Detect and Trace: An Australian Field Trial Using Machine-Learning Tools to Combat Illegal Wildlife Trade

The illegal wildlife trade is a global problem that continues to harm individuals, wildlife populations, ecosystems, and humans at an increasing rate. While efforts are underway globally to address the issue through a coordinated approach, the testing of new technologies in real-world settings remains limited. Here, we present the outcomes of an opportunistic Australian trial that tested two machine-learning tools during real-world seizures, including associated radiation-exposure safety data. During the seven-month trial, 116 animals were intercepted, representing reptiles and crustacea across five Genera: <i>Tiliqua</i>, <i>Egernia, Oedura, Chelodina</i>, and <i>Euastacus</i>. Of the 18 seized consignments, totalling 48 parcels, scanned through the RTT^®110 CT X-ray baggage scanner, automated AI detected smuggled wildlife 56% of the time using the most successful algorithm (AT.3), and captured 48 high-resolution 3D X-ray images, which allowed identification of concealed wildlife. In addition, 33 <i>Tiliqua</i> sp. were scanned using the Olympus Vanta pXRF and the data analysed using previously published machine-learning provenance models. Common blue-tongue lizards (<i>Tiliqua scincoides</i>) were less likely to be wild-caught than shingleback lizards (<i>Tiliqua rugosa</i>). Alongside expert statements, provenance results were provided to enforcement agencies. Following the trial, there was a significant reduction in the number of seized parcels being exported through postal pathways. This trial demonstrates the impact of integrating new technology to support intelligence-led enforcement processes and reduce wildlife trafficking.