Novel detection of provenance in the illegal wildlife trade using elemental data

Deep learning-based image classification of turtles imported into Korea

Although turtles play a key role in maintaining healthy and balanced environments, these are endangered due to global trade to meet the high demand for food, medicine, and pets in Asia. In addition, imported non-native turtles have been controlled as alien invasive species in various countries, including Korea. Therefore, a rapid and accurate classification of imported turtles is needed to conserve and detect those in native ecosystems. In this study, eight Single Shot MultiBox Detector (SSD) models using different backbone networks were used to classify 36 imported turtles in Korea. The images of these species were collected from Google and were identified using morphological features. Then, these were divided into 70% for training, 15% for validation, and 15% for test sets. In addition, data augmentation was applied to the training set to prevent overfitting. Among the eight models, the Resnet18 model showed the highest mean Average Precision (mAP) at 88.1% and the fastest inference time at 0.024 s. The average correct classification rate of 36 turtles in this model was 82.8%. The results of this study could help in management of the turtle trade, specifically in improving detection of alien invasive species in the wild.

Differentiating wild from captive animals: an isotopic approach

Background

Wildlife farming can be an important but complex tool for conservation. To achieve conservation benefits, wildlife farming should meet a variety of criteria, including traceability conditions to identify the animals' origin. The traditional techniques for discriminating between wild and captive animals may be insufficient to prevent doubts or misdeclaration, especially when labels are not expected or mandatory. There is a pressing need to develop more accurate techniques to discriminate between wild and captive animals and their products. Stable isotope analysis has been used to identify animal provenance, and some studies have successfully demonstrated its potential to differentiate wild from captive animals. In this literature review, we examined an extensive collection of publications to develop an overall picture of the application of stable isotopes to distinguish between wild and captive animals focusing on evaluating the patterns and potential of this tool.

Survey methodology

We searched peer-reviewed publications in the Web of Science database and the references list from the main studies on the subject. We selected and analyzed 47 studies that used δ13C, δ15N, δ2H, δ18O, and δ34S in tissues from fish, amphibians, reptiles, birds, and mammals. We built a database from the isotope ratios and metadata extracted from the publications.

Results

Studies have been using stable isotopes in wild and captive animals worldwide, with a particular concentration in Europe, covering all main vertebrate groups. A total of 80.8% of the studies combined stable isotopes of carbon and nitrogen, and 88.2% used at least one of those elements. Fish is the most studied group, while amphibians are the least. Muscle and inert organic structures were the most analyzed tissues (46.81% and 42.55%). δ13C and δ15N standard deviation and range were significantly higher in the wild than in captive animals, suggesting a more variable diet in the first group. δ13C tended to be higher in wild fishes and in captive mammals, birds, reptiles, and amphibians. δ15N was higher in the wild terrestrial animals when controlling for diet. Only 5.7% of the studies failed to differentiate wild and captive animals using stable isotopes.

Conclusions

This review reveals that SIA can help distinguish between wild and captive in different vertebrate groups, rearing conditions, and methodological designs. Some aspects should be carefully considered to use the methodology properly, such as the wild and captivity conditions, the tissue analyzed, and how homogeneous the samples are. Despite the increased use of SIA to distinguish wild from captive animals, some gaps remain since some taxonomic groups (e.g., amphibians), countries (e.g., Africa), and isotopes (e.g., δ2H, δ18O, and δ34S) have been little studied.

Determining the Provenance of Traded Wildlife in the Philippines

The illegal wildlife trade is a significant threat to global biodiversity, often targeting already threatened species. In combating the trade, it is critical to know the provenance of the traded animal or part to facilitate targeted conservation actions, such as education and enforcement. Here, we present and compare two methods, portable X-ray fluorescence (pXRF) and stable isotope analysis (SIA), to determine both the geographic and source provenance (captive or wild) of traded animals and their parts. Using three critically endangered, frequently illegally traded Philippine species, the Palawan forest turtle (Siebenrockiella leytensis), the Philippine cockatoo (Cacatua haematuropygia), and the Philippine pangolin (Manis culionensisis), we demonstrate that using these methods, we can more accurately assign provenance using pXRF data (x¯ = 83%) than SIA data (x¯ = 47%). Our results indicate that these methods provide a valuable forensic tool that can be used in combating the illegal wildlife trade.

Detecting illegal wildlife trafficking via real time tomography 3D X-ray imaging and automated algorithms

Wildlife trafficking is a global problem involving the deliberate and illegal transport of wildlife across international borders. Animals are either removed directly from their natural environment or bred specifically to fuel demand driven by activities such as the illegal pet trade or for purported medicinal reasons. In Australia, wildlife trafficking poses a serious environmental and biosecurity risk through the removal of native species and the introduction of exotic invasive wildlife. This has the potential to impact the natural ecosystem and Australia’s multibillion-dollar agricultural industry. To help detect and restrict this activity, innovative technologies such as 3D X-ray CT technology using Real Time Tomography has been trialed to create wildlife detection algorithms for deployment across Australian mail/traveller luggage pathways. Known species of trafficked Australian wildlife and additional model species of exotics were scanned to create an image reference library for algorithm detection. A total of 294 scans from 13 species of lizards, birds and fish were used to develop initial wildlife algorithms with a detection rate of 82% with a false alarm rate at 1.6%. In combination with human and biosecurity dog detection, this innovative technology is a promising complementary platform for wildlife detection at Australian international borders, with potential worldwide applications.

Image Classification of Amazon Parrots by Deep Learning: A Potentially Useful Tool for Wildlife Conservation

Simple Summary

Most parrot species are threatened with extinction because of habitat loss and commercial trade. Parrot conservation is vital because parrots play an important role in the ecosystem. The Amazon parrots are one of the most endangered parrot species. Monitoring their wild population and global trade is essential for their conservation. However, this is becoming more challenging because it requires manual analysis of large-scale image data. Furthermore, the morphological identification of the Amazon parrots can be difficult because they have similar morphological features. Deep learning-based object detection models are useful tools for monitoring wild populations and global trade. In this study, 26 Amazon parrot species were classified using eight object detection models. The object detection model, which showed the highest accuracy, classified the 26 Amazon parrot species at 90.7% on average. The continuous development of deep learning models for classifying Amazon parrots might help to improve the ability to monitor their wild populations and global trade.

Abstract

Parrots play a crucial role in the ecosystem by performing various roles, such as consuming the reproductive structures of plants and dispersing plant seeds. However, most are threatened because of habitat loss and commercial trade. Amazon parrots are one of the most traded and illegally traded parrots. Therefore, monitoring their wild populations and global trade is crucial for their conservation. However, monitoring wild populations is becoming more challenging because the manual analysis of large-scale datasets of images obtained from camera trap methods is labor-intensive and time consuming. Monitoring the wildlife trade is difficult because of the large quantities of wildlife trade. Amazon parrots can be difficult to identify because of their morphological similarity. Object detection models have been widely used for automatic and accurate species classification. In this study, to classify 26 Amazon parrot species, 8 Single Shot MultiBox Detector models were assessed. Among the eight models, the DenseNet121 model showed the highest mean average precision at 88.9%. This model classified the 26 Amazon parrot species at 90.7% on average. Continuous improvement of deep learning models classifying Amazon parrots may support monitoring wild populations and the global trade of these species.

Portable X-ray fluorescence for bone lead measurements of Australian eagles

Lead (Pb) toxicity from ammunition has been shown to be a threat to scavenging birds across the globe. Toxic levels of lead have recently been found in Australia's largest bird of prey, the wedge-tailed eagle (Aquila audax), through inductively coupled plasma mass spectrometry (ICP-MS) analysis of liver and bone samples. However, ICP-MS is consumptive (causing damage to archived specimens), time-consuming, and expensive. For these reasons, portable X-ray fluorescence (XRF) devices have been optimized to measure bone lead in North American avian species, humans, and other environmental samples. In this study, we assessed portable XRF for bone lead measurement in Australian raptors in two parts. First, we validated the method using tissues from wedge-tailed eagles from Tasmania (A. a. fleayi), analysing bone samples taken from sites on the femur immediately adjacent to sites for which we had ICP-MS data (n = 89). Second, we measured lead via portable XRF in the skulls of wedge-tailed eagles from south-eastern mainland Australia (A. a. audax) collected during a criminal prosecution (n = 92). Portable XRF bone lead measurement demonstrated an excellent correlation with ICP-MS results using root-transformed regression (R² = 0.88). Calculated equivalent ICP-MS values revealed that greater than 50% of the eagles from mainland Australia had elevated lead levels (>10 mg/kg) and 13% had severe lead exposure (>20 mg/kg). Our results support previous studies of North American avian species and suggest that portable XRF could be a useful and inexpensive option for measurement of bone lead in Australian scavenger species.

China’s Conservation Strategy Must Reconcile Its Contemporary Wildlife Use and Trade Practices

China’s supply-side conservation efforts in the past decades have led to two bewildering juxtapositions: a rapidly expanding farming industry vs. overexploitation, which remains one of the main threats to Chinese vertebrates. COVID-19 was also the second large-scale zoonotic disease outbreak since the 2002 SARS. Here, we reflect on China’s supply-side conservation strategy by examining its policies, laws, and practices concerning wildlife protection and utilization, and identify the unintended consequences that likely have undermined this strategy and made it ineffective in protecting threatened wildlife and preventing zoonotic diseases. We call for China to overhaul its conservation strategy to limit and phase out risky and unsustainable utilization, while improving legislation and enforcement to establish full chain-of-custody regulation over existing utilization.

The global magnitude and implications of legal and illegal wildlife trade in China

China is one of the largest consumer markets in the international legal and illegal wildlife trade. An increasing demand for wildlife and wildlife products is threatening biodiversity, both within China and in other countries where wildlife destined for the Chinese market is being sourced. We analysed official data on legal imports of CITES-listed species in five vertebrate classes (mammals, reptiles, amphibians, birds and fish), and on enforcement seizures of illegally traded wildlife, during 1997–2016. This is the first study that collates and analyses publicly available data on China's legal and illegal wildlife trade and considers a broad range of species. Specifically, we estimated the scale and scope of the legal and illegal wildlife trade, quantified the diversity of species involved, and identified the major trading partners, hotspots and routes associated with illegal trade. Our findings show that substantial quantities of wildlife have been extracted globally for the Chinese market: during 1997–2016 over 11.5 million whole-organism equivalents and 5 million kg of derivatives of legally traded wildlife, plus over 130,000 illegally traded animals (alive and dead) and a substantial amount of animal body parts and products, were imported into China. Although measures to reduce demand and alleviate poverty are crucial to curb unsustainable and illegal wildlife trade in the longer term, China's wildlife regulators and enforcers must take urgent measures to disrupt the supply chains from source to market.

Forensic identification of the keratin fibers of South American camelids by ambient ionization mass spectrometry: Vicuña, alpaca and guanaco

RATIONALE

The keratin fleece of the endangered vicuña (Vicugna vicugna) commands a high value in international markets, and this trade has caused illegal poaching and a substantial decrease in vicuña populations. Morphological analysis of hairs does not have the resolution to determine the species of origin of camelid natural fibers. In addition, commerce in camelid fleece also includes the legal trade of alpaca (Vicugna pacos) and guanaco (Lama guanicoe) wool.

METHODS

The keratin fiber spectra of vicuña (n = 19), guanaco (n = 20) and alpaca (n = 20) were collected using X-ray fluorescence (XRF) spectrometry, Horizontal attenuated total reflectance Fourier transform infrared (HATR-FTIR) spectroscopy and direct analysis in real time time-of-flight mass spectrometry (DART-TOFMS). Analysis with each technique evaluated the data to determine if the three taxa could be separated using either descriptive or multivariate statistics.

RESULTS

XRF analysis showed that the elements detected and their relative concentrations were similar in all three species, whereas HATR-FTIR analysis could identify alpaca fleece but could not differentiate vicuña from guanaco. Ions detected by ambient ionization using DART-TOFMS, in either positive- or negative-ion mode, gave the best results and showed that each taxonomic group is distinctive. Multivariate analysis of the mass spectra created robust models which resolved each species (LOOCV = 99.9%). The analyses of eight validation samples were correctly assigned to the appropriate species and demonstrated the reliability of DART-TOFMS to infer taxonomic source.

CONCLUSIONS

The DART-TOFMS spectra of unmodified keratin fibers infer that the chemotype of each species is heavily influenced by fatty acids, cholesterol and its analogs, and that these ions are useful in separating the fleece of vicuña, alpaca and guanaco. We posit that the etiological source of these chemotype differences is consistent with genetic modulations and is less influenced by diet. Accurate taxonomic identification of fleece is important to identify violations and assists in the protection of rare species.

Pet or pest? Stable isotope methods for determining the provenance of an invasive alien species

The illegal pet trade facilitates the global dispersal of invasive alien species (IAS), providing opportunities for new pests to establish in novel recipient environments. Despite the increasing threat of IAS to the environment and economy, biosecurity efforts often lack suitable, scientifically-based methods to make effective management decisions, such as identifying an established IAS population from a single incursion event. We present a proof-of-concept for a new application of a stable isotope technique to identify wild and captive histories of an invasive pet species. Twelve red-eared slider turtles (Trachemys scripta elegans) from historic Australian incursions with putative wild, captive and unknown origins were analysed to: (1) present best-practice methods for stable isotope sampling of T. s. elegans incursions; (2) effectively discriminate between wild and captive groups using stable isotope ratios; and (3) present a framework to expand the methodology for use on other IAS species. A sampling method was developed to obtain carbon (δ13C) and nitrogen (δ15N) stable isotope ratios from the keratin layer of the carapace (shells), which are predominantly influenced by dietary material and trophic level respectively. Both δ13C and δ15N exhibited the potential to distinguish between the wild and captive origins of the samples. Power simulations demonstrated that isotope ratios were consistent across the carapace and a minimum of eight individuals were required to effectively discriminate wild and captive groups, reducing overall sampling costs. Statistical classification effectively separated captive and wild groups by δ15N (captive: δ15N‰ ≥ 9.7‰, minimum of 96% accuracy). This study outlines a practical and accessible method for detecting IAS incursions, to potentially provide biosecurity staff and decision-makers with the tools to quickly identify and manage future IAS incursions.