Primary Disease Gradients of Wheat Stripe Rust in Large Field Plots

Abstract

Field data on disease gradients are essential for understanding the spread of plant diseases. In particular, dispersal far from an inoculum source can drive the behavior of an expanding focal epidemic. In this study, primary disease gradients of wheat stripe rust, caused by the aerially dispersed fungal pathogen Puccinia striiformis, were measured in Madras and Hermiston, OR, in the spring of 2002 and 2003. Plots were 6.1 m wide by 128 to 171 m long, and inoculated with urediniospores in an area of 1.52 by 1.52 m. Gradients were measured as far as 79.2 m downwind and 12.2 m upwind of the focus. Four gradient models-the power law, the modified power law, the exponential model, and the Lambert's general model-were fit to the data. Five of eight gradients were better fit by the power law, modified power law, and Lambert model than by the exponential, revealing the non-exponentially bound nature of the gradient tails. The other three data sets, which comprised fewer data points, were fit equally well by all the models. By truncating the largest data sets (maximum distances 79.2, 48.8, and 30.5 m) to within 30.5, 18.3, and 6.1 m of the focus, it was shown how the relative suitability of dispersal models can be obscured when data are available only at a short distance from the focus. The truncated data sets were also used to examine the danger associated with extrapolating gradients to distances beyond available data. The power law and modified power law predicted dispersal at large distances well relative to the Lambert and exponential models, which consistently and sometimes severely underestimated dispersal at large distances.

The Population Genetic Structure of Clonal Organisms Generated by Exponentially Bounded and Fat-Tailed Dispersal

“…Of special interest were values of b # 2. Theoretical and experimental results suggest this is the relevant range in wind dispersal of small particles like fungal spores and pollen (Mccartney 1987;Mccartney and Bainbridge 1987;Ferrandino 1993;Sackett and Mundt 2005;Klein et al 2006;Shaw et al 2006). If not stated otherwise, simulations used values of 1.2 # b # 4.…”

“…The median dispersal for powdery mildew conidiospores is estimated to be 3.1 m for field disease gradients (half the distance of the exponential model; Fitt et al 1987) or 1 m to several meters (modeled from deposition velocities and impaction efficiency, with 50% deposition rate; Mccartney 1987). Modeled estimates for wheat yellow rust are 2.8 m-6.5 m (calculated from exponential fits to the three largest data sets for upwind dispersal in Sackett and Mundt 2005). Estimates by O'hara and Brown (1998) are similar.…”

“…For fungal spores, the question of the best-fitting dispersal function is hindered by the necessity of large experimental plots free from too much background infection. A recent study addressing the above problems showed that dispersal of the wheat stripe or yellow rust fungus (Puccinia striiformis) fitted a power-law model well if enough sufficiently distant spore traps were used (Sackett and Mundt 2005). Moreover, although several physical processes underlie wind dispersal, theoretical arguments strongly propose that LDD of small objects can be modeled by a single function that will have inverse power-law behavior in the tails (Shaw 1995;Kot et al 1996;Stockmarr 2002;Aylor 2003;Shaw et al 2006).…”

The Effects of Dispersal Gradient and Pathogen Life Cycle Components on Epidemic Velocity in Computer Simulations

“…The baseline simulated epidemic was constructed to mimic a field experiment conducted near Hermiston, OR, during the spring of 2002. This experiment was chosen from four possible experiments (two locations, 2 years) for the precision and extent of the dispersal data available (45). A full description of the experiment is given by Cowger et al (10).…”

“…Dispersal in the model was based on field data extending as far as 79 m from the source (45). Spatiotemporal field data was also available over the course of the epidemic, allowing corroboration of simulation results (10).…”