Digital footprints of wildlife crime: linking public search behavior to illegal trade dynamics for six keystone mammal taxa in China

Abstract

Wildlife crime poses a severe threat to global biodiversity, driving species extinctions and enabling organized crime networks. While China has strengthened legal frameworks to combat illegal wildlife trade, the shift of illicit activities to online platforms challenges traditional enforcement methods. This study integrates judicial records (2011–2019) with Baidu Search Index (BSI) data to investigate the spatiotemporal dynamics of wildlife crime and its linkage to public search behavior, focusing on six keystone taxa: elephants, rhinos, tigers, bears, pangolins, and musk deer. Using spatial autocorrelation analysis and temporal correlation tests, we assessed how search trends reflect demand-side drivers and crime patterns. Key findings reveal a rise-and-decline trend in wildlife crime and most of the BSI values during the 2010s. Crimes involving exotic species (e.g., elephants, rhinos) clustered in coastal trafficking hubs, while offenses against native taxa (bears, musk deer) dispersed across source regions. Public search behavior exhibited species-specific patterns: derivative products correlated with exotic species crimes, whereas vernacular names aligned with native species offenses, reflecting divergent motivations. Spatial analysis identified High-High crime-search hotspots and demand-supply mismatches. This study pioneers the use of BSI as a real-time proxy for monitoring wildlife crime, demonstrating its utility in pinpointing demand hotspots and informing targeted interventions. Methodologically, integrating digital behavioral data with judicial analytics offers a transformative approach for conservation policy, albeit constrained by semantic ambiguities and rural data gaps. These insights advocate for adaptive, data-driven strategies to curb biodiversity loss and transnational trafficking networks.