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Higher temperature extremes exacerbate negative disease effects in a social mammal

Abstract

One important but understudied way in which climate change may impact the fitness of individuals and populations is by altering the prevalence of infectious disease outbreaks. This is especially true in social species where endemic diseases are widespread. Here we use 22 years of demographic data from wild meerkats (Suricata suricatta) in the Kalahari, where temperatures have risen steadily, to project group persistence under interactions between weather extremes and fatal tuberculosis outbreaks caused by infection with Mycobacterium suricattae. We show that higher temperature extremes increase the risk of outbreaks within groups by increasing physiological stress as well as the dispersal of males, which are important carriers of tuberculosis. Explicitly accounting for negative effects of tuberculosis outbreaks on survival and reproduction in groups more than doubles group extinction risk in 12 years under projected temperature increases. Synergistic climate–disease effects on demographic rates may therefore rapidly intensify climate-change impacts in natural populations.

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Fig. 1: Changes in maximum temperatures and disease outbreaks in meerkat groups in the Kalahari.
Fig. 2: Observed relationships between temperature and rainfall deviations, clinical TB and group dynamics in meerkats.
Fig. 3: Weather extremes impact the occurrence of clinical TB directly and indirectly via dispersal.
Fig. 4: Climate–TB interactions affect predictions of key demographic rates in meerkats.
Fig. 5: Temperature–disease interactions under climate change destabilize meerkat groups.
Fig. 6: Climate change increases group extinction via clinical TB outbreaks.

Data availability

All data to construct and project the individual-based model have been deposited on Zenodo: https://doi.org/10.5281/zenodo.5784649.

Code availability

All R scripts to construct and project the individual-based model have been deposited on Zenodo: https://doi.org/10.5281/zenodo.5784649.

References

  1. 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 

  2. Fuller, A. et al. Physiological mechanisms in coping with climate change. Physiol. Biochem. Zool. 83, 713–720 (2010).

    Google Scholar 

  3. Sinervo, B. et al. Erosion of lizard diversity by climate change and altered thermal niches. Science 328, 894–899 (2010).

    CAS  Google Scholar 

  4. Brawn, J. D., Benson, T. J., Stager, M., Sly, N. D. & Tarwater, C. E. Impacts of changing rainfall regime on the demography of tropical birds. Nat. Clim. Change 7, 133–136 (2016).

    Google Scholar 

  5. Summers, B. A. Climate change and animal disease. Vet. Pathol. 46, 1185–1186 (2009).

    CAS  Google Scholar 

  6. Randall, C. J. & van Woesik, R. Contemporary white-band disease in Caribbean corals driven by climate change. Nat. Clim. Change 5, 375–379 (2015).

    Google Scholar 

  7. Munson, L. et al. Climate extremes promote fatal co-infections during canine distemper epidemics in African lions. PLoS ONE 3, e2545 (2008).

    Google Scholar 

  8. Rohr, J. R. et al. Frontiers in climate change–disease research. Trends Ecol. Evol. 26, 270–277 (2011).

    Google Scholar 

  9. Zarnetske, P. L., Skelly, D. K. & Urban, M. C. Biotic multipliers of climate change. Science 336, 1516–1518 (2012).

    CAS  Google Scholar 

  10. Cohen, J. M., Sauer, E. L., Santiago, O., Spencer, S. & Rohr, J. R. Divergent impacts of warming weather on wildlife disease risk across climates. Science 370, eabb1702 (2020).

    CAS  Google Scholar 

  11. Cornwallis, C. K. et al. Cooperation facilitates the colonization of harsh environments. Nat. Ecol. Evol. 1, 0057 (2017).

    Google Scholar 

  12. Koenig, W. D. & Dickinson, J. L. (eds) Cooperative Breeding in Vertebrates: Studies of Ecology, Evolution, and Behavior (Cambridge Univ. Press, 2016).

  13. Groenewoud, F. & Clutton-Brock, T. Meerkat helpers buffer the detrimental effects of adverse environmental conditions on fecundity, growth and survival. J. Anim. Ecol. 90, 641–652 (2020).

    Google Scholar 

  14. Langwig, K. E. et al. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol. Lett. 15, 1050–1057 (2012).

    Google Scholar 

  15. Vicente, J., Delahay, R. J., Walker, N. J. & Cheeseman, C. L. Social organization and movement influence the incidence of bovine tuberculosis in an undisturbed high-density badger Meles meles population. J. Anim. Ecol. 76, 348–360 (2007).

    CAS  Google Scholar 

  16. Bermejo, M. et al. Ebola outbreak killed 5000 gorillas. Science 314, 1564 (2006).

    CAS  Google Scholar 

  17. Hanya, G. et al. Mass mortality of Japanese macaques in a western coastal forest of Yakushima. Ecol. Res. 19, 179–188 (2004).

    Google Scholar 

  18. Angulo, E. et al. Allee effects in social species. J. Anim. Ecol. 87, 47–58 (2018).

    Google Scholar 

  19. Woodroffe, R., Groom, R. & McNutt, J. W. Hot dogs: high ambient temperatures impact reproductive success in a tropical carnivore. J. Anim. Ecol. 86, 1329–1338 (2017).

    Google Scholar 

  20. Brandell, E. E., Dobson, A. P., Hudson, P. J., Cross, P. C. & Smith, D. W. A metapopulation model of social group dynamics and disease applied to Yellowstone wolves. Proc. Natl Acad. Sci. USA 118, 33649227 (2021).

    Google Scholar 

  21. Clutton-Brock, T. H. & Manser, M. in Cooperative Breeding in Vertebrates: Studies of Ecology, Evolution, and Behavior (eds Koenig, W. D. & Dickinson, J. L.) 294–317 (Cambridge Univ. Press, 2016).

  22. Drewe, J. A. Who infects whom? Social networks and tuberculosis transmission in wild meerkats. Proc. R. Soc. B 277, 633–642 (2010).

    Google Scholar 

  23. Parsons, S. D. C., Drewe, J. A., van Pittius, N. C. G., Warren, R. M. & van Helden, P. D. Novel cause of tuberculosis in meerkats, South Africa. Emerg. Infect. Dis. 19, 2004–2007 (2013).

    Google Scholar 

  24. Duncan, C., Manser, M., & Clutton-Brock, T. H. Decline and fall: the causes of group failure in cooperatively breeding meerkats. Ecol. Evol. https://doi.org/10.1002/ece3.7655 (2021).

  25. Drewe, J. A., Foote, A. K., Sutcliffe, R. L. & Pearce, G. P. Pathology of Mycobacterium bovis infection in wild meerkats (Suricata suricatta). J. Comp. Pathol. 140, 12–24 (2009).

    CAS  Google Scholar 

  26. van Wilgen, N. J., Goodall, V. & Holness, S. Rising temperatures and changing rainfall patterns in South Africa’s national parks. Aquat. Microb. Ecol. 36, 706–721 (2016).

    Google Scholar 

  27. Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).

    CAS  Google Scholar 

  28. Fischer, E. M., Beyerle, U. & Knutti, R. Robust spatially aggregated projections of climate extremes. Nat. Clim. Change 3, 1033–1038 (2013).

    Google Scholar 

  29. Bourne, A. R., Cunningham, S. J., Spottiswoode, C. N. & Ridley, A. R. Hot droughts compromise interannual survival across all group sizes in a cooperatively breeding bird. Ecol. Lett. 23, 1776–1788 (2020).

    Google Scholar 

  30. Van de Ven, T. M. F. N., Fuller, A. & Clutton‐Brock, T. H. Effects of climate change on pup growth and survival in a cooperative mammal, the meerkat. Funct. Ecol. 34, 194–202 (2020).

    Google Scholar 

  31. Katale, B. Z. et al. Prevalence and risk factors for infection of bovine tuberculosis in indigenous cattle in the Serengeti ecosystem, Tanzania. BMC Vet. Res. 9, 267 (2013).

    Google Scholar 

  32. Paniw, M., Maag, N., Cozzi, G., Clutton-Brock, T. & Ozgul, A. Life history responses of meerkats to seasonal changes in extreme environments. Science 363, 631–635 (2019).

    CAS  Google Scholar 

  33. Dwyer, R. A., Witte, C., Buss, P., Goosen, W. J. & Miller, M. Epidemiology of tuberculosis in multi-host wildlife systems: implications for black (Diceros bicornis) and white (Ceratotherium simum) rhinoceros. Front. Vet. Sci. 7, 580476 (2020).

    Google Scholar 

  34. Patterson, S., Drewe, J. A., Pfeiffer, D. U. & Clutton-Brock, T. H. Social and environmental factors affect tuberculosis related mortality in wild meerkats. J. Anim. Ecol. 86, 442–450 (2017).

    Google Scholar 

  35. Dubuc, C. et al. Increased food availability raises eviction rate in a cooperative breeding mammal. Biol. Lett. 13, 20160961 (2017).

    Google Scholar 

  36. Maag, N., Cozzi, G., Clutton-Brock, T. H. & Ozgul, A. Density‐dependent dispersal strategies in a cooperative breeder. Ecology 99, 1932–1941 (2018).

    Google Scholar 

  37. Ekernas, L. S. & Cords, M. Social and environmental factors influencing natal dispersal in blue monkeys, Cercopithecus mitis stuhlmanni. Anim. Behav. 73, 1009–1020 (2007).

    Google Scholar 

  38. Ozgul, A., Bateman, A. W., English, S., Coulson, T. & Clutton-Brock, T. H. Linking body mass and group dynamics in an obligate cooperative breeder. J. Anim. Ecol. 83, 1357–1366 (2014).

    Google Scholar 

  39. Tomlinson, A. J., Chambers, M. A., Wilson, G. J., McDonald, R. A. & Delahay, R. J. Sex-related heterogeneity in the life-history correlates of Mycobacterium bovis infection in European badgers (Meles meles). Transbound. Emerg. Dis. 60, 37–45 (2013).

    Google Scholar 

  40. Courchamp, F., Grenfell, B. & Clutton-Brock, T. H. Population dynamics of obligate cooperators. Proc. R. Soc. B 266, 557–563 (1999).

    Google Scholar 

  41. Lerch, B. A., Nolting, B. C. & Abbott, K. C. Why are demographic Allee effects so rarely seen in social animals? J. Anim. Ecol. 87, 1547–1559 (2018).

    Google Scholar 

  42. Borg, B. L., Brainerd, S. M., Meier, T. J. & Prugh, L. R. Impacts of breeder loss on social structure, reproduction and population growth in a social canid. J. Anim. Ecol. 84, 177–187 (2015).

    Google Scholar 

  43. Brown, P. T. & Caldeira, K. Greater future global warming inferred from Earth’s recent energy budget. Nature 552, 45–50 (2017).

    CAS  Google Scholar 

  44. Zscheischler, J. et al. Future climate risk from compound events. Nat. Clim. Change 8, 469–477 (2018).

    Google Scholar 

  45. Blois, J. L., Zarnetske, P. L., Fitzpatrick, M. C. & Finnegan, S. Climate change and the past, present, and future of biotic interactions. Science 341, 499–504 (2013).

    CAS  Google Scholar 

  46. Blackwood, J. C., Streicker, D. G., Altizer, S. & Rohani, P. Resolving the roles of immunity, pathogenesis, and immigration for rabies persistence in vampire bats. Proc. Natl Acad. Sci. USA 110, 20837–20842 (2013).

    CAS  Google Scholar 

  47. Fenner, A. L., Godfrey, S. S. & Michael Bull, C. Using social networks to deduce whether residents or dispersers spread parasites in a lizard population. J. Anim. Ecol. 80, 835–843 (2011).

    Google Scholar 

  48. Paniw, M. et al. The myriad of complex demographic responses of terrestrial mammals to climate change and gaps of knowledge: a global analysis. J. Anim. Ecol. 90, 1398–1407 (2021).

    Google Scholar 

  49. McDonald, J. L. et al. Demographic buffering and compensatory recruitment promotes the persistence of disease in a wildlife population. Ecol. Lett. 19, 443–449 (2016).

    Google Scholar 

  50. Plowright, R. K., Sokolow, S. H., Gorman, M. E., Daszak, P. & Foley, J. E. Causal inference in disease ecology: investigating ecological drivers of disease emergence. Front. Ecol. Environ. 6, 420–429 (2008).

    Google Scholar 

  51. Russell, R., DiRenzo, G. V., Szymanski, J., Alger, K. & Grant, E. H. C. Principles and mechanisms of wildlife population persistence in the face of disease. Front. Ecol. Evol. 8, 344 (2020).

    Google Scholar 

  52. Baudouin, A. et al. Disease avoidance, and breeding group age and size condition the dispersal patterns of western lowland gorilla females. Ecology 100, e02786 (2019).

    Google Scholar 

  53. Townsend, A. K., Hawley, D. M., Stephenson, J. F. & Williams, K. E. G. Emerging infectious disease and the challenges of social distancing in human and non-human animals. Proc. R. Soc. B 287, 20201039 (2020).

    CAS  Google Scholar 

  54. Schisler, G. J., Bergersen, E. P. & Walker, P. G. Effects of multiple stressors on morbidity and mortality of fingerling rainbow trout infected with Myxobolus cerebralis. Trans. Am. Fish. Soc. 129, 859–865 (2000).

    Google Scholar 

  55. Härkönen, T., Harding, K., Rasmussen, T. D., Teilmann, J. & Dietz, R. Age- and sex-specific mortality patterns in an emerging wildlife epidemic: the phocine distemper in European harbour seals. PLoS ONE 2, e887 (2007).

    Google Scholar 

  56. Clutton-Brock, T. H. et al. Reproduction and survival of suricates (Suricata suricatta) in the southern Kalahari. Afr. J. Ecol. 37, 69–80 (1999).

    Google Scholar 

  57. Clutton-Brock, T. H., Hodge, S. J. & Flower, T. P. Group size and the suppression of subordinate reproduction in Kalahari meerkats. Anim. Behav. 76, 689–700 (2008).

    Google Scholar 

  58. Bateman, A. W., Ozgul, A., Coulson, T. & Clutton-Brock, T. H. Density dependence in group dynamics of a highly social mongoose, Suricata suricatta. J. Anim. Ecol. 81, 628–639 (2012).

    Google Scholar 

  59. Adler, R. F. et al. The Global Precipitation Climatology Project (GPCP) monthly analysis (new version 2.3) and a review of 2017 global precipitation. Atmosphere 9, 138 (2018).

    Google Scholar 

  60. Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010).

    CAS  Google Scholar 

  61. Parding, K. M. et al. GCMeval – an interactive tool for evaluation and selection of climate model ensembles. Clim. Serv. 18, 100167 (2020).

    Google Scholar 

  62. Delahay, R. J., Langton, S., Smith, G. C., Clifton-Hadley, R. S. & Cheeseman, C. L. The spatio-temporal distribution of Mycobacterium bovis (bovine tuberculosis) infection in a high-density badger population. J. Anim. Ecol. 69, 428–441 (2000).

    Google Scholar 

  63. Delahay, R. J. et al. Long-term temporal trends and estimated transmission rates for Mycobacterium bovis infection in an undisturbed high-density badger (Meles meles) population. Epidemiol. Infect. 141, 1445–1456 (2013).

    CAS  Google Scholar 

  64. Buzdugan, S. N., Chambers, M. A., Delahay, R. J. & Drewe, J. A. Diagnosis of tuberculosis in groups of badgers: an exploration of the impact of trapping efficiency, infection prevalence and the use of multiple tests. Epidemiol. Infect. 144, 1717–1727 (2016).

    CAS  Google Scholar 

  65. Akaike, H. in Selected Papers of Hirotugu Akaike (eds Parzen, E. et al.) 199–213 (Springer, 1998).

  66. Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. B Stat. 73, 3–36 (2011).

    Google Scholar 

  67. Grimm, V. et al. The ODD protocol: a review and first update. Ecol. Model. 221, 2760–2768 (2010).

    Google Scholar 

  68. Wood, S. N. Statistical inference for noisy nonlinear ecological dynamic systems. Nature 466, 1102–1104 (2010).

    CAS  Google Scholar 

  69. Fronzek, S., Carter, T. R., Räisänen, J., Ruokolainen, L. & Luoto, M. Applying probabilistic projections of climate change with impact models: a case study for sub-Arctic palsa mires in Fennoscandia. Clim. Change 99, 515–534 (2010).

    Google Scholar 

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Acknowledgements

We thank the many volunteers and field managers of the Kalahari Meerkat Project (KMP) for their contribution to data collection, in particular D. Gaynor and T. Vink for organization of the project and databases; the Trustees of the Kalahari Research Centre and the Directors of the Kalahari Meerkat Project for access to the data used in this paper; the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP; and the climate modelling groups (listed in Supplementary Table 3.2) for producing and making available their model output. Data collection was logistically supported by the Mammal Research Institute of the University of Pretoria. The long-term research on meerkats is currently supported by a European Research Council Advanced Grant (no. 742808 and no. 294494) to T.C.-B., and by grants from the University of Zurich and the MAVA foundation to M.M. F.G. and C.D. were supported by a European Research Council Advanced Grant (no. 742808) awarded to T.C.-B. M.P. was supported by MSCA-IF-EF-ST no. 894223. Analysis of data was supported by a Swiss National Science Foundation Grant (31003A_182286) to A.O.

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T.C.-B. and M.M. led the long-term study and data collection; M.P., A.O. and T.C.-B. conceived the ideas for the paper and its structure; M.P., C.D. and F.G. designed the analyses; M.P. conducted the analyses with help from A.O. and wrote the manuscript; J.A.D. guided the TB epidemiology aspects of the study; all authors discussed the results and commented on the manuscript.

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Correspondence to Maria Paniw.

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Nature Climate Change thanks Jens Christensen, Isabel Smallegange 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 Annual trends in total rainfall at the study site of meerkat demographic data collection.

Blue lines depict observed values, as interpolated by NOAA GPCP Precipitation Data, while black lines depict mean predictions (± 95 % prediction intervals as shaded areas) from simple linear models fit to rainfall values through time.

Extended Data Fig. 2 Life-cycle diagram of Kalahari meerkats (Suricata suricatta) from which the individual based model was parameterized.

Circles depict life-history stages. Rectangles depict nine major stages to aid visualization. Transitions (T) among stages, depicted by solid arrows, occur in discrete one-month intervals. Transitions depicted by black arrows are deterministic (that is, depend on rules set in the IBM and not on sampling from a distribution). Other transitions include survival (S) and, conditional on survival, growth (G). Adult helper (H) and dominant (D) females transition among pregnancy categories: first-month pregnant (P1), second-month pregnant (P2), and with their own litter of dependent, weaning pups (L). NP transition to P1 or remain NP. P1 either transition to P2 or abort. If they abort, they can become P1or NP. P2 give birth to a litter and wean it for a month (L), or they abort. In the latter case, they transition to P1 or NP. Females with litters (L) can remain NP or become pregnant again (P1). Surviving adult dominant and helper females contribute new recruits (R) of a given mass distribution (D) to one-month old pups, depicted by dashed arrows. Survival and growth of adult dominant and helper males (M) differ depending on whether the male was natal (N) to the group or immigrated (I) into a group.

Extended Data Fig. 3 Structure of the individual-based model (IBM) to simulate meerkat group dynamics.

White/green boxes represent models of stage-specific demographic rates and group transition to a TB state, while blue boxes show conditions to decide the structure of a given demographic-rate model. Green boxes represent models in which new individuals are added to the group (through birth or immigration). Stages include pups, juveniles (juv), subadults (sub), and adult helpers (H) and dominants (D). The IBM is initiated with a group under specified characteristics (grey box; see Supplementary Table 3.1), and individuals go through life events (modelled probabilistically) in distinct one-month steps. At the end of each month, individual and group characteristics are updated, new individuals can immigrate into the group, and the group can remain without apparent TB cases or show clinical TB cases for the first time (upon which the entire group is considered TB affected). Monthly simulations of group dynamics are based on different scenarios of climate change. For each simulation, several output metrics are calculated.

Extended Data Fig. 4 Simulated meerkat group sizes overlap the distribution of observed group sizes.

Simulations were based on an individual-based model which projected dynamics of 10 groups in discrete monthly intervals for the entire life span of a group starting from a specific date. Group size refers to the number of subadult and adult individuals (> 6 months old). Red line: Average observed group size for groups that were monitored for > 6 years. Black line: average simulated group size across 1000 simulation runs from IBM simulations initiated with large group sizes. Grey lines: group size in each simulation run. Insert shows distribution of all simulated and observed group sizes. Total number (n) of data points to calculate observed and simulated distributions in insert is n = 650 and n = 256076, respectively. The higher discrepancies between observed and simulated values at lower group sizes in the insert is due to demographic stochasticity implemented in the IBM, which has a higher impact in smaller groups, causing more variable simulation outcomes compared to observed data.

Extended Data Fig. 5 Simulated adult mass of meerkats falls within the distribution of observed masses.

Simulations were based on an individual-based model which projected dynamics of 10 groups, including mass change, in discrete monthly intervals. Total number (n) of data points to calculate observed and simulated distributions is n = 647 and n = 256076, respectively.

Extended Data Fig. 6 Proportion of IBM simulations that result in group extinction increases under scenarios of higher temperature extremes.

Boxplots summarize the distribution of changes in group extinction probability (proportion of 1000 total simulations that result in group extinction), compared to a baseline, when increasingly more extreme years are sampled in IBM simulations (scenarios are depicted on the x axis). Red/orange points show changes for the ten initial group sizes and two probabilities with which extreme years were sampled in simulations. Back points depict average values for each scenario connected by lines to better visualize changes in extinction probability.

Extended Data Fig. 7 Effects of climate and TB perturbations on relationship between group extinction and clinical TB.

Boxplots and violin plots show the distribution of (a) the proportion of 1000 IBM simulation runs that resulted in clinical TB occurring in groups within 12 years among 10 IBM configurations initiated with different group characteristics. The simulations were run under either a baseline scenario (no climate change) or sampling temperature and rainfall from extreme years (2015 & 2016) with probability of 0.75 (climate change). In (b), distributions (across IBM configurations initiated with small or large group sizes) show the proportion of simulations where clinical TB occurred in at least the last three months prior to group failure or prior to the end of simulations (12 years).

Extended Data Fig. 8 Temperatures become more extreme in the future under four greenhouse-gas Relative Concentration Pathways (RCPs) across different global circulation models (GCMs).

Plots show values of historical (1997–2018) and future (2019—2100) monthly means of maximum temperatures for four RCP across 21 GCMs (different subplots; see Supplementary Table 3.2 for details on GCMs). Red horizontal line depicts year 2019.

Extended Data Fig. 9 Temperatures deviations become more extreme in the future under four greenhouse-gas Relative Concentration Pathways (RCPs) across different global circulation models (GCMs).

Plots show monthly standardised deviations from long-term (1997–2018) seasonal means of historic (1997–2018) and future (2019–2100) monthly maximum temperatures for four RCP across 21 GCMs (different subplots; see Supplementary Table 3.2 for details on GCMs). Red horizontal line depicts year 2019.

Extended Data Fig. 10 Climate-change impact risks for the Kalahari.

Plots show the distribution of GCM outputs as proportion of hot years (that is, >6, 7, 8, 9, or >10 months if above-average temperatures) by mid century (2041–2061) and end century (2079–2100) for each Relative Concentration Pathway (RCP) and historic/current conditions (1997–2018). Number indicate the proportion of models for which hot years in each category will at least double; and numbers in parentheses indicate proportion of models for which hot years will occur with a probability of > 0.75.

Supplementary information

Supplementary Information

Supplementary discussion, Figs. 1.1–1.9, 2.1, 3.1–3.16 and 4.1, and Tables 1.1, 1.2, 3.1–3.3 and 5.1.

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Paniw, M., Duncan, C., Groenewoud, F. et al. Higher temperature extremes exacerbate negative disease effects in a social mammal. Nat. Clim. Chang. 12, 284–290 (2022). https://doi.org/10.1038/s41558-022-01284-x

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