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Plant pathogen infection risk tracks global crop yields under climate change

  • 1.

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

    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).

    CAS 

    Google Scholar 

  • 3.

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

    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).

    Google Scholar 

  • 5.

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

    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).

    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).

    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).

    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).

    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).

    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).

    Google Scholar 

  • 12.

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

    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).

    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).

    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).

    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).

    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).

    Google Scholar 

  • 18.

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

    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).

    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).

    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).

    Google Scholar 

  • 22.

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

    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).

    Google Scholar 

  • 24.

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

    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).

    Google Scholar 

  • 27.

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

    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).

    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).

    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).

    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).

    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).

    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).

    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).

    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).

    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).

    Google Scholar 

  • 39.

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

    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).

    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).

    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).

    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).

    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).

    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).

    CAS 

    Google Scholar 

  • 48.

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

    Google Scholar 


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