PLoS ONE (Jan 2015)

Effects of Weather Variables on Ascospore Discharge from Fusarium graminearum Perithecia.

  • Valentina Manstretta,
  • Vittorio Rossi

DOI
https://doi.org/10.1371/journal.pone.0138860
Journal volume & issue
Vol. 10, no. 9
p. e0138860

Abstract

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Fusarium graminearum is a predominant component of the Fusarium head blight (FHB) complex of small grain cereals. Ascosporic infection plays a relevant role in the spread of the disease. A 3-year study was conducted on ascospore discharge. To separate the effect of weather on discharge from the effect of weather on the production and maturation of ascospores in perithecia, discharge was quantified with a volumetric spore sampler placed near maize stalk residues bearing perithecia with mature ascospores; the residues therefore served as a continuous source of ascospores. Ascospores were discharged from perithecia on 70% of 154 days. Rain (R) and vapor pressure deficit (VPD) were the variables that most affected ascospore discharge, with 84% of total discharges occurring on days with R≥0.2 mm or VPD≤11 hPa, and with 70% of total ascospore discharge peaks (≥ 30 ascospores/m3 air per day) occurring on days with R≥0.2 mm and VPD≤6.35 hPa. An ROC analysis using these criteria for R and VPD provided True Positive Proportion (TPP) = 0.84 and True Negative Proportion (TNP) = 0.63 for occurrence of ascospore discharge, and TPP = 0.70 and TNP = 0.89 for occurrence of peaks. Globally, 68 ascospores (2.5% of the total ascospores sampled) were trapped on the 17 days when no ascospores were erroneously predicted. When a discharge occurred, the numbers of F. graminearum ascospores sampled were predicted by a multiple regression model with R2 = 0.68. This model, which includes average and maximum temperature and VPD as predicting variables, slightly underestimated the real data and especially ascospore peaks. Numbers of ascospores in peaks were best predicted by wetness duration of the previous day, minimum temperature, and VPD, with R2 = 0.71. These results will help refine the epidemiological models used as decision aids in FHB management programs.