Research Article

# Global wildlife trade across the tree of life

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Science  04 Oct 2019:
Vol. 366, Issue 6461, pp. 71-76
DOI: 10.1126/science.aav5327

## A heavy toll

Trade in wildlife, and their parts, is well recognized for a few key species, such as elephants and rhinos, but it occurs globally, across a wide array of species. Scheffers et al. looked across tens of thousands of vertebrate species and found that one in every five species is affected by trade of some sort. The impacts of trade tend to be concentrated in certain phylogenetic groups, thus the potential for long-term impact on certain lineages is substantial. This analysis allows for prediction of potential for trade where it does not yet occur, facilitating proactive prevention.

Science, this issue p. 71

## Abstract

Wildlife trade is a multibillion dollar industry that is driving species toward extinction. Of >31,500 terrestrial bird, mammal, amphibian, and squamate reptile species, ~24% (N = 7638) are traded globally. Trade is strongly phylogenetically conserved, and the hotspots of this trade are concentrated in the biologically diverse tropics. Using different assessment approaches, we predict that, owing to their phylogenetic replacement and trait similarity to currently traded species, future trade will affect up to 4064 additional species—totaling 11,702 species at risk of extinction from trade. Our assessment underscores the need for a strategic plan to combat trade with policies that are proactive rather than reactive, which is especially important because species can quickly transition from being safe to being endangered as humans continue to harvest and trade across the tree of life.

The tree of life is being pruned by human activities at an unprecedented rate (1). Yet, although we understand the global footprint of land degradation and deforestation and how that manifests in species loss (2), we have a limited understanding of the global extent and patterns of the wildlife trade. The trade of wildlife for luxury foods and medicinal parts and as pets is now so substantial that it represents one of the most prominent drivers of vertebrate extinction risk globally (3). Each year, billions of wild plants and animals are traded to meet a rapidly expanding global demand (4, 5), a demand so insatiable that, globally, US$8 billion to$21 billion is reaped annually from the illegal trade, making it one of the world’s largest illegitimate businesses (5, 6).

The high demand for wildlife products and pets has driven pronounced losses in enigmatic species such as tigers, elephants, rhinos, and poison dart frogs (7). Some subspecies are already extinct [e.g., the last Javan rhino, Rhinoceros sondaicus annamiticus, was shot for its horn in 2010 in Vietnam (8)] or on the cusp of extinction in the wild (e.g., Bali myna, Leucopsar rothschildi)—all because of trade. There is an insidious aspect to this market force in that these emblematic species only represent a tiny, though well publicized, fraction of animal species traded. If cultural preferences change, wildlife trade can rapidly drive a species toward extinction. For instance, the emergence of widespread demand in East Asia for pangolin scales and meat has triggered major declines in some species [e.g., Sunda pangolin (Manis javanica)] in just two decades (9), and growing demand for the ivory-like casque of the helmeted hornbill (Rhinoplax vigil) resulted in tens of thousands of birds traded annually since around 2012 (10). Both species are now critically endangered (11). Moreover, wildlife trade indirectly places substantial pressure on biodiversity through the introduction of pathogens, including the globally lethal amphibian fungus Batrachochytrium dendrobatidis (12), and invasive species, such as the Burmese python (Python bivittatus) in Florida, USA (13).

## What species are traded?

Trade in wildlife affects ~24% of all extant terrestrial vertebrate species on Earth. Our assessment shows that 7638 of the 31,745 vertebrate species have been reported as traded, with a higher percentage of all birds (45% of 10,278 species) and mammals (23% of 5420 species) globally traded than reptiles (13% of 9563 species) and amphibians (8% of 6484 species) (Fig. 1 and table S1). Our assessment across the CITES and IUCN databases yields a total that is 26 to 147% higher than prior recorded estimates [e.g., (3, 14, 15)]. Traded species are in higher categories of threat compared with nontraded species (especially among mammals and birds; Fig. 1 and table S2), confirming wildlife trade as a driver of extinction risk.

We found that trade occurs in 75% of all terrestrial vertebrate families (393 of 526 families; table S1). This pattern is evident across all terrestrial vertebrate groups considered, with birds and mammals showing the highest percentage of families traded (birds: 79%, N = 192; mammals: 78%, N = 106), followed by reptiles (74%, N = 54) and amphibians (55%, N = 41). Despite this broad exploitation, humans are targeting specific components of the tree of life (Fig. 2 and fig. S1), as indicated by a significant phylogenetic signal in wildlife trade for all taxa (fig. S2). Mammals showed a signal as strong as expected under a Brownian motion model of evolution (fig. S2), indicating higher levels of phylogenetic clustering relative to birds, reptiles, and amphibians (16). Highly traded families—those with >50% of their species traded—make up more than one third (35%; 182 of 526 families) of the total families, which breaks down to 43% of mammal (N = 59), 39% of bird (N = 94), 33% of reptile (N = 24), and 7% of amphibian (N = 5) families (tables S1 and S3).

Nonrandomness in trade across the tree of life implies high susceptibility for select clades, likely because of similar traits (such as voice quality, folklore, ivory, etc.). In exploring this, we found that large-bodied species are more traded than small-bodied species, a pattern that holds regardless of IUCN threat category (fig. S3 and table S4), and that the probability of being traded is positively related to body size (fig. S4). Over millennia, primitive human societies affected large-bodied species through hunting for subsistence, which changed contemporary biogeographical patterns of animal body size (17, 18). Our analysis shows that this pattern continues with modern humans through the wildlife trade.

Trade also targets species that are unique and/or distinctive in traits. In our assessment of evolutionary distinctiveness (a measure of phylogenetic isolation) (19), which may yield species with unique traits (19, 20), our results suggest that the probability of being traded increases with evolutionary distinctiveness (fig. S4), but the distribution of evolutionary distinctiveness did not differ between traded and nontraded species (fig. S5). Furthermore, mean family-wide evolutionary distinctiveness predicts the proportion of traded birds, mammals, and reptiles (fig. S6) (linear model: standardized coefficients = 0.43, 0.44, and 0.43, respectively, all P values < 0.01), but not for amphibians. Humans have long admired birds’ aesthetic attributes, including song and plumage complexity, and perhaps this long-standing admiration is reflected in the bird trade.

In total, on the basis of those species with a probability >95 and >90% in any one of the four assessment schemes described above, we predict that future trade will affect between 405 and 4064 additional species (Fig. 3 and table S5), amounting to between 230 and 1475 bird, 99 and 301 mammal, 4 and 329 amphibian, and 72 and 1959 reptile species, with a >95 and >90% probability of future trade, respectively. As a precaution, we recommend that conservation attention be given not just to currently traded species but also to those species with the highest probabilities of being targeted by trade in the future (see table S5 for the complete list of species and their probability of future trade).

## Where are the hotspots of traded species?

Although the footprint of trade spans all of Earth’s habitable continents, we uncovered a pantropical dominance in the trade for vertebrates (Fig. 4 and fig. S7). Biogeographical patterns in trade richness closely match patterns in species richness (Fig. 4 and table S6). South America, Central to Southeast Africa, the Himalayas, Southeast Asia, and Australia are the main epicenters of the wildlife trade, containing areas with the highest numbers of traded species (i.e., top 5 and 25% richest cells in trade) (Fig. 4 and fig. S7).

## Tackling global wildlife trade

Species possessing rare phenotypes, such as conspicuous plumage color, body shape and size, behavior, and/or (perceived) medicinal value tend to bring high market prices. Trade follows a rarity-value feedback model, whereby increasing rarity drives both higher demand and higher prices for a species (22, 24), with this positive feedback loop shown in both legal and illegal wildlife trade. For example, in Europe, CITES-listed pets command a higher price than non–CITES-listed species (24). Trade also quickly shifts to conspecifics as the availability of a targeted species declines, which likely explains why we uncovered a strong phylogenetic signal in the trade of all vertebrate groups (fig. S2). For instance, as Asian pangolin species decline, they are increasingly replaced in trade by African pangolins, with strength of demand for African pangolin meat and scales in Asia now high despite a relative price increase of 211% versus 4.6% baseline inflation (25). On the basis of identified morphological and phylogenetic correlates of trade, we predict an increase between 5 and 53% (probabilities >95 and >90%, respectively) in the total number of traded vertebrate species (Fig. 3 and table S5), which amounts to as many as 11,702 species at risk of current and future trade.

That trade tracks cultural [e.g., the Harry Potter–inspired trade of owls in Asia (26)] and economic vogue suggests that abundant species may not be safe. Often, species are flagged for conservation only after a severe decline is documented [e.g., pangolins (25)]. Our study offers two possible rectifications of this issue.

First, with the strong predictive strength of phylogeny revealed in our analysis, we can circumvent cryptic, yet-to-come declines by flagging species that are currently of little concern but have a high likelihood of being traded in the future because of their evolutionary proximity to traded species (Fig. 3 and table S5). For instance, some highly colorful bird groups at high risk of future trade include Zosterops white-eyes (n = 55), Serinus finches (n = 31), and Turdus thrushes (n = 31), whereas Rhinella beaked toads (n = 58) and Sciurus tree squirrels (n = 20) are the highest-risk amphibian and mammal genera, respectively. Reptiles yielded the largest number of species at risk of future trade. Here, Liolaemus iguanian lizards (n = 229), Atractus (n = 135) and Tantilla (n = 61) colubrid ground snakes, Bothrops (n = 40) pit vipers, and Lycodon wolf snakes (n = 48) are all genera at high risk of future trade. We caution, however, that our identification of a species as potentially traded in the future does not reveal the potential trade volume of this species.

Second, the IUCN Red List, the largest assessor of species threat for conservation, needs to ensure that any evidence of trade is recorded in species threat accounts, regardless of current IUCN status. For example, we found that IUCN indicates 4545 traded species omitted by CITES, whereas CITES indicates an additional 723 traded species omitted by IUCN (fig. S8). In turn, future IUCN assessments would benefit from different analytical approaches that incorporate extinction risk from trade [e.g., (21, 27)], as well as increased communication among all conservation groups that document and monitor trade (27).

More broadly, our global assessment of wildlife trade underscores the need for a strategic plan to combat trade. Evolutionary history’s ability to predict trade suggests that policies can be proactive rather than reactive in approach. Both online black markets and mainstream marketplaces, such as eBay or Facebook (28), facilitate a large volume of transactions with few regulations to stifle trade activity. Advanced machine-learning computer systems can be used by vendors to monitor and stem this activity (29, 30). Stricter penalties to merchants of trade, as well as consumer pressure for more sustainable and cheaper alternatives [e.g., humanely harvested horn from the least-rare rhino species (31)], may hasten the adoption of these techniques. Our comprehensive list of traded and at-risk species can inform these computerized search systems.

Our global maps of trade hotspots are an important first step in prioritization. The identification of many tropical regions as epicenters of traded species diversity suggests that combating the surge of illegal wildlife trade will likely require action at the local community level (32), combined with targeting key countries that import and export wildlife (33), especially those countries in hotspot areas that share continuous borders (34). In many areas, hunting for wildlife trade occurs out of sheer necessity—that is, it occurs in impoverished areas where harvesting wildlife to sell to middlemen represents the only source of cash income (32). Borrowing from other programs to halt criminal trading of humans, arms, and drugs, wildlife trade policies would gain strength if they were linked to transnational agreements such as the United Nations Collaborative Programme on Reducing Emissions from Deforestation and Forest Degradation (REDD). This may also offer economic incentives for protection rather than exploitation within local communities. For instance, carbon-trading schemes could increase the value of carbon in areas that are combating wildlife trade—with the ecological co-benefit of areas that maintain large-bodied vertebrates yielding higher carbon stocks over the long term (35).

## Methods summary

We compiled information on traded birds, mammals, amphibians, and squamate reptiles using the CITES list and IUCN Red List. We identified species traded through the IUCN application programming interface platform and IUCN web interface and classified each as being traded as pets and/or products (see supplementary materials for details). We superimposed range maps of all species in a 110 km × 110 km global grid and recorded species presence or absence in each cell. We determined total (pet and product) trade richness as the number of traded species in each cell. We defined hotspots as the upper 25% and upper 5% richest cells for traded species and assessed the correlation between spatial patterns in total, traded, and threatened species richness.

We used updated time-calibrated species-level phylogenetic trees for each vertebrate group from which we obtained one maximum clade credibility tree, and we used these trees in downstream analyses. We tested whether closely related species are traded more than random ones using the D-statistic. We used phylogenetic analysis of variance to test whether traded and nontraded species differ in body size and evolutionary distinctiveness, and phylogenetic logistic regression to test whether these traits influence the probability of a species being traded. We determined risk of future trade by (i) identifying for each nontraded species the proportion of all species traded in their respective family and (ii) for each nontraded species, averaging its phylogenetic distance with the 10 closest related species that are traded.

## Supplementary Materials

science.sciencemag.org/content/366/6461/71/suppl/DC1

Materials and Methods

Figs. S1 to S8

Tables S1 to S10

References (3662)

## References and Notes

Acknowledgments: We dedicate this paper to all researchers and park guards who have lost their lives in protecting wildlife from illegal trade. Funding: B.R.S. received financial support from the UF/IFAS Early Career Seed Grant. Author contributions: B.R.S., B.F.O., and D.P.E. conceptualized the idea and methodology, I.L. and B.F.O. collected data, B.F.O. analyzed data, B.R.S. and D.P.E. wrote the original draft, and all authors revised the work. Competing interests: None declared. Data and materials availability: All data related to this work are provided in the supplementary materials.
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