Optimizing Entomofauna Sampling: A Multi-Stimulus Trap for Sustainable Pest Management in Agroforestry

Monitoring insect diversity is crucial for understanding ecosystem dynamics and supporting sustainable agricultural practices. Efficient trapping methods are essential tools for assessing entomofauna in complex agroforestry systems. This study aimed to develop and optimize a trap to assess entomofauna diversity in agroforestry systems. The research was conducted in two agroforestry systems (AFS) located in a transition zone between the Pantanal and Cerrado biomes. Initially, a "Mixed trap", developed by combining light, food, and color attractants, was compared with traps using each attraction principle individually. Subsequently, the influence of trap size and light source was evaluated. Specimen collections were carried out independently for each experiment, and data were analyzed using rarefaction and extrapolation curves, as well as species abundance, richness, and diversity through Hill numbers and the Shannon and Simpson indexes. The Mixed trap showed the highest species richness and diversity. Although it also yielded high values in the Hill diversity indexes, the Light trap proved less efficient, capturing fewer species and lower overall diversity. Insects from the orders Diptera, Thysanoptera, Coleoptera, and Hymenoptera were eudominant in the Mixed trap—an important finding, as these groups include key pest species. Regarding design variables, traps made from 5 L PET bottles equipped with LED lights demonstrated superior performance in capturing a broader range of insect groups. In conclusion, the Mixed trap is more efficient for sampling diverse insect groups. The integration of multiple attractant stimuli in a single trap strengthens entomofauna monitoring in agroforestry systems and supports more sustainable pest management strategies.