Dataset Information

Beyond banning wildlife trade: COVID-19, conservation and development.

ABSTRACT: One of the immediate responses to COVID-19 has been a call to ban wildlife trade given the suspected origin of the pandemic in a Chinese market selling and butchering wild animals. There is clearly an urgent need to tackle wildlife trade that is illegal, unsustainable or carries major risks to human health, biodiversity conservation or meeting acceptable animal welfare standards. However, some of the suggested actions in these calls go far beyond tackling these risks and have the potential to undermine human rights, damage conservation incentives and harm sustainable development. There are a number of reasons for this concerns. First calls for bans on wildlife markets often include calls for bans on wet markets, but the two are not the same thing, and wet markets can be a critical underpinning of informal food systems. Second, wildlife trade generates essential resources for the world's most vulnerable people, contributing to food security for millions of people, particularly in developing countries. Third, wildlife trade bans have conservation risks including driving trade underground, making it even harder to regulate, and encouraging further livestock production. Fourth, in many cases, sustainable wildlife trade can provide key incentives for local people to actively protect species and the habitat they depend on, leading to population recoveries. Most importantly, a singular focus on wildlife trade overlooks the key driver of the emergence of infectious diseases: habitat destruction, largely driven by agricultural expansion and deforestation, and industrial livestock production. We suggest that the COVID-19 crisis provides a unique opportunity for a paradigm shift both in our global food system and also in our approach to conservation. We make specific suggestions as to what this entails, but the overriding principle is that local people must be at the heart of such policy shifts.

SUBMITTER: Roe D

PROVIDER: S-EPMC7388857 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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Publications

Beyond banning wildlife trade: COVID-19, conservation and development.

Roe Dilys D Dickman Amy A Kock Richard R Milner-Gulland E J EJ Rihoy Elizabeth E 't Sas-Rolfes Michael M

World development 20200729

One of the immediate responses to COVID-19 has been a call to ban wildlife trade given the suspected origin of the pandemic in a Chinese market selling and butchering wild animals. There is clearly an urgent need to tackle wildlife trade that is illegal, unsustainable or carries major risks to human health, biodiversity conservation or meeting acceptable animal welfare standards. However, some of the suggested actions in these calls go far beyond tackling these risks and have the potential to un ...[more]

PMID: 32834392

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Project description:Wildlife trade is a multi-billion-dollar sector that impacts a wide range of species, and thus is of significant research and conservation interest. Wildlife trade has also become a prominent topic in the public-facing media, where coverage has intensified following the outbreak of the global COVID-19 pandemic due to the potential connection between wildlife trade and the origin of the SARS Cov2 virus. Given the importance of the media in shaping public understanding and discourse of complex topics such as wildlife trade, this could impact the implementation of and public support for policy decisions. In this study, we followed a standardised protocol to extract wildlife trade-related discussion from 285 professional opinion pieces (NGO reports or articles in conservation-themed forums) and 107 scientific articles published in two time periods: "pre-COVID" (June 1-December 31, 2019) and "during-COVID" (January 1-May 31, 2020). We compared opinion pieces and scientific articles across the two time periods and to each other to investigate potential differences in the presentation of wildlife trade and associated speakers. We found a shift in the way that wildlife trade was discussed in professional opinion pieces between the periods, in that the discussion became less specific in terms of defining the legality and purpose of trade, and the animal groups involved in the "during-COVID" period. The generalised framing of wildlife trade in our dataset also coincided with an increased discussion of highly generalised management strategies, such as blanket bans on wildlife trade. We also found that publications included more quotes from researchers in the "during-COVID" period. In both professional opinion pieces and scientific articles, we found that quotations or research were often from speakers whose affiliation region was different to the geographic range of the trade they were speaking about. This highlights the importance of incorporating local knowledge and considering the diversity of speakers and interviewees in both research and the public-facing media about the wildlife trade.

Project description:Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing models, such as text classifiers. Here, we test the ability of a suite of text classifiers to extract relevant advertisements from wildlife trade occurring on the Internet. We collected data from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). We found that text classifiers can predict, with a high degree of accuracy, which listings are relevant (ROC AUC ≥ 0.98, F1 score ≥ 0.77). Furthermore, in an attempt to answer the question 'how much data is required to have an adequately performing model?', we conducted a sensitivity analysis by simulating decreases in sample sizes to measure the subsequent change in model performance. From our sensitivity analysis, we found that text classifiers required a minimum sample size of 33% (c. 5.5k listings) to accurately identify relevant listings (for our dataset), providing a reference point for future applications of this sort. Our results suggest that text classification is a viable tool that can be applied to the online trade of wildlife to reduce time dedicated to data cleaning. However, the success of text classifiers will vary depending on the advertisements and websites, and will therefore be context dependent. Further work to integrate other machine learning tools, such as image classification, may provide better predictive abilities in the context of streamlining data processing for wildlife trade related online data.

Dataset Information

Beyond banning wildlife trade: COVID-19, conservation and development.

Publications

Beyond banning wildlife trade: COVID-19, conservation and development.

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