Process‐aware prioritization of <i>Vespa velutina</i> management under observation bias control

Abstract Biological invasions are a major driver of biodiversity loss, with invasive social vespids posing particular challenges due to colony‐level organization, worker mobility and broad prey use. The Asian yellow‐legged hornet ( Vespa velutina ) exemplifies this issue, reducing honey‐bee productivity, disrupting native insect assemblages and creating recurring management costs across Europe and East Asia. Risk mapping highlights potential spread but often overlooks observation bias, species interactions and seasonal dynamics, limiting its value for actionable decision‐making. We aimed to develop a process‐aware prioritization framework for V. velutina in the Republic of Korea that links uneven surveillance data to budget‐feasible management actions by explicitly accounting for ecological interactions, observation bias, and seasonal colony dynamics. From 2019 to 2023, standardized lure‐trap surveys collected 46,813 individuals across 92 grid cells, representing eight Vespa spp. Trait‐based win probabilities were used to define a Competitive Pressure Index (CPI), while a negative‐binomial observation model controlled for sampling effort. Seasonal abundance was projected using a colony dynamics model, and budget‐constrained plans were evaluated with bootstrap and sensitivity analyses. Prioritization scores were clustered and strongly correlated with CPI, with V. velutina most disadvantaged against V. mandarinia , V. dybowskii and V. ducalis . Budget‐feasible plans reduced V. velutina abundance by ~50% relative to baseline. Uncertainty analysis showed narrow confidence intervals, and global sensitivity analysis identified encounter‐rate intensity as the dominant driver, while life‐history parameters had negligible influence. This demonstrates that robust prioritization can be achieved even under irregular monitoring and competitive interactions. Synthesis and applications . Our framework converts irregular survey data into transparent, actionable priorities that connect observation, mechanism and projection. It provides managers with budget‐feasible guidance on where and how strongly to intervene, translating weak signals from citizen science and trapping into evidence‐supported actions that are based on transparent, reproducible criteria with quantified uncertainty. Although applied to V. velutina , the approach is transferable to other invasive social insects and broadly relevant to programs facing clustered sources, interaction‐mediated impacts and seasonal amplification. By grounding risk assessment in ecological processes and uncertainty analysis, this framework supports more accountable, effective and adaptive invasive species management.