climatic Change, Springer, vol. 151(2), pages 275-287, November.
    2. Kyle Lesinger & Di Tian & Courtney P. Leisner & Alvaro Sanz-Saez, 2020. "Impact of climate change on storage conditions for major agricultural commodities across the contiguous United States," Climatic Change, Springer, vol. 162(3), pages 1287-1305, October.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0189496. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.