Algorithms Predict Risk of Plant Disease and Infestation Worldwide

Pests and diseases have a major impact on food supplies and the surrounding ecosystem. It is particularly damaging when a pest invades a new region in which native plants have little natural resistance. To help governments better understand the risks of outbreaks before they even happen, researchers in Mexico have developed a new technique that can predict the risk of plant disease and infestation worldwide.
The new system is a series of algorithms that help predict outbreaks. The technique that the researchers use is based on the principle that closely related plants growing near each other are prone to infestation by the same pests or pathogens. So, the team studied the geographical distribution of closely related plants and based on that, generated maps of potential disease hotspots.
The team tested their algorithms by applying them to an invasive pest known as the redbay ambrosia beetle. This invasive pest is present in North America, and it transmits Laurel Wilt Disease, a serious and sometimes deadly disease for the laurel family.
Using their technique and algorithms, the researchers created maps showing areas of the world most likely to suffer infestation or interaction between the beetles and plants. These maps accurately described the native territories of the beetles, as well as the recent invasive behavior of some beetles.

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