AI-driven insights into microbiome dynamics: Enhancing pest management through machine learning and statistical modelling

The spotted-wing drosophila (SWD) (Drosophila suzukii Matsumura) is an invasive fruit fly that threatens commercially vital berries such as grapes, blackberries, and elderberries. The prevalence of this invasive fruit fly is escalating globally, posing significant challenges for fruit growers and necessitating effective pest control measures to safeguard crops and minimize economic losses. Recent research into the microbiome has highlighted the role of host-associated microorganisms ("microbiota") in various animals, including Drosophila, influencing health, development, aging, pesticide metabolism, and disease immunity. Moreover, lipids, such as triacylglycerol (TAG), crucial for animal vitality, are increasingly recognized for their distribution influenced by the gut microbiome. However, it remains unclear how the composition of Drosophila microbiota affects its TAG levels. This study explores the relationship between SWD microbiota collected from grapes, elderberries, and blackberries and their lipid content. Employing a blend of ecological and artificial intelligence modeling, we characterize microbial assembly and interactions, identify microbes under positive/negative selection, and pinpoint microbial genes associated with this selection. Understanding these gene associations with ecologically significant host traits and microbial interactions is pivotal for developing sustainable technologies to reduce the fly population and control the spread of plant pathogens. Ultimately, this research contributes to more effective pest management strategies for maintaining crop yields and economic stability.