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Dispersal kernels may be scalable: Implications from a plant pathogen
Author(s) -
Farber Daniel H.,
De Leenheer Patrick,
Mundt Christopher C.
Publication year - 2019
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/jbi.13642
Subject(s) - biological dispersal , propagule , spatial ecology , kernel (algebra) , biology , sampling (signal processing) , seed dispersal , ecology , population , mathematics , computer science , demography , sociology , filter (signal processing) , combinatorics , computer vision
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. Main Conclusions Organisms spread by aerially dispersed small propagules that exhibit LDD may follow similar dispersal patterns over a several hundred‐ or thousand‐fold expanse of spatial scale. Given that the primary mechanisms driving aerial dispersal remains constant, it may be possible to extrapolate across scales when empirical data are unavailable at a scale of interest.