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Abrupt expansion of climate change risks for species globally

Nature Ecology & Evolution volume 7, pages 1060–1071 (2023)Cite this article

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Abstract

Climate change is already exposing species to dangerous temperatures driving widespread population and geographical contractions. However, little is known about how these risks of thermal exposure will expand across species’ existing geographical ranges over time as climate change continues. Here, using geographical data for approximately 36,000 marine and terrestrial species and climate projections to 2100, we show that the area of each species’ geographical range at risk of thermal exposure will expand abruptly. On average, more than 50% of the increase in exposure projected for a species will occur in a single decade. This abruptness is partly due to the rapid pace of future projected warming but also because the greater area available at the warm end of thermal gradients constrains species to disproportionately occupy sites close to their upper thermal limit. These geographical constraints on the structure of species ranges operate both on land and in the ocean and mean that, even in the absence of amplifying ecological feedbacks, thermally sensitive species may be inherently vulnerable to sudden warming-driven collapse. With higher levels of warming, the number of species passing these thermal thresholds, and at risk of abrupt and widespread thermal exposure, increases, doubling from less than 15% to more than 30% between 1.5 °C and 2.5 °C of global warming. These results indicate that climate threats to thousands of species are expected to expand abruptly in the coming decades, thereby highlighting the urgency of mitigation and adaptation actions.

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Fig. 1: The spatiotemporal dynamics of thermal exposure across species geographical ranges.
Fig. 2: The abruptness, timing and magnitude of thermal exposure across the geographical ranges of species.
Fig. 3: Partitioning the causes of abrupt thermal exposure.
Fig. 4: The warm-skewed structure of species’ geographical ranges.
Fig. 5: Increasing risks of abrupt thermal exposure with the magnitude of global warming.

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Data availability

Climate and biodiversity data are freely available for download or upon request from the original sources. Data generated for this project are available at https://doi.org/10.6084/m9.figshare.22723889.

Code availability

The code used to conduct the analysis is available at https://doi.org/10.6084/m9.figshare.22723889.

References

  1. Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLoS Biol. 14, e2001104 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  3. 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).

    Article  Google Scholar 

  4. 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).

    Article  CAS  PubMed  Google Scholar 

  5. Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).

    Article  PubMed  Google Scholar 

  6. Wilson, R. J. et al. Changes to the elevational limits and extent of species ranges associated with climate change. Ecol. Lett. 8, 1138–1146 (2005).

    Article  PubMed  Google Scholar 

  7. Hartmann, H. et al. Climate change risks to global forest health: emergence of unexpected events of elevated tree mortality worldwide. Annu. Rev. Plant Biol. 73, 673–702 (2022).

    Article  PubMed  Google Scholar 

  8. 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).

    Article  CAS  PubMed  Google Scholar 

  9. Urban, M. C. Climate change. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Trisos, C. H., Merow, C. & Pigot, A. L. The projected timing of abrupt ecological disruption from climate change. Nature 580, 496–501 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Stanton, J. C., Shoemaker, K. T., Pearson, R. G. & Akçakaya, H. R. Warning times for species extinctions due to climate change. Glob. Change Biol. 21, 1066–1077 (2015).

    Article  Google Scholar 

  12. Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl Acad. Sci. USA 109, 8606–8611 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Trull, N., Böhm, M. & Carr, J. Patterns and biases of climate change threats in the IUCN Red List. Conserv. Biol. 32, 135–147 (2018).

    Article  PubMed  Google Scholar 

  15. Akçakaya, H. R., Butchart, S., Mace, G. M., Stuart, S. N. & Hilton-Taylor, C. Use and misuse of the IUCN Red List Criteria in projecting climate change impacts on biodiversity. Glob. Change Biol. 12, 2037–2043 (2006).

    Article  Google Scholar 

  16. Smale, D. A. & Wernberg, T. Extreme climatic event drives range contraction of a habitat-forming species. Proc. Biol. Sci. 280, 20122829 (2013).

    PubMed  PubMed Central  Google Scholar 

  17. Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).

    Article  CAS  PubMed  Google Scholar 

  18. Ratnayake, H. U., Kearney, M. R., Govekar, P., Karoly, D. & Welbergen, J. A. Forecasting wildlife die-offs from extreme heat events. Anim. Conserv. 22, 386–395 (2019).

    Article  Google Scholar 

  19. Hansen, B. B., Grøtan, V., Herfindal, I. & Lee, A. M. The Moran effect revisited: spatial population synchrony under global warming. Ecography 43, 1591–1602 (2020).

    Article  Google Scholar 

  20. Dietzel, A., Connolly, S. R., Hughes, T. P. & Bode, M. The spatial footprint and patchiness of large-scale disturbances on coral reefs.Glob. Change Biol. 27, 4825–4838 (2021).

    Article  CAS  Google Scholar 

  21. Biggs, R., Carpenter, S. R. & Brock, W. A. Turning back from the brink: detecting an impending regime shift in time to avert it. Proc. Natl Acad. Sci. USA 106, 826–831 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Tittensor, D. P. et al. Global patterns and predictors of marine biodiversity across taxa. Nature 466, 1098–1101 (2010).

    Article  CAS  PubMed  Google Scholar 

  23. Pigot, A. L., Owens, I. P. F. & Orme, C. D. L. The environmental limits to geographic range expansion in birds. Ecol. Lett. 13, 705–715 (2010).

    Article  PubMed  Google Scholar 

  24. Stewart, S. B. et al. Climate extreme variables generated using monthly time‐series data improve predicted distributions of plant species. Ecography 44, 626–639 (2021).

    Article  Google Scholar 

  25. Stillman, J. H. Heat waves, the new normal: summertime temperature extremes will impact animals, ecosystems, and human communities. Physiology 34, 86–100 (2019).

    Article  CAS  PubMed  Google Scholar 

  26. Sognnaes, I. et al. A multi-model analysis of long-term emissions and warming implications of current mitigation efforts. Nat. Clim. Change 11, 1055–1062 (2021).

    Article  Google Scholar 

  27. 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).

    Article  Google Scholar 

  28. Elsen, P. R. & Tingley, M. W. Global mountain topography and the fate of montane species under climate change. Nat. Clim. Change 5, 772–776 (2015).

    Article  Google Scholar 

  29. Terborgh, J. On the notion of favorableness in plant ecology. Am. Nat. 107, 481–501 (1973).

    Article  Google Scholar 

  30. Gross, K. & Snyder-Beattie, A. A general, synthetic model for predicting biodiversity gradients from environmental geometry. Am. Nat. 188, E85–E97 (2016).

    Article  PubMed  Google Scholar 

  31. Howells, E. J., Berkelmans, R., van Oppen, M. J. H., Willis, B. L. & Bay, L. K. Historical thermal regimes define limits to coral acclimatization. Ecology 94, 1078–1088 (2013).

    Article  PubMed  Google Scholar 

  32. Frieler, K. et al. Limiting global warming to 2 °C is unlikely to save most coral reefs. Nat. Clim. Change 3, 165–170 (2013).

    Article  Google Scholar 

  33. Paquette, A. & Hargreaves, A. L. Biotic interactions are more often important at species’ warm versus cool range edges. Ecol. Lett. 24, 2427–2438 (2021).

    Article  PubMed  Google Scholar 

  34. Sunday, J. M., Bates, A. E. & Dulvy, N. K. Thermal tolerance and the global redistribution of animals. Nat. Clim. Change 2, 686–690 (2012).

    Article  Google Scholar 

  35. Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).

    Article  CAS  PubMed  Google Scholar 

  36. Simmonds, E. G., Cole, E. F., Sheldon, B. C. & Coulson, T. Phenological asynchrony: a ticking time-bomb for seemingly stable populations? Ecol. Lett. 23, 1766–1775 (2020).

    Article  PubMed  Google Scholar 

  37. Socolar, J. B., Epanchin, P. N., Beissinger, S. R. & Tingley, M. W. Phenological shifts conserve thermal niches in North American birds and reshape expectations for climate-driven range shifts. Proc. Natl Acad. Sci. USA 114, 12976–12981 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Suggitt, A. J. et al. Extinction risk from climate change is reduced by microclimatic buffering. Nat. Clim. Change 8, 713–717 (2018).

    Article  Google Scholar 

  39. Foden, W. B. et al. Identifying the world’s most climate change vulnerable species: a systematic trait-based assessment of all birds, amphibians and corals. PLoS ONE 8, e65427 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Armstrong McKay, D. I. et al. Exceeding 1.5 °C global warming could trigger multiple climate tipping points. Science 377, eabn7950 (2022).

    Article  PubMed  Google Scholar 

  41. Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).

    Article  CAS  PubMed  Google Scholar 

  42. Ramiadantsoa, T., Stegner, M. A., Williams, J. W. & Ives, A. R. The potential role of intrinsic processes in generating abrupt and quasi-synchronous tree declines during the Holocene. Ecology 100, e02579 (2019).

    Article  PubMed  Google Scholar 

  43. Sagarin, R. D., Gaines, S. D. & Gaylord, B. Moving beyond assumptions to understand abundance distributions across the ranges of species. Trends Ecol. Evol. 21, 524–530 (2006).

    Article  PubMed  Google Scholar 

  44. Waldock, C., Stuart-Smith, R. D., Edgar, G. J., Bird, T. J. & Bates, A. E. The shape of abundance distributions across temperature gradients in reef fishes. Ecol. Lett. 22, 685–696 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  45. Burrows, M. T. et al. The pace of shifting climate in marine and terrestrial ecosystems. Science 334, 652–655 (2011).

    Article  CAS  PubMed  Google Scholar 

  46. Hughes, T. P. et al. Global warming impairs stock–recruitment dynamics of corals. Nature 568, 387–390 (2019).

    Article  CAS  PubMed  Google Scholar 

  47. Suttle, K. B., Thomsen, M. A. & Power, M. E. Species interactions reverse grassland responses to changing climate. Science 315, 640–642 (2007).

    Article  CAS  PubMed  Google Scholar 

  48. Dullinger, S. et al. Extinction debt of high-mountain plants under twenty-first-century climate change. Nat. Clim. Change 2, 619–622 (2012).

    Article  Google Scholar 

  49. Cotto, O. et al. A dynamic eco-evolutionary model predicts slow response of alpine plants to climate warming. Nat. Commun. 8, 15399 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).

    Article  PubMed  Google Scholar 

  51. Roll, U. et al. The global distribution of tetrapods reveals a need for targeted reptile conservation. Nat. Ecol. Evol. 1, 1677–1682 (2017).

    Article  PubMed  Google Scholar 

  52. Brinton, E., Ohman, M. D., Townsend, A. W., Knight, M. D. & Bridgeman, A. L. Euphausiids of the World Ocean (Springer, 2000).

  53. Jereb, P. & Roper, C. F. E. Cephalopods of the World: an Annotated and Illustrated Catalogue of Cephalopod Species Known to Date (FAO, 2005).

  54. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  56. Chen, D. et al. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) 147–286 (Cambridge Univ. Press, 2021).

  57. Hawkins, E. & Sutton, R. Time of emergence of climate signals.Geophys. Res. Lett. https://doi.org/10.1029/2011GL050087 (2012).

    Article  Google Scholar 

  58. Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Müller, H. G. & Wang, J. L. Hazard rate estimation under random censoring with varying kernels and bandwidths. Biometrics 50, 61–76 (1994).

    Article  PubMed  Google Scholar 

  60. R Core Team. R: A language and environment for statistical computing (R Foundation for Statistical Computing, 2020).

  61. Santafe, G., Calvo, B., Perez, A. & Lozano, J. A. bde: Bounded density estimation. R package v.1.0.1 https://CRAN.R-project.org/package=bde (2015).

  62. Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Assis, J. et al. Bio‐ORACLE v2.0: extending marine data layers for bioclimatic modelling. Glob. Ecol. Biogeogr. 27, 277–284 (2017).

    Article  Google Scholar 

  64. Jetz, W. & Rahbek, C. Geometric constraints explain much of the species richness pattern in African birds. Proc. Natl Acad. Sci. USA 98, 5661–5666 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Storch, D. et al. Energy, range dynamics and global species richness patterns: reconciling mid-domain effects and environmental determinants of avian diversity. Ecol. Lett. 9, 1308–1320 (2006).

    Article  PubMed  Google Scholar 

  66. Lovell, R. S. L., Blackburn, T. M., Dyer, E. E. & Pigot, A. L. Environmental resistance predicts the spread of alien species. Nat. Ecol. Evol. 5, 322–329 (2021).

    Article  PubMed  Google Scholar 

  67. Anderegg, W. R. L., Kane, J. M. & Anderegg, L. D. L. Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Change 3, 30–36 (2013).

    Article  Google Scholar 

  68. IUCN Species Survival Commission. IUCN Red List Categories and Criteria (IUCN, 2001).

  69. 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).

    Article  PubMed  Google Scholar 

  70. Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).

    Article  CAS  PubMed  Google Scholar 

  71. Roskov, Y. et al. Species 2000 & ITIS Catalogue of Life, 2017 Annual Checklist; http://www.catalogueoflife.org/annual-checklist/2017 (DVD, 2017).

Download references

Acknowledgements

We thank the many people who have contributed to and maintain the freely available biodiversity and climate datasets on which this project depends. We are grateful to J. Bridle for insightful discussions. This study has been supported by a Royal Society UK University Research Fellowship and Natural Environment Research Council grant no. NE/W006618/1 to A.L.P., a Royal Society UK & African Academy of Sciences Future Leaders–African Independent Research Fellowship Programme and National Science Foundation grant no. DBI-1639145 to C.H.T., National Science Foundation grant no. DBI-1565046 to C.M. and a NASA Ecological Forecasting Team Applied Sciences Program grant no. 80NSSC21K1183 to A.W.

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Authors and Affiliations

  1. Centre for Biodiversity and Environment Research, Department of Genetics, Evolution and Environment, University College London, London, UK

    Alex L. Pigot

  2. Eversource Energy Center and Department of Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA

    Cory Merow

  3. Department of Geography, University at Buffalo, Buffalo, NY, USA

    Adam Wilson

  4. African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa

    Christopher H. Trisos

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A.L.P. designed and conducted the analyses and wrote the first draft of the manuscript. C.H.T., C.M. and A.W. contributed to study design, analysis and manuscript writing.

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Correspondence to Alex L. Pigot.

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Extended data

Extended Data Fig. 1 Metrics of species exposure dynamics.

Exemplar patterns of thermal exposure are shown for four species (a) Pristimantis malkini, (b) Telescopus beetzi, (c) Pectinia pygmaeus and (d) Abudefduf declivifrons for a single run of the Whole Atmosphere Community Climate Model (CESM2-WACCM) under an intermediate greenhouse gas emissions scenario (SSP2-4.5). Black lines show the cumulative % of grid cells thermally exposed across each species’ geographic range. Magnitude is the percentage of grid cells in the geographic range exposed by 2100. Onset and median timing is the first and median year of grid cell exposure respectively. Abruptness is the maximum percentage of 21st century exposure occurring in any single decadal window. The light grey window indicates the decade of worst exposure, with the dark grey block indicating the magnitude of exposure events occurring in that decade.

Extended Data Fig. 2 Predictability in the year of thermal exposure across species geographic ranges.

Boxplots show the distribution of independent slope estimates from a linear model predicting the year of thermal exposure from (a) the magnitude of warming between the beginning (2005-2014) and end (2091-2100) of the 21st century and (b) the grid cell warming tolerance (WT), calculated as the difference between the grid cell temperature at the beginning of the century (2005-2014) and the species’ upper realised thermal limit. Only species where at least 10 grid cells are exposed this century are included (n = 14,403). Slope estimates are the median across CMIP6 climate models under an intermediate greenhouse gas emissions scenario (SSP2-4.5). Boxes show the 25th and 75th percentile and the whiskers ± 1.5 x Interquartile range.

Extended Data Fig. 3 Variation in the magnitude, abruptness and timing of thermal exposure across species geographic ranges.

Plots show the covariation across species between each pair of exposure metrics. Species values are the median metric scores across CMIP6 climate models with warmer colours indicating a higher density of points. Magnitude is the percentage of grid cells in the species’ geographic range thermally exposed by 2100. Timing is the year of onset of grid cell exposure within each species geographic range. Abruptness is the maximum percentage of 21st century exposure occurring in any single decadal window. Rows show different greenhouse gas emission scenarios. Sample sizes vary across plots because timing scores are only calculated for species that are thermally exposed before 2100 and abruptness is only shown for species with at least 10 grid cells thermally exposed by 2100.

Extended Data Fig. 4 Skew in grid cell warming tolerances across terrestrial and marine species geographic ranges based on (a-c) simulated and (d-f) observed climate data.

Histograms show: (a,d) the interval (10%) of the realised thermal niche with the highest density of grid cells, (b,e) the skew in grid cell warming tolerances within species geographic ranges and (c,f) the proportion of each species geographic range that occurs in the warm half of the species’ realised thermal niche. Results are based on (a-c) the median scores across CMIP6 climate models under an intermediate greenhouse gas emission scenario (SSP2-4.5) and (d-f) air and sea-surface temperature from observed weather data. Metrics were only calculated for species occurring in at least 30 grid cells on land (n = 15,195 species) or in the ocean (n = 3,519 species).

Extended Data Fig. 5 The warm-skewed availability of air and sea-surface temperatures globally.

Top: Density plots show the distribution of average (2005-2014) maximum mean monthly (MMT) air (land) and sea-surface temperatures (ocean) calculated from the historical run of each CMIP6 climate model. Each curve shows a different CMIP6 model. Bottom: Density plots show the mean maximum monthly air (1970-2000) and sea-surface temperature (2000-2014) from observed spatially interpolated weather and satellite data. Each curve shows the distribution when temperatures are averaged at different spatial grain sizes (from 1 to 768 km).

Extended Data Fig. 6 The warm-skewed structure of species geographic ranges compared to a null model of random grid cell occupancy.

Histograms show: (a) the interval (10%) of the realised thermal niche with the greatest density of grid cells, (b) the skew in grid cell warming tolerances within species geographic ranges and (c) the proportion of each species geographic range that occurs in the warm half of the species’ realised thermal niche. Colors denote species where grid cells within observed species geographic ranges are skewed towards the warm (red) or cold (blue) edge of the realised thermal niche according to each metric. Brackets show the expected pattern (minimum and maximum values across 20 replicate simulations) under a null model of random grid cell occupancy. Results are based on average air and sea-surface temperature from observed weather data. Metrics were only calculated for species occurring in at least 30 grid cells (n = 18,714 species).

Extended Data Fig. 7 The area of species’ existing geographic ranges at risk of abrupt thermal exposure.

(a-c) Scatter plots show the relationship between the abruptness of thermal exposure and the number of 100 km grid cells in the species’ geographic range exposed by 2100 (that is area of thermal exposure). Abruptness is the maximum percentage of 21st century exposure occurring in any single decadal window. In (b, d) the distribution of abruptness scores is shown after removing species where fewer than n grid cells (n = 10, 25, 50, 100, 250 grid cells) are thermally exposed by 2100. Results for different thresholds are indicated by colored lines, corresponding to the vertical lines in (a, c). Values are the abruptness and area of thermal exposure for each species across CMIP6 climate models under an intermediate SSP2-4.5 (a-b) and high SSP5-8.5 (c-d) greenhouse gas emission scenario (n = 35,863 species).

Extended Data Table 1 Species and taxonomic groups
Full size table
Extended Data Table 2 Climate models
Full size table
Extended Data Table 3 Abruptness of projected thermal exposure
Full size table

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Pigot, A.L., Merow, C., Wilson, A. et al. Abrupt expansion of climate change risks for species globally. Nat Ecol Evol 7, 1060–1071 (2023). https://doi.org/10.1038/s41559-023-02070-4

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