Artificial Intelligence-Aided Meta-Analysis of Toxicological Assessment of Agrochemicals in Bees

Abstract

The lack of consensus regarding pollinator decline in various parts of the planet has generated intense debates in different spheres. Consequently, much research has attempted to identify the leading causes of this decline, and a multifactorial synergism (i.e., different stressors acting together and mutually potentiating the harmful effects) seems to be the emerging consensus explaining this phenomenon. The emphasis on some stressor groups such as agrochemicals, and pollinators such as the honey beeApis mellifera, can hide the real risk of anthropogenic stressors on pollinating insects. In the present study, we conducted a systematic review of the literature to identify general and temporal trends in publications, considering the different groups of pollinators and their exposure to agrochemicals over the last 76 years. Through an artificial intelligence (AI)-aided meta-analysis, we quantitatively assessed trends in publications on bee groups and agrochemicals. Using AI tools through machine learning enabled efficient evaluation of a large volume of published articles. Toxicological assessment of the impact of agrochemicals on insect pollinators is dominated by the order Hymenoptera, which includes honey bees. Although honey bees are well-explored, there is a lack of published articles exploring the toxicological assessment of agrochemicals for bumble bees, solitary bees, and stingless bees. The data gathered provide insights into the current scenario of the risk of pollinator decline imposed by agrochemicals and serve to guide further research in this area.Systematic Review Registrationhttps://asreview.nl/.