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

Biology

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

A. Hinsley, D. W. S. Challender, et al.

This groundbreaking study by A. Hinsley and colleagues unveils the unforeseen impact of unsustainable wildlife trade on innovation and market shifts. With a staggering 130% annual rise in patents related to endangered species from 1970 to 2020, it reveals how businesses are adapting with novel products and methods despite stricter regulations. Discover how this research leverages machine learning to provide essential insights for proactive wildlife trade management!

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Playback language: English
Abstract
Unsustainable wildlife trade threatens numerous species. This study uses machine learning to analyze patent filings (1970-2020) related to six traded taxa (rhinoceroses, pangolins, bears, sturgeon, horseshoe crabs, and caterpillar fungus) to detect early warnings of market shifts. A 130% annual increase in patents was found, exceeding background rates. Innovation led to diversification, including new products (e.g., rhino horn fertilizer) and farming methods. Stricter regulation did not consistently reduce patenting, highlighting how businesses predict and adapt to market changes. Patents offer valuable data for proactive wildlife trade management.
Publisher
Nature Communications
Published On
Aug 01, 2024
Authors
A. Hinsley, D. W. S. Challender, S. Masters, D. W. Macdonald, E. J. Milner-Gulland, J. Fraser, J. Wright
Tags
unsustainable wildlife trade
machine learning
patent analysis
innovation
market shifts
endangered species
wildlife management

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