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Assessing the illegal hunting of native wildlife in China

Nature volume 623, pages 100–105 (2023)Cite this article

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Abstract

Illegal harvesting and trading of wildlife have become major threats to global biodiversity and public health1,2,3. Although China is widely recognized as an important destination for wildlife illegally obtained abroad4, little attention has been given to illegal hunting within its borders. Here we extracted 9,256 convictions for illegal hunting from a nationwide database of trial verdicts in China spanning January 2014 to March 2020. These convictions involved illegal hunting of 21% (n = 673) of China’s amphibian, reptile, bird and mammal species, including 25% of imperilled species in these groups. Sample-based extrapolation indicates that many more species were taken illegally during this period. Larger body mass and range size (for all groups), and proximity to urban markets (for amphibians and birds) increase the probability of a species appearing in the convictions database. Convictions pertained overwhelmingly to illegal hunting for commercial purposes and involved all major habitats across China. A small number of convictions represented most of the animals taken, indicating the existence of large commercial poaching operations. Prefectures closer to urban markets show higher densities of convictions and more individual animals taken. Our results suggest that illegal hunting is a major, overlooked threat to biodiversity throughout China.

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Fig. 1: Illegal hunting of species in four vertebrate groups in China as represented in court verdicts from January 2014 to March 2020.
Fig. 2: The proportion of imperilled and non-imperilled species illegally hunted in China, as represented in court verdicts from January 2014 to March 2020.
Fig. 3: Sample-size-based estimates, using court verdicts, of the number of species that were illegally hunted in China.
Fig. 4: Geographic patterns and socioeconomic drivers of illegal hunting convictions in 336 Chinese prefectures.

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Data availability

The court verdicts are available in OpenLaw (http://openlaw.cn/). The number of verdicts (all verdicts, type 1 and type 2 verdicts), the number of individual animals hunted, and ecological and socioeconomic variables at prefecture level and species traits for four vertebrate groups were uploaded to Figshare, https://doi.org/10.6084/m9.figshare.22114913. Source data are provided with this paper.

Code availability

R code for analysing the relationships between illegal hunting intensity (number of court verdicts and number of individuals hunted) and ecological and socioeconomic factors in 336 Chinese prefectures and predicting the probability that a species is known to be illegally hunted using species traits were uploaded to Figshare, https://doi.org/10.6084/m9.figshare.22114913.

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Acknowledgements

The authors thank the Chinese law enforcement officers, law workers and conservationists who combat illegal hunting in China and generated these court verdicts. We thank R. Senior, F. Guo, Y. Zeng and Y. Chen for their help with analyses, and Y. Liu, T. Mu, B. Lin, C. L. Crawford, B. Lee, Y. He and F. Hua for their comments on earlier versions of this manuscript. The work of D. Liang and D. Wilcove on this project was funded by grants from the High Meadows Foundation and the Ma Huateng Foundation to Princeton University.

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Authors and Affiliations

  1. Princeton School of Public and International Affairs, Princeton University, Princeton, NJ, USA

    Dan Liang, Liang Ma & David S. Wilcove

  2. Department of Ecology and Evolutionary Biology, The University of Tennessee, Knoxville, TN, USA

    Xingli Giam

  3. School of Ecology, Sun Yat-sen University, Shenzhen, China

    Sifan Hu & Liang Ma

  4. State Key Laboratory of Biological Control, School of Life Sciences, Sun Yat-sen University, Guangzhou, China

    Sifan Hu

  5. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA

    David S. Wilcove

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Contributions

D.L., X.G. and D.S.W. conceived the idea. D.L. and S.H. performed data collection and extraction. D.L., X.G. and L.M. analysed the data. D.L. drafted the paper with inputs from D.S.W. and X.G. All authors revised and approved the final draft of the manuscript.

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Correspondence to Dan Liang.

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Extended data figures and tables

Extended Data Fig. 1 The number of individual animals illegally taken in court verdicts.

a: frequency distribution of the number of individual animals illegal hunted. Each bar on the x-axis represents 1% of 8,059 court verdicts with information on the number of individual animals taken, ordered by the number of individual animals that were illegally hunted. The total number of individual animals reported as taken in court verdicts is 3,158,622. Note that a small number of court verdicts (5%) account for disproportionately large numbers of animals (90%) hunted illegally. b: the numbers of individual animals illegally hunted in the court verdicts (8,059 court verdicts for four vertebrate groups combined; 1,559 verdicts for amphibians, 339 verdicts for reptiles, 4,001 court verdicts for birds, and 2,161 verdicts for mammals) in different geographical regions. For visualization purposes, the number of individuals (y-axis) is log-transformed. The middle line represents median values; lower and upper box hinges represent 25th and 75th percentiles, respectively; lower and upper whiskers represent minimum (25th percentiles – 1.5 * interquartile range) and maximum (75th percentiles + 1.5 * interquartile range), respectively. Dots are outliers, which represent court cases involving vast numbers of illegally hunted animals. Note that small numbers of court verdicts with large numbers of illegally hunted animals are widely distributed across different regions.

Extended Data Fig. 2 Motivation for illegal hunting and use of hunted animals in court verdicts.

a: percentage of court verdicts of different motivations for hunting and uses of animals. We obtained data on the motivation for hunting and use of illegally obtained animals in 3,721 and 5,748, respectively, of 8,645 court verdicts that contained taxonomic information. b: the numbers of animals hunted in court verdicts for commercial versus non-commercial purposes. We obtained data from 3,434 court verdicts with information about motivation and the number of animals hunted. For visualization purposes, the number of individual animals hunted (y-axis) is log-transformed. The middle line represents median values; lower and upper box hinges represent 25th and 75th percentiles, respectively; lower and upper whiskers represent minimum (25th percentiles – 1.5 * Interquartile Range) and maximum (75th percentiles + 1.5 * Interquartile Range), respectively. Dots are outliers, which represent court cases involving especially large numbers of illegally hunted animals. Note that many more animals were taken for commercial purposes than for non-commercial purposes.

Extended Data Fig. 3 Illegal hunting of animals in different habitat types.

a: percentage of court verdicts detected in different habitats. Information on the habitats where illegal hunting occurred was present in 8,454 (91%) court verdicts. The y-axis shows the percentage of court verdicts involving hunting in a particular habitat type. Habitat types were categorized following the habitat type categories used by IUCN. b: the numbers of animals hunted in court verdicts in different habitats, reported in court verdicts. We obtained data from 7,897 court verdicts with information about habitats and the number of animals hunted. For visualization purposes, the number of individuals (y-axis) is log-transformed. The middle line represents median values; lower and upper box hinges represent 25th and 75th percentiles, respectively; lower and upper whiskers represent minimum (25th percentiles – 1.5 * Interquartile Range) and maximum (75th percentiles + 1.5 * Interquartile Range), respectively. Dots are outliers, which represent court cases involving vast numbers of illegally hunted animals. Note that large numbers of animals were illegally hunted in all major habitats including anthropogenic landscapes.

Extended Data Fig. 4 Geographic patterns and socioeconomic drivers for the number of animals illegally hunted in 336 Chinese prefectures.

Figures in the left column show the mean coefficient estimates with 95% confidence interval (CI) bars for ecological, geographic, and socioeconomic variables in the global model for all four vertebrate groups combined (a) and for each vertebrate group individually (c: amphibians; e: reptiles; g: birds; i: mammals) (For models, see Supplementary Table 10). Negative and positive values of coefficients indicate negative and positive relationships with the number of court verdicts per km2, respectively. Coefficients with 95% CI that do not overlap with zero indicate variables that are significantly correlated with the number of court verdicts per km2. Maps in the right column show relative hunting intensity (number of type 1 verdicts per km2; scaled to 0-1 by the maximum values and the color scale is on a log scale) for all four vertebrates combined (b) and for each vertebrate group individually (d: amphibians; f: reptiles; h: birds; j: mammals).

Extended Data Fig. 5 Geographic patterns and socioeconomic drivers for the number of the type 1 (crime of illegal hunting wildlife in general) verdicts in 336 Chinese prefectures.

Panels in the left column show the mean coefficient estimates with 95% confidence interval (CI) bars for ecological, geographic, and socioeconomic variables in the global model for all four vertebrate groups combined (a) and for each vertebrate group individually (c: amphibians; e: reptiles; g: birds; i: mammals). (For models, see Supplementary Table 8). Negative and positive coefficient values indicate negative and positive relationships with the number of court verdicts per km2, respectively. Coefficients with 95% CI that do not overlap with zero indicate variables that are statistically significantly correlated with the number of court verdicts per km2. Maps in the right column show relative hunting intensity (number of type 1 verdicts per km2; scaled to 0-1 by the maximum values and the color scale is on a log scale) for all vertebrates combined (b) and for each vertebrate group individually (d: amphibians; f: reptiles; h: birds; j: mammals).

Extended Data Fig. 6 Geographic patterns and socioeconomic drivers for the number of the type 2 (crime of illegal hunting, killing imperiled wildlife) verdicts in 336 Chinese prefectures.

Figures in the left column show the mean coefficient estimates with 95% confidence interval (CI) bars for ecological, geographic, and socioeconomic variables in the global model for all four vertebrate groups combined (a) and for each vertebrate group individually (c: amphibians; e: reptiles; g: birds; i: mammals) (For models, see Supplementary Table 9). Negative and positive values of coefficients indicate negative and positive relationships with the number of court verdicts per km2, respectively. Coefficients with 95% CI that do not overlap with zero indicate variables that are significantly correlated with the number of court verdicts per km2. Maps in the right column show relative hunting intensity (number of type 1 verdicts per km2; scaled to 0-1 by the maximum values and the color scale is on a log scale) for all group vertebrates combined (b) and for each vertebrate group individually (d: amphibians; f: reptiles; h: birds; j: mammals).

Extended Data Fig. 7 Partial response curves of the global models showing the relationships between socioeconomic variables and number of all verdicts in each prefecture for each taxonomic group.

From top to bottom, rows represent all vertebrate groups combined, followed by amphibians, reptiles, birds and mammals. Error bands represented 95% confidence intervals. Significant associations are marked with a red “*” (see Supplementary Table 6 for the definitions of all socioeconomic variables and Supplementary Table 7 for coefficients of each variable in the global models for each taxonomic group). Note that ‘latitude’ was removed due to its high association with species richness (in the cases of vertebrates, amphibians, and reptiles) (Supplementary Table 19).

Extended Data Fig. 8 Partial response curves of the global models showing the relationships between socioeconomic variables and number of all individuals being hunted in each prefecture for each taxonomic group.

From top to bottom, rows represent all vertebrate groups combined, followed by amphibians, reptiles, birds and mammals. Error bands represented 95% confidence intervals. Significant associations are marked with a red “*” (see Supplementary Table 6 for the definitions of all socioeconomic variables and Supplementary Table 10 for coefficients of each variable in the global models for each taxonomic group). Note that ‘latitude’ was removed due to its high association with species richness (in the cases of vertebrates, amphibians, and reptiles) (Supplementary Table 19).

Extended Data Fig. 9 Partial response curves of the global models showing the relationships between socioeconomic variables and number of the type 1 (the crime of illegal hunting) and type 2 verdicts (the crime of illegal hunting and killing imperiled wildlife) in each prefecture for each taxonomic group.

a: type 1 verdicts; b: type 2 verdicts. Error bands represented 95% confidence intervals. Significant associations are marked with a red “*” (see Supplementary Table 6 for the definitions of all socioeconomic variables and Supplementary Tables 8-9 for coefficients of each variable in the global models for each taxonomic group). Note that ‘latitude’ was removed due to its high association with species richness (in the cases of China-listed reptiles) (Supplementary Table 19).

Extended Data Fig. 10 The predicted geographic patterns of the number of court verdicts and the number of animals illegally hunted per km2 in 336 Chinese prefectures.

Figures in columns a-c show the predicted number of court verdicts (scaled to 0-1 by the maximum values) per 1000 km2 based on the top models for all verdicts (a), type 1 verdicts (b), and type 2 verdicts (c), scaled to 0-1 by the maximum values (for models, see Supplementary Tables 7−9). Maps in column d show the predicted number of animals hunted per 1,000 km2, scaled to 0-1 by the maximum values and color scale is on log (for top models, see Supplementary Table 10). From top to bottom, rows show the four vertebrate groups combined, amphibians, reptiles, birds, and mammals.

Extended Data Fig. 11 The differences between the observed and predicted number of court verdicts (all verdicts, the type 1 verdicts, and the type 2 verdicts) and total animals illegally hunted per 1000 km2 in 336 Chinese prefectures.

Figures in columns a-c show the differences between the observed and predicted number of court verdicts per 1,000 km2 for all verdicts (a), type 1 verdicts (b), and type 2 verdicts (c). Maps in column d show the differences between the observed and predicted number of individual animals hunted per 1000 km2. From top to bottom, rows show the four vertebrate groups combined; amphibians; reptiles; birds; and mammals. Blue colors indicate prefectures where the observed number of verdicts (or individuals) is greater than the predicted number of verdicts (or individuals), while red colors indicate prefectures where the observed number of verdicts (or individuals) is smaller than the predicted number of verdicts (or individuals).

Extended Data Fig. 12 Prefectures shown in orange (n = 25) are where D.L. observed shorebird hunting while conducting fieldwork along the China coasts.

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Liang, D., Giam, X., Hu, S. et al. Assessing the illegal hunting of native wildlife in China. Nature 623, 100–105 (2023). https://doi.org/10.1038/s41586-023-06625-0

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