Juniperus ashei pollen, a significant aeroallergen, has been recorded during December and January in Tulsa, Oklahoma, over the past 20 years. The nearest upwind source for this pollen is populations growing in southern Oklahoma and central Texas, at distances of 200 km and 600 km respectively. Long-distance dispersal of J. ashei pollen into the Tulsa area shows a strong correlation with the trajectories of wind blowing across southern populations before traveling north towards eastern Oklahoma. The strong tie between climatic conditions and the occurrence of this aeroallergen within the Tulsa, Oklahoma, atmosphere provided a unique opportunity to forecast the dispersal, entrainment, and downwind deposition of this significant aeroallergen. Forecasts of long-distance J. ashei pollen dispersal began during the winter of 1998/1999. Each forecast uses defined climatic parameters to signal pollination at each source site. Coupled to these estimates of pollen release, forecast weather conditions and modeled wind trajectories are used to determine the threat of dispersal to downwind communities. The accuracy of these forecasts was determined by comparing the forecast "threat" to aerobiological records for the same period collected in the "Tulsa region". Analysis of the two seasons revealed only a single occurrence of "high" or "very high" pollen concentrations in Tulsa not directly linked to "moderate" or "severe" forecast threats from the southern source areas.
Cucurbit downy mildew caused by the obligate oomycete, Pseudoperonospora cubensis, is considered one of the most economically important diseases of cucurbits worldwide. In the continental United States, the pathogen overwinters in southern Florida and along the coast of the Gulf of Mexico. Outbreaks of the disease in northern states occur annually via long-distance aerial transport of sporangia from infected source fields. An integrated aerobiological modeling system has been developed to predict the risk of disease occurrence and to facilitate timely use of fungicides for disease management. The forecasting system, which combines information on known inoculum sources, long-distance atmospheric spore transport and spore deposition modules, was tested to determine its accuracy in predicting risk of disease outbreak. Rainwater samples at disease monitoring sites in Alabama, Georgia, Louisiana, New York, North Carolina, Ohio, Pennsylvania and South Carolina were collected weekly from planting to the first appearance of symptoms at the field sites during the 2013, 2014, and 2015 growing seasons. A conventional PCR assay with primers specific to P. cubensis was used to detect the presence of sporangia in rain water samples. Disease forecasts were monitored and recorded for each site after each rain event until initial disease symptoms appeared. The pathogen was detected in 38 of the 187 rainwater samples collected during the study period. The forecasting system correctly predicted the risk of disease outbreak based on the presence of sporangia or appearance of initial disease symptoms with an overall accuracy rate of 66 and 75%, respectively. In addition, the probability that the forecasting system correctly classified the presence or absence of disease was ≥ 73%. The true skill statistic calculated based on the appearance of disease symptoms in cucurbit field plantings ranged from 0.42 to 0.58, indicating that the disease forecasting system had an acceptable to good performance in predicting the risk of cucurbit downy mildew outbreak in the eastern United States.
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