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This article reviews extant multidisciplinary literature to uncover existing themes and directions in the knowledge of the overlap between natural resource scarcity and illicit supply chain activity. In doing so, the authors present a novel review of this nascent, complex, and multidisciplinary research area. This review has uncovered 127 articles that have not been synthesized or organized in a meaningful way with the supply chain literature. It extracts insights and develops a comprehensive process framework encompassing the following: (a) antecedents associated with natural resource extraction, which foments the opportunity for illicit activity to thrive; (b) resulting economic, social, and environmental outcomes from illicit activity as it relates to natural resource extraction; and (c) potential moderating processes, which either enable or inhibit illicit activity to occur, including firm‐level tactics that businesses can employ to counteract illicit activity throughout the supply chain and to promote sustainable long‐term operations. An extensive agenda is presented suggesting future research paths, methodologies, theories, and potential contributions.

 

As a result of declining biodiversity and increasing rates of urbanization, the illegal urban wildmeat trade is projected to become an integral sector of the wildlife crime industry. Adequate assessment of urban wildmeat trafficking requires investigation into the roles and behaviors of individuals who engage in wildlife crimes. However, akin to much of wildlife crime literature, women's engagement within the urban wildmeat trade have received little investigation. The objectives of our investigation were to (1) explore relationships between women and wildlife products across the supply chain, and (2) determine whether a significant relationship exists between women and specific wildlife products. Through systematic social observations, we evaluate the gendered dimensions of urban wildmeat trafficking in the Republic of Congo between the urban centers of Brazzaville and Pointe-Noire. We place particular emphasis on species of conservation concern, namely great apes, African pangolins, and dwarf crocodiles. Results indicate that there are gendered variations at the species and the geographic level, indicating that women are sourcing and sending their products to different locations than men, and that women are specializing in trade of different species. We attest that urban wildmeat trafficking prevention strategies implement a gender-aware approach due to the unique ways that individuals engage with the industry and how that engagement is gendered. 

Wildlife trafficking (WT), the illegal trade of wild fauna, flora, and their parts, directly threatens biodiversity and conservation of trafficked species, while also negatively impacting human health, national security, and economic development. Wildlife traffickers obfuscate their activities in plain sight, leveraging legal, large, and globally linked transportation networks. To complicate matters, defensive interdiction resources are limited, datasets are fragmented and rarely interoperable, and interventions like setting checkpoints place a burden on legal transportation. As a result, interpretable predictions of which routes wildlife traffickers are likely to take can help target defensive efforts and understand what wildlife traffickers may be considering when selecting routes. We propose a data-driven model for predicting trafficking routes on the global commercial flight network, a transportation network for which we have some historical seizure data and a specification of the possible routes that traffickers may take. While seizure data has limitations such as data bias and dependence on the deployed defensive resources, this is a first step towards predicting wildlife trafficking routes on real-world data. Our seizure data documents the planned commercial flight itinerary of trafficked and successfully interdicted wildlife. We aim to provide predictions of highly-trafficked flight paths for known origin-destination pairs with plausible explanations that illuminate how traffickers make decisions based on the presence of criminal actors, markets, and resilience systems. We propose a model that first predicts likelihoods of which commercial flights will be taken out of a given airport given input features, and then subsequently finds the highest-likelihood flight path from origin to destination using a differentiable shortest path solver, allowing us to automatically align our model’s loss with the overall goal of correctly predicting the full flight itinerary from a given source to a destination. We evaluate the proposed model’s predictions and interpretations both quantitatively and qualitatively, showing that the predicted paths are aligned with observed held-out seizures, and can be interpreted by policy-makers 

Wildlife trafficking is a global phenomenon posing many negative impacts on socio-environmental systems. Scientific exploration of wildlife trafficking trends and the impact of interventions is signifi-cantly encumbered by a suite of data reuse challenges. We describe a novel, open-access data directory on wildlife trafficking and a corresponding visualization tool that can be used to identify data for multiple purposes, such as exploring wildlife trafficking hotspots and convergence points with other crime, discovering key drivers or deterrents of wildlife trafficking, and uncovering structural patterns. Keyword searches, expert elicitation, and peer- reviewed publications were used to search for extant sources used by industry and non-profit organizations, as well as those leveraged to publish academic research articles. The open-access data direc-tory is designed to be a living document and searchable according to multiple measures. The directory can be instrumental in the data- driven analysis of unsustainable illegal wildlife trade, supply chain structure via link prediction models, the value of demand and supply reduction initiatives via multi-item knapsack problems, or trafficking behavior and transportation choices via network inter-diction problems. 

Abstract We have more data about wildlife trafficking than ever before, but it remains underutilized for decision-making. Central to effective wildlife trafficking interventions is collection, aggregation, and analysis of data across a range of source, transit, and destination geographies. Many data are geospatial, but these data cannot be effectively accessed or aggregated without appropriate geospatial data standards. Our goal was to create geospatial data standards to help advance efforts to combat wildlife trafficking. We achieved our goal using voluntary, participatory, and engagement-based workshops with diverse and multisectoral stakeholders, online portals, and electronic communication with more than 100 participants on three continents. The standards support data-to-decision efforts in the field, for example indictments of key figures within wildlife trafficking, and disruption of their networks. Geospatial data standards help enable broader utilization of wildlife trafficking data across disciplines and sectors, accelerate aggregation and analysis of data across space and time, advance evidence-based decision making, and reduce wildlife trafficking. 

We describe a novel database on wildlife trafficking that can be used for exploring supply chain coordination via game-theoretic collaboration models, geographic spread of wildlife products trafficked via multi-item knapsack problems, or illicit network interdiction via multi-armed bandit problems.

A publicly available visualization of this dataset is available at: https://public.tableau.com/views/IWTDataDirectory-Gore/Sheet2?:language=en-US&:display_count=n&:origin=viz_share_link