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Understanding complex drivers of wildlife crime to design effective conservation interventions

Abstract:
In conservation understanding the drivers of behavior and developing robust interventions to promote behavioral change is challenging and requires a multifaceted approach. This is particularly true for efforts to address illegal wildlife use, where pervasive—and sometimes simplistic—narratives often obscure complex realities. We used an indirect questioning approach, the unmatched count technique, to investigate the drivers and prevalence of wildlife crime in communities surrounding 2 national parks in Uganda and combined scenario interviews and a choice experiment to predict the performance of potential interventions designed to tackle these crimes. Although poverty is often assumed to be a key driver of wildlife crime, we found that better‐off households and those subject to human–wildlife conflict and those that do not receive any benefits from the parks’ tourism revenue sharing were more likely to be involved in certain types of wildlife crime, especially illegal hunting. The interventions predicted to have the greatest impact on reducing local participation in wildlife crime were those that directly addressed the drivers including, mitigating damage caused by wildlife and generating financial benefits for park‐adjacent households. Our triangulated approach provided insights into complex and hard‐to‐access behaviors and highlighted the importance of going beyond single‐driver narratives.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1111/cobi.13330

Authors

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Institution:
University of Oxford
Department:
Zoology
Role:
Author


Publisher:
Wiley
Journal:
Conservation Biology More from this journal
Volume:
33
Issue:
6
Pages:
1296-1306
Publication date:
2019-07-03
Acceptance date:
2019-04-07
DOI:
EISSN:
1523-1739
ISSN:
0888-8892


Language:
English
Keywords:
Pubs id:
pubs:987669
UUID:
uuid:a6525c5a-4c19-4e57-82d4-ddd8b7562db4
Local pid:
pubs:987669
Source identifiers:
987669
Deposit date:
2019-04-08
ARK identifier:

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