We compiled a dataset consisting of all the species involved in the illegal wildlife trade along with the reason (i.e., use-type) they were being traded. In total, the dataset includes c. 4.9k distinct taxa representing c. 3.3k species and contains c. 11k taxa-use combinations from 110 unique use-types. Our dataset can be used to conduct large-scale broad searches of the Internet to find illegally traded wildlife.
We found a pattern between US reptile trade and smuggling of live reptiles to Australia. Almost all species smuggled to Aus are legal in US trade and are popular We compared illegal smuggling of reptiles into Australia to the legal pet trade of reptiles in the US. We provide the first empirical risk watch-list for desirable reptile species being trafficked into Australia. Our findings give insight into the drivers of illegal wildlife trade and our approach provides a framework for anticipating future trends in wildlife smuggling.
The Internet can be vast source of data for the wildlife trade. However, data collected from the Internet is often numerous and messy, making data cleaning a task the requires a lot time and effort. Here, we tested if text classification can be used to speed up the process of data cleaning in relation to online data collected on the wildlife trade. We found that text classification models can predict with great accuracy relaxant advertisements, including the taxonomy of relevant species, using the text found in online advertisements. We recommend using text classification as a method to make data cleaning more efficient. Future efforts should try to pair text classification with image classification for improved efficiency.
Australian reptiles face serious conservation threats from illegal poaching fuelled by international demand and the exotic pet trade. We investigated the extent of illegal trade in a charismatic Australian lizard: the shingleback, also known as the bobtail or sleepy lizard. Using government records, media reports, and online advertisements, we found clear evidence that many shinglebacks have been illegally poached from the wild and are smuggled overseas to be traded as pets. Not only are our findings concerning from a conservation and animal welfare perspective, but they also highlight a major legal loophole. Once shinglebacks are illegally smuggled out of Australia, there are no legal actions available to prevent or regulate overseas trade. To address this, we recommend using an existing and under-utilised legislative tool (Appendix III of CITES, an international treaty) to protect Australian shinglebacks and help to curtail global trade.
The international trade in exotic vertebrate pets provides key social and economic benefits but also drives associated ecological, ethical, and human health impacts. Here, we represent and review the structure of the pet trade as a network composed of different market actors (nodes) and trade flows (links). As a case study of how data-informed networks can realize this goal, we quantified spatial and temporal patterns in pets imported to the United States. Our framework and case study illustrate how network approaches can help to inform and manage the effects of the growing demand for exotic pets.
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