Assessing Long-Distance Atmospheric Transport of Soilborne Plant Pathogens

Pathogenic fungi are a leading cause of crop disease and primarily spread through microscopic, durable spores adapted differentially for both persistence and dispersal. Computational Earth System Models and air pollution models have been used to simulate atmospheric spore transport for aerial-dispersal-adapted (airborne) rust diseases, but the importance of atmospheric spore transport for soil-dispersal-adapted (soilborne) diseases remains unknown. This study adapts the Community Atmosphere Model, the atmospheric component of the Community Earth System Model, to simulate the global transport of the plant pathogenic soilborne fungus Fusarium oxysporum, F. oxy. Our sensitivity study assesses the model's accuracy in long-distance aerosol transport and the impact of deposition rate on long-distance spore transport in Summer 2020 during a major dust transport event from Northern Sub-Saharan Africa to the Caribbean and southeastern U.S. We find that decreasing wet and dry deposition rates by an order of magnitude improves representation of long distance, trans-Atlantic dust transport. Simulations also suggest that a small number of viable spores can survive trans-Atlantic transport to be deposited in agricultural zones. This number is dependent on source spore parameterization, which we improved through a literature search to yield a global map of F. oxy spore distribution in source agricultural soils. Using this map and aerosol transport modeling, we show how viable spore numbers in the atmosphere decrease with distance traveled and offer a novel danger index for viable spore deposition in agricultural zones

A multi-omics approach to solving problems in plant disease ecology.

The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions

Assessing long-distance atmospheric transport of soilborne plant pathogens

Pathogenic fungi are a leading cause of crop disease and primarily spread through microscopic, durable spores adapted differentially for both persistence and dispersal via soil, animals, water, and/or the atmosphere. Computational Earth system models and air pollution models have been used to simulate atmospheric spore transport for aerial-dispersal-adapted (airborne) rust diseases, but the importance of atmospheric spore transport for soil-dispersal-adapted (soilborne) diseases remains unknown. While a few existing simulation studies have focused on intra continental dispersion, transoceanic and inter continental atmospheric transport of soilborne spores entrained in agricultural dust aerosols is understudied and may contribute to disease spread. This study adapts the Community Atmosphere Model, the atmospheric component of the Community Earth System Model, to simulate the global transport of the plant pathogenic soilborne fungus Fusarium oxysporum ( F. oxy ). Our sensitivity study assesses the model’s accuracy in long-distance aerosol transport and the impact of deposition rate on simulated long-distance spore transport in Summer 2020 during a major dust transport event from Northern Sub-Saharan Africa to the Caribbean and southeastern United States (U.S.). We find that decreasing wet and dry deposition rates by an order of magnitude improves representation of long-distance, trans-Atlantic dust transport. Simulations also suggest that a small number of spores can survive trans-Atlantic transport to be deposited in agricultural zones. This number is dependent on source spore parameterization, which we improved through a literature search to yield a global map of F. oxy spore distribution in source agricultural soils. Using this map and aerosol transport modeling, we show how potentially viable spore numbers in the atmosphere decrease with distance traveled and offer a novel danger index for modeled viable spore deposition in agricultural zones. Our work finds that intercontinental transport of viable spores to cropland is greatest between Eurasia, North Africa, and Sub-Saharan Africa, suggesting that future observational studies should concentrate on these regions

Assessing long-distance atmospheric transport of soilborne plant pathogens

Please cite as: Brodsky, H., Calderón, R., Hamilton, D., Li, L., Miles, A., Pavlick, R., Gold, K., Crandall, S., Mahowald, N. (2023) Assessing Long-Distance Atmospheric Transport of Soilborne Plant Pathogens [dataset] Cornell University eCommons Repository. https://doi.org/10.7298/ddgx-ht24These files contain data supporting results in Brodsky et al. Assessing Long-Distance Atmospheric Transport of Soilborne Plant Pathogens. In Brodsky et al. we modify an atmospheric transport model to simulate the global transport of a plant pathogenic soilborne fungus. Pathogenic fungi are a leading cause of crop disease and primarily spread through microscopic, durable spores adapted differentially for both persistence and dispersal via soil, animals, water, and/or the atmosphere. Computational Earth System Models and air pollution models have been used to simulate atmospheric spore transport for aerial-dispersal-adapted (airborne) rust diseases, but the importance of atmospheric spore transport for soil-dispersal- adapted (soilborne) diseases remains unknown. While a few existing simulation studies have focused on intracontinental dispersion, transoceanic and intercontinental atmospheric transport of soilborne spores entrained in agricultural dust aerosols is understudied and may contribute to disease spread. This study adapts the Community Atmosphere Model, the atmospheric component of the Community Earth System Model, to simulate the global transport of the plant pathogenic soilborne fungus Fusarium oxysporum (F. oxy). Our sensitivity study assesses the model’s accuracy in long-distance aerosol transport and the impact of deposition rate on long- distance spore transport in Summer 2020 during a major dust transport event from Northern Sub- Saharan Africa to the Caribbean and southeastern U.S. We find that decreasing wet and dry deposition rates by an order of magnitude improves representation of long-distance, trans- Atlantic dust transport. Simulations also suggest that a small number of viable spores can survive trans-Atlantic transport to be deposited in agricultural zones. This number is dependent on source spore parameterization, which we improved through a literature search to yield a global map of F. oxy spore distribution in source agricultural soils. Using this map and aerosol transport modeling, we show how viable spore numbers in the atmosphere decrease with distance traveled and offer a novel danger index for viable spore deposition in agricultural zones. Our work finds that intercontinental transport of viable spores to cropland is greatest between Eurasia, North Africa, and Sub-Saharan Africa, suggesting that future observational studies should concentrate on these regions.HKB, RC, KMG, SGC, RP, and NMM would like to acknowledge the support of NASA (80NSSC20K1533). Additionally, a portion of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004). We would also like to acknowledge high-performance computing support from Cheyenne (doi:10.5065/D6RX99HX) provided by NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation