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.

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

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.

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.

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.

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

Annual reports on wildlife trade 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

CITES WCMC Trade database (2010-2014) :

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).

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/).

AbstractDNA barcoding has revolutionised the identification of illegally traded material of endangered species as it overcomes the lack of resolution encountered with morphological identification. Nonetheless, in recently evolved and highly diverse clades, such as the relatives of Aloe vera, the lack of interspecific sequence variation in standardised markers compromises the barcoding efficacy. We present a new DNA barcoding tool using 189 nuclear markers, optimised for aloes (Asphodelaceae, Alooideae). We built a comprehensive sequence reference dataset from taxonomically verified sources for >300 species and validated its reliability for identification using phylogenomic inference. Seven anonymised samples from verified botanical collections and ten plants seized at London Heathrow Airport were correctly identified to species level, including a critically endangered species from Madagascar. Commercially purchased samples were confirmed to be the species as advertised. An accurate, reliable DNA barcoding method for aloe identification introduces new assurance to regulatory processes for endangered plants in trade.This dataset is associated with the manuscript by Yannick Woudstra et al. (2024) entitled 'An updated DNA barcoding tool for Aloe vera and CITES-restricted relatives'It contains the following files:Alooideae target capture tool reference sequences (“Aloeref.fasta”)Accession information fileSequencing information fileExtinction risk & geographical distribution information fileR script for producing Figure 1 (of main manuscript)Data file to produce plots for Figure 1 (“IUCN_renumbered.csv”)Co-phylogeny of reference database topologies from concatenation vs. coalescent-based methodologiesPhylogeny with distribution of CITES-appendix listingsRaw sequencing data are deposited in the NCBI short read archive (SRA) consists of the following bioprojects:PRJNA1120785: Alooideae target capture design – pilot studyPRJNA1120847: Alooideae reference database (aloes) & identifications of unknown aloe materialPRJNA1122593: Alooideae reference database (non-aloes)

DNA metabarcoding provides great potential for species identification in complex samples such as food supplements and traditional medicines. Such a method would aid CITES (the Convention on International Trade in Endangered Species of Wild Fauna and Flora) enforcement officers to combat wildlife crime by preventing illegal trade of endangered plant and animal species. The objective of this research was to develop a multi-locus DNA metabarcoding method for forensic wildlife species identification and to evaluate the applicability and reproducibility of this approach across different laboratories.

A DNA metabarcoding method was developed that makes use of 12 DNA barcode markers that have demonstrated universal applicability across a wide range of plant and animal taxa, and that facilitate the identification of species in samples containing degraded DNA. The DNA metabarcoding method was developed based on Illumina MiSeq amplicon sequencing of well-defined experimental mixtures, for which a bioinformatics pipeline with user-friendly web interface was developed. The performance of the DNA metabarcoding method was assessed in an international validation trial by 16 laboratories, in which the method was found to be highly reproducible and sensitive enough to identify species present in a mixture at 1% dry weight content.

The advanced multi-locus DNA metabarcoding method assessed in this study provides reliable and detailed data on the composition of complex food products, including information on the presence of CITES-listed species. The method can provide improved resolution for species identification, while verifying species with multiple DNA barcodes contributes to an enhanced quality assurance.

Metinės prekybos laukiniais augalais ir gyvūnais ataskaitos pagal Tarybos reglamentą (EB) Nr. 338/97 dėl laukinės faunos ir floros rūšių apsaugos kontroliuojant jų prekybą (CITES) su pakeitimais, padarytais Reglamentu (EB) Nr. 2019/1010 dėl ataskaitų teikimo

Rapporti annwali dwar il-kummerċ ta’ organiżmi selvaġġi skont ir-Regolament tal-Kunsill (KE) Nru 338/97 dwar il-protezzjoni ta’ speċi ta’ fawna u flora selvaġġi billi jkun regolat il-kummerċ fihom (CITES) kif emendat bir-Regolament dwar l-Allinjament tar-Rappurtar 2019/1010

Sprawozdania roczne dotyczące handlu dziką fauną i florą na mocy rozporządzenia Rady (WE) nr 338/97 w sprawie ochrony gatunków dzikiej fauny i flory w drodze regulacji handlu nimi (CITES), zmienione rozporządzeniem w sprawie dostosowania sprawozdań 2019/1010

Número e volume de apreensões de animais, plantas e derivados efetuadas por trimestre ao abrigo da Convenção sobre o Comércio Internacional das Espécies da Fauna e da Flora Selvagens Ameaçadas de Extinção.

Este conjunto de dados foi consolidado no relativo aos «dados de transparência da Força Fronteiriça» de 2015.

Výroční zprávy o obchodu s volně žijícími a planě rostoucími druhy podle nařízení Rady (ES) č. 338/97 o ochraně druhů volně žijících živočichů a planě rostoucích rostlin regulováním obchodu s nimi (CITES) ve znění nařízení o sladění podávání zpráv 2019/1010