Voluntary consensus based geospatial data standards for the global illegal trade in wild fauna and flora

Gore, Meredith L.; Schwartz, Lee R.; Amponsah‐Mensah, Kofi; Barbee, Emily C.; Canney, Susan M.; Carbo-Penche, Maria; Cronin, Drew T.; Hilend, Rowan; Laituri, Melinda; Luna, David; Maina, Faith; Mey, Christian de; Mumford, Kathleena; Mugo, Robinson; Nduguta, Redempta; Nyce, Christopher; McEvoy, John F.; McShea, William J.; Mandimbihasina, Angelo; Salafsky, Nick; Smetana, David; Tait, Alexander N.; Wittig, Tim; Wright, Dawn J.; Naess, Leah Wanambwa

doi:10.1038/s41597-022-01371-w

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…Without more accurate and representative data we have no way to identify vulnerable wildlife populations that may be most susceptible to the impacts of high levels of international trade, no knowledge of where to most e ciently and effectively direct sustainability interventions, and no idea who to engage in the codesign of such interventions, especially around sourcing 15 . Although particular industries, such as sheries, now collate more comprehensive data to enable more sustainable regulations, the majority of sectors such as the exotic pet trade, medicinal trade, or even elements of fashion trade lack comprehensive and usable data 16 .…”

Section: Wildlife Trade Data Remain Fragmented and Incompletementioning

confidence: 99%

The magnitude of legal wildlife trade and implications for species survival

Hughes,

Marshall,

Alamshah

et al. 2024

Preprint

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The unsustainable use of wildlife is a primary driver of global biodiversity loss. No comprehensive global dataset exists on what species are in trade, their geographic origins, and trade's ultimate impacts, which limits our ability to sustainably manage trade. The United States (US) is one of the world's largest importers of wildlife, trade data being compiled in the US Law Enforcement Management Information System (LEMIS), the only comprehensive publicly-accessible wildlife trade database of non-CITES listed species. In total, 21,097 species and over 2.85 billion individuals were traded over the past 22 years (2000-2022). When LEMIS data is combined with CITES records, the US imported over 29,445 wild species, including over 50% of all globally described species in some taxonomic groups. For most taxa, around half of the individuals are declared as sourced from the wild. Although LEMIS provides the only means to assess trade volumes for many taxa, without any associated data on most wild populations, it is impossible to assess the impact or sustainability of trade, or any potential risk of pest or pathogen spread. Furthermore, these insights underscore the likely under-estimation of trade, and the urgent need for other countries to adopt similar mechanisms to accurately record trade.

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Section: Wildlife Trade Data Remain Fragmented and Incompletementioning

confidence: 99%

The magnitude of legal wildlife trade and implications for species survival

Hughes,

Marshall,

Alamshah

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Esteemed organizations like WWF, 9 TRAFFIC, 10 and EAGLE 11 actively combat violators engaged in the systematic illegal hunting, transportation, and sale of wildlife and their products (Dalberg 2012). Historically, significant efforts have been invested in providing data (Gore et al 2022(Gore et al , 2023b and conducting analyses (Gore et al 2019(Gore et al , 2023a focusing on offline trafficking. However, with the advent of the internet, wildlife traffickers have adapted their strategies, actively promoting and selling products online (Lavorgna 2014).…”

Section: Related Workmentioning

confidence: 99%

Wildlife Product Trading in Online Social Networks: A Case Study on Ivory-Related Product Sales Promotion Posts

Mou,

Yue,

Lee

et al. 2024

ICWSM

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Wildlife trafficking (WLT) has evolved into a pressing global concern, as traffickers increasingly utilize online platforms such as e-commerce websites and social networks to expand their illicit trade. This paper addresses the pivotal challenge of detecting and recognizing promotional behaviors related to the sale of wildlife products within online social networks—a critical step in combating these environmentally detrimental activities. To confront these illicit operations effectively, our research undertakes the following key initiatives: 1. Data Collection and Labeling: We employ a network-based approach to gather a scalable dataset pertaining to wildlife product trading. Through a human-in-the-loop machine learning process, this dataset is meticulously labeled, distinguishing between positive class samples containing wildlife product selling posts and hard-negatives representing regular posts misclassified as potential WLT posts, subsequently rectified by human annotators. 2. Machine Learning Framework Development: We present a robust framework that benchmarks machine learning results on the collected dataset. This framework autonomously identifies suspicious wildlife selling posts and accounts, effectively harnessing the multi-modal nature of online social networks. 3. In-depth Analysis of Trading Behaviors: Our research delves into a comprehensive analysis of trading posts, illuminating the systematic and organized selling behaviors prevalent in the current landscape. By providing detailed insights into the nature of these behaviors, we contribute valuable information for understanding and countering illegal wildlife product trading. Moreover, we emphasize our commitment to openness and collaboration by making our code and dataset openly available, thereby fostering cooperative efforts towards the development of more effective strategies in combating illegal wildlife trafficking.

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“…Domain-specific challenges to applying computer science and operations research models to WTN are complicated by a lack of interoperable data about conservation biology and conservation crime ( 1 ) as well as a lack of common standards ( 30 ). Machine learning can help fill in data gaps by using available data to build models for predicting likely linkages and routes that have not been detected yet.…”

Section: Quantitative Models Can Advance Understanding About Illicit ...mentioning

confidence: 99%

Advancing interdisciplinary science for disrupting wildlife trafficking networks

Gore

Barbee

Dilkina

et al. 2023

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

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Wildlife trafficking, whether local or transnational in scope, undermines sustainable development efforts, degrades cultural resources, endangers species, erodes the local and global economy, and facilitates the spread of zoonotic diseases. Wildlife trafficking networks (WTNs) occupy a unique gray space in supply chains—straddling licit and illicit networks, supporting legitimate and criminal workforces, and often demonstrating high resilience in their sourcing flexibility and adaptability. Authorities in different sectors desire, but frequently lack knowledge about how to allocate resources to disrupt illicit wildlife supply networks and prevent negative collateral impacts. Novel conceptualizations and a deeper scientific understanding of WTN structures are needed to help unravel the dynamics of interaction between disruption and resilience while accommodating socioenvironmental context. We use the case of ploughshare tortoise trafficking to help illustrate the potential of key advancements in interdisciplinary thinking. Insights herein suggest a significant need and opportunity for scientists to generate new science-based recommendations for WTN-related data collection and analysis for supply chain visibility, shifts in illicit supply chain dominance, network resilience, or limits of the supplier base.

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Voluntary consensus based geospatial data standards for the global illegal trade in wild fauna and flora

Cited by 7 publications

References 14 publications

The magnitude of legal wildlife trade and implications for species survival

The magnitude of legal wildlife trade and implications for species survival

Wildlife Product Trading in Online Social Networks: A Case Study on Ivory-Related Product Sales Promotion Posts

Advancing interdisciplinary science for disrupting wildlife trafficking networks

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