Abstract
Climate change may soon cause a catastrophic loss of global biodiversity. For decades, tropical species have widely been considered more vulnerable than temperate species. However, some studies have suggested the opposite. Using a global-scale dataset from resurvey studies spanning 5,151 plant and animal species encompassing 39,157 sites, we show that climate-related local extinctions were significantly more frequent among temperate (49% of surveyed species) than tropical species (33%). We then tested whether these more frequent temperate extinctions were explained by greater sensitivity to warming among temperate species, by faster warming at higher latitudes, or both. We found that extinction probabilities increased significantly with the magnitude of recent warming in temperate regions, and that temperate species also showed a general trend towards higher sensitivity to warming. Overall, our findings challenge the long-held view that climate change more strongly impacts tropical species and suggest that temperate species are increasingly vulnerable.
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Data availability
The compiled dataset of species’ local extinction records, associated climate variables and metadata generated in this study are available via figshare at https://doi.org/10.6084/m9.figshare.25974661 (ref. 107) and are also provided in Supplementary Data 1–3. Original survey data were compiled from multiple published sources, and details of all contributing studies are provided in Supplementary Table 1. Climate data were obtained from the following publicly available global datasets: ERA5 from the Copernicus Climate Data Store (https://cds.climate.copernicus.eu), high-resolution climatologies from CHELSA (https://www.chelsa-climate.org/datasets/chelsa_daily), Climatic Research Unit (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.07/) and the National Oceanic and Atmospheric Administration daily Optimum Interpolation Sea Surface Temperature (https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/). Land-cover data were obtained from the European Space Agency Climate Change Initiative Land Cover (https://cds.climate.copernicus.eu) and HILDA+ datasets (https://doi.org/10.1594/PANGAEA.921846). Additional biodiversity and species distribution data were obtained from publicly available sources, including GBIF (https://www.gbif.org/), International Union for Conservation of Nature (https://iucn.org/) and FishBase (https://www.fishbase.se/). Source data are provided with this paper.
Code availability
The R codes used for downscaling climate data and statistical analyses are available via figshare at https://doi.org/10.6084/m9.figshare.25974661 (ref. 107).
References
Thomas, C. D. et al. Extinction risk from climate change. Nature 427, 145–148 (2004).
Google Scholar
Urban, M. C. Accelerating extinction risk from climate change. Science 348, 571–573 (2015).
Google Scholar
Wiens, J. J. & Zelinka, J. How many species will Earth lose to climate change?. Glob. Change Biol. 30, e17125 (2024).
Google Scholar
Urban, M. C. Climate change extinctions. Science 386, 1123–1128 (2024).
Google Scholar
Bellard, C., Bertelsmeier, C., Leadley, P., Thuiller, W. & Courchamp, F. Impacts of climate change on the future of biodiversity. Ecol. Lett. 15, 365–377 (2012).
Google Scholar
Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).
Google Scholar
Sheldon, K. S. Climate change in the tropics: ecological and evolutionary responses at low latitudes. Annu. Rev. Ecol. Evol. Syst. 50, 303–333 (2019).
Google Scholar
Pinsky, M. L., Comte, L. & Sax, D. F. Unifying climate change biology across realms and taxa. Trends Ecol. Evol. 37, 672–682 (2022).
Google Scholar
Lawlor, J. A. et al. Mechanisms, detection and impacts of species redistributions under climate change. Nat. Rev. Earth Environ. 5, 351–368 (2024).
Google Scholar
Tewksbury, J. J., Huey, R. B. & Deutsch, C. A. Putting the heat on tropical animals. Science 320, 1296–1297 (2008).
Google Scholar
Calosi, P., Bilton, D. T. & Spicer, J. I. Thermal tolerance, acclimatory capacity and vulnerability to global climate change. Biol. Lett. 4, 99–102 (2008).
Google Scholar
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl Acad. Sci. USA 105, 6668–6672 (2008).
Google Scholar
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).
Google Scholar
Dillon, M. E., Wang, G. & Huey, R. B. Global metabolic impacts of recent climate warming. Nature 467, 704–706 (2010).
Google Scholar
Helmuth, B., Kingsolver, J. G. & Carrington, E. Biophysics, physiological ecology, and climate change: does mechanism matter?. Annu. Rev. Physiol. 67, 177–201 (2005).
Google Scholar
Wiens, J. J. Climate-related local extinctions are already widespread among plant and animal species. PLOS Biol. 14, e2001104 (2016).
Google Scholar
Román-Palacios, C. & Wiens, J. J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl Acad. Sci. USA 117, 4211–4217 (2020).
Google Scholar
Huey, R. B. et al. Why tropical forest lizards are vulnerable to climate warming. Proc. R. Soc. B 276, 1939–1948 (2009).
Google Scholar
Perez, T. M., Stroud, J. T. & Feeley, K. J. Thermal trouble in the tropics. Science 351, 1392–1393 (2016).
Google Scholar
Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
Google Scholar
Buckley, L. B. & Kingsolver, J. G. Evolution of thermal sensitivity in changing and variable climates. Annu. Rev. Ecol. Evol. Syst. 52, 563–586 (2021).
Google Scholar
Grinder, R. M. & Wiens, J. J. Niche width predicts extinction from climate change and vulnerability of tropical species. Glob. Change Biol. 29, 618–630 (2023).
Google Scholar
Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).
Google Scholar
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).
Google Scholar
Kingsolver, J. G., Diamond, S. E. & Buckley, L. B. Heat stress and the fitness consequences of climate change for terrestrial ectotherms. Funct. Ecol. 27, 1415–1423 (2013).
Google Scholar
Murali, G., Iwamura, T., Meiri, S. & Roll, U. Future temperature extremes threaten land vertebrates. Nature 615, 461–467 (2023).
Google Scholar
Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B 281, 20132612 (2014).
Google Scholar
Jørgensen, L. B., Ørsted, M., Malte, H., Wang, T. & Overgaard, J. Extreme escalation of heat failure rates in ectotherms with global warming. Nature 611, 93–98 (2022).
Google Scholar
Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).
Google Scholar
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).
Google Scholar
Duffy, K., Gouhier, T. C. & Ganguly, A. R. Climate-mediated shifts in temperature fluctuations promote extinction risk. Nat. Clim. Change 12, 1037–1044 (2022).
Google Scholar
Freeman, B. G., Miller, E. T. & Strimas-Mackey, M. Recent abundance changes at species’ range limits in the North and Central American avifaunas. Glob. Ecol. Biogeogr. 34, e70059 (2025).
Google Scholar
Manabe, S. & Wetherald, R. T. The effects of doubling the CO2 concentration on the climate of a general circulation model. J. Atmospheric Sci. 32, 3–15 (1975).
Google Scholar
IPCC Climate Change 2023: Synthesis Report (eds Core Writing Team et al.) (IPCC, 2023).
Pottier, P. et al. Vulnerability of amphibians to global warming. Nature 639, 954–961 (2025).
Google Scholar
Hampe, A. & Petit, R. J. Conserving biodiversity under climate change: the rear edge matters. Ecol. Lett. 8, 461–467 (2005).
Google Scholar
Holzmann, K. L., Walls, R. L. & Wiens, J. J. Accelerating local extinction associated with very recent climate change. Ecol. Lett. 26, 1877–1886 (2023).
Google Scholar
Lenoir, J. et al. Species better track climate warming in the oceans than on land. Nat. Ecol. Evol. 4, 1044–1059 (2020).
Google Scholar
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).
Google Scholar
Rantanen, M. et al. The Arctic has warmed nearly four times faster than the globe since 1979. Commun. Earth Environ. 3, 168 (2022).
Google Scholar
Taheri, S., Naimi, B., Rahbek, C. & Araújo, M. B. Improvements in reports of species redistribution under climate change are required. Sci. Adv. 7, eabe1110 (2021).
Google Scholar
Parker, E. J., Weiskopf, S. R., Oliver, R. Y., Rubenstein, M. A. & Jetz, W. Insufficient and biased representation of species geographic responses to climate change. Glob. Change Biol. 30, e17408 (2024).
Google Scholar
Freeman, B. G., Song, Y., Feeley, K. J. & Zhu, K. Montane species track rising temperatures better in the tropics than in the temperate zone. Ecol. Lett. 24, 1697–1708 (2021).
Google Scholar
Smith, K. E. et al. Biological impacts of marine heatwaves. Annu. Rev. Mar. Sci. 15, 119–145 (2023).
Google Scholar
Fredston, A. L. et al. Marine heatwaves are not a dominant driver of change in demersal fishes. Nature 621, 324–329 (2023).
Google Scholar
Scheffers, B. R. et al. The broad footprint of climate change from genes to biomes to people. Science 354, aaf7671 (2016).
Google Scholar
Paquette, A. & Hargreaves, A. L. Biotic interactions are more often important at species’ warm versus cool range edges. Ecol. Lett. 24, 2427–2438 (2021).
Google Scholar
Malanoski, C. M., Farnsworth, A., Lunt, D. J., Valdes, P. J. & Saupe, E. E. Climate change is an important predictor of extinction risk on macroevolutionary timescales. Science 383, 1130–1134 (2024).
Google Scholar
Lancaster, L. T. & Humphreys, A. M. Global variation in the thermal tolerances of plants. Proc. Natl Acad. Sci. USA 117, 13580–13587 (2020).
Google Scholar
O’Sullivan, O. S. et al. Thermal limits of leaf metabolism across biomes. Glob. Change Biol. 23, 209–223 (2017).
Google Scholar
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).
Google Scholar
Miraldo, A. et al. An Anthropocene map of genetic diversity. Science 353, 1532–1535 (2016).
Google Scholar
Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).
Google Scholar
Alexander, J. M., Diez, J. M. & Levine, J. M. Novel competitors shape species’ responses to climate change. Nature 525, 515–518 (2015).
Google Scholar
Cahill, A. E. et al. How does climate change cause extinction? Proc. R. Soc. B 280, 20121890 (2013).
Google Scholar
Outhwaite, C. L., McCann, P. & Newbold, T. Agriculture and climate change are reshaping insect biodiversity worldwide. Nature 605, 97–102 (2022).
Google Scholar
Freeman, B. G., Scholer, M. N., Ruiz-Gutierrez, V. & Fitzpatrick, J. W. Climate change causes upslope shifts and mountaintop extirpations in a tropical bird community. Proc. Natl Acad. Sci. USA 115, 11982–11987 (2018).
Google Scholar
Freeman, B. G. & Class Freeman, A. M. Rapid upslope shifts in New Guinean birds illustrate strong distributional responses of tropical montane species to global warming. Proc. Natl Acad. Sci. USA 111, 4490–4494 (2014).
Google Scholar
Evans, M. E. K. et al. Tree rings reveal the transient risk of extinction hidden inside climate envelope forecasts. Proc. Natl Acad. Sci. USA 121, e2315700121 (2024).
Google Scholar
Hoffmann, S., Irl, S. D. & Beierkuhnlein, C. Predicted climate shifts within terrestrial protected areas worldwide. Nat. Commun. 10, 4787 (2019).
Google Scholar
Xu, X., Huang, A., Belle, E., De Frenne, P. & Jia, G. Protected areas provide thermal buffer against climate change. Sci. Adv. 8, eabo0119 (2022).
Google Scholar
Dornelas, M. et al. BioTIME: a database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).
Google Scholar
Maureaud, A. A. et al. FISHGLOB_data: an integrated dataset of fish biodiversity sampled with scientific bottom-trawl surveys. Sci. Data 11, 24 (2024).
Google Scholar
Kapfer, J. et al. Resurveying historical vegetation data – opportunities and challenges. Appl. Veg. Sci. 20, 164–171 (2017).
Google Scholar
Snethlage, M. A. et al. A hierarchical inventory of the world’s mountains for global comparative mountain science. Sci. Data 9, 149 (2022).
Google Scholar
Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience 57, 573–583 (2007).
Google Scholar
Forister, M. L. et al. Compounded effects of climate change and habitat alteration shift patterns of butterfly diversity. Proc. Natl Acad. Sci. USA 107, 2088–2092 (2010).
Google Scholar
Tingley, M. W. & Beissinger, S. R. Detecting range shifts from historical species occurrences: new perspectives on old data. Trends Ecol. Evol. 24, 625–633 (2009).
Google Scholar
Enriquez-Urzelai, U., Bernardo, N., Moreno-Rueda, G., Montori, A. & Llorente, G. Are amphibians tracking their climatic niches in response to climate warming? A test with Iberian amphibians. Clim. Change 154, 289–301 (2019).
Google Scholar
Bustamante, M. R., Ron, S. R. & Coloma, L. A. Cambios en la diversidad en siete comunidades de anuros en los Andes de Ecuador. Biotropica 37, 180–189 (2005).
Google Scholar
Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).
Google Scholar
Chamberlain, S., Oldoni, D. & Waller, J. rgbif: Interface to the global biodiversity information facility API. R v.3.8.0 (2022).
Feeley, K. J. & Stroud, J. T. Where on Earth are the “tropics”? Front. Biogeogr. https://doi.org/10.21425/F5FBG38649 (2018).
Google Scholar
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
Google Scholar
Karger, D. N. et al. Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4, 170122 (2017).
Google Scholar
Karger, D. N., Wilson, A. M., Mahony, C., Zimmermann, N. E. & Jetz, W. Global daily 1 km land surface precipitation based on cloud cover-informed downscaling. Sci. Data 8, 307 (2021).
Google Scholar
Karger, D. N. et al. CHELSA-W5E5: daily 1 km meteorological forcing data for climate impact studies. Earth Syst. Sci. Data 15, 2445–2464 (2023).
Google Scholar
Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
Google Scholar
Banzon, V., Smith, T. M., Chin, T. M., Liu, C. & Hankins, W. A long-term record of blended satellite and in situ sea-surface temperature for climate monitoring, modeling and environmental studies. Earth Syst. Sci. Data 8, 165–176 (2016).
Google Scholar
Reynolds, R. W. et al. Daily high-resolution-blended analyses for sea surface temperature. J. Clim. 20, 5473–5496 (2007).
Google Scholar
Wu, Z., Huang, N. E., Long, S. R. & Peng, C.-K. On the trend, detrending, and variability of nonlinear and nonstationary time series. Proc. Natl Acad. Sci. USA 104, 14889–14894 (2007).
Google Scholar
Kim, D. & Oh, H.-S. EMD: a package for empirical mode decomposition and Hilbert spectrum. R J. 1, 40–46 (2009).
Google Scholar
Buckley, L. B. & Huey, R. B. Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob. Change Biol. 22, 3829–3842 (2016).
Google Scholar
Vogel, M. M. et al. Regional amplification of projected changes in extreme temperatures strongly controlled by soil moisture-temperature feedbacks. Geophys. Res. Lett. 44, 1511–1519 (2017).
Google Scholar
Seneviratne, S. I., Donat, M. G., Pitman, A. J., Knutti, R. & Wilby, R. L. Allowable CO2 emissions based on regional and impact-related climate targets. Nature 529, 477–483 (2016).
Google Scholar
O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Surv. Data Ser. 691, 4–9 (2012).
Karger, D. N., Chauvier, Y. & Zimmermann, N. E. chelsa-cmip6 1.0: a python package to create high resolution bioclimatic variables based on CHELSA ver. 2.1 and CMIP6 data. Ecography 2023, e06535 (2023).
Google Scholar
Hijmans, R. J., Phillips, S., Leathwick, J., Elith, J. & Hijmans, M. R. J. Package ‘dismo’. Circles 9, 1–68 (2017).
Hobday, A. J. et al. A hierarchical approach to defining marine heatwaves. Prog. Oceanogr. 141, 227–238 (2016).
Google Scholar
Schlegel, R. & Smit, A. J. heatwaveR: a central algorithm for the detection of heatwaves and cold-spells. J. Open Source Softw. 3, 821 (2018).
Google Scholar
McKee, T. B., Doesken, N. J. & Kleist, J. The relationship of drought frequency and duration to time scales. In Proc. 8th Conference on Applied Climatology 179–183 (American Meteorological Society, 1993).
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2013).
Magnusson, A. et al. Package ‘glmmTMB’. R package v.0.2.0 (2017).
Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. & Makowski, D. performance: an R package for assessment, comparison and testing of statistical models. J. Open Source Softw. 6, 3139 (2021).
Google Scholar
Gareth, J., Daniela, W., Trevor, H. & Robert, T. An Introduction to Statistical Learning: With Applications in R (Spinger, 2013).
Hartig, F. & Hartig, M. F. Package ‘DHARMa’. R package v.0.4.7 (2022).
Lenth, R. emmeans: estimated marginal means, aka least-squares means. R package v.1.8.5 https://cir.nii.ac.jp/crid/1370584340724217473 (2023).
Antonakis, J., Bastardoz, N. & Rönkkö, M. On ignoring the random effects assumption in multilevel models: review, critique, and recommendations. Organ. Res. Methods 24, 443–483 (2021).
Google Scholar
Byrnes, J. E. K. & Dee, L. E. Causal inference with observational data and unobserved confounding variables. Ecol. Lett. 28, e70023 (2025).
Google Scholar
Wooldridge, J. M. Econometric Analysis of Cross Section and Panel Data (MIT, 2010).
Silk, M. J., Harrison, X. A. & Hodgson, D. J. Perils and pitfalls of mixed-effects regression models in biology. PeerJ 8, e9522 (2020).
Google Scholar
Seaman, S., Pavlou, M. & Copas, A. Review of methods for handling confounding by cluster and informative cluster size in clustered data. Stat. Med. 33, 5371–5387 (2014).
Google Scholar
Schielzeth, H. Simple means to improve the interpretability of regression coefficients. Methods Ecol. Evol. 1, 103–113 (2010).
Google Scholar
Lüdecke, D. ggeffects: tidy data frames of marginal effects from regression models. J. Open Source Softw. 3, 772 (2018).
Google Scholar
Stuble, K. L. et al. The promise and the perils of resurveying to understand global change impacts. Ecol. Monogr. 91, e01435 (2021).
Google Scholar
Pinsky, M. L., Selden, R. L. & Kitchel, Z. J. Climate-driven shifts in marine species ranges: scaling from organisms to communities. Annu. Rev. Mar. Sci. 12, 153–179 (2020).
Google Scholar
Murali, G., Karger, D. N. & Wiens, J. J. Temperate local extinctions from climate change are outpacing tropical extinctions. figshare https://doi.org/10.6084/m9.figshare.25974661 (2026).
Acknowledgements
We are grateful to the researchers who carried out the original surveys and resurveys, without which our study would not have been possible. We thank the following researchers for kindly sharing their survey data with us: M. Campos-Cerqueira, K. J. Feeley, B. G. Freeman, K. J. Iknayan, J. M. Kerner, J. J. Kirchman, S. B. Rumpf, N. Sillero, A. M. Van Tatenhove and S. Zorio. We also thank J. Lenoir for his feedback on an earlier version of the paper. We acknowledge the University of Arizona’s High-Performance Computing facility for providing computational resources.
Funding
This work was funded by a Fulbright-Kalam Postdoctoral Fellowship awarded to G.M. through the US-India Educational Foundation (USIEF) and was also partly supported by the DST–INSPIRE Faculty Award (DST/INSPIRE/04/2023/001823) to G.M.
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Contributions
The study was conceptualized by G.M. and J.J.W., who also acquired funding and administered the project. Methodology was developed by G.M., D.N.K. and J.J.W. G.M. carried out the investigation, performed the visualization and wrote the original draft of the paper. J.J.W. supervised. All three authors contributed to revisions and editing.
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Nature Climate Change thanks I-Ching Chen, Michael Moore and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Local extinctions are more frequent among temperate species than tropical species under the stringent criterion for species inclusion.
The analysis included n = 4,425 species across habitats and taxonomic groups. The percentage of warm-edge local extinction across habitats and taxonomic groups for tropical (red) and temperate (blue) species are shown. (a) The percentage of local extinction as a half-pie chart for species across habitats and major groups within each zone. (b and c) The percentage of warm-edge local extinctions per site. The percentage is calculated as the number of species that had warm-edge local extinction at that site divided by the total number of species with their warm-edge range limits at that site. Note that the actual number of species present at each site might be higher than the number of species having warm-edge range limits there. Statistical significance was assessed using GLMMs. All tests were two-sided. P values were adjusted for multiple comparisons using the false discovery rate method. Significance is indicated as: ns=Padj > 0.05; *=Padj > 0.01; ***=Padj < 0.01. For full statistical analysis results, see Supplementary Tables 18–31, and Supplementary Methods section ‘Sensitivity analyses for temperate versus tropical local extinctions under the stringent criterion’. Credit: silhouettes in a, Phylopic under a Creative Commons license CC0 1.0.
Source data
Extended Data Fig. 2 Local extinctions are more frequent among temperate species than tropical species after accounting for differences in survey efforts.
The analysis included n = 3,996 species across habitats and taxonomic groups, using only studies with identical or greater resurvey effort (n = 35 studies; see Supplementary Methods section ‘Sensitivity analyses for differences in survey efforts’). Analyses of terrestrial insects were not shown because data from tropical regions were entirely lacking after filtering for comparable resurvey effort. The percentage of warm-edge local extinction across habitats and taxonomic groups for tropical (red) and temperate (blue) species are shown. (a) The percentage of local extinction as a half-pie chart for species across habitats and major groups within each zone. (b and c) The percentage of warm-edge local extinctions per site. The percentage is calculated as the number of species that had warm-edge local extinction at that site divided by the total number of species with their warm-edge range limits at that site. Note that the actual number of species present at each site might be higher than the number of species having warm-edge range limits there. Statistical significance was assessed using GLMMs. All tests were two-sided. P values were adjusted for multiple comparisons using the false discovery rate method. Significance is indicated as: ns=Padj > 0.05; *=Padj > 0.01; ***=Padj < 0.01. For full statistical analysis results, see Supplementary Tables 32–44. Credit: silhouettes in a, Phylopic under a Creative Commons license CC0 1.0.
Source data
Extended Data Fig. 3 Robustness of latitudinal patterns in warm-edge local extinctions under group-mean-centering analyses.
Coefficients from GLMMs in which latitude is decomposed into between-study and within-study components using a group-mean-centering approach. The analysis included n = 5,119 species across habitats and taxonomic groups (n = 56 studies; see Supplementary Methods section ‘Sensitivity analyses using the group-mean-centering approach’). Panels show the between-study latitude effect only. Results are shown at the (a) species-level and (b) site-level. All species-level models used the full hierarchical nested random-effects structure to account for non-independence among studies, blocks, and sites (1|Study_ID/Block_ID/Site_ID), and included taxonomic random effects (1|Family/Genus). Site-level models included (1 | Study_ID/Block_ID) random effects. Model coefficient estimates are shown as circles, and the 95% CIs are shown as lines. Statistically significant coefficients are shown in black, and nonsignificant coefficients are shown in grey. All tests were two-sided. P values are adjusted for false discovery rate. Results are consistent with the main species-level and site-level analyses (Main Fig. 2), confirming that the global latitudinal gradient in warm-edge extinction is robust to study-level clustering and taxonomic structure. For full statistical analysis results, see Supplementary Tables 45–58. Credit: silhouettes in a, Phylopic under a Creative Commons license CC0 1.0.
Source data
Extended Data Fig. 4 Null-model resampling of background local extinctions at the species level and site level.
(a) Distributions of resampled background local extinction percentages (100,000 resampled replicates) for tropical (left; n = 584) and temperate (right; n = 2,274) species. Vertical lines indicate the observed percentage of warm-edge local extinctions in each region for species included in this analysis (584 tropical and 2,274 temperate species). In both cases, the observed values exceed the upper 95% quantile of the resampled distributions (that is, background local extinctions; P value in the top left corner), indicating that warm-edge extinctions occurred more frequently than expected from the resampled background extinctions. (b) Percentage of sites for which the observed warm-edge local extinction percentage exceeded the 95th percentile of the corresponding resampled background extinction distribution for that site. (c) Violin plot of the median of site-level resampled background extinction percentages (that is, from 100,000 resampled replicates per site) across tropical and temperate sites. Median values are represented by black diamonds. The percentage of local extinctions is colored by region: tropical (red) and temperate (blue). See Supplementary Methods section ‘Null-model resampling of background local extinction’. Note that many species were excluded from these analyses because they occur only at a single site (that is, have no background sites).
Source data
Extended Data Fig. 5 Relationship between amount of warming and background local extinctions among species and sites.
(a) Species-level model coefficient estimates testing the relationship between the probability of background local extinction and the amount of warming in BIO1 (ΔBIO1) at these sites, comparing tropical and temperate species. (b) Site-level model coefficient estimates testing the relationship between site-level background local extinction frequency and ΔBIO1 for these sites, comparing tropical and temperate sites. In both panels, colored distributions represent observed estimates (that is, based on observed background local extinctions), and white distributions represent randomised (null) estimates (that is, based on randomised background local extinctions). The percentage of background local extinctions is colored by region: tropical (red) and temperate (blue). Positive values indicate higher background extinction probabilities under greater warming. Overlap with zero indicates no association between extinction and warming. Each point represents a randomised replicate (n = 10,000 randomisations). See also Supplementary Methods section ‘Linking background local extinction to climate change’.
Source data
Extended Data Fig. 6 Cool-edge local extinction is more frequent among temperate than tropical species.
Results are based on the analysis of species included under the stringent criterion (for the cool-edge) with usable data on cool-edge persistence and extinction over time (n = 3,196 species; see Supplementary Methods). The percentage of cool-edge local extinction across habitats and taxonomic groups for tropical (red) and temperate (blue) species are shown. (a) The percentage of local extinction is shown as a half-pie chart for species across habitats and major groups within each zone. (b and c) The percentage of cool-edge local extinctions per site. The percentage is calculated as the number of species that had cool-edge local extinction at that site divided by the total number of species with their cool-edge range limits at that site. Note that the actual number of species present at each site can be higher than the number of species having cool-edge range limits there. Statistical significance was assessed using GLMMs. All tests were two-sided. P values were adjusted for multiple comparisons using the false discovery rate method. Significance is indicated as: ns=Padj > 0.05; *=Padj > 0.01; ***=Padj < 0.01. For full statistical analysis results, see Supplementary Tables 59–72, and Supplementary Methods section ‘Cool-edge local extinction across latitude’. Credit: silhouettes in a, Phylopic under a Creative Commons license CC0 1.0.
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Extended Data Fig. 7 The percentage of species with cool-edge range expansion.
The results are presented across habitats and taxonomic groups, shown separately for tropical (red) and temperate (blue) species. Results are based on the analysis of species included under the stringent criterion with usable data on cool-edge expansion (n = 3,196 species; see Supplementary Methods section ‘Cool-edge expansions across latitude’). The percentages of species with cool-edge range expansion are shown as lollipop plots for all species (a), in terrestrial, freshwater, and marine habitats (b), and for major taxonomic groups within each habitat (c). P values are from GLMM likelihood-ratio tests for the latitude effect, using the same model structure as in the warm-edge extinction analysis. All tests were two-sided. P values are adjusted for false discovery rate (Padj).
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Extended Data Fig. 8 Extent of land-use change surrounding survey sites across multiple spatial scales.
Extent of land use changes per site (% of area within circular buffers) across three spatial scales for 32,075 non-marine sites. Lollipop plots indicate the percentage of sites that showed visible land-use change across defined bins for different buffer distances. (a) 564 m radius (~1 km2), (b) 2 km radius (~12.57 km2), and (c) 5 km radius (~78.54 km2). Land use change is grouped into six bins representing increasing levels of transformation within each buffer. The values on top of the line segments represent the number of sites, with the percentage of sites in parentheses. Note that a 564 m buffer radius was used to match the resolution of the different land-use data we used based on the survey start and end years (more details in Supplementary Methods section ‘Supplementary data validation and verification’).
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Extended Data Fig. 9 Study-level covariates show no systematic influence on reported warm-edge local extinctions.
Relationships between study-level covariates and the percentage of warm-edge local extinctions reported per study. Panels show (a–b) median elevational resolution of survey sites, (c–d) survey plot or transect area (log10-scaled), (e–f) number of sites surveyed (log10-scaled), and (g–h) number of species surveyed (log10-scaled). Left plots show all studies combined under liberal criterion, right plots show separate regressions for temperate and tropical studies. The R2 and P values (from simple linear regression) are given at the top of the panel. The red dashed line represents the fit of a linear regression model with 95% CIs as grey shading. Across all covariates, regression slopes were shallow and non-significant (all P > 0.05), indicating that methodological variation among studies did not systematically affect warm-edge local extinction estimates or the observed latitudinal pattern in warm-edge local extinctions. See Supplementary Figures 8–11 for results under the stringent criterion for including species, and Supplementary Methods section ‘Study-level covariate analyses’.
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Supplementary information
Supplementary Information (download PDF )
Supplementary Methods, Text, Figs. 1–21, Tables 1–82 and References.
Reporting Summary (download PDF )
Supplementary Data 1 (download XLSX )
Data on local extinction for all 5,151 species.
Supplementary Data 2 (download XLSX )
Local extinction data based on all 1,610 sites included.
Supplementary Data 3 (download XLSX )
Estimated climate and climate change for each species at their warmest-edge site.
Source data
Source Data Fig. 1 (download XLSX )
Source data on species-level warm-edge local extinctions.
Source Data Fig. 2 (download XLSX )
Source data on species-level and site-level warm-edge local extinctions.
Source Data Fig. 3 (download XLSX )
Statistical source data for model estimates of climatic drivers of warm-edge local extinctions.
Source Data Fig. 4 (download XLSX )
Source data for the estimated climate change for each species.
Source Data Extended Data Fig. 1 (download XLSX )
Source data for species-level warm-edge local extinctions, including a column indicating the stringency criterion.
Source Data Extended Data Fig. 2 (download XLSX )
Source data for species-level warm-edge local extinctions, including sampling effort comparability classification.
Source Data Extended Data Fig. 3 (download XLSX )
Statistical source data from group-mean-centering GLMM analyses.
Source Data Extended Data Fig. 4 (download XLSX )
Source data for null-model resampling of background local extinctions.
Source Data Extended Data Fig. 5 (download XLSX )
Statistical source data for model estimates of relationships between background local extinctions and warming.
Source Data Extended Data Fig. 6 (download XLSX )
Source data on species-level and site-level cool-edge local extinctions.
Source Data Extended Data Fig. 7 (download XLSX )
Source data for latitudinal patterns in cool-edge dispersal.
Source Data Extended Data Fig. 8 (download XLSX )
Source data for site-level land-use change across multiple spatial scales.
Source Data Extended Data Fig. 9 (download XLSX )
Source data for study-level covariate effects on study-level warm-edge local extinctions.
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Murali, G., Karger, D.N. & Wiens, J.J. Temperate local extinctions from climate change are outpacing tropical extinctions.
Nat. Clim. Chang. (2026). https://doi.org/10.1038/s41558-026-02669-y
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DOI: https://doi.org/10.1038/s41558-026-02669-y
Source: Ecology - nature.com

