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• W4387004972 abstract "Abstract Aim Online reptile trade poses new challenges to species conservation and requires automated monitoring. Range‐restricted and endemic reptile species are especially vulnerable to wildlife trade and unsustainable exploitation. In this study, we investigated the magnitude and geographic distribution of online trade of 96 endemic and range‐restricted reptile species from the Lesser Antilles. Location Global. Methods We developed methods for automated collection, filtering and processing of wildlife trade content for the targeted species from publicly accessible online platforms. Results We identified 599 relevant advertisements originating from 231 different advertisers and 41 websites focusing on 43 species. Species advertised included threatened species according to the International Union for the Conservation of Nature (IUCN) Red List and species listed in the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendices. Among threatened species, five are Critically Endangered, three are Endangered and two are Vulnerable according to the IUCN Red List. Moreover, three of the six most advertised species were classified as Near Threatened. Germany was the country with the highest number of advertisements ( N = 124), followed by the United States ( N = 55), the Netherlands ( N = 15) and United Kingdom ( N = 15). Based on data from sale advertisements that included price and currency data, prices ranged from one to over a thousand Euros. Main Conclusions We present a framework for automated analysis of online trade in reptiles that can be extended to other taxonomic groups. Our results highlight countries, such as Germany and the United States, where enhanced monitoring actions would be important to assess the origin (i.e. captive bred or wild sourced individuals) and the legality of the trade. Immediate conservation actions, such as population monitoring, are also needed to ensure wildlife trade is not threatening the persistence of endemic reptile populations in the wild." @default.
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• W4387004972 date "2023-09-25" @default.
• W4387004972 modified "2023-09-27" @default.
• W4387004972 title "Using automated content analysis to monitor global online trade in endemic reptile species" @default.
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• W4387004972 doi "https://doi.org/10.1111/ddi.13771" @default.
• W4387004972 hasPublicationYear "2023" @default.
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