A trait-based thermal risk-index framework integrating stage-structured insect demography, host resources and the Next-Generation Matrix

The biology and population dynamics of terrestrial arthropods are strongly influenced by environmental conditions, particularly temperature, but thermal responses alone do not determine whether a population can establish when development depends on host resources. Here, we extend an existing physiologically based risk-index framework by explicitly coupling stage-structured insect demography with host-resource dynamics and deriving the resulting population-level invasion threshold through the Next-Generation Matrix (NGM) approach. The formulation integrates temperature-dependent development, fecundity and mortality across egg, larval, pupal and adult stages with host-resource production and depletion, yielding the basic reproduction number ( R 0 ) as a mechanistically interpretable risk index, where R 0 >1 identifies conditions permitting population growth. We demonstrate the framework using Busseola fusca and Chilo partellus , two major maize stemborers in Malawi. The stage-structured formulation produced distinct species-specific invasion windows: R 0 >1 occurred between 19.4–30.6 °C for B. fusca and 24.5–36.7 °C for C. partellus , with maximum reproductive potential at 25.9 °C and 31.2 °C, respectively. Global sensitivity analysis identified female oviposition, adult mortality, resource decay and resource production as dominant drivers of the population-level threshold. Spatial application reproduced contrasting geographic patterns, with broader reproductive potential for B. fusca across central and northern Malawi and a more restricted warm-lowland pattern for C. partellus . Independent larval observations were positively associated with predicted stage-transition success, with stronger agreement for B. fusca (Spearman ρ =0.60) than for C. partellus ( ρ =0.30). The framework therefore provides a general route for translating stage-specific physiological traits and resource dependence into an NGM-based population invasion threshold that can support mechanistic pest risk assessment and early-warning applications.