ArticleProceedings of the National Academy of Sciences of the United States of America2015
Quantitative methods of identifying the key nodes in the illegal wildlife trade network.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 99 citations in OpenAlex.
- Impacts of wildlife trade on terrestrial biodiversity.Nature ecology & evolution · 2021Pooled it
- The Medicine Quality Monitoring Globe: A public health intelligence system for collecting and curating reports of substandard and falsified medical products from publicly available Internet news media.PLOS digital health · 2026Article
- Detect and Trace: An Australian Field Trial Using Machine-Learning Tools to Combat Illegal Wildlife Trade.Animals : an open access journal from MDPI · 2026Article
- A game theoretic treatment of contagion in trade networks.PLoS computational biology · 2025Article
- Using online reports of seahorse seizures to track their illegal trade.Conservation biology : the journal of the Society for Conservation Biology · 2025Article
- Hide and seek with falsified medicines: Current challenges and physico-chemical and biological approaches for tracing the origin of trafficked products.Forensic science international · 2025Review
- Curbing the trade in pangolin scales in China by revealing the characteristics of the illegal trade network.Scientific reports · 2025Article
- Inter-city movement pattern of notifiable infectious diseases in China: a social network analysis.The Lancet regional health. Western Pacific · 2025Article
- The uncertain role of substandard and falsified medicines in the emergence and spread of antimicrobial resistance.Nature communications · 2023Review
- Determining the Provenance of Traded Wildlife in the Philippines.Animals : an open access journal from MDPI · 2023Article
- Current wildlife crime (Indian scenario): major challenges and prevention approaches.Biodiversity and conservation · 2023Review
- Concerned or Apathetic? Using Social Media Platform (Twitter) to Gauge the Public Awareness about Wildlife Conservation: A Case Study of the Illegal Rhino Trade.International journal of environmental research and public health · 2022Article
- Prosecution records reveal pangolin trading networks in China, 2014-2019.Zoological research · 2021Article
- Trading Animal Lives: Ten Tricky Issues on the Road to Protecting Commodified Wild Animals.Bioscience · 2021Article
- Understanding wildlife crime in China: Socio-demographic profiling and motivation of offenders.PloS one · 2021Article
- Research on ecosystem services of water conservation and soil retention: a bibliometric analysis.Environmental science and pollution research international · 2021Article
- Article
- What drives disease flows between locations?Transactions in GIS : TG · 2020Article
- Synergistic China-US Ecological Research is Essential for Global Emerging Infectious Disease Preparedness.EcoHealth · 2020Review
- Novel detection of provenance in the illegal wildlife trade using elemental data.Scientific reports · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
Innovative approaches are needed to combat the illegal trade in wildlife. Here, we used network analysis and a new database, HealthMap Wildlife Trade, to identify the key nodes (countries) that support the illegal wildlife trade. We identified key exporters and importers from the number of shipments a country sent and received and from the number of connections a country had to other countries over a given time period. We used flow betweenness centrality measurements to identify key intermediary countries. We found the set of nodes whose removal from the network would cause the maximum disruption to the network. Selecting six nodes would fragment 89.5% of the network for elephants, 92.3% for rhinoceros, and 98.1% for tigers. We then found sets of nodes that would best disseminate an educational message via direct connections through the network. We would need to select 18 nodes to reach 100% of the elephant trade network, 16 nodes for rhinoceros, and 10 for tigers. Although the choice of locations for interventions should be customized for the animal and the goal of the intervention, China was the most frequently selected country for network fragmentation and information dissemination. Identification of key countries will help strategize illegal wildlife trade interventions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.