Early warning of trends in commercial wildlife trade through novel machine-learning analysis of patent filing

A. Hinsley (), D. W. S. Challender, S. Masters, D. W. Macdonald, E. J. Milner-Gulland, J. Fraser and J. Wright ()
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A. Hinsley: University of Oxford
D. W. S. Challender: University of Oxford
S. Masters: Naturalis Biodiversity Centre
D. W. Macdonald: University of Oxford
E. J. Milner-Gulland: University of Oxford
J. Fraser: University of Oxford
J. Wright: Oxford Martin School

Nature Communications, 2024, vol. 15, issue 1, 1-10

Abstract: Abstract Unsustainable wildlife trade imperils thousands of species, but efforts to identify and reduce these threats are hampered by rapidly evolving commercial markets. Businesses trading wildlife-derived products innovate to remain competitive, and the patents they file to protect their innovations also provide an early-warning of market shifts. Here, we develop a novel machine-learning approach to analyse patent-filing trends and apply it to patents filed from 1970-2020 related to six traded taxa that vary in trade legality, threat level, and use type: rhinoceroses, pangolins, bears, sturgeon, horseshoe crabs, and caterpillar fungus. We found 27,308 patents, showing 130% per-year increases, compared to a background rate of 104%. Innovation led to diversification, including new fertilizer products using illegal-to-trade rhinoceros horn, and novel farming methods for pangolins. Stricter regulation did not generally correlate with reduced patenting. Patents reveal how wildlife-related businesses predict, adapt to, and create market shifts, providing data to underpin proactive wildlife-trade management approaches.

Date: 2024
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DOI: 10.1038/s41467-024-49688-x

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