A frequently ignored but critical aspect of microbial dispersal is survival in the atmosphere. We exposed spores of two closely related, morphologically dissimilar, and economically important fungal pathogens to typical atmospheric environments and modeled their movement in the troposphere. We first measured the mortality of Alternaria solani and A. alternata conidia exposed to ranges of solar radiation, relative humidity, and temperature. We then measured survival in an advantageous environment over 12 days. A. solani conidia are nearly 10 times larger than A. alternata conidia and most die after 24 hours. By contrast, over half of A. alternata conidia remained viable at 12 days. The greater viability of the smaller spores is counterintuitive as larger spores are assumed to be more durable. To elucidate the consequences of survival rates for dispersal, we deployed models of atmospheric spore movement across North American. We predict 99% of the larger A. solani conidia settle within 24 hours, with a maximum dispersal distance of 100 km. By contrast, most A. alternata conidia remain airborne for more than 12 days and long-distance dispersal is possible, e.g., from Wisconsin to the Atlantic Ocean. We observe that the larger conidia of A. solani survive poorly but also land sooner and move over shorter distances as compared to the smaller conidia of A. alternata. Our data relating larger spore size to poorer survival in the atmosphere and shorter distances travelled likely translate to other fungal species and highlight the potential for starkly different dispersal dynamics among even closely related fungi.
First attempt to couple in an efficient and economic way real-time data and ensemble predictions.• First assessment of the added value of real-time observations in a wind calibration.• Real-time data can be easily and economically ingested in an EMOSbased calibration.• Ingestion of real-time data produces noticeable benefits vs. static calibrations.• Real-time data provide added value to the whole wind predictive probability density.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.