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Statistical Modelling Of Spatial And Temporal Patterns In Human-Elephant Conflict

Francis, Chloe (2021) Statistical Modelling Of Spatial And Temporal Patterns In Human-Elephant Conflict. Master of Science by Research (MScRes) thesis, University of Kent,. (doi:10.22024/UniKent/01.02.89705) (Access to this publication is currently restricted. You may be able to access a copy if URLs are provided) (KAR id:89705)

Language: English

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The Assam Haathi Project is run by Chester Zoo and Eco-Systems-India. The aim of the project is to manage and reduce human-elephant conflict by making communities aware of harmless methods to stop elephants from destroying their livelihoods and prevent villagers harming elephants in retaliation. The task force educate and provide villagers with new and safer deterrents to use on the elephants rather than ones they may currently use, such as guns. The project (based in Assam, India) began in 2004 as deforestation has forced elephants to venture into villages in order to search for other sources of food and shelter. Since the start of the project, trained community members have recorded crop-raiding incidents. In this thesis, we derive meaningful biological conclusions related to questions concerning the project by applying various statistical methods to the elephant data; these methods include capture-recapture, distance sampling and generalised linear models. Capture-recapture methods are applied to estimate elephant population size and distance sampling methods are used to investigate how the probability of detecting elephants herds varies spatially. Generalised linear models are used to investigate which of the mitigations used play a positive effect at reducing human-elephant conflict across the two Assam study sites - Goalpara and Sonitpur. We go on to discover that based on this data, there is some significant evidence to suggest that spotlights and electric fences are mitigations which are likely to reduce crop loss caused by elephants.

Item Type: Thesis (Master of Science by Research (MScRes))
Thesis advisor: Cole, Diana
Thesis advisor: McCrea, Rachel
DOI/Identification number: 10.22024/UniKent/01.02.89705
Uncontrolled keywords: Statistics
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
SWORD Depositor: System Moodle
Depositing User: System Moodle
Date Deposited: 11 Aug 2021 06:30 UTC
Last Modified: 13 Aug 2021 13:52 UTC
Resource URI: (The current URI for this page, for reference purposes)
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