ILLWILD

Illegal wildlife trade (IWT) is increasing worldwide, with far reaching consequences for animal populations and ecosystems, as well as for human health and society. IWT is changing rapidly, and digital media has become one of its main channels. However, no systematic studies have characterized the magnitude and geographic range of IWT on digital media and the subsequent impacts on biodiversity conservation. Here, I will: (1) explore and quantify the global scale of IWT on digital media for endangered mammal and bird species; (2) identify geographic hotspots and explore the socio-economic drivers of IWT; and (3) identify top priority species for which the impact of individual offtake for IWT through digital media is higher in terms of population decline. To do so, I will focus on the digital trade of bird and mammal species included in Appendix I of CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora); since international trade for those species is not permitted. I will use state-of-the-art machine-learning algorithms to detect and quantify illegal trade of those species on several digital media platforms. I will integrate this information with potential geographic and socio-economic drivers of illegal wildlife trade to establish appropriate conservation planning strategies. Finally, I will carry out population models to understand the contribution of IWT to mortality rates and population trends. The proposed study combines artificial intelligence, social sciences and conservation science, thus providing a novel, multidisciplinary approach to understand the extent and patterns of IWT through digital media platforms and inform decision-making to ultimately help halt biodiversity loss.

2021

2023