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Conservation biology : the journal of the Society for Conservation Biology
12, 2019;33 (6): 1307-1317.

Network analysis of a stakeholder community combatting illegal wildlife trade.

Andrea Moshier, Janna Steadman, David L Roberts
Author Information
  1. Andrea Moshier: Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, Marlowe Building, University of Kent, Canterbury, Kent CT2 9NF, U.K. ORCID
  2. Janna Steadman: Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, Marlowe Building, University of Kent, Canterbury, Kent CT2 9NF, U.K.
  3. David L Roberts: Durrell Institute of Conservation and Ecology, School of Anthropology and Conservation, Marlowe Building, University of Kent, Canterbury, Kent CT2 9NF, U.K. ORCID
PMID: 31006914 DOI: 10.1111/cobi.13336

Abstract

The illegal wildlife trade has emerged as a growing and urgent environmental issue. Stakeholders involved in the efforts to curb wildlife trafficking include nongovernmental organizations (NGOs), academia, and state government and enforcement bodies. The extent to which these stakeholders work and communicate among each other is fundamental to effectively combatting illicit trade. Using the United Kingdom as a case study, we used a social network analysis and semistructured interviews of stakeholders to assess communication relationships in the counter wildlife trafficking community. The NGOs consistently occupied 4 of the 5 most central positions in the generated networks, whereas academic institutions routinely occupied 4 of the 5 most peripheral positions. However, NGOs were the least diverse in their communication practices compared with the other stakeholder groups. Stakeholders identified personal relationships as the most important aspect of functioning communication. Participant insights also showed that stakeholder-specific variables (e.g., ethical and confidentiality concerns), competition, and fundraising can have a confounding effect on intercommunication. Evaluating communication networks and intrastakeholder communication trends is essential to creating cohesive, productive, and efficient responses to the challenges of combatting illegal wildlife trade. Article impact statement: Communication among those combatting illegal wildlife trade is confounded by stakeholder variables (ethics, confidentiality), competition, and fundraising.

Keywords

NGO ONG academic académicos análisis de redes sociales aplicación centralidad de redes community comunidad enforcement network centrality social network analysis

References

  1. Baden-Fuller C, Morgan MS. 2010. Business models as models. Long Range Planning 43:156-171.
  2. Baker J. 1999. The international wildlife conservation regime and the convention on international trade in endangered species. Human Dimensions of Wildlife 4:18-39.
  3. Borgatti SP, Everett MG, Freeman LC. 2002. Ucinet for windows: software for social network analysis. Analytic Technologies, Harvard, Massachusetts.
  4. Borgatti SP, Everett MG, Johnson JC. 2013. Analyzing social networks. Sage, London.
  5. Buchy M, Ahmed S. 2007. Social learning, academics and NGOs: Can the collaborative formula work? Action Research 5:358-377.
  6. Cerotti PR, Clifton J. 1998. How Australian organizations use global communications. Group Decision and Negotiation 7:435-446.
  7. CITES (Convention on the International Trade in Endangered Species of Wild Flora and Fauna). 2017. What is CITES? Geneva, Switzerland. Available from https://www.cites.org/eng/disc/what.php (accessed July 2017).
  8. Cordella A, Tempini N. 2015. E-government and organizational change: reappraising the role of ICT and bureaucracy in public service delivery. Government Information Quarterly 32:279-286.
  9. Crane A, Livesey SM. 2003. Are you talking to me? Stakeholder communication and the risks and rewards of dialogue. Pages 1-32 in Andriof J, Waddocl S, Husted B, Sutherland Rahman S, editor. Unfolding stakeholder thinking 2: relationships, communication, reporting and performance. Routledge, Abingdon, United Kingdom.
  10. Cronin B. 2016. Social network analysis. Pages 237-252 in Lee F, Cronin B, editors. Handbook of research methods and applications in heterodox economics. Edward Elgar Publishing, Cheltenham.
  11. Cross R, Parker A, Prusak L, Borgatti SP. 2001. Knowing what we know: Supporting knowledge creation and sharing in social networks. Organizational Dynamics 30:100-120.
  12. DEFRA (Department for Environment Food & Rural Affairs). 2017. Illegal wildlife trade (IWT) challenge fund. DEFRA, London. Available from https://www.gov.uk/government/collections/illegal-wildlife-trade-iwt-challenge-fund (accessed August 2017).
  13. Dunn OJ. 1964. Multiple comparisons using rank sums. Technometrics 6:241-252.
  14. Fahlman RC. 2015. Elephant crime intelligence system assessment. World Bank, Washington, D.C.
  15. Groce JE, Farrelly MA, Jorgensen BS, Cook CN. 2018. Using social-network research to improve outcomes in natural resource management. Conservation Biology 33:53-65.
  16. Haas TC, Ferreira SM. 2015. Federated Databases and actionable intelligence: using social network analysis to disrupt transnational wildlife trafficking criminal networks. Security Informatics 4:1-14.
  17. Hinsley A, Lee TE, Harrison JR, Roberts DL. 2016. Estimating the extent and structure of trade in horticultural orchids via social media. Conservation Biology 30:1038-1047.
  18. IBM Corp. 2016. SPSS statistics for windows. Version 22.0. IBM, Armonk, New York.
  19. Illes A. 2016. Wildlife crime in the United Kingdom. European Union, Brussels. Available from 445 http://www.europarl.europa.eu/supporting-analyses (accessed August 2017).
  20. Jarvis RM, Borrelle SB, Bollard Breen B, Towns DR. 2015. Conservation, mismatch and the research-implementation gap. Pacific Conservation Biology 21:105-107.
  21. Joseph H. 2013. The open access movement grows up: taking stock of a revolution. PLoS Biology 11. https://doi.org/10.1371/journal.pbio.1001686.
  22. Kampourakis K. 2016. Publish or perish? Science & Education 25:249-250.
  23. Kossinets G. 2006. Effects of missing data in social networks. Social Networks 28:247-268.
  24. Kraut RE, Streeter LA. 1995. Coordination in software development. Communications of the ACM 38:69-81.
  25. Kurland J, Pires SF. 2016. Assessing U.S. wildlife trafficking patterns: how criminology and conservation science can guide strategies to reduce the illegal wildlife trade. Deviant Behavior 9625:1-17.
  26. Mace G, et al. 2000. correspondence it’s time to work together and stop duplicating conservation efforts. Nature 405:393-394.
  27. Marijnen E. 2017. The ‘green militarisation’ of development aid: the European Commission and the Virunga National Commission and the Virunga National Park, DR Congo. Third World Quarterly https://doi.org/10.1080/01436597.2017.1282815.
  28. Mbaru EK, Barnes ML. 2017. Key players in conservation diffusion: using social network analysis to identify critical injection points. Biological Conservation 210:222-232.
  29. McPherson M, Smith-Lovin L, Cook JM. 2001. Birds of a feather: homophily in social networks. Annual Review of Sociology 27:415-444.
  30. Newing H, Eagle CM, Puri RK, Watson CW. 2011. Conducting research in conservation: social science methods and practice. Routledge, Abingdon.
  31. Nurse A. 2013. Privatising the green police: The role of NGOs in wildlife law enforcement. Crime, Law and Social Change 59:305-318.
  32. Olsen R. 2011. Self-selection bias. Pages 809-812 in Lavrakas PJ, editor. Encyclopedia of survey research methods. Sage Publications, Thousand Oaks, California.
  33. Parr J. 2011. Illegal wildlife trade: a need for institutional mapping a response to Bennett. Oryx 45:480-481.
  34. Prell C, Hubacek K, Reed M. 2009. Stakeholder analysis and social network analysis in natural resource management. Society and Natural Resources 22:501-518.
  35. QSR International. 2015. NVivo qualitative data analysis software. Version 11. QSR International, Australia.
  36. Research Councils UK. 2017. RCUK policy on open access and supporting guidance. Available from http://www.rcuk.ac.uk/research/openaccess/policy/ (accessed August 2017).
  37. Schneider JL. 2008. Reducing the illicit trade in endangered wildlife. Journal of Contemporary Criminal Justice 24:274-295.
  38. Sonnert G, Holton GJ. 2002. Ivory bridges: connecting science and society. The MIT Press, Cambridge, Massachusetts.
  39. Stewart A. 2000. The role of the wildlife liaison officer in police. Police Journal 73:80-84.
  40. The Royal Foundation of The Duke and Duchess of Cambridge and Prince Harry. 2017. United for Wildlife, London, United Kingdom. Available from http://www.royalfoundation.com/our-work/united-for-wildlife/ (accessed July 20, 2006).
  41. UK Environmental Law Association. 2017. Available from http://www.environmentlaw.org.uk/rte.asp?id=251 (accessed August 1, 2017).
  42. United Kingdom Home Office. 2013. List of UK police forces. Available from https://www.police.uk/forces/ (accessed August 2018).
  43. Utermohlen M, Baine P. 2017. Flying under the radar wildlife trafficking in the air transport sector. Available from www.routespartnership.org. (accessed August 2018).
  44. Wang DJ, Shi X, Mcfarland DA, Leskovec J. 2012. Measurement error in network data: A re-classification. Social Networks 34:396-409.
  45. Wildlife and Countryside LINK. 2017. Link strategy. Wildlife and Countryside Link, London. Available from https://www.wcl.org.uk/link-strategy.asp (accessed July 2017).
  46. Wyatt T. 2009. Exploring the organization of Russia Far East's illegal wildlife trade: two case studies of the illegal fur and illegal falcon trades. Global Crime 10:144-154.
  47. Xia J. 2013. The open access divide. Publications 1:113-139.

MeSH Term

Animals
Animals, Wild
Conservation of Natural Resources
Humans
United Kingdom
Journal Article
Article has an altmetric score of 7

Word Cloud

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