Spatially explicit ecological modeling improves empirical characterization of plant pathogen dispersal. Issue 2 (9th April 2023) - Karisto, Petteri; Suffert, Frédéric; Mikaberidze, Alexey
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. Abstract : Plants and their pathogens produce offspring that needs to move (or disperse) to a different location to be able to survive and reproduce further. It is important to know how far they can move because this would inform ways to protect endangered species and manage weeds and diseases in crops. To measure dispersal, we typically record the numbers of dispersed individuals at different distances from the source. However, since the source itself can have a substantial area, we may not know where exactly each individual originated, and this introduces a large uncertainty into measurements. We have overcome this problem by formulating a mathematical theory of dispersal that sums over all dispersal events across the entire source area and thereby makes dispersal measurements much more accurate. Using this theory, we demonstrated that three important pathogens of major crops (wheat and potato) disperse over substantially shorter distances than previously thought. This means that they will shift or expand the geographic areas where they cause problems slower than expected.