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Uncertainty in ensembles of global biodiversity scenarios

Author

Listed:
  • Wilfried Thuiller

    (Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d’Écologie Alpine)

  • Maya Guéguen

    (Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d’Écologie Alpine)

  • Julien Renaud

    (Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, Laboratoire d’Écologie Alpine)

  • Dirk N. Karger

    (Swiss Federal Research Institute WSL)

  • Niklaus E. Zimmermann

    (Swiss Federal Research Institute WSL)

Abstract

While there is a clear demand for scenarios that provide alternative states in biodiversity with respect to future emissions, a thorough analysis and communication of the associated uncertainties is still missing. Here, we modelled the global distribution of ~11,500 amphibian, bird and mammal species and project their climatic suitability into the time horizon 2050 and 2070, while varying the input data used. By this, we explore the uncertainties originating from selecting species distribution models (SDMs), dispersal strategies, global circulation models (GCMs), and representative concentration pathways (RCPs). We demonstrate the overwhelming influence of SDMs and RCPs on future biodiversity projections, followed by dispersal strategies and GCMs. The relative importance of each component varies in space but also with the selected sensitivity metrics and with species’ range size. Overall, this means using multiple SDMs, RCPs, dispersal assumptions and GCMs is a necessity in any biodiversity scenario assessment, to explicitly report associated uncertainties.

Suggested Citation

  • Wilfried Thuiller & Maya Guéguen & Julien Renaud & Dirk N. Karger & Niklaus E. Zimmermann, 2019. "Uncertainty in ensembles of global biodiversity scenarios," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-09519-w
    DOI: 10.1038/s41467-019-09519-w
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    Cited by:

    1. An T. N. Dang & Lalit Kumar & Michael Reid, 2020. "Modelling the Potential Impacts of Climate Change on Rice Cultivation in Mekong Delta, Vietnam," Sustainability, MDPI, vol. 12(22), pages 1-21, November.
    2. Veerkamp, C.J. & Loreti, M. & Benavidez, R. & Jackson, B & Schipper, A.M., 2023. "Comparing three spatial modeling tools for assessing urban ecosystem services," Ecosystem Services, Elsevier, vol. 59(C).
    3. Abdulwahab, Umarfarooq A. & Hammill, Edd & Hawkins, Charles P., 2022. "Choice of climate data affects the performance and interpretation of species distribution models," Ecological Modelling, Elsevier, vol. 471(C).
    4. Mariana García Criado & Isla H. Myers-Smith & Anne D. Bjorkman & Signe Normand & Anne Blach-Overgaard & Haydn J. D. Thomas & Anu Eskelinen & Konsta Happonen & Juha M. Alatalo & Alba Anadon-Rosell & Is, 2023. "Plant traits poorly predict winner and loser shrub species in a warming tundra biome," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    5. Joaquin Romano & Emilio Pérez-Chinarro & Byron V. Coral, 2020. "Network of Landscapes in the Sustainable Management of Transboundary Biosphere Reserves," Land, MDPI, vol. 9(9), pages 1-24, September.
    6. 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).
    7. Isaac Eckert & Andrea Brown & Dominique Caron & Federico Riva & Laura J. Pollock, 2023. "30×30 biodiversity gains rely on national coordination," Nature Communications, Nature, vol. 14(1), pages 1-11, December.

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