Spatially-explicit modeling improves empirical characterization of dispersal

Dispersal is a key ecological process. An individual dispersal event has a source and a destination, both are well localized in space and can be seen as points. A probability to move from a source point to a destination point can be described by a specific probability function, the dispersal kernel. However, when we measure dispersal, the source of dispersing individuals is usually an area, which distorts the shape of the observed dispersal gradient compared to the underlying dispersal kernel. Here, we show with simulations, how different source geometries affect the gradient shape depending on the type of the kernel. We present an explicit mathematical approach for estimating the dispersal kernel from a dispersal gradient data independently of the source dimension. Further, we demonstrate the value of the approach by analysing three experimental dispersal datasets with a conventional method and the proposed method, to show how the estimated dispersal kernels differ between the methods. We use three pre-existing datasets from field experiments measuring dispersal of important plant pathogens. Our results demonstrate how analysis of dispersal data can be improved to achieve more rigorous measures of dispersal. The proposed approach leads to a general measure of dispersal in contrast to results from the conventional method that depend on the design of the dispersal source. This enables a direct comparison between outcomes of different experiments and allows acquiring more knowledge from a large number of previous empirical studies of dispersal.