A Scale‐Aware Maturity Framework for Insect Digital‐Twins Benchmarked Against the Global DT Literature

ABSTRACT Digital‐twin (DT) research has expanded rapidly across engineering and cyber‐physical systems, yet explicit adoption and consolidation of DT concepts in entomology remain limited and fragmented. This study addresses this imbalance through a two‐track synthesis. A Web of Science–based bibliometric analysis of the global DT domain from 2014 to 2025 establishes an external benchmark for growth dynamics, thematic structure, and prevailing implementation emphases. Because insect‐specific DT publications remain too sparse for stable bibliometric inference, entomological evidence is organized through a scale‐aware conceptual framework spanning individual, colony, and population or ecosystem levels, combined with a four‐level maturity scheme that distinguishes monitoring, shadowing, predictive, and intervening systems. The maturity lens clarifies that operational progress depends less on increasing model complexity than on strengthening data–model coupling and decision relevance. The transition from monitoring to shadowing requires explicit observation‐process models that map imperfect measurements to latent biological states while accounting for detection bias, missingness, and measurement error. The transition from shadowing to prediction is constrained by structural model error and uncertainty propagation under nonstationary environmental forcing. Movement toward intervention maturity requires an explicit decision layer that formalizes trade‐offs among effectiveness, cost, regulatory constraints, and ecological side effects. Representative case studies demonstrate how operational coherence can be achieved under scale‐specific constraints, while also revealing dominant bottlenecks including external validity at the individual scale, identifiability at the colony scale, and effort–abundance confounding at landscape scales. The resulting framework provides criteria for cumulative comparison and a roadmap for operationally coherent insect DTs that support risk‐aware decision making.