Bayesian Networks for Understanding Human-Wildlife Conflict in Conservation

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

doi:10.1007/978-3-030-42553-1_14

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

Environmental Geography

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-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 language	English
Title of host publication	Case Studies in Applied Bayesian Data Science
Subtitle of host publication	CIRM Jean-Morlet Chair, Fall 2018
Editors	Kerrie L. Mengersen, Pierre Pudlo, Christian P. Robert
Publisher	Springer
Pages	347-370
Number of pages	24
ISBN (Electronic)	9783030425531
ISBN (Print)	9783030425524
DOIs	https://doi.org/10.1007/978-3-030-42553-1_14
Publication status	Published - 2020

Publication series

Name	Lecture Notes in Mathematics
Volume	2259
ISSN (Print)	0075-8434
ISSN (Electronic)	1617-9692

Funding

Acknowledgements The authors acknowledge financial and organisation support from the Cambridge University, Queensland University, Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers, Cheetah Conservation Botswana and the Lupunaluz Foundation in Peru. The authors acknowledge financial and organisation support from the Cambridge University, Queensland University, Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers, Cheetah Conservation Botswana and the Lupunaluz Foundation in Peru. This work is the outcome of two substantive studies undertaken with a range of experts and community members. We thank them all. In particular, for the cheetah study we thank the Mokolodi Nature Reserve for hosting the workshop and Wabotlhe and Brian for reviewing the network, and for the jaguar study we thank the research teams at QUT ACEMS and Vanessa Hunter and Lupunaluz for organising the research trip.

Funders
Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers
Cambridge University
Lupunaluz Foundation in Peru
Queensland University
University of Cambridge
University of Queensland
Qingdao Technological University
Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 15 Life on Land

Keywords

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

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10.1007/978-3-030-42553-1_14

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Davis, J., Good, K., Hunter, V., Johnson, S., & Mengersen, K. L. (2020). Bayesian Networks for Understanding Human-Wildlife Conflict in Conservation. In K. L. Mengersen, P. Pudlo, & C. P. Robert (Eds.), Case Studies in Applied Bayesian Data Science: CIRM Jean-Morlet Chair, Fall 2018 (pp. 347-370). (Lecture Notes in Mathematics; Vol. 2259). Springer. https://doi.org/10.1007/978-3-030-42553-1_14

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Davis, J, Good, K, Hunter, V, Johnson, S & Mengersen, KL 2020, Bayesian Networks for Understanding Human-Wildlife Conflict in Conservation. in KL Mengersen, P Pudlo & CP Robert (eds), Case Studies in Applied Bayesian Data Science: CIRM Jean-Morlet Chair, Fall 2018. Lecture Notes in Mathematics, vol. 2259, Springer, pp. 347-370. https://doi.org/10.1007/978-3-030-42553-1_14

Bayesian Networks for Understanding Human-Wildlife Conflict in Conservation. / Davis, Jac; Good, Kyle; Hunter, Vanessa et al.
Case Studies in Applied Bayesian Data Science: CIRM Jean-Morlet Chair, Fall 2018. ed. / Kerrie L. Mengersen; Pierre Pudlo; Christian P. Robert. Springer, 2020. p. 347-370 (Lecture Notes in Mathematics; Vol. 2259).

Research output: Chapter in Book / Report / Conference proceeding › Chapter › Academic › peer-review

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Bayesian Networks for Understanding Human-Wildlife Conflict in Conservation

Abstract

Publication series

Funding

UN SDGs

Keywords

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