Encoding and Decoding Illegal Wildlife Trade Networks

The illegal wildlife trade (IWT) is a key driver of biodiversity loss and a barrier to desired transformations in socio-environmental systems. IWT is known to exploit licit networks, such as the global airline flight network, yet the ability of science to support societal efforts to reduce IWT remains underdeveloped. Research on IWT relies on aggregate, context-specific, and biased counts of observed incidents leading to potential policy misguidance. Our study utilizing this data uses computational methods, centrality analysis and predictive modeling, to help encode and decode IWT-related airline flight networks to improve sensemaking with implications for sustainable futures. Methods advance existing analyses by revealing factors traffickers consider when selecting routes, identifying different airports' roles within the IWT network, and uncovering trafficking hotspots not observed in data. Computational science methods using these data sources can help overcome existing limitations and transform knowledge into action to reduce IWT and biodiversity loss. The files included herein include our cleaned dataset on an airport level, documenting incident counts from TRAFFIC’s Wildlife Trade Portal and ROUTES. Other covariates considered as potential features in our predictive model are included and broken down per airport. Additionally, the network files of the full flight and trafficking networks are available. A supplementary data file including definitions of all these features has been included as well.