Project description
Understanding illegal wildlife trade through digital media
Illegal wildlife trade (IWT) is a major threat to the future existence of species around the world. Digital media is transforming how people exchange and sell species; however, systematic studies on the magnitude and geographic range of IWT on digital media and the subsequent impacts do not exist. Addressing this, the EU-funded ILLWILD project will examine and quantify, using cutting-edge machine learning algorithms, the global scale of IWT on digital media for endangered species. It will further identify geographical hotspots and the socio-economic drivers for it and shed light on the most at-risk species through this channel. The project's work will aid in stopping biodiversity loss.
Objective
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.
Fields of science
- natural sciencesbiological scienceszoologyornithology
- natural sciencesbiological sciencesecologyecosystems
- natural sciencesbiological scienceszoologymammalogy
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- social scienceseconomics and businessbusiness and managementcommerce
Keywords
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
00014 Helsingin Yliopisto
Finland