Advancement of methods in insect science: from genes to ecosystems

Innovative advances in methods drive discovery in insect science. For example, the development of broadly conserved cytochrome c oxidase subunit I (COI) primers enabled widespread, comparable molecular identification across metazoans and directly catalyzed large-scale DNA barcoding and biodiversity-monitoring programs (Folmer et al. 1994). Leveraging this advancement, whole-genome sequencing of Drosophila melanogaster then translated those molecular resources into a methodological foundation for functional genetics and comparative genomics that has underpinned modern population- and evolutionary-genomic studies (Adams et al. 2000). Likewise, the application of systematic, network-level sampling and quantitative analysis revealed how interaction topology determines community vulnerability and thus showed that rigorous sampling designs and analytical methods are central to reliable ecological inference and conservation action (Memmott et al. 2004). Methodological advances often arrive quietly—a new reagent here, a tweak to an imaging pipeline there, a re-purposing of a technology already available, to gain more insights into a physiological system—but together, they reshape what we can measure, how accurately we measure it, and who can take those measurements. This special collection gathers 7 such advances that are united by a pragmatic ambition: to make insect science more precise, less invasive, more reproducible, and more affordable so insights can scale from museum cabinets to landscapes and from laboratories to management programs. The articles span chemical marking, automated image analysis, metabarcoding, nondestructive DNA extraction, low-cost tracking, thermal physiology, and deep-learning bioacoustics—a deliberately interdisciplinary set chosen for clarity of validation and potential for broad uptake. A clear throughline in the collection is attention to operational constraints. Faraji et al. (2025) tackle a classic problem for public-health programs: how to produce reliable mosquito counts at scale. Their ImageJ-based optical counting pipeline maintains