Abstract
<jats:p>The recognition of microbiota as a critical component of insect biology and evolution has driven a rapid expansion of insect-microbe interaction research. Yet while high-throughput sequencing has democratized access to microbial community profiling, it has also normalized low-resolution, artifact-prone approaches that often yield limited biological insights. Insects pose distinct challenges for microbiota characterization. Microbial communities are frequently low in biomass, highly variable across individuals and tissues, and often dominated by one or a few vertically transmitted endosymbionts. The fact that these features violate key assumptions of generic microbiome workflows is commonly overlooked, leading to misleading inferences from compositional data. Here, we summarize the properties of insect-associated microbiota that constrain the use of standard analytical approaches and highlight recurring pitfalls in their study. We emphasize two pervasive issues: (i) contamination, a major and often underappreciated source of bias in low-biomass samples, and (ii) the routine application of off-the-shelf diversity metrics and ordination-based analyses without consideration of their assumptions or biological relevance. We explain how microbiota quantification, appropriate controls and contamination-aware data filtering approaches, and analytical frameworks that prioritize hypothesis-driven inference, can improve rigor and lead to biologically meaningful conclusions. As data generation becomes cheaper and easier, progress in the field will depend on aligning such methodological and analytical choices with the biology of insect–microbe interactions rather than on increasing sequencing effort alone.</jats:p>