Pathogen and pest communities in agroecosystems across climate gradients: anticipating future challenges in the highland tropics - Mouafo-Tchinda, R.A.; Sula, A.I.P.; Etherton, B.A.; Okonya, J.S.; Nakato, G.V.; Xing, Y.; Robledo, J.; Adhikari, A.; Blomme, G.; Kantungeko, D.; Nduwayezu, A.; Kreuze. J.F.; et al. (2026)
CONTEXT Tropical agricultural systems must respond to current and future pathogen and pest communities. An important research gap is how climate change may shift the geographic distribution of tropical pathogens and pests. OBJECTIVE We evaluated the geographic risk of 27 pathogens and pests in four food security crops (banana, cassava, potato, and sweetpotato) in the Great Lakes region of Africa, and potential future risk under climate change. We analyzed model performance for each pathogen and pest, assessing the potential for changes in geographic distribution, and for decision support systems to facilitate management. METHODS Cropland connectivity analysis identified locations likely important in the spread of crop-specific pathogens and pests. We surveyed the 27 economically important pathogens and pests in Rwanda and Burundi, mapping the distribution of each across climate gradients and quantifying associations. We used machine learning to model each species as a function of environmental variables, including host landscape. We also evaluated future temperatures across altitudes under climate change scenarios. RESULTS AND CONCLUSIONS Among ten algorithms evaluated, random forests and support vector machines generally performed best for predicting severity or 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. SIGNIFICANCE These findings indicate how crop health in the region requires adaptation to multiple sustainability challenges. The results also indicate which pathogen and pest species have the potential for development of decision support models.