title: Quantitative methods of identifying the key nodes in the illegal wildlife trade network.
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.
authors.affiliation: Department of Clinical Studies-New Bolton Center, University of Pennsylvania School of Veterinary Medicine, Kennett Square, PA 19348; nikkitapatel@gmail.com.
authors.forename: Nikkita Gunvant
authors.initials: NG
authors.lastname: Patel
authors.affiliation: Department of Clinical Studies-New Bolton Center, University of Pennsylvania School of Veterinary Medicine, Kennett Square, PA 19348;
authors.forename: Chris
authors.initials: C
authors.lastname: Rorres
authors.affiliation: Metabiota, San Francisco, CA 94104;
authors.forename: Damien O
authors.initials: DO
authors.lastname: Joly
authors.affiliation: Department of Pediatrics, Harvard Medical School, Boston, MA 02215; Boston Children's Hospital, Boston, MA 02215;
authors.forename: John S
authors.initials: JS
authors.lastname: Brownstein
authors.affiliation: Department of Clinical Studies-New Bolton Center, University of Pennsylvania School of Veterinary Medicine, Kennett Square, PA 19348;
authors.forename: Ray
authors.initials: R
authors.lastname: Boston
authors.affiliation: Department of Biostatistics and Epidemiology, University of Pennsylvania, Philadelphia, PA 19104.
authors.forename: Michael Z
authors.initials: MZ
authors.lastname: Levy
authors.affiliation: Department of Clinical Studies-New Bolton Center, University of Pennsylvania School of Veterinary Medicine, Kennett Square, PA 19348;
authors.forename: Gary
authors.initials: G
authors.lastname: Smith
mesh_terms: D000818:Animals; D000835:Animals, Wild; D003132:Commerce / Q000331:legislation & jurisprudence*; D003247:Conservation of Natural Resources / Q000331:legislation & jurisprudence / Q000379:methods*; D003415:Crime; D038622:Internationality
keywords: elephant; key player; network analysis; rhinoceros; wildlife trade