Countering plant crime online: cross-disciplinary collaboration in the FloraGuard study

Abstract

The illegal online trade in plants has potentially devastating impacts upon species poached for sale in digital markets, yet the scale of this threat to endangered species of flora remains relatively undetermined. Effectively monitoring and analysing the online trade in plants, requires an efficient means of searching the vastness of cyberspace, and the expertise to differentiate legal from potentially illegal wildlife trade (IWT). Artificial Intelligence (AI) offers a means of improving the efficiency of both search and analysis techniques, although the complexities of wildlife trade, and the need to monitor thousands of different species, makes the automation of this technology extremely challenging. In this contribution, we review a novel socio-technical approach to addressing this problem. Combining expertise in information and communications technology, criminology, law enforcement and conservation science, this cross-disciplinary technique combines AI algorithms with human judgement and expertise, to search for and iteratively analyse potentially relevant online content. We suggest that by coupling the scalability of search algorithms with a sufficient level of human input required to evaluate wildlife trade data, the proposed methodological approach offers significant advantages over manual search techniques. We conclude by examining the high level of cross-disciplinary collaboration required to develop this technique, which may provide a useful case study for conservation practitioners and law enforcement agencies, seeking to tackle this technology-driven threat to biodiversity.

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Identifiers

ORCID for Anita Lavorgna: orcid.org/0000-0001-8484-1613

ORCID for Stuart Middleton: orcid.org/0000-0001-8305-8176

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