A trait-based framework for quantifying arthropod invasion potential: Predictive modeling with Tropilaelaps mites as a case study
Over the past decade, the global rise in invasive species has accelerated at an unprecedented rate, intensifying threats to ecosystems, human health, and economies worldwide. Newly invasive taxa, such as Tropilaelaps mites, are of particular concern for apiculture and agroecosystems. Despite growing concern about the spread of Tropilaelaps mites and other arthropods, limited resources are available to assess their invasive potential. We characterized 118 invasive arthropod species using available literature to identify key biological and ecological traits associated with invasive potential. We developed predictive generalized linear mixed models (GLMMs) to determine the traits most important for predicting invasive potential (number of invaded regions), and the top-performing models were subsequently applied to Tropilaelaps mercedesae. Several traits were identified as significant predictors of invasiveness, including the degree of human association, resilience at small population sizes, diet breadth, maximum annual number of generations, altitude range, and the interaction between human association and temperature range. Notably, T. mercedesae was predicted to be capable of invading 160 regions, ranking it within the top 10% most invasive species among those evaluated (12th out of 119), ranked just below the cosmopolitan Varroa destructor mite. These findings position T. mercedesae as a high-risk, yet under-recognized, invasive threat. Collectively, this demonstrates the power of predictive trait-based modeling to inform invasion risk prior to widespread establishment and underscores the urgency of reallocating resources toward surveillance, research, and proactive management strategies rather than relying on costly, often ineffective post-establishment eradication.