Researchers develop game-changing method to help combat illegal trade: 'We urgently need to address it more effectively'

It is now possible to identify wildlife trafficking hubs at airports before an incident is even reported. 

As researchers from the University of Southern California and University of Maryland shared, they collaborated to analyze nearly 2,000 airports worldwide. They studied predictive models, historical trafficking data patterns, and airport features to identify airports that may be used for illegal wildlife trading. 

They published "Encoding and decoding illegal wildlife trade networks reveals key airport characteristics and undetected hotspots" in the journal Communications Earth & Environment. 

The researchers' novel use of artificial intelligence and network science can help conservation groups stop various forms of illegal wildlife trading. Their technology uncovered hidden hotspots, and 307 airports were identified as potentially involved.

These airports were located in the United States, Mexico, Italy, China, Indonesia, and the Philippines. 

"The most significant outcome of our model is the practical insights it offers to those invested in combating the illegal wildlife trade, such as how to allocate limited resources and prioritize where interventions are needed to make the most impact," lead author Hannah Murray said. 

The researchers noted the importance of this development because illegal trade is the second-largest wildlife threat following habitat loss. Data-driven tools can catch wildlife traffickers before they complete their deals, which contribute to biodiversity loss in native habitats and the spread of invasive species. 

"We urgently need to address it more effectively in order to preserve key biodiversity," USC computer science associate professor Bistra Dilkina said.

The model is one of the latest and most promising efforts to stop illegal wildlife traders and use innovative solutions to prevent poaching. 

This model stands out for its potential to be proactive rather than reactive to offenders who are getting increasingly skilled with their illicit activities. In addition to detecting possible animal trading, the model can predict a high likelihood of flora crimes. Therefore, it protects endangered plants and animals subject to extinction due to unauthorized global trade.

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"To our knowledge, our study is one of the first to both encode and decode statistics on [the illegal wildlife trade] on an airport level, adding more granular insights," the researchers concluded. "Additionally, the usage of this scaled-down data to build a predictive pipeline may unearth lower-level insights into how traffickers select the airports they visit and also highlight potential undetected hotspots of IWT activity."

With this newfound knowledge about wildlife trafficking hotspots, airports can implement enhanced security measures, such as increasing screening. The researchers also suggested additional training for airline crew members and greater collaboration between conservation groups and the transportation sector. 

They hope their study paves the way for additional research using a data-driven approach to fight illegal wildlife trading. In the future, their model could also be applied to other unlawful activities, such as human and drug trafficking. 

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