The global wildlife trade network is a massive system that has been shown to threaten biodiversity, introduce non-native species and pathogens, and cause chronic animal welfare concerns. Despite its scale and impact, comprehensive characterization of the global wildlife trade is hampered by data that are limited in their temporal or taxonomic scope and detail. To help fill this gap, we present data on 15 years of the importation of wildlife and their derived products into the United States (2000–2014), originally collected by the United States Fish and Wildlife Service. We curated and cleaned the data and added taxonomic information to improve data usability. These data include >2 million wildlife or wildlife product shipments, representing >60 biological classes and >3.2 billion live organisms. Further, the majority of species in the dataset are not currently reported on by CITES parties. These data will be broadly useful to both scientists and policymakers seeking to better understand the volume, sources, biological composition, and potential risks of the global wildlife trade. Resources in this dataset:Resource Title: United States LEMIS wildlife trade data curated by EcoHealth Alliance (Version 1.1.0) - Zenodo. File Name: Web Page, url: https://doi.org/10.5281/zenodo.3565869 Over 5.5 million USFWS LEMIS wildlife or wildlife product records spanning 15 years and 28 data fields. These records were derived from >2 million unique shipments processed by USFWS during the time period and represent >3.2 billion live organisms. We provide the final cleaned data as a single comma-separated value file. Original raw data as provided by the USFWS are also available. Although relatively large (~1 gigabyte), the cleaned data file can be imported into a software environment of choice for data analysis. Alternatively, the assocated R package provides access to a release of the same cleaned dataset but with a data download and manipulation framework that is designed to work well with this large dataset. Both the Zenodo data repository and the R package contain a metadata file describing each of the data fields as well as a lookup table to retrieve full values for the abbreviated codes used throughout the dataset. Contents: lemis_2000_2014_cleaned.csv: This file represents the compiled, cleaned LEMIS data from 2000-2014. This data is identical to the version 1.1.0 dataset available through the lemis R package. lemis_codes.csv: Full values for all coded values used in the LEMIS data. Identical to the output from the lemis R package function "lemis_codes()". lemis_metadata.csv: Data fields and field descriptions for all variables in the LEMIS data. Identical to the output from the lemis R package function "lemis_metadata()". raw_data.zip: This archive contains all of the raw LEMIS data files that are processed and cleaned with the code contained in the 'data-raw' subdirectory of the lemis R package repository.Resource Software Recommended: R package,url: https://github.com/ecohealthalliance/lemis

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 Unite..., 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 ..., , Variable descriptions:

Year = year when the recorded trade occurred.

Class = taxonomic class of traded taxa.

Order = taxonomic order of traded taxa.

Family = taxonomic family of traded taxa.

Genus = taxonomic genus of traded taxa.

CITES_Taxon = scientific name ascribed to traded taxa by CITES.

HBW_BL_binomial = Genus and species names of traded taxa based on the Handbook of Birds of the World and BirdLife Taxonomic Checklist v6.

HBW_BL_Genus = Genus of traded taxa based on the Handbook of Birds of the World and BirdLife Taxonomic Checklist v6.

HBW_BL_Species = Species of traded taxa based on the Handbook of Birds of the World and BirdLife Taxonomic Checklist v6.

HBW_BL_Common = Common name of traded taxa based on the Handbook of Birds of the World and BirdLife Taxonomic Checklist v6.

App. = CITES Appendix ascribed to traded taxa.

2021_IUCN_category = IUCN Red List classification based on the 2021 update.

Importer = ISO2 Alpha code for importing CITES party.

Exporter = ISO2...

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.

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

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.

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.

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

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

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.

🇬🇧 영국

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

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

Supplementary Summary 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 SummaryFileDetails.txt. Please refer to the main publication and connected DOIs for details on data generation.

CITES WCMC Trade database (2010-2014) :

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.

Biological invasions rank among the top five threatening factors affecting biodiversity, but ongoing changes in climate and land cover might exacerbate risks. We used species distribution models for 609 traded bird species on the CITES list to examine the combined effects of projected climate change and land-cover change worldwide on the potential range expansion of bird species with commercial value as pets. The maps of potential invasion (may be inferred as the invasion risk) have been provided in the main manuscript and here, the potential invasion dataset for the current and future times is provided including the species distribution maps, all as GeoTiff files. The maps for the future time are provided for different future years and over a range of climate scenarios (SSP245, SSP370, and SSP585). Methods The data are the outcomes of the species distribution models (SDMs), trained by using 609 species data (known to be traded from appendix II of CITES [Convention on International Trade in Endangered Species] database; available online at https://trade.cites.org) and the Worldclim climate dataset (version 2.1). The sdm R package (https://onlinelibrary.wiley.com/doi/full/10.1111/ecog.01881) was used to fit the models and generate the ensemble of predictions (for the current time) and projections (for the future times). The details are provided in the main manuscript published in Global Change Biology. The zip files contain the species distribution maps for each individual species (for the current and future times), and the individual GeoTiff files (not those that are within the zip files) are the maps based on combining all the species to generate potential invasion risk (also presented in the manuscript).

The datasets used to construct Figure 1 in the manuscript "Overtrading of widespread generalist amphibians is a global biodiversity time-bomb" written by Amael Borzee and me. The raw datasets composed of three tables, by which Table 1 and 2 provided here included the datasets of international amphibians trade between China and other countries were retrieved from CITES (https://trade.cites.org/). The data collected the record of international amphibians trade between 1986 and 2019. There is also a dataset of domestic illegal amphibians trade that was collected from court cases in China related with poaching of amphibians, retrieved from “China judgement online” (https://wenshu.court.gov.cn). The court cases were all reported between 2014 and 2022.

Table S1. Table of the Indian Star Tortoise trade transactions (1975–2013): Explanation note: Table to show the Indian Star Tortoise trade transactions (1975-2013) as recorded by the Convention on International Trade in Endangered Species of Wild Fauna and Flora World Conservation Monitoring Centre (CITES WCMC) database (http://trade.cites.org/).