Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal

Abstract

Abstract Dispersal is a key ecological process, but it remains difficult to measure. By recording numbers of dispersed individuals at different distances from the source, one acquires a dispersal gradient. Dispersal gradients contain information on dispersal, but they are influenced by the spatial extent of the source. How can we separate the two contributions to extract knowledge about dispersal? One could use a small, point‐like source for which a dispersal gradient represents a dispersal kernel, which quantifies the probability of an individual dispersal event from a source to a destination. However, the validity of this approximation cannot be established before conducting measurements. This represents a key challenge hindering progress in characterization of dispersal. To overcome it, we formulated a theory that incorporates the spatial extent of sources to estimate dispersal kernels from dispersal gradients. Using this theory, we re‐analyzed published dispersal gradients for three major plant pathogens. We demonstrated that the three pathogens disperse over substantially shorter distances compared to conventional estimates. This method will allow the researchers to re‐analyze a vast number of existing dispersal gradients to improve our knowledge about dispersal. The improved knowledge has potential to advance our understanding of species' range expansions and shifts, and inform management of weeds and diseases in crops.

Keywords

• dispersal ecology
• dispersal gradient
• dispersal kernel
• dispersal theory
• experimental design
• mathematical modeling