Dispersal Kernels may be Scalable: Implications from a Plant Pathogen.

Area of Science:

• Plant Pathology
• Ecology
• Epidemiology

Background:

• Understanding spatial scale's impact on dispersal is key for spatiotemporal population dynamics.
• Long-distance dispersal (LDD) is significant in many plant disease systems.
• Investigating dispersal gradients of wheat rusts over multiple scales provides crucial ecological insights.

Purpose of the Study:

• To investigate the dispersal gradients of two wheat rust species over multiple spatial scales.
• To determine if a single dispersal kernel can accurately model dispersal across different scales.
• To assess if disease spread modeling yields similar spatiotemporal results regardless of the data's spatial scale.

Main Methods:

• Empirical primary disease gradients of cereal rust were compared across local, field-wide, and regional spatial scales.
• Dispersal was analyzed using an inverse-power law dispersal kernel.
• Spatially explicit simulations were conducted using a compartmental time-step model to examine scale-dependent disease spread.

Main Results:

• A single inverse-power law dispersal kernel effectively fit dispersal data from all three spatial scales.
• Simulations of epidemic spread at different spatial resolutions produced comparable spatiotemporal patterns.
• Dispersal kernel data from one spatial scale accurately represented spatiotemporal disease spread at larger scales.

Conclusions:

• Wheat rusts, with their small, aerially dispersed propagules exhibiting LDD, demonstrate consistent dispersal patterns across vast spatial scales (hundreds to thousands of fold).
• The fundamental mechanisms of aerial dispersal are constant, enabling extrapolation of findings across scales.
• This research supports the possibility of using data from one spatial scale to predict disease spread at another, even when data is limited.