Quantitative methods of identifying the key nodes in the illegal wildlife trade network

Nikkita Gunvant Patel; Chris Rorres; Damien O Joly; John S Brownstein; Ray Boston; Michael Z Levy; Gary Smith

doi:10.1073/pnas.1500862112

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Quantitative methods of identifying the key nodes in the illegal wildlife trade network

Journal article

Open access

Peer reviewed

Quantitative methods of identifying the key nodes in the illegal wildlife trade network

Nikkita Gunvant Patel, Chris Rorres, Damien O Joly, John S Brownstein, Ray Boston, Michael Z Levy and Gary Smith

Proceedings of the National Academy of Sciences - PNAS, v 112(26), pp 7948-7953

30 Jun 2015

DOI: https://doi.org/10.1073/pnas.1500862112

PMID: 26080413

Featured in Collection : UN Sustainable Development Goals @ Drexel

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Abstract

Animals

Animals, Wild

Commerce - legislation & jurisprudence

Conservation of Natural Resources - legislation & jurisprudence

Conservation of Natural Resources - methods

Crime

Internationality

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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Details

Title: Quantitative methods of identifying the key nodes in the illegal wildlife trade network
Creators: Nikkita Gunvant Patel - University of Pennsylvania
Chris Rorres - University of Pennsylvania
Damien O Joly - Metabiota (United States)
John S Brownstein - Harvard University
Ray Boston - University of Pennsylvania
Michael Z Levy - University of Pennsylvania
Gary Smith - University of Pennsylvania
Publication Details: Proceedings of the National Academy of Sciences - PNAS, v 112(26), pp 7948-7953
Publisher: PNAS
Grant note: T32 AI070077 / NIAID NIH HHS
Resource Type: Journal article
Language: English
Academic Unit: [Retired Faculty]
Web of Science ID: WOS:000357079400036
Scopus ID: 2-s2.0-84937930099
Other Identifier: 991021879624404721