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.2021 May 5;7(19):eabf7679.
doi: 10.1126/sciadv.abf7679. Print 2021 May.

International socioeconomic inequality drives trade patterns in the global wildlife market

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International socioeconomic inequality drives trade patterns in the global wildlife market

Jia Huan Liew et al. Sci Adv..

Abstract

The wildlife trade is a major cause of species loss and a pathway for disease transmission. Socioeconomic drivers of the wildlife trade are influential at the local scale yet rarely accounted for in multinational agreements aimed at curtailing international trade in threatened species. In recent decades (1998-2018), approximately 421,000,000 threatened (i.e., CITES-listed) wild animals were traded between 226 nations/territories. The global trade network was more highly connected under conditions of greater international wealth inequality, when rich importers may have a larger economic advantage over poorer exporting nations/territories. Bilateral trade was driven primarily by socioeconomic factors at the supply end, with wealthier exporters likely to supply more animals to the global market. Our findings suggest that international policies for reducing the global wildlife trade should address inequalities between signatory states, possibly using incentive/compensation-driven programs modeled after other transnational environmental initiatives (e.g., REDD+).

Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

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Figures

Fig. 1
Fig. 1. Trends in the international trade of threatened wild animals.
A total of 226 nations/territories making up 8342 unique exporter-importer pairs participated in the global market between 1998 and 2018, where trade network connectivity generally increased with international wealth inequality. (A) Each tile represents the linear relationship (depicted in inset) between a network index (row) and a measure of global wealth/wealth inequality (column). Tile colors represent slope coefficients indicating the direction and strength of respective statistical relationships, while panel textures identify whether current or lagged (from the previous year) measures of wealth/wealth inequality are more parsimonious predictors of the topology of the global trade network. Shaded regions in line plots within the inset represent 95% credible intervals. (B) Dashed line marks the beginning of the time period analyzed in this paper. The number of individuals traded is represented by bars, while dots represent species counts. Appendix numbers in the legend refer to CITES appendices. (C) Width of arrows indicates volume of trade flows in terms of estimated number of individual animals. The inner track of the chord diagram represents nations/territories identified by ISO 3166 country codes, while the outer track represents biogeographic region (21).
Fig. 2
Fig. 2. Socioeconomic dimensions of the global wildlife market.
Wild animals in the trade mostly flowed from poor developing economies to rich developed nations/territories, but wealthier exporters are sources of higher trade volumes. (A) Map of the 30 top participants and the 15 largest trade links in the global wild animal trade between 1998 and 2018. The size of the pie charts and width of the arrows are indicative of total individual animals traded, detailed by numbers associated with each participating nation/territory. (B) Bars represent the scaled average difference in socioeconomic indicators between exporters and importers. Actual numerical differences are denoted by the numbers associated with each bar (±SD). (C) Data points represent the scaled posterior distribution of slope coefficients (median ± maximum/minimum value within the 95% credible interval) describing the effects of the top five socioeconomic predictors of bilateral trade volume. The slope coefficients indicate the direction and strength of respective statistical relationships.
Fig. 3
Fig. 3. Animal group-specific socioeconomic drivers of the global wildlife trade network.
Statistical relationships between group-specific trade networks and indicators of global wealth/wealth inequality were highly variable, but overall trends (Fig. 1) were mirrored in several animal groups. The significance of tile colors and textures is as described in Fig. 1. Groups analyzed were (top left) amphibians, anthozoans, arachnids, birds, bivalves, and fishes and (top right) hydrozoans, insects, mammals, reptiles, sharks/rays, and snails.
Fig. 4
Fig. 4. Animal group-specific predictors of bilateral trade volumes.
More wild-caught animals were traded between similarly wealthy exporter-importer pairs (i.e., lower socioeconomic disparity) across nearly all groups analyzed. The color of each tile represents slope coefficients indicating the direction and strength of statistical relationships, while the histogram represents the frequency with which respective socioeconomic indicators constitute one of five top variables of importance predicting group-specific bilateral trade volume. Icons represent the animal groups as defined in Fig. 3. This figure has been truncated by removing socioeconomic predictors that were selected as a variable of importance for only a single animal group (full diagram in fig. S14).
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