Advancing interdisciplinary science for disrupting wildlife trafficking networks

Abstract

Wildlife trafficking, whether local or transnational in scope, undermines sustainable development efforts, degrades cultural resources, endangers species, erodes the local and global economy, and facilitates the spread of zoonotic diseases. Wildlife trafficking networks (WTNs) occupy a unique gray space in supply chains-straddling licit and illicit networks, supporting legitimate and criminal workforces, and often demonstrating high resilience in their sourcing flexibility and adaptability. Authorities in different sectors desire, but frequently lack knowledge about how to allocate resources to disrupt illicit wildlife supply networks and prevent negative collateral impacts. Novel conceptualizations and a deeper scientific understanding of WTN structures are needed to help unravel the dynamics of interaction between disruption and resilience while accommodating socioenvironmental context. We use the case of ploughshare tortoise trafficking to help illustrate the potential of key advancements in interdisciplinary thinking. Insights herein suggest a significant need and opportunity for scientists to generate new science-based recommendations for WTN-related data collection and analysis for supply chain visibility, shifts in illicit supply chain dominance, network resilience, or limits of the supplier base.

Keywords: green security games; illegal wildlife trade; illicit networks; operations research; supply chain resilience.

Fig. 1.

Interdisciplinary approaches can advance scientific thinking about WTNs to help support efforts to disrupt them and help minimize their negative collateral impacts. The strengths of individual disciplines combine and mutually enhance one another.

Fig. 2.

Scientific thinking about WTNs can be advanced by quantitative computer science and operations engineering models that both inform on-the-ground interventions and consider the impacts of interventions on the resilience of the network. Feedbacks are filtered through the local socioenvironmental context.

Fig. 3.

Selected (A) supply chain network features and locations for ploughshare trafficking, and (B) different paths used for trafficking ploughshares, derived using participatory mapping methods in Soalala, Madagascar, 2018. Line color denotes boat, foot, or bike path.