Legal and illegal wildlife trade 433
characteristics associated with the magnitude of trade. One of the challenges facing analyses of legal and illegal trade in CITES-listed species is the variation in reporting rates and enforcement over time; this is minimized here because we only analyse import and seizure data from one country, the USA, which has consistently submitted CITES annual reports over time. The USA also has the Law Enforcement Management Information System (LEMIS) database for re- cording trade in CITES-listed and non-listed species, and for recording imports (i.e. seizures) that do not comply with CITES, other USA domestic regulations, or regulations in other countries (through the Lacey Act). Extracts from the LEMIS database are then compiled into the CITES annual report for the USA. This makes it an ideal country for an analysis of wildlife trade, and in particular because it is one of the main global importers of wildlife and wildlife products, both legally and illegally (Petrossian et al., 2016; UNODC, 2016). We consider both legal trade and seizures, to analyse and
provide insight into howtheymay interact (althoughwe note that seizures do not equate to illegal trade, asmuch of the il- licit trade is undetected, and seizures can happen for various reasons). Our goal is to inform both current international discussions on legal and illegal wildlife trade, and to provide both an evidence base and a baseline for further analysis and material relevant to decision-making at CITES and at nation- al levels. The latter could include, for example, informing the ongoing work of the inter-agency task force that ensures the USA meets the obligations under its Eliminate, Neutralize, Disrupt Wildlife Trafficking Act of
2016.In late 2017 this task force announced an evidence-based list of focus coun- tries (U.S. Department of State, 2018) with which it will work collaboratively to combat wildlife trafficking; this list is expected to be revised and updated over time.
Methods
Trade data Data were extracted from the CITES Trade Database, managed by the United Nations Environment Programme– World Conservation Monitoring Centre on behalf of the CITES Secretariat, which compiles all international wildlife trade data submitted by national CITES authorities in their annual reports to the Convention (see details in UNEP- WCMC, 2013). This database contains data on reported legal trade and seizures into the USA of CITES-listed species over a period of 36 years. There are limitations to these data. Firstly, species not listed under CITES are not included, which, among others, excludes numerous species of reptiles destined for the USA pet trade (Robinson et al., 2015), and manyspeciesofplants tradedforpurposes suchasornamental gardens (Hinsley et al., 2017). Secondly, as the objective of
CITES is to regulate international wildlife trade, trade within national borders ofCITES-listed species is unregulated unless relevant national legislation is inplace (for example, since July 2016, there has beena near totalban onthedomestic commer- cial trade in African elephant ivory across states within the USA; UNODC, 2016). In our analysis we used all available USA trade records
of legal and seized imports recorded in the CITES Trade Database before 4 July 2016 and spanning 1979–2014. Trade data were extracted in the form of ‘comparative tabulation’, which aggregates shipments by year containing the same species and where all other parameters match, as explained in the CITES Trade Database manual (UNEP- WCMC, 2013) and in Robinson & Sinovas (2018). Our analysis uses only data reported by the USA (i.e. importer- reported quantities), and includes re-exports (i.e. cases where wildlife products are imported into the USA from a country that differs from the species’ country of origin). Although comparing importer-reported to exporter-reported data can provide insights should discrepancies be present, given the numerous reasons and interpretations for such disparities (Robinson & Sinovas, 2018), here we focus only on the importer-reported data. For each record we extracted information on (1) year of
the trade or seizure event, (2) taxonomy (i.e. class, order, family, genus and species), (3) exporting country, (4) coun- try of origin (reporting origin was inconsistent, and thus not considered in our analysis), (5) quantity of product, (6) unit (e.g. t, number), (7) type of product (e.g. live, processed leather, hunting trophy), and (8) product source (e.g. wild- caught, bred in captivity, illegally sourced/seized). The CITES Trade Database contains a range of different units (i.e. numbers, weights, volumes and lengths) as is appropri- ate for the specific record, with data converted in various ways before further analysis (Supplementary Table 1). We used only records measured in either weight or numbers in our analysis, as measures of volume and length were very sparse for all species groups in the dataset, except plants. As a result, the majority of timber products are excluded from the analysis. This reduced the number of products from 94 to 82 (Supplementary Table 2). We analysed data across all 82 types of products and
focus on four product types with both high relevance to conservation and the greatest quantity of data: (1) live animals and plants, (2) processed leather products (i.e. not unprocessed skins or pelts), (3) meat (for consumption), and (4) trophies (Supplementary Tables 2 & 3). Species were grouped into 21 taxonomic units (Supplementary Table 2). The analysis included individual exporting coun- tries as well as UN Environment Programme regions (Brooks et al., 2016). IUCN Red List status was appended to records using the package rredlist (Chamberlain, 2018) in R (R Core Team, 2017) and the 2018-2 version of the Red List (IUCN, 2018).
Oryx, 2021, 55(3), 432–441 © The Author(s), 2019. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605319000541
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