Bayesian Networks for Understanding Human-Wildlife Conflict in Conservation

Jac Davis, Kyle Good, Vanessa Hunter, Sandra Johnson, Kerrie L. Mengersen*

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingChapterAcademicpeer-review

Abstract

Human-wildlife conflict is a major threat to survival and viability of many native animal species worldwide. Successful management of this conflict requires evidence-based understanding of the complex system of factors that motivate and facilitate it. However, for many affected species, data on this sensitive subject are too sparse for many statistical techniques. This study considers two iconic wild cats under threat in diverse locations and employs a Bayesian Network approach to integrate expert-elicited information into a probabilistic model of the factors affecting human-wildlife conflict. The two species considered are cheetahs in Botswana and jaguars in the Peruvian Amazon. Results of the individual network models are presented and the relative importance of different conservation management strategies are presented and discussed. The study highlights the strengths of the Bayesian Network approach for quantitatively describing complex, data-poor real world systems.

Original languageEnglish
Title of host publicationCase Studies in Applied Bayesian Data Science
Subtitle of host publicationCIRM Jean-Morlet Chair, Fall 2018
EditorsKerrie L. Mengersen, Pierre Pudlo, Christian P. Robert
PublisherSpringer
Pages347-370
Number of pages24
ISBN (Electronic)9783030425531
ISBN (Print)9783030425524
DOIs
Publication statusPublished - 2020

Publication series

NameLecture Notes in Mathematics
Volume2259
ISSN (Print)0075-8434
ISSN (Electronic)1617-9692

Keywords

  • Amazon
  • Bayesian network
  • Botswana
  • Cheetah
  • Conservation
  • Human-wildlife conflict
  • Jaguar
  • Peru

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