Dataset of seized wildlife and their intended uses
Oliver C. Stringham,
Stephanie Moncayo,
Eilish Thomas,
Sarah Heinrich,
Adam Toomes,
Jacob Maher,
Katherine G.W. Hill,
Lewis Mitchell,
Joshua V. Ross,
Chris R. Shepherd,
Phillip Cassey
Affiliations
Oliver C. Stringham
Invasion Science & Wildlife Ecology Lab, University of Adelaide, SA 5005, Australia; School of Mathematical Sciences, University of Adelaide, SA 5005, Australia; Corresponding author at: The University of Adelaide, North Terrace Campus, Adelaide, SA, 5000, Australia.
Stephanie Moncayo
Invasion Science & Wildlife Ecology Lab, University of Adelaide, SA 5005, Australia
Eilish Thomas
Invasion Science & Wildlife Ecology Lab, University of Adelaide, SA 5005, Australia
Sarah Heinrich
Invasion Science & Wildlife Ecology Lab, University of Adelaide, SA 5005, Australia
Adam Toomes
Invasion Science & Wildlife Ecology Lab, University of Adelaide, SA 5005, Australia
Jacob Maher
Invasion Science & Wildlife Ecology Lab, University of Adelaide, SA 5005, Australia
Katherine G.W. Hill
Invasion Science & Wildlife Ecology Lab, University of Adelaide, SA 5005, Australia
Lewis Mitchell
School of Mathematical Sciences, University of Adelaide, SA 5005, Australia
Joshua V. Ross
School of Mathematical Sciences, University of Adelaide, SA 5005, Australia
Chris R. Shepherd
Monitor Conservation Research Society, Big Lake Ranch, BC, Canada
Phillip Cassey
Invasion Science & Wildlife Ecology Lab, University of Adelaide, SA 5005, Australia
The illegal wildlife trade (IWT) threatens conservation and biosecurity efforts. The Internet has greatly facilitated the trade of wildlife, and researchers have increasingly examined the Internet to uncover illegal trade. However, most efforts to locate illegal trade on the Internet are targeted to one or few taxa or products. Large-scale efforts to find illegal wildlife on the Internet (e-commerce, social media, dark web) may be facilitated by a systematic compilation of illegally traded wildlife taxa and their uses. Here, we provide such a dataset. We used seizure records from three global wildlife trade databases to compile the identity of seized taxa along with their intended usage (i.e., use-type). Our dataset includes c. 4.9k distinct taxa representing c. 3.3k species and contains c. 11k taxa-use combinations from 110 unique use-types. Further, we acquired over 45k common names for seized taxa from over 100 languages. Our dataset can be used to conduct large-scale broad searches of the Internet to find illegally traded wildlife. Further, our dataset can be filtered for more targeted searches of specific taxa or derived products.
