ABSTRACT
E-commerce has become a booming market for wildlife trafficking, as online platforms are increasingly more accessible and easier to navigate by sellers, while still lacking adequate supervision. Artificial intelligence models, and specifically deep learning, have been emerging as promising tools for the automated analysis and monitoring of digital online content pertaining to wildlife trade. Here, we used and fine-tuned freely available artificial intelligence models (i.e., convolutional neural networks) to understand the potential of these models to identify instances of wildlife trade. We specifically focused on pangolin species, which are among the most trafficked mammals globally and receiving increasing trade attention since the COVID-19 pandemic. Our convolutional neural networks were trained using online images (available from iNaturalist, Flickr and Google) displaying both traded and non-traded pangolin settings. The trained models showed great performances, being able to identify over 90 % of potential instances of pangolin trade in the considered imagery dataset. These instances included the showcasing of pangolins in popular marketplaces (e.g., wet markets and cages), and the displaying of commonly traded pangolin parts and derivates (e.g., scales) online. Nevertheless, not all instances of pangolin trade could be identified by our models (e.g., in images with dark colours and shaded areas), leaving space for further research developments. The methodological developments and results from this exploratory study represent an advancement in the monitoring of online wildlife trade. Complementing our approach with other forms of online data, such as text, would be a way forward to deliver more robust monitoring tools for online trafficking.
ABSTRACT
The SARS-CoV-2 pandemic has caused immense social and economic costs worldwide. Most experts endorse the view that the virus has a zoonotic origin with the final spillover being associated with wildlife trade. Besides human consumption, wild animals are also extensively traded as pets. Information on zoonotic diseases has been reported to reduce consumer demand for exotic pets. We conducted a global survey and collected 162 responses from international experts on exotic pet trade (traders, academics, NGOs, enforcement entities) to understand how the legal and illegal trade of exotic pets is expected to be affected by the ongoing coronavirus pandemic. Our results suggest that legal purchase of exotic pets is perceived as decreasing during the first pandemic wave due to: lower availability of animals for trade, suppliers' inability to reach consumers and social distancing measures. The general perception is that in the future (i.e., next five years), both demand and supply of legally traded exotic pets are expected to either remain unchanged or decrease only temporarily. The consumer demand for illegal exotic pets is also expected to remain unchanged following the outbreak. The top two challenges reported by respondents, when considering the consequences of the pandemic for the exotic pet trade, are inadequate enforcement of national regulations and increased illegal trade. Our results suggest that the negative consequences of a zoonotic outbreak may not dissuade consumers of exotic pets. Worldwide, the transit/storing conditions and lack of health screenings of traded live animals are conducive to spreading diseases. Consumer demand is a key driver of trade, and enforcement of trade regulations will remain challenging, unless factors driving consumer demand are adequately incorporated in problem-solving frameworks. We emphasize the complexity of trade dynamics and the need to go beyond bans on wildlife trade. Stronger law enforcement, implemented along with initiatives dissuading consumption of wild exotic pets, are essential to sustainably satisfy the market demand.