Environment and host infection history jointly predict disease risk in a multi-pathogen system
Forecasting disease epidemics may require considering both abiotic and biotic conditions. Abiotic conditions can influence pathogen dispersal, survival, and infection, while prior infection by one pathogen species can alter host susceptibility to subsequent pathogens, creating historical contingencies. Yet, the relative importance of environmental conditions, within-host pathogen interactions, and their interplay in predicting seasonal disease dynamics remains underexplored. We analyzed approximately 30,000 plant-level observations of three foliar fungal diseases on the grass species tall fescue in a North Carolina old field, and meteorological data, from 2017-2024. Using such datasets to make ecological predictions that are accurate and interpretable may require multiple modeling approaches. Temporally explicit random forest models predicted future disease with over 80% accuracy for two of the three diseases and revealed environmental thresholds for disease prediction in environmental variables including temperature and humidity. Survival analyses showed that pathogen-pathogen interactions rivaled environmental conditions in predicting risk of crown rust, which was strongly facilitated by prior anthracnose infection. Across all pathogens, Bayesian hierarchical models recovered evidence that facilitative interactions between coinfecting pathogens were modulated by effects of environmental conditions, particularly temperature. Together, these complementary approaches show that a host's current disease state can matter as much as its environment in predicting disease, identify key environmental factors associated with disease risk, and show that modulation of environmental drivers by pathogen coinfections can improve predictions of seasonal disease epidemics. Other disease forecasts may be improved by accounting not just for environmental drivers, but also how those drivers propagate through pathogen species interactions.