Quantifying the benefit of early climate change mitigation in avoiding biodiversity loss

Journal name:
Nature Climate Change
Year published:
Published online

Climate change is expected to have significant influences on terrestrial biodiversity at all system levels, including species-level reductions in range size and abundance, especially amongst endemic species1, 2, 3, 4, 5, 6. However, little is known about how mitigation of greenhouse gas emissions could reduce biodiversity impacts, particularly amongst common and widespread species. Our global analysis of future climatic range change of common and widespread species shows that without mitigation, 57±6% of plants and 34±7% of animals are likely to lose ≥50% of their present climatic range by the 2080s. With mitigation, however, losses are reduced by 60% if emissions peak in 2016 or 40% if emissions peak in 2030. Thus, our analyses indicate that without mitigation, large range contractions can be expected even amongst common and widespread species, amounting to a substantial global reduction in biodiversity and ecosystem services by the end of this century. Prompt and stringent mitigation, on the other hand, could substantially reduce range losses and buy up to four decades for climate change adaptation.

At a glance


  1. Global greenhouse gas emissions and temperature rise in the AVOID scenarios.
    Figure 1: Global greenhouse gas emissions and temperature rise in the AVOID scenarios.

    a,b, Global greenhouse gas emissions (in gigatonnes of carbon equivalent per year, GtCeqyr−1; a) and projected annual global mean near-surface temperature rise in the AVOID scenarios (b), labelled A1B-xxxx-y-z, where xxxx refers to the year during which global greenhouse gas emissions peak, y refers to the rate (% yr−1) at which emissions subsequently decline, and z refers to whether the final emissions floor level is set to high (H) or low (L). The key in a also applies to b. The shaded bars provide a 10–90% range for temperature rise, and the solid lines indicate the median values. (see Supplementary Information for details).

  2. Proportion of species losing [ge]50% of their range by the 2080s under various dispersal and mitigation scenarios.
    Figure 2: Proportion of species losing ≥50% of their range by the 2080s under various dispersal and mitigation scenarios.

    af, Proportion of species losing ≥50% of their range by the 2080s with realistic dispersal, under the baseline scenario (red), and in the mitigation scenarios with emissions peaking in 2030 (green) or 2016 (blue), respectively, for plants (a), animals (b), amphibians (c), birds (d), mammals (e) and reptiles (f). The shaded areas show the uncertainties arising from use of a range of GCM patterns for creating downscaled climate projections, as well as over the use of two (green) or three (blue) different mitigation scenarios. Red lines show trends for emission pathway SRES A1B without mitigation; green and blue pathways show those with mitigation in which global greenhouse gas emissions peak in 2030 and in 2016, respectively. The corresponding green and blue dashed arrows in a show the adaptation time bought in the AVOID2030 and the AVOID2016 scenarios (2038–2080 and 2048–2080, respectively); the dashed arrows are represented by blue and green stars in bf.

  3. Species richness in the 2080s.
    Figure 3: Species richness in the 2080s.

    ad, Species richness of animal (a,c) and plant (b,d) species in the 2080s under realistic dispersal for the stringent mitigation case in which global greenhouse gas emissions peak in 2016 and are subsequently reduced at 5% annually (c,d) compared with the no mitigation case SRES A1B (a,b). The colour scale in a also applies to parts bd. e,f, The species richness change that is avoided by such mitigation. White areas are those where no data exist in the GBIF network. Species richness gains occur only on the edges of these white areas, where they are artefacts of data paucity, and hence are not shown. The colour scale in e also applies to f.


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Author information


  1. Tyndall Centre for Climate Change Research, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK

    • R. Warren &
    • J. Price
  2. Centre for Tropical Biodiversity and Climate Change, School of Marine and Tropical Biology, James Cook University, Townsville, Queensland 4811, Australia

    • J. VanDerWal,
    • J. A. Welbergen,
    • I. Atkinson,
    • L. P. Shoo &
    • S. E. Williams
  3. eResearch Centre, Division of Research and Innovation, James Cook University, Townsville, Queensland 4811, Australia

    • J. VanDerWal &
    • I. Atkinson
  4. International Center for Tropical Agriculture (CIAT), AA6713 Cali, Colombia

    • J. Ramirez-Villegas &
    • A. Jarvis
  5. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), c/o CIAT, AA6713 Cali, Colombia

    • J. Ramirez-Villegas &
    • A. Jarvis
  6. Institute for Climate and Atmospheric Science (ICAS), School of Earth and Environment, University of Leeds, Leeds LS2 9JT, UK

    • J. Ramirez-Villegas
  7. Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ, UK

    • T. J. Osborn
  8. School of Biological Sciences, The University of Queensland, St Lucia, Queensland 4072, Australia

    • L. P. Shoo
  9. Met Office Hadley Centre, Department of Meteorology, University of Reading, Reading EX1 3PB, UK

    • J. Lowe


J.P. assembled the team, coordinated and advised. R.W. generated and provided the climate projections in collaboration with T.J.O. and J.L. J.R-V. cleaned and processed the GBIF data. R.W., J.V., J.P., L.P.S., A.J. and S.E.W. designed the model experiments. J.V. performed the model experiments and analysis. R.W., J.V., J.A.W., J.R-V. and J.P. wrote the paper. I.A. facilitated and advised on computational issues surrounding modelling and data storage.

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The authors declare no competing financial interests.

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