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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. 

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. 

Classifying whether collected information related to emerging topics and domains is fake/incorrect is not an easy task because we do not have enough labeled data in the domains. Given labeled data from source domains (e.g., gossip and health) and limited labeled data from a newly emerging target domain (e.g., COVID-19 and Ukraine war), simply applying knowledge learned from source domains to the target domain may not work well because of different data distribution. To solve the problem, in this paper, we propose an energy-based domain adaptation with active learning for early misinformation detection. Given three real world news datasets, we evaluate our proposed model against two baselines in both domain adaptation and the whole pipeline. Our model outperforms the baselines, improving at least 5% in the domain adaptation task and 10% in the whole pipeline, showing effectiveness of our proposed approach. 

The notion that indigenous people and local communities can effectively prevent conservation crime rests upon the assumption that they are informal guardians of natural resources. Although informal guardianship is a concept typically applied to “traditional” crimes, urban contexts, and the global North, it has great potential to be combined with formal guardianship (such as ranger patrols) to better protect wildlife, incentivize community participation in conservation, and address the limitations of formal enforcement in the global South. Proactive crime prevention is especially important for illegal snare hunting, a practice that has led to pernicious defaunation and which has proved difficult to control due to its broad scope. This paper uses interview data with community members in protected areas in Viet Nam where illegal snare hunting is commonplace to 1) analyze the conditions for informal guardianship in the study locations; 2) explore how community members can become more effective informal guardians; and 3) examine how formal and informal guardianship mechanisms can be linked to maximize deterrence and limit displacement of illegal snaring. Results indicate that conditions for informal guardianship exist but that respondent willingness to intervene depends upon the location, offender activity, and type of offender (outsider versus community member). While respondents generated numerous strategies for wildlife crime prevention, they also listed crime displacement mechanism offenders used to avoid detection. We discuss how informal guardianship can be integrated with formal guardianship into an overall model of situational crime prevention to protect wildlife and incentivize community-led deterrence of illegal snaring.