Modern food systems facilitate rapid dispersal of pests and pathogens through multiple pathways. The complexity of spread dynamics and data inadequacy make it challenging to model the phenomenon and also to prepare for emerging invasions.
This study presents a generic framework to study the spatio-temporal spread of invasive species as a multi-scale propagation process over a time-varying network accounting for climate, biology, seasonal production, trade and demographic information. Machine learning techniques are used in a novel manner to capture model variability and analyse parameter sensitivity.
The researchers applied the framework to understand the spread of a devastating pest of tomato, Tuta absoluta, in South and Southeast Asia, a region at the frontier of its current range. Analysis with respect to historical invasion records suggests that even with modest self-mediated spread capabilities, the pest can quickly expand its range through domestic city-to-city vegetable trade.
Their models forecast that within 5–7 years, Tuta absoluta will invade all major vegetable growing areas of mainland Southeast Asia assuming unmitigated spread. Monitoring high-consumption areas can help in early detection, and targeted interventions at major production areas can effectively reduce the rate of spread.