Pathogen and pest communities in food security crops across climate gradients: Anticipating future challenges in the Great Lakes region of Africa

Mouafo-Tchinda, R.; Plex Sula, A.; Etherton, B. A.; Okonya, J.; Nakato, G. V.; Xing, Y.; Robledo Buritica, J.; Adhikari, A.; Blomme, G.; Kantungeko, D.; Nduwayezu, A.; Kreuze, J. F.; Kroschel, J.; Legg, J.; Garrett, K. A.

doi:10.1101/2025.01.08.631994

bioRxiv · 10.1101/2025.01.08.631994

Pathogen and pest communities in food security crops across climate gradients: Anticipating future challenges in the Great Lakes region of Africa

Mouafo-Tchinda, R.·Plex Sula, A.·Etherton, B. A.·Okonya, J.·Nakato, G. V.·Xing, Y.·Robledo Buritica, J.·Adhikari, A.·Blomme, G.·Kantungeko, D.·Nduwayezu, A.·Kreuze, J. F.·Kroschel, J.·Legg, J.·Garrett, K. A.

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Abstract

CONTEXTTropical cropping systems must adapt to the current and future geographic distribution of pathogen and pest communities. An important research gap is how climate change may shift the distribution of pathogens and pests in tropical lowlands and highlands. OBJECTIVEWe evaluated the current geographic risk of 27 pathogens and pests in the production of four food security crops (banana, cassava, potato, and sweetpotato) in the Great Lakes region of Africa, and the potential future risk under climate change. Models for each pathogen and pest indicate the potential for changes in geographic distribution, with model fit indicating the potential for decision support systems to facilitate management. METHODSFirst, cropland connectivity analysis identified locations likely important in the spread of crop-specific pathogens and pests, such as locations in Rwanda and Burundi. Second, we surveyed the 27 economically important pathogens and pests in Rwanda and Burundi, mapping the distribution of each across climate gradients and quantifying patterns of association. Third, we used machine learning to develop models of each species as a function of environmental variables, including host landscape variables. We also evaluated the increase in temperature across altitudes under future climate change scenarios in this region. RESULTS AND CONCLUSIONSAmong the ten machine-learning algorithms evaluated, random forests and support vector machines generally performed best for predicting severity and infestation. Host landscape variables were useful predictors for some species. Based on climate matching, 44% of the pathogens and pests could become more common with warmer temperatures at higher altitudes, while 17% may become less common. SIGNIFICANCEThis study indicates adaptation priorities for crop health in a region with multiple challenges to agricultural sustainability. The models developed here also indicate which species may have more potential and relevance for future development of pathogen and pest forecasts.

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Mouafo-Tchinda, R., Plex Sula, A., Etherton, B. A., Okonya, J., Nakato, G. V., Xing, Y., Robledo Buritica, J., Adhikari, A., Blomme, G., Kantungeko, D., Nduwayezu, A., Kreuze, J. F., Kroschel, J., Legg, J., Garrett, K. A.. 2025-01-13. Pathogen and pest communities in food security crops across climate gradients: Anticipating future challenges in the Great Lakes region of Africa. https://doi.org/10.1101/2025.01.08.631994

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