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Plant pathogen infection risk tracks global crop yields under climate change

Nature Climate Change volume 11, pages 710–715 (2021)Cite this article

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Abstract

Global food security is strongly determined by crop production. Climate change-induced losses to production can occur directly or indirectly, including via the distributions and impacts of plant pathogens. However, the likely changes in pathogen pressure in relation to global crop production are poorly understood. Here we show that temperature-dependent infection risk, r(T), for 80 fungal and oomycete crop pathogens will track projected yield changes in 12 crops over the twenty-first century. For most crops, both yields and r(T) are likely to increase at high latitudes. In contrast, the tropics will see little or no productivity gains, and r(T) is likely to decline. In addition, the United States, Europe and China may experience major changes in pathogen assemblages. The benefits of yield gains may therefore be tempered by the greater burden of crop protection due to increased disease and unfamiliar pathogens.

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Fig. 1: Summary of infection cardinal temperatures for 80 plant pathogens included in this study.
Fig. 2: Average change in Rr and pathogen turnover under RCP 6.0 across all months.
Fig. 3: Effect of RCP and pathogen restriction method on change in Rr and pathogen turnover.
Fig. 4: Effect of RCP on average change in Rr and pathogen turnover across all months.
Fig. 5: Changes in crop yield and r(T) under RCP 6.0 by latitude.

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Data availability

The fungal and oomycete cardinal temperature data are available in Dryad42 (https://doi.org/10.5061/dryad.tqjq2bvw6) and from ref. 16. The data on annual crop yield projections used in this study are from the Inter-Sectoral Model Intercomparison Project (https://www.isimip.org). The fungal and oomycete host plant data and geographical distributions (the Plantwise database) were used under license for the current study and are available with permission from CABI. The FAOSTAT commodity list is available from http://www.fao.org. The global gridded climate data and climate projections are available from WorldClim (https://www.worldclim.org). The global gridded crop distribution data used in this study are available from EarthStat (https://www.earthstat.org) and MIRCA2000 (https://www.uni-frankfurt.de/45218031/data_download). The fungal and oomycete names and name disambiguation data were obtained from Species Fungorum (http://www.speciesfungorum.org/) and MycoBank (http://www.mycobank.org/). The annual per capita GDP (PPP) data were obtained from the World Bank (https://data.worldbank.org/). Coupled Model Intercomparison Project 5 single-level monthly near-surface RH data were obtained from the Climate Data Store (https://cds.climate.copernicus.eu). Administrative boundaries for the maps were obtained from GADM (https://www.gadm.org). The coastal outlines were obtained from package rworldmap version 1.3–6 for R version 4.0.1.

Code availability

All analyses were conducted using existing functions for R version 4.0.1. No substantial custom code was used. R code used for data manipulation is available from the corresponding author on reasonable request.

References

  1. Fones, H. N. et al. Threats to global food security from emerging fungal and oomycete crop pathogens. Nat. Food 1, 332–342 (2020).

    Article  Google Scholar 

  2. Chaloner, T. M., Gurr, S. J. & Bebber, D. P. Geometry and evolution of the ecological niche in plant-associated microbes. Nat. Commun. 11, 2955 (2020).

    Article  CAS  Google Scholar 

  3. Bebber, D. P. Range-expanding pests and pathogens in a warming world. Annu. Rev. Phytopathol. 53, 335–356 (2015).

    Article  CAS  Google Scholar 

  4. Bebber, D. P. et al. Many unreported crop pests and pathogens are probably already present. Glob. Change Biol. 25, 2703–2713 (2019).

    Article  Google Scholar 

  5. Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Syst. 37, 637–669 (2006).

    Article  Google Scholar 

  6. Bebber, D. P., Ramotowski, M. A. T. & Gurr, S. J. Crop pests and pathogens move polewards in a warming world. Nat. Clim. Change 3, 985–988 (2013).

    Article  Google Scholar 

  7. Yan, Y., Wang, Y.-C., Feng, C.-C., Wan, P.-H. M. & Chang, K. T.-T. Potential distributional changes of invasive crop pest species associated with global climate change. Appl. Geogr. 82, 83–92 (2017).

    Article  Google Scholar 

  8. Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40, 677–697 (2009).

    Article  Google Scholar 

  9. Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).

    Article  Google Scholar 

  10. Bondeau, A. et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 13, 679–706 (2007).

    Article  Google Scholar 

  11. Bregaglio, S., Donatelli, M. & Confalonieri, R. Fungal infections of rice, wheat, and grape in Europe in 2030–2050. Agron. Sustain. Dev. 33, 767–776 (2013).

    Article  Google Scholar 

  12. Bebber, D. P. Climate Change effects on Black Sigatoka disease of banana. Philos. Trans. R. Soc. B 374, 20180269 (2019).

    Article  Google Scholar 

  13. Delgado-Baquerizo, M. et al. The proportion of soil-borne pathogens increases with warming at the global scale. Nat. Clim. Change 10, 550–554 (2020).

    Article  Google Scholar 

  14. Ostberg, S., Schewe, J., Childers, K. & Frieler, K. Changes in crop yields and their variability at different levels of global warming. Earth Syst. Dyn. 9, 479–496 (2018).

    Article  Google Scholar 

  15. Rosenzweig, C. et al. Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc. Natl Acad. Sci. USA 111, 3268–3273 (2014).

    Article  CAS  Google Scholar 

  16. Magarey, R. D., Sutton, T. B. & Thayer, C. L. A simple generic infection model for foliar fungal plant pathogens. Phytopathology 95, 92–100 (2005).

    Article  CAS  Google Scholar 

  17. Bebber, D. P., Holmes, T. & Gurr, S. J. The global spread of crop pests and pathogens. Glob. Ecol. Biogeogr. 23, 1398–1407 (2014).

    Article  Google Scholar 

  18. Soberón, J. & Nakamura, M. Niches and distributional areas: concepts, methods, and assumptions. Proc. Natl Acad. Sci. USA 106, 19644–19650 (2009).

    Article  Google Scholar 

  19. Bebber, D. P., Holmes, T., Smith, D. & Gurr, S. J. Economic and physical determinants of the global distributions of crop pests and pathogens. N. Phytol. 202, 901–910 (2014).

    Article  Google Scholar 

  20. Sparks, A. H., Forbes, G. A., Hijmans, R. J. & Garrett, K. A. Climate change may have limited effect on global risk of potato late blight. Glob. Change Biol. 20, 3621–3631 (2014).

    Article  Google Scholar 

  21. Chen, X. M. Epidemiology and control of stripe rust [Puccinia striiformis f. sp. tritici] on wheat. Can. J. Plant Pathol. 27, 314–337 (2005).

    Article  Google Scholar 

  22. Zhan, J. & McDonald, B. A. Thermal adaptation in the fungal pathogen Mycosphaerella graminicola. Mol. Ecol. 20, 1689–1701 (2011).

    Article  Google Scholar 

  23. Robin, C., Andanson, A., Saint-Jean, G., Fabreguettes, O. & Dutech, C. What was old is new again: thermal adaptation within clonal lineages during range expansion in a fungal pathogen. Mol. Ecol. 26, 1952–1963 (2017).

    Article  Google Scholar 

  24. Rowlandson, T. et al. Reconsidering leaf wetness duration determination for plant disease management. Plant Dis. 99, 310–319 (2014).

    Article  Google Scholar 

  25. IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

  26. Dunn, R. J. H., Willett, K. M., Ciavarella, A. & Stott, P. A. Comparison of land surface humidity between observations and CMIP5 models. Earth Syst. Dyn. 8, 719–747 (2017).

    Article  Google Scholar 

  27. Větrovský, T. et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat. Commun. 10, 5142 (2019).

    Article  Google Scholar 

  28. Liu, X. et al. Warming affects foliar fungal diseases more than precipitation in a Tibetan alpine meadow. N. Phytol. 221, 1574–1584 (2019).

    Article  Google Scholar 

  29. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

  30. Sohl, T. L., Wimberly, M. C., Radeloff, V. C., Theobald, D. M. & Sleeter, B. M. Divergent projections of future land use in the United States arising from different models and scenarios. Ecol. Model. 337, 281–297 (2016).

    Article  Google Scholar 

  31. Müller, C. et al. Exploring uncertainties in global crop yield projections in a large ensemble of crop models and CMIP5 and CMIP6 climate scenarios. Environ. Res. Lett. 16, 034040 (2021).

    Article  Google Scholar 

  32. Folberth, C. et al. Parameterization-induced uncertainties and impacts of crop management harmonization in a global gridded crop model ensemble. PLoS ONE 14, e0221862 (2019).

    Article  CAS  Google Scholar 

  33. Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22, GB1022 (2008).

    Article  Google Scholar 

  34. Portmann, F. T., Siebert, S. & Döll, P. MIRCA2000—global monthly irrigated and rainfed crop areas around the year 2000: a new high-resolution data set for agricultural and hydrological modeling. Glob. Biogeochem. Cycles 24, 1–24 (2010).

    Article  Google Scholar 

  35. Liu, J., Williams, J. R., Zehnder, A. J. B. & Yang, H. GEPIC—modelling wheat yield and crop water productivity with high resolution on a global scale. Agric. Syst. 94, 478–493 (2007).

    Article  Google Scholar 

  36. Liu, W. et al. Global investigation of impacts of PET methods on simulating crop–water relations for maize. Agric. Meteorol. 221, 164–175 (2016).

    Article  Google Scholar 

  37. Williams, J. R. & Sharpley, A. N. EPIC—Erosion/Productivity Impact Calculator: 1. Model Documentation (USDA, 1989).

  38. Watanabe, M. et al. Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. J. Clim. 23, 6312–6335 (2010).

    Article  Google Scholar 

  39. Collins, W. J. et al. Development and evaluation of an Earth-system model—HadGEM2. Geosci. Model Dev. 4, 1051–1075 (2011).

    Article  Google Scholar 

  40. Dunne, J. P. et al. GFDL’s ESM2 global coupled climate–carbon earth system models. Part I: physical formulation and baseline simulation characteristics. J. Clim. 25, 6646–6665 (2012).

    Article  Google Scholar 

  41. Dufresne, J.-L. et al. Climate change projections using the IPSL-CM5 Earth system model: from CMIP3 to CMIP5. Clim. Dyn. 40, 2123–2165 (2013).

    Article  Google Scholar 

  42. Bebber, D. P., Chaloner, T. M. & Gurr, S. J. Fungal and Oomycete Cardinal Temperatures (the Togashi Dataset) (Dryad, 2020); https://doi.org/10.5061/DRYAD.TQJQ2BVW6

  43. Viswanath, K. et al. Simulation of leaf blast infection in tropical rice agro-ecology under climate change scenario. Clim. Change 142, 155–167 (2017).

    Article  Google Scholar 

  44. Boixel, A.-L., Delestre, G., Legeay, J., Chelle, M. & Suffert, F. Phenotyping thermal responses of yeasts and yeast-like microorganisms at the individual and population levels: proof-of-concept, development and application of an experimental framework to a plant pathogen. Microb. Ecol. 78, 42–56 (2019).

    Article  Google Scholar 

  45. Hijmans, R. J. et al. raster: Geographic data analysis and modeling. R package v.3.1-5 (2020).

  46. Yan, W. & Hunt, L. A. An equation for modelling the temperature response of plants using only the cardinal temperatures. Ann. Bot. 84, 607–614 (1999).

    Article  Google Scholar 

  47. Wiens, J. A., Stralberg, D., Jongsomjit, D., Howell, C. A. & Snyder, M. A. Niches, models, and climate change: assessing the assumptions and uncertainties. Proc. Natl Acad. Sci. USA 106, 19729–19736 (2009).

    Article  CAS  Google Scholar 

  48. Chen, Y. A new methodology of spatial cross-correlation analysis. PLoS ONE 10, e0126158 (2015).

    Article  Google Scholar 

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Acknowledgements

T.M.C. is supported by BBSRC SWBio DTP studentship no. BB/M009122/1. D.P.B. and S.J.G. are supported by BBSRC grant no. BB/N020847/1 and the Global Burden of Crop Loss project (Bill and Melinda Gates Foundation). S.J.G. is supported by a CIFAR Fellowship, ‘The Fungal Kingdom: Threats and Opportunities’.

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Authors and Affiliations

  1. Department of Biosciences, University of Exeter, Exeter, UK

    Thomas M. Chaloner, Sarah J. Gurr & Daniel P. Bebber

  2. Department of Biology, Utrecht University, Utrecht, Netherlands

    Sarah J. Gurr

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Contributions

D.P.B. and T.M.C. developed the concept, collated the data, conducted the analyses and prepared the figures. D.P.B. wrote the manuscript with contributions from T.M.C. and S.J.G.

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Correspondence to Daniel P. Bebber.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks the anonymous reviewers for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–16, Tables 1–8 and Appendix.

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Source data

Source Data Fig. 1

Numerical data used to generate graphs.

Source Data Fig. 2a

Numeric data for maps presented in Geotiff raster file format.

Source Data Fig. 2b,d

Numeric data used to generate maps and graphs.

Source Data Fig. 2c

Numeric data for maps presented in Geotiff raster file format.

Source Data Fig. 3

Numerical data used to generate heat maps. Note that Rr and pathogen turnover values are not directly comparable between Fig. 3a and Fig. 3b excel sheets. This is because Rr and pathogen turnover values in Fig. 3a are scaled to enable colour saturation comparison between the RCPs in Fig. 3a (see scale bars in Fig. 3a).

Source Data Fig. 4

Numerical data used to generate graphs.

Source Data Fig. 5

Numerical data used to generate graphs.

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Chaloner, T.M., Gurr, S.J. & Bebber, D.P. Plant pathogen infection risk tracks global crop yields under climate change. Nat. Clim. Chang. 11, 710–715 (2021). https://doi.org/10.1038/s41558-021-01104-8

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