Monitoring internet trade to inform species conservation actions

[摘要] Specimens, parts and products of threatened species are commonly traded on the internet. This could threaten the survival of some wild populations. We outline 2 methods to monitor internet sales of species to assess potential threats and inform conservation actions. Our first method combines systematic monitoring of online offers of plants for sale with expert consultation. Our second method utilises a computational model, trained to expert-classified records using probabilistic inference, to predict unknown properties of the traded taxa. We used these methods to monitor internet trade in 5 genera of succulent plant species endemic to Madagascar, some of which have recently been listed for trade regulation under the Convention on International Trade in Endangered Species (CITES). This revealed potential threats to wild populations: for instance, almost all species recorded were of high conservation concern, yet most offers for live plants were of apparently wild-collected specimens (85%). Our model predicted with 89% accuracy whether the plants were classified as propagated or wild collected by an expert, although accuracy dropped for data collected in the following summer. Our results highlight potential threats by internet trade to the survival of some CITES and non-CITES listed plant species from Madagascar. These should be addressed by further conservation actions and policy. More generally, our results reveal how standardised internet surveys can provide information on levels of trade in wild-collected threatened species that could impact on natural populations, and can provide data that can be incorporated into models to facilitate future monitoring and enforcement.

[学科分类] 动物科学

[关键词] Adenia;Commiphora;Operculicarya;Uncarina;Machine learning;Infer.NET;Naive Bayes classifier