Dispersal kernels may be scalable: Implications from a plant pathogen

Abstract

Aim:Understanding how spatial scale of study affects observed dispersal patterns can provide insights to spatiotemporal population dynamics, particularly in systems with significant long-distance dispersal (LDD). We aimed to investigate the dispersal gradients of two rusts of wheat with spores of similar size, mass and shape, over multiple spatial scales. We hypothesized that a single dispersal kernel could fit the dispersal from all spatial scales well, and that it would be possible to obtain similar results in spatiotemporal increase of disease when modelling based on differing scales.Location: Central Oregon and St. Croix Island.Taxa: Puccinia striiformis f. sp. tritici, Puccinia graminis f. sp. tritici, Triticum aestivum. Methods:We compared empirically derived primary disease gradients of cereal rust across three spatial scales: local (inoculum source and sampling unit = 0.0254 m, spatial extent = 1.52 m) field-wide (inoculum source = 1.52 m, sampling unit = 0.305 m and spatial extent = 91.4 m) and regional (inoculum source and sampling unit = 152 m, spatial extent = 10.5 km). We then examined whether disease spread in spatially explicit simulations depended upon the scale at which data were collected by constructing a compartmental time-step model. Results:The three data sets could be fit well by a single power law dispersal kernel.Simulating epidemic spread at different spatial resolutions resulted in similar patterns of spatiotemporal spread. Dispersal kernel data obtained at one spatial scale can be used to represent spatiotemporal disease spread at a larger spatial scale.

Methods for quantifying rain-splash dispersal of Neonectria ditissima conidia in apple canopies

“…Rain-splash dispersal is important in the spread of many microorganisms, whereby spores become suspended in water droplets which are subsequently redistributed by gravity, droplet splash and aerial dispersion (Madden, 1997;Pielaat and van den Bosch, 1998;Pietravalle et al, 2001;Saint-Jean et al, 2004;Farber et al, 2019). Determining and quantifying propagule movement can be difficult, and detecting an inoculum source or sampling inoculum both have many practical difficulties across spatial and temporal scales (Lacey and West, 2006;Mahaffee et al, 2023).…”

“…Knowledge of the distance that splash droplets travel and the variability between occasions or situations, is useful for determining the nature of disease spread from a focal point through a crop (Shaw, 1991;Zadoks and Van den Bosch, 1994). Information about dispersal of pathogens is critical for understanding epidemics, particularly in defining how fast and far disease can spread (Fountaine et al, 2010;Cunniffe et al, 2016;Farber et al, 2019;Mahaffee et al, 2023).…”

“…Empirical models describing dispersal gradients commonly use power or exponential functions. These are valuable to comparative epidemiology and incorporation into spatio-temporal modelling at larger spatial scales (Madden, 1997;Saint-Jean et al, 2004;Lacey and West, 2006;Farber et al, 2019), yet, under field settings dispersal and variability are often not adequately quantified.…”