Context

The Convention on International Trade in Endangered Species of Wild Fauna and Flora, or CITES for short, is an international treaty organization tasked with monitoring, reporting, and providing recommendations on the international species trade. CITES is a division of the IUCN, which is one of the principal international organization focused on wildlife conversation at large. It is not a part of the UN (though its reports are read closely by the UN).

CITES is one of the oldest conservation organizations in existence. Participation in CITES is voluntary, but almost every member nation in the UN (and, therefore, almost every country worldwide) participates. Countries participating in CITES are obligated to report on roughly 5000 animal species and 29000 plant species brought into or exported out of their countries, and to honor limitations placed on the international trade of these species.

Protected species are organized into three appendixes. Appendix I species are those whose trade threatens them with extinction. Two particularly famous examples of Class I species are the black rhinoceros and the African elephant, whose extremely valuable tusks are an alluring target for poachers exporting ivory abroad. There are 1200 such species. Appendix II species are those not threatened with extinction, but whose trade is nevertheless detrimental. Most species in cites, around 21000 of them, are in Appendix II. Finally, Appendix III animals are those submitted to CITES by member states as a control mechanism. There are about 170 such species, and their export or import requires permits from the submitting member state(s).

This dataset records all legal species imports and exports carried out in 2016 (and a few records from 2017) and reported to CITES. Species not on the CITES lists are not included; nor is the significant, and highly illegal, ongoing black market trading activity.

Content

This dataset contains records on every international import or export conducted with species from the CITES lists in 2016. It contains columns identifying the species, the import and export countries, and the amount and characteristics of the goods being traded (which range from live animals to skins and cadavers).

For further details on individual rows and columns refer to the metadata on the /data tab. A much more detailed description of each of the fields is available in the original CITES documentation.

Acknowledgements

This dataset was originally aggregated by CITES and made available online through this downloader tool. The CITES downloader goes back to 1975, however it is only possible to download fully international data two years at a time (or so) due to limitations in the number of rows allowed by the data exporter. If you would like data going further back, check out the downloader. Be warned, though, this data takes a long time to generate!

This data is prepared for CITES by UNEP, a division of the UN, and hence likely covered by the UN Data License.

Inspiration

• What is the geospatial distribution of the international plant/animal trade?
• How much export/import activity is there for well-known species, like rhinos, elephants, etcetera?
• What percent of the trade is live, as opposed to animal products (ivory, skins, cadavers, etcetera)?

The global legal wildlife trade is worth US$4-20 billion to the world’s economy every year. Raptors frequently enter the wildlife trade for use as display animals, by falconers or hobbyists for sport and recreation. Using data from the Convention on International Trade in Endangered Species of Wild Fauna and Flora’s (CITES) Trade Database, we examined trends in the global, legal commercial trade of CITES-listed raptors between 1975-2020. Overall 272 raptor species were traded, totalling 188,149 individuals, with the number of traded raptors increasing over time. Hybrid Falcons (N = 50,366) were most commonly traded, comprising more than a third of the global diurnal CITES-listed raptor trade, followed by Gyrfalcons (Falco rusticolus; N = 30,510), Saker Falcons (F. cherrug; N = 21,679), Peregrine Falcons (F. peregrinus; N = 13,390) and Northern White-faced Owls (Ptilopsis leucotis; N = 6,725). More than half of wild-caught diurnal raptors were classified as globally threatened. The United Kingdom was the largest exporter of live raptors and the United Arab Emirates was the largest importer. More affluent countries were likely to import more raptors than those less affluent. Larger-bodied diurnal species were traded more relative to their smaller-bodied conspecifics. Following the introduction of the European Union’s Wild Bird Trade Ban in 2005, the number of traded wild-caught raptors declined. Despite its limitations, the CITES Trade Database provides an important baseline of the legal trade of live raptors for commercial purposes. However, better understanding of illegal wildlife trade networks and smuggling routes, both on-the-ground and online, are essential for future conservation efforts. Methods Trade data for all CITES-listed raptor species were downloaded from the open-access CITES Trade Database (https://trade.cites.org/) on 21st November 2021, using a compiled Comparative Tabulation Table from UNEP/WCMC. The following search terms were used to filter the CITES trade data: “Year Range” was set to include all records between 1975 to 2020, “Source” was set to “ALL”, “Purpose” was set to “COMMERCIAL” denoted by the letter (T) and “Trade Terms” was set to “LIVE” which filtered trade records for only live birds. The “Source” variable relates to the original source of the specimens traded (CITES Secretariat and UNEP-WCMC 2022) and allows the data set to be subset by, but not limited to: specimens bred in captivity (denoted by the letter “C”), specimens bred in captivity for commercial purposes (“D”), specimens taken from the wild (“W”) and ranched specimens including those that are reared in a controlled environment, taken as eggs or juveniles from the wild, which would otherwise have a low chance of survival to adulthood (“R”) (CITES Secretariat and UNEP-WCMC 2022). For the purposes of this study, we combined all records where the “Source” was denoted either by: “C” or “D” and hereafter refer to these as ‘Captive-bred’, and “W” or “R” hereafter ‘Wild-caught’. Taxonomic filtering was applied using the “Search by Taxon” function. The taxonomic orders “FALCONIFORMES” and “STRIGIFORMES” were used to obtain trade records for species within these orders. Species within the families Accipitridae, Cathartidae, Falconidae, Panionidae and Sagittariidae are pooled under the order “FALCONIFORMES” on the CITES Trade Database, and those in the families Strigidae and Tytonidae are pooled under the order “STRIGIFORMES”. For consistency between subgroups, all taxonomies were standardised to species-level, following the Handbook of Birds of the World and BirdLife Taxonomic Checklist v6 (http://datazone.birdlife.org/species/taxonomy). Following geopolitical changes since 1975, we pooled trade records under the former “Serbia and Montenegro” (denoted by ISO Alpha-2 code: CS ex-YU) with records under “Serbia” (RS). Other geopolitical country name changes included “Former Czechoslovakia” (non-ISO code ZC), “Former East Germany” (DD) and “Former Soviet Union” (SU). Trade records under these names were pooled with data for “Czech Republic” (CZ), “Germany” (DE) and “Russian Federation” (RU), respectively.

The data used in this dataset was retrieved from the CITES Trade Database at "trade.cites.org". This dataset contains exporter-reported directional trade records for CITES-listed reptiles between 2000 and 2020. This dataset only contains trade records with the source code (C, D, F, R, W, and X) and purpose code (P or T). All records were converted to whole organism equivalents.

Up to date and Complete Collection of the CITES Wildlife Trade Database .csv files from 1975 to 2022. Files are in order but are still RAW

Data and code used in the paper: Searching the web builds fuller picture of arachnid trade. Throughout the methods we have indicated the stage of analysis each data component was used and the code script connected. We have numbered to code and data supplements to reflect as closely as possible the order in which data generation and summary was undertaken. The following provide additional details linked to each of the data files.

Data S1 - Website data: lang = language of the search engine used, ad hoc websites had language described after discovery; engine = the search engine used; page = the page on which the website appeared from the search engine; searchdate = search date in YYYY-mm-dd HH:MM:SS; link = link to the webpage, redacted to protect website identity; reviewdate = date revewied for arachnids being sold and search strategy; sells = whether the website sells arachnids (1 == sells); allow = whether the site explcicilt forbids automated searching (1 == allows, NA when search method was not fully automated, e.g., single page); type = the type of the website (e.g., trade, classified ads); order = whether arachnids where organised in a particular ways; target = a refined target URL to start search; method = the search method chosen, see methods for details; refine = any refinement or filter than could constrain the scope of the website to be searched; spages = the number of pages required to cycle through to cover the entire stock (also separated by ; if multiple cycles where needed or multiple single pages could be easily collected); prelimCheck = whether the website passed initial checks for arachnid selling; notes = any details that might need special attention during searches; webID = code used for subsequent data summary.

Data S2 - Raw keyword searches outputs: species keywords. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (only applies to Data S3); webID = the website ID.

Data S3 – Raw keyword searches outputs: genus keywords. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (multiple detections separated by ;); webID = the website ID.

Data S4 - Raw keyword search outputs: temporal sample. sp = the modern species or genus that a keyword is associated with; page = the number of the page the keyword was detected on; keyw = the exact keyword that was detected; spORgen = whether the keyword was a species binomial or just genus; termsSurrounding = the words surrounding a genus keyword detection (multiple detections separated by ;); webID = the website ID; timestamp.parse = the timestamp extracted from the archived web page; year = a simplified timestamp including only the year.

Data S5 - LEMIS data used. An arachnid filtered version of 74,75.

Data S6 - CITES trade database data used 76.

Data S7 - CITES appendices data used 77.

Data S8 - IUCN Redlist data used 78.

Data S9 - Compiled final dataset, with data deriving from WSC, Scorpion files, ITIS, WAM and the data collection process. speciesId = a numeric code, one per species; clade = the clade the species belongs to; family = the family the species belongs to; genus = the genus of the species; species = the species epithet; author = the species authority name; year = the species authority year; parentheses = whether parentheses are needed with the authority; distribution = WSC original distribution descriptions; invalid = whether the species is considered valid; source = the species source, either World Spider Catalogue, Scorpion files, ITIS or WAM; accName = the species binomial being used as our accepted name; allNames = the accepted species binomial and all synonyms; allGenera = the accepted genus, and all other genera the species has belonged to at one point; onlineTradeSnap = whether the species was detected via a match to the accName in the snapshot data; onlineTradeSnap_Any = whether the species was detected via any synonym in the snapshot data; onlineTradeSnap_genus = whether the genus was detected via a match to the genus in the snapshot data; onlineTradeSnap_genusAny = whether the genus was detected via any synonym in the snapshot data; onlineTradeTemp = whether the species was detected via a match to the accName in the temporal data; onlineTradeTemp_Any = whether the species was detected via any synonym in the temporal data; onlineTradeTemp_genus = whether the genus was detected via a match to the genus in the temporal data; onlineTradeTemp_genusAny = whether the genus was detected via any synonym in the temporal data; onlineTradeEither = whether the species was detected via a match to the accName in the temporal data or snapshot data; onlineTradeEither_Any = whether the species was detected via any synonym in the temporal data or snapshot data; LEMIStrade = whether the species was detected via a match to the accName in the LEMIS data; LEMIStrade_Any = whether the species was detected via any synonym in the LEMIS data; LEMIStrade_genus = whether the genus was detected via any synonym in the LEMIS data; LEMIStrade_genusAny = whether the genus was detected via any synonym in the LEMIS data; CITEStrade = whether the species was detected via a match to the accName in the CITES trade database data; CITEStrade_Any = whether the species was detected via any synonym in the CITES trade database data; CITEStrade_genus = whether the genus was detected via any synonym in the CITES trade database data; CITEStrade_genusAny = whether the genus was detected via any synonym in the CITES trade database data; CITESapp = the CITES appendix the species is listed under using an exact match to the accName; CITESapp_Any = the CITES appendix the species is listed under using any match to any of the species’ synonyms; redlist = the IUCN Redlist category the species is listed under using an exact match to the accName; redlist_Any = the IUCN Redlist category the species is listed under using any match to any of the species’ synonyms; extactMatchTraded = the species is detected in any of the trade sources via a match to the accName; anyMatchTraded = the species is detected in any of the trade sources via a match to any species’ synonym.

Data S10 - Forum listings of “What species are you currently keeping” from an online fora posted between 9th September 2021 and 9th October 2021, to provide an idea of online discussions. Each user with a separate list is provided in a separate tab. Morph_collector is the same as poster1, but the potential cryptic species or morphs are noted separately to make them clearer.

Data S11 – Distribution information for spiders. Only two columns used in summaries: accName = the accepted name used throughout summaries; NAME = the country name the spider occurs in.

Data S12 - Distribution information for scorpions. species = the accepted name used throughout summaries; NAME = the country name the scorpions occurs in.

Code S1 - Search URL Extract.R

Code S2 - Retrieve web data.R

Code S3 - Temporal Classified Ads.R

Code S4 - Keyword Generation.R

Code S5 - Keyword Search.R

Code S6 - LEMIS filter and summary.R

Code S7 - Compiling results.R

Code S8 - Summary Figures.R

Code S9 - Temporal Figures.R

Code S10 - New description figure.R

Code S11 - Term exploration.R

Code S12 - LEMIS summary and mapping.R

Annual reports on illegal wildlife trade (report on annual seizures) under Council Regulation (EC) No 338/97 on the protection of species of wild fauna and flora by regulating trade therein (CITES) as amended by Reporting Alignment Regulation 2019/1010

The global illegal exotic pet trade is a major driver of biodiversity loss, particularly affecting reptile species listed under the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendix II. CITES is an international agreement used to monitor the import and export of listed fauna and flora, ensuring trade is not detrimental to the survival of listed species in the wild. This study investigates South Africa’s role in the international trade of reptiles, using the endemic Sungazer lizard (Smaug giganteus) as a case study. Analysis of CITES Trade Database records reveals systemic reporting inaccuracies, including discrepancies between importer and exporter data, misuse of source and purpose codes, and evidence of wildlife laundering. Trade peaks in 1988 and 2013 correspond to increased species visibility due to CITES listing and cultural associations with popular media, respectively, illustrating the influence of anthropogenic and media-driven factors on global wildlife trade demand. Evidence of potentially illegal exports from non-range countries and suspicious declarations of captive-bred specimens suggests that South Africa may be both a source and transit country in illicit reptile trade networks. The lack of enforcement, coupled with the ease of smuggling, exacerbates the threat to S. giganteus and similar threatened reptile species. These findings underscore the urgent need to strengthen CITES data reliability, enforce trade regulations, and enhance protection for South African reptile species vulnerable to overexploitation.

Llc Cites Project Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

The WLRS (Wildlife Licensing and Registration Service) A60 Database (CITES) is used to monitor the control of trade in endangered species.

Unsustainable trade in big cats affects all species in the genus, Panthera, and is one of the foremost threats to their conservation. To provide further insight into the impact of policy interventions intended to address this issue, we examine the case study of the Republic of Korea (South Korea), which in the early 1990s was one of the world’s largest importers of tiger (Panthera tigris) bone and a major manufacturer of tiger-derived medicinal products. In 1993, South Korea became a Party to the Convention on International Trade in Endangered Species (CITES) and introduced a ban on commercial trade in CITES Appendix I-listed big cats a year later. We used an expert-based questionnaire survey and an exploration of the CITES trade database to investigate what has since happened to big cat trade in South Korea. Expert opinion suggested that big cat trade has likely substantially reduced since the early 1990s, as a result of the trade ban and broad socioeconomic changes. However, illegal trade has not been eradicated entirely and we were able to confirm that products reportedly derived from big cats were still publicly available for sale on a range of Korean online marketplaces, sometimes openly. The items most commonly reported by respondents from post-1994 trade and supported by expert-led evidence were tiger and leopard (Panthera pardus) skins and tiger bone wine. Although South Korea may provide a useful case study of a historically significant consumer country for tiger which has made strong progress in addressing unsustainable levels of big cat trade within a short period of time, there remains a need to address recalcitrant small-scale, illegal trade. We also recommend further investigation regarding reports of South Korean nationals being involved in illegal trade in tiger-derived products in Southeast Asia.

We collected data on the wildlife trade of seven turtle and tortoise species endemic to Indonesia and Malaysia (Amyda cartilaginea, Batagur borneoensis, Cuora amboinensis, Carettochelys insculpta, Heosemys annandalii, Heosemys grandis, and Heosemys spinosa). We collated data for: the operations and economics of three breeding farms and one ranching facility; species life-history traits; and species international legal trade and confiscation data. We collected data for the facilities (one in Malaysia and three in Indonesia) using field visits and a semi-structured questionnaire. We conducted a literature review to compile relevant information on species’ life-history traits to estimate breeding viability. We downloaded species-specific data on international trade from the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Trade Database for the exporting countries (Malaysia and Indonesia) for 2000–2015. We compared legal trade with confiscation data obtained from CITES. The data in this article can provide insights into the operations of turtle breeding farms in Southeast Asia. The data can be used as a reference for the inspection of breeding farms and for legislative bodies to determine whether captive breeding for select turtle species is feasible.

Methods

Data on the facilities was obtained by the inspection of four breeding farms (one registered facility in Malaysia, and three facilities in Indonesia) and interview facility operators using a standardized questionnaire. We conducted a literature search for species life-history traits and downloaded data on international commercial trade from the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES): UNEP WCMC CITES Trade Database for the exporting countries (Malaysia and Indonesia) from 2000–2015. We compared data from legal trade with confiscation data obtained from CITES, CoP17 Doc annex 1.

Usage Notes

A readme file is provided.

Appendix B10 Species selected through the Review of Significant Trade (RST) stage 1a process (modified for Appendix III species). Quantities are in gross exports. Data extracted from the CITES Trade Database 2th March 2025.

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) was concluded on 3 March 1973 and entered into force on 1 July 1975. CITES provides a global framework for the legal and sustainable international trade in CITES-listed species. Today, CITES regulates trade in more than 36, 000 species of wild animals and plants. CITES is widely regarded as one of the most important international conservation instruments. Since 1975, the Conference of the Parties has adapted this framework to changing circumstances and, through the adoption of Resolutions and Decisions, has demonstrated an ability to construct practical solutions to increasingly complex wildlife trade and conservation challenges. The Conference of the Parties adopted its first strategic plan, the Strategic Vision through 2005, and an Action Plan at its 11th meeting (Gigiri, 2000). These were subsequently extended until the end of 2007 at the 13th meeting of the Conference of the Parties (Bangkok, 2004). Initially, at its 14th meeting (The Hague, 2007), and with amendments agreed at its 16th (Bangkok, 2013) and 17th (Johannesburg, 2016) meetings, the Conference of the Parties agreed a new Strategic Vision for CITES for the period 2008-2020. The agreed amendments describe the contribution of CITES’ activities to the achievement of the Strategic Plan for Biodiversity 2011-2020 and the relevant Aichi Biodiversity Targets adopted by the Parties to the Convention on Biological Diversity, as well as to the achievement of the 2030 Agenda for Sustainable Development, and its Goals and targets relevant to CITES. With this new Strategic Vision, the Conference of the Parties to CITES outlines the Convention’s direction for the 2021-2030 timeframe in fulfilment of its mandate. It is additionally recognized that Parties’ efforts to implement the Convention may also provide benefit to, and draw strength from, efforts being undertaken in other fora, and in this sense highlights the linkages between CITES and, inter alia, the processes and actions listed below: – the 2030 Agenda for Sustainable Development and its Sustainable Development Goals and targets relevant to CITES, including those for terrestrial and marine wildlife; – the Strategic Plan for Biodiversity 2011-2020 and the post-2020 Biodiversity Framework being developed by Parties to the Convention on Biological Diversity; – the findings of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services 2019 Global Assessment Report on Biodiversity and Ecosystem Services; and – relevant resolutions of the United Nations General Assembly. The CITES Strategic Vision provides a framework for the future development of the CITES Resolutions and Decisions and provides guidance on goals and objectives to be achieved. The Conference of the Parties, through its Resolutions and Decisions, will determine actions to be taken by Parties, the Committees or the Secretariat, as appropriate. The Strategic Vision also serves the Parties as a tool for the prioritization of activities, and decisions on how best to fund them, in light of the need for the efficient and transparent use of resources.

The number and volume of seizures of animals, plants and derivatives made per quarter under the Convention on International Trade in Endangered Species. This data set has been consolidated into that on 'Border Force transparency data' from 2015.

This statistic shows the share of CITES member parties by the maximum penalty possible for violation of regulations of CITES in 2015. The maximum penalty possible for a violation of a CITES regulation in thirty-one percent of CITE member party countries was only a fine in 2015.

The Convention on International Trade in Endangered Species of Wild Fauna and Flora

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) is a treaty designed to protect wild animal and plant species from extinction due to international trade. The treaty has been in effect since July 1, 1975. There are currently 182 member parties to the agreement and over 35,000 species covered under CITES traded as live animals or products derived from them.

Parties which have agreed to be bound to CITES are legally obligated to follow its regulations. The treaty provides a framework with which the member parties can create laws to ensure adherence within their nation. This framework is a licensing system through which all species covered under CITES must pass through in order to be imported or exported.

One of the shortcomings of CITES is that it approaches conservation from the angle of allowing unregulated trade in all species until they come under the review. This can allow a species to decline without hindrance in legal trade. With the expansion of wealth in Asian countries such as China, previously unthreatened species such as pangolin are now endangered.

Mammals, such as the pangolin are the most frequently seized category of the wild animal trade, while rosewood trees are the species most subject to illegal trade overall.

Legal imports and exports of live big cats (genus: Panthera) to South Korea,1994–2021.

Main Data for Marshall et al. Almost 30,000 wild species: how much do we really know about legal wildlife trade?, that aimed to describe the patterns present in 20 years of LEMIS wildlife import records.Summary: An analysis of 22 years of publicly accessible US wildlife trade data, of almost 30,000 species and over 2.85 billion individuals, provides insights into global trade trends and emphasises the importance of accessible trade data in biodiversity conservation efforts.Explanation of files and fields is in MainDataFileDetails.txt. Users of this data should be aware that the data still contains errors even after the correction of names and removal of outliers. Caution should be exercised when using and interpreting this trade dataset. The associated publication and supplementary discussion contains details on the limitations of this data. Further details on the cleaning of the pre-2014 data can be found Eskew, E. A., White, A. M., Ross, N., Smith, K. M., Smith, K. F., Rodríguez, J. P., ... & Daszak, P. (2020). United States wildlife and wildlife product imports from 2000–2014. Scientific Data, 7(1), 22. and Eskew, E. A., White, A. M., Ross, N., Smith, K. M., Smith, K. F., Rodríguez, J. P., ... & Daszak, P. (2019). United States LEMIS wildlife trade data curated by EcoHealth Alliance. Zenodo Dataset, 10. https://doi.org/10.5281/ZENODO.3565869

CITES WCMC Trade database (2010-2014) :

The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) was adopted in 1975 in an effort to manage the international biodiversity trade. Meetings regulating the implementation of CITES have since been held every 2-3 years with the involvement of diverse stakeholders representing country Party-signatories, non-Party states, international organizations, private sector interests, and NGOs. These meetings and their outcomes are of interest to environmental science scholars, social scientists, journalists, and advocacy organizations. Yet, no usable data on meeting attendees and their details exists. This limits researchers' and advocates' abilities to study or track CITES meeting attendance patterns, and their associated causes and effects. Applying NLP techniques to PDF attendance rosters, we build the first CITES attendee-level dataset, covering 18,686 attendee records for all meetings to date. The dataset contains rich information on attendee geo-locations, names, affiliations and genders, and variables associated with attendee delegations, among others. Summaries and validations underscore the promise of our data and suggest new avenues for research on international wildlife conservation.

Felid body parts that expert participants reported being traded in South Korea.