Scale of unregulated international trade in Australian reptiles and amphibians.

Reptiles and amphibians are popular in the exotic pet trade, where Australian species are valued for their rarity and uniqueness. Despite a near-complete ban on the export of Australian wildlife, smuggling and subsequent international trade frequently occur in an unregulated and unmonitored manner. In 2022, Australia listed over 100 squamates in Appendix III of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) to better monitor this trade. We investigated current trade and assessed the value of this Australian CITES listing using web-scraping methods to monitor the online pet trade in Australian reptiles and amphibians, with additional data from published papers, trade databases, and seizure records. Despite the export ban, we identified 170 endemic herpetofauna (reptile and amphibian) species in international trade, 33 of which were not recorded previously in the international market, including 6 newly recorded genera. Ninety-two traded species were included in CITES appendices (59 added in 2022), but at least 78 other traded species remained unregulated. Among these, 5 of the 10 traded threatened species were unlisted, and we recommend they be considered for inclusion in CITES Appendix III. We also recommend the listing of all Diplodactylidae genera in Appendix III. Despite this family representing the greatest number of Australian species in trade, only one genus (of 7 traded) was included in the recent CITES amendments. Overall, a large number of Australian reptile and amphibian species are traded internationally and, although we acknowledge the value of Australia's recent CITES listing, we recommend the consideration of other taxa for similar inclusion in CITES.

Escala del mercado internacional no regulado de reptiles y anfibios australianos Resumen Los reptiles y anfibios son populares en el comercio de mascotas exóticas, en el que las especies australianas son valoradas por su rareza y singularidad. A pesar de la prohibición casi total de la exportación de fauna silvestre australiana, el contrabando y el comercio internacional posterior se producen con frecuencia de forma no regulada y no supervisada. En 2022, Australia incluyó más de 100 escamosos en el apéndice III de la Convención sobre el Comercio Internacional de Especies Amenazadas de Fauna y Flora Silvestres (CITES) para controlar mejor este comercio. Investigamos el comercio actual y evaluamos el valor de esta inclusión en CITES con métodos de raspado web para monitorear el comercio virtual de reptiles y anfibios australianos como mascotas, con datos adicionales de artículos publicados, bases de datos comerciales y registros de incautaciones. A pesar de la prohibición de las exportaciones, identificamos 170 especies endémicas de herpetofauna (reptiles y anfibios) en el comercio internacional, 33 de las cuales no se habían registrado previamente en el mercado internacional, incluidos 6 géneros registrados recientemente. Noventa y dos especies comercializadas se incluyeron en los apéndices de CITES (59 añadidas en 2022), pero al menos otras 78 especies comercializadas permanecieron sin regular. Entre ellas, cinco de las diez especies amenazadas comercializadas no estaban incluidas y recomendamos que se considere su inclusión en el apéndice III de CITES. También recomendamos la inclusión de todos los géneros de Diplodactylidae en el apéndice III. A pesar de que esta familia representa el mayor número de especies australianas en el comercio, sólo un género (de 7 comercializados) fue incluido en las recientes enmiendas de CITES. En general, un gran número de especies de reptiles y anfibios australianos son objeto de comercio internacional y, aunque reconocemos el valor de la reciente inclusión de Australia en CITES, recomendamos que se consideren otros taxones para su similar inclusión.

Forecasting potential invaders to prevent future biological invasions worldwide.

The ever-increasing and expanding globalisation of trade and transport underpins the escalating global problem of biological invasions. Developing biosecurity infrastructures is crucial to anticipate and prevent the transport and introduction of invasive alien species. Still, robust and defensible forecasts of potential invaders are rare, especially for species without known invasion history. Here, we aim to support decision-making by developing a quantitative invasion risk assessment tool based on invasion syndromes (i.e., generalising typical attributes of invasive alien species). We implemented a workflow based on 'Multiple Imputation with Chain Equation' to estimate invasion syndromes from imputed datasets of species' life-history and ecological traits and macroecological patterns. Importantly, our models disentangle the factors explaining (i) transport and introduction and (ii) establishment. We showcase our tool by modelling the invasion syndromes of 466 amphibians and reptile species with invasion history. Then, we project these models to amphibians and reptiles worldwide (16,236 species [c.76% global coverage]) to identify species with a risk of being unintentionally transported and introduced, and risk of establishing alien populations. Our invasion syndrome models showed high predictive accuracy with a good balance between specificity and generality. Unintentionally transported and introduced species tend to be common and thrive well in human-disturbed habitats. In contrast, those with established alien populations tend to be large-sized, are habitat generalists, thrive well in human-disturbed habitats, and have large native geographic ranges. We forecast that 160 amphibians and reptiles without known invasion history could be unintentionally transported and introduced in the future. Among them, 57 species have a high risk of establishing alien populations. Our reliable, reproducible, transferable, statistically robust and scientifically defensible quantitative invasion risk assessment tool is a significant new addition to the suite of decision-support tools needed for developing a future-proof preventative biosecurity globally.

Dataset of seized wildlife and their intended uses.

The illegal wildlife trade (IWT) threatens conservation and biosecurity efforts. The Internet has greatly facilitated the trade of wildlife, and researchers have increasingly examined the Internet to uncover illegal trade. However, most efforts to locate illegal trade on the Internet are targeted to one or few taxa or products. Large-scale efforts to find illegal wildlife on the Internet (e-commerce, social media, dark web) may be facilitated by a systematic compilation of illegally traded wildlife taxa and their uses. Here, we provide such a dataset. We used seizure records from three global wildlife trade databases to compile the identity of seized taxa along with their intended usage (i.e., use-type). Our dataset includes c. 4.9k distinct taxa representing c. 3.3k species and contains c. 11k taxa-use combinations from 110 unique use-types. Further, we acquired over 45k common names for seized taxa from over 100 languages. Our dataset can be used to conduct large-scale broad searches of the Internet to find illegally traded wildlife. Further, our dataset can be filtered for more targeted searches of specific taxa or derived products.

Text classification to streamline online wildlife trade analyses.

Automated monitoring of websites that trade wildlife is increasingly necessary to inform conservation and biosecurity efforts. However, e-commerce and wildlife trading websites can contain a vast number of advertisements, an unknown proportion of which may be irrelevant to researchers and practitioners. Given that many wildlife-trade advertisements have an unstructured text format, automated identification of relevant listings has not traditionally been possible, nor attempted. Other scientific disciplines have solved similar problems using machine learning and natural language processing models, such as text classifiers. Here, we test the ability of a suite of text classifiers to extract relevant advertisements from wildlife trade occurring on the Internet. We collected data from an Australian classifieds website where people can post advertisements of their pet birds (n = 16.5k advertisements). We found that text classifiers can predict, with a high degree of accuracy, which listings are relevant (ROC AUC ≥ 0.98, F1 score ≥ 0.77). Furthermore, in an attempt to answer the question 'how much data is required to have an adequately performing model?', we conducted a sensitivity analysis by simulating decreases in sample sizes to measure the subsequent change in model performance. From our sensitivity analysis, we found that text classifiers required a minimum sample size of 33% (c. 5.5k listings) to accurately identify relevant listings (for our dataset), providing a reference point for future applications of this sort. Our results suggest that text classification is a viable tool that can be applied to the online trade of wildlife to reduce time dedicated to data cleaning. However, the success of text classifiers will vary depending on the advertisements and websites, and will therefore be context dependent. Further work to integrate other machine learning tools, such as image classification, may provide better predictive abilities in the context of streamlining data processing for wildlife trade related online data.

A guide to using the internet to monitor and quantify the wildlife trade.

The unrivaled growth in e-commerce of animals and plants presents an unprecedented opportunity to monitor wildlife trade to inform conservation, biosecurity, and law enforcement. Using the internet to quantify the scale of the wildlife trade (volume and frequency) is a relatively recent and rapidly developing approach that lacks an accessible framework for locating relevant websites and collecting data. We produced an accessible guide for internet-based wildlife trade surveillance. We detailed a repeatable method involving a systematic internet search, with search engines, to locate relevant websites and content. For data collection, we highlight web-scraping technology as an efficient way to collect data in an automated fashion at regularly timed intervals. Our guide is applicable to the multitude of trade-based contexts because researchers can tailor search keywords for specific taxa or derived products and locations of interest. We provide information for working with the diversity of websites used in wildlife trade. For example, to locate relevant content on social media (e.g., posts or groups), each social media platform should be examined individually via the site's internal search engine. A key advantage of using the internet to study wildlife trade is the relative ease of access to an increasing amount of trade-related data. However, not all wildlife trade occurs online and it may occur on unobservable sections of the internet.

Resumen Una Guía para Usar el Internet para Monitorear y Cuantificar el Mercado de Fauna El crecimiento incomparable del comercio en línea de animales y plantas representa una oportunidad sin precedentes para monitorear el mercado de fauna y así orientar a la conservación, la bioseguridad y la aplicación de la ley. El uso del internet para cuantificar la escala del mercado de fauna (volumen y frecuencia) es una estrategia relativamente reciente y de rápido desarrollo que carece de un marco de trabajo accesible para la localización de sitios web relevantes y para la recolección de datos. Realizamos una guía accesible para la vigilancia del mercado de fauna en internet. Detallamos un método repetible que involucra una búsqueda sistemática por internet, por medio de buscadores, para localizar sitios web y contenidos relevantes. Para la recolección de datos, resaltamos la tecnología de web scraping como una manera eficiente de obtener datos de manera automatizada a intervalos regulares de tiempo. Nuestra guía puede aplicarse a la multitud de contextos basados en el mercado porque los investigadores pueden adaptar las palabras de búsqueda a taxones específicos o productos derivados y a localidades de interés. Proporcionamos información para poder trabajar con la diversidad de sitios web que se usan para el mercado de fauna. Por ejemplo, para localizar contenido relevante en las redes sociales (p. ej.: publicaciones o grupos), cada plataforma social debería ser examinada individualmente por medio del buscador interno del sitio. Una ventaja importante de usar el internet para estudiar el mercado de fauna es el acceso relativamente sencillo a una creciente cantidad de datos relacionados con el mercado. Sin embargo, no todo el mercado de fauna ocurre en línea y puede que suceda en secciones inobservables del internet.