Applying Interdisciplinary Science To Fight Wildlife Trafficking

Wildlife trafficking endangers animals, fuels illegal activities, and harms local and global economies. It also poses serious consequences to human health as it destroys ecosystems that support surrounding communities and spreads animal-borne diseases. Despite these dangers, wildlife trafficking still isn’t universally seen as a severe problem by conservation officials or law enforcement agencies. 

Wildlife trafficking networks, or WTNs, are usually defined as transnational networks that cross source, transit, and destination regions. These networks are dynamic and diverse, exploiting both legal and illegal avenues. Because of these characteristics, the network and supply chain characteristics of WTNs remain poorly understood by the people trying to stop them. Much of the information that exists is only useful on a case-by-case basis. 

This article argues that given the broad scope and diversity of WTNs, it’s necessary to use interdisciplinary and cross-sectoral approaches to gather and analyze data effectively. Integrating knowledge from fields like conservation biology, criminology, operations research, and computational science can improve data quality and help in understanding the scope and success of WTNs across different contexts. The authors highlight three concepts that can be applied to WTNs to strengthen pre-existing knowledge: supply chain management, operations research, and computational science.

The concepts of supply chain management (SCM) can help officials understand the structure, strengths, and weaknesses of illegal supply chains. SCM explores the flow of products and services from suppliers to consumers. In WTNs, understanding these flows can reveal weak points where interventions could be most effective. Knowledge of the physical structure of WTNs can also offer great benefits. For example, knowing how WTNs are physically structured by identifying nodes (storage points) and edges (connecting pathways) can lead to more targeted and efficient interdiction (tactics used to disrupt traffickers).

Operations research provides tools to optimize decision-making in uncertain situations. There are many decision-making trade-offs that both traffickers and the authorities fighting them have to make. For example, stopping the trade of non-perishable products like jaguar teeth requires different tactics than those needed for perishable products like tiger meat​ because traffickers use different strategies for each.

The authors suggest using computer models and algorithms to predict responses to different interdiction activities, though they note the current lack of quality data that can be used in the first place. One potential way to address this is by applying reinforcement learning, a machine learning technique that trains software to make optimal decisions in different contexts. It mimics the human process of decision-making by balancing known actions with new strategies that might lead to better payoffs.

Other concepts in computational science, like bilevel optimization and path prediction models, can also help tackle WTNs. Bilevel optimization considers the goals of both traffickers and enforcement agencies, helping predict the effectiveness of different strategies. Path prediction models can help predict transit routes from source to destination based on network resilience, making it easier to identify interdiction sites.

The authors share the case study of ploughshare tortoises to highlight the potential of using interdisciplinary approaches to combat trafficking. These tortoises, located in Madagascar’s Baly Bay National Park, are some of the rarest species of tortoises in the world. Because of this, they’re targeted because of their distinct appearance and high value in the illegal pet trade. The researchers used cultural and geographic insights from local stakeholders to develop participatory mapping that they then digitized. This helped them pinpoint different nodes where the tortoises are stored and the edges they’re transported via. After identifying waterways as a major mode of transport, they applied path prediction analysis and bilevel optimization to compare different disruption strategies, helping them narrow down the most vulnerable points in the trafficking network.

The authors conclude that through better data sharing and the application of these new techniques, protectors will be able to more efficiently identify and disrupt the processes of WTNs. Combining ideas from different fields of science is crucial to fighting the serious threat of wildlife trafficking.