As anthropogenic climate change continues the risks to biodiversity will increase over time, with future projections indicating that a potentially catastrophic loss of global biodiversity is on the horizon1,2,3. However, our understanding of when and how abruptly this climate-driven disruption of biodiversity will occur is limited because biodiversity forecasts typically focus on individual snapshots of the future. Here we use annual projections (from 1850 to 2100) of temperature and precipitation across the ranges of more than 30,000 marine and terrestrial species to estimate the timing of their exposure to potentially dangerous climate conditions. We project that future disruption of ecological assemblages as a result of climate change will be abrupt, because within any given ecological assemblage the exposure of most species to climate conditions beyond their realized niche limits occurs almost simultaneously. Under a high-emissions scenario (representative concentration pathway (RCP) 8.5), such abrupt exposure events begin before 2030 in tropical oceans and spread to tropical forests and higher latitudes by 2050. If global warming is kept below 2 °C, less than 2% of assemblages globally are projected to undergo abrupt exposure events of more than 20% of their constituent species; however, the risk accelerates with the magnitude of warming, threatening 15% of assemblages at 4 °C, with similar levels of risk in protected and unprotected areas. These results highlight the impending risk of sudden and severe biodiversity losses from climate change and provide a framework for predicting both when and where these events may occur.
Subscribe to Journal
Get full journal access for 1 year
only $3.90 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
All datasets used here are publicly available. Expert verified range maps are available from https://www.iucnredlist.org/resources/spatial-data-download and http://datazone.birdlife.org/species/requestdis. Climate change projections for RCP 8.5, RCP 4.5 and RCP 2.6 for CMIP5 are available from https://esgf-node.llnl.gov/search/cmip5/. Maps of projected risk to biodiversity from climate change are available to view at https://climatehorizons.users.earthengine.app/view/biodiversity-risk.
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).
Warren, R., Price, J., Graham, E., Forstenhaeusler, N. & VanDerWal, J. The projected effect on insects, vertebrates, and plants of limiting global warming to 1.5 °C rather than 2 °C. Science 360, 791–795 (2018).
Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B 285, 20180792 (2018).
Weber, C. et al. Mitigation scenarios must cater to new users. Nat. Clim. Change 8, 845–848 (2018).
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).
Hughes, T. P. et al. Global warming transforms coral reef assemblages. Nature 556, 492–496 (2018).
Barnosky, A. D. et al. Approaching a state shift in Earth’s biosphere. Nature 486, 52–58 (2012).
Scheffer, M. et al. Early-warning signals for critical transitions. Nature 461, 53–59 (2009).
Harris, R. M. B. et al. Biological responses to the press and pulse of climate trends and extreme events. Nat. Clim. Change 8, 579–587 (2018).
Bay, R. A., Rose, N. H., Logan, C. A. & Palumbi, S. R. Genomic models predict successful coral adaptation if future ocean warming rates are reduced. Sci. Adv. 3, e1701413 (2017).
Chevin, L.-M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).
Colwell, R. K. & Rangel, T. F. Hutchinson’s duality: the once and future niche. Proc. Natl Acad. Sci. USA 106, 19651–19658 (2009).
Feeley, K. J. & Silman, M. R. Biotic attrition from tropical forests correcting for truncated temperature niches. Glob. Change Biol. 16, 1830–1836 (2010).
The IUCN Red List of Threatened Species https://www.iucnredlist.org/ (IUCN, 2017).
van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).
Stuart-Smith, R. D., Edgar, G. J. & Bates, A. E. Thermal limits to the geographic distributions of shallow-water marine species. Nat. Ecol. Evol. 1, 1846–1852 (2017).
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).
Hawkins, E. & Sutton, R. Time of emergence of climate signals. Geophys. Res. Lett. 39, L01702 (2012).
Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).
Colwell, R. K., Brehm, G., Cardelús, C. L., Gilman, A. C. & Longino, J. T. Global warming, elevational range shifts, and lowland biotic attrition in the wet tropics. Science 322, 258–261 (2008).
IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).
Williams, J. W., Jackson, S. T. & Kutzbach, J. E. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl Acad. Sci. USA 104, 5738–5742 (2007).
Liautaud, K., van Nes, E. H., Barbier, M., Scheffer, M. & Loreau, M. Superorganisms or loose collections of species? A unifying theory of community patterns along environmental gradients. Ecol. Lett. 22, 1243–1252 (2019).
Araújo, M. B. et al. Heat freezes niche evolution. Ecol. Lett. 16, 1206–1219 (2013).
Crisp, M. D. et al. Phylogenetic biome conservatism on a global scale. Nature 458, 754–756 (2009).
White, A. E., Dey, K. K., Mohan, D., Stephens, M. & Price, T. D. Regional influences on community structure across the tropical–temperate divide. Nat. Commun. 10, 2646 (2019).
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).
Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).
Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).
Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).
Mahony, C. R. & Cannon, A. J. Wetter summers can intensify departures from natural variability in a warming climate. Nat. Commun. 9, 783 (2018).
Valladares, F. et al. The effects of phenotypic plasticity and local adaptation on forecasts of species range shifts under climate change. Ecol. Lett. 17, 1351–1364 (2014).
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).
Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).
Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).
Lister, B. C. & Garcia, A. Climate-driven declines in arthropod abundance restructure a rainforest food web. Proc. Natl Acad. Sci. USA 115, E10397–E10406 (2018).
Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).
Burke, K. D. et al. Pliocene and Eocene provide best analogs for near-future climates. Proc. Natl Acad. Sci. USA 115, 13288–13293 (2018).
Bird Species Distribution Maps of the World v.2.0 (Birdlife International, 2012).
Brinton, E., Ohman, M. D., Townsend, A. W., Knight, M. D. & Bridgeman, A. L. Euphausiids of the World Ocean (Springer, 2000).
Jereb, P. & Roper, C. F. E. (eds) Cephalopods of the World: An Annotated and Illustrated Catalogue of Cephalopod Species Known to Date Vol. 1 (FAO, 2005).
Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).
Hurlbert, A. H. & Jetz, W. Species richness, hotspots, and the scale dependence of range maps in ecology and conservation. Proc. Natl Acad. Sci. USA 104, 13384–13389 (2007).
Jetz, W., Sekercioglu, C. H. & Watson, J. E. M. Ecological correlates and conservation implications of overestimating species geographic ranges. Conserv. Biol. 22, 110–119 (2008).
Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).
Faurby, S. & Araújo, M. B. Anthropogenic range contractions bias species climate change forecasts. Nat. Clim. Change 8, 252–256 (2018).
Schulzweida, U. CDO User Guide v.1.9.6 https://doi.org/10.5281/zenodo.2558193 (2019).
R Core Team. R: a language and environment for statistical computing. http://www.R-project.org/ (R Foundation for Statistical Computing, 2019).
Kay, J. E. et al. The Community Earth System Model (CESM) Large Ensemble Project: a community resource for studying climate change in the presence of internal climate variability. Bull. Am. Meteorol. Soc. 96, 1333–1349 (2015).
Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Shannon, C. E. A mathematical theory of communication. Bell Syst. Tech. J. 27, 623–656 (1948).
We thank G. Mace and O. Petchey for comments on pre-submission drafts of the manuscript. This study has been supported by the following institutions and grants: the Royal Society, UK, to A.L.P.; the National Socio-Environmental Synthesis Center under funding received from the National Science Foundation DBI-1639145 and the FLAIR Fellowship Programme: a partnership between the African Academy of Sciences and the Royal Society funded by the UK Government’s Global Challenges Research Fund, to C.H.T.; and NSF grants 1565046 and 1661510, to C.M.
The authors declare no competing interests.
Peer review information Nature thanks Joanne Bennett, Anthony Richardson, Jennifer Sunday and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Spatial distribution of the magnitude, abruptness and timing of assemblage exposure for alternative climate variables.
a–c, Shown is the median value across 22 CMIP5 climate models for MAT (a), MMT (b) and precipitation (c) under RCP 2.6, RCP 4.5 and RCP 8.5.
Extended Data Fig. 2 Comparing the magnitude, timing and abruptness of assemblage exposure across alternative climate variables.
a–c, Patterns of exposure to both MAT and precipitation combined are very similar to patterns of exposure to MAT only, highlighting the importance of changes in temperature in driving exposure. d–i, Patterns of exposure to unprecedented temperatures show both similarities and differences depending on whether temperature is quantified using MAT or MMT. More species are exposed and exposure occurs earlier for MAT compared with MMT, but spatial variation in the magnitude (d, g) and timing (e, h) of exposure are strongly correlated between temperature variables. Variation in the abruptness of assemblage exposure is less strongly correlated between MAT and MMT (f), but both variables confirm the abruptness of projected exposure (i). Values are the median across 22 CMIP5 climate models under RCP 8.5, with hotter colours indicating a higher density of points. Points falling along the dashed 1:1 line indicate a perfect correspondence between metrics. The correlation between metrics (Spearman’s ρ), and the mean difference in the timing of exposure (years), is shown.
Extended Data Fig. 3 Uncertainty in species local exposure metrics across 22 CMIP5 climate models under RCP 8.5.
Uncertainty (standard deviation, SD) in the magnitude of exposure is greatest around the boundaries of the tropics, with little geographic variation in uncertainty in timing or abruptness.
Density plots (left) show the distribution of abruptness values for different CMIP5 climate models (n = 22, lines) and RCPs on land (red) and in the ocean (blue). Histograms (right) show the median abruptness across climate models under RCP 8.5 for each group of organisms. Abruptness is calculated as the percentage of exposure times occurring within the decadal window of maximum exposure (colours). Abruptness is also shown for an alternative metric based on the Shannon entropy index (grey) with values scaled between 0 and 100, indicating the most gradual and the most abrupt distribution of exposure times possible for a given assemblage, respectively. Exposure is consistently abrupt across climate models, RCP scenarios, metrics and organism groups.
a–h, On land (left) and in the ocean (right) the median timing of exposure (a, b) is weakly correlated (Spearman’s ρ) with the timing of local climate emergence. The magnitude of exposure (c, d) is weakly correlated with the magnitude of warming between the start (2000–2020) and the end (2090–2100) of the twenty-first century. The abruptness of exposure (percentage of local species exposure times that occur in the decade of maximum exposure) is only partly correlated with the maximum rate of warming (maximum difference in mean temperature between successive decades) (e, f) or the percentage of species with nowhere warmer within 1,000 km of their range (g, h). Values are the median across 22 CMIP5 climate models under RCP 8.5. Hotter colours indicate a higher density of points.
a–c, Bivariate plots showing the strong correlation among alternative metrics for the timing of local assemblage exposure: the median year of local species exposure, the mean year of local species exposure and the mid-point of the decadal window of worst (that is, maximum) local species exposure. d–f, Bivariate plots showing the weaker correlation between the magnitude, abruptness and timing of exposure across assemblages. Values are the median across 22 CMIP5 climate models under RCP 8.5, with hotter colours indicating a higher density of points. In a–c, points falling along the dashed 1:1 line indicate a perfect correspondence between metrics. The correlation between metrics (Spearman’s ρ) is shown, as well as (for a–c) the mean difference in the timing of exposure (years).
Extended Data Fig. 7 Accumulation of exposure to unprecedented temperatures at decadal time snapshots from 2030 to 2100.
Light grey indicates zero local species exposure. Maps show the median across 22 CMIP5 climate models under RCP 8.5, highlighting the immediate onset of exposure in the tropics that spreads to higher latitudes later in the century.
a–d, The cumulative exposure to unprecedented temperatures of all local species populations (that is, species X site aggregated across all sites) increases smoothly over time at the global scale. Global horizon profiles are shown when species are weighted by the inverse of their geographic range size (equivalent to the mean percentage of the geographic range exposed) (a, b) or are given equivalent weighting (d–f). In d–f, dynamics are dominated by species with many local populations (that is, large geographic ranges). Variability in exposure across 22 climate models (thin lines) is shown for each RCP scenario (median, thick line).
Extended Data Fig. 9 The global distribution in the risk of high-magnitude and abrupt assemblage exposure events under different representative concentration pathways.
Maps show the probability of abrupt exposure calculated across 22 CMIP5 climate models. The risk of abrupt exposure was calculated on the basis of all species in an assemblage (left column) and for each group of organisms separately (right column). The maps highlight the greater risk of abrupt exposure events under intermediate (RCP 4.5) and especially under high (RCP 8.5) emission pathways, and when considering taxonomic groups separately.
Extended Data Fig. 10 Abruptness of horizon profiles for terrestrial vertebrates in 100-km grid cells with low or high spatial temperature heterogeneity.
Red, low heterogeneity; grey, high heterogeneity. Abruptness is calculated as the percentage of species exposure times in the decade of maximum exposure. Temperature heterogeneity is the range in temperatures at 1-km resolution within each 100-km cell. Assemblages with abrupt exposure have lower temperature heterogeneity, which suggests that quantifying species niches at finer spatial resolutions is unlikely to alter the abrupt nature of assemblage exposure dynamics.
About this article
Cite this article
Trisos, C.H., Merow, C. & Pigot, A.L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020). https://doi.org/10.1038/s41586-020-2189-9
Remote Sensing (2020)