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Airborne propagules of Phytophthora and related taxa in SW Spain including a predictive model

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Abstract

The airborne sporangia of Oomycota, including Phytophthora species and other species, are usually recorded by volumetric spore traps. The aim of this work was to create a predictive model for the seasonality and weather parameters that may affect sporangia distribution by analysing their presence over the course of a year. Three Hirst spore traps were located in the SW of Spain (Plasencia, Don Benito and Zafra), and the presence of sporangia was recorded throughout 2012–2013. Phytophthora sporangia were recorded on 76–110 days per year. Autumn and spring were the seasons when the highest concentration peaks of airborne sporangia were recorded; these peaks were recorded on the same day in each of the three locations. When analysing weekly sums of the daily concentration of sporangia, there was a statistically significant correlation between the sporangia concentration and amount of rain in the three locations studied; however, this correlation was not observed when comparing the individual daily concentrations. When comparing the daily concentration to the daily rain levels, the highest correlation coefficient and statistically significant correlation was reached on the second day after rain. The proposed predictive model considered the previous rain to be the main environmental factor. Holm oak and cork oak woods in Plasencia may be responsible for a higher concentration of airborne Phytophthora-type sporangia. Rain seems to have a direct influence on the concentration of airborne sporangia, but the highest concentrations appear on the second day after rain.

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Acknowledgments

This work was made possible by research projects PRI06A190 and PRI BS10008 financed by the Regional Government, Junta de Extremadura (Spain) and the European Regional Development Fund.

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Correspondence to Rafael Tormo Molina.

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Manzano, J.M.M., Molina, R.T., Rodríguez, S.F. et al. Airborne propagules of Phytophthora and related taxa in SW Spain including a predictive model. Eur J Plant Pathol 143, 473–483 (2015). https://doi.org/10.1007/s10658-015-0700-1

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  • DOI: https://doi.org/10.1007/s10658-015-0700-1

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