Spatially-explicit modeling improves empirical characterization of dispersal: theory and a case study

Dispersal is a key ecological process, but remains difficult to measure. By recording numbers of dispersed individuals at different distances from the source one can acquire a dispersal gradient. Although dispersal gradients contain information on dispersal, they are influenced by the spatial extent of the source. How can we separate the two contributions to extract knowledge on 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 point. However, the validity of this approximation cannot be established before conducting measurements. We formulated a theory that incorporates the spatial extent of sources to estimate dispersal kernels from dispersal gradients. We re-analyzed published dispersal gradients for three major plant pathogens. We also demonstrated using simulations that this approach provides more accurate estimates of dispersal kernels across biologically plausible scenarios. We concluded that the three plant pathogens disperse over substantially shorter distances compared to conventional estimates. Using this method, a significant proportion of published dispersal gradients can be re-analyzed to improve our knowledge about spatial scales of dispersal. Thus, our results can boost progress in characterization of dispersal across taxa.