Assessing trends and seasonal changes in elephant poaching risk at the small area level using spatio-temporal Bayesian modeling

Parinaz Rashidi (Corresponding Author), A.K. Skidmore, Tiejun Wang, Roshanak Darvishzadeh, Shadrack Ngene, A. Vrieling

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
59 Downloads (Pure)

Abstract

Knowledge about changes in wildlife poaching risk at fine spatial scale can provide essential background intelligence for law enforcement and crime prevention. We assessed interannual trends and seasonal changes in elephant poaching risk for Kenya’s Greater Tsavo ecosystem for 2002 to 2012 using spatio-temporal Bayesian modeling. Poaching data were obtained from the Kenya Wildlife Service’s database on elephant mortality. The novelty of our paper is (1) combining space and time when defining poaching risk for elephant; (2) the inclusion of environmental risk factors to improve the accuracy of the spatio-temporal Bayesian model; and (3) the separate analysis of dry and wet seasons to understand season-dependent poaching patterns. Although Tsavo’s overall poaching level increased over time, the risk of poaching differed significantly across space. Three of the 34 spatial units had a consistently high poaching risk regardless of whether models included environmental risk factors. Adding risk factors enhanced the model’s predictive power. We found that highest poaching risk areas differed between the wet and dry season. The findings improve our understanding of elephant poaching and highlight high risk areas within Tsavo where action to reduce elephant poaching is required.

Original languageEnglish
Pages (from-to)622-636
Number of pages15
JournalInternational journal of geographical information science
Volume32
Issue number3
DOIs
Publication statusPublished - 2018

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poaching
elephant
trend
modeling
risk factor
environmental risk
wet season
dry season
crime prevention
Crime
predictive model
law enforcement
Law enforcement
crime
Kenya
Ecosystems
intelligence
mortality
inclusion

Keywords

  • ITC-ISI-JOURNAL-ARTICLE
  • ITC-HYBRID
  • UT-Hybrid-D

Cite this

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title = "Assessing trends and seasonal changes in elephant poaching risk at the small area level using spatio-temporal Bayesian modeling",
abstract = "Knowledge about changes in wildlife poaching risk at fine spatial scale can provide essential background intelligence for law enforcement and crime prevention. We assessed interannual trends and seasonal changes in elephant poaching risk for Kenya’s Greater Tsavo ecosystem for 2002 to 2012 using spatio-temporal Bayesian modeling. Poaching data were obtained from the Kenya Wildlife Service’s database on elephant mortality. The novelty of our paper is (1) combining space and time when defining poaching risk for elephant; (2) the inclusion of environmental risk factors to improve the accuracy of the spatio-temporal Bayesian model; and (3) the separate analysis of dry and wet seasons to understand season-dependent poaching patterns. Although Tsavo’s overall poaching level increased over time, the risk of poaching differed significantly across space. Three of the 34 spatial units had a consistently high poaching risk regardless of whether models included environmental risk factors. Adding risk factors enhanced the model’s predictive power. We found that highest poaching risk areas differed between the wet and dry season. The findings improve our understanding of elephant poaching and highlight high risk areas within Tsavo where action to reduce elephant poaching is required.",
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Assessing trends and seasonal changes in elephant poaching risk at the small area level using spatio-temporal Bayesian modeling. / Rashidi, Parinaz (Corresponding Author); Skidmore, A.K.; Wang, Tiejun; Darvishzadeh, Roshanak; Ngene, Shadrack; Vrieling, A.

In: International journal of geographical information science, Vol. 32, No. 3, 2018, p. 622-636.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Skidmore, A.K.

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AU - Ngene, Shadrack

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AB - Knowledge about changes in wildlife poaching risk at fine spatial scale can provide essential background intelligence for law enforcement and crime prevention. We assessed interannual trends and seasonal changes in elephant poaching risk for Kenya’s Greater Tsavo ecosystem for 2002 to 2012 using spatio-temporal Bayesian modeling. Poaching data were obtained from the Kenya Wildlife Service’s database on elephant mortality. The novelty of our paper is (1) combining space and time when defining poaching risk for elephant; (2) the inclusion of environmental risk factors to improve the accuracy of the spatio-temporal Bayesian model; and (3) the separate analysis of dry and wet seasons to understand season-dependent poaching patterns. Although Tsavo’s overall poaching level increased over time, the risk of poaching differed significantly across space. Three of the 34 spatial units had a consistently high poaching risk regardless of whether models included environmental risk factors. Adding risk factors enhanced the model’s predictive power. We found that highest poaching risk areas differed between the wet and dry season. The findings improve our understanding of elephant poaching and highlight high risk areas within Tsavo where action to reduce elephant poaching is required.

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