Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.784390
Title: Characterising the extent of illegal online trade in wildlife using novel approaches
Author: Yeo, Lydia Mary
ISNI:       0000 0004 7969 9407
Awarding Body: University of Kent
Current Institution: University of Kent
Date of Award: 2018
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Abstract:
The illegal, international trade in wildlife poses serious and pressing threats at a number of levels. Traded species are increasingly threatened with extinction and these harms extend to compromised biodiversity and ecosystem instability. Associated threats include biosecurity issues such as disease introduction (including zoonoses) and the ingress of alien species. There is acute awareness of both the critical need for enhanced understanding of the extent and nature of the illegal wildlife trade and how challenging it is to achieve this. The online trading environment presents a particular case where challenges are amplified since it is growing rapidly, diverse and complex to monitor and regulate. Mirroring patterns in conventional trade, the online environment is increasingly being used as a means to conduct legal and illegal wildlife trade. Its attractiveness for illegal trade is illustrated by recent experience where, in response to ivory trade bans, trade shifted from physical trading outlets to online media. The research focus of this thesis is to contribute towards addressing a key area of unmet need that underpins counter-illegal wildlife trade measures. Specifically, bridging an informational "gap" which the United Nations General Assembly (UNGA) has acknowledged under UN Resolution A/71/L.88 "Tackling Illicit Wildlife Trafficking" (2017). Under UN A/71/L.88 the UNGA has tasked the United Nations Office on Drugs and Crime (UNODC) with collecting information on patterns and flows of illicit wildlife trafficking as a support to addressing the trade. The UNODC describes bridging the informational gap as essential to successful counter illegal wildlife trade measures. I translate this imperative to the fast-growing online environment for illegal wildlife trade where the lack of information is a compelling unmet need. I apply two approaches to researching illegal online wildlife trade and the behaviours associated with it. These are: a) "Measurement" (modelling) of online trade postings by application of two different mark recapture (MRC) models to downloaded encounter history data for the online ivory trade (Chapters 3 and 4) and b) "Asking" people who may be involved with illegal (online) wildlife trade to share this information through an online survey incorporating sensitive question models (Chapter 5). In my initial MRC study I build on prior research into online trade in CITES-listed species to evaluate population parameters associated with (illegal) online trade in elephant ivory within the UK. Online media operate "24/7" and, currently, no suitable technology exists to monitor and interrogate this trade continuously. MRC offers a resource-efficient means to monitor trade since it can be applied to estimate trading population parameters based on incomplete observation. I assess study outcomes to identify population parameter inferences and potential actions to address trade based on these. I indicate opportunities for MRC application to enhance understanding of the illegal, online trade in ivory and, potentially, other wildlife trade commodities. I then explore application of the complex, multi-parameter multi-state open robust design (MSORD) model to time-separated sets of encounter histories of online "ivory" trade items (UK trade). My intent is to examine the suitability of MSORD for modelling data from snap-shot online wildlife trade monitoring studies to derive maximum information and resource benefit from them. In this way, to build knowledge and understanding of the illegal online trade in ivory (and potentially other wildlife trade commodities). I shift focus to engage with people more directly to understand their involvement in illegal wildlife trade, preferred transaction routes i.e. face to face or online, and how this balance may be changing. I apply sensitive question models (including a novel model) and direct questioning to investigate potentially sensitive purchasing behaviours in a reptile keeper community, principally UK-based. I discuss study outcomes in terms of comparative model performance and consider significant results in the context of the reptile trade. Aspects particular to sensitive question model application are discussed and suggestions for future research made, informed by learnings from this study. Considered as a whole, the outcomes from this thesis have potential for application to increase knowledge and understanding of the illegal online trade in wildlife and contribute towards bridging the informational gap described. The MRC approaches applied may offer resource-sparing means to monitor online trade and better understand trading population parameters. This enhanced understanding could provide a basis for informed policy development and coordinated interventions ranging from educational, to law enforcement. Behavioural elements of trading populations (such as participation in illegal wildlife trade, sensitivities to it and preferred routes for purchasing items) may be further explored using sensitive question models. This research indicates that illegal, online wildlife trade is ongoing in the (mainly) UK trading populations I have assessed, despite initiatives and enforcement actions designed to address it. This leads me to consider the effectiveness of such initiatives, and factors that may influence this. I suggest that ensuring clear understanding of the extent and nature of trade being conducted, including the behaviours that underpin it, is essential to designing suitable interventions with an increased likelihood of success. I recommend further, coordinated research as indicated in this thesis as part of a wider initiative to deepen understanding of illegal (online) wildlife trade as a support to effective counter-measures and biodiversity conservation.
Supervisor: Roberts, David ; McCrea, Rachel Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.784390  DOI: Not available
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