Tracking Biosecurity Through the Diversity and Network Structure of International Trade

Effective and evidence-based biosecurity measures are essential to prevent trade disruption, protect industries and contain the chains of biological invasions. There are increasing demands for analysts to use quantitative data to monitor this system, with the goals of early detection and forecasting. However, standard approaches often struggle with the incomplete and complex nature of trade data, which tends to include non-normality, temporal and spatial autocorrelation, and limited observations. In this study, a time series of open access import data spanning three years was used to generate measures of diversity indices and network topology, alongside detailed analyses of import pathways and interception records of harmful organisms, revealing their dynamic patterns across different trade routes. Patterns of annual seasonality were evident across the board. A combination of Inverse Simpson’s diversity and network Linkage density optimised the monitoring power of import data for interceptions of harmful taxa. Traditional correlations to total number of interceptions remained intractable, but machine learning tools demonstrated predictive power to forecast these temporal patterns. Combined, these methods provide a novel approach for biosecurity monitoring in plant and animal trade across international borders. These indicators complement more conventional economic metrics, giving actionable insights into trade complexity and biosecurity status.