Analysis of deforestation and protected area effectiveness in Indonesia: A comparison of Bayesian spatial models

Published Date

March 2015, Vol.31:285–295, doi:10.1016/j.gloenvcha.2015.02.004

Title 

Analysis of deforestation and protected area effectiveness in Indonesia: A comparison of Bayesian spatial models

• Author 

• Cyrille Brun ^a,b
• Alex R. Cook ^c,d,,
• Janice Ser Huay Lee ^e
• Serge A. Wich ^f
• Lian Pin Koh ^g
• Luis R. Carrasco ^h,,

• aDepartment of Statistics and Applied Probability, National University of Singapore, Singapore, Singapore
• bDepartment of Applied Mathematics, Ecole Polytechnique in Palaiseau, France
• cSaw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
• dYale-NUS College, National University of Singapore, Singapore, Singapore
• eWoodrow Wilson School of Public and International Affairs and Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA
• fResearch Centre for Evolutionary Psychology and Palaeoecology, School of Natural Sciences and Psychology, Liverpool John Moores University, United Kingdom
• gEnvironmental Institute, School of Earth and Environmental Sciences, The University of Adelaide, Adelaide, Australia
• hDepartment of Biological Sciences, National University of Singapore, Singapore, Singapore

Received 15 May 2014. Revised 11 January 2015. Accepted 13 February 2015. Available online 14 March 2015.

Highlights

• •
  Biodiversity-focused protected areas do not slow down deforestation in Indonesia.
• •
  Logging concessions and forest plantations slow down deforestation.
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  Deforestation is explained by high agricultural rents and low transport costs.
• •
  Illegal logging presents persisting localized hotspots driving deforestation.

Abstract

Tropical deforestation in Southeast Asia is one of the leading causes of carbon emissions and reductions of biodiversity. Spatially explicit analyses of the dynamics of deforestation in Indonesia are needed to support sustainable land use planning but the value of such analyses has so far been limited by data availability and geographical scope. We use remote sensing maps of land use change from 2000 to 2010 to compare Bayesian computational models: autologistic and von Thünen spatial-autoregressive models. We use the models to analyze deforestation patterns in Indonesia and the effectiveness of protected areas. Cross-validation indicated that models had an accuracy of 70–85%. We find that the spatial pattern of deforestation is explained by transport cost, agricultural rent and history of nearby illegal logging. The effectiveness of protected areas presented mixed results. After controlling for multiple confounders, protected areas of category Ia, exclusively managed for biodiversity conservation, were shown to be ineffective at slowing down deforestation. Our results suggest that monitoring and prevention of road construction within protected areas, using logging concessions as buffers of protected areas and geographical prioritization of control measures in illegal logging hotspots would be more effective for conservation than reliance on protected areas alone, especially under food price increasing scenarios.

Keywords

• Conservation planning
• Food security
• Landscape modeling
• Markov Chain Monte Carlo
• Spatial autoregressive models
• von Thünen model

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• ⁎ 
  Corresponding author. Tel.: +65 91377291.
• ⁎⁎ 
  Corresponding author at: Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore.

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